US20150218640A1 - Biomarker signature method, and apparatus and kits therefor - Google Patents

Abstract

The present invention discloses methods, kits, and apparatus as well as reagents and compositions associated therewith for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject. Also disclosed is a biomarker signature for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject. The present invention further discloses methods, kits and apparatus, as well as reagents and compositions associated therewith, for identifying biomarkers for use in a biomarker signature.

Description

• This application claims priority to Australian Provisional Application No. 2014900363 entitled “Biomarker signature method, and apparatus and kits therefor” filed 6 Feb. 2014, the entire contents of which are incorporated herein by reference.
STATEMENT REGARDING SEQUENCE LISTING
• The Sequence Listing associated with this application is provided in text format in lieu of a paper copy, and is hereby incorporated by reference into the specification. The name of the text file containing the Sequence Listing is DAVI_—035_—01US_ST25.txt. The text file is 4.4 MB, was created on Feb. 6, 2015, and is being submitted electronically via EFS-Web.
BACKGROUND OF THE INVENTION
• The present invention relates to method, kit and apparatus and to reagents and compositions associated therewith for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject, to a biomarker signature for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject, and to a method, kit and apparatus, as well as reagents and compositions associated therewith, for identifying biomarkers for use in a biomarker signature.
DESCRIPTION OF THE PRIOR ART
• The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavor to which this specification relates.
• The analysis of gene expression products for diagnostic purposes is known. Such analysis requires identification of one or more genes that can be used to generate a signature for use in distinguishing between different conditions. However, such identification can require the analysis of many gene expression products, which can be mathematically complex, computationally expensive and hence difficult. Much of the biomarker discovery process is devoted to identifying a subset of the data that may have relevant import, from which a signature is derived using a combination of these values to produce a model for diagnostic or prognostic use.
• WO2004/044236 describes a method of determining the status of a subject. In particular, this is achieved by obtaining subject data including respective values for each of a number of parameters, the parameter values being indicative of the current biological status of the subject. The subject data are compared to predetermined data that includes values for at least some of the parameters and an indication of the condition. The status of the subject, and in particular, the presence and/or absence of the one or more conditions, can then be determined in accordance with the results of the comparison.
• US2010/0028876 describes methods for diagnosing biological states or conditions based on ratios of gene expression data from cell or tissue samples, such as cancer cell or tissue samples, by differentiating between cell types, including cancer cell types. The invention provides sets of genes that are expressed differentially in normal and cancer lung cells and tissues to be able to differentiate these cells and tissues. Such cellular differentiation is important in diagnosing cancer and cancer types. The sets of genes are identified by the degree (fold change) of up or down regulation. These sets of genes can be used to discriminate between normal and malignant cells or tissues, and between classes of malignant cells or tissues. Accordingly, diagnostic assays for classification of tumors, prediction of tumor outcome, selecting and monitoring treatment regimens and monitoring tumor progression/regression also are provided.
• However, traditional methods for biomarker identification and traditional combinations of biomarkers use a relatively large number of biomarkers, which in turn makes tests expensive to perform, limiting their use in practice. In addition, the prior art does not describe the use of immune system biomarker ratios, or a method of identifying minimal sets of immune system biomarker ratios useful in determining the presence, absence, degree or prognosis of immune system-mediated medical conditions.
SUMMARY OF THE PRESENT INVENTION
• In one broad form the present invention seeks to provide a method for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:
• • a) determining a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject;
  • b) determining a derived biomarker value using the pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers; and,
  • c) determining the indicator using the derived biomarker value.
• Typically the method includes:
• • a) determining a first derived biomarker value using a first pair of biomarker values, the first derived biomarker value being indicative of a ratio of concentrations of first and second immune system biomarkers;
  • b) determining a second derived biomarker value using a second pair of biomarker values, the second derived biomarker value being indicative of a ratio of concentrations of third and fourth immune system biomarkers; and,
  • c) determining the indicator by combining the first and second derived biomarker values.
• Typically the method includes combining the derived biomarker values using a combining function, the combining function being at least one of:
• • a) an additive model;
  • b) a linear model;
  • c) a support vector machine;
  • d) a neural network model;
  • e) a random forest model;
  • f) a regression model;
  • g) a genetic algorithm;
  • h) an annealing algorithm;
  • i) a weighted sum;
  • j) a nearest neighbor model; and,
  • k) a probabilistic model.
• Typically the method is performed at least in part using an electronic processing device.
• Typically the method includes, in at least one electronic processing device:
• • a) obtaining at least two pairs of measured biomarker values, each measured biomarker value being a measured value of a corresponding immune system biomarker of the biological subject;
  • b) determining a first derived biomarker value indicative of a ratio of concentrations of first and second immune system biomarkers;
  • c) determining a second derived biomarker value indicative of a ratio of third and fourth immune system biomarkers; and,
  • d) determining the indicator by combining the first and second derived biomarker values.
• Typically the method includes, in at least one processing device, generating a representation of the indicator.
• Typically the representation includes:
• • a) an alphanumeric indication of the indicator;
  • b) a graphical indication of a comparison of the indicator to one or more indicator references;
  • c) an alphanumeric indication of a likelihood of the subject having at least one medical condition.
• Typically the method includes:
• • a) comparing the indicator to an indicator reference; and,
  • b) determining a likelihood in accordance with results of the comparison.
• Typically the indicator reference is based on at least one of:
• • a) an indicator threshold range;
  • b) an indicator threshold; and,
  • c) an indicator distribution.
• Typically the indicator reference is derived from indicators determined for a number of individuals in a reference population.
• Typically the indicator reference is based on a distribution of indicators determined for a group of a reference population, the group consisting of individuals diagnosed as having the medical condition or lacking the medical condition.
• Typically the reference population includes:
• • a) a plurality of individuals of different sexes;
  • b) a plurality of individuals of different ethnicities;
  • c) a plurality of healthy individuals;
  • d) a plurality of individuals suffering from at least one diagnosed medical condition;
  • e) a plurality of individuals lacking the at least one diagnosed medical condition;
  • f) a plurality of individuals showing clinical signs of at least one medical condition;
  • g) first and second groups of individuals, each group of individuals suffering from a respective diagnosed medical condition; and,
  • h) first and second groups of individuals, the first group of individuals suffering from a diagnosed medical condition, and the second group lacking the diagnosed medical condition.
• Typically the indicator is for use in determining the likelihood that a biological subject has at least one medical condition, and wherein the reference population includes:
• • a) individuals presenting with clinical signs of the at least one medical condition;
  • b) individuals diagnosed as having the at least one medical condition;
  • c) individuals diagnosed as lacking the at least one medical condition; and,
  • d) healthy individuals.
• Typically the indicator reference is retrieved from a database.
• Typically the likelihood is based on a probability generated using the results of the comparison.
• Typically the indicator is for determining a likelihood of the subject having a first or second condition, and wherein the method includes:
• • a) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of first and second conditions; and,
  • b) determining the likelihood in accordance with the results of the comparison.
• Typically the method includes:
• • a) determining first and second indicator probabilities using the results of the comparisons; and,
  • b) combining the first and second indicator probabilities to determine a condition probability indicative of the likelihood.
• Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively.
• Typically the method includes:
• • a) obtaining a sample taken from the biological subject, the sample including polynucleotide expression products; and,
  • b) quantifying at least some of the polynucleotide expression products within the sample to determine the pair of biomarker values.
• Typically the method includes, determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.
• Typically the method includes:
• • a) quantifying polynucleotide expression products by:
  • b) amplifying at least some polynucleotide expression products in the sample; and,
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and,
  • d) determining the indicator by determining a difference between the amplification amounts.
• Typically the amplification amount is at least one of:
• • a) a cycle time;
  • b) a number of cycles;
  • c) a cycle threshold;
  • d) an amplification time; and,
  • e) relative to an amplification amount of another amplified product.
• Typically the method includes determining:
• • a) a first derived biomarker value by determining a difference between the amplification amounts of a first pair of polynucleotide expression products;
  • b) a second derived biomarker value by determining a difference between the amplification amounts of a second pair of polynucleotide expression products;
  • c) determining the indicator by adding the first and second derived biomarker values.
• Typically the immune system biomarker is a biomarker of an immune system of the biological subject that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult.
• Typically:
• • a) the at least two immune system biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
  • b) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Typically the mutual correlation range is at least one of:
• • a) ±0.8;
  • b) ±0.7;
  • c) ±0.6;
  • d) ±0.5;
  • e) ±0.4;
  • f) ±0.3;
  • g) ±0.2; and,
  • h) ±0.1.
• Typically each immune system biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.
• Typically the condition correlation range is at least one of:
• • a) ±0.9;
  • b) ±0.8;
  • c) ±0.7;
  • d) ±0.6;
  • e) ±0.5; and,
  • f) ±0.4.
• Typically the performance threshold is indicative of an explained variance of at least one of:
• • a) 0.4;
  • b) 0.5;
  • c) 0.6;
  • d) 0.7;
  • e) 0.8; and,
  • f) 0.9.
• Typically the immune system biomarker value is indicative of a level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.
• In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method includes:
• • a) determining a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
  • b) determining a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene; and,
  • c) determining the indicator using the first and second pairs of biomarker values.
• In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one inflammatory response syndrome (IRS) immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: NUMB, RAB27A, USP3//LOC100130855, HIF1A, LBXCOR1//PIAS1//CALML4, SQRDL, C20orf74, IL10RB, PARP4, DNTTIP1, MTMR6//LOC646482, LAMP2, MAPK1, SERINC3//TTPAL, IGSF6//METTL9, RP2, C18orf32, LOC284757, MTMR10//MTMR15, SLC12A6, LCP1, CHP, PRR13, C20orf177, ZFP106, DICER1, PHF12, IFNAR1, BNIP2, UBE2A, NIN, MBD2//SNORA37, TM9SF2, RAB8B, CLIP1, WAS, DNAJC3, CDADC1, KIAA0317, MED13L, INTS6, PDK3, MYO5A, NUPL1, VEZF1, CUL4B, USP9X//USP9Y, RPS6KA3, IL17RA//CECR7, ELF1, TMX4, TAOK1, ELMO2, STAT5B//STAT5A, PAN3//EEF1A1//CHCHD2, SIPA1L1//SNORD56B//LOC145474//LOC283567, OSBPL1A, SYNJ1, U2AF1, NPEPPS//TBC1D3F//LOC440434, AP1G1, SNTB2, ZNF230//ZNF222, LYRM1, ME2, GALNT1, DYRK1A, ZMYM2, ARID4A, TOB1, DOCK11, ACTR10, ZMYM5//ZMYM2, FNDC3A, NUFIP2, STRADA, SPG11//ISLR, SPATA13//C1QTNF9, BRWD3, BACH1, CLTC, LIG4, C21orf41//BACH1, KPNB1, DHRS7, USP8, LACTB, SYNE2, ZDHHC20//LOC728099, EAPP, MED13//LOC100129112, TAOK3, NLGN3, CIT, RIPK3, CP110, ABHD2, GNA13, GGNBP2, PXN and PTPN1 (hereafter referred to interchangeably herein as “group A IRS immune system biomarker genes” or “group A IRS biomarker genes”); and
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: AGFG1, BMX//HNRPDL, MCM5, TRIM28, GRWD1, ZNF574, ARRDC2, PELP1, SHPK, GPS1, FAM38A, FBXO31, C16orf58//LOC100128371, NLRC3, JMJD8, CDK10, TRAPPC2L, PRMT7, BRF1, MTA1/LOC647310//LOC100128343, PLD4, DDX54//CCDC42B, PLBD1, IRAK3, FGD4, ARG1, RANGAP1, UNC84B, SAMSN1//LOC388813, PFKL, S100A12, KIF22, LRRN1, CCDC134, LZTR1, GZMM, ICAM2, TMC8, LAT//SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC 100289169//LOC728734//LOC729602//LOC100288442//LOC100288332, CLEC4D, CDK5RAP1, PPP1R16B, DAZAP1, LMF1, EDC4, IL21R//LOC283888, JMJD7-PLA2G4B//JMJD7//PLA2G4B, TMEM120B//RHOF, ENTPDH/C10orf131, ACSL1, ZC3H7B, CHERP//C19orf44//CALR3, U2AF2, PYGL, SOS2, ANKRD22, MEGF9, MGAM//LOC100124692, IL1R2, IL2RB, FCAR, IL27RA, DHX37, PATZ1, PRDM15, NOSIP, RPTOR, SPG7, DNAJA3, VNN1, SEPT9, THAP11, LPCAT2, MAN2C1, PITPNM2, NOC2L//SAMD11//LOC401010, ZMYM3, FTSJ1, KCNE1, ACTR5, FAM110A, FAM134C, LLGL2, INF2, KDM2B, ACSL4, B4GALT5, CD79B, BCL11B, ERLIN1, TLR4, EVL, SRGN, SLC37A3, GPR141, MSL3, MMP9//LOC100128028, MAP2K6, PHTF1, KLHL36, POLR3E, PCNX, PNKP, TMEM104, TRPV2, SEPT1, APH1B, POLE, MED24, MPI, C12orf49, PES1, ERCC1//CD3EAP, CD177, CPD, MEF2A//LYSMD4, C14orf43, RPLP0, CDC25B, SYMPK, ARHGEF18//LOC100128573, PSTPIP2, HERC2//HERC2P2//HERC2P3//LOC440248, MAPK14, F5, PLCG1, ZNF416, AARS, KLHL2, APOBEC3A//APOBEC3B, CMTM1/CKLF, USP11, MAP3K14//LOC100133991, GOLGA3, TMEM204, S100A8, IL1R1, DHPS, PPP2R1A, UBTF, DRG2, DNMT1, USP36, ZBTB4, TSC2, KIAA0195, KIAA0182, ALOX5AP, TGIF2, ST20//C15orf37, FN3KRP, ABCD4, ZFP64, NEO1, PPIL2//YPEL1, RNPS1, NF2, SERPINB1, DDX51, PRPF6, TIMM22, SYS1//SYS1-DBNDD2//DBNDD2, RAB31, KRI1, SMARCA4, CLUAP1, C16orf67, C20orf4, CHTF8//HAS3, NPTN, CSRP2BP, AES, ODZ1, MTMR15//MTMR10, SIRPD, EEF2//SNORD37, DKC1/SNORA36A//SNORA56, CEACAM4, C12orf43, RANBP3, EEF2K, LOC338799, PLP2, AKAP8, ELAC2, AKAP1, TBC1D4, ALOX5, WSB1, BAZ1A, ETS2, GGA2, CSTF2//RAD21, METTL9, GYG1, CRAMP1L//HN1L, EVI2B, PPP1R13B, POPS, C20orf3, WDR59, KCNJ15, PGLYRP1, ELAVL1, SLC25A1, PSMD3, CDC42EP3, FTSJ3, C2CD2, RBM19, CDH26, TRMT2B, GTF2F1//LOC100130856, SNRPN//SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD 116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@, SLC9A8, RPA1, ADARB1, AFG3L2, MCTP2, DACH1, SEH1L, RRP1B, ZNF335, WDR73, TAF15, MOSPD2, WIPIMARSG, ARRB2, PLIN5ARG1, SNRPD3//C22orf13, CTNNBL1, ZNF175, NCF4, DDX27, FBXO21, TDP1, ATXN2L, ILF3, VAPA, DDX19B//DDX19A, NCOR2, KL, MTHFS, TOM1L2//LOC246315, APOBEC3D, EXD2, CDR2//RRN3//LOC100131998//LOC653390, ADCY4, DHX33, CKLF, GTF3C1, PRKCSH, DHX35, HSPH1, CCDC92, BCOR, CCPG1//PIGB//DYX1C1, MCM3AP, FPR1, ZNF460, AKAP8L, DCAF7, RNF24, NSMCE1, PDHA1, SAFB2//SAFB, ITM2B, ZNF236, PI4KA//PI4KAP1//PI4KAP2//LOC100293141, CSTB, C14orf138, ITGAL, ARID3A, COG7, TYROBP, HP//HPR, SRCAP//SNORA30, COG1, GK//GK3P//FTL//LOC652904, C15orf63//SERF2, SERPINA6, SMG6//C17orf6, INO80, C16orf62, RAB35, PEF1, C14orf101, TMEM185A, LIMK2, CTCF, DIABLO//B3GNT4, VPS33A, UNQ1887, TBCB//POLR2I, ABHD13, SLC24A6, EDNRB, CAl2, ANAPC5, TMC3, TRIAP1, ABHD12B, TDRD9, EIF2B1, CXorf59, LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LOC 644397, SDS, SYCP2, TBC1D8B, TMEM31, GUCY1B2, PFN1, SLC24A3, ABCC11//LONP2 and ZNF257//ZNF492//ZNF99//ZNF98//LOC646864 (hereafter referred to interchangeably herein as “group B IRS immune system biomarker genes” or “group B IRS biomarker genes”).
• In some embodiments, the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: LTBP3, LPHN1, NR1D1//THRA, METTL9, PLD4, MAPK14, FAM102A, MYBBP1A//SPNS2, FLJ10232, SEMA4C, LMF1, PLBD1, MAN1C1, B4GALT5, ENGASE, NDRG2, TLR5, WDR4, PATZ1, CD177, LILRA5, SIRPD, ADAMTS10, TCF7, GGT7, GYG1, CDAN1, BRF1, GPR84, TMC6//LOC100131096, PGAP3, GRAP//SNORD3B-1//SNORD3B-2//LOC400581, SHPK, NOG, PVRIG//PILRB//STAG3, TDRD9, TMC8, C14orf21, NLRC4, APBA2, TBL3, LDOC1L, C16orf58//LOC100128371, KLHL3, IRAK3, JMJD7-PLA2G4B//JMJD7//PLA2G4B, RAB31, IL10RB, NFATC1//LOC100127994, S100A12, SLC2A3//SLC2A14, GPA33, PLXDC1, BCL6//LOC100131635, GTPBP3, SQRDL, CD7, TMEM177//LOC100125918, FBXO31, CACNA1E, LILRA4, FAM38A, UBE2J1, SBF1//SBF1P1, TMEM204, FCAR, CDK4/MARCH9/C3HC4, ZBTB4, PIWIL4, RNASE2//LOC643332, PRMT7, AGFG1, CMTM1/CKLF, MCTP1, ARL11, FMNL3//PRPF40B, FAM151B, BAHD1, OSBPL7, ZAP70, TUBGCP6, MAP2K6, NPTN, C3AR1, CD247, S100A8, TBCD, LMNB1/PCIF1, PFKL, GRWD1, PYGL, UPP1, OMG, SAMSN1//LOC388813, BLCAP, PTPRS, FAM20A, CARD6, SPPL2B, IL2RB, SORT1, BST1, TAF1C//ADAD2, SEMA4F, NCAPH2, MCTP2, ZAK, CCR7, MAN2C1, NEURL4//GPS2//D4S234E, BMX//HNRPDL, TRAPPC6A, LPCAT2, C19orf60, SLC4A10, C14orf101, TP53I3, IL1RN, AIM2, UBE2R2, PNKP, ZNF70, SEPT1, NEO1, MPRIP, DPH1//OVCA2, C16orf67, CD58, RAB27A, EEF2K, CLIC1, MBLAC2, IFNGR2, CRTC1//MAML2, CACNB1, GALNT3, C19orf6, C20orf74, RALB, GPRASP1, CA4, ETS2, RP2, MARS2, RAB32, FAIM3, C20orf24//SLA2, ZNF549, PIGL, PHTF1, IL18R1, IPO4, ZFP106, SLC12A4, DNTTIP1, S100A11, ZNF544, ATXN1, GNLY, MID2, BACH2, INF2, ARFGAP1, MSL3, SOS2, ARL8A, PTPRJ//OR4B1, NAT9, RHOT2//FBXL16, PNPLA1, DNAJC13, GNG5, FAM129C, PXK, C10orf119, BATF, LMO7, KLF2, NRD1, CLCN7, GLA, CFLAR, SYCP2, IMAGE5303689, LPGAT1, PTGDR, LAMP2, ZNF607, INSL3//JAK3, DUSP3, PCNX, CD79B, IRAK1, ZNF550//ZNF549, LOC100130950, SPTLC2, CTSA, RAP2C, ADCY9, MED12L, MTHFD2, CAP1, TOR1AIP2//TOR1AIP1//IFRG15, CHP, TSEN2, LYRM1, UBE2A, NUPL1, YIPF1, FRMD3, KAL1, CLTC, FLVCR2//RPS24, WSB2, KIAA0040, JAG1, GPR183, N6AMT1, ZNF563, AP3B2, SERPINB2//SERPINB10, CDH2, ITFG1, EDEM2, RNF135, HPSE, DSC1, FOXN2, RASSF2, ZNF420, ZFP28, UBE2G2//SUMO3, PTTG1IP, PRKCB, KIT, PLEK, MAP4K4, GBA//GBAP, CLIP1, EDEM3, SERINC3//TTPAL, TPST2, HNRPLL, TP53BP2, KCND1, GCLM, RIT1, OSCAR, DDX59, EDNRB, ELMO2, RRAGC, AFTPH, DCUN1D3//LYRM1, RSBN1, IFI30, SNX1, PTPN1, SEP15, ARMCX5, ALAS1, NFKBIA, STXBP2//L00554363//LOC100131801, C15orf24, SRI, ASGR2, NSF//LOC728806, TRIM69, SEC23A, PLAUR, RAB3GAP2//AURKAPSH/AURKA//SNORA36B, MAOB//NAT13, DEDD, SEC23B, COPA, EGF, STRADA, SIAE, C5, SLC30A1, ANXA4, NKG7, ABHD12B, TESK2, LONRF3, PIKFYVE, SH3BGRL3, ARMCX3, NEU1, SPAST, STX6//KIAA1614, TADA3L, LIN37//PSENEN, UBR3, WDR90, RTN2 and TMUB2 (hereafter referred interchangeably herein as “group C IRS immune system biomarker genes” or “group C IRS biomarker genes”); and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: CNNM3, SLC25A45, UNC84B, ARHGEF18//LOC100128573, PIK3IP1, EPHX2, SEPT9, ITPKB, TSPYL2//GPR173, GALT, USP11, CBX7//LOC100128400, MAP3K14//LOC100133991, CDK5RAP1, KLHL36, SPG7, ZNF574, RASA3, KLHL22//KRT18, PYGO2, USP36, LCK, SKI, C5orf45//SQSTM1, PIK3C2B//LOC100130573, ANGEL1, ZCCHC14, CIRBP, ZMIZ2, TMEM120B//RHOF, NOC2L//SAMD11//LOC401010, PPP1R13B, ZNF416, PBXIP1, SMYD5//NOTO, ZNF529, EDC4, LENG8, TBC1D22A//LOC100289878, CORO7, COG8//PDF, CUL9, RASGRF2, CHERP//C19orf44//CALR3, POLR3E, CNNM4, TSC2, XYLT2//LOC100130580, TP53, LMBR1L, AKT1, SLC7A6//SLC7A6OS, LDLRAP1, SGSM2, ZNF764//ZNF747, AKAP1, RNPS1, ICAM2, KIF3A, TGIF2, VAC14//LOC100130894, CXXC5, DCAF15, TARBP1, RCAN3, IMP4, LUC7L, SIN3B, TRMT2B, POFUT2, AXIN1, PPIL2//YPEL1, NLRP1//LOC728392, TMEM63A, TMEM208, ZNF362, GNG7, CASS4, ZNF287, DGKE, CEP68, ASXL1, CLUAP1, WDR89, CD40LG, MGC57346//C17orf69, PPIE//CCDC25, FCHO1, TNPO2//SNORD41, CSNK1E//LOC400927, CLYBL, XAB2, METT10D//LOC284009, PRPF6, AKAP8, SH3BP1//PDXP, TOM1L2//LOC246315, ZNF329, ZNF274, FAM119A, SMYD3, UNC84A//C7orf20, ZNF256, PSD4, CCDC130, LIMD2//MAP3K3, ELP2, ZNF8, AFG3L2, TXK, DDX27, FBXL12, TNRC6C, TADA1L, KIAA0355//FLJ21369, ZNF211, ZNF808//ZNF578//ZNF611, SRCAP//SNORA30, BANP//RUNDC2C, ADARB1, CCDC71, KTI12, TCF25, XYLTH/LYRM2//ZC3H11A, DET1, ABCF3, PRKCZ, KIAA0141, CHI3L1, RPGRIP1, TTC31, MTMR15//MTMR10, MEF2D, TMEM50B, GLOD4, PRPF8, C14orf43, P2RX5, MSH2, PCCA, DENND4B, SLC43A2, MAPK8IP3, TUBGCP5, C19orf2, SEH1L, CCDC104, TRIM62, TDRKH, COG1, POLR1B, AFG3L1, TYK2, RBM3, UBTF, RP11-9412.2//NBPF16//NBPF11//NBPF15//NBPF8//NBPF20//NBPF10//NBPF14//NBP F1//LOC100288142//NBPF12//KIAAl245//LOC100290137, ZNF41, ZNF461, PI4KA//P14KAP1//PI4KAP2//LOC100293141, THEM4, BCL11A, CC2D1B, WDR73, BBS2//OGFOD1, RRN3//LOC653390//LOC730092//LOC100131998, NOP58, NUCKS1, ZNHIT6, RXRB, AKT3, FANCM, ERN1, FAM117B, COX11//TOM1L1, ACVR2A, RP3-402G11.5, AHCTF1, CLN8, NVL, SAPS2, DPEP3, PDE3B, DPEP2, GGA1, CCDC50, SNRPN//SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@, C17orf65//ASB16, ZNF317, SNRNP200, CXorf26, MTBP, NOL11//SNORA38B, CCNL2, ALDOC, PITPNC1, FASTKD2, ZZZ3, PIK3R5, WDR82, GLDN, CHML, C15orf40, DIDO1, CLCC1//GPSM2//C1orf62, SLC35D1, SCRN1, C15orf63//SERF2, ZNF460, SAFB2//SAFB, C16orf54, DDX18, CTPS2, ZNF382, ZNF101, LIPT 1//MRPL30, ITGA6, KIF21B, INPP5B, SF3A1//CCDC157, ODF2L, NUAK2, CHCHD5, AHSA2//USP34, YLPM1, TERF2, ZNF830, MAN2B1//MORG1, GPATCH8, SHC1, SEPT4, SFRS2, TMC3, OTUD5, NARG1L, MKL1//KIAA1659, YTHDF1, SLC14A2, GGA3 and EXOC7 (hereafter referred to interchangeably herein as “group D IRS immune system biomarker genes” or “group D IRS biomarker genes”).
• In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: ALKBH5//FLJ13773, RPS19BP1, RFXANK//MEF2B//LOC729991, NA, CDC6, C19orf56, NA, ABCC2, THAP11, RTN2, MAZ, TAX1BP3, NUTF2, MPZL3, FBXW5, HIST1H2BM, CETP, PQLC1, H2AFX, KIAA0101//CSNK1G1, STK17B, SMARCD3, LOC100134934//CDK3, LPCAT4, LPP, MPZL2, ANKRD9, PRR13//PCBP2, MDS2, RBM33, GATAD2B//PLIN2, PPTC7, MYBL2, OIP5, PLA2G7, CRIPT, RNF186, CCDC125, TLE3, C3orf35, SAP130, MXD1, ZHX2, CDK5RAP3, ENTPD1//C10orf131, NDUFB7, POGZ, DOK3, MCMI, IL17F, CLPS and DUSP11 (hereafter referred to interchangeably herein as “group E IRS immune system biomarker genes” or “group E IRS biomarker genes”); and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: PIWIL4, C11orf82, ACRC, CLPTM1L, MAGED2, PLACE, ZDHHC4, OTX1, INSIG1, BATF, MFSD11, DNASE1L1//RPL10, C15orf24, CDS2, KEAP1, ARD1A, POLR2G, AHCY, SLC39A9, CUGBP1, FAM96B, TM7SF3, CTSZ, CD63, SPPL2A, ST3GAL2, SEC13, TM9SF1, IRAK2, GOSR2, ADIPOR2, TG, GABRR2, TPST2, DERL2, CCDC101//LOC388242, VWA5A//OR10D1P, CD300A, MRPS34, PSMA7, MAPK6, JKAMP, TLR10, RAG1AP1, NEU1, SLC30A1, PDGFC, ATOX1, CYBASC3, TMEM205//hCG_—29977, FAM108B1, ACSS2, HIST1H4L, AGTRAP, RNF114, UBQLN2, EDF1, C20orf197, UBE2E1, RER1, ANKRD10, SEC22C, TM2D3, SLC15A2, TRIM28, COX15, CCDC109A, CSTF1, AIP, ACTR1A, HIST1H4I, YIF1B, TSPAN31, VPS26B, CNIH, TGFBR1, NIPA2//CYFIP1, DDAH2, BID, CYB5R1, CEACAM4, KIT, GAB2, JAG1, RPGRIP1, VAT1, GNB5//LOC100129973, SSR4//IDH3G, LAMP1, MRPL41, RUNX2, ITFG1, DNASE1, ZNRD1//NCRNA00171, NLRP1//LOC728392, STT3A, MGAT4B//SQS™1, KIAAl257//ACAD9//LOC100132731, KLHL6, PTPN6, GALK2, DAD1//OR6J1, PDLIM5, TMEM147, TRAM1//LOC286190, LZTR1, TNPO1, ACSL5, C22orf37, PLK1, SYNE2, PSMD3, FLJ27255, PRKCD, RAB34, RPN2//EEF1A2, SLC35B1, KCNIP3, PDE3B, PXMP2//PGAM5, SDF2, HIST1H3I, LOC284757, TMEM33//DCAF4L1, CSNK2A2, LSM10, PTTGlIP, ADRB3//GOT1L1, PLXNA2, DIAPH2, BICD2, HAL, RPS6KC1, TMEM106C, CD1E, SLC35A5, C7orf26, IMP3, PICALM, ARF1, FHOD1//SLC9A5, C19orf55, TOMM40L//NR1I3, INSIG2, NEK9, HCG27, SDHB, CUBN, PRDX3, CEPT1//DRAM2, ERGIC1, KPNA3, VAV1, ELM01, CUGBP2, LASP1, COL9A2, MEGF9, ELF4, SUZ12P, SULT1A2//SULT1A1, FAM123C, FAR2, IER2, RGS2, MYBPH, MFAP3, RCHY1, MGAT1, MFSD4, CDH2, TMEM184C, CTRB2//CTRB1, MPP4, PHF12, SLBP, ADAM19, HTR1B, TRIM55, CRNN, KLHDC7A, YIPF5, SLC11A1, GABBR1, CAMKV, SLC35F5, CHRNG, CXCL14, METTLE, PHC2, GPR153, TNFRSF10D, BAT2L, GALNT2, DENND3//C8orf60, CLDN3, F11, CCDC93, FLJ46365, CYP21A2, ETV5, TRPM2, IL20, NBL1//Clorf151, NGEF, POU6F2, PTEN//PTENP1, NPC1L1, CYP4B1, NFIC, PPARGC1A, PLIN3, THPO, TIMP4, CELSR2, DMBX1, CAMK2B, PPFIA1, HCLS1, SLC6A20, C17orf66//RSL24D1, PIWIL2, DAZL//DAZ4//DAZ3//DAZ2, GAL3ST2, TPD52L1, C19orf34, RASGEF1C, BAALC//FLJ10489, NR4A2//FLJ46875, HAPLN1, CLDN18, TAS1R1, TIMD4, SKI, CCDC48, MEGF10, OSBPL6, DNAH2, ARID1B, PGC, DCST1, SDK1, CHIA, REG4//NBPF7, TMEM49//CLTC//MIR21, TMEM44, NDUFB6//DFFB, COL25A1, EPHX4, NCRNA00085, NTRK3, PKHD1, SLC2A7, NTRK1, ABHD1//PREB, SLC4A9, GPNMB, SLC5A1, SLC7A8, RTCD1, PROC, C17orf64, FLJ14100//Clorf86, NUP50, UNKL, C10orf18, TMEM61, C9orf68, CYTSA, MORN3, RAB17, CNNM3, CCDC28B, SH2D6, BARHL2, T, SNRK, TCP11, KDR, ENAM, UNKL, SPRR2C, GPR17//LOC100291428//LIMS2, Clorf175//TTC4, CACNA1D, C2orf62, LOC100132686, UNQ6126, TRIM15, GPR113//SELI, IL22, SCN10A, FAAH, MBOAT7, C7orf51, KIAA1530, TRPM8, C1orf95, DDC//LOC100129427, GABRA1, HCRTR1, DCST2, CHODL, PANS//EEF1A1//CHCHD2, LYPD6B, UGT3A1 and SERPINA6 (hereafter referred to interchangeably herein as “group F IRS immune system biomarker genes” or “group F IRS biomarker genes”).
• In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first, second, third and fourth IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: RPS19BP1, RFXANK//MEF2B//LOC729991, C19orf56, RTN2, HIST1H2BM, CETP, PQLC1, H2AFX, KIAA0101//CSNK1G1, LOC100134934//CDK3, LPCAT4, LPP, MPZL2, ANKRD9, RBM33, MYBL2, PLA2G7,01P5, CRIPT, RNF186, C3orf35, ZHX2, NDUFB7 and DUSP11 (hereafter referred to interchangeably herein as “group G IRS immune system biomarker genes” or “group G IRS biomarker genes”);
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: PIWIL4, C11orf82, ACRC, PLACE, ZDHHC4, OTX1, INSIG1, BATF, MFSD11, C15orf24, CDS2, POLR2G, SLC39A9, FAM96B, TM7SF3, SPPL2A, ADIPOR2, GOSR2, DERL2, TPST2, VWA5A//OR10D1P, CCDC101//LOC388242, MAPK6, PSMA7, JKAMP, TLR10, RAG1AP1, SLC30A1, PDGFC, ATOX1, TMEM205//hCG29977, FAM108B1, UBQLN2, EDF1, C20orf197, RER1, UBE2E1, ANKRD10, SEC22C, TM2D3, SLC15A2, CCDC109A, HIST1H4I, TSPAN31, TGFBR1, CNIH, DDAH2, NIPA2//CYFIP1, BID, CYB5R1, KIT, RPGRIP1, MRPL41, RUNX2, ITFG1, ZNRD1//NCRNA00171, NLRP1//LOC728392, KIAA1257//ACAD9//LOC100132731, KLHL6, GALK2, DAD1//OR6J1, PDLIM5, TRAMH/LOC286190, TNPO1, ACSL5, SYNE2, RPN2//EEF1A2, SLC35B1, KCNIP3, TMEM33//DCAF4L1, CSNK2A2, LSM10, PLXNA2, DIAPH2, HAL, RPS6KC1, SLC35A5, PICALM, C19orf55, INSIG2, SDHB, PRDX3, CEPTH/DRAM2, KPNA3, SULT1A2//SULT1A1, FAR2, MYBPH, MFAP3, RCHY1, CDH2, TMEM184C, CTRB2//CTRB1, SLBP, CRNN, YIPF5, CHRNG, SLC35F5, METTLE, CLDN3, CCDC93, CYP21A2, NBL1//Clorf151, NGEF, POU6F2, NPC1L1, PPARGC1A, THPO, CELSR2, DMBX1, SLC6A20, C17orf66//RSL24D1, GAL3ST2, C19orf34, BAALC//FLJ10489, CLDN18, TAS1R1, CCDC48, OSBPL6, SDK1, TMEM49//CLTC//MIR21, TMEM44, NDUFB6//DFFB, NCRNA00085, NTRK3, NTRK1, SLC4A9, SLC5A1, RTCD1, FLJ14100//C1orf86, PROC, C17orf64, UNKL, C9orf68, MORN3, RAB17, CNNM3, CCDC28B, BARHL2, UNKL, LOC100132686, UNQ6126, IL22, FAAH, C7orf51, DCST2, LYPD6B and SERPINA6 (hereafter referred to interchangeably herein as “group H IRS immune system biomarker genes” or “group H IRS biomarker genes”);
  • c) the third IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: ALKBH5//FLJ13773, RNA243, ARD1A, CDC6, AHCY, MRPS34, CYBASC3, HIST1H4L, RNF114, TRIM28, CSTF1, CEACAM4, GAB2, GNB5//LOC100129973, THAP11, SSR4//IDH3G, STT3A, NUTF2, MPZL3, TMEM147, RAB34, PDE3B, PXMP2//PGAM5, HIST1H3I, LOC284757, TMEM106C, STK17B, IMP3, HCG27, CUBN, ERGIC1, ELM01, CUGBP2, COL9A2, MEGF9, SUZ12P, FAM123C, RGS2, PRR13//PCBP2, PHF12, ADAM19, GATAD2B//PLIN2, SLC11A1, PPTC7, PHC2, BAT2L, DENND3//C8orf60, FLJ46365, ETV5, CCDC125, PTEN//PTENP1, TLE3, NFIC, TIMP4, PPFIA1, HCLS1, SAP130, MXD1, NR4A2//FLJ46875, SKI, ARID1B, ENTPDH/C10orf131, POGZ, DOK3, REG4//NBPF7, MCMI, SLC7A8, NUP50, C10orf18, TMEM61, SH2D6, SNRK, SPRR2C, CACNA1D, TRIM15, CLPS, MBOAT7, KIAA1530, C1orf95, GABRA1, HCRTR1, CHODL and PAN3//EEF1A1//CHCHD2 (hereafter referred to interchangeably herein as “group I IRS immune system biomarker genes” or “group I IRS biomarker genes”); and,
  • d) the fourth IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: CLPTM1L, MAGED2, DNASE1L1//RPL10, KEAP1, CUGBP1, CTSZ, CD63, ST3GAL2, SEC13, TM9SF1, IRAK2, GABRR2, TG, CD300A, NEU1, ACSS2, AGTRAP, UPF0627, COX15, ABCC2, ACTR1A, AIP, YIF1B, VPS26B, JAG1, VAT1, LAMP1, DNASE1, MAZ, TAX1BP3, MGAT4B//SQSTM1, FBXW5, PTPN6, LZTR1, PLK1, C22orf37, PSMD3, FLJ27255, PRKCD, SDF2, PTTG1IP, ADRB3//GOT1L1, BICD2, CD1E, C7orf26, ARF1, FHOD1//SLC9A5, TOMM40L//NR1I3, NEK9, VAV1, SMARCD3, LASP1, ELF4, IER2, MGAT1, MFSD4, MDS2, MPP4, HTR1B, TRIM55, KLHDC7A, GABBR1, CAMKV, CXCL14, GPR153, TNFRSF10D, GALNT2, F11, IL20, TRPM2, CYP4B1, PLIN3, CAMK2B, PIWIL2, DAZL//DAZ4//DAZ3//DAZ2, TPD52L1, RASGEF1C, HAPLN1, TIMD4, CDK5RAP3, MEGF10, DNAH2, PGC, DCST1, CHIA, COL25A1, EPHX4, PKHD1, SLC2A7, ABHD1//PREB, GPNMB, CYTSA, T, TCP11, ENAM, KDR, GPR17//LOC100291428//LIMS2, Clorf175//TTC4, IL17F, C2orf62, GPR113//SELI, SCN10A, TRPM8, DDC//LOC100129427 and UGT3A1 (hereafter referred to interchangeably herein as “group J IRS immune system biomarker genes” or “group J IRS biomarker genes”).
• In specific embodiments, the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product and the fourth IRS immune system biomarker is a LAMP1 expression product.
• In some embodiments, the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: N4BP2L2//CG030, FAM96A, MINPP1, MORC3//DOPEY2, LSM8, PLEKHA3, MITD1, ATF4, B2M, TMX3, ZNF273, PLEKHF2, UNQ2999, DPM1, OCLM, NADK, GPR65, SFRS3, ZNF28, PFDN4, COQ10B, SLC30A6//DDX50, KPNA5, ATP6V1G1, HAUS2, hCG_—2039148, RAB33B, BET1, UBE2V2, ATP6AP2, SUBH/TMEM183A, TMEM188, ABHD3, LAPTM4A, RNF138, CCDC82, TMEM179B, PAPD4, VAMP2, CCDC126, ATG3, CHCHD5, RBM39//LOC643167, NAT8B, HAT1, CNOT6L, ZBED5, GOLT1B, TTC33, ACTN1, ACTR6, GNB4, TMEM208, CCNH, C4orf34, HAUS6//SCARNA8, TCTN1, SF3B14, TMEM138, TTC35, DLEU2//DLEU2L, MFSD8, COX7A2L, UGGT2, CEPTH/DRAM2, LEMD3, CREBZF, RPL21P44, ANGEL2, UFM1, XP01, CALM2//C2orf61, PLDN, CLK1//PPIL3, Clorf84, PPA2, FPGT//TNNI3K, ORC4L, SLC25A14, H3F3B//H3F3C, FAM188A, MCM9, KLHL9, NACA, PAFAH1B2, CRLS1, TSSK4, LOC152217, ZNF568, ATP6V0D1//LOC100132855, CDC37L1, FNTA, SHFM1, JKAMP, TMEM126B, MRPL47, DENND1B, ATP6V0E1//SNORA74B, LIN7C, HAUS3//POLN, SLC30A7, VAMP3, OBFC2A, MAGT1, STARD3NL, C5orf15, PSMD10, RERE, RNF139, SFT2D2, SKP1, RNPC3//AMY2B, MYOM1, TIPRL, HPRT1, TRIM21, VRK2, CDKN1B, ANKRA2, RAP2B, FAM127B//FAM127C//FAM127A, FAM126A, TMEM161B, UNQ1887, FANCF, SELT, CYP20A1, RWDD1, ARPP19, SC5DL, TICAM2//TMED7//TMED7-TICAM2, STAM, LEPROTL1, RNF44, DCP1B, TNNT3, UCHL5, UPRT, SON, PIK3C3, SFRS1//FLJ44342, FBXO22//FBXO22OS, SCFD1, Cllorf31, TMTC3, CCDC132, TMBIM4//LOC100133322, ATAD1, APH1A, MYNN, HADHB, PIGN, RNA243, SLC38A9, C10orf84, CDKN1A, ATP7A, RPAP2, ZNF451, GSK3B//LOC100129275, TMSB10, KCTD5//PRO0461//PDPK1, VPS29, WIPF3//ZNRF2//LOC441208, SFRS5 and C11orf73 (hereafter referred to interchangeably herein as “group K IRS immune system biomarker genes” or “group K IRS biomarker genes”); and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: C13orf1, PRKCB, APOBEC3A//APOBEC3B, SFRS9, NCAPD2//SCARNA10//FADS1, GATS, LOC284757, TSHZ2, JAK1, MAPK13, RPN1, GNAS, CYTSA, TRPM6, C22orf30, PCMTD1//PXDNL, CCDC69, ARSD, MLL3//BAGE2, NCORH/C20orf191//LOC100131704, MRPS7, VEZF1, GSR, POU2F1, VPS4A, SMG7, PTP4A2, OSBP, GLCCI1thcag7.903, DOCK2, PCNX, GLTP, FBXO18, YY2, TCF20, NR2C2//MRPS25, TEX2, BAMBI, WHSC1L1, UBTF, FAH, GEMIN4, DDEF1IT1, FAM50A, VPS13D, SATB1//TBC1D5, PARP6, SETD5, PHF21A, IRF4, ZNF217, UBE4A, HIVEP1, HDLBP, GNAI2, MED13L, FOXP1, NSD1, DDOST, TMBIM6, ABLIM1, SYNRG//LOC100131822, KDM3B, ASH1L, NCOA2, GPRIN3, NCOA1, KLF3, LOC100288114//MGC9913, VPS26B, AHCYL1, CDC6, PLCG2, IL16, GIT2, TACC3, MAP4K4, NEK9, FAM149B1//FAM149B2, VPS8, ATXN7, WDFY4, ZC3H11A//RP11-74E24.2, THRAP3, ZNF346, AP3M2, CD14, CLASP1, ABCC2, ATXN7L1//RINT1//EFCAB10, IN080D, CTPS, LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LO C644397, DNASE1, LRRN3, ZNF318, PRKAR2A, MRPS15, ANKHD1-EIF4EBP3//ANKHDP/EIF4EBP3, BTF3L4, DGKA, C10orf119, MBD5, Cllorf30, CDC2L5, DPP4, DCTN4, TP53BP2, IMPDH2, GOT2, ELMO1and PARP1 (hereafter referred to interchangeably herein as “group L IRS immune system biomarker genes” or “group L IRS biomarker genes”).
• In some embodiments, the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: EEF1DP3, GIMAP7, ZNF839, PYGL, TNFAIP8L2, SFRS9, VIM, GLTP, WDFY4, APPL2, C4orf3, PLD1, LIN7A, ELP2, ZDHHC3, UBAPP/K1F24, C20orf177, FAM149B1//FAM149B2, E2F3, SPATA1, DACH1, FAM47E//STBD1, SVIL//hCG1783494, METTL9, LRRC42, NUPL1, UPP1, AFF2, SLC16A4, SET, CA4, HCK, C16orf72, EXT1, NOP58, FRZB, C9orf6//IKBKAP, VASP, ASB8//PHB, GTDC1, SLC39A9, FBXO34//KIAA0831, RABGEF1//tcag7.967//tcag7.951//KCTD7//LOC10029333 3, SLC28A3, WIPIMARSG, NFE2, GOLGAH/SCAI, C9orf84, RPS6KA2, PSMA7, C19orf59, ICA1, TOR1AIP2//TOR1AIP1//IFRG15, MSRA, FPR1, TP53I3, FOXL2, CD63, PIGC, CENPBD1, CYB5R1, GNB2, ZNF197, KLF7, NSFL1C, USF2, PARP6, MAP9, TSPO, CSTB, DDA1, SLC36A4, GFOD2, OCRL, ZNF232, APH1B, TALDOP/INTS8, DENND2C//BCAS2, RAB11FIP2, LARS, PLP2, EIF4E2, DNASE1L1//RPL10, AFTPH, TMCO3, RPA2, UQCRC1, ZDHHC3, and ACTR1A (hereafter referred to interchangeably herein as “group M IRS immune system biomarker genes” or “group M IRS biomarker genes”); and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: CD6, ITPA, PVRIG//PILRB//STAG3, FLT3LG, IL12RB1, MAP3K14//LOC100133991, FAM102A, TMC8, TMEM208, TMEM109, C1orf84, NADK, SEPT1, UBA7, CD5, C12orf62, C20orf112, FOXP4, EIF4A2//SNORA4, ZNF487//LOC439911, KCTD13, IL18BP//NUMA1, KPNA5, EDC4, ZNF587//ZNF417, NBR2, RPL28, ZNF738, SHFM1, CNO, C9orf82, RPL5//SNORA66//SNORD21//FAM69A, VAMP2, SIT1, SFRS3, LCK, IRF9, MRPS21, NEFM//LOC100129717, RCN2, BET1, C19orf6, SH2D1A, GLS, OR1C1, RBM14//RBM4, CCDC97, TUT1, MRPL14, ENOPH1, NAGK, DPM1, MPV17, SH2D3C, TMEM204, C3orf42, ARSD, FAM96A, LSM8, ATP5G1, KTI12, ARL4C, C11orf31, C16orf58//LOC100128371, CCDC109B//HIGD1A//CCDC13, NUDT21, ZFP106, ACTR6, LIX1L, MEF2D, ZNF407, TMEM18, NAT11, DNAJC24, PLEKHA3, GPN2, SMCR7, C7orf23//DMTF1, PPM1G, CNOT6L, NACA, FNTA, GRIA2, N4BP2L2//CG030, ENOSF1/TYMS, THBS4, LUC7L2, MOCS2, ZNF383, AKNA, UBE2Z, FLJ34077, SH3KBP1, POLR2F//LOC100131530, ILIA, UBE2V2, KIAA1919, PRKCB, SHOC2, RBM46, GRPEL2, KCNG3, PCDH10, XAB2, VPS52, MCCC2, NSMCE4A, PTP4A2, SNX2, COQ10A, C6orf182, RNF44, MOGS, DIRAS3, Mitochondrial, KIAA1826, SGK196, NSUN5//NSUN5B//NSUN5C, Mitochondrial, MORF4L2, MAK16//C8orf41, PILRB//PVRIG//STAG3, SAAL1, TMX3, PTPRG, MAPK1, DNAJA3, LEAP2, LMOD3, ASB6, MTMR10//MTMR15, HIBADH, MORC1, CORO1A//LOC606724, SFXN2, HSN2, AAAS, INHBA, MRPS7, LRRFIP1, KCTD7//RABGEF1, DCDC2//KAAG1, SLCO3A1, DENND4B, CFTR, MOG, QRFPR, BAT2//SNORA38, ITPR3, C3orf22, TNFRSF4, ZNF646//ZNF668, GCM2, CDK4//TSPAN31, FXC1, RNMT, CHST4, POLR3E, PDE8B, C6orf146, CHCHD5, TARBP1, TADA1L, PREX2, TRAP1//DNASE1, ZNHIT6, RGP1//GBA2, SMCP, ZC3H15, TRAPPC4, SARNP//DNAJC14, GNAS, C14orf104, IL20RA, WAC, SLIT3, C4orf39, TSEN54, PPP1R13B, TRIM35, HGC6.3, YIPF3, HQ0644/PRO0644, FAM13C//PHYHIPL, BCCIP//DHX32, ACADM, SUB1//TMEM183A, KPNA1, SPAG17//WDR3, KIAA1549, DIABLO//B3GNT4, ZBTB44, EIF1AD, RUNX1T1, TRIL, PTPLB, DHDDS, UPK1A, OTUB1, Clorf182, HAPLN2, SOBP, RYR3, LRRC17, TKTL2, TMBIM6, and GDNF (hereafter referred to interchangeably herein as “group N IRS immune system biomarker genes” or “group N IRS biomarker genes”).
• In some embodiments, the indicator is for determining a likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: SIRPG//SIRPA, GATA3, FAM102A, UPF3A, ATP13A5, CACNA1I and RANBP17//USP12 (hereafter referred to interchangeably herein as “group O IRS immune system biomarker genes” or “group O IRS biomarker genes”); and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from the following IRS immune system biomarker genes: GABRA6, HAPLN1, YSK4, FOXL2, TLL1, MECOM, COL3A1, HRG, SLC22A3, C8orf45, SCN7A and SNTG1 (hereafter referred to interchangeably herein as “group P IRS immune system biomarker genes” or “group P IRS biomarker genes”).
• In another broad form the present invention seeks to provide apparatus for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the apparatus including at least one electronic processing device that:
• • a) determines a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject;
  • b) determines a derived biomarker value using the pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers; and,
  • c) determines the indicator using the derived biomarker value.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a first pair and a second pair of reverse transcribed mRNAs, wherein the first pair comprises a PLAC8 reverse transcribed mRNA and a PLA2G7 reverse transcribed mRNA and wherein the second pair comprises a CEACAM4 reverse transcribed mRNA and a LAMP1 reverse transcribed mRNA.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group A IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group B IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group C IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group D IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group E IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group F IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least two pairs of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least two pairs of reverse transcribed mRNAs comprising a first pair and a second pair of reverse transcribed mRNAs, wherein the first pair comprises a reverse transcribed mRNA from a first IRS immune system biomarker gene and a reverse transcribed mRNA from a second IRS immune system biomarker gene, and wherein the second pair comprises a reverse transcribed mRNA from a third IRS immune system biomarker gene and a reverse transcribed mRNA from a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group K IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group L IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group M IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group N IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group O IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group P IRS immune system biomarker genes.
• The at least one oligonucleotide primer or probe can be hybridized to an individual one of the reverse transcribed mRNAs.
• The reverse transcribed mRNAs can be derived from components of the immune system.
• The reverse transcribed mRNAs can be derived from leukocytes.
• The reverse transcribed mRNAs can be derived from blood cells.
• The reverse transcribed mRNAs can be derived from peripheral blood cells.
• The composition can further comprise a labeled reagent for detecting the reverse transcribed mRNAs.
• The labeled reagent can be a labeled said at least one oligonucleotide primer or probe.
• The labeled reagent can be a labeled said reverse transcribed mRNA.
• The labeled reagent can be a labeled oligonucleotide linker or tag for labeling a said reverse transcribed mRNA.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of the PLA2G7 gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of the PLAC8 gene, wherein the second pair of reagents comprises: (iii) a reagent that allows quantification of a polynucleotide expression product of the CEACAM4 gene; and (iv) a reagent that allows quantification of a polynucleotide expression product of the LAMP1 gene.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and a healthy condition, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of ipSIRS and a healthy condition, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least two pairs of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, and wherein the second pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of a third IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of severe sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the pair of biomarker values comprises at least one of:
• • a) a first pair of biomarker values comprising first and second biomarker values corresponding to first and second biomarkers, wherein the first immune system biomarker represents a polynucleotide expression product of the PLA2G7 gene and wherein the second immune system biomarker representing a polynucleotide expression product of the PLAC8 gene, and
  • b) a second pair of biomarker values comprises third and fourth biomarker values corresponding to third and fourth immune system biomarkers, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of the CEACAM4 gene and wherein the fourth immune system biomarker represents a polynucleotide expression product of the LAMP1 gene.
• Typically the indicator-determining method comprises: determining the first pair and second pair of biomarker values and determining a first derived biomarker value calculated using the first pair of biomarker values and a second derived biomarker value calculated using the second pair of biomarker values; and determining the indicator based on a combination of the first and second derived biomarker values.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression of inSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating inSIRS based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of inSIRS in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression of ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating ipSIRS based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of ipSIRS in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to of first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least two pairs of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least two derived biomarker values using the at least two pairs of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of each pair of immune system biomarkers; and (c) determining the indicator based on the at least two derived biomarker values, wherein the at least one pair of biomarker values comprises a first pair of biomarker values comprising first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, and a second pair of biomarker values comprising third and fourth biomarker values corresponding to third and fourth immune system biomarkers, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of a third IRS immune system biomarker gene and wherein the fourth immune system biomarker represents a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of mild sepsis and septic shock, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of severe sepsis and septic shock, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes.
• In some embodiments, the method comprises taking the sample from the subject and obtaining the indicator according to the indicator-determining method.
• In some embodiments, the method comprises: sending the sample taken from the subject to a laboratory at which the indicator is determined.
• Typically, the sample comprises cells obtained from the subject or a nucleic acid sample thereof.
• In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of inSIRS and ipSIRS, respectively; and,
  • e) determining a likelihood of the subject having inSIRS or ipSIRS in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between inSIRS and a healthy condition in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of inSIRS and healthy condition, respectively; and,
  • e) determining a likelihood of the subject having inSIRS or the healthy condition in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between ipSIRS and a healthy condition in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of ipSIRS and healthy condition, respectively; and,
  • e) determining a likelihood of the subject having ipSIRS or the healthy condition in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of inSIRS and ipSIRS, respectively; and,
  • e) determining a likelihood of the subject having inSIRS or ipSIRS in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of inSIRS and ipSIRS, respectively; and,
  • e) determining a likelihood of the subject having inSIRS or ipSIRS in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between mild sepsis and severe sepsis in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of mild sepsis and severe sepsis, respectively; and,
  • e) determining a likelihood of the subject having mild sepsis or severe sepsis in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between mild sepsis and septic shock in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of mild sepsis and septic shock, respectively; and,
  • e) determining a likelihood of the subject having mild sepsis or septic shock in accordance with the results of the comparison.
• In another broad form the present invention seeks to provide a method for differentiating between severe sepsis and septic shock in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being indicative of a concentration of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes;
  • c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and,
  • d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of severe sepsis and septic shock, respectively; and,
  • e) determining a likelihood of the subject having severe sepsis or septic shock in accordance with the results of the comparison.
• Typically the method includes determining:
• • a) a first derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression products using the first pair of biomarker values;
  • b) a second derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression products using the first pair of biomarker values; and,
  • c) determining the indicator by combining the first and second derived biomarker values.
• Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with inSIRS and ipSIRS respectively.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of:
    • i) a first pair of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of:
    • i) a first pair of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes;
    • ii) a second pair of polynucleotide expression products of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the second IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:
• • a) obtaining a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) amplifying at least some polynucleotide expression products in the sample;
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes;
  • d) determining the indicator by determining a difference between the amplification amounts; and,
  • e) using the indicator to assess the likelihood of a biological subject having a medical condition.
• Typically the method includes determining:
• • a) a first derived biomarker value by determining a difference between the amplification amounts of the first pair of polynucleotide expression products;
  • b) a second derived biomarker value by determining a difference between the amplification amounts of the second pair of polynucleotide expression products;
  • c) determining the indicator by adding the first and second derived biomarker values.
• Typically the method includes determining:
• • a) comparing the indicator to first and second indicator references, wherein the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, one of the first and second groups consisting of individuals diagnosed with the medical condition; and,
  • b) determining a likelihood of the subject having the medical condition in accordance with the results of the comparison.
• Typically the amplification amount is at least one of:
• • a) a cycle time;
  • b) a number of cycles;
  • c) a cycle threshold;
  • d) an amplification time; and,
  • e) relative to an amplification amount of another amplified product.
• In another broad form the present invention seeks to provide a method for use in assessing the likelihood of a biological subject having a medical condition, the method including, in one or more processing devices:
• • a) determining a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • b) determining an indicator indicative of a ratio of the concentrations of the polynucleotide expression products using the pair of biomarker values;
  • c) retrieving previously determined first and second indicator references from a database, the first and second indicator references being determined based on indicators determined from first and second groups of a reference population, one of the groups consisting of individuals diagnosed with the medical condition;
  • d) comparing the indicator to the first and second indicator references;
  • e) using the results of the comparison to determine a probability indicative of the subject having the medical condition; and,
  • f) generating a representation of the probability, the representation being displayed to a user to allow the user to assess the likelihood of a biological subject having at least one medical condition.
• Typically the method includes determining:
• • a) a first derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression products using the first pair of biomarker values;
  • b) a second derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression products using the first pair of biomarker values; and,
  • c) determining the indicator by combining the first and second derived biomarker values.
• In another broad form the present invention seeks to provide apparatus for determining an indicator used in determining the likelihood of a biological subject having at least one medical condition, the apparatus including:
• • a) a sampling device that obtains a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) a measuring device that quantifies polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • c) at least one processing device that:
    • i) receives an indication of the pair of biomarker values from the measuring device;
    • ii) determines an indicator using a ratio of the concentration of the first and second polynucleotide expression products using the biomarker values; and, iii) compares the indicator to at least one indicator reference; and,
    • iv) determines a likelihood of the subject having the at least one medical condition using the results of the comparison; and,
    • v) generates a representation of the indicator and the likelihood for display to a user.
• In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) in a measuring device:
    • i) amplifying at least some polynucleotide expression products in the sample;
    • ii) determining an amplification amount representing a degree of amplification required to obtain a defined level of polynucleotide expression products including:
      • (1) amplification amounts for a first pair of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
      • (2) amplification amounts for a second pair of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • c) in a processing system:
    • i) retrieving the amplification amounts;
    • ii) determining an indicator by:
      • (1) determining a first derived biomarker value indicative of a ratio of concentrations of the first pair of polynucleotide expression products by determining a difference between the amplification amounts for the first pair;
      • (2) determining a second derived biomarker value indicative of a ratio of concentrations of the second pair of polynucleotide expression products by determining a difference between the amplification amounts for the second pair;
      • (3) determining the indicator by adding the first and second derived biomarker values;
    • iii) retrieving previously determined first and second indicator references from a database, wherein the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with inSIRS and ipSIRS respectively;
    • iv) comparing the indicator to the first and second indicator references;
    • v) using the results of the comparison to determine a probability of the subject being classified within the first or second group;
    • vi) generating a representation at least partially indicative of the indicator and the probability; and,
    • vii) providing the representation to a user to allow the user to assess the likelihood of a biological subject having at least one medical condition.
• In another broad form the present invention seeks to provide a method for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:
• • a) determining a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject;
  • b) determining the indicator using a combination of the plurality of biomarker values, wherein:
    • i) at least two biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
    • ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Typically the method includes:
• • c) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker of the biological subject; and,
  • d) determining the indicator by applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.
• Typically the function includes at least one of:
• • a) multiplying two biomarker values;
  • b) dividing two biomarker values;
  • c) adding two biomarker values;
  • d) subtracting two biomarker values;
  • e) a ratio of two biomarker values;
  • f) a weighted sum of at least two biomarker values;
  • g) a log sum of at least two biomarker values; and,
  • h) a sigmoidal function of at least two biomarker values.
• Typically the method includes determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.
• Typically the method includes combining at least two biomarker values to determine an indicator value representing the indicator.
• Typically the method includes combining at least two biomarker values using a combining function, the combining function being at least one of:
• • a) an additive model;
  • b) a linear model;
  • c) a support vector machine;
  • d) a neural network model;
  • e) a random forest model;
  • f) a regression model;
  • g) a genetic algorithm;
  • h) an annealing algorithm;
  • i) a weighted sum;
  • j) a nearest neighbor model; and,
  • k) a probabilistic model.
• Typically at least one of the at least two biomarkers is a derived biomarker.
• Typically the method includes:
• • a) determining a first derived biomarker value, the first derived biomarker value being indicative of a ratio of concentrations of the first and second immune system biomarkers;
  • b) determining a second derived biomarker value, the second derived biomarker value being indicative of a ratio of concentrations of the third and fourth measured immune system biomarkers; and,
  • c) adding the first and second derived biomarker values to generate an indicator value.
• Typically the method is performed at least in part using an electronic processing device.
• Typically the method includes, in the electronic processing device:
• • a) receiving a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding immune system biomarker;
  • b) applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker; and,
  • c) combining at least one derived biomarker value and at least one other biomarker value to determine the indicator.
• Typically the mutual correlation range is at least one of:
• • a) ±0.8;
  • b) ±0.7;
  • c) ±0.6;
  • d) ±0.5;
  • e) ±0.4;
  • f) ±0.3;
  • g) ±0.2; and,
  • h) ±0.1.
• Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.
• Typically the condition correlation range is at least one of:
• • a) ±0.9;
  • b) ±0.8;
  • c) ±0.7;
  • d) ±0.6;
  • e) ±0.5; and,
  • f) ±0.4.
• Typically the performance threshold is indicative of an explained variance of at least one of:
• • a) 0.4;
  • b) 0.5;
  • c) 0.6;
  • d) 0.7;
  • e) 0.8; and,
  • f) 0.9.
• Typically the biomarker value is indicative of a level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.
• Typically the method includes generating a representation of the indicator.
• Typically the representation includes:
• • a) an alphanumeric indication of the indicator;
  • b) a graphical indication of a comparison of the indicator to one or more indicator references;
  • c) an alphanumeric indication of a likelihood of the subject having at least one medical condition.
• Typically the method includes:
• • a) comparing the indicator to an indicator reference; and,
  • b) determining a likelihood in accordance with results of the comparison.
• Typically the indicator reference is based on at least one of:
• • a) an indicator threshold range;
  • b) an indicator threshold; and,
  • c) an indicator distribution.
• Typically the indicator reference is derived from indicators determined for a number of individuals in a reference population.
• Typically the indicator reference is based on a distribution of indicators determined for a group of a reference population, the group consisting of individuals diagnosed with the medical condition.
• Typically the reference population includes:
• • a) a plurality of individuals of different sexes;
  • b) a plurality of individuals of different ethnicities;
  • c) a plurality of healthy individuals;
  • d) a plurality of individuals suffering from at least one diagnosed medical condition;
  • e) a plurality of individuals showing clinical signs of at least one medical condition; and,
  • f) first and second groups of individuals, each group of individuals suffering from a respective diagnosed medical condition.
• Typically the indicator is for use in determining the likelihood that a biological subject has at least one medical condition, and wherein the reference population includes:
• • a) individuals presenting with clinical signs of the at least one medical condition;
  • b) individuals diagnosed with the at least one medical condition; and,
  • c) healthy individuals.
• Typically the indicator reference is retrieved from a database.
• Typically the likelihood is based on a probability generated using the results of the comparison.
• Typically the indicator is for determining a likelihood of the subject having a first or second condition, and wherein the method includes:
• • a) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of first and second conditions; and,
  • b) determining the likelihood in accordance with the results of the comparison.
• Typically the method includes:
• • a) determining first and second indicator probabilities using the results of the comparisons; and,
  • b) combining the first and second indicator probabilities to determine a condition probability indicative of the likelihood.
• Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively.
• Typically the method includes:
• • a) obtaining a sample taken from the biological subject, the sample including polynucleotide expression products;
  • b) quantifying at least some of the polynucleotide expression products within the sample to determine at least a pair of biomarker values;
  • c) determining the indicator at least in part using the pair of biomarker values;
• Typically the method includes, determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.
• Typically the method includes:
• • a) quantifying polynucleotide expression products by:
  • b) amplifying at least some polynucleotide expression products in the sample; and,
  • c) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and,
  • d) determining the indicator by determining a difference between the amplification amounts.
• Typically the amplification amount is at least one of:
• • a) a cycle time;
  • b) a number of cycles;
  • c) a cycle threshold;
  • d) an amplification time; and,
  • e) relative to an amplification amount of another amplified product.
• Typically the method includes determining:
• • a) a first derived biomarker value by determining a difference between the amplification amounts of a first pair of polynucleotide expression products;
  • b) a second derived biomarker value by determining a difference between the amplification amounts of a second pair of polynucleotide expression products;
  • c) determining the indicator by adding the first and second derived biomarker values.
• Typically the immune system biomarker is an IRS biomarker of an immune system of the biological subject that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult.
• Typically the indicator is for determining a likelihood of the subject having at least one of inSIRS and ipSIRS, and wherein the method includes:
• • a) determining a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
  • b) determining a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene; and,
  • c) determining the indicator using the first and second pairs of biomarker values.
• Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method includes:
• • a) determining a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
  • b) determining a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene; and,
  • c) determining the indicator using the first and second pairs of biomarker values.
• Typically the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS immune system biomarker genes; and
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS immune system biomarker genes.
• Typically the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS immune system biomarker genes; and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS immune system biomarker genes.
• Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS immune system biomarker genes; and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS immune system biomarker genes.
• Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first, second, third and fourth IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS immune system biomarker genes;
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS immune system biomarker genes;
  • c) the third IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS immune system biomarker genes; and,
  • d) the fourth IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS immune system biomarker genes.
• Typically the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product and the fourth IRS immune system biomarker is a LAMP1 expression product.
• Typically the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS immune system biomarker genes; and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS immune system biomarker genes.
• Typically the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS immune system biomarker genes; and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS immune system biomarker genes.
• Typically the indicator is for determining a likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:
• • a) the first IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS immune system biomarker genes; and,
  • b) the second IRS immune system biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS immune system biomarker genes.
• In another broad form the present invention seeks to provide apparatus for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the apparatus including a processing device that:
• • a) determines a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject;
  • b) determines the indicator using a combination of the plurality of biomarker values, wherein:
    • i) at least two biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
    • ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• In one broad form the present invention seeks to provide a method for determining an indicator for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the method including:
• • a) determining a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding biomarker of the biological subject;
  • b) determining the indicator using a combination of the plurality of biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition, wherein:
    • i) at least two markers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
    • ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Typically the method includes:
• • a) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker of the biological subject; and,
  • b) applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.
• Typically the function includes at least one of:
• • a) multiplying two biomarker values;
  • b) dividing two biomarker values;
  • c) adding two biomarker values;
  • d) subtracting two biomarker values;
  • e) a weighted sum of at least two biomarker values;
  • f) a log sum of at least two biomarker values; and,
  • g) a sigmoidal function of at least two biomarker values.
• Typically the method includes determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.
• Typically the method includes combining at least two biomarker values to determine an indicator value representing the indicator.
• Typically the method includes combining at least two biomarker values using a combining function, the combining function being at least one of:
• • a) an additive model;
  • b) a linear model;
  • c) a support vector machine;
  • d) a neural network model;
  • e) a random forest model;
  • f) a regression model;
  • g) a genetic algorithm;
  • h) an annealing algorithm;
  • i) a weighted sum;
  • j) a nearest neighbor model; and,
  • k) a probabilistic model.
• Typically at least one of the at least two biomarkers is a derived biomarker.
• Typically the method includes:
• • a) determining a first derived biomarker value, the first derived biomarker value being a ratio of first and second measured biomarker values;
  • b) determining a second derived biomarker value, the second derived biomarker value being a ratio of third and fourth measured biomarker values; and,
  • c) adding the first and second derived biomarker values to generate an indicator value.
• Typically the method includes:
• • a) determining an indicator value;
  • b) comparing the indicator value to at least one indicator value range; and,
  • c) using a result of the comparison in diagnosing the presence, absence, degree or prognosis of at least one condition.
• Typically the method is performed at least in part using an electronic processing device.
• Typically the method includes, in the electronic processing device:
• • a) receiving a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker of the biological subject;
  • b) applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker; and,
  • c) combining at least one derived biomarker value and at least one other biomarker value to determine an indicator value.
• Typically the method includes generating a representation in accordance with the at least one indicator value.
• Typically the method includes:
• • a) comparing the indicator value to at least one indicator value range; and,
  • b) displaying a result of the comparison.
• Typically the mutual correlation range is at least one of:
• • a) ±0.8;
  • b) ±0.7;
  • c) ±0.6;
  • d) ±0.5;
  • e) ±0.4;
  • f) ±0.3;
  • g) ±0.2; and,
  • h) ±0.1.
• Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.
• Typically the condition correlation range is at least one of:
• • a) ±0.9;
  • b) ±0.8;
  • c) ±0.7;
  • d) ±0.6;
  • e) ±0.5; and,
  • f) ±0.4.
• Typically the performance threshold is indicative of an explained variance of at least one of:
• • a) 0.4;
  • b) 0.5;
  • c) 0.6;
  • d) 0.7;
  • e) 0.8; and,
  • f) 0.9.
• Typically the biomarker value is indicative of a level or abundance of a molecule or entity selected from one or more of:
• • a) A nucleic acid molecule;
  • b) A proteinaceous molecule;
  • c) An amino acid
  • d) A carbohydrate;
  • e) A lipid;
  • f) A steroid;
  • g) An inorganic molecule;
  • h) An ion;
  • i) A drug;
  • j) A chemical;
  • k) A metabolite;
  • l) A toxin;
  • m) A nutrient;
  • n) A gas;
  • o) A cell;
  • p) A pathogenic organism; and,
  • q) A non-pathogenic organism.
• In another broad form the present invention seeks to provide apparatus for determining an indicator for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the apparatus including an electronic processing device that:
• • a) determines a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding biomarker of the biological subject;
  • b) determines the indicator using a combination of the plurality of biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition, wherein:
    • i) at least two biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
    • ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• In another broad form the present invention seeks to provide a diagnostic signature for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the diagnostic signature defining a combination of at least two biomarker values corresponding to values of biomarkers that can be measured for or derived from the biological subject, wherein:
• • a) at least two biomarkers have a mutual correlation for the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
  • b) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of the at least two biomarkers to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being a variance explained of at least 0.3.
• Typically the diagnostic signature defines a function to be applied to at least one measured biomarker value to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.
• Typically the function includes at least one of:
• • a) multiplying two biomarker values;
  • b) dividing two biomarker values;
  • c) adding two biomarker values;
  • d) subtracting two biomarker values;
  • e) a weighted sum of at least two biomarker values;
  • f) a log sum of at least two biomarker values; and,
  • g) a sigmoidal function of at least two biomarker values.
• Typically the at least one derived biomarker value corresponds to a ratio of two measured biomarker values.
• Typically the diagnostic signature defines a combination of at least two biomarker values for determining an indicator value representing the indicator.
• Typically the diagnostic signature defines a combining function for combining at least two biomarker values, the combining function being at least one of:
• • a) an additive model;
  • b) a linear model;
  • c) a support vector machine;
  • d) a neural network model;
  • e) a random forest model;
  • f) a regression model;
  • g) a genetic algorithm;
  • h) an annealing algorithm; and,
  • i) a weighted sum.
• Typically at least one of the at least two biomarkers is a derived biomarker.
• Typically the diagnostic signature defines:
• • a) a first derived biomarker value, the first derived biomarker value being a ratio of first and second measured biomarker values;
  • b) a second derived biomarker value, the second derived biomarker value being a ratio of third and fourth measured biomarker values; and,
  • c) a combination of the first and second derived biomarker values to generate an indicator value.
• Typically the diagnostic signature defines at least one indicator value range and wherein comparison of at least one indicator value to the at least one indicator value range is used in diagnosing the presence, absence, degree or prognosis of at least one condition.
• Typically the mutual correlation range is at least one of:
• • a) ±0.8;
  • b) ±0.7;
  • c) ±0.6;
  • d) ±0.5;
  • e) ±0.4;
  • f) ±0.3;
  • g) ±0.2; and,
  • h) ±0.1.
• Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.
• Typically the condition correlation range is at least one of:
• • a) ±0.9;
  • b) ±0.8;
  • c) ±0.7;
  • d) ±0.6;
  • e) ±0.5; and,
  • f) ±0.4.
• Typically the performance threshold is indicative of an explained variance of at least one of:
• • a) 0.4;
  • b) 0.5;
  • c) 0.6;
  • d) 0.7;
  • e) 0.8; and,
  • f) 0.9.
• Typically the biomarker value is indicative of a level or abundance of a molecule or entity selected from one or more of:
• • a) A nucleic acid molecule;
  • b) A proteinaceous molecule;
  • c) An amino acid
  • d) A carbohydrate;
  • e) A lipid;
  • f) A steroid;
  • g) An inorganic molecule;
  • h) An ion;
  • i) A drug;
  • j) A chemical;
  • k) A metabolite;
  • l) A toxin;
  • m) A nutrient;
  • n) A gas;
  • o) A cell;
  • p) A pathogenic organism; and,
  • q) A non-pathogenic organism.
• In another broad form the present invention seeks to provide a method of identifying biomarkers for use in a diagnostic signature, the diagnostic signature being for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the method including:
• • a) for a number of candidate biomarkers, ranking the candidate biomarkers in accordance with the ability of each biomarker to distinguish between the presence, absence, degree or prognosis of at least one condition in a biological subject;
  • b) selecting at least two candidate biomarkers in accordance with the ranking, the at least two biomarkers having a mutual correlation for the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9;
  • c) determining a performance value of a combination of the at least two candidate biomarkers; and,
  • d) defining a diagnostic signature in accordance with the combination of the at least two biomarkers if the performance value is greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Typically the method includes determining a combination of at least two candidate biomarkers using a combining function, the combining function being at least one of:
• • a) an additive model;
  • b) a linear model;
  • c) a support vector machine;
  • d) a neural network model;
  • e) a random forest model;
  • f) a regression model;
  • g) a genetic algorithm;
  • h) an annealing algorithm; and,
  • i) a weighted sum.
• Typically the method includes:
• • a) selecting a next combining function;
  • b) determining if a performance value of a combination of the at least two candidate biomarkers determined by the next combining function is greater than or equal to a performance threshold; and,
  • c) if the performance value is not greater than or equal to a performance threshold, repeating steps a) and b) for successive combining functions.
• Typically the method includes:
• • a) selecting two candidate biomarkers;
  • b) determining if a performance value of a combination of the two candidate biomarkers is greater than or equal to a performance threshold; and,
  • c) if the performance value is not greater than or equal to a performance threshold:
    • i) combining the selected candidate biomarkers with at least one additional candidate biomarker; and,
    • ii) repeating steps a) and b) with at least one additional candidate biomarker.
• Typically the method includes combining a number of candidate biomarkers up to a limit.
• Typically the method includes:
• • a) selecting a highest ranked candidate biomarker;
  • b) selecting a next highest ranked candidate biomarker;
  • c) for the selected candidate markers, determining if the mutual correlation for the candidate biomarkers within the mutual correlation range; and,
  • d) if not, repeating steps b) and c) until two candidate biomarkers are selected having a mutual correlation within the mutual correlation range.
• Typically the method includes:
• • a) defining at least two groups of candidate biomarkers, candidate biomarkers in different groups having a mutual correlation within the mutual correlation range;
  • b) ranking the candidate biomarkers in each group; and,
  • c) selecting candidate biomarkers from the different groups.
• Typically the method includes:
• • a) using reference data for at least one individual to define a number of groups indicative of the presence, absence, degree or prognosis of the at least one condition; and,
  • b) using at least one analysis technique to identify a number of candidate biomarkers that are potentially useful for distinguishing the groups.
• Typically the method includes using reference values measured for reference biomarkers for the at least one individual to identify the candidate biomarkers.
• Typically the method includes using reference values to filter reference biomarkers to determine candidate biomarkers.
• Typically at least one of the candidate biomarkers is a derived biomarker derived from at least one of the reference biomarkers using a function.
• Typically the derived biomarkers are derived from filtered biomarkers.
• Typically the method includes:
• • a) applying a function to at least one of the reference values to determine at least one derived reference biomarker value, the at least one derived reference biomarker value being indicative of a value of a corresponding derived reference biomarker; and,
  • b) determining at least one candidate biomarker using the at least one derived reference biomarker value.
• Typically the method includes:
• • a) using reference data for at least one individual to define a number of groups indicative of the presence, absence, degree or prognosis of the at least one condition; and,
  • b) for each group, combining a range of at least two reference biomarker values to determine an indicator value range for the group.
• Typically the mutual correlation range is at least one of:
• • a) ±0.8;
  • b) ±0.7;
  • c) ±0.6;
  • d) ±0.5;
  • e) ±0.4;
  • f) ±0.3;
  • g) ±0.2; and,
  • h) ±0.1.
• Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.
• Typically the condition correlation range is at least one of:
• • a) ±0.9;
  • b) ±0.8;
  • c) ±0.7;
  • d) ±0.6;
  • e) ±0.5; and,
  • f) ±0.4.
• Typically the performance threshold is indicative of an explained variance of at least one of:
• • a) 0.4;
  • b) 0.5;
  • c) 0.6;
  • d) 0.7;
  • e) 0.8; and,
  • f) 0.9.
• In another broad form the present invention seeks to provide apparatus for identifying markers for use in a diagnostic signature, the diagnostic signature being for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the apparatus including an electronic process device that:
• • a) for a number of candidate biomarkers, ranks the candidate biomarkers in accordance with the ability of each biomarker to distinguish between the presence, absence, degree or prognosis of at least one condition in a biological subject;
  • b) selects at least two candidate biomarkers in accordance with the ranking, the at least two biomarkers having a mutual correlation for the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9;
  • c) determines a performance value of a combination of the at least two candidate biomarkers; and,
  • d) defines a diagnostic signature in accordance with the combination of the at least two biomarkers if the performance value is greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or a healthy condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes.
• In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of ipSIRS or a healthy condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from ipSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes.
• In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes.
• In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least four IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a second IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a third IRS biomarker group, and wherein at least one other of the at least four IRS biomarkers is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes.
• Suitably, the first IRS biomarker is a PLA2G7 expression product, the second IRS biomarker is a PLAC8 expression product, the third IRS biomarker is a CEACAM4 expression product and the fourth IRS biomarker is a LAMP1 expression product.
• In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of mild sepsis or severe sepsis in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes.
• In another form the present invention seeks to provide a method for diagnosing the presence or absence of mild sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes.
• In another form the present invention seeks to provide a method for diagnosing the presence or absence of severe sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes.
• In another broad form the present invention seeks to provide a kit comprising: (i) a reagent that allows quantification of a first IRS biomarker; and (ii) a reagent that allows quantification of a second IRS biomarker, wherein the first and second IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a stage of ipSIRS selected from mild sepsis, severe sepsis and septic shock, which at least one condition lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the first and second IRS biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the first and second IRS biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.
• Suitably, the kit further comprises: (iii) a reagent that allows quantification of a third IRS biomarker; and (iv) a reagent that allows quantification of a fourth IRS biomarker, wherein the third and fourth IRS biomarkers have a mutual correlation in respect of at the least one condition that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the third and fourth IRS biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the third and fourth IRS biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.
• Suitably, the kit is for diagnosing the presence or absence of inSIRS or a healthy condition, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes.
• Suitably, the kit is for diagnosing the presence or absence of ipSIRS or a healthy condition, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes.
• Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes.
• Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein the first IRS biomarker is selected from a first IRS biomarker group, wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the third IRS biomarker is selected from a third IRS biomarker group and wherein the fourth IRS biomarker is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes.
• Suitably, the first IRS biomarker is a PLA2G7 expression product, the second IRS biomarker is a PLAC8 expression product, the third IRS biomarker is a CEACAM4 expression product and the fourth IRS biomarker is a LAMP1 expression product.
• Suitably, the kit is for diagnosing the presence or absence of mild sepsis or severe sepsis, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes.
• Suitably, the kit is for diagnosing the presence or absence of mild sepsis or septic shock, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes.
• Suitably, the kit is for diagnosing the presence or absence of severe sepsis or septic shock, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes.
• In another broad form the present invention seeks to provide a method for treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis or septic shock) in a subject, the method comprising (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of the at least one condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (c) administering to the subject, on the basis that the indicator indicates the presence of inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the indicator indicates the presence of ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.
• Suitably, the method further comprises: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker.
• Suitably, the function includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.
• In another broad form the present invention seeks to provide a method of monitoring the efficacy of a particular treatment regimen in a subject towards a desired health state, the method comprising: a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject after treatment with a treatment regimen; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, and (c) determining that the treatment regimen is effective for changing the health status of the subject to the desired health state on the basis that the indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.
• In another broad form the present invention seeks to provide a method of correlating a biomarker signature with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject having the condition and for whom an effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of the condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the condition, or to provide a prognosis for the condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with an effective treatment regimen for the condition.
• In another broad form the present invention seeks to provide a method of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject after treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at the least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the post-treatment indicator indicates whether the treatment regimen is effective for treating the condition in the subject on the basis that post-treatment indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.
• In another broad form the present invention seeks to provide a method of correlating a biomarker signature with a positive or negative response or a side effect to a treatment regimen, the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with a positive or negative response to the treatment regimen.
• In another broad form the present invention seeks to provide a method of determining a positive or negative response to a treatment regimen and/or a side effect of a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method: (a) correlating a reference biomarker signature with a positive or negative response or a side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a control biological subject or control group, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen, based on the correlation of the reference biomarker signature with the positive or negative response or side effect to the treatment regimen.
• In another broad form the present invention seeks to provide a method of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen by a biological subject, the method comprising: (a) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the sample biomarker signature is correlated with a positive or negative response to the treatment regimen and/or to a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen based on the sample biomarker signature.
BRIEF DESCRIPTION OF THE DRAWINGS
• An example of the present invention will now be described with reference to the accompanying drawings, in which:—
• FIG. 1A is a flowchart of an example of a method for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition in a biological subject;
• FIG. 1B is a flowchart of an example of a method for identifying biomarkers for use in a biomarker signature;
• FIG. 2 is a schematic diagram of an example of a distributed computer architecture;
• FIG. 3 is a schematic diagram of an example of a processing system of FIG. 2;
• FIG. 4 is a schematic diagram of an example of a computer system of FIG. 2;
• FIG. 5 is a flowchart of a specific example of a method for identifying biomarkers for use in a biomarker signature;
• FIG. 6A is a flowchart of a first example of a method for selecting candidate biomarkers;
• FIG. 6B is a flowchart of a second example of a method for selecting candidate biomarkers;
• FIG. 7 is a flowchart of a second example of a method for use in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition in a biological subject;
• FIG. 8A is a plot of 941 mRNA biomarkers against the AUC for differentiating between healthy condition and post-surgical inflammation (PS) (also referred to herein as “infection-negative SIRS” (inSIRS)), for individual biomarkers having an AUC greater than 0.7;
• FIG. 8B is a box and whisker plot showing the best mRNA biomarker for separating healthy condition and PS;
• FIG. 8C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating healthy condition and PS with all derived biomarkers having an AUC of 1.0;
• FIG. 8D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating healthy condition and PS;
• FIGS. 8E and 8F are two plots showing the correlation to each other of the biomarkers in each group;
• FIGS. 8G and 8H are two plots demonstrating the AUC of the biomarkers in each group (groups 1 and 2);
• FIG. 8I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;
• FIG. 9A is a plot of the 941 mRNA biomarkers against the AUC for differentiating between healthy condition and sepsis (also referred to herein as “infection-positive SIRS” (ipSIRS)) with all individual biomarkers having an AUC greater than 0.7;
• FIG. 9B is a box and whisker plot showing the best mRNA biomarker for separating healthy condition and sepsis;
• FIG. 9C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating healthy condition and sepsis with all derived biomarkers having an AUC of 1.0;
• FIG. 9D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating healthy condition and sepsis;
• FIGS. 9E and 9F show the correlation to each other of the biomarkers groups correlated to each group;
• FIGS. 9G and 9H are two plots demonstrating the AUC of the biomarkers in each group (bucketgroups 1 and 2);
• FIG. 9I is a box and whisker plot showing that when biomarkers are derived from bucketgroup 1 and bucketgroup 2 that a greater overall AUC is obtained;
• FIG. 10A is a plot of the 359 mRNA biomarkers against the AUC for differentiating between PS and sepsis with all individual biomarkers having had an AUC greater than 0.7;
• FIG. 10B is a box and whisker plot showing the best mRNA biomarker for separating PS and sepsis;
• FIG. 10C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating PS and sepsis with all derived biomarkers having an AUC of 0.9;
• FIG. 10D is a box and whisker plot of the best derived biomarker for separating PS and sepsis;
• FIG. 10E is a plot showing the correlation of the biomarkers in each bucketgroup to the condition;
• FIG. 10F is a box and whisker plot showing that when biomarkers are derived from bucketgroup 1 and bucketgroup 2 that a greater overall AUC is obtained;
• FIG. 10G is a plot demonstrating the AUC of the biomarkers in each of four bucketgroups;
• FIG. 10H is a box and whisker plot showing that when biomarkers are derived from each of the four bucketgroups a greater overall AUC is obtained;
• FIG. 11A is a plot of the 66 mRNA biomarkers against the AUC for differentiating between mild sepsis and severe sepsis with all individual biomarkers selected having an AUC greater than 0.7;
• FIG. 11B is a box and whisker plot showing the best mRNA biomarker for separating mild sepsis and severe sepsis;
• FIG. 11C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating mild sepsis and severe sepsis with all derived biomarkers have an AUC of at least 0.87;
• FIG. 11D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating mild sepsis and severe sepsis;
• FIGS. 11E and 11F are plots showing the correlation to each other of the biomarkers in each group;
• FIGS. 11G and 11H are plots demonstrating the AUC of the biomarkers in each group;
• FIG. 11I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;
• FIG. 12A is a plot of the 48 mRNA biomarkers against the AUC for differentiating between mild sepsis and septic shock (also referred to herein as “infection-positive SIRS-shock” (ipSIRS-shock)) with all individual biomarkers having an AUC greater than 0.7;
• FIG. 12B is a box and whisker plot showing the best mRNA biomarker for separating mild sepsis and septic shock;
• FIG. 12C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating mild sepsis and septic shock with all derived biomarkers having an AUC of at least 0.793;
• FIG. 12D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating mild sepsis and septic shock;
• FIGS. 12E and 12F are plots showing the correlation to each other of the biomarkers in each group;
• FIGS. 12G and 12H are plots demonstrating the AUC of the biomarkers in each group;
• FIG. 12I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;
• FIG. 13A is a plot of the 61 mRNA biomarkers against the AUC for differentiating between severe sepsis and septic shock with all individual biomarkers selected having an AUC greater than 0.7;
• FIG. 13B is a box and whisker plot showing the best mRNA biomarker for separating severe sepsis and septic shock;
• FIG. 13C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating severe sepsis and septic shock with all derived biomarkers have an AUC of at least 0.821;
• FIG. 13D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating severe sepsis and septic shock;
• FIGS. 13E and 13F are plots showing the correlation to each other of the biomarkers in each group;
• FIGS. 13G and 13H are plots demonstrating the AUC of the biomarkers in each group;
• FIG. 13I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;
• FIG. 14 is a user interface illustrating an example of a thermal cycler protocol;
• FIG. 15 is a diagram of an example of a report; and,
• FIGS. 16A to 16L are box and whisker plots for the top twelve biomarker combinations for distinguishing between healthy condition and PS;
• FIGS. 17A to 17L are box and whisker plots for the top twelve biomarker combinations for distinguishing between healthy condition and sepsis;
• FIGS. 18A to 18L are box and whisker plots for the top twelve biomarker combinations for distinguishing between PS and sepsis;
• FIGS. 19A to 19L are box and whisker plots for the top twelve biomarker combinations for distinguishing between sepsis and severe sepsis;
• FIGS. 20A to 20L are box and whisker plots for the top twelve biomarker combinations for distinguishing between severe sepsis and septic shock;
• FIGS. 21A to 21L are box and whisker plots for the top twelve biomarker combinations for distinguishing between sepsis and septic shock;
• FIG. 22 is a graph of the effect on AUC of adding biomarkers to a biomarker signature;
• FIG. 23 is a graph showing an example of the ability of the biomarker signature to distinguish between PS and sepsis for two patient populations;
• FIG. 24 is a flowchart of an example of a method for determining indicator references;
• FIGS. 25A and 25B are a flowchart of an example of a method for validating an indicator derived from biomarker measurements;
• FIG. 26 is an example showing the comparison of an indicator value to an indicator reference; and,
• FIGS. 27A and 27B are example representation of indicator values.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
• An example of a method for determining an indicator for use in diagnosing the presence, absence or degree of at least one condition in or of a biological subject, or in monitoring the progression of at least one condition in or of the subject, or in prognosing at least one condition in or of the subject, will now be described with reference to FIG. 1A.
• For the purpose of explanation, a number of different terms will be used. For example, the term “biomarker” refers to a measurable parameter, or combination of parameters, that can be used as an indicator of a biological state and includes, without limitation, proteins, nucleic acids, carbohydrates, lipids, metabolites, gases, steroids, ions, nutrients, toxins, cells, pathogenic organisms, non-pathogenic organisms, organic compounds and inorganic compounds. Biomarkers also encompass non-blood-borne factors, non-analyte physiological markers of health status, or other factors or biomarkers not measured from samples (e.g., biological samples such as bodily fluids), such as “clinical” or “phenotypic” parameters, including, without limitation, age, ethnicity, gender, species, breed, genetic information, white blood cell count, diastolic blood pressure and systolic blood pressure, bone density, height, weight, waist and hip circumference, body-mass index, as well as others such as Type I or Type II diabetes mellitus or gestational diabetes mellitus (collectively referred to here as diabetes), resting heart rate, homeostatic model assessment (HOMA), HOMA insulin resistance (HOMA-IR), intravenous glucose tolerance (SI(IVGT)), resting heart rate, β cell function, macrovascular function, microvascular function, atherogenic index, low-density lipoprotein/high-density lipoprotein ratio, intima-media thickness, body temperature, sequential organ failure assessment (SOFA) and the like. The “biomarkers” could also include “immune response biomarkers”, which will be described in more detail below.
• The term “biomarker value” refers to a value measured or derived for at least one corresponding biomarker of the biological subject and which is typically at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject. Thus, the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject, or alternatively could be derived biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.
• Biomarker values can be of any appropriate form depending on the manner in which the values are determined. For example, the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in one preferred example, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a technique such as PCR, sequencing or the like. In this case, the biomarker values can be in the form of amplification amounts, or cycle times, which are a logarithmic representation of the concentration of the biomarker within a sample, as will be appreciated by persons skilled in the art and as will be described in more detail below.
• The term “reference biomarkers” is used to refer to biomarkers whose activity has been quantified for a sample population of one or more individuals having one or more conditions, stages of one or more conditions, subtypes of one or more conditions or different prognoses. The term “reference data” refers to data measured for one or more individuals in a sample population, and may include quantification of the level or activity of the biomarkers measured for each individual, information regarding any conditions of the individuals, and optionally any other information of interest.
• The term “candidate biomarkers” refers to a subset of the reference biomarkers that have been identified as being potentially useful in distinguishing between different groups of individuals, such as individuals suffering from different conditions, or having different stages or prognoses. The number of candidate biomarkers will vary, but is typically about 200.
• The term “signature biomarkers” is used to refer to a subset of the candidate biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of signature biomarkers will vary, but is typically of the order of 10 or less.
• The term “biomarker signature” means a combination of at least two biomarker values corresponding to values of biomarkers that can be measured for or derived from one or more biological subjects, which combination is characteristic for a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition.
• The terms “biological subject”, “subject”, “individual” and “patient” are used interchangeably herein to refer to an animal subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the phylum Chordata, subphylum vertebrata including primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines (e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars etc.), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subject is a primate (e.g., a human, ape, monkey, chimpanzee).
• As used herein, the term SIRS (“systemic inflammatory response syndrome”) refers to a clinical response arising from a non-specific insult with two or more of the following measurable clinical characteristics; a body temperature greater than 38° C. or less than 36° C., a heart rate greater than 90 beats per minute, a respiratory rate greater than 20 per minute, a white blood cell count (total leukocytes) greater than 12,000 per mm³ or less than 4,000 per mm³, or a band neutrophil percentage greater than 10%. From an immunological perspective, it may be seen as representing a systemic response to insult (e.g., major surgery) or systemic inflammation. As used herein, “inSIRS” (which includes within its scope “post-surgical” (PS) inflammation) includes the clinical response noted above but in the absence of a systemic infectious process. By contrast, “ipSIRS” (also referred to herein as “sepsis”) includes the clinical response noted above but in the presence of a presumed or confirmed infection. Confirmation of infection can be determined using microbiological culture or isolation of the infectious agent. From an immunological perspective, ipSIRS may be seen as a systemic response to microorganisms, whether it is a local, peripheral or systemic infection.
• As used herein, the term “degree” of a condition refers to the seriousness, severity, stage or state of a condition. For example, a condition may be characterized as mild, moderate or severe. A person of skill in the art would be able to determine or assess the degree of a particular condition. For example, the degree of a condition may be determined by comparing the likelihood or length of survival of a subject having a condition with the likelihood or length of survival in other subjects having the same condition. In other embodiments, the degree of a condition may be determined by comparing the clinical signs of a subject having a condition with the degree of the clinical signs in other subjects having the same condition.
• It will be appreciated that the above described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.
• In this example, the method includes determining a plurality of biomarker values at step 100, each biomarker value being indicative of a value measured or derived for at least one biomarker of the biological subject.
• The biomarker values and biomarkers corresponding to the biomarker values can be of any appropriate form and in particular can relate to any attribute of a subject for which a value can be quantified. This technique is particularly suited to high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, or any combination of such technologies and in one preferred example, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule.
• The biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject, or alternatively could be derived biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values. As used herein, biomarkers to which a function has been applied are referred to as “derived markers”.
• The biomarker values may be determined in any one of a number of ways. In one example, the process of determining the biomarker values can include measuring the biomarker values, for example by performing tests on the biological subject. More typically however, the step of determining the biomarker values includes having an electronic processing device receive or otherwise obtain biomarker values that have been previously measured or derived. This could include for example, retrieving the biomarker values from a data store such as a remote database, obtaining biomarker values that have been manually input, using an input device, or the like.
• At step 110, the indicator is determined using a combination of the plurality of biomarker values, the indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition.
• The biomarker values can be combined in any one of a number of ways and this can include for example adding, multiplying, subtracting, or dividing biomarker values to determine an indicator value. This step is performed so that multiple biomarker values can be combined into a single indicator value, providing a more useful and straightforward mechanism for allowing the indicator to be interpreted and hence used in diagnosing the presence, absence or degree of the at least one condition in the subject, or prognosing the at least one condition in the subject.
• Assuming the method is performed using an electronic processing device, at step 120 an indication of the indicator is optionally displayed or otherwise provided to the user. In this regard, the indication could be a graphical or alphanumeric representation of an indicator value. Alternatively however, the indication could be the result of a comparison of the indicator value to predefined thresholds or ranges, or alternatively could be an indication of the presence, absence, degree or prognosis for at least one condition, derived using the indicator.
• In order to ensure an effective diagnosis or prognosis can be determined, at least two of the biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9. This requirement means that the two biomarkers are not entirely correlated in respect of each other when considered in the context of the condition(s) being diagnosed or prognosed. In other words, at least two of the biomarkers in the combination respond differently as the condition changes, which adds significantly to their ability when combined to discriminate between at least two conditions, to diagnose the presence, absence or degree of at least one condition, and/or to provide a prognosis of at least condition in or of the biological subject. As used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).
• Typically, the requirement that biomarkers have a low mutual correlation means that the biomarkers may relate to different biological attributes or domains such as but not limited to different molecular functions, different biological processes and different cellular components. Illustrative examples of molecular function include addition of or removal of one of more of the following moieties to or from a protein, polypeptide, peptide, nucleic acid (e.g., DNA, RNA): linear, branched, saturated or unsaturated alkyl (e.g., C₁-C₂₄ alkyl); phosphate; ubiquitin; acyl; fatty acid, lipid, phospholipid; nucleotide base; hydroxyl and the like. Molecular functions also include signaling pathways, including without limitation, receptor signaling pathways and nuclear signaling pathways. Non-limiting examples of molecular functions also include cleavage of a nucleic acid, peptide, polypeptide or protein at one or more sites; polymerization of a nucleic acid, peptide, polypeptide or protein; translocation through a cell membrane (e.g., outer cell membrane; nuclear membrane); translocation into or out of a cell organelle (e.g., Golgi apparatus, lysosome, endoplasmic reticulum, nucleus, mitochondria); receptor binding, receptor signaling, membrane channel binding, membrane channel influx or efflux; and the like.
• Illustrative examples of biological processes include: stages of the cell cycle such as meiosis, mitosis, cell division, prophase, metaphase, anaphase, telophase and interphase, stages of cell differentiation; apoptosis; necrosis; chemotaxis; immune responses including adaptive and innate immune responses, pro-inflammatory immune responses, autoimmune responses, tolerogenic responses and the like. Other illustrative examples of biological processes include generating or breaking down adenosine triphosphate (ATP), saccharides, polysaccharides, fatty acids, lipids, phospholipids, sphingolipids, glycolipids, cholesterol, nucleotides, nucleic acids, membranes (e.g., cell plasma membrane, nuclear membrane), amino acids, peptides, polypeptides, proteins and the like.
• Representative examples of cellular components include organelles, membranes, as for example noted above, and others.
• It will be appreciated that the use of biomarkers that have different biological attributes or domains provides further information than if the biomarkers were related to the same or common biological attributes or domains.
• In this regard, it will be appreciated if the at least two biomarkers are highly correlated to each other, the use of both biomarkers would add little diagnostic/prognostic improvement compared to the use of a single one of the biomarkers. Accordingly, the method uses biomarkers that are not well correlated with each other, thereby ensuring that the inclusion of each biomarker in the method adds significantly to the discriminative ability of the indicator.
• Despite this, in order to ensure that the indicator can accurately be used in performing the discrimination between at least two conditions or the diagnosis of the presence, absence or degree of at least one condition or the prognosis of at least one condition, the indicator has a performance value that is greater than or equal to a performance threshold. The performance threshold may be of any suitable form but is to be typically indicative of an explained variance of at least 0.3, or an equivalent value of another performance measure.
• It has been found that utilizing a combination of biomarkers that have a mutual correlation between ±0.9 and using these in a combination that provides an explained variance of at least 0.3, this allows an indicator to be defined that is suitable for ensuring that an accurate discrimination, diagnosis or prognosis can be obtained whilst minimizing the number of biomarkers that are required.
• It will be appreciated that in this context, the biomarkers used within the above-described method can define a biomarker signature for the at least one condition, which includes a minimal number of biomarkers, whilst maintaining sufficient performance to allow the biomarker signature to be used in making a clinically relevant diagnosis, prognosis, or differentiation. Minimizing the number of biomarkers used minimizes the costs associated with performing diagnostic or prognostic tests and in the case of nucleic acid expression products, allows the test to be performed utilizing relatively straightforward techniques such as nucleic acid array, and polymerase chain reaction (PCR) processes, or the like, allowing the test to be performed rapidly in a clinical environment.
• Furthermore, producing a single indicator value allows the results of the test to be easily interpreted by a clinician or other medical practitioner, so that test can be used for reliable diagnosis in a clinical environment.
• An example of the process for generating a suitable biomarker signature for use in the method of FIG. 1A will now be described with reference to FIG. 1B.
• In particular, it is typical to generate a biomarker signature by analyzing a large number of biomarkers and then selecting a combination of biomarkers that meet the above described criteria.
• In this example, at step 150 the process includes ranking a number of candidate biomarkers in accordance with the ability of each biomarker to distinguish between the presence, absence, degree or prognosis of at least one condition in a biological subject.
• The candidate biomarkers can be obtained in any appropriate matter, but typically this would involve acquiring reference data including reference biomarker values relating to a number of reference biomarkers that have been measured or derived for one or more reference individuals having different presences, absences, degrees or prognoses of the one or more conditions of interest. Thus, it will be appreciated that the candidate biomarkers can include measured and/or derived biomarkers, as will be described in more detail below.
• The reference data typically includes measurements of a plurality of reference biomarkers, the measurements including information regarding the activity, such as the level, abundance or functional activity, of any expression product or measurable molecule, as will be described in more detail below. The reference data may also include information such as clinical data regarding one or more conditions suffered by each individual. This can include information regarding a presence, absence, degree or progression of a condition, phenotypic information, such as details of phenotypic traits, genetic or genetically regulated information, amino acid or nucleotide related genomics information, results of other tests including imaging, biochemical and hematological assays, other physiological scores such as a SOFA (Sequential Organ Failure Assessment) score, or the like and this is not intended to be limiting, as will be apparent from the description below.
• The candidate biomarkers can include some or all of the reference biomarkers, depending on the preferred implementation. Thus, for example, reference biomarker values could be analyzed to determine correlations between the reference biomarkers and the at least one condition, with the reference biomarkers being coarsely filtered to remove those with a low correlation, for example with a correlation with the condition that is below 0.3.
• At step 160 at least two candidate biomarkers are selected based on the ranking and a mutual correlation. In particular, at least two candidate biomarkers are selected which have a mutual correlation within a mutual correlation range of ±0.9. Thus, this process excludes any biomarkers which are highly mutually correlated, when considered in the context of the one or more conditions, and which would not therefore add significantly to the ability to discriminate between the presence, absence, degree or prognosis of at least one condition.
• At step 170 a performance value of a combination of the selected candidate biomarkers is determined. As mentioned above the combination may be any combination of the candidate biomarker values, such as addition, subtraction, multiplication, or division of the candidate biomarker values, and this will not therefore be described in any further detail.
• At step 180 it is determined if the performance value of the combination exceeds a performance threshold, the performance threshold being equivalent to an explained variance of at least 0.3. If so, the combination of the candidate biomarkers can be used to define a biomarker signature. Otherwise, the previous steps can be repeated, for example by determining alternative combinations of the candidate biomarkers, selecting different candidate biomarkers, or adding additional candidate biomarkers as will be described in more detail below. In this regard, it will be appreciated that other measures could be used, and reference to an explained variance of at least 0.3 is intended to be a particular example for illustrative purposes.
• Accordingly, the above described method can be utilized to select a combination of candidate biomarkers that are suitable for use as signature biomarkers in a biomarker signature for diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, for example using the method of FIG. 1A above.
• In one example, this is achieved by ensuring that at least two of the biomarkers used are not highly mutually correlated, thereby ensuring that each of these biomarkers contributes to the performance of the resulting signature.
• A number of further features will now be described.
• In one example, the method includes determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker of the biological subject and applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.
• The function used will therefore vary depending on the preferred implementation. In one example, the function includes at least one of multiplying two biomarker values; dividing two biomarker values; adding two biomarker values; subtracting two biomarker values; a weighted sum of at least two biomarker values; a log sum of at least two biomarker values; and, a sigmoidal function of at least two biomarker values.
• More typically the function is division of two biomarker values, so that the derived biomarker value corresponds to a ratio of two measured biomarker values. There are a number of reasons why the ratio might be preferred. For example, use of a ratio is self-normalizing, meaning variations in measuring techniques will automatically be accommodated. For example, if the input concentration of a sample is doubled, the relative proportions of biomarkers will remain the same. As a result, the type of function therefore has a stable profile over a range of input concentrations, which is important because input concentration is a known variable for expression data. Additionally, many biomarkers are nodes on biochemical pathways, so the ratio of biomarkers gives information about the relative activation of one biological pathway to another, which is a natural representation of biological change within a system. Finally, ratios are typically easily interpreted.
• The method typically includes combining at least two biomarker values to determine an indicator value representing the indicator. This is usually achieved by combining at least two biomarker values using a combining function, such as: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; and a weighted sum.
• In one example, at least one of the at least two biomarkers is a derived biomarker and in a preferred example, the combining function is addition of derived biomarker values that are ratios, in which case the method includes determining first and second derived biomarker values from ratios of first and second and third and fourth measured biomarker values, and then adding the first and second derived biomarker values to generate an indicator value.
• In one example, the method includes determining an indicator value, comparing the indicator value to at least one indicator value range, and using a result of the comparison in diagnosing the presence, absence, degree or prognosis of at least one condition.
• The above-described process is typically performed using an electronic processing device, forming part of a processing system such as a computer system or the like. In this case, the method typically involves having the electronic processing device receive a plurality of measured biomarker values, apply a function to at least one of the measured biomarker values to determine the at least one derived biomarker value and combining at least one derived biomarker value and at least one other biomarker value to determine an indicator value.
• The electronic processing device can then generate a representation in accordance with the at least one indicator value, for example by displaying a numerical indication of the indicator value. More typically however the electronic processing device compares the indicator value to at least one indicator value range and displays a result of the comparison. This can be used to compare the indicator to defined ranges representing specific stages, progressions or prognoses of one or more conditions, allowing an indication of the respective stage, progression or prognosis to be displayed.
• The mutual correlation range is typically at least one of: ±0.8; ±0.7; ±0.6; ±0.5; ±0.4; ±0.3; ±0.2; and ±0.1. In this regard, it will be appreciated that the smaller the mutual correlation range used, the less correlated the biomarkers will be and hence the more useful these will be in discriminating between the specific stages, progressions or prognoses of one or more conditions.
• It is also typical for the biomarkers used to have at least a minimum correlation with the condition. In one example, each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3. However, it will be appreciated that the greater the range, the greater the correlation between the biomarker and condition, and hence the more use this will be in performing a diagnosis. Accordingly, the condition correlation is more typically one of: ±0.9; ±0.8; ±0.7; ±0.6; ±0.5; and ±0.4.
• Furthermore, it will be appreciated that the greater the performance of the indicator, the greater use the indicator will be in performing the diagnosis. Accordingly, the performance threshold is typically indicative of an explained variance of at least one of: 0.4; 0.5; 0.6; 0.7; 0.8; and 0.9.
• As described above, the biomarker value can be of any suitable form. However, the technique is particularly suited to biomarker values indicative of a level or abundance of a molecule selected from one or more of: a nucleic acid molecule; a proteinaceous molecule; an amino acid; a carbohydrate; a lipid; a steroid; an inorganic molecule; an ion; a drug; a chemical; a metabolite; a toxin; a nutrient; a gas; a cell; a pathogenic organism; and a non-pathogenic organism.
• When determining biomarkers for use in a biomarker signature, the method typically includes selecting a combining function, determining if a performance value of a combination of the at least two candidate biomarkers determined by the combining function is greater than or equal to a performance threshold and if the performance value is not greater than or equal to a performance threshold, repeating these steps for successive different combining functions. Thus, this allows a number of different combining functions to be tried successively, allowing the best combining function to be identified.
• The method typically further includes selecting two candidate biomarkers, determining if a performance value of a combination of the two candidate biomarkers is greater than or equal to a performance threshold and if not, combining the selected candidate biomarkers with at least one additional candidate biomarker before repeating the steps with at least one additional candidate biomarker. This allows a larger number of biomarkers to be used in the event two biomarkers are insufficient, and this can be repeated, with increasing numbers of candidate biomarkers used in combination and compared to the performance threshold until the required performance is reached, or up until a defined number limit of candidate biomarkers is reached.
• To ensure the required mutual correlation is met when selecting the candidate biomarkers, the method typically includes selecting a highest and a next highest ranked candidate biomarker, for the selected candidate biomarkers, determining if the mutual correlation for the candidate biomarkers within the mutual correlation range and if not repeating these steps until two candidate biomarkers are selected having a mutual correlation within the mutual correlation range. Alternatively however this can be achieved by defining at least two groups of candidate biomarkers, candidate biomarkers in different groups having a mutual correlation within the mutual correlation range, ranking the candidate biomarkers in each group and selecting candidate biomarkers from the different groups.
• In general, the candidate biomarkers are determined by using reference data for at least one individual to define a number of groups indicative of the presence, absence, degree or prognosis of the at least one condition and then using at least one analysis technique to identify a number of candidate biomarkers that are potentially useful for distinguishing the groups. The groups can also be used to establish a range of at least two reference biomarker values to determine an indicator value range for the group.
• In one example, reference values measured for reference biomarkers for the at least one individual can then be used to identify the candidate biomarkers, for example by filtering reference biomarkers to determine candidate biomarkers based on a correlation of each biomarker with the condition.
• In one example, the process is performed by one or more processing systems operating as part of a distributed architecture, an example of which will now be described with reference to FIG. 2.
• In this example, the arrangement includes a number of processing systems 201 and computer systems 203 interconnected via one or more communications networks, such as the Internet 202, and/or a number of local area networks (LANs) 204. It will be appreciated that the configuration of the networks 202, 204 is for the purpose of example only, and in practice the processing and computer systems 201, 203 can communicate via any appropriate mechanism, such as via wired or wireless connections, including, but not limited to mobile networks, private networks, such as an 802.11 networks, the Internet, LANs, WANs, or the like, as well as via direct or point-to-point connections, such as Bluetooth, or the like.
• The use of separate terms “processing system” and “computer system” is for illustrative purposes and to enable distinction between different devices, optionally having different functionality. For example, the processing and computer systems 201, 203 could represent servers and clients respectively, as will become apparent from the following description. However, this is not intended to be limiting and in practice any suitable computer network architecture can be used.
• An example of a suitable processing system 201 is shown in FIG. 3. In this example, the processing system 201 includes an electronic processing device, such as at least one microprocessor 300, a memory 301, an optional input/output device 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example the external interface 303 can be utilized for connecting the processing system 201 to peripheral devices, such as the communications networks 202, 204, databases 211, other storage devices, or the like. Although a single external interface 303 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless or the like) may be provided.
• In use, the microprocessor 300 executes instructions in the form of applications software stored in the memory 301 to perform required processes, such as communicating with other processing or computer systems 201, 203. Thus, actions performed by a processing system 201 are performed by the processor 300 in accordance with instructions stored as applications software in the memory 301 and/or input commands received via the I/O device 302, or commands received from other processing or computer systems 201, 203. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.
• Accordingly, it will be appreciated that the processing systems 201 may be formed from any suitable processing system, such as a suitably programmed computer system, PC, web server, network server, or the like. In one particular example, the processing systems 201 are standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing system could be or could include any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.
• As shown in FIG. 4, in one example, the computer systems 203 include an electronic processing device, such as at least one microprocessor 400, a memory 401, an input/output device 402, such as a keyboard and/or display, and an external interface 403, interconnected via a bus 404 as shown. In this example the external interface 403 can be utilized for connecting the computer system 203 to peripheral devices, such as the communications networks 202, 204, databases, other storage devices, or the like. Although a single external interface 403 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless or the like) may be provided.
• In use, the microprocessor 400 executes instructions in the form of applications software stored in the memory 401 to perform required processes, for example to allow communication with other processing or computer systems 201, 203. Thus, actions performed by a processing system 203 are performed by the processor 400 in accordance with instructions stored as applications software in the memory 401 and/or input commands received from a user via the I/O device 402. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.
• Accordingly, it will be appreciated that the computer systems 203 may be formed from any suitable processing system, such as a suitably programmed PC, Internet terminal, lap-top, hand-held PC, smart phone, PDA, tablet, or the like. Thus, in one example, the processing system 300 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing systems 203 can be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.
• It will also be noted that whilst the processing and computer systems 201, 203 are shown as single entities, it will be appreciated that this is not essential, and instead one or more of the processing and/or computer systems 201, 203 can be distributed over geographically separate locations, for example by using processing systems provided as part of a cloud based environment.
• Examples of the above-described method(s) will now be described in further detail. For the purpose of these examples, it is assumed that the process is performed by one or more of the processing systems 201, acting as diagnostic servers. Interaction by a user is via a user computer system 203, which is used to allow a user to submit raw data, for example obtained from measurements on a subject, with the processing system 201 generating the indicator, allowing this to be displayed on the computer system 203.
• However, it will be appreciated that the above-described configuration assumed for the purpose of the following examples is not essential, and numerous other configurations may be used. It will also be appreciated that the partitioning of functionality between the processing and computer systems 201, 203 may vary, depending on the particular implementation.
• An example of a specific method for identifying biomarkers for using a biomarker signature will now be described with reference to FIG. 5.
• In this example, at step 500 reference data is obtained from at least one individual. The reference data is typically in the form of measured biomarker values obtained for at least one individual for different stages of the at least one condition.
• The reference data may be acquired in any appropriate manner but typically this involves obtaining gene expression product data from a plurality of individuals, selected to include individuals diagnosed with one or more conditions of interest, as well as healthy individuals. Detection of either types of gene expression in use of any of the methods described herein is encompassed by the present invention. The terms “expression” or “gene expression” refer to production of RNA only or production of RNA and translation of RNA into proteins or polypeptides. Thus, the term “expression products” encompass (i) polynucleotides including RNA transcripts and corresponding nucleic acids including complementary cDNA copies of RNA transcripts, and (ii) polypeptides encoded by RNA transcripts. The conditions are typically medical, veterinary or other health status conditions and may include any illness, disease, stages of disease, disease subtypes, severities of disease, diseases of varying prognoses or the like. The terms “healthy individual”, “healthy subject” and the like are used herein to refer to a subject, in particular a mammal, having no diagnosed disease, disorder, infirmity, or ailment. The condition of such an individual or subject is referred to herein as a “healthy condition” such that in one example a condition can include healthy. In specific embodiments, a healthy subject lacks SIRS (e.g., inSIRS or ipSIRS).
• In order to achieve this, gene expression product data are collected, for example by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantification process, such as a nucleic acid amplification process, including PCR (Polymerase Chain Reaction) or the like, in order to assess the activity, and in particular, level or abundance of a number of reference biomarkers. Quantified values indicative of the relative activity are then stored as part of the reference data.
• Example reference biomarkers could include expression products such as nucleic acid or proteinaceous molecules, as well as other molecules relevant in making a clinical assessment. The number of biomarkers measured for use as reference biomarkers will vary depending upon the preferred implementation, but typically include a large number such as, 1000, 5000, 10000 or above, although this is not intended to be limiting.
• The individuals also typically undergo a clinical assessment allowing any conditions to be clinically identified, and with an indication of any assessment or condition forming part of the reference data. Whilst any conditions can be assessed, in one example the process is utilized specifically to identify conditions such as SIRS (Systemic Inflammatory Response Syndrome) (M S Rangel-Frausto, D Pittet, M Costigan, T Hwang, C S Davis, and R P Wenzel, “The Natural History of the Systemic Inflammatory Response Syndrome (SIRS). a Prospective Study.”, JAMA: the Journal of the American Medical Association 273, no. 2 (Jan. 11, 1995): 117-123.). SIRS is an overwhelming whole body reaction that may have an infectious or non-infectious etiology, whereas sepsis is SIRS that occurs during infection. Both are defined by a number of non-specific host response parameters including changes in heart and respiratory rate, body temperature and white cell counts (Mitchell M Levy et al., “2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference”, Critical Care Medicine 31, no. 4 (April 2003): 1250-1256.; K Reinhart, M Bauer, N C Riedemann, and C S Hartog, “New Approaches to Sepsis: Molecular Diagnostics and Biomarkers”, Clinical Microbiology Reviews 25, no. 4 (Oct. 3, 2012): 609-634) To differentiate these conditions they are referred herein to as SIRS (both conditions), infection-negative SIRS (SIRS without infection, hereafter referred to as “inSIRS”) and infection-positive SIRS (sepsis, SIRS with a known or suspected infection, hereafter referred to as “ipSIRS”) The causes of SIRS are multiple and varied and can include, but are not limited to, trauma, burns, pancreatitis, endotoxemia, surgery, adverse drug reactions, and infections (local and systemic). It will be appreciated from the following, however, that this can be applied to a range of different conditions, and reference to SIRS or sepsis is not intended to be limiting.
• Additionally, the reference data may include additional biomarkers such as one or more phenotypic or clinical parameters of the individuals and/or their relatives. Phenotypic parameters can include information such as the gender, ethnicity, age, hair color, eye color, height, weight, waist and hip circumference, or the like. Also, in the case of the technology being applied to individuals other than humans, this can also include information such as designation of a species, breed or the like. Clinical traits may include genetic information, white blood cell count, diastolic blood pressure and systolic blood pressure, bone density, body-mass index, diabetes, resting heart rate, HOMA, HOMA-IR, IVGT, resting heart rate, β cell function, macrovascular function, microvascular function, atherogenic index, low-density lipoprotein/high-density lipoprotein ratio, intima-media thickness, body temperature, SOFA and the like.
• Accordingly, in one example the reference data can include for each of the reference individuals information relating to at least one and desirably to a plurality of reference biomarkers and a presence, absence, degree or progression of a condition.
• The reference data may be collected from individuals presenting at a medical center with clinical signs relating to relevant any conditions of interest, and may involve follow-on consultations in order to confirm clinical assessments, as well as to identify changes in biomarkers, and/or clinical signs, and/or severity of clinical signs, over a period of time. In this latter case, the reference data can include time series data indicative of the progression of a condition, and/or the activity of the reference biomarkers, so that the reference data for an individual can be used to determine if the condition of the individual is improving, worsening or static. It will also be appreciated that the reference biomarkers are preferably substantially similar for the individuals within the sample population, so that comparisons of measured activities between individuals can be made.
• This reference data could also be collected from a single individual over time, for example as a condition within the individual progresses, although more typically it would be obtained from multiple individuals each of which has a different stage of the one or more conditions of interest.
• It will be appreciated that once collected, the reference data can be stored in the database 211 allowing this to be subsequently retrieved by the processing system 201 for subsequent analysis. The processing system 201 also typically stores an indication of an identity of each of the reference biomarkers.
• In one example, the measurements are received as raw data, which then undergoes preliminary processing. Such raw data corresponds to information that has come from a source without modification, such as outputs from instruments such as PCR machines, array (e.g., microarray) scanners, sequencing machines, clinical notes or any other biochemical, biological, observational data, or the like. This step can be used to convert the raw data into a format that is better suited to analysis. In one example this is performed in order to normalize the raw data and thereby assist in ensuring the biomarker values demonstrate consistency even when measured using different techniques, different equipment, or the like. Thus, the goal of normalization is to remove the variation within the samples that is not directly attributable to the specific analysis under consideration. For example, to remove variances caused by differences in sample processing at different sites. Classic examples of normalization include z-score transformation for generic data, or popular domain specific normalizations, such as RMA normalization for microarrays.
• However, it will also be appreciated that in some applications, such as a single sample experiment run on a single data acquisition machine, this step may not strictly be necessary, in which case the function can be a Null function producing an output identical to the input.
• In one example, the preferred approach is a paired function approach over log normalized data. Log normalization is a standard data transformation on microarray data, because the data follow a log-normal distribution when coming off the machine. Applying a log transform turns the data into process-friendly normal data.
• At step 505 different groups are defined using the reference data.
• Prior to this occurring, the processing system 201 optionally removes a validation subgroup of individuals from the reference data to allow the processing system 201 to determine the candidate biomarkers using the reference data without the validation subgroup so that the validation subgroup can be subsequently used to validate the candidate biomarkers or signatures including a number of the candidate biomarkers. Thus, data from the validation subgroup is used to validate the efficacy of the candidate or signature biomarkers in identifying the presence, absence, degree, stage, severity, prognosis or progression of any one or more of the conditions to ensure the potential or signature biomarkers are effective.
• In one example, this is achieved by having the processing system 201 flag individuals within the validation subgroup or alternatively store these in either an alternative location within the database 211 or an alternative database to the reference data. The validation subgroup of individuals is typically selected randomly and may optionally be selected to include individuals having different phenotypic traits. When a validation subgroup of individuals is removed, the remaining individuals will simply be referred to as reference data for ease throughout the remaining description.
• The reference data (i.e., excluding the validation subgroup) are classified into groups. The groups may be defined in any appropriate manner and may be defined based on any one or more of an indication of a presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with the individuals.
• For example, a first selection of groups may be to identify one or more groups of individuals suffering from SIRS, one or more groups of individuals suffering ipSIRS, one or more groups of individuals suffering inSIRS, and one or more groups of healthy individuals. Further groups may also be defined for individuals suffering from other conditions. The groups may include overlapping groups, so for example it may be desirable to define groups of healthy individuals and individuals having SIRS, with further being defined to distinguish inSIRS patients from ipSIRS patients, as well as different degree of inSIRS or ipSIRS, with these groups having SIRS in common, but each group of patients differing in whether a clinician has determined the presence of an infection or not. Additionally, further subdivision may be performed based on phenotypic traits, so groups could be defined based on gender, ethnicity or the like so that a plurality of groups of individuals suffering from a condition are defined, with each group relating to a different phenotypic trait.
• It will also be appreciated, however, that identification of different groups can be performed in other manners, for example on the basis of particular activities of biomarkers within the biological samples of the reference individuals, and accordingly, reference to conditions is not intended to be limiting and other information may be used as required.
• The manner in which classification into groups is performed may vary depending on the preferred implementation. In one example, this can be performed automatically by the processing system 201, for example, using unsupervised methods such as Principal Components Analysis (PCA), or supervised methods such as k-means or Self Organizing Map (SOM). Alternatively, this may be performed manually by an operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI), and define respective groups using appropriate input commands.
• At step 510 biomarkers are filtered based on their ability to distinguish between the groups. This process typically examines the activity of the reference biomarkers for individuals within and across the groups, to identify reference biomarkers whose activities differ between and hence can distinguish groups. A range of different analysis techniques can be utilized including, for example, regression or correlation analysis techniques. Examples of the techniques used can include established methods for parameterized model building such as Partial Least Squares, Random Forest or Support Vector Machines, usually coupled to a feature reduction technique for the selection of the specific subset of the biomarkers to be used in a signature.
• Such techniques are known and described in a number of publications. For example, the use of Partial Least Squares is described in “Partial least squares: a versatile tool for the analysis of high-dimensional genomic data” by Boulesteix, Anne-Laure and Strimmer, Korbinian, from Briefings in Bioinformatics 2007 vol 8. no. 1, pg 32-44. Support Vector machines are described in “LIBSVM: a library for support vector machines” by Chang, C. C. and Lin, C. J. from ACM Transactions on Intelligent Systems and Technology (TIST), 2011 vol 2, no. 3, pg 27. Standard Random Forest in R language is described in “Classification and Regression by random Forest” by Liaw, A. and Wiener, M., in R news 2002, vol 2, no. 3, pg 18-22.
• The analysis techniques are implemented by the processing system 201, using applications software, which allows the processing system 201 to perform multiple ones of the analysis techniques in sequence. This is advantageous as the different analysis techniques typically have different biases and can therefore be used to identify different potential biomarkers that can distinguish the groups, thereby reducing the risk of clinically relevant biomarkers being overlooked.
• In one example, the process involves filtering out any biomarkers that demonstrate a correlation with the groups, and hence with the condition, that is below a certain correlation threshold, such as 0.3.
• At step 515 derived biomarkers are generated from the filtered reference biomarkers using one or more functions. The nature of the derived biomarkers and the functions used will vary depending upon the preferred implementation. For example, functions can include division, subtraction, multiplication, addition of two markers, sigmoidal functions applied to the product of two biomarkers, negative logs of the division of two biomarkers, least-squares regression applied to two vectors of markers to produce a function (equation) output, concordance correlation coefficient of two vectors of categorical biomarkers, or the like.
• In general, the function is selected based on a number of rules. These rules can include: utility, functions that provide the best results; interpretability, functions that can be understood in terms of biological function; output, functions that produce informative outputs; simplicity; performance assessment; least number of biomarkers for best performance; number of biomarkers at a statistical overfitting threshold or the like.
• In one example, the preferred function is division, with the resulting biomarkers being different ratios. It will be appreciated that the division can be performed in multiple different ways, so that for three biomarkers, nine different derived biomarkers can be determined.
• At step 520 a performance measure is determined for each of the candidate biomarkers, including the filtered reference biomarkers and any derived markers. The performance measure may be of any suitable form and typically includes a correlation or performance explained measure that is indicative of a correlation of the corresponding biomarker and its ability to distinguish between groups. In one example, the performance function used to determine the performance measure is a standard univariate statistical test over all candidate biomarkers. Other examples however include a t-test, a non-parametric equivalent or area under receiver operator curve, chi squared or regression analyses or their equivalents, extensions or derivatives may be used.
• The outcome of the applying the performance function to each in the candidate selection step is a ranked list of biomarkers, with the top N ranked biomarkers proceeding to the next stage. The biomarkers falling below this threshold are no longer considered. The threshold applied may be an absolute number or proportion of all biomarkers, or determined by performance, such as a p value ≦0.05. The threshold should be chosen to contain a sufficiently large number of biomarkers to bias towards including sufficiently independent biomarkers (i.e., low mutual correlation).
• At step 525, the processing system 201 selects a next two candidate biomarkers based on the performance measure and on a mutual correlation. In this regard, two markers that are highly correlated with each other in terms of the context of the condition will not necessarily improve the ability to distinguish a particular presence, absence, degree or prognosis of the condition any more than a single one of the markers. Accordingly, it is typical to select biomarkers that have a high performance measure in respect of the condition, but which have a mutual correlation that falls below a mutual correlation threshold. The mutual correlation threshold used will vary depending upon the preferred implementation, and is typically selected to be as low as possible, as described above. Examples of the manner in which the biomarkers are selected will be described in more detail below with respect to FIGS. 6A and 6B.
• At step 530, the processing system 201 determines a performance of a next candidate biomarker combination. In this regard, the processing system 201 will use a combining function such as addition, to combine the biomarker values of the selected candidate biomarkers and use this to determine and indicator value based on the combination of biomarker values. The performance can be determined in any suitable manner, such as using statistical measurements, correlation measurements, concordance measurements or aggregate performance measurements such as averages. In one particular example, the performance measure is a ‘variance explained’ (VE). A VE of “1” means that using the biomarkers, you can perfectly classify/predict the disease. A VE of “0.8” means that your markers account for 80% of the result in practice.
• Accordingly, at step 535 the processing system 201 compares the performance of the indicator to a performance threshold and determines if this is exceeded at step 540. In the event that the threshold is exceeded, this indicates that the selected combination of markers provides the required degree of discrimination allowing the presence, absence, degree or prognosis of the condition to be determined.
• In the event that the threshold is not exceeded at step 540, it is determined if all combinations have been considered at step 545. In this regard, it is possible that multiple different combinations of the two selected biomarkers to be tried, so if each possible combination has not been considered, the processing system 201 returns to step 530 to determine the performance of a next candidate biomarker combination. In this regard, the combinations used will typically be ordered in terms of preference, so that preferred combinations are tried first, with less preferred combinations being tried only in the event that preferred combinations prove unsuccessful.
• Once all candidate biomarker combinations have been considered for the two candidate biomarkers selected, and if the performance threshold has still not been exceeded, the process moves onto step 550 to compare the current number of candidate biomarkers being considered to a limit. In this regard, the limit is used to control the overall number of biomarkers in the biomarker signature, thereby minimizing signature size and hence the cost of performing the associated measurements and diagnosis.
• If the limit has not been exceeded an additional biomarker is added based on the correlation and performance at step 560, with the process moving onto step 530 to determine a performance of the next candidate biomarker combination. Otherwise if a limit has been reached, then an alternative next two candidate biomarkers are selected at step 525. Thus, this process allows additional candidate biomarkers to be progressively included, with a combination of the multiple candidate biomarkers being compared to the performance threshold, to determine if the required performance is met. If this is not achieved before the number of candidate biomarkers reaches the limit, the process is recommenced using two different candidate biomarkers.
• Once the performance threshold has been exceeded at step 540, the selected candidate biomarkers can be defined as signature biomarkers for inclusion in a biomarker signature for the one or more conditions at step 565.
• It should be noted that before the biomarker signature is finalized at step 565, additional checks might be performed, to ensure that the candidate biomarkers included in the signature should not be excluded for any reason. For example, candidate biomarkers might be excluded for cost considerations as some combinations of candidate biomarkers may cost more than others. For example, a larger number of biomarkers may cost more than a smaller number, and the additional cost may not be justified by a small improvement in performance. Alternatively, the cost might be increased if multiple different tests are required in order to measure required biomarker values.
• Biomarkers might also be excluded from use for legal reasons, for example if their use is restricted for approval or intellectual property reasons. Some biomarkers may be difficult to measure from a technical perspective, for example very low expression in vivo, which increases variability and therefore reduces robustness.
• The performance of each biomarker combination panel may also include some variability, typically expressed as confidence intervals around the reported performance. Although a point estimate for one panel may be higher than for another, if the difference given the variability is not significant, the combinations may be considered equivalent.
• Once a particular combination of signature biomarkers has been defined, at step 570 the processing system 201 can determine an indicator value range associated with each group. In particular, the range of reference biomarker values for the signature biomarkers within each group are used to calculate indicator value ranges for each group. These can then be compared to an indicator value calculated for a biological subject having an unknown presence absence, degree or progression of the at least one condition and used to identify a group to which the subject would belong and hence the presence, absence, degree or progression of the condition.
• Thus, the above-described process iteratively assesses the biomarkers, initially selecting two biomarkers, with various combinations of these being considered to determine if these have the required performance for use in diagnosing the presence, absence, degree or progression of a condition. In the event that the required performance is not provided, additional biomarkers can be added and further combinations tried. Thus the process can consider three biomarkers, four biomarkers, five biomarkers, six biomarkers, seven biomarkers, eight biomarkers, nine biomarkers or more. Typically this is performed to a limit which may be defined based for example on the number of biomarkers that can practically be measured within given cost or process parameters. In the event that the required performance is not obtained the process moves onto to select alternative candidate biomarkers with this being repeated.
• Thus it will be appreciated that the above process initially selects those biomarkers which have the suitable performance and which are not highly correlated on the basis that these provide the maximum performance. The ability of these biomarkers to distinguish is then tested and in the event that this is insufficient, further biomarkers can be added to a limit. If this still does not provide the required discriminatory performance alternative biomarkers can be selected.
• The process of selecting two candidate biomarkers at step 525 can be achieved in any number of ways depending upon the preferred implementation and examples of this will now be described with reference to FIGS. 6A and 6B.
• In the example of FIG. 6A, at step 600 biomarkers are grouped according to their mutual similarity. Thus, highly correlated biomarkers are put together in common groups. Biomarkers within the group are ranked at step 610 based on their performance measure in terms of their correlation with the condition, with the highest ranked biomarkers from two of the groups being selected at step 620 to define the next two candidate biomarkers. It will be appreciated if additional candidate biomarkers are required, these can be selected from different groups to the first two candidate biomarkers.
• An alternative process is shown in FIG. 6B. In this example, at step 650 biomarkers are ranked based on their performance at discriminating the condition(s). At step 660 a next highest biomarker is selected, with the remaining biomarkers being re-ranked on the combination of their similarity with the selected biomarker, for example using mutual information, as well as their performance. The next highest biomarker is then selected at step 680 with this process being repeated as required.
• Accordingly, it will be appreciated that the above-described processes provide mechanisms for selecting a combination of biomarkers, and more typically derived biomarkers, that can be used to form a biomarker signature, which in turn can be used in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition. In this regard, the biomarker signature defines the biomarkers that should be measured (i.e., the signature biomarkers), how derived biomarker values should be determined for measured biomarker values, and then how biomarker values should be subsequently combined to generate an indicator value. The biomarker signature can also specify defined indicator value ranges that indicate a particular presence, absence, degree or prognosis of one or more conditions.
• An example of the method of using a biomarker signature described above will now be described with reference to FIG. 7.
• In this example, at step 700 a plurality of measured biomarker values are measured for a biological subject whose condition is unknown, with these typically being provided to the processing system 201, for example by download from measuring equipment or the like.
• After any required processing, such as normalization or the like, at step 710, the processing system 201 applies one or more functions to the measured biomarker values to determine any required derived biomarker values. A derived biomarker value and another biomarker value (i.e., another derived biomarker value or a measured biomarker value) are combined to generate an indicator value, which can then be displayed or otherwise used in determining the presence, absence, degree or prognosis of one or more conditions. Thus, this can involve simply displaying the indicator value, allowing an assessment to be made by a medical practitioner or alternatively may involve further processing, such as comparing the indicator to defined indicator value ranges that indicate a particular presence, absence, degree or prognosis of one or more conditions, with the results of the comparison being displayed.
• Accordingly, in the above-described method the biomarker signature defines the biomarker values that need to be measured and/or derived, allowing the processing system 201 to automatically generate an indicator value based on received measured biomarker values. Once this has been completed, the processing system 201 can compare the indicator value to the indicator value ranges, and either display results of the comparison, or alternative interpret the results of the comparison, allowing an indicator to be displayed that is indicative of the presence, absence, degree or prognosis of a condition. This can then be used by a medical practitioner as required in performing a medical diagnosis of the biological subject.
• Using the above-described methods it has been identified that the use of ratios of “immune system biomarkers” is particularly beneficial when assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition.
• As used herein, the term “immune system biomarker” refers to a biomarker of the host's immune system that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult, including metabolic, toxic, neurotoxic, iatrogenic, thermal or chemical insults, illustrative examples of which include trauma, surgery, drugs including chemotherapeutic drugs, radiation, disease including pathogenic infection, metabolic disease and ischemia, as well as foreign or implanted substances.
• The term “immune system”, as used herein, refers to cells, molecular components and mechanisms, including antigen-specific and non-specific categories of the adaptive and innate immune systems, respectively, that provide a defense against damage and insults and matter, the latter comprised of antigenic molecules, including but not limited to tumors, pathogens, and self-reactive cells.
• The term “innate immune system” refers to a host's non-specific reaction to insult to include antigen-nonspecific defense cells, molecular components and mechanisms that come into action immediately or within several hours after exposure to almost any insult or antigen. Elements of the innate immunity include for example phagocytic cells (monocytes, macrophages, dendritic cells, polymorphonuclear leukocytes such as neutrophils, reticuloendothelial cells such as Küpffer cells, and microglia), cells that release inflammatory mediators (basophils, mast cells and eosinophils), natural killer cells (NK cells) and physical barriers and molecules such as keratin, mucous, secretions, complement proteins, immunoglobulin M (IgM), acute phase proteins, fibrinogen and molecules of the clotting cascade, and cytokines. Effector compounds of the innate immune system include chemicals such as lysozymes, IgM, mucous and chemoattractants (e.g., cytokines or histamine), complement and clotting proteins.
• The term “adaptive immune system” refers to antigen-specific cells, molecular components and mechanisms that emerge over several days, and react with and remove a specific antigen. The adaptive immune system develops throughout a host's lifetime. The adaptive immune system is based on leukocytes, and is divided into two major sections: the humoral immune system, which acts mainly via immunoglobulins produced by B cells, and the cell-mediated immune system, which functions mainly via T cells.
• Accordingly, in one example, an indicator is determined that correlates to a ratio of immune system biomarkers, which can be used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition.
• In this example, the method includes determining a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject.
• The biomarker values are used to determine a derived biomarker value using the pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers.
• Thus, if the biomarker values are the concentrations of the biomarkers, then the derived biomarker value will be based on a ratio of the biomarker values. However, if the biomarker values are related to the concentrations of the biomarkers, for example if they are logarithmically related by virtue of the biomarker values being based on PCR cycle times, or the like, then the biomarker values may be combined in some other manner, such as by subtracting the cycle times to determine a derived biomarker value indicative of a ratio of the concentrations.
• The derived biomarker is then used to determine the indicator, either by using the derived biomarker value as an indicator value, or by performing additional processing, such as comparing the derived biomarker value to a reference or the like, as will be described in more detail below.
• In any event, combining biomarker values to determine a ratio of concentrations of immune system biomarkers, and then using this to determine an indicator allows indicators to be determined for use in determining a likelihood of a subject suffering from a range of different conditions, depending on the immune system biomarkers selected, which as it will be appreciated can be performed using the above described process.
• A number of further features will now be described.
• In one example, the process involves determining a first derived biomarker value using a first pair of biomarker values, the first derived biomarker value being indicative of a ratio of concentrations of first and second immune system biomarkers, determining a second derived biomarker value using a second pair of biomarker values, the second derived biomarker value being indicative of a ratio of concentrations of third and fourth immune system biomarkers and determining the indicator by combining the first and second derived biomarker values. Thus, in this example, two pairs of derived biomarker values can be used, which can assist in increasing the ability of the indicator to reliably determine the likelihood of a subject having a condition.
• The derived biomarker values could be combined using a combining function such as an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model.
• In one example, the indicator is compared to an indicator reference, with a likelihood being determined in accordance with results of the comparison. The indicator reference is typically derived from indicators determined for a number of individuals in a reference population. The reference population typically includes individuals having different characteristics, such as a plurality of individuals of different sexes; and/or ethnicities, with different groups being defined based on different characteristics, with the subject's indicator being compared to indicator references derived from individuals with similar characteristics. The reference population can also include a plurality of healthy individuals, a plurality of individuals suffering from at least one diagnosed medical condition, a plurality of individuals showing clinical signs of at least one medical condition and/or first and second groups of individuals, each group of individuals suffering from a respective diagnosed medical condition.
• It will be appreciated that the individuals selected will depend on the intended use of the indicator. In particular, when the indicator is for use in determining the likelihood that a biological subject has a specific medical condition, the sample population includes individuals presenting with clinical signs of the specific medical condition, individuals diagnosed with the specific medical condition and healthy individuals. This ensures that the assessment of indicator validity applies regardless of not or whether the individual has the specific condition or not.
• It will also be appreciated that the sample population could also include a plurality of individuals of different sexes, ethnicities, ages, or the like, allowing the control value ranges to be common across populations. However, this is not essential, and alternatively control value thresholds could be established that are specific to a particular sub-set of the population. In this case, it would be necessary to ensure that the control value threshold ranges used are appropriate for the subject under consideration.
• The indicator can also be used for determining a likelihood of the subject having a first or second condition, in other words to distinguish between the conditions. In this case, this would typically be achieved by comparing the indicator to first and second indicator references, the first and second indicator references being indicative of first and second conditions and determining the likelihood in accordance with the results of the comparison. In particular, this can include determining first and second indicator probabilities using the results of the comparisons and combining the first and second indicator probabilities, for example using a Bayes method, to determine a condition probability corresponding to the likelihood of the subject having one of the conditions. In this situation the first and second conditions could include two medical conditions, or a single medical condition and a healthy condition.
• In this case, the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively. In this regard, this can be achieved by determining first and second groups of individuals, each group of individuals having a presence or absence of a diagnosed medical condition and determining first and second indicator references for the first and second groups respectively. This allows the indicator to be used to distinguish between first and second conditions, which could include different medical conditions, as well as healthy conditions. It will also be appreciated that whilst two groups are described, this is not essential and three or more groups could also be defined.
• The process is usually performed using at least one electronic processing device, such as a suitably programmed computer system or the like.
• In this case, the electronic processing device typically obtains at least two pairs of measured biomarker values, either by receiving these from a measuring or other quantifying device, or by retrieving these from a database or the like. The processing device then determines a first derived biomarker value indicative of a ratio of concentrations of first and second immune system biomarkers and a second derived biomarker value indicative of a ratio of third and fourth immune system biomarkers. The processing device then determines the indicator by combining the first and second derived biomarker values.
• The processing device can then generate a representation of the indicator, for example by generating an alphanumeric indication of the indicator, a graphical indication of a comparison of the indicator to one or more indicator references or an alphanumeric indication of a likelihood of the subject having at least one medical condition.
• The method would also typically include obtaining a sample taken from the biological subject, the sample including polynucleotide expression products and quantifying at least some of the polynucleotide expression products within the sample to determine the pair of biomarker values. This can be achieved using any suitable technique, and will depend on the nature of the immune system biomarkers.
• For example, if the indicator is based on a ratio of concentrations of the polynucleotide expression products, this process would typically include quantifying polynucleotide expression products by amplifying at least some polynucleotide expression products in the sample, determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products and determining the indicator by determining a difference between the amplification amounts. In this regard, the amplification amount is generally a cycle time, a number of cycles, a cycle threshold and an amplification time.
• In this case, the method includes determining a first derived biomarker value by determining a difference between the amplification amounts of a first pair of polynucleotide expression products, determining a second derived biomarker value by determining a difference between the amplification amounts of a second pair of polynucleotide expression products and determining the indicator by adding the first and second derived biomarker values.
• As previously discussed, the at least two immune system biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9 and the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Typically the mutual correlation range is one of ±0.8; ±0.7; ±0.6; ±0.5; ±0.4; ±0.3; ±0.2; and, ±0.1.
• Typically each immune system biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3 and more typically ±0.9; ±0.8; ±0.7; ±0.6; ±0.5; and, ±0.4. Typically the performance threshold is indicative of an explained variance of at least one of 0.4; 0.5; 0.6; 0.7; 0.8; and, 0.9.
• The above-described method has been used to identify 1650 biomarkers of inflammatory response syndromes (also referred to interchangeably herein as “IRS biomarkers” or “IRS immune system biomarkers”), which are useful for assisting in distinguishing: (1) between SIRS affected subjects (i.e., subjects having inSIRS or ipSIRS) and healthy subjects or subjects not affected by SIRS; (2) between subjects with inSIRS and subjects with ipSIRS; and/or (3) between subjects with different stages of ipSIRS (e.g., sepsis, severe sepsis and septic shock). Based on this identification, the present inventors have developed various methods, compositions, apparatus and kits, which take advantage of these biomarkers to provide an indicator for use in diagnosing the presence, absence or degree of at least one condition, or for prognosing at least one condition, wherein the at least one condition is selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS, or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, illustrative examples of which include mild sepsis, severe sepsis and septic shock). In advantageous embodiments, the methods and kits involve monitoring the expression of IRS biomarker genes in cells of the immune systems, including blood cells (e g., immune cells such as leukocytes), which may be reflected in changing patterns of RNA levels or protein production that correlate for example with the presence of active disease or response to disease.
• The IRS biomarkers are expression products of genes (also referred to interchangeably herein as “IRS biomarker genes” or IRS immune system biomarker genes”), including polynucleotide and polypeptide expression products. As used herein, polynucleotide expression products of IRS biomarker genes are referred to herein as “IRS biomarker polynucleotides.” Polypeptide expression products of the IRS biomarker genes are referred to herein as “IRS biomarker polypeptides.” The term “gene”, as used herein, refers to a stretch of nucleic acid that codes for a polypeptide or for an RNA chain that has a function. While it is the exon region of a gene that is transcribed to form RNA (e.g., mRNA), the term “gene” also includes regulatory regions such as promoters and enhancers that govern expression of the exon region.
• Suitably, the IRS biomarker genes are selected from the group consisting of: PRKCZ, SKI, RER1, TAS1R1, VAMP3, AGTRAP, VPS13D, KLHDC7A, NBLH/Clorf151, MDS2, RCAN3, LDLRAP1, MAN1C1, SH3BGRL3, DHDDS, HCRTR1, CCDC28B, LCK, ZNF362, THRAP3, PPIE//CCDC25, CAP1, CTPS, Clorf84, FAAH, DMBX1, CYP4B1, BTF3L4, LRRC42, Clorf175//TTC4, TMEM61, FPGT//TNNI3K, ACADM, SPATA1, EPHX4, RPAP2, RPL5//SNORA66//SNORD21//FAM69A, RTCD1, SLC30A7, RNPC3//AMY2B, CELSR2, AHCYL1, CEPT1//DRAM2, CHIA, LIX1L, UPF0627, MRPS21, TNFAIP8L2, SMCP, DCST1, RAG1AP1, Clorf182, HAPLN2, NTRK1, CD1E, TOMM40L//NR1I3, POU2F1, TIPRL, SFT2D2, CACNA1E, SMG7, OCLM, RGS2, ZC3H11A//RP11-74E24.2, MFSD4, IL20, RPS6KC1, C1orf95, ARF1, GALNT2, TNFRSF4, NADK, FLJ14100//C1orf86, GPR153, RERE, SLC2A7, SDHB, RNF186, DDOST, GPN2, RPA2, PEF1, PTP4A2, TRIM62, PHC2, LSM10, MRPS15, RRAGC, COL9A2, TESK2, NRD1, KTI12, CC2D1B, YIPF1, JAK1, SLC35D1, DIRAS3, ZZZ3, GNG5, ZNHIT6, ODF2L, SEP15, BARHL2, GCLM, CLCCH/GPSM2//Clorf62, SORT1, SLC16A4, PHTF1, RSBN1, DENND2C//BCAS2, CD58, SPAG17//WDR3, REG4//NBPF7, RP11-94I2.2//NBPF16//NBPF11//NBPF15//NBPF8//NBPF20//NBPF10//NBPF14//NBPF1//LOC100288142//NBPF12//KIAA1245//LOC100290137, APH1A, POGZ, TDRKH, THEM4, S100A11, CRNN, SPRR2C, S100A12, S100A8, GATAD2B//PLIN2, DENND4B, PBXIP1, PYGO2, SHC1, DCST2, GBA//GBAP, ASH1L, RITZ, MEF2D, AIM2, COPA, DEDD, TADA1L, GPA33, CD247, F5, PIGC, KIAA0040, TOR1AIP2//TOR1AIP1//IFRG15, STX6//KIAA1614, EDEM3, UCHL5, DENND1B, DDX59, KIF21B, ARL8A, CYB5R1, MYBPH, CHI3L1, PIK3C2B//LOC100130573, NUAK2, NUCKS1, FAIM3, PLXNA2, SLC30A1, LPGAT1, ANGEL2, RAB3GAP2//AURKAPSH/AURKA//SNORA36B, TP53BP2, NVL, TMEM63A, PARP1, ITPKB, TARBP1, CHML, AKT3, SMYD3, AHCTF1, OR1C1, NCOA1, HADHB, ABHD1//PREB, SPAST, SLC30A6//DDX50, CRIPT, MSH2, FOXN2, CCDC104, VRK2, AHSA2//USP34, OTX1, AFTPH, CEP68, PLEK, ANXA4, MXD1, NAGK, SMYD5//NOTO, MTHFD2, TTC31, SEMA4F, TMSB10, SH2D6, GNLY, KCNIP3, CNNM4, CNNM3, ZAP70, LIP T1//MRPL30, MAP4K4, IL1R2, IL1R1, IL18R1, POLR1B, CHCHD5, IL1RN, PSD4, DDX18, INSIG2, TMEM177//LOC100125918, RALB, PROC, GPR17//LOC100291428//LIMS2, IMP4, FAM123C, ACVR2A, MBD5, LYPD6B, SLC4A10, UBR3, HAT1, ITGA6, ZAK, OSBPL6, PLEKHA3, ZC3H15, COL3A1, GLS, OBFC2A, COQ10B, MARS2, CFLAR, NOP58, FAM117B, CYP20A1, FASTKD2, PIKFYVE, C2orf62, SLC11A1, AGFG1, CHRNG, EIF4E2, TRPM8, LRRFIP1, GAL3ST2, TMEM18, LAPTM4A, SF3B14, TP53I3, UNQ2999, GPR113//SELI, MPV17, PPM1G, NLRC4, CDC42EP3, HNRPLL, COX7A2L, KCNG3, CALM2//C2orf61, BCL11A, XPO1, NAT8B, DUSP11, MOGS, SNRNP200, SEMA4C, MITD1, ILIA, SLC35F5, CCDC93, CLASP1, SAP130, YSK4, GTDC1, ORC4L, NR4A2//FLJ46875, DPP4, GALNT3, SCN7A, FRZB, STK17B, CLKH/PPIL3, MPP4, INO80D, KLF7, FAM119A, NGEF, ARL4C, RAB17, HDLBP, LRRN1, SETD5, IRAK2, C3orf42, TSEN2, NR2C2//MRPS25, UBE2E1, C3orf35, SNRK, ZNF197, GNAI2, ALAS1, PRKCD, CACNA1D, PXK, PTPRG, ATXN7, SLC35A5, SLC15A2, CCDC48, DNAJC13, CLDN18, GYG1, SELT, MED12L, RAP2B, MYNN, ABCF3, VPS8, HRG, EIF4A2//SNORA4, LPP, CCDC50, LOC152217, TADA3L, SEC13, TIMP4, METTLE, DAZL//DAZ4//DAZ3//DAZ2, SATB1//TBC1D5, SCN10A, SEC22C, ZDHHC3, ZDHHC3, SLC6A20, UQCRC1, PRKAR2A, IMPDH2, CCDC71, UBA7, CAMKV, WDR82, LMOD3, FOXP1, MORC1, ATG3, GSK3B//LOC100129275, HCL51, KPNA1, PTPLB, C3orf22, RPN1, KIAA1257//ACAD9//LOC100132731, FOXL2, MECOM, PLD1, GNB4, MRPL47, KLHL6, THPO, ETV5, BCL6//LOC100131635, ATP13A5, TMEM44, KIAA1530, TACC3, CNO, BST1, KLF3, TMEM33//DCAF4L1, KIT, ENAM, FAM47E//STBD1, ENOPH1, PDLIM5, CCDC109B//HIGD1A//CCDC13, EGF, PCDH10, RAB33B, TMEM184C, RBM46, GRIA2, C4orf39, KLHL2, TLL1, F11, SLBP, HAUS3//POLN, PPARGC1A, TLR10, C4orf34, TXK, RPL21P44, KDR, RCHY1, CNOT6L, PLACE, HPSE, GPRIN3, PPA2, COL25A1, C4orf3, QRFPR, MFSD8, MAP9, PDGFC, TKTL2, ACSL1, SUBH/TMEM183A, CARD6, MCCC2, TNPO1, PDE8B, PAPD4, THBS4, FAM151B, RASGRF2, SNX2, LMNB1//PCIF1, MEGF10, LEAP2, TCF7, KDM3B, CXXC5, SLC4A9, ANKHD1-EIF4EBP3//ANKHD1//EIF4EBP3, KIAA0141, GRPEL2, MFAP3, GABRA6, GABRA1, DOCK2, RANBP17//USP12, ERGIC1, ATP6V0E1//SNORA74B, ZNF346, NSD1, CLPTM1L, UGT3A1, GDNF, TTC33, hCG_—2039148, MOCS2, SLC38A9, CCDC125, ANKRA2, HAPLN1, CCNH, TMEM161B, MBLAC2, MCTP1, TICAM2//TMED7//TMED7-TICAM2, KIF3A, C5orf15, SKP1, CXCL14, KLHL3, CD14, YIPF5, LARS, DCTN4, CCDC69, ATOX1, TIMD4, ADAM19, SLIT3, RNF44, DOK3, MGAT4B//SQSTM1, C5orf45//SQSTM1, RASGEF1C, MGAT1, IRF4, HIVEP1, E2F3, HIST1H4I, HIST1H2BM, MOG, ZNRD1//NCRNA00171, TRIM15, HCG27, BAT2//SNORA38, CYP21A2, ITPR3, MAPK14, MAPK13, PNPLA1, SFRS3, CDKN1A, FOXP4, CUL9, RUNX2, ZNF451, SOBP, C6orf182, KIAA1919, RWDD1, KPNA5, TPD52L1, ARG1, RAB32, ARID1B, SLC22A3, SERPINB1, C6orf146, GCM2, ATXN1, DCDC2//KAAG1, HIST1H3I, HIST1H4L, GABBR1, RNA243, DDAH2, CLIC1, NEU1, RXRB, VPS52, TCP11, CLPS, PGC, ZNF318, YIPF3, MRPL14, PLA2G7, PKHD1, IL17F, HTR1B, GABRR2, UBE2J1, BACH2, MCM9, VNN1, IL20RA, FLJ27255, T, RPS6KA2, HGC6.3, UNC84A//C7orf20, SDK1, ZDHHC4, C7orf26, GLCCI1thcag7.903, GPNMB, CCDC126, WIPF3//ZNRF2//LOC441208, GPR141, STARD3NL, POU6F2, CDC2L5, ZMIZ2, UPP1, ZNF273, KCTD7//RABGEF1, RABGEF1thcag7.967//tcag7.951//KCTD7//LOC100293333, CCDC132, PVRIG//PILRB//STAG3, PILRB//PVRIG//STAG3, C7orf51, GNB2, LRRC17, LRRN3, CFTR, LSM8, LUC7L2, MGAM//LOC100124692, GIMAP7, INSIG1, RBM33, ICA1, FAM126A, HIBADH, TRIL, SCRN1, ELM01, INHBA, CAMK2B, NPC1L1, DDC//LOC100129427, NSUN5//NSUN5B//NSUN5C, CLDN3, C7orf23//DMTF1, SRI, BET1, MCMI, GATS, ATXN7L1//RINT1//EFCAB10, KIAA1549, SLC37A3, SMARCD3, MLL3//BAGE2, CLN8, MSRA, PIWIL2, NEFM//LOC100129717, EPHX2, LEPROTL1, MAK16//C8orf41, AP3M2, FNTA, SGK196, UBE2V2, FLJ46365, SNTG1, TRIM55, C8orf45, PREX2, PLEKHF2, BAALC//FLJ10489, TTC35, MTBP, ZHX2, RNF139, TG, DENND3//C8orf60, TNFRSF10D, TRIM35, GSR, WHSC1L1, PCMTDJ//PXDNL, NCOA2, TRAMMLOC286190, RUNX1T1, EXT1, DDEF1IT1, CDC37L1, UBE2R2, UBAP1//KIF24, GALT, RGP1//GBA2, TGFBR1, C9orf6//IKBKAP, IMAGE5303689, ATP6V1G1, TLR4, SET, MRPL41, C9orf68, HAUS6//SCARNA8, KLHL9, C9orf82, NDUFB6//DFFB, SIT1, FAM108B1, TRPM6, FRMD3, SLC28A3, BICD2, C9orf84, AKNA, MEGF9, C5, GOLGA1//SCAI, SH2D3C, FAM102A, FLJ10232, ASB6, BAT2L, EDF1, FBXW5, C10orf18, FBXO18, GATA3, CUGBP2, VIM, STAM, WAC, BAMBI, ZNF487//LOC439911, ALOX5, WDFY4, SRGN, CCDC109A, FAM149B1//FAM149B2, MINPP1, PTEN//PTENP1, ENTPD1//C10orf131, ABCC2, SFXN2, SHOC2, ACSL5, BCCIP//DHX32, FAM188A, CUBN, SVIL//hCG1783494, FAM13C//PHYHIPL, ATAD1, ANKRD22, FLJ34077, COX15, ERLIN1, ACTR1A, ABLIM1, RAB11FIP2, C10orf84, PRDX3, C10orf119, NSMCE4A, TALDOMINTS8, TNNT3, FXC1, PDE3B, DNAJC24, PTPRJ//OR4B1, C11orf31, TMEM109, CD6, CDS, TMEM138, POLR2G, TMEM179B, NAT11, OTUB1, RBM14//RBM4, AIP, PPFIA1, IL18BP//NUMA1, C11orf30, C11orf82, TMEM126B, C11orf73, PIWIL4, LOC100132686, PAFAH1B2, UBE4A, TRAPPC4, SC5DL, VWA5A//OR10D1P, STT3A, VPS26B, TRIM21, ZBED5, SAAL1, FANCF, LIN7C, PHF21A, CUGBP1, OSBP, CYBASC3, TUT1, SLC25A45, LTBP3, EIF1AD, GAB2, CREBZF, PICALM, SLC36A4, CCDC82, KIAA1826, MPZL3, MPZL2, H2AFX, SIAE, ZBTB44, HSN2, ADIPOR2, NCAPD2//SCARNA10//FADS1, PTPN6, CLEC4D, CDKN1B, GOLT1B, FAR2, FGD4, TMEM106C, TMBIM6, C12orf62, PRR13//PCBP2, DGKA, COQ10A, TSPAN31, CDK4/MARCH9/C3HC4, LEMD3, IRAK3, TMTC3, ACTR6, TCTN1, PXMP2//PGAM5, DCP1B, SLC2A3//SLC2A14, C3AR1, PLBD1, TM7SF3, ASB8//PHB, LMBR1L, FMNL3//PRPF40B, AAAS, NFE2, GPR84, CD63, SARNP//DNAJC14, NACA, CDK4//TSPAN31, TMBIM4//LOC100133322, IL22, LIN7A, HAL, APPL2, GLTP, GIT2, VPS29, PPTC7, DDX54//CCDC42B, SLC24A6, SDS, RBM19, MED13L, C12orf49, FBXO21, WSB2, TAOK3, CIT, RAB35, RPLP0, PXN, TRIAP1, SFRS9, POPS, UNQ1887, C12orf43, ANAPC5, KDM2B, MORN3, TMEM120B//RHOF, LOC338799, DIABLO//B3GNT4, VPS33A, CLIP1, PITPNM2, EIF2B1, CCDC92, NCOR2, DHX37, DDX51, POLE, GOLGA3, ZMYM2, SPATA13//C1QTNF9, NUPL1, PAN3//EEF1A1//CHCHD2, ALOX5AP, EEF1DP3, KL, UFM1, NARG1L, ITM2B, FNDC3A, CDADC1, ARL11, LMO7, DNAJC3, TM9SF2, CLYBL, PCCA, ABHD13, LAMP1, TMCO3, UPF3A, ZMYM5//ZMYM2, ZDHHC20//LOC728099, PARP4, MTMR6//LOC646482, HSPH1, N4BP2L2//CG030, ELFT, LCP1, KPNA3, C13orf1, DLEU2//DLEU2L, GUCY1B2, INTS6, DACH1, TBC1D4, EDNRB, UGGT2, GPR183, LIG4, ANKRD10, RASA3, RNASE2//LOC6433 32, RPGRIP1, IRF9, TSSK4, C14orf21, SCFD1, FANCM, ABHD12B, PTGDR, FBXO34//KIAA0831, C14orf101, ACTR10, ARID4A, JKAMP, HIF1A, SYNE2, EXD2, SLC39A9, SFRS5, PCNX, SIPA1L1//SNORD56B//LOC145474//LOC283567, YLPM1, BATF, FLVCR2//RPS24, GPR65, TDP1, EVL, ZNF839, TDRD9, INF2, PLD4, MTA1//LOC647310//LOC100128343, NDRG2, DAD1//OR6J1, SLC7A8, IPO4, TM9SF1, ADCY4, RIPK3, EAPP, BAZ1A, NFKBIA, SEC23A, C14orf104, C14orf138, SOS2, NIN, PYGL, CNIH, DHRS7, WDR89, ACTN1, NUMB, C14orf43, ABCD4, KIAA0317, NEK9, ANGE1, SPTLC2, SERPINA6, DICER1, BCL11B, ANKRD9, PPP1R13B, AKT1, BRF1, TUBGCP5, SNRPN//SNURF//IP W//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@, APBA2, MTMR15//MTMR10, RYR3, BAHD1, CHP, JMJD7-PLA2G4B//JMJD7//PLA2G4B, HAUS2, C15orf63//SERF2, B2M, TRIM69, PLDN, SQRDL, GALK2, USPS, GLDN, MAPK6, LACTB, RAB8B, APH1B, USP3//LOC1001308 55, SNX1, LBXCORH/PIAS1//CALML4, NE01, MPI, FBXO22//FBXO220S, RCN2, FAH, IL16, ABHD2, SLCO3A1, MCTP2, MEF2A//LYSMD4, NIPA2//CYFIP1, HERC2//HERC2P2//HERC2P3//LOC440248, MTMR10//MTMR15, C15orf24, SLC12A6, LPCAT4, INO80, OIP5, ZFP106, CDAN1, SPG11//ISLR, SPPL2A, GNB5//LOC100129973, MYO5A, ARPP19, RAB27A, CCPG1//PIGB//DYX1C1, BNIP2, CAl2, FAM96A, KIAA0101//CSNK1G1, TLE3, PARP6, NPTN, MAN2C1, IMP3, MTHFS, ST20//C15orf37, TMC3, AP3B2, C15orf40, WDR73, NTRK3, DET1, TM2D3, WDR90, RHOT2//FBXL16, TMEM204, CRAMP1L//HN1L, MAPK8IP3, TBL3, TSC2, KCTD5//PRO0461//PDPK1, CLUAP1, DNASE1, DNAJA3, CP110, C16orf62, LYRM1, METTL9, EEF2K, POLR3E, PLK1, PRKCB, IL21R//LOC283888, SULT1A2//SULT1A1, ATXN2L, LAT//SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPINH/LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332, KIF22, MAZ, CORO1A//LOC606724, ITGAL, SRCAP//SNORA30, ZNF646//ZNF668, C16orf67, TMEM188, LPCAT2, CETP, CKLF, CMTM1//CKLF, TMEM208, CTCF, THAP11, NUTF2, EDC4, SLC7A6//SLC7A60S, PRMT7, SNTB2, VPS4A, DDX19B//DDX19A, CHST4, HP//HPR, PLCG2, KLHL36, KIAA0182, BANP//RUNDC2C, TRAPPC2L, SPG7, CDK10, TCF25, AFG3L1, LUC7L, AXIN1, JMJD8, LMF1, UNKL, UNKL, CLCN7, MRPS34, RNPS1, NLRC3, TRAP1//DNASE1, ADCY9, CORO7, C16orf72, RRN3//LOC653390//LOC730092//LOC100131998, XYLTH/LYRM2//ZC3H11A, DCUN1D3//LYRM1, IGSF6//METTL9, CDR2//RRN3//LOC100131998//LOC653390, COG7, GGA2, NSMCE1, GTF3C1, CCDC101//LOC388242, C16orf54, KCTD13, SEPT1, ZNF764//ZNF747, C16orf58//LOC100128371, ITFG1, ABCC11//LONP2, NUDT21, BBS2//OGFOD1, CSNK2A2, GOT2, FAM96B, FHOD1//SLC9A5, ATP6VOD1//LOC100132855, GFOD2, SLC12A4, DPEP3, DPEP2, CHTF8//HAS3, COG8//PDF, TERF2, AARS, ST3 GAL2, VA C14//LOC100130894, AP1G1, WDR59, CTRB2//CTRB1, TAF1C//ADAD2, FBXO31, ZCCHC14, FAM38A, CENPBD1, TIMM22, RPA1, DPH1//OVCA2, SGSM2, ARRB2, LOC100130950, DNAH2, PIGL, TRPV2, MPRIP, DRG2, ALKBH5//FLJ13773, SMCR7, WSB1, TAOK1, CPD, SUZ12P, RNF135, ZNF830, TAF15, GGNBP2, LASP1, PSMD3, CDC6, NBR2, TMUB2, MGC57346//C17orf69, NSF//LOC728806, GOSR2, NPEPPS//TBC1D3F//LOC440434, KPNB1, CDK5RAP3, ATP5G1, UBE2Z, XYLT2//LOC100130580, NOG, DGKE, AKAP1, TMEM49//CLTC//MIR21, CLTC, CA4, C17orf64, DCAF7, PITPNC1, NOL11//SNORA38B, MAP2K6, COG1, CD300A, TMEM104, MRPS7, KIAA0195, T5EN54, LLGL2, LOC100134934//CDK3, MFSD11, SEPT9, TNRC6C, TMC8, ENGASE, RPTOR, GPS1, FN3KRP, TBCD, GEMIN4, GLOD4, SLC43A2, PRPF8, SMG6//C17orf6, METT10D//LOC284009, SHPK, TAX1BP3, P2RX5, MYBBP1A//SPNS2, PELP1, PFN1, ZNF232, DHX33, DERL2, NLRP1//LOC728392, ASGR2, NEURL4//GPS2//D4S234E, ZBTB4, TP53, VAMP2, PIK3R5, ELAC2, NCOR1//C20orf191//LOC100131704, ZNF287, TOM1L2//LOC246315, GRAP//SNORD3B-1//SNORD3B-2//LOC400581, ALDOC, SDF2, RAB34, PHF12, NUFIP2, OMG, EVI2B, C17orf66//RSL24D1, SYNRG//LOC100131822, PLXDC1, CACNB1, PGAP3, MED24, NR1D1//THRA, CCR7, STAT5B//STAT5A, FAM134C, VAT1, DUSP3, C17orf65//ASB16, UBTF, GPATCH8, MAP3K14//LOC100133991, OSBPL7, SLC35B1, TOB1, COX11//TOM1L1, VEZF1, SFRS1//FLJ44342, SEPT4, MED13//LOC100129112, LIMD2//MAP3K3, STRADA, FTSJ3, CD79B, ICAM2, ERN1, TEX2, LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LOC644397, GNA13, WIPI1//ARSG, FAM20A, NAT9, GGA3, H3F3B//H3F3C, EXOC7, SFRS2, TMC6//LOC100131096, USP36, CD7, RAB31, VAPA, SEH1L, HQ0644/PRO0644, RNMT, RNF138, GALNT1, ELP2, PIK3C3, SLC14A2, ME2, SERPINB2//SERPINB10, ZNF407, ZNF236, NFATC1//LOC100127994, ENOSF1//TYMS, MYOM1, AFG3L2, ABHD3, OSBPL1A, CDH2, DSC1, PSTPIP2, C18orf32, MBD2//SNORA37, PIGN, TMX3, PQLC1, GZMM, ARID3A, CIRBP, DAZAP1, SPPL2B, NFIC, VAV1, ARHGEF18//LOC100128573, STXBP2//L00554363//LOC100131801, C19orf59, ZNF317, ILF3, SMARCA4, PRKCSH, IER2, CCDC130, DCAF15, IL27RA, KLF2, SIN3B, DDA1, GTPBP3, FAM129C, FCHO1, ARRDC2, IFI30, C19orf60, CRTC1//MAML2, RFXANK//MEF2B//LOC729991, ZNF101, ZNF738, ZNF257//ZNF492//ZNF99//ZNF98//LOC646864, C19orf2, KIAA0355//FLJ21369, USF2, TMEM147, LIN37//PSENEN, C19orf55, TBCB//POLR2I, ZNF382, ZNF568, ZNF420, ZNF383, CCDC97, ZNF574, CD177, ZNF230//ZNF222, VASP, GRWD1, FLT3LG, ZNF175, NCRNA00085, PPP2R1A, ZNF808//ZNF578//ZNF611, LENG8, FCAR, RPL28, U2AF2, LOC100288114//MGC9913, ZFP28, ZNF460, ZNF549, ZNF211, ZNF587//ZNF417, ZNF274, ZNF544, ZNF8, TRIM28, C19orf6, C19orf34, GNG7, AES, EEF2//SNORD37, PLIN5ARG1, PLIN3, PTPRS, SAFB2//SAFB, RANBP3, GTF2F1//LOC100130856, XAB2, ELAVL1, ADAMTS10, FBXL12, DNMT1, TYK2, KEAP1, KRI1, TMEM205//hCG29977, ZNF563, MAN2B1//MORG1, C19orf56, DHPS, TNPO2//SNORD41, LPHN1, NDUFB7, AKAP8, AKAP8L, CHERP//C19orf44//CALR3, INSL3//JAK3, IL12RB1, UPK1A, TYROBP, ZNF529, ZNF461, ZNF607, YIF1B, PRR13, CEACAM4, PLAUR, TRAPPC6A, ERCC1//CD3EAP, RTN2, SYMPK, PGLYRP1, NOSIP, PNKP, NKG7, FPR1, ZNF28, OSCAR, MBOAT7, LILRA5, LILRA4, ZNF550//ZNF549, ZNF416, ZNF256, ZNF329, FAM110A, ITPA, CDC25B, CDS2, CRLS1, CSRP2BP, SEC23B, SLC24A3, HCK, ASXL1, ACSS2, C20orf4, TGIF2, C20orf24//SLA2, RPN2//EEF1A2, CTNNBL1, ACTR5, PPP1R16B, DHX35, PLCG1, MYBL2, SYS1//SYS1-DBNDD2//DBNDD2, DNTTIP1, CTSA, MMP9//LOC100128028, DDX27, SLC9A8, RNF114, PTPN1, TSHZ2, PFDN4, CSTF1, CASS4, GNAS, C20orf177, CDH26, C20orf197, LOC284757, ARFGAP1, PRPF6, NSFL1C, SIRPD, SIRPG//SIRPA, RNF24, RASSF2, TMX4, JAG1, C20orf74, C20orf3, C20orf112, CDK5RAP1, AHCY, GGT7, EDEM2, RBM39//LOC643167, BLCAP, SERINC3//TTPAL, ZNF335, ELMO2, B4GALT5, DPM1, ZFP64, ZNF217, CTSZ, SYCP2, PSMA7, DID01, YTHDF1, CHODL, BACH1, C21orf41//BACH1, IL10RB, IFNAR1, IFNGR2, SON, MORC3//DOPEY2, DYRK1A, KCNJ15, ETS2, RRP1B, PFKL, TRPM2, ADARB1, SAMSN1//LOC388813, N6AMT1, SYNJ1, TMEM50B, KCNE1, PRDM15, C2CD2, WDR4, U2AF1, CSTB, UBE2G2//SUMO3, PTTGlIP, POFUT2, MCM3AP, IL17RA//CECR7, C22orf37, LZTR1, PPIL2//YPEL1, CYTSA, SNRPD3//C22orf13, NF2, LIMK2, SLC5A1, MCM5, NCF4, GGA1, SH3BP1//PDXP, POLR2F//LOC100131530, APOBEC3A//APOBEC3B, APOBEC3D, ATF4, CACNA1I, ZC3H7B, CCDC134, TSPO, NUP50, TBC1D22A//LOC100289878, RP3-402G11.5, SAPS2, NCAPH2, BID, SLC25A1, KLHL22//KRT18, PI4KA//PI4KAP1//PI4KAP2//LOC100293141, MAPK1, ZNF70, TPST2, SF3A1//CCDC157, PES1, PIK3IP1, PATZ1, C22orf30, IL2RB, CSNK1E//LOC400927, UNC84B, CBX7//LOC100128400, RPS19BP1, MKL1//KIAA1659, RANGAP1, TCF20, LDOC1L, UNQ6126, TUBGCP6, SBF1//SBF1P1, MSL3, MOSPD2, BMX//HNRPDL, PDHA1, YY2, PDK3, GK//GK3P//FTL//LOC652904, CXorf59, ATP6AP2, USP9X//USP9Y, RP2, USP11, RBM3, FTSJ1, WAS, PLP2, TSPYL2//GPR173, MAGED2, UBQLN2, NLGN3, ACRC, UPRT, CXorf26, ATP7A, DIAPH2, CSTF2//RAD21, ARMCX3, ARMCX5, GPRASP1, TMEM31, TBC1D8B, MID2, DOCK11, LONRF3, UBE2A, SH2D1A, OCRL, SLC25A14, HPRT1, CD40LG, AFF2, SSR4//IDH3G, FAM50A, DKC1//SNORA36A//SNORA56, ARSD, KAL1, CTPS2, RPS6KA3, BCOR, MAOB//NAT13, ZNF41, OTUD5, KCND1, ZMYM3, MAGT1, BRWD3, TRMT2B, GLA, MORF4L2, PSMD10, ACSL4, LAMP2, CUL4B, ODZ1, ELF4, RAP2C, FAM127B//FAM127C//FAM127A, TMEM185A, ARD1A, IRAK1, DNASE1L1//RPL10, SH3KBP1, Mitochondrial, Mitochondrial, CCNL2, INPP5B, TLR5, ADRB3//GOT1L1, NOC2L//SAMD11//LOC401010 and SHFM1 (hereafter referred to interchangeably herein as “the full list of IRS immune system biomarker genes” or “full list IRS biomarker genes”).
• The methods, compositions, apparatus and kits of the present invention take advantage of the IRS biomarkers broadly described above and elsewhere herein to provide an indicator for use in diagnosing the presence, absence or degree of the at least one condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock), or in providing a prognosis of the at least one condition, which may involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values (also referred to herein as a “biomarker signature”), the indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• In advantageous embodiments, the diagnostic or prognostic methods, compositions, apparatus and kits of the present invention involve: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker. The function suitably includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.
• In some embodiments, the diagnostic or prognostic methods, compositions, apparatus and kits involve: determining at least one derived IRS biomarker value corresponding to a ratio of two measured IRS biomarker values. In these examples, the diagnostic or prognostic methods, apparatus and kits suitably include combining at least two IRS biomarker values to determine an indicator value representing the indicator and in illustrative examples of this type, the at least two IRS biomarker values are combined using a combining function (e.g., any one or more of: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; weighted sum; a nearest neighbor model; and a probabilistic model). Suitably, the diagnostic or prognostic methods, apparatus and kits include: (a) determining a first derived IRS biomarker value, the first derived IRS biomarker value being a ratio of first and second measured IRS biomarker values; (b) determining a second derived IRS biomarker value, the second derived IRS biomarker value being a ratio of third and fourth measured IRS biomarker values; and (c) adding the first and second derived IRS biomarker values to generate an indicator value.
• In some embodiments, the methods, compositions, kits and apparatus of the present invention are useful for diagnosing that inSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes as defined herein.
• In other embodiments, the methods, apparatus and kits are useful for diagnosing that ipSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from ipSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes, as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes as defined herein.
• In still other embodiments, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes as defined herein.
• In other embodiments, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least four IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a second IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a third IRS biomarker group, and wherein at least one other of the at least four IRS biomarkers is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes as defined herein, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes as defined herein, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes as defined herein, and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes as defined herein.
• In still other embodiments, the methods, apparatus and kits are useful for diagnosing that mild sepsis or severe sepsis is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes as defined herein.
• In still other embodiments, the methods, apparatus and kits are useful for diagnosing that mild sepsis or septic shock is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes as defined herein.
• In still other embodiments, the methods, apparatus and kits are useful for diagnosing that severe sepsis or septic shock is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes as defined herein.
• As used herein, the terms “diagnosis”, “diagnosing” and the like are used interchangeable herein to encompass determining the likelihood that a subject will develop a condition, or the existence or nature of a condition in a subject. These terms also encompass determining the severity of disease or episode of disease, as well as in the context of rational therapy, in which the diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dose or dosage regimen), and the like. By “likelihood” is meant a measure of whether a biological subject with particular measured or derived biomarker values actually has a condition (or not) based on a given mathematical model. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased likelihood may be determined simply by determining the subject's measured or derived biomarker values for at least two IRS biomarkers and placing the subject in an “increased likelihood” category, based upon previous population studies. The term “likelihood” is also used interchangeably herein with the term “probability”.
• In some embodiments, the biomarkers, including IRS biomarkers, are obtained from a biological sample. The term “biological sample” as used herein refers to a sample that may be extracted, untreated, treated, diluted or concentrated from an animal. The biological sample is suitably a biological fluid such as whole blood, serum, plasma, saliva, urine, sweat, ascitic fluid, peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal fluid, tissue biopsy, and the like. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof. Typically, the biological sample comprises blood cells such as mature, immature or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes, macrophages, dendritic cells natural killer cells, or fraction of such cells (e.g., a nucleic acid or protein fraction). In specific embodiments, the biological sample comprises leukocytes including peripheral blood mononuclear cells (PBMC). By “obtained from” is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.
• The term “nucleic acid” or “polynucleotide” as used herein includes RNA, mRNA, miRNA, cRNA, cDNA mtDNA, or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA.
• “Protein”, “polypeptide” and “peptide” are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.
• In some embodiments, biomarker signatures are determined through analysis of measured or derived IRS biomarker values for IRS biomarkers of one or more control subjects that have or do not have a condition. These biomarkers are referred to herein as “reference IRS biomarkers”. In specific examples, individual control subjects are selected from “healthy control subjects”, “non-healthy control subjects”, “SIRS control subjects”, “inSIRS control subjects”, “ipSIRS control subjects”, “control subjects with a particular stage of ipSIRS”, illustrative examples of which include “mild sepsis control subjects”, “severe sepsis control subjects” and “septic shock control subjects”, etc.), which are also referred to herein as control groups (e.g., “healthy control group”, “non-healthy control group”, “SIRS control group”, “inSIRS control group”, “ipSIRS control group”, “ipSIRS stage group”, illustrative examples of which include “mild sepsis control group”, “severe sepsis control group”, and “septic shock control group”, etc.).
• Suitably, an individual measured or derived IRS biomarker value corresponds to the level or amount of a respective IRS biomarker or to a function that is applied to that level or amount. As used herein the terms “level” and “amount” are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or concentrations of IRS biomarkers in a sample.
• In some embodiments, the presence, absence, degree or prognosis of at least one condition in a biological subject is established by determining a plurality of IRS biomarker values, wherein each IRS biomarker value is indicative of a value measured or derived for at least one IRS biomarker in a biological sample obtained from the biological subject. These biomarkers are referred to herein as “sample IRS biomarkers”. In accordance with the present invention, a sample IRS biomarker corresponds to a reference IRS biomarker (also referred to herein as a “corresponding IRS biomarker”). By “corresponding IRS biomarker” is meant an IRS biomarker that is structurally and/or functionally similar to a reference IRS biomarker. Representative corresponding IRS biomarkers include expression products of allelic variants (same locus), homologues (different locus), and orthologues (different organism) of reference IRS biomarker genes. Nucleic acid variants of reference IRS biomarker genes and encoded IRS biomarker polynucleotide expression products can contain nucleotide substitutions, deletions, inversions and/or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference IRS polypeptide.
• Generally, variants of a particular IRS biomarker gene or polynucleotide will have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters. In some embodiments, the IRS biomarker gene or polynucleotide displays at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to a nucleotide sequence selected from any one of SEQ ID NO: 1-1650, as summarized in Table 1.
• Corresponding IRS biomarkers also include amino acid sequences that display substantial sequence similarity or identity to the amino acid sequence of a reference IRS biomarker polypeptide. In general, an amino acid sequence that corresponds to a reference amino acid sequence will display at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence selected from any one of SEQ ID NO: 1651-3284, as summarized in Table 2.
• In some embodiments, calculations of sequence similarity or sequence identity between sequences are performed as follows:
• To determine the percentage identity of two amino acid sequences, or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparison, when a position in the first sequence is occupied by the same or similar amino acid residue (i.e., conservative substitution) at the corresponding position in the second sequence, then the molecules are similar at that position.
• The percentage identity between the two sequences is a function of the number of identical amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. By contrast, the percentage similarity between the two sequences is a function of the number of identical and similar amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.
• The comparison of sequences and determination of percentage identity or percentage similarity between sequences can be accomplished using a mathematical algorithm. In certain embodiments, the percentage identity or similarity between amino acid sequences is determined using the Needleman and Wunsch, (1970, J. Mol. Biol. 48: 444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at http://www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at http://www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. An non-limiting set of parameters (and the one that should be used unless otherwise specified) includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.
• In some embodiments, the percentage identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.
• The nucleic acid and protein sequences described herein can be used as a “query sequence” to perform a search against public databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul, et al., (1990, J Mol Biol., 215: 403-10). BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to YYYYY protein molecules of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res, 25: 3389-3402). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.
• Corresponding IRS biomarker polynucleotides also include nucleic acid sequences that hybridize to reference IRS biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term “hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions” describes conditions for hybridization and washing. “Hybridization” is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms “match” and “mismatch” as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.
• Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1% v/v to at least about 15% v/v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42° C., and at least about 1 M to at least about 2 M salt for washing at 42° C. Low stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by two washes in 0.2×SSC, 0.1% SDS at least at 50° C. (the temperature of the washes can be increased to 55° C. for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v/v to at least about 30% v/v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42° C., and at least about 0.1 M to at least about 0.2 M salt for washing at 55° C. Medium stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at 60-65° C. One embodiment of medium stringency conditions includes hybridizing in 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 60° C. High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42° C., and about 0.01 M to about 0.02 M salt for washing at 55° C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 0.2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 1% SDS for washing at a temperature in excess of 65° C. One embodiment of high stringency conditions includes hybridizing in 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 65° C.
• In certain embodiments, a corresponding IRS biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65° C.
• Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al., supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to 1.104.
• The IRS biomarkers disclosed herein each have significant sensitivity and specificity for diagnosing the presence, absence or degree of at least condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock). Accordingly, it is feasible to use individual IRS biomarkers in methods, apparatus and kits that do not rely on the use of low mutual correlation between biomarkers to diagnose the presence, absence or degree of the at least condition. In illustrative examples of this type, the invention contemplates methods, kits and apparatus that are useful for diagnosing that inSIRS or a healthy condition is present or absent in a biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and a healthy condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group A and group B IRS biomarker genes as defined herein.
• In other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that ipSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from ipSIRS and a healthy condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group C and group D IRS biomarker genes as defined herein.
• In still other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and ipSIRS, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group E and group F IRS biomarker genes as defined herein.
• In still other illustrative examples, the methods, apparatus and kits are useful for diagnosing that mild sepsis or severe sepsis is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and severe sepsis, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group K and group L IRS biomarker genes as defined herein.
• In still other illustrative examples, the methods, apparatus and kits are useful for diagnosing that mild sepsis or septic shock is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group M and group N IRS biomarker genes as defined herein.
• In other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that severe sepsis or septic shock is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from severe sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group O and group P IRS biomarker genes as defined herein.
• The biomarkers may be quantified or detected using any suitable technique. In specific embodiments, the biomarkers, including the IRS biomarkers, are quantified using reagents that determine the level or abundance of individual biomarkers. Non-limiting reagents of this type include reagents for use in nucleic acid- and protein-based assays.
• In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al., 1989, supra; and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A⁺ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the IRS biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., (“PCR Protocols”, Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate IRS biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90/07641. Polymerase chain reaction methodologies are well known in the art.
• In certain advantageous embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.
• In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting or RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by “blotting” on to the filter. Subsequently, the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection/quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of an IRS biomarker nucleic acid detected with the progression or severity of the disease. As used herein, the term “probe” refers to a molecule that binds to a specific sequence or sub-sequence or other moiety of another molecule. Unless otherwise indicated, the term “probe” typically refers to a nucleic acid probe that binds to another nucleic acid, also referred to herein as a “target polynucleotide”, through complementary base pairing. Probes can bind target polynucleotides lacking complete sequence complementarity with the probe, depending on the stringency of the hybridization conditions. Probes can be labeled directly or indirectly and include primers within their scope. By “primer” is meant an oligonucleotide which, when paired with a strand of DNA, is capable of initiating the synthesis of a primer extension product in the presence of a suitable polymerizing agent. The primer is preferably single-stranded for maximum efficiency in amplification but can alternatively be double-stranded. A primer must be sufficiently long to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including application, temperature to be employed, template reaction conditions, other reagents, and source of primers. For example, depending on the complexity of the target sequence, the primer may be at least about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500, to one base shorter in length than the template sequence at the 3′ end of the primer to allow extension of a nucleic acid chain, though the 5′ end of the primer may extend in length beyond the 3′ end of the template sequence. In certain embodiments, primers can be large polynucleotides, such as from about 35 nucleotides to several kilobases or more. Primers can be selected to be “substantially complementary” to the sequence on the template to which it is designed to hybridize and serve as a site for the initiation of synthesis. By “substantially complementary”, it is meant that the primer is sufficiently complementary to hybridize with a target polynucleotide. Desirably, the primer contains no mismatches with the template to which it is designed to hybridize but this is not essential. For example, non-complementary nucleotide residues can be attached to the 5′ end of the primer, with the remainder of the primer sequence being complementary to the template. Alternatively, non-complementary nucleotide residues or a stretch of non-complementary nucleotide residues can be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the sequence of the template to hybridize therewith and thereby form a template for synthesis of the extension product of the primer.
• Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed nucleic acid probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to IRS biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed IRS biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.
• In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more IRS biomarker polynucleotides under conditions favoring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., 1989, supra.
• Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).
• cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.
• Usually the target IRS biomarker polynucleotides are detectably labeled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labeled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu³⁴), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.
• The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.
• After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.
• The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a colored colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated color spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different IRS biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’. A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al. (U.S. Pat. No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al. (U.S. Pat. No. 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labeled microbeads, the reaction may be detected using flow cytometry.
• In certain embodiments, the IRS biomarker is a target RNA (e.g., mRNA) or a DNA copy of the target RNA whose level is measured using at least one nucleic acid probe that hybridizes under at least low, medium, or high stringency conditions to the target RNA or to the DNA copy, wherein the nucleic acid probe comprises at least 15 (e.g., 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of an IRS biomarker polynucleotide. In some embodiments, the measured level or abundance of the target RNA or its DNA copy is normalized to the level or abundance of a reference RNA or a DNA copy of the reference RNA. Suitably, the nucleic acid probe is immobilized on a solid or semi-solid support. In illustrative examples of this type, the nucleic acid probe forms part of a spatial array of nucleic acid probes. In some embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array). In other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using a polymerase chain reaction (PCR)). In still other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nuclease protection assay.
• In other embodiments, IRS biomarker protein levels are assayed using protein-based assays known in the art. For example, when an IRS biomarker protein is an enzyme, the protein can be quantified based upon its catalytic activity or based upon the number of molecules of the protein contained in a sample. Antibody-based techniques may be employed including, for example, immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).
• In specific embodiments, protein-capture arrays that permit simultaneous detection and/or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays, to profile protein expression in bodily fluids, such as in sera of healthy or diseased subjects, as well as in subjects pre- and post-drug treatment.
• Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, Bio-Rad and Sigma). Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogram. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210). The antigen-binding molecules of such arrays may recognize at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.
• Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.
• Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include color coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.
• In operation, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186), is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.
• In specific embodiments, the IRS biomarker is a target polypeptide whose level is measured using at least one antigen-binding molecule that is immuno-interactive with the target polypeptide. In these embodiments, the measured level of the target polypeptide is normalized to the level of a reference polypeptide. Suitably, the antigen-binding molecule is immobilized on a solid or semi-solid support. In illustrative examples of this type, the antigen-binding molecule forms part of a spatial array of antigen-binding molecule. In some embodiments, the level of antigen-binding molecule that is bound to the target polypeptide is measured by immunoassay (e.g., using an ELISA). Reference herein to “immuno-interactive” includes reference to any interaction, reaction, or other form of association between molecules and in particular where one of the molecules is, or mimics, a component of the immune system.
• All the essential reagents required for detecting and quantifying the biomarkers of the invention, including IRS biomarkers, may be assembled together in a kit. In some embodiments, the kit comprises: (i) a reagent that allows quantification (e.g., determining the level or abundance) of a first biomarker; and (ii) a reagent that allows quantification (e.g., determining the level or abundance) of a second biomarker, wherein the first and second biomarkers have a mutual correlation in respect of at least one condition (e.g., at least one of a healthy condition and one or more diseases such as but not limited to inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock)) that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the first and second biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the first and second biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3. In some embodiments, the kit further comprises (iii) a reagent that allows quantification (e.g., determining the level or abundance) of a third biomarker; and (iv) a reagent that allows quantification (e.g., determining the level or abundance) of a fourth biomarker, wherein the third and fourth biomarkers have a mutual correlation in respect of at the least one condition that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the third and fourth biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the third and fourth biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.
• In advantageous embodiments, the kits of the present invention are useful for diagnosing the presence, absence or degree of at least one condition, or for providing a prognosis for at least one condition, wherein the at least one condition is selected from a healthy condition, inSIRS, ipSIRS or a stage of ipSIRS. In these embodiments, IRS biomarkers are suitably selected from a group as broadly described above and elsewhere herein.
• In the context of the present invention, “kit” is understood to mean a product containing the different reagents necessary for carrying out the methods of the invention packed so as to allow their transport and storage. Materials suitable for packing the components of the kit include crystal, plastic (polyethylene, polypropylene, polycarbonate and the like), bottles, vials, paper, envelopes and the like. Additionally, the kits of the invention can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit. The instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like. Alternatively or in addition, the media can contain Internet addresses that provide the instructions.
• A “reagent that allows quantification of a biomarker” means a compound or material, or set of compounds or materials, which allow quantification of the biomarker. In specific embodiments, the compound, material or set of compounds or materials permit determining the expression level of a gene (e.g., an IRS biomarker gene), including without limitation the extraction of RNA material, the determination of the level of a corresponding RNA, etc., primers for the synthesis of a corresponding cDNA, primers for amplification of DNA, and/or probes capable of specifically hybridizing with the RNAs (or the corresponding cDNAs) encoded by the genes, TaqMan probes, etc.
• The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) a biomarker polynucleotide (e.g., an IRS biomarker polynucleotide) (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a biomarker polynucleotide (e.g., an IRS biomarker polynucleotide). Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq, Sequenase™, DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) a biomarker polypeptide (e.g., an IRS biomarker polypeptide) (which may be used as a positive control), (ii) an antibody that binds specifically to a biomarker polypeptide (e.g., an IRS biomarker polypeptide). The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify the expression of a biomarker gene (e.g., an IRS biomarker gene).
• The reagents described herein, which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a chip or chamber, a microarray or a kit adapted for use with the assays described in the examples or below, e.g., RT-PCR or Q PCR techniques described herein. The term “microarray” refers to an arrangement of hybridizable array elements, e.g., probes (including primers), ligands, biomarker nucleic acid sequence or protein sequences on a substrate.
• The reagents also have utility in compositions for detecting and quantifying the biomarkers of the invention. For example, a reverse transcriptase may be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample, to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as “cDNA”). The nucleic acid sample is suitably derived from components of the immune system, representative examples of which include components of the innate and adaptive immune systems as broadly discussed for example above. In specific embodiments, the reverse transcribed RNA is derived blood cells (e.g., peripheral blood cells). Suitably, the reverse transcribed RNA is derived leukocytes.
• The reagents are suitably used to quantify the reverse transcribed transcripts. For example, oligonucleotide primers that hybridize to the reverse transcribed transcript can be used to amplify at least a portion of the reverse transcribed transcript via a suitable nucleic acid amplification technique, e.g., RT-PCR or Q PCR techniques described herein. Alternatively, oligonucleotide probes may be used to hybridize to the reverse transcribed transcript for the quantification, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide primer or probe is hybridized to a complementary nucleic acid sequence of a reverse transcribed transcript in the compositions of the invention. The compositions typically comprise labeled reagents for detecting and/or quantifying the reverse transcribed transcripts. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to RNA transcripts or reverse transcribed RNA, labeled RNA, labeled reverse transcribed RNA as well as labeled oligonucleotide linkers or tags (e.g., a labeled RNA or DNA linker or tag) for labeling (e.g., end labeling such as 3′ end labeling) RNA or reverse transcribed RNA. The primers, probes, RNA or reverse transcribed RNA (i.e., cDNA) (whether labeled or non-labeled) may be immobilized or free in solution. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to reverse transcribed and transcripts as well as labeled reverse transcribed transcripts. The label can be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.
• The present invention also encompasses non-reverse transcribed RNA embodiments in which cDNA is not made and the RNA transcripts are directly the subject of the analysis. Thus, in other embodiments, reagents are suitably used to quantify RNA transcripts directly. For example, oligonucleotide probes can be used to hybridize to transcripts for quantification of immune system biomarkers of the invention, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide probe is hybridized to a complementary nucleic acid sequence of an immune system biomarker transcript in the compositions of the invention. In illustrative examples of this type, the compositions may comprise labeled reagents that hybridize to transcripts for detecting and/or quantifying the transcripts. Representative reagents of this type include labeled oligonucleotide probes that hybridize to transcripts as well as labeled transcripts. The primers or probes may be immobilized or free in solution.
• The term “immobilized” means that a molecular species of interest is fixed to a solid support, suitably by covalent linkage. This covalent linkage can be achieved by different means depending on the molecular nature of the molecular species. Moreover, the molecular species may be also fixed on the solid support by electrostatic forces, hydrophobic or hydrophilic interactions or Van-der-Waals forces. The above described physico-chemical interactions typically occur in interactions between molecules. In particular embodiments, all that is required is that the molecules (e.g., nucleic acids or polypeptides) remain immobilized or attached to a support under conditions in which it is intended to use the support, for example in applications requiring nucleic acid amplification and/or sequencing or in in antibody-binding assays. For example, oligonucleotides or primers are immobilized such that a 3′ end is available for enzymatic extension and/or at least a portion of the sequence is capable of hybridizing to a complementary sequence. In some embodiments, immobilization can occur via hybridization to a surface attached primer, in which case the immobilized primer or oligonucleotide may be in the 3′-5′ orientation. In other embodiments, immobilization can occur by means other than base-pairing hybridization, such as the covalent attachment.
• The term “solid support” as used herein refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized. Non-limiting examples of solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers. In some embodiments, the solid supports are in the form of membranes, chips or particles. For example, the solid support may be a glass surface (e.g., a planar surface of a flow cell channel). In some embodiments, the solid support may comprise an inert substrate or matrix which has been “functionalized”, such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides. By way of non-limiting example, such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass. The molecules (e.g., polynucleotides) can be directly covalently attached to the intermediate material (e.g., a hydrogel) but the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate). The support can include a plurality of particles or beads each having a different attached molecular species.
• The present invention also extends to the management of inSIRS, ipSIRS or particular stages of ipSIRS, or prevention of further progression of inSIRS, ipSIRS or particular stages of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), or assessment of the efficacy of therapies in subjects following positive diagnosis for the presence of inSIRS, ipSIRS or particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock) in a subject. The management of inSIRS or ipSIRS conditions is generally highly intensive and can include identification and amelioration of the underlying cause and aggressive use of therapeutic compounds such as, vasoactive compounds, antibiotics, steroids, antibodies to endotoxin, anti-tumor necrosis factor agents, recombinant protein C. In addition, palliative therapies as described for example in Cohen and Glauser (1991, Lancet 338: 736-739) aimed at restoring and protecting organ function can be used such as intravenous fluids and oxygen and tight glycemic control. Therapies for ipSIRS are reviewed in Healy (2002, Ann Pharmacother. 36(4): 648-54) and Brindley (2005, CJEM. 7(4): 227) and Jenkins (2006, J Hosp Med. 1(5): 285-295).
• Typically, the therapeutic agents will be administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose. The dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time such as a reduction in, or relief from, the symptoms of inSIRS, ipSIRS or particular stages of ipSIRS. The quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner. In determining the effective amount of the active compound(s) to be administered in the treatment or prevention of inSIRS, ipSIRS or particular stages of ipSIRS, the medical practitioner or veterinarian may evaluate severity of any symptom associated with the presence of inSIRS, ipSIRS or particular stages of ipSIRS including, inflammation, blood pressure anomaly, tachycardia, tachypnea fever, chills, vomiting, diarrhea, skin rash, headaches, confusion, muscle aches, seizures. In any event, those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.
• The therapeutic agents may be administered in concert with adjunctive (palliative) therapies to increase oxygen supply to major organs, increase blood flow to major organs and/or to reduce the inflammatory response. Illustrative examples of such adjunctive therapies include non-steroidal-anti-inflammatory drugs (NSAIDs), intravenous saline and oxygen.
• In specific embodiments of the present invention, the methods, apparatus and kits described above and elsewhere herein are contemplated for use in methods of treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis or septic shock) in a subject. These methods (also referred to herein as “treatment methods”) generally comprise: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of the at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (c) administering to the subject, on the basis that the indicator indicates the presence of inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the indicator indicates the presence of ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.
• In advantageous embodiments, the treatment methods comprise: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker. The function suitably includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.
• In some embodiments the methods, apparatus and kits of the present invention are used for monitoring, treatment and management of conditions that can lead to inSIRS or ipSIRS, illustrative examples of which include retained placenta, meningitis, endometriosis, shock, toxic shock (i.e., sequelae to tampon use), gastroenteritis, appendicitis, ulcerative colitis, Crohn's disease, inflammatory bowel disease, acid gut syndrome, liver failure and cirrhosis, failure of colostrum transfer in neonates, ischemia (in any organ), bacteremia, infections within body cavities such as the peritoneal, pericardial, thecal, and pleural cavities, burns, severe wounds, excessive exercise or stress, hemodialysis, conditions involving intolerable pain (e.g., pancreatitis, kidney stones), surgical operations, and non-healing lesions. In these embodiments, the methods or kits of the present invention are typically used at a frequency that is effective to monitor the early development of inSIRS, ipSIRS or particular stages of ipSIRS, to thereby enable early therapeutic intervention and treatment of that condition. In illustrative examples, the diagnostic methods or kits are used at least at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hour intervals or at least 1, 2, 3, 4, 5 or 6 day intervals, or at least weekly, fortnightly or monthly.
• The present invention can be practiced in the field of predictive medicine for the purpose of diagnosis or monitoring the presence or development of a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS in a subject, and/or monitoring response to therapy efficacy.
• The biomarker signatures and corresponding indicators of the present invention further enable determination of endpoints in pharmacotranslational studies. For example, clinical trials can take many months or even years to establish the pharmacological parameters for a medicament to be used in treating or preventing inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). However, these parameters may be associated with a biomarker signature and corresponding indicator of a health state (e.g., a healthy condition). Hence, the clinical trial can be expedited by selecting a treatment regimen (e.g., medicament and pharmaceutical parameters), which results in a biomarker signature associated with a desired health state (e.g., healthy condition). This may be determined for example by: a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject after treatment with a treatment regimen; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, and (c) determining that the treatment regimen is effective for changing the health status of the subject to the desired health state (e.g., healthy condition) on the basis that the indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen. As used herein, the term “degree” refers to the extent or stage of a condition. Thus, for example, mild sepsis is a stage or degree of sepsis that is lower than severe sepsis. Similarly, severe sepsis is a stage or degree of sepsis that is lower than septic shock. Accordingly, this aspect of the present invention advantageously provides methods of monitoring the efficacy of a particular treatment regimen in a subject (for example, in the context of a clinical trial) already diagnosed with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS. These methods take advantage of measured or derived IRS biomarker values that correlate with treatment efficacy to determine, for example, whether measured or derived IRS biomarker values of a subject undergoing treatment partially or completely normalize during the course of or following therapy or otherwise shows changes associated with responsiveness to the therapy.
• As used herein, the term “treatment regimen” refers to prophylactic and/or therapeutic (i.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise. The term “treatment regimen” encompasses natural substances and pharmaceutical agents (i.e., “drugs”) as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, and combinations thereof.
• Accordingly, the invention provides methods of correlating a biomarker signature with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), wherein the methods generally comprise: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject having the condition and for whom an effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of the condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the condition, or to provide a prognosis for the condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with an effective treatment regimen for the condition. The term “correlating” generally refers to determining a relationship between one type of data with another or with a state. In specific embodiments, an indicator or biomarker signature is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.
• The invention further provides methods of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). These methods generally comprise: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject after treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at the least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the post-treatment indicator indicates whether the treatment regimen is effective for treating the condition in the subject on the basis that post-treatment indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.
• The invention can also be practiced to evaluate whether a subject is responding (i.e., a positive response) or not responding (i.e., a negative response) to a treatment regimen or has a side effect to a treatment regimen. This aspect of the invention provides methods of correlating a biomarker signature with a positive or negative response or a side effect to a treatment regimen, which generally comprise: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with a positive or negative response to the treatment regimen. As used herein, the term “positive response” means that the result of the treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition. By contrast, the term “negative response” means that the treatment regimen provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.
• The invention also encompasses methods of determining a positive or negative response to a treatment regimen and/or a side effect of a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS. These methods generally comprise: (a) correlating a reference biomarker signature with a positive or negative response or a side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a control biological subject or control group, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen, based on the correlation of the reference biomarker signature with the positive or negative response or side effect to the treatment regimen.
• In related embodiments, the present invention further contemplates methods of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen by a biological subject. These methods generally comprise: (a) determining a sample biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the sample biomarker signature is correlated with a positive or negative response to the treatment regimen and/or to a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen based on the sample biomarker signature.
• This above methods can be practiced to identify responders or non-responders relatively early in the treatment process, i.e., before clinical manifestations of efficacy. In this way, the treatment regimen can optionally be discontinued, a different treatment protocol can be implemented and/or supplemental therapy can be administered. Thus, in some embodiments, a sample IRS biomarker signature is obtained within about 2 hours, 4 hours, 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, 12 weeks, 4 months, six months or longer of commencing therapy.
• A number of non-limiting example signatures for use in diagnosing respective conditions will now be described. For the purpose of illustration, the above-described process was used to select biomarkers that provided the theoretical best diagnostic biomarkers, selected from combinations including measured and/or derived biomarkers. Following this, other measured and/or derived biomarkers were grouped based on their correlation to the best diagnostic biomarkers, with the ability of biomarkers within these groups to act as a diagnostic signature then being assessed.
• The results set out in detail below highlight that as long as the above-described criteria are met, the resulting signatures provide the required discriminatory ability for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject.
Signature Derivation
• An illustrative process for the identification of mRNA biomarkers for use in diagnostic algorithms will now be described.
Summary
• Peripheral blood samples were obtained from healthy controls and patients retrospectively diagnosed by a panel of physicians with either inSIRS or ipSIRS (blood culture positive). ipSIRS patients were further classified retrospectively into “mild”, “severe” or “shock” based on clinical parameters. Total RNA from patient samples was then used in gene expression analysis (GeneChip® and/or quantitative PCR (qPCR)). Gene expression data were analyzed using a variety of statistical approaches to identify individual and derived markers. Derived markers were divided into groups based on how they correlated to each of the markers in the top-performing (based on AUC) ratio. This ratio approach provides the best diagnostic power with respect to AUC for separating: healthy and post-surgical (PS) (also referred to herein as “inSIRS”) conditions; healthy and sepsis (also referred to herein as “ipSIRS”); inSIRS and ipSIRS; mild ipSIRS and severe ipSIRS; mild ipSIRS and septic shock; and severe ipSIRS and septic shock.
Clinical Trials
• Clinical trials were performed to determine whether certain mRNA transcripts could distinguish between healthy controls and various patient groups and within patient groups. Intensive care sepsis, post-surgical and inSIRS patients, as well as healthy controls were prospectively enrolled and attended a single visit where blood was collected for gene expression and mRNA analyses using Affymetrix exon arrays and/or quantitative real-time PCR (qRT-PCR). A definitive diagnosis of infection-positive SIRS (mild, severe or shock) or inSIRS was unlikely to be known at the time patients were enrolled, and thus confirmation of a diagnosis and the assignment of patients to the cohorts were made retrospectively.
• Patients who had clinical signs and/or symptoms of ipSIRS or inSIRS were consented and enrolled into the study as soon as possible after they had been identified, in most cases within 24 hours of admission. Final assessment of whether the participant had inSIRS, ipSIRS (mild, severe, or shock) was made retrospectively as clinical information and blood culture results became available.
• Study participants were all over 18 years and either they or their surrogate decision maker signed and dated the clinical trial information sheet and consent form. All of the control participants were considered to be in good health based on a brief physical examination and their medical history at the time of enrolment.
• Patients or their surrogate decision maker were offered the opportunity to participate in this study if the patient presented with signs and symptoms of either inSIRS or ipSIRS at the time of admission to ICU (using criteria based on the American College of Physicians and the Society of Critical Care Medicine standard definitions). That is, inSIRS and ipSIRS participants needed a variable combination of clinical conditions including two or more of the following within the last 24 hours: temperature >38° C. or <36° C.; heart rate >90 beats/min; respiratory rate >20 breathes/min or a PaCO₂ of <4.3 kPa (<32 mm Hg); and evidence of a white blood cell count <4,000 cells/mm³ (<4×10⁹ cells/L) or >12,000 cells/mm³ (>12×10⁹ cells/L) or >10% immature neutrophils (band forms). Participants were excluded if they had any chronic systemic immune-inflammatory disorders including SLE, Crohn's disease, insulin-dependent diabetes mellitus (IDDM); were transplant recipients or were currently receiving chemotherapy treatment for cancer. Most patients had other underlying co-morbidities. All study participants were 18 years of age or older and had a body mass index of less than 40.
• Demography, vital signs measurements (blood pressure, heart rate, respiratory rate, oxygen saturation, temperature), hematology (full blood count), clinical chemistry (urea, electrolytes, liver function enzymes, blood glucose) as well as microbial status was recorded.
• Blood was collected for the purpose of extraction of high quality RNA into PAXgene™ tubes (PreAnalytix Inc., Valencia, Calif., USA). Blood for bacterial culture was collected into BacTec Plus Aerobic (10 ml) and BacTec Plus Anaerobic (10 mL) tubes (Becton Dickinson) tubes for the detection of aerobic and anaerobic bacterial growth respectively.
• A PAXgene blood RNA kit available from Qiagen Inc. (Valencia, Calif., USA) was used to isolate total RNA from PAXgene tubes. Isolation begins with a centrifugation step to pellet nucleic acids in the PAXgene blood RNA tube. The pellet is washed and re-suspended and incubated in optimized buffers together with Proteinase K to bring about protein digestion. An additional centrifugation is carried out to remove residual cell debris and the supernatant is transferred to a fresh microcentrifuge tube. Ethanol is added to adjust binding conditions, and the lysate is applied to the PAXgene RNA spin column. During brief centrifugation, RNA is selectively bound to the silica-gel membrane as contaminants pass through. Remaining contaminants are removed in three efficient wash steps and RNA is then eluted in Buffer BR5. Determination of RNA quantity and quality was performed using an Agilent Bioanalyzer and Absorbance 260/280 ratio using a spectrophotometer.
Processing of Samples
• Measurement of specific mRNA levels in a tissue sample can be achieved using a variety of technologies. A common and readily available technology that covers most of the known human mRNAs is GeneChip® analysis using Affymetrix technology. Details on the technology and methodology can be found at www.affymetrix.com. GeneChip® analysis has the advantage of being able to analyze thousands of RNA transcripts at a time. Another common and readily available technology is qPCR (quantitative polymerase chain reaction), which has the advantage of being able to analyze, in real-time and quantitatively, hundreds of RNA transcripts at a time. Details on one of these technologies (TaqMan®), chemistries and methodologies can be found on the Life Technologies website including published protocols entitled; “Protocol: Introduction to TaqMan SYBR Green Chemistries for Real-Time PCR” and “TaqMan Gene Expression Assays Protocol.” Both GeneChip® and qPCR were used in the discovery and proof-of-concept stages for biomarker identification. qPCR was used exclusively for biomarker feasibility testing.
Analysis, Interpretation of Data and Selection of Biomarkers and Derived Biomarkers
• Healthy Control Versus inSIRS
• A list of 941 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of Healthy and inSIRS was generated. FIG. 8A plots these markers against the AUC and FIG. 8B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (AGFG1-ArfGAP with FG repeats 1). The conditions of Healthy and inSIRS are perfectly separated when using this mRNA biomarker alone.
• From the 941 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 1.0. A plot of the AUC of these derived markers for separating the conditions of Healthy and inSIRS is shown in FIG. 8C with the top performing ratio shown as a box and whisker plot in FIG. 8D. The AUC for AGFG1 and PVRIG (poliovirus receptor related immunoglobulin domain containing) has a ratio is 1.0.
• All 941 individual markers were then broken into two groups—those that correlate to AGFG1 and those that correlate to PVRIG. FIGS. 8E and 8F demonstrate the correlation between the two groups based on their similarity to either AGFG1 or PVRIG. In these plots the groups are referred to as either group 1 (AGFG1) or group 2 (PVRIG). It can be seen that each “group” contains those markers that are most highly correlated to each other.
• The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance Healthy versus inSIRS (PS)) as shown in FIGS. 8G and 8H.
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating Healthy and inSIRS is obtained than when markers are chosen from within groups (p<2.558e-13) as demonstrated in FIG. 8I, which shows a greater overall AUC is obtained compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.97, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.9.
• Healthy Control Versus ipSIRS
• A list of 941 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of Healthy and ipSIRS was generated. FIG. 9A plots these markers against the AUC and FIG. 9B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (LTBP3—latent transforming growth factor beta binding protein 3). The conditions of Healthy and ipSIRS are perfectly separated when using this mRNA biomarker alone.
• From the 941 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 1.0. A plot of the AUC of these derived markers for separating the conditions of Healthy and inSIRS is shown in FIG. 9C with the top performing ratio shown as a box and whisker plot in FIG. 9D. The AUC for LTBP3 and LPHN1 (latrophilin 1) as a ratio is 1.0.
• All 941 individual markers were then broken into two groups—those that correlate to LTBP3 and those that correlate to LPHN1. Both plots in FIGS. 9E and 9F demonstrate the correlation between the two groups based on their similarity to either LTBP3 or LPHN1. It can be seen that each “group” contains those markers that are most highly correlated to each other.
• The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance Healthy versus ipSIRS (sepsis)), as shown in FIGS. 9G and 9H.
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating Healthy and ipSIRS is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 9I, which shows an improved AUC compared to using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.97, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.8.
• inSIRS Versus ipSIRS
• A list of 359 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of inSIRS and ipSIRS was generated. FIG. 10A plots these markers against the AUC and FIG. 10B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (PIWIL4—piwi-like RNA mediated gene silencing 4). The conditions of inSIRS (PS) and ipSIRS (sepsis) are well separated when using this mRNA biomarker alone.
• From the 359 individual markers 1000 derived markers (ratios) were generated that had an AUC greater than 0.9. A plot of the AUC of these derived markers for separating the conditions of inSIRS and ipSIRS is shown in FIG. 10C with the top performing ratio of PLA2G7 (phospholipase A2, Group VII, (platelet activating factor acetyl hydrolase, plasma)) and PLAC8 (placenta-specific 8) shown as a box and whisker plot in FIG. 10D.
• All 359 individual markers were then broken into two groups—those that correlate to PLA2G7 and those that correlate to PLAC8. The plot in FIG. 10E demonstrates that the markers in each “group” correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance inSIRS (PS) versus ipSIRS (sepsis)).
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating inSIRS (PS) and ipSIRS (sepsis) is obtained than when markers are chosen from within groups (p<5.78e-5) as demonstrated in FIG. 10F compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.80, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.8.
• In an alternative embodiment, the markers were broken into four groups—those that correlate to CEACAM4 (bucket 3), those that correlate to LAMP1 (bucket 4), those that correlate to PLA2G7 (bucket 1) and those that correlate to PLAC8 (bucket 2). The plots in FIG. 10G demonstrate the markers in each group correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (i.e., inSIRS (PS) versus ipSIRS (sepsis)). Like the two-bucket embodiment discussed above, choosing mRNAs from four groups to create derived markers results in an overall better AUC for separating inSIRS (PS) and ipSIRS (sepsis), as compared to when markers are chosen from within groups (p<0.2564, as demonstrated in FIG. 10H compared to when using markers from any one of groups 1 to 4. The mean AUC for markers derived from groups 1 to 4 is over 0.8 whereas the mean AUC for markers derived from any one of groups 1 to 4 is less than 0.8
• Mild ipSIRS Versus Severe ipSIRS
• A list of 66 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of mild ipSIRS and severe ipSIRS was generated. FIG. 11A plots these markers against the AUC and FIG. 11B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (N4BP2L2-NEDD4 binding protein 2-like 2). The conditions of mild ipSIRS and severe ipSIRS are well separated when using this mRNA biomarker alone.
• From the 66 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 0.87. A plot of the AUC of these derived markers for separating the conditions of mild ipSIRS and severe ipSIRS is shown in FIG. 11C with the top performing ratio shown as a box and whisker plot in FIG. 11D. The AUC for N4BP2L2 and ZC3H11A (zinc finger CCCH-type containing 11A) as a ratio is 0.983.
• All 66 individual markers were then broken into two groups—those that correlate to N4BP2L2 and those that correlate to ZC3H11A. Both plots in FIGS. 11E and 11F demonstrate the correlation between the two groups based on their similarity to either N4BP2L2 or ZC3H11A. In these plots the groups are referred to as either group 1 (N4BP2L2) or group 2 (ZC3H11A). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance mild ipSIRS versus severe ipSIRS).
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating mild ipSIRS and severe ipSIRS is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 11I, compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.89, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.6.
• Mild ipSIRS Versus ipSIRS—Shock
• A list of 48 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of mild ipSIRS and ipSIRS—shock was generated. FIG. 12A plots these markers against the AUC and FIG. 12B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (CD6-CD6 molecule). The conditions of mild ipSIRS and ipSIRS shock are well separated when using this mRNA biomarker alone.
• From the 48 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of at least 0.793. A plot of the AUC of these derived markers for separating the conditions of mild ipSIRS and ipSIRS shock is shown in FIG. 12C with the top performing ratio shown as a box and whisker plot in FIG. 12D. The AUC for VAMP2 and UBAP1 (ubiquitin associated protein 1) as a ratio is 0.978.
• All 48 individual markers were then broken into two groups—those that correlate to VAMP2 and those that correlate to UBAP1. Both plots in FIGS. 12E and 12F demonstrate the correlation between the two groups based on their similarity to either VAMP2 or UBAP1. In these plots the groups are referred to as either group 1 (VAMP2) or group 2 (UBAP1). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance mild ipSIRS versus ipSIRS—shock).
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating mild ipSIRS and ipSIRS—shock is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 12I, compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.87, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.65.
• Severe ipSIRS Versus ipSIRS—Shock
• A list of 61 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of severe ipSIRS and ipSIRS—shock was generated. FIG. 13A plots these markers against the AUC and FIG. 13B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (SIRPG—signal regulatory protein gamma) The conditions of severe ipSIRS and ipSIRS—shock are well separated when using this mRNA biomarker alone.
• From the 61 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of at least 0.821. A plot of the AUC of these derived markers for separating the conditions of severe ipSIRS and ipSIRS—shock is shown in FIG. 13C with the top performing ratio shown as a box and whisker plot in FIG. 13D. The AUC for GATA3 (GATA binding protein 3) and MECOM (MDS1 and EVI1 complex locus) as a ratio is 0.936.
• All 61 individual markers were then broken into two groups—those that correlate to GATA3 and those that correlate to MECOM. Both plots in FIGS. 13E and 13F demonstrate the correlation between the two groups based on their similarity to either GATA3 or MECOM. In these plots the groups are referred to as either group 1 (GATA3) or group 2 (MECOM). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance severe ipSIRS versus ipSIRS—shock).
• By choosing mRNAs from these two groups to create derived markers a better AUC for separating severe ipSIRS and ipSIRS—shock is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 13I compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.82, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.7.
Signature Usage
• Use of the above described markers and resulting signatures in patient populations and benefits in respect of differentiating inSIRS and ipSIRS, will now be described.
• An assay capable of differentiating patients with inSIRS and ipSIRS can be used in multiple patient populations including:
• 1) Intensive Care Unit (medical and surgical ICU)
  2) Post-surgical and medical wards
3) Emergency Department
• 4) Medical clinics.
• Patients admitted to intensive care (ICU) often have ipSIRS, or develop ipSIRS during their ICU stay. The ultimate aim of intensive care is to ensure the patient survives and is discharged to a general ward in the minimum time. Patients in intensive care with diagnosed ipSIRS are usually administered a number of therapeutic compounds—many of which have opposing actions on the immune system and many of which could be counterproductive depending on the severity of ipSIRS (mild sepsis, severe sepsis, septic shock). Monitoring intensive care patients on a regular basis with biomarkers of the present invention will allow medical practitioners to differentiate inSIRS from ipSIRS and determine the stage of ipSIRS and hence choice of therapies, when to start and stop therapies, and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers will therefore allow medical intensivists to tailor and modify therapies to ensure patients survive and spend less time in intensive care. Less time in intensive care leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.
• Surgical and general medical patients often develop inSIRS post-surgery or as a consequence of their condition or procedures and have a higher risk of developing ipSIRS. Post-operative and medical care in such patients therefore involves monitoring for signs of inSIRS and ipSIRS and differentiating between these two conditions. The treatment and management of inSIRS and ipSIRS patients post-surgically and in general wards is different, since inSIRS patients can be put on mild anti-inflammatory drugs or anti-pyretics and ipSIRS patients must be started on antibiotics as soon as possible for best outcomes. Monitoring post-surgical and medical patients on a regular basis with biomarkers of the present invention will allow nursing and medical practitioners to differentiate inSIRS and ipSIRS at an early stage and hence make informed decisions on choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers will therefore allow medical practitioners to tailor and modify therapies to ensure patients recover quickly from surgery or other condition and do not develop ipSIRS. Less time in hospital and less complications leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.
• Further, patients presenting to emergency departments often have a fever, which is one (of four) of the clinical signs of inSIRS. Such patients need to be assessed to determine if they have either inSIRS or ipSIRS. As mentioned above, the treatment and management of pyretic, inSIRS and septic patients are different. By way of example, a patient with a fever without other inSIRS clinical signs and no obvious source of infection may be sent home, or provided with other non-hospital services, without further hospital treatment. However, a patient with a fever may have early ipSIRS and not admitting such a patient may put their life at risk. Because these biomarkers can differentiate inSIRS and ipSIRS they will allow medical practitioners to triage emergency department patients quickly and effectively. Accurate triage decision-making insures that patients requiring hospital treatment are given it, and those that don't are provided with other appropriate services.
• Further still, patients presenting to medical clinics often have any one of the four clinical signs of inSIRS (increased heart rate, increased respiratory rate, abnormal white blood cell count, fever or hypothermia). Many different clinical conditions can present with one of the four clinical signs of inSIRS and such patients need to be assessed to determine if they have either inSIRS or ipSIRS and to exclude other differential diagnoses. By way of example, a patient with colic might also present with clinical signs of increased heart rate. Differential diagnoses could be (but not limited to) appendicitis, urolithiasis, cholecystitis, pancreatitis, enterocolitis. In each of these conditions it would be important to determine if there was a systemic inflammatory response (inSIRS) or whether an infection was contributing to the systemic response to the condition (ipSIRS). The treatment and management of patients with and without systemic inflammation and/or infection are different. Because these biomarkers can differentiate patients with a systemic inflammatory response to infection from those with a systemic inflammatory response without infection (inSIRS and ipSIRS), and determine the degree of systemic involvement, the use of them will allow medical practitioners to determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue.
• Determining the Extent of Systemic Inflammation in Sick Patients and Those with inSIRS and ipSIRS
• As mentioned above, patients presenting to medical clinics often have any one of the four clinical signs of inSIRS. However, many different clinical conditions can present with one of the four clinical signs of inSIRS and such patients need to be assessed to determine if they have inSIRS, and if so the extent of inSIRS, or ipSIRS, and if so the extent of ipSIRS, and to exclude other differential diagnoses.
• By way of example, a patient with respiratory distress is likely to present with clinical signs of increased respiratory rate. Differential diagnoses could be (but not limited to) asthma, pneumonia, congestive heart failure, physical blockage of airways, allergic reaction, collapsed lung, pneumothorax. In each of these conditions it would be important to determine if there was a systemic inflammatory response (inSIRS) or whether an infection was contributing to the condition. The treatment and management of patients with and without systemic inflammation and/or infection are different. Because these biomarkers can differentiate patients with a systemic inflammatory response to infection from those with a systemic inflammatory response without infection (inSIRS and ipSIRS), and determine the degree of systemic involvement, the use of them will allow medical practitioners to determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue. Patients with a collapsed lung, pneumothorax or a physical blockage are unlikely to have a large systemic inflammatory response and patients with congestive heart failure, allergic reaction or asthma are unlikely to have a large systemic inflammatory response due to infection. The extent of both inSIRS and ipSIRS, as indicated by biomarkers presented in this patent, also provides clinicians with information on next treatment and management steps. For example, a patient with respiratory distress and a strong biomarker response indicating ipSIRS is likely to be immediately hospitalized, placed on antibiotics and a chest X-ray performed. A patient with respiratory distress and a strong biomarker response indicating inSIRS but not ipSIRS is likely to be hospitalized and chest X-rayed along with other investigative diagnostic procedures, such as MRI, ECG, and angiogram. A patient with respiratory distress with a short history and no inSIRS or ipSIRS is likely to undergo further examination at a local clinic rather than requiring hospitalization.
• Again, and as mentioned above, patients presenting to emergency departments often have a fever, which is one (of four) of the clinical signs of inSIRS. Such patients need to be assessed to determine if they have either inSIRS or ipSIRS. Further it is important to determine how sick they are to be able to make a Judgment call on whether to admit the patient or not. Accurate triage decision-making insures that patients requiring hospital treatment are given it, and those that don't are provided with other appropriate services.
• Patients in ICU often have inSIRS and ipSIRS and it is important to differentiate these two conditions as treatment regimens differ. In patients with inSIRS it is important to determine the extent of the inflammatory response so that appropriate treatments and management regimens can be put in place. For example, a patient newly determined to have inSIRS that is not extensive may be able to be put on mild medication such a non-steroidal anti-inflammatory. A patient newly determined to have extensive inSIRS (e.g. trauma) may require stronger anti-inflammatory medication such as steroids to reduce the potential impact of the side effects of inflammation (swelling). In patients with ipSIRS it is also important to determine the extent of the inflammatory response to infection so that appropriate treatments and management regimens can be put in place or stopped. For example, for a patient with a persistent strong ipSIRS response the clinician may consider either changing, or adding to, the antibiotic treatment regimen in the absence of traditional bacterial culture and sensitivity results. Further, patients that are known to have had ipSIRS and have been on antimicrobial therapy for an extended period but have since demonstrated (by testing using biomarkers) that they no longer have either an inSIRS or ipSIRS can therefore be safely taken off intravenous antibiotics.
• Determining the Severity of ipSIRS
• Patients admitted to intensive care (ICU) often have ipSIRS, or develop ipSIRS during their ICU stay. It is known that the severity of sepsis can be considered to be a continuum from less severe, or sepsis, to more severe, or severe sepsis, to the most severe, or septic shock. More severe sepsis (ipSIRS) requires more aggressive, immediate and tailored intervention compared to sepsis (although all are acute conditions). Patients in intensive care with diagnosed ipSIRS are usually administered a number of therapeutic compounds—many of which have opposing actions on the immune system and many of which could be counterproductive depending on the severity of ipSIRS (sepsis, severe sepsis, septic shock). Monitoring intensive care patients on a regular basis with biomarkers of the present invention will allow medical practitioners to determine the severity of ipSIRS (mild, severe or shock) and hence choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers disclosed herein will therefore allow medical practitioners to tailor, modify or cease therapies and/or care to ensure patients survive and spend less time in intensive care. Less time in intensive care leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.
First Example Workflow
• A first example workflow will now be described. The workflow involves up to seven steps depending upon availability of automated platforms. The assay uses quantitative, real-time determination of the amount of each transcript in the sample based on the detection of fluorescence on a qRT-PCR instrument (e.g. Applied Biosystems 7500 Fast Dx Real-Time PCR Instrument, Applied Biosystems, Foster City, Calif., catalogue number 440685; K082562). Transcripts are each amplified, detected, and quantified in a separate reaction well using a probe that is visualized in the FAM channel (by example). The reported score is calculated using interpretive software provided separately to the kit but designed to integrate with RT-PCR machines.
• The workflow below describes the use of manual processing and a pre-prepared kit.
Pre-Analytical
• Blood collection
• Total RNA isolation
Analytical
• Reverse transcription (generation of cDNA)
• qPCR preparation
• qPCR
• Software, Interpretation of Results and Quality Control
• Output.
Kit Contents
• Diluent
• RT Buffer
• RT Enzyme Mix
• qPCR Buffer
• Primer/Probe Mix
• AmpliTaq Gold® (or similar)
• High Positive Control
• Low Positive Control
• Negative Control
Blood Collection
• The specimen used is a 2.5 mL sample of blood collected by venipuncture using the PAXgene® collection tubes within the PAXgene® Blood RNA System (Qiagen, kit catalogue #762164; Becton Dickinson, Collection Tubes catalogue number 762165; K042613). An alternate collection tube is Tempus® (Life Technologies).
Total RNA Isolation
• Blood (2.5 mL) collected into a PAXgene RNA tube is processed according to the manufacturer's instructions. Briefly, 2.5 mL sample of blood collected by venipuncture using the PAXgene™ collection tubes within the PAXgene™ Blood RNA System (Qiagen, kit catalogue #762164; Becton Dickinson, Collection Tubes catalogue number 762165; K042613). Total RNA isolation is performed using the procedures specified in the PAXgene™ Blood RNA kit (a component of the PAXgene™ Blood RNA System). The extracted RNA is then tested for purity and yield (for example by running an A_260/280 ratio using a Nanodrop® (Thermo Scientific)) for which a minimum quality must be (ratio >1.6). RNA should be adjusted in concentration to allow for a constant input volume to the reverse transcription reaction (below). RNA should be processed immediately or stored in single-use volumes at or below −70° C. for later processing.
Reverse Transcription
• Determine the appropriate number of reaction equivalents to be prepared (master mix formulation) based on a plate map and the information provided directly below. Each clinical specimen is run in singleton.
• • a) Each batch run must include the following specimens:
  • b) High Control, Low Control, Negative Control, and No Template Control (Test Diluent instead of sample) in singleton each
    Program the ABI 7500 Fast Dx Instrument as detailed below.
  • c) Launch the software.
  • d) Click Create New Document
  • e) In the New Document Wizard, select the following options:
    • i) Assay: Standard Curve (Absolute Quantitation)
    • ii) Container: 96-Well Clear
    • iii) Template: Blank Document (or select a laboratory-defined template)
    • iv) Run Mode: Standard 7500
    • v) Operator: Enter operator's initials
    • vi) Plate name: [default]
  • f) Click Finish
  • g) Select the Instrument tab in the upper left
  • h) In the Thermal Cycler Protocol area, Thermal Profile tab, enter the following times:
    • i) 25° C. for 10 minutes
    • ii) 45° C. for 45 minutes
    • iii) 93° C. for 10 minutes
    • iv) Hold at 25° C. for 60 minutes
• In a template-free area, remove the test Diluent and RT-qPCR Test RT Buffer to room temperature to thaw. Leave the RT-qPCR Test RT Enzyme mix in the freezer and/or on a cold block.
• In a template-free area, assemble the master mix in the order listed below.
RT Master Mix—Calculation:

• 	Per well	×N

  	RT-qPCR Test RT Buffer	3.5 μL	3.5 × N
  	RT-qPCR Test RT Enzyme mix	1.5 μL	1.5 × N
  	Total Volume
  	5 μL	5 × N

• Gently vortex the master mix then pulse spin. Add the appropriate volume (5 μL) of the RT Master Mix into each well at room temperature.
• Remove clinical specimens and control RNAs to thaw. (If the specimens routinely take longer to thaw, this step may be moved upstream in the validated method.)
• Vortex the clinical specimens and control RNAs, then pulse spin. Add 10 μL of control RNA or RT-qPCR Test Diluent to each respective control or negative well.
• Add 10 μL of sample RNA to each respective sample well (150 ng total input for RT; OD₂₆₀/OD₂₈₀ ratio greater than 1.6). Add 10 μL of RT-qPCR Test Diluent to the respective NTC well.
• Note: The final reaction volume per well is 15 μL.

• 	Samples

  	RT Master Mix	5 μL
  	RNA sample
  	10 μL
  	Total Volume (per well)	15 μL

• Mix by gentle pipetting. Avoid forming bubbles in the wells.
• Cover wells with a seal.
• Spin the plate to remove any bubbles (1 minute at 400×g).
• Rapidly transfer to ABI 7500 Fast Dx Instrument pre-programmed as detailed above.
• Click Start. Click Save and Continue. Before leaving the instrument, it is recommended to verify that the run started successfully by displaying a time under Estimated Time Remaining.
• qPCR master mix may be prepared to coincide roughly with the end of the RT reaction. For example, start about 15 minutes before this time. See below.
• When RT is complete (i.e. resting at 25° C.; stop the hold at any time before 60 minutes is complete), spin the plate to collect condensation (1 minute at 400×g).
• qPCR Preparation
• Determine the appropriate number of reaction equivalents to be prepared (master mix formulation) based on a plate map and the information provided in RT Preparation above.
• Program the ABI 7500 Fast Dx with the settings below.
• • a) Launch the software.
  • b) Click Create New Document
  • c) In the New Document Wizard, select the following options:
    • i) Assay: Standard Curve (Absolute Quantitation)
    • ii) Container: 96-Well Clear
    • iii) Template: Blank Document (or select a laboratory-defined template)
    • iv) Run Mode: Standard 7500
    • v) Operator: Enter operator's initials
    • vi) Plate name: Enter desired file name
  • d) Click Next
  • e) In the Select Detectors dialog box:
    • i) Select the detector for the first biomarker, and then click Add>>.
    • ii) Select the detector second biomarker, and then click Add>>, etc.
    • iii) Passive Reference: ROX
  • f) Click Next
  • g) Assign detectors to appropriate wells according to plate map.
    • i) Highlight wells in which the first biomarker assay will be assigned
    • ii) Click use for the first biomarker detector
    • iii) Repeat the previous two steps for the other biomarkers
    • iv) Click Finish
  • h) Ensure that the Setup and Plate tabs are selected
  • i) Select the Instrument tab in the upper left
  • j) In the Thermal Cycler Protocol area, Thermal Profile tab, perform the following actions, with the results shown in FIG. 14:
    • i) Delete Stage 1 (unless this was completed in a laboratory-defined template).
    • ii) Enter sample volume of 25 μL.
    • iii) 95° C. 10 minutes
    • iv) 40 cycles of 95° C. for 15 seconds, 63° C. for 1 minute
    • v) Run Mode: Standard 7500
    • vi) Collect data using the “stage 2, step 2 (63.0@1:00)” setting
  • k) Label the wells as below using this process: Right click over the plate map, then select Well Inspector. With the Well Inspector open, select a well or wells. Click back into the Well Inspector and enter the Sample Name. Close the Well Inspector when completed.
    • i) CONH for High Control
    • ii) CONL for Low Control
    • iii) CONN for Negative Control
    • iv) NTC for No Template Control
    • v) [Accession ID] for clinical specimens
  • l) Ensure that detectors and quenchers are selected as listed below.
    • i) FAM for CEACAM biomarker 1; quencher=none
    • ii) FAM for LAMP1 biomarker 2; quencher=none, etc.
    • iii) FAM for PLA2G7; quencher=none
    • iv) FAM for PLACE; quencher=none
    • v) Select “ROX” for passive reference
      qPCR
• In a template-free area, remove the assay qPCR Buffer and assay Primer/Probe Mixes for each target to room temperature to thaw. Leave the assay AmpliTaq Gold in the freezer and/or on a cold block.
• Still in a template-free area, prepare qPCR Master Mixes for each target in the listed order at room temperature.
• qPCR Master Mixes—Calculation Per Sample

• 	Per well	×N

  	qPCR Buffer	11 μL	11 × N
  	Primer/Probe Mix	3.4 μL	3.4 × N
  	AmpliTaq Gold ®	0.6 μL	0.6 × N
  	Total Volume
  	15 μL	15 × N

• Gently mix the master mixes by flicking or by vortexing, and then pulse spin. Add 15 μL of qPCR Master Mix to each well at room temperature.
• In a template area, add 130 μL of SeptiCyte Lab Test Diluent to each cDNA product from the RT Reaction. Reseal the plate tightly and vortex the plate to mix thoroughly.
• Add 10 μL of diluted cDNA product to each well according to the plate layout.
• Mix by gentle pipetting. Avoid forming bubbles in the wells.
• Cover wells with an optical seal.
• Spin the plate to remove any bubbles (1 minute at 400×g).
• Place on real-time thermal cycler pre-programmed with the settings above.
• Click Start. Click Save and Continue. Before leaving the instrument, it is recommended to verify that the run started successfully by displaying a time under Estimated Time Remaining.
• Note: Do not open the qPCR plate at any point after amplification has begun. When amplification has completed, discard the unopened plate.
Software, Interpretation of Results and Quality Control
• Software is specifically designed to integrate with the output of PCR machines and to apply an algorithm based on the use of multiple biomarkers. The software takes into account appropriate controls and reports results in a desired format.
• When the run has completed on the ABI 7500 Fast Dx Instrument, complete the steps below in the application 7500 Fast System with 21 CFR Part 11 Software, ABI software SDS v1.4.
• Click on the Results tab in the upper left corner.
• Click on the Amplification Plot tab in the upper left corner.
• In the Analysis Settings area, select an auto baseline and manual threshold for all targets. Enter 0.01 as the threshold.
• Click on the Analyze button on the right in the Analysis Settings area.
• From the menu bar in the upper left, select File then Close.
• Complete the form in the dialog box that requests a reason for the change. Click OK.
• Transfer the data file (.sds) to a separate computer running the specific assay RT-qPCR Test Software.
• Launch the assay RT-qPCR Test Software. Log in.
• From the menu bar in the upper left, select File then Open.
• Browse to the location of the transferred data file (.sds). Click OK.
• The data file will then be analyzed using the assay's software application for interpretation of results.
Interpretation of Results and Quality Control Results
• Launch the interpretation software. Software application instructions are provided separately.
• Following upload of the .sds file, the Software will automatically generate classifier scores for controls and clinical specimens.
• Controls
• The Software compares each CON (control) specimen (CONH, CONL, CONN) to its expected result. The controls are run in singleton.

• Control specimen

  Designation	Name	Expected result
  CONH	High Control	Score range
  CONL	Low Control	Score range
  CONN	Negative Control	Score range
  NTC	No Template Control	Fail (no Ct for all targets)

• If CONH, CONL, and/or CONN fail the batch run is invalid and no data will be reported for the clinical specimens. This determination is made automatically by the interpretive software. The batch run should be repeated starting with either a new RNA preparation or starting at the RT reaction step.
• If NTC yields a result other than Fail (no Ct for all targets), the batch run is invalid and no data may be reported for the clinical specimens. This determination is made by visual inspection of the run data. The batch run should be repeated starting with either a new RNA preparation or starting at the RT reaction step.
• If a second batch run fails, please contact technical services. If both the calibrations and all controls are valid, then the batch run is valid and specimen results will be reported.
• Specimens
• Note that a valid batch run may contain both valid and invalid specimen results.
• Analytical criteria (e.g. Ct values) that qualify each specimen as passing or failing (using pre-determined data) are called automatically by the software.
• Scores out of range—reported.
• Quality Control
• Singletons each of the Negative Control, Low Positive Control, and High Positive Control must be included in each batch run. The batch is valid if no flags appear for any of these controls.
• A singleton of the No Template Control is included in each batch run and Fail (no Ct for all targets) is a valid result indicating no amplifiable material was detectable in the well.
• The negative control must yield a Negative result. If the negative control is flagged as Invalid, then the entire batch run is invalid.
• The low positive and high positive controls must fall within the assigned ranges. If one or both of the positive controls are flagged as Invalid, then the entire batch run is invalid.
Example Output
• A possible example output from the software is presented below in FIG. 15. The format of such a report depends on many factors including; quality control, regulatory authorities, cut-off values, the algorithm used, laboratory and clinician requirements, likelihood of misinterpretation.
• In this instance the assay is called “SeptiCyte Lab Test”. The result is reported as a number (5.8), a call (“Sepsis Positive”), a position on a 0-12 scale, and a probability of the patient having sepsis based on historical results and the use of a pre-determined cut-off (using results from clinical trials). Results of controls within the assay are also reported. Other information that could be reported might include: previous results and date and time of such results, probability of severe sepsis or septic shock, a scale that provides cut-off values for historical testing results that separate the conditions of healthy, inSIRS and ipSIRS (mild, severe and shock) such that those patients with higher scores are considered to have more severe inSIRS or ipSIRS.
Second Example Workflow
• A second example workflow will now be described. Machines have been, and are being, developed that are capable of processing a patient sample at point-of-care, or near point-of-care. Such machines require few molecular biology skills to run and are aimed at non-technical users. The idea is that the sample would be pipetted directly into a disposable cartridge that is then inserted into the machine. The user presses “Start” and within 2-3 hours a result is generated. The cartridge contains all of the required reagents to perform Steps 2-5 in the example workflow above and the machine has appropriate software incorporated to allow Steps 6 and 7 to be performed.
• Fresh, whole, anti-coagulated blood can be pipetted into an Idylla Cartridge (Biocartis NV) or similar (Unyvero, Curetis AG; Enigma ML, Enigma Diagnostics; DiagCore, STAT Diagnostica; Savannah, Quidel Corp; ePlex, GenMark Dx), and on-screen instructions on the Idylla machine followed to test for differentiating inSIRS and ipSIRS (by example). Inside the Idylla machine RNA is first extracted from the whole blood and is then converted into cDNA. The cDNA is then used in qRT-PCR reactions. The reactions are followed in real time and Ct values calculated. On-board software generates a result output (see Figure XX). Appropriate quality control measures for RNA quality, no template controls, high and low template controls and expected Ct ranges ensure that results are not reported erroneously.
Example Biomarker Ratios
• Example biomarker ratios (the top 12 based on AUC) that are capable of separating different conditions are shown in the box and whisker plots as listed below, with each showing perfect separation.
• • FIGS. 16A to 16L show Healthy Versus inSIRS (Post-Surgical)
  • FIGS. 17A to 17L show Healthy Versus ipSIRS (Sepsis)
  • FIGS. 18A to 18L show inSIRS (Post-Surgical) Versus ipSIRS (Sepsis)
  • FIGS. 19A to 19L show Sepsis Versus Severe Sepsis
  • FIGS. 20A to 20L show Severe Sepsis Versus Septic Shock
  • FIGS. 21A to 21L show Sepsis Versus Septic Shock
Example Algorithm Combining Biomarker Ratios
• Biomarker ratios (derived markers) can be used in combination to increase the diagnostic power for separating various conditions. Determining which markers to use, and how many, for separating various conditions can be achieved by calculating Area Under Curve (AUC).
• FIG. 22 shows the effect on AUC (in this instance for separating inSIRS and ipSIRS) of adding biomarkers to the diagnostic signature. Diagnostic power significantly increases (adjusted p-value=0.0175) between a single mRNA biomarker (in this instance PLA2G7, AUC of 0.88, 95% CI 0.79-0.97) compared to the power of the two best performing markers in combination (in this instance PLA2G7 and PLAC8, AUC of 0.96, 95% CI 0.91-1.0). Combinations of two, three, four and five biomarkers produced equally as good differentiation of inSIRS and ipSIRS without significant differences. For commercial development of derived markers other factors come into play such as cost-effectiveness, assay complexity and capabilities of the qRT-PCR platform.
• In this example, the addition of markers beyond 3 or 4 does not significantly improve performance and, conversely, a decline in AUC is observed in signatures of ≧5 genes probably because when a statistical model is forced to include biomarkers that add little additional information data over-fitting and addition of noise occurs.
• As such, and by example, a 4-gene signature (0.986, 95% CI 0.964-1.00) offers the appropriate balance between simplicity, practicality and commercial risk for separating inSIRS and ipSIRS. Further, an equation using four markers weighs each biomarker equally which also provides additional robustness in cases of analytical or clinical variability.
• One example equation that provides good diagnostic power for separating inSIRS and ipSIRS (amongst others) is:
• 
  Diagnostic Score=(PLA2G7−PLAC8)+(CEACAM4−LAMP1)
• The value for each biomarker is a Ct value from a PCR. When clinical samples from patients with inSIRS and ipSIRS were tested using these four markers in a PCR the Ct values for each of the markers was found to fall between 26 and 34. In this patient population the first biomarker within each bracket pair has a higher value than the second biomarker within each bracket pair. Thus, the “Diagnostic Score” has been found to have values between 0 and 12. However, in theory the “Diagnostic Score could potentially be highest Ct value +/− highest Ct value.
• In FIG. 23 shows results of PCR and the use of the above algorithm have been calculated for two patient populations (N=63 for “Discovery” and N=70 for “Feasibility”). Each patient was clinically and retrospectively (note, not at the time the sample was taken) confirmed as having either inSIRS (black dots) or ipSIRS (red dots). Each patient sample has also had a SeptiCyte score calculated (Y axis on left hand side). On a scale of 0-12 it can be seen that patients with confirmed ipSIRS (red dots) obtain a higher Diagnostic Score compared to those with confirmed inSIRS. Further, it can be seen that an arbitrary cut-off line can be drawn that more or less separates the two conditions depending upon the desired false negative or false positive rate (compared to a retrospective diagnosis of inSIRS or ipSIRS using clinical data). In this instance the line is drawn at a “SeptiCyte Score” of 4 such that the number of false negative ipSIRS calls in the Discovery Dataset is zero and the number of false negative ipSIRS calls in the Feasibility Dataset is 2. Conversely the number of false positive ipSIRS calls in the Discovery Dataset is four and the number of false positive ipSIRS calls in the Feasibility Dataset is 9. Clearly in this instance whether a patient sample is false positive or false negative depends on the artificial gold standard of a retrospective clinical call of inSIRS or ipSIRS.
• Accordingly, in one example, when used for determining a likelihood of the subject having inSIRS or ipSIRS, the method can include determining a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene, determining a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene and then determining an indicator using the first and second pairs of biomarker values.
• As previously discussed, the indicator could then be compared to indicator references specifically established to distinguish between inSIRS and ipSIRS.
• An example process of a process for establishing indicator references will now be described in more details with reference to FIG. 24.
• In this example, at step 2400 the processing system 201 determines reference data in the form of measured biomarker values obtained for a reference population. The reference data may be acquired in any appropriate manner but typically this involves obtaining gene expression product data from a plurality of individuals.
• In order to achieve this, gene expression product data are collected, for example by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantification process, such as a nucleic acid amplification process, including PCR (Polymerase Chain Reaction) or the like, in order to assess the activity, and in particular, level or abundance of a number of reference biomarkers. Quantified values indicative of the relative activity are then stored as part of the reference data.
• In one example, the measurements are received as raw data, which then undergoes preliminary processing. Such raw data corresponds to information that has come from a source without modification, such as outputs from instruments such as PCR machines, array (e.g., microarray) scanners, sequencing machines, clinical notes or any other biochemical, biological, observational data, or the like. This step can be used to convert the raw data into a format that is better suited to analysis. In one example this is performed in order to normalize the raw data and thereby assist in ensuring the biomarker values demonstrate consistency even when measured using different techniques, different equipment, or the like. Thus, the goal of normalization is to remove the variation within the samples that is not directly attributable to the specific analysis under consideration. For example, to remove variances caused by differences in sample processing at different sites. Classic examples of normalization include z-score transformation for generic data, or popular domain specific normalizations, such as RMA normalization for microarrays.
• However, it will also be appreciated that in some applications, such as a single sample experiment run on a single data acquisition machine, this step may not strictly be necessary, in which case the function can be a Null function producing an output identical to the input.
• In one example, the preferred approach is a paired function approach over log normalized data. Log normalization is a standard data transformation on microarray data, because the data follow a log-normal distribution when coming off the machine. Applying a log transform turns the data into process-friendly normal data.
• The individuals are selected to include individuals diagnosed with one or more conditions of interest, as well as healthy individuals The conditions are typically medical, veterinary or other health status conditions and may include any illness, disease, stages of disease, disease subtypes, severities of disease, diseases of varying prognoses or the like, and in the current example would include at least some individuals with inSIRS and some individuals with ipSIRS. In this regard, the individuals also typically undergo a clinical assessment allowing the conditions to be clinically identified, and with an indication of any assessment or condition forming part of the reference data.
• The biomarker values measured will depend on the predominant condition that is being assessed so, for example, in the case of determining the likelihood of a subject having inSIRS or ipSIRS, the biomarkers used will be LAMP1, CEACAM4, PLAC8 and PLA2G7, as discussed above.
• Once collected, the reference data can be stored in the database 211 allowing this to be subsequently retrieved by the processing system 201 for subsequent analysis, or could be provided directly to the processing system 201 for analysis.
• As part of the above process, at step 2410 the measurements are validated using traditional prior art techniques, to ensure that the measurements have been performed successfully, and hence are valid.
• At step 2420, each individual with the reference population is typically allocated to a group. The groups may be defined in any appropriate manner and may be defined based on any one or more of an indication of a presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with the individuals.
• For example, a first selection of groups may be to identify one or more groups of individuals suffering from SIRS, one or more groups of individuals suffering ipSIRS, and one or more groups of individuals suffering inSIRS. Further groups may also be defined for individuals suffering from other conditions. The groups may include overlapping groups, so for example it may be desirable to define groups of healthy individuals and individuals having SIRS, with further being defined to distinguish inSIRS patients from ipSIRS patients, as well as different degree of inSIRS or ipSIRS, with these groups having SIRS in common, but each group of patients differing in whether a clinician has determined the presence of an infection or not. Additionally, further subdivision may be performed based on characteristics of the individuals, phenotypic traits, measurement protocols or the like, so groups could be defined based on these parameters so that a plurality of groups of individuals suffering from a condition are defined, with each group relating to a different phenotypic trait, measurement protocol or the like.
• It will also be appreciated, however, that identification of different groups can be performed in other manners, for example on the basis of particular activities of biomarkers within the biological samples of the reference individuals, and accordingly, reference to conditions is not intended to be limiting and other information may be used as required.
• The manner in which classification into groups is performed may vary depending on the preferred implementation. In one example, this can be performed automatically by the processing system 201, for example, using unsupervised methods such as Principal Components Analysis (PCA), or supervised methods such as k-means or Self Organizing Map (SOM). Alternatively, this may be performed manually by an operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI), and define respective groups using appropriate input commands.
• At step 2430, first and second derived biomarker values are determined representing respective indicator values. The first and second indicator values In₁, In₂ are determined on a basis of ratios of concentrations of first and second, and third and fourth biomarkers respectively:
• • In₁=(PLA2G7/PLACE)
  • In₂=(CEACAM4/LAMP1)
• The indicator values are then used to establish indicator references at step 2440, which are then used in analyzing measured indicator values for a subject to establish a likelihood of the subject having a condition.
• In particular, indicator values for each reference group are statistically analyzed to establish a range or distribution of indicator values that is indicative of each group, and an example distribution is shown in FIG. 26, as will be discussed in more detail below.
• A further example will now be described with reference to FIGS. 25A and 25B.
• In this example, at step 2500 a sample is acquired from the subject. The sample could be any suitable sample such as a peripheral blood sample, or the like, depending on the nature of the biomarker values being determined At step 2505 the sample undergoes preparation allowing this to be provided to a measuring device and used in a quantification process at step 2510. For this purpose of this example, the quantification process involves PCR amplification, with the measuring device being a PCR machine, although other suitable techniques could be used. In this instance, amplifications times At(PLA2G7), At(PLAC8), At(CEACAM4), At(LAMP1) are determined for each of the four biomarkers at step 2515, with the amplification times being transferred from the measuring device to the processing system 201 allowing the processing system 201 to perform analysis of the corresponding biomarker values.
• Accordingly, at step 2520 the processing system 201 calculates ratios using the amplifications times. In this regard, as the amplification times represent a log value, the ratios are determined by subtracting amplifications times as will be appreciated by a person skilled in the art.
• Accordingly, in this example the indicator values would be determined as follows:
• • In₁=At(PLA2G7)−At(PLAC8)
  • In₂=At(CEACAM4)−At(LAMP1)
• At step 2525 the processing system 201 determines an indicator value by combining the ratios for the indicator values, as follows:
• • In=In₁+In₂
• The processing system 201 then compares the indicator value to one or more respective indicator references at step 2530.
• As previously described, the indicator references are derived for a reference population and are used to indicate the likelihood of a subject suffering from inSIRS or ipSIRS. To achieve this, the reference population is grouped based on a clinical assessment into groups having/not having the conditions or a measure of severity, risk or progression stage of the condition, with this then being used to assess threshold indicator values that can distinguish between the groups or provide a measure of severity, risk or progression stage.
• The comparison is performed by comparing the indicator to an indicator distribution determined for each group in the reference population. In the current example, there are two reference groups, with one corresponding to individuals diagnosed with inSIRS and the other for individuals diagnosed with ipSIRS. In this instance, the results of the comparison can be used to determine a likelihood of the individual having ipSIRS as opposed to inSIRS. This can be achieved using a number of different methods, depending on the preferred implementation.
• An example of a reference distribution is shown in FIG. 26, which shows the distribution of indicator values for a reference population containing both inSIRS and ipSIRS samples. The density (y axis) describes how common scores are in the reference population. In FIG. 26, the most common values for the inSIRS population are in the range 1 to 8, and for the ipSIRS population are mostly in the range 5 to 13. By way of example, let us assume that the calculated indicator value for a new sample is 4. A value of 4 in the inSIRS population has a high density at this value (A), while the ipSIRS population has a low density at this value (B), meaning that this sample is more likely to be inSIRS. Conversely, if a sample has an indicator score of 10, this value in the ipSIRS reference population has a high density (C), while the inSIRS population has a low density (D), meaning this it is more probable that this sample with an indicator value of 10 belongs to the ipSIRS population.
• In practice this process can be performed by determining a basic probability based on score bands. For a given score band (i.e. 4-6), the proportion of individuals with SIRS or SEPSIS is calculated. For example, if 40% of the scores between 4 and 6 were SEPSIS, then if an subject has an indicator value between 4 and 6, they have a 40% probability of sepsis. Thus, for a given range within the reference distribution, the probability of belonging to one group or another (SIRS/SEPSIS) can simply be the proportion of that group within the range.
• An alternative technique is a standard Bayes method. In this case, the technique uses a distribution of inSIRS scores, a distribution of ipSIRS scores and an indicator value for the subject. In this example, a standard score or equivalent is used to generate a probability of the indicator value belonging to the inSIRS distribution: pr(inSIRS) and separately to the ipSIRS distribution: pr(ipSIRS). The Bayes method is used to generate the probability of ipSIRS given the individual distributions.
• Thus, given derived biomarker distributions for two or more groups (i.e. inSIRS/ipSIRS), the probability of membership for a single unknown sample into each distribution can be calculated (p-value) using for example a standard score (z-score). Then the p-values for each distribution can be combined into an overall probability for each class (i.e. inSIRS/ipSIRS) using for example Bayes rule or any other probability calculation method (including frequentist or empirical or machine learned methods).
• Thus, once the indicator value has been derived and compared to the indicator distributions, the results of this comparison are used by the processing system 201 to calculate a likelihood of the subject having ipSIRS at step 2535, with this being used to generate a representation of the results at step 2540, which is provided for display at step 2545, for example to a clinician or medical practitioner. This can be achieved by displaying the representation on a client device, such as part of an email, dashboard indication or the like.
• An example of the representation is shown in FIGS. 27A and 27B.
• In this example, the representation 2700 includes a pointer 2710 that moves relative to a linear scale 2720. The linear scale is divided into regions 2721, 2722, 2723, 2724 which indicate whether the subject is suffering from level 1, 2, 3 or 4. Corresponding indicator number values are displayed at step 2730 with an indication of whether the corresponding value represents a likelihood of SIRS (inSIRS) or SEPSIS (ipSIRS) being shown at step 2740. An alphanumeric indication of the score is shown at step 2751 together with an associated probability of the biological subject having SEPSIS at step 2752.
• As shown in this example, regions of the linear scale where the pointer is situated are highlighted with the diagnosis that is most unlikely being greyed out to make it absolutely clear where the subject sits on the scale. This results in a representation which when displayed at step 2545 is easy for a clinician to readily understand and to make a rapid diagnosis.
• It will be appreciated from the above that a method can be provided for use in assessing the likelihood of a biological subject having inSIRS or ipSIRS the method including, in one or more processing devices:
• • a) determining a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • b) determining an indicator indicative of a ratio of the concentrations of the polynucleotide expression products using the pair of biomarker values;
  • c) retrieving previously determined first and second indicator references from a database, the first and second indicator references being determined based on indicators determined from first and second groups of a reference population, one of the groups consisting of individuals diagnosed with the medical condition;
  • d) comparing the indicator to the first and second indicator references;
  • e) using the results of the comparison to determine a probability indicative of the subject having the medical condition; and,
  • f) generating a representation of the probability, the representation being displayed to a user to allow the user to assess the likelihood of a biological subject having at least one medical condition.
• Similarly apparatus can be provided for determining the likelihood of a biological subject having inSIRS or ipSIRS, the apparatus including:
• • a) a sampling device that obtains a sample taken from a biological subject, the sample including polynucleotide expression products;
  • b) a measuring device that quantifies polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:
    • i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
    • ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • c) at least one processing device that:
    • i) receives an indication of the pair of biomarker values from the measuring device;
    • ii) determines an indicator using a ratio of the concentration of the first and second polynucleotide expression products using the biomarker values; and,
    • iii) compares the indicator to at least one indicator reference; and,
    • iv) determines a likelihood of the subject having the at least one medical condition using the results of the comparison; and,
    • v) generates a representation of the indicator and the likelihood for display to a user.
• A further method that can be provided includes differentiating between inSIRS and ipSIRS in a biological subject, the method including:
• • a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;
  • b) in a measuring device:
    • i) amplifying at least some polynucleotide expression products in the sample;
    • ii) determining an amplification amount representing a degree of amplification required to obtain a defined level of polynucleotide expression products including:
      • (1) amplification amounts for a first pair of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;
      • (2) amplification amounts for a second pair of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;
  • c) in a processing system:
    • i) retrieving the amplification amounts;
    • ii) determining an indicator by:
      • (1) determining a first derived biomarker value indicative of a ratio of concentrations of the first pair of polynucleotide expression products by determining a difference between the amplification amounts for the first pair;
      • (2) determining a second derived biomarker value indicative of a ratio of concentrations of the second pair of polynucleotide expression products by determining a difference between the amplification amounts for the second pair;
      • (3) determining the indicator by adding the first and second derived biomarker values;
    • iii) retrieving previously determined first and second indicator references from a database, wherein the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with inSIRS and ipSIRS respectively;
    • iv) comparing the indicator to the first and second indicator references;
    • v) using the results of the comparison to determine a probability of the subject being classified within the first or second group;
    • vi) generating a representation at least partially indicative of the indicator and the probability; and,
    • vii) providing the representation to a user to allow the user to assess the likelihood of a biological subject having at least one medical condition.
• Additionally, a method can be provided for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:
• • a) determining a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject;
  • b) determining the indicator using a combination of the plurality of biomarker values, wherein:
    • i) at least two biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and,
    • ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.
• Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers.
• Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described.

• 	TABLE 1
  		SEQ
  	HGNC § Gene Name	ID NO

  	PRKCZ	1
  	SKI	2
  	RER1	3
  	TAS1R1	4
  	VAMP3	5
  	AGTRAP	6
  	VPS13D	7
  	KLHDC7A	8
  	NBL1//C1orf151	9
  	MDS2	10
  	RCAN3	11
  	LDLRAP1	12
  	MAN1C1	13
  	SH3BGRL3	14
  	DHDDS	15
  	HCRTR1	16
  	CCDC28B	17
  	LCK	18
  	ZNF362	19
  	THRAP3	20
  	PPIE//CCDC25	21
  	CAP1	22
  	CTPS	23
  	C1orf84	24
  	FAAH	25
  	DMBX1	26
  	CYP4B1	27
  	BTF3L4	28
  	LRRC42	29
  	C1orf175//TTC4	30
  	TMEM61	31
  	FPGT//TNNI3K	32
  	ACADM	33
  	SPATA1	34
  	EPHX4	35
  	RPAP2	36
  	RPL5//SNORA66//	37
  	SNORD21//FAM69A
  	RTCD1	38
  	SLC30A7	39
  	RNPC3//AMY2B	40
  	CELSR2	41
  	AHCYL1	42
  	CEPT1//DRAM2	43
  	CHIA	44
  	LIX1L	45
  	UPF0627	46
  	MRPS21	47
  	TNFAIP8L2	48
  	SMCP	49
  	DCST1	50
  	RAG1AP1	51
  	C1orf182	52
  	HAPLN2	53
  	NTRK1	54
  	CD1E	55
  	TOMM40L//NR1I3	56
  	POU2F1	57
  	TIPRL	58
  	SFT2D2	59
  	CACNA1E	60
  	SMG7	61
  	OCLM	62
  	RGS2	63
  	ZC3H11A//RP11-74E24.2	64
  	MFSD4	65
  	IL20	66
  	RPS6KC1	67
  	C1orf95	68
  	ARF1	69
  	GALNT2	70
  	TNFRSF4	71
  	NADK	72
  	FLJ14100//C1orf86	73
  	GPR153	74
  	RERE	75
  	SLC2A7	76
  	SDHB	77
  	RNF186	78
  	DDOST	79
  	GPN2	80
  	RPA2	81
  	PEF1	82
  	PTP4A2	83
  	TRIM62	84
  	PHC2	85
  	LSM10	86
  	MRPS15	87
  	RRAGC	88
  	COL9A2	89
  	TESK2	90
  	NRD1	91
  	KTI12	92
  	CC2D1B	93
  	YIPF1	94
  	JAK1	95
  	SLC35D1	96
  	DIRAS3	97
  	ZZZ3	98
  	GNG5	99
  	ZNHIT6	100
  	ODF2L	101
  	SEP15	102
  	BARHL2	103
  	GCLM	104
  	CLCC1//GPSM2//	105
  	C1orf62
  	SORT1	106
  	SLC16A4	107
  	PHTF1	108
  	RSBN1	109
  	DENND2C//BCAS2	110
  	CD58	111
  	SPAG17//WDR3	112
  	REG4//NBPF7	113
  	RP11-94I2.2//NBPF16//	114
  	NBPF11//NBPF15//
  	NBPF8//NBPF20//
  	NBPF10//NBPF14//
  	NBPF1//LOC100288142//
  	NBPF12//KIAA1245//
  	LOC100290137
  	APH1A	115
  	POGZ	116
  	TDRKH	117
  	THEM4	118
  	S100A11	119
  	CRNN	120
  	SPRR2C	121
  	S100A12	122
  	S100A8	123
  	GATAD2B//PLIN2	124
  	DENND4B	125
  	PBXIP1	126
  	PYGO2	127
  	SHC1	128
  	DCST2	129
  	GBA//GBAP	130
  	ASH1L	131
  	RIT1	132
  	MEF2D	133
  	AIM2	134
  	COPA	135
  	DEDD	136
  	TADA1L	137
  	GPA33	138
  	CD247	139
  	F5	140
  	PIGC	141
  	KIAA0040	142
  	TOR1AIP2//TOR1AIP1//	143
  	IFRG15
  	STX6//KIAA1614	144
  	EDEM3	145
  	UCHL5	146
  	DENND1B	147
  	DDX59	148
  	KIF21B	149
  	ARL8A	150
  	CYB5R1	151
  	MYBPH	152
  	CHI3L1	153
  	PIK3C2B//LOC100130573	154
  	NUAK2	155
  	NUCKS1	156
  	FAIM3	157
  	PLXNA2	158
  	SLC30A1	159
  	LPGAT1	160
  	ANGEL2	161
  	RAB3GAP2//AURKAPS1//	162
  	AURKA//SNORA36B
  	TP53BP2	163
  	NVL	164
  	TMEM63A	165
  	PARP1	166
  	ITPKB	167
  	TARBP1	168
  	CHML	169
  	AKT3	170
  	SMYD3	171
  	AHCTF1	172
  	OR1C1	173
  	NCOA1	174
  	HADHB	175
  	ABHD1//PREB	176
  	SPAST	177
  	SLC30A6//DDX50	178
  	CRIPT	179
  	MSH2	180
  	FOXN2	181
  	CCDC104	182
  	VRK2	183
  	AHSA2//USP34	184
  	OTX1	185
  	AFTPH	186
  	CEP68	187
  	PLEK	188
  	ANXA4	189
  	MXD1	190
  	NAGK	191
  	SMYD5//NOTO	192
  	MTHFD2	193
  	TTC31	194
  	SEMA4F	195
  	TMSB10	196
  	SH2D6	197
  	GNLY	198
  	KCNIP3	199
  	CNNM4	200
  	CNNM3	201
  	ZAP70	202
  	LIPT1//MRPL30	203
  	MAP4K4	204
  	IL1R2	205
  	IL1R1	206
  	IL18R1	207
  	POLR1B	208
  	CHCHD5	209
  	IL1RN	210
  	PSD4	211
  	DDX18	212
  	INSIG2	213
  	TMEM177//LOC100125918	214
  	RALB	215
  	PROC	216
  	GPR17//LOC100291428//	217
  	LIMS2
  	IMP4	218
  	FAM123C	219
  	ACVR2A	220
  	MBD5	221
  	LYPD6B	222
  	SLC4A10	223
  	UBR3	224
  	HAT1	225
  	ITGA6	226
  	ZAK	227
  	OSBPL6	228
  	PLEKHA3	229
  	ZC3H15	230
  	COL3A1	231
  	GLS	232
  	OBFC2A	233
  	COQ10B	234
  	MARS2	235
  	CFLAR	236
  	NOP58	237
  	FAM117B	238
  	CYP20A1	239
  	FASTKD2	240
  	PIKFYVE	241
  	C2orf62	242
  	SLC11A1	243
  	AGFG1	244
  	CHRNG	245
  	EIF4E2	246
  	TRPM8	247
  	LRRFIP1	248
  	GAL3ST2	249
  	TMEM18	250
  	LAPTM4A	251
  	SF3B14	252
  	TP53I3	253
  	UNQ2999	254
  	GPR113//SELI	255
  	MPV17	256
  	PPM1G	257
  	NLRC4	258
  	CDC42EP3	259
  	HNRPLL	260
  	COX7A2L	261
  	KCNG3	262
  	CALM2//C2orf61	263
  	BCL11A	264
  	XPO1	265
  	NAT8B	266
  	DUSP11	267
  	MOGS	268
  	SNRNP200	269
  	SEMA4C	270
  	MITD1	271
  	IL1A	272
  	SLC35F5	273
  	CCDC93	274
  	CLASP1	275
  	SAP130	276
  	YSK4	277
  	GTDC1	278
  	ORC4L	279
  	NR4A2//FLJ46875	280
  	DPP4	281
  	GALNT3	282
  	SCN7A	283
  	FRZB	284
  	STK17B	285
  	CLK1//PPIL3	286
  	MPP4	287
  	INO80D	288
  	KLF7	289
  	FAM119A	290
  	NGEF	291
  	ARL4C	292
  	RAB17	293
  	HDLBP	294
  	LRRN1	295
  	SETD5	296
  	IRAK2	297
  	C3orf42	298
  	TSEN2	299
  	NR2C2//MRPS25	300
  	UBE2E1	301
  	C3orf35	302
  	SNRK	303
  	ZNF197	304
  	GNAI2	305
  	ALAS1	306
  	PRKCD	307
  	CACNA1D	308
  	PXK	309
  	PTPRG	310
  	ATXN7	311
  	SLC35A5	312
  	SLC15A2	313
  	CCDC48	314
  	DNAJC13	315
  	CLDN18	316
  	GYG1	317
  	SELT	318
  	MED12L	319
  	RAP2B	320
  	MYNN	321
  	ABCF3	322
  	VPS8	323
  	HRG	324
  	EIF4A2//SNORA4	325
  	LPP	326
  	CCDC50	327
  	LOC152217	328
  	TADA3L	329
  	SEC13	330
  	TIMP4	331
  	METTL6	332
  	DAZL//DAZ4//DAZ3//DAZ2	333
  	SATB1//TBC1D5	334
  	SCN10A	335
  	SEC22C	336
  	ZDHHC3a	337
  	ZDHHC3b	338
  	SLC6A20	339
  	UQCRC1	340
  	PRKAR2A	341
  	IMPDH2	342
  	CCDC71	343
  	UBA7	344
  	CAMKV	345
  	WDR82	346
  	LMOD3	347
  	FOXP1	348
  	MORC1	349
  	ATG3	350
  	GSK3B//LOC100129275	351
  	HCLS1	352
  	KPNA1	353
  	PTPLB	354
  	C3orf22	355
  	RPN1	356
  	KIAA1257//ACAD9//	357
  	LOC100132731
  	FOXL2	358
  	MECOM	359
  	PLD1	360
  	GNB4	361
  	MRPL47	362
  	KLHL6	363
  	THPO	364
  	ETV5	365
  	BCL6//LOC100131635	366
  	ATP13A5	367
  	TMEM44	368
  	KIAA1530	369
  	TACC3	370
  	CNO	371
  	BST1	372
  	KLF3	373
  	TMEM33//DCAF4L1	374
  	KIT	375
  	ENAM	376
  	FAM47E//STBD1	377
  	ENOPH1	378
  	PDLIM5	379
  	CCDC109B//HIGD1A//	380
  	CCDC13
  	EGF	381
  	PCDH10	382
  	RAB33B	383
  	TMEM184C	384
  	RBM46	385
  	GRIA2	386
  	C4orf39	387
  	KLHL2	388
  	TLL1	389
  	F11	390
  	SLBP	391
  	HAUS3//POLN	392
  	PPARGC1A	393
  	TLR10	394
  	C4orf34	395
  	TXK	396
  	RPL21P44	397
  	KDR	398
  	RCHY1	399
  	CNOT6L	400
  	PLAC8	401
  	HPSE	402
  	GPRIN3	403
  	PPA2	404
  	COL25A1	405
  	C4orf3	406
  	QRFPR	407
  	MFSD8	408
  	MAP9	409
  	PDGFC	410
  	TKTL2	411
  	ACSL1	412
  	SUB1//TMEM183A	413
  	CARD6	414
  	MCCC2	415
  	TNPO1	416
  	PDE8B	417
  	PAPD4	418
  	THBS4	419
  	FAM151B	420
  	RASGRF2	421
  	SNX2	422
  	LMNB1//PCIF1	423
  	MEGF10	424
  	LEAP2	425
  	TCF7	426
  	KDM3B	427
  	CXXC5	428
  	SLC4A9	429
  	ANKHD1-EIF4EBP3//	430
  	ANKHD1//EIF4EBP3
  	KIAA0141	431
  	GRPEL2	432
  	MFAP3	433
  	GABRA6	434
  	GABRA1	435
  	DOCK2	436
  	RANBP17//USP12	437
  	ERGIC1	438
  	ATP6V0E1//SNORA74B	439
  	ZNF346	440
  	NSD1	441
  	CLPTM1L	442
  	UGT3A1	443
  	GDNF	444
  	TTC33	445
  	hCG_2039148	446
  	MOCS2	447
  	SLC38A9	448
  	CCDC125	449
  	ANKRA2	450
  	HAPLN1	451
  	CCNH	452
  	TMEM161B	453
  	MBLAC2	454
  	MCTP1	455
  	TICAM2//TMED7//	456
  	TMED7-TICAM2
  	KIF3A	457
  	C5orf15	458
  	SKP1	459
  	CXCL14	460
  	KLHL3	461
  	CD14	462
  	YIPF5	463
  	LARS	464
  	DCTN4	465
  	CCDC69	466
  	ATOX1	467
  	TIMD4	468
  	ADAM19	469
  	SLIT3	470
  	RNF44	471
  	DOK3	472
  	MGAT4B//SQSTM1	473
  	C5orf45//SQSTM1	474
  	RASGEF1C	475
  	MGAT1	476
  	IRF4	477
  	HIVEP1	478
  	E2F3	479
  	HIST1H4I	480
  	HIST1H2BM	481
  	MOG	482
  	ZNRD1//NCRNA00171	483
  	TRIM15	484
  	HCG27	485
  	BAT2//SNORA38	486
  	CYP21A2	487
  	ITPR3	488
  	MAPK14	489
  	MAPK13	490
  	PNPLA1	491
  	SFRS3	492
  	CDKN1A	493
  	FOXP4	494
  	CUL9	495
  	RUNX2	496
  	ZNF451	497
  	SOBP	498
  	C6orf182	499
  	KIAA1919	500
  	RWDD1	501
  	KPNA5	502
  	TPD52L1	503
  	ARG1	504
  	RAB32	505
  	ARID1B	506
  	SLC22A3	507
  	SERPINB1	508
  	C6orf146	509
  	GCM2	510
  	ATXN1	511
  	DCDC2//KAAG1	512
  	HIST1H3I	513
  	HIST1H4L	514
  	GABBR1	515
  	RNA243	516
  	DDAH2	517
  	CLIC1	518
  	NEU1	519
  	RXRB	520
  	VPS52	521
  	TCP11	522
  	CLPS	523
  	PGC	524
  	ZNF318	525
  	YIPF3	526
  	MRPL14	527
  	PLA2G7	528
  	PKHD1	529
  	IL17F	530
  	HTR1B	531
  	GABRR2	532
  	UBE2J1	533
  	BACH2	534
  	MCM9	535
  	VNN1	536
  	IL20RA	537
  	FLJ27255	538
  	T	539
  	RPS6KA2	540
  	HGC6.3	541
  	UNC84A//C7orf20	542
  	SDK1	543
  	ZDHHC4	544
  	C7orf26	545
  	GLCCI1//tcag7.903	546
  	GPNMB	547
  	CCDC126	548
  	WIPF3//ZNRF2//	549
  	LOC441208
  	GPR141	550
  	STARD3NL	551
  	POU6F2	552
  	CDC2L5	553
  	ZMIZ2	554
  	UPP1	555
  	ZNF273	556
  	KCTD7//RABGEF1	557
  	RABGEF1//tcag7.967//	558
  	tcag7.951//KCTD7//
  	LOC100293333
  	CCDC132	559
  	PVRIG//PILRB//STAG3	560
  	PILRB//PVRIG//STAG3	561
  	C7orf51	562
  	GNB2	563
  	LRRC17	564
  	LRRN3	565
  	CFTR	566
  	LSM8	567
  	LUC7L2	568
  	MGAM//LOC100124692	569
  	GIMAP7	570
  	INSIG1	571
  	RBM33	572
  	ICA1	573
  	FAM126A	574
  	HIBADH	575
  	TRIL	576
  	SCRN1	577
  	ELMO1	578
  	INHBA	579
  	CAMK2B	580
  	NPC1L1	581
  	DDC//LOC100129427	582
  	NSUN5//NSUN5B//	583
  	NSUN5C
  	CLDN3	584
  	C7orf23//DMTF1	585
  	SRI	586
  	BET1	587
  	MCM7	588
  	GATS	589
  	ATXN7L1//RINT1//	590
  	EFCAB10
  	KIAA1549	591
  	SLC37A3	592
  	SMARCD3	593
  	MLL3//BAGE2	594
  	CLN8	595
  	MSRA	596
  	PIWIL2	597
  	NEFM//LOC100129717	598
  	EPHX2	599
  	LEPROTL1	600
  	MAK16//C8orf41	601
  	AP3M2	602
  	FNTA	603
  	SGK196	604
  	UBE2V2	605
  	FLJ46365	606
  	SNTG1	607
  	TRIM55	608
  	C8orf45	609
  	PREX2	610
  	PLEKHF2	611
  	BAALC//FLJ10489	612
  	TTC35	613
  	MTBP	614
  	ZHX2	615
  	RNF139	616
  	TG	617
  	DENND3//C8orf60	618
  	TNFRSF10D	619
  	TRIM35	620
  	GSR	621
  	WHSC1L1	622
  	PCMTD1//PXDNL	623
  	NCOA2	624
  	TRAM1//LOC286190	625
  	RUNX1T1	626
  	EXT1	627
  	DDEF1IT1	628
  	CDC37L1	629
  	UBE2R2	630
  	UBAP1//KIF24	631
  	GALT	632
  	RGP1//GBA2	633
  	TGFBR1	634
  	C9orf6//IKBKAP	635
  	IMAGE5303689	636
  	ATP6V1G1	637
  	TLR4	638
  	SET	639
  	MRPL41	640
  	C9orf68	641
  	HAUS6//SCARNA8	642
  	KLHL9	643
  	C9orf82	644
  	NDUFB6//DFFB	645
  	SIT1	646
  	FAM108B1	647
  	TRPM6	648
  	FRMD3	649
  	SLC28A3	650
  	BICD2	651
  	C9orf84	652
  	AKNA	653
  	MEGF9	654
  	C5	655
  	GOLGA1//SCAI	656
  	SH2D3C	657
  	FAM102A	658
  	FLJ10232	659
  	ASB6	660
  	BAT2L	661
  	EDF1	662
  	FBXW5	663
  	C10orf18	664
  	FBXO18	665
  	GATA3	666
  	CUGBP2	667
  	VIM	668
  	STAM	669
  	WAC	670
  	BAMBI	671
  	ZNF487//LOC439911	672
  	ALOX5	673
  	WDFY4	674
  	SRGN	675
  	CCDC109A	676
  	FAM149B1//FAM149B2	677
  	MINPP1	678
  	PTEN//PTENP1	679
  	ENTPD1//C10orf131	680
  	ABCC2	681
  	SFXN2	682
  	SHOC2	683
  	ACSL5	684
  	BCCIP//DHX32	685
  	FAM188A	686
  	CUBN	687
  	SVIL//hCG_1783494	688
  	FAM13C//PHYHIPL	689
  	ATAD1	690
  	ANKRD22	691
  	FLJ34077	692
  	COX15	693
  	ERLIN1	694
  	ACTR1A	695
  	ABLIM1	696
  	RAB11FIP2	697
  	C10orf84	698
  	PRDX3	699
  	C10orf110	700
  	NSMCE4A	701
  	TALDO1//INTS8	702
  	TNNT3	703
  	FXC1	704
  	PDE3B	705
  	DNAJC24	706
  	PTPRJ//OR4B1	707
  	C11orf31	708
  	TMEM109	709
  	CD6	710
  	CD5	711
  	TMEM138	712
  	POLR2G	713
  	TMEM179B	714
  	NAT11	715
  	OTUB1	716
  	RBM14//RBM4	717
  	AIP	718
  	PPFIA1	719
  	IL18BP//NUMA1	720
  	C11orf30	721
  	C11orf82	722
  	TMEM126B	723
  	C11orf73	724
  	PIWIL4	725
  	LOC100132686	726
  	PAFAH1B2	727
  	UBE4A	728
  	TRAPPC4	729
  	SC5DL	730
  	VWA5A//OR10D1P	731
  	STT3A	732
  	VPS26B	733
  	TRIM21	734
  	ZBED5	735
  	SAAL1	736
  	FANCF	737
  	LIN7C	738
  	PHF21A	739
  	CUGBP1	740
  	OSBP	741
  	CYBASC3	742
  	TUT1	743
  	SLC25A45	744
  	LTBP3	745
  	EIF1AD	746
  	GAB2	747
  	CREBZF	748
  	PICALM	749
  	SLC36A4	750
  	CCDC82	751
  	KIAA1826	752
  	MPZL3	753
  	MPZL2	754
  	H2AFX	755
  	SIAE	756
  	ZBTB44	757
  	HSN2	758
  	ADIPOR2	759
  	NCAPD2//SCARNA10//	760
  	FADS1
  	PTPN6	761
  	CLEC4D	762
  	CDKN1B	763
  	GOLT1B	764
  	FAR2	765
  	FGD4	766
  	TMEM106C	767
  	TMBIM6	768
  	C12orf62	769
  	PRR13//PCBP2	770
  	DGKA	771
  	COQ10A	772
  	TSPAN31	773
  	CDK4/MARCH9/C3HC4	774
  	LEMD3	775
  	IRAK3	776
  	TMTC3	777
  	ACTR6	778
  	TCTN1	779
  	PXMP2//PGAM5	780
  	DCP1B	781
  	SLC2A3//SLC2A14	782
  	C3AR1	783
  	PLBD1	784
  	TM7SF3	785
  	ASB8//PHB	786
  	LMBR1L	787
  	FMNL3//PRPF40B	788
  	AAAS	789
  	NFE2	790
  	GPR84	791
  	CD63	792
  	SARNP//DNAJC14	793
  	NACA	794
  	CDK4//TSPAN31	795
  	TMBIM4//LOC100133322	796
  	IL22	797
  	LIN7A	798
  	HAL	799
  	APPL2	800
  	GLTP	801
  	GIT2	802
  	VPS29	803
  	PPTC7	804
  	DDX54//CCDC42B	805
  	SLC24A6	806
  	SDS	807
  	RBM19	808
  	MED13L	809
  	C12orf49	810
  	FBXO21	811
  	WSB2	812
  	TAOK3	813
  	CIT	814
  	RAB35	815
  	RPLP0	816
  	PXN	817
  	TRIAP1	818
  	SFRS9	819
  	POP5	820
  	UNQ1887	821
  	C12orf43	822
  	ANAPC5	823
  	KDM2B	824
  	MORN3	825
  	TMEM120B//RHOF	826
  	LOC338799	827
  	DIABLO//B3GNT4	828
  	VPS33A	829
  	CLIP1	830
  	PITPNM2	831
  	EIF2B1	832
  	CCDC92	833
  	NCOR2	834
  	DHX37	835
  	DDX51	836
  	POLE	837
  	GOLGA3	838
  	ZMYM2	839
  	SPATA13//C1QTNF9	840
  	NUPL1	841
  	PAN3//EEF1A1//CHCHD2	842
  	ALOX5AP	843
  	EEF1DP3	844
  	KL	845
  	UFM1	846
  	NARG1L	847
  	ITM2B	848
  	FNDC3A	849
  	CDADC1	850
  	ARL11	851
  	LMO7	852
  	DNAJC3	853
  	TM9SF2	854
  	CLYBL	855
  	PCCA	856
  	ABHD13	857
  	LAMP1	858
  	TMCO3	859
  	UPF3A	860
  	ZMYM5//ZMYM2	861
  	ZDHHC20//LOC728099	862
  	PARP4	863
  	MTMR6//LOC646482	864
  	HSPH1	865
  	N4BP2L2//CG030	866
  	ELF1	867
  	LCP1	868
  	KPNA3	869
  	C13orf1	870
  	DLEU2//DLEU2L	871
  	GUCY1B2	872
  	INTS6	873
  	DACH1	874
  	TBC1D4	875
  	EDNRB	876
  	UGGT2	877
  	GPR183	878
  	LIG4	879
  	ANKRD10	880
  	RASA3	881
  	RNASE2//LOC643332	882
  	RPGRIP1	883
  	IRF9	884
  	TSSK4	885
  	C14orf21	886
  	SCFD1	887
  	FANCM	888
  	ABHD12B	889
  	PTGDR	890
  	FBXO34//KIAA0831	891
  	C14orf101	892
  	ACTR10	893
  	ARID4A	894
  	JKAMP	895
  	HIF1A	896
  	SYNE2	897
  	EXD2	898
  	SLC39A9	899
  	SFRS5	900
  	PCNX	901
  	SIPA1L1//SNORD56B//	902
  	LOC145474//LOC283567
  	YLPM1	903
  	BATF	904
  	FLVCR2//RPS24	905
  	GPR65	906
  	TDP1	907
  	EVL	908
  	ZNF839	909
  	TDRD9	910
  	INF2	911
  	PLD4	912
  	MTA1//LOC647310//	913
  	LOC100128343
  	NDRG2	914
  	DAD1//OR6J1	915
  	SLC7A8	916
  	IPO4	917
  	TM9SF1	918
  	ADCY4	919
  	RIPK3	920
  	EAPP	921
  	BAZ1A	922
  	NFKBIA	923
  	SEC23A	924
  	C14orf104	925
  	C14orf138	926
  	SOS2	927
  	NIN	928
  	PYGL	929
  	CNIH	930
  	DHRS7	931
  	WDR89	932
  	ACTN1	933
  	NUMB	934
  	C14orf43	935
  	ABCD4	936
  	KIAA0317	937
  	NEK9	938
  	ANGEL1	939
  	SPTLC2	940
  	SERPINA6	941
  	DICER1	942
  	BCL11B	943
  	ANKRD9	944
  	PPP1R13B	945
  	AKT1	946
  	BRF1	947
  	TUBGCP5	948
  	SNRPN *	949
  	APBA2	950
  	MTMR15//MTMR10	951
  	RYR3	952
  	BAHD1	953
  	CHP	954
  	JMJD7-PLA2G4B//	955
  	JMJD7//PLA2G4B
  	HAUS2	956
  	C15orf63//SERF2	957
  	B2M	958
  	TRIM69	959
  	PLDN	960
  	SQRDL	961
  	GALK2	962
  	USP8	963
  	GLDN	964
  	MAPK6	965
  	LACTB	966
  	RAB8B	967
  	APH1B	968
  	USP3//LOC100130855	969
  	SNX1	970
  	LBXCOR1//PIAS1//	971
  	CALML4
  	NEO1	972
  	MPI	973
  	FBXO22//FBXO22OS	974
  	RCN2	975
  	FAH	976
  	IL16	977
  	ABHD2	978
  	SLCO3A1	979
  	MCTP2	980
  	MEF2A//LYSMD4	981
  	NIPA2//CYFIP1	982
  	HERC2//HERC2P2//	983
  	HERC2P3//LOC440248
  	MTMR10//MTMR15	984
  	C15orf24	985
  	SLC12A6	986
  	LPCAT4	987
  	INO80	988
  	OIP5	989
  	ZFP106	990
  	CDAN1	991
  	SPG11//ISLR	992
  	SPPL2A	993
  	GNB5//LOC100129973	994
  	MYO5A	995
  	ARPP19	996
  	RAB27A	997
  	CCPG1//PIGB//DYX1C1	998
  	BNIP2	999
  	CA12	1000
  	FAM96A	1001
  	KIAA0101//CSNK1G1	1002
  	TLE3	1003
  	PARP6	1004
  	NPTN	1005
  	MAN2C1	1006
  	IMP3	1007
  	MTHFS	1008
  	ST20//C15orf37	1009
  	TMC3	1010
  	AP3B2	1011
  	C15orf40	1012
  	WDR73	1013
  	NTRK3	1014
  	DET1	1015
  	TM2D3	1016
  	WDR90	1017
  	RHOT2//FBXL16	1018
  	TMEM204	1019
  	CRAMP1L//HN1L	1020
  	MAPK8IP3	1021
  	TBL3	1022
  	TSC2	1023
  	KCTD5//PRO0461//	1024
  	PDPK1
  	CLUAP1	1025
  	DNASE1	1026
  	DNAJA3	1027
  	CP110	1028
  	C16orf62	1029
  	LYRM1	1030
  	METTL9	1031
  	EEF2K	1032
  	POLR3E	1033
  	PLK1	1034
  	PRKCB	1035
  	IL21R//LOC283888	1036
  	SULT1A2//SULT1A1	1037
  	ATXN2L	1038
  	LAT ¶	1039
  	KIF22	1040
  	MAZ	1041
  	CORO1A//LOC606724	1042
  	ITGAL	1043
  	SRCAP//SNORA30	1044
  	ZNF646//ZNF668	1045
  	C16orf67	1046
  	TMEM188	1047
  	LPCAT2	1048
  	CETP	1049
  	CKLF	1050
  	CMTM1//CKLF	1051
  	TMEM208	1052
  	CTCF	1053
  	THAP11	1054
  	NUTF2	1055
  	EDC4	1056
  	SLC7A6//SLC7A6OS	1057
  	PRMT7	1058
  	SNTB2	1059
  	VPS4A	1060
  	DDX19B//DDX19A	1061
  	CHST4	1062
  	HP//HPR	1063
  	PLCG2	1064
  	KLHL36	1065
  	KIAA0182	1066
  	BANP//RUNDC2C	1067
  	TRAPPC2L	1068
  	SPG7	1069
  	CDK10	1070
  	TCF25	1071
  	AFG3L1	1072
  	LUC7L	1073
  	AXIN1	1074
  	JMJD8	1075
  	LMF1	1076
  	UNKL	1077
  	UNKL	1078
  	CLCN7	1079
  	MRPS34	1080
  	RNPS1	1081
  	NLRC3	1082
  	TRAP1//DNASE1	1083
  	ADCY9	1084
  	CORO7	1085
  	C16orf72	1086
  	RRN3//LOC653390//	1087
  	LOC730092//
  	LOC100131998
  	XYLT1//LYRM2//ZC3H11A	1088
  	DCUN1D3//LYRM1	1089
  	IGSF6//METTL9	1090
  	CDR2//RRN3//	1091
  	LOC100131998//
  	LOC653390
  	COG7	1092
  	GGA2	1093
  	NSMCE1	1094
  	GTF3C1	1095
  	CCDC101//LOC388242	1096
  	C16orf54	1097
  	KCTD13	1098
  	SEPT1	1099
  	ZNF764//ZNF747	1100
  	C16orf58//LOC100128371	1101
  	ITFG1	1102
  	ABCC11//LONP2	1103
  	NUDT21	1104
  	BBS2//OGFOD1	1105
  	CSNK2A2	1106
  	GOT2	1107
  	FAM96B	1108
  	FHOD1//SLC9A5	1109
  	ATP6V0D1//LOC100132855	1110
  	GFOD2	1111
  	SLC12A4	1112
  	DPEP3	1113
  	DPEP2	1114
  	CHTF8//HAS3	1115
  	COG8//PDF	1116
  	TERF2	1117
  	AARS	1118
  	ST3GAL2	1119
  	VAC14//LOC100130894	1120
  	AP1G1	1121
  	WDR59	1122
  	CTRB2//CTRB1	1123
  	TAF1C//ADAD2	1124
  	FBXO31	1125
  	ZCCHC14	1126
  	FAM38A	1127
  	CENPBD1	1128
  	TIMM22	1129
  	RPA1	1130
  	DPH1//OVCA2	1131
  	SGSM2	1132
  	ARRB2	1133
  	LOC100130950	1134
  	DNAH2	1135
  	PIGL	1136
  	TRPV2	1137
  	MPRIP	1138
  	DRG2	1139
  	ALKBH5//FLJ13773	1140
  	SMCR7	1141
  	WSB1	1142
  	TAOK1	1143
  	CPD	1144
  	SUZ12P	1145
  	RNF135	1146
  	ZNF830	1147
  	TAF15	1148
  	GGNBP2	1149
  	LASP1	1150
  	PSMD3	1151
  	CDC6	1152
  	NBR2	1153
  	TMUB2	1154
  	MGC57346//C17orf69	1155
  	NSF//LOC728806	1156
  	GOSR2	1157
  	NPEPPS//TBC1D3F//	1158
  	LOC440434
  	KPNB1	1159
  	CDK5RAP3	1160
  	ATP5G1	1161
  	UBE2Z	1162
  	XYLT2//LOC100130580	1163
  	NOG	1164
  	DGKE	1165
  	AKAP1	1166
  	TMEM49//CLTC//MIR21	1167
  	CLTC	1168
  	CA4	1169
  	C17orf64	1170
  	DCAF7	1171
  	PITPNC1	1172
  	NOL11//SNORA38B	1173
  	MAP2K6	1174
  	COG1	1175
  	CD300A	1176
  	TMEM104	1177
  	MRPS7	1178
  	KIAA0195	1179
  	TSEN54	1180
  	LLGL2	1181
  	LOC100134934//CDK3	1182
  	MFSD11	1183
  	SEPT9	1184
  	TNRC6C	1185
  	TMC8	1186
  	ENGASE	1187
  	RPTOR	1188
  	GPS1	1189
  	FN3KRP	1190
  	TBCD	1191
  	GEMIN4	1192
  	GLOD4	1193
  	SLC43A2	1194
  	PRPF8	1195
  	SMG6//C17orf6	1196
  	METT10D//LOC284009	1197
  	SHPK	1198
  	TAX1BP3	1199
  	P2RX5	1200
  	MYBBP1A//SPNS2	1201
  	PELP1	1202
  	PFN1	1203
  	ZNF232	1204
  	DHX33	1205
  	DERL2	1206
  	NLRP1//LOC728392	1207
  	ASGR2	1208
  	NEURL4//GPS2//D4S234E	1209
  	ZBTB4	1210
  	TP53	1211
  	VAMP2	1212
  	PIK3R5	1213
  	ELAC2	1214
  	NCOR1//C20orf191//	1215
  	LOC100131704
  	ZNF287	1216
  	TOM1L2//LOC246315	1217
  	GRAP//SNORD3B-1//	1218
  	SNORD3B-2//LOC400581
  	ALDOC	1219
  	SDF2	1220
  	RAB34	1221
  	PHF12	1222
  	NUFIP2	1223
  	OMG	1224
  	EVI2B	1225
  	C17orf66//RSL24D1	1226
  	SYNRG//LOC100131822	1227
  	PLXDC1	1228
  	CACNB1	1229
  	PGAP3	1230
  	MED24	1231
  	NR1D1//THRA	1232
  	CCR7	1233
  	STAT5B//STAT5A	1234
  	FAM134C	1235
  	VAT1	1236
  	DUSP3	1237
  	C17orf65//ASB16	1238
  	UBTF	1239
  	GPATCH8	1240
  	MAP3K14//LOC100133991	1241
  	OSBPL7	1242
  	SLC35B1	1243
  	TOB1	1244
  	COX11//TOM1L1	1245
  	VEZF1	1246
  	SFRS1//FLJ44342	1247
  	SEPT4	1248
  	MED13//LOC100129112	1249
  	LIMD2//MAP3K3	1250
  	STRADA	1251
  	FTSJ3	1252
  	CD79B	1253
  	ICAM2	1254
  	ERN1	1255
  	TEX2	1256
  	LRRC37A3//LRRC37A2//	1257
  	LRRC37A//ARL17P1//
  	LRRC37A4//
  	LOC100294335//LOC644397
  	GNA13	1258
  	WIPI1//ARSG	1259
  	FAM20A	1260
  	NAT9	1261
  	GGA3	1262
  	H3F3B//H3F3C	1263
  	EXOC7	1264
  	SFRS2	1265
  	TMC6//LOC100131096	1266
  	USP36	1267
  	CD7	1268
  	RAB31	1269
  	VAPA	1270
  	SEH1L	1271
  	HQ0644/PRO0644	1272
  	RNMT	1273
  	RNF138	1274
  	GALNT1	1275
  	ELP2	1276
  	PIK3C3	1277
  	SLC14A2	1278
  	ME2	1279
  	SERPINB2//SERPINB10	1280
  	ZNF407	1281
  	ZNF236	1282
  	NFATC1//LOC100127994	1283
  	ENOSF1//TYMS	1284
  	MYOM1	1285
  	AFG3L2	1286
  	ABHD3	1287
  	OSBPL1A	1288
  	CDH2	1289
  	DSC1	1290
  	PSTPIP2	1291
  	C18orf32	1292
  	MBD2//SNORA37	1293
  	PIGN	1294
  	TMX3	1295
  	PQLC1	1296
  	GZMM	1297
  	ARID3A	1298
  	CIRBP	1299
  	DAZAP1	1300
  	SPPL2B	1301
  	NFIC	1302
  	VAV1	1303
  	ARHGEF18//LOC100128573	1304
  	STXBP2//LOC554363//	1305
  	LOC100131801
  	C19orf59	1306
  	ZNF317	1307
  	ILF3	1308
  	SMARCA4	1309
  	PRKCSH	1310
  	IER2	1311
  	CCDC130	1312
  	DCAF15	1313
  	IL27RA	1314
  	KLF2	1315
  	SIN3B	1316
  	DDA1	1317
  	GTPBP3	1318
  	FAM129C	1319
  	FCHO1	1320
  	ARRDC2	1321
  	IFI30	1322
  	C19orf60	1323
  	CRTC1//MAML2	1324
  	RFXANK//MEF2B//LOC729991	1325
  	ZNF101	1326
  	ZNF738	1327
  	ZNF257//ZNF492//ZNF99//	1328
  	ZNF98//LOC646864
  	C19orf2	1329
  	KIAA0355//FLJ21369	1330
  	USF2	1331
  	TMEM147	1332
  	LIN37//PSENEN	1333
  	C19orf55	1334
  	TBCB//POLR2I	1335
  	ZNF382	1336
  	ZNF568	1337
  	ZNF420	1338
  	ZNF383	1339
  	CCDC97	1340
  	ZNF574	1341
  	CD177	1342
  	ZNF230//ZNF222	1343
  	VASP	1344
  	GRWD1	1345
  	FLT3LG	1346
  	ZNF175	1347
  	NCRNA00085	1348
  	PPP2R1A	1349
  	ZNF808//ZNF578//	1350
  	ZNF611
  	LENG8	1351
  	FCAR	1352
  	RPL28	1353
  	U2AF2	1354
  	LOC100288114//MGC9913	1355
  	ZFP28	1356
  	ZNF460	1357
  	ZNF549	1358
  	ZNF211	1359
  	ZNF587//ZNF417	1360
  	ZNF274	1361
  	ZNF544	1362
  	ZNF8	1363
  	TRIM28	1364
  	C19orf6	1365
  	C19orf34	1366
  	GNG7	1367
  	AES	1368
  	EEF2//SNORD37	1369
  	PLIN5//LRG1	1370
  	PLIN3	1371
  	PTPRS	1372
  	SAFB2//SAFB	1373
  	RANBP3	1374
  	GTF2F1//LOC100130856	1375
  	XAB2	1376
  	ELAVL1	1377
  	ADAMTS10	1378
  	FBXL12	1379
  	DNMT1	1380
  	TYK2	1381
  	KEAP1	1382
  	KRI1	1383
  	TMEM205//hCG_29977	1384
  	ZNF563	1385
  	MAN2B1//MORG1	1386
  	C19orf56	1387
  	DHPS	1388
  	TNPO2//SNORD41	1389
  	LPHN1	1390
  	NDUFB7	1391
  	AKAP8	1392
  	AKAP8L	1393
  	CHERP//C19orf44//	1394
  	CALR3
  	INSL3//JAK3	1395
  	IL12RB1	1396
  	UPK1A	1397
  	TYROBP	1398
  	ZNF529	1399
  	ZNF461	1400
  	ZNF607	1401
  	YIF1B	1402
  	PRR13	1403
  	CEACAM4	1404
  	PLAUR	1405
  	TRAPPC6A	1406
  	ERCC1//CD3EAP	1407
  	RTN2	1408
  	SYMPK	1409
  	PGLYRP1	1410
  	NOSIP	1411
  	PNKP	1412
  	NKG7	1413
  	FPR1	1414
  	ZNF28	1415
  	OSCAR	1416
  	MBOAT7	1417
  	LILRA5	1418
  	LILRA4	1419
  	ZNF550//ZNF549	1420
  	ZNF416	1421
  	ZNF256	1422
  	ZNF329	1423
  	FAM110A	1424
  	ITPA	1425
  	CDC25B	1426
  	CDS2	1427
  	CRLS1	1428
  	CSRP2BP	1429
  	SEC23B	1430
  	SLC24A3	1431
  	HCK	1432
  	ASXL1	1433
  	ACSS2	1434
  	C20orf4	1435
  	TGIF2	1436
  	C20orf24//SLA2	1437
  	RPN2//EEF1A2	1438
  	CTNNBL1	1439
  	ACTR5	1440
  	PPP1R16B	1441
  	DHX35	1442
  	PLCG1	1443
  	MYBL2	1444
  	SYS1//SYS1-	1445
  	DBNDD2//DBNDD2
  	DNTTIP1	1446
  	CTSA	1447
  	MMP9//LOC100128028	1448
  	DDX27	1449
  	SLC9A8	1450
  	RNF114	1451
  	PTPN1	1452
  	TSHZ2	1453
  	PFDN4	1454
  	CSTF1	1455
  	CASS4	1456
  	GNAS	1457
  	C20orf177	1458
  	CDH26	1459
  	C20orf197	1460
  	LOC284757	1461
  	ARFGAP1	1462
  	PRPF6	1463
  	NSFL1C	1464
  	SIRPD	1465
  	SIRPG//SIRPA	1466
  	RNF24	1467
  	RASSF2	1468
  	TMX4	1469
  	JAG1	1470
  	C20orf74	1471
  	C20orf3	1472
  	C20orf112	1473
  	CDK5RAP1	1474
  	AHCY	1475
  	GGT7	1476
  	EDEM2	1477
  	RBM39//LOC643167	1478
  	BLCAP	1479
  	SERINC3//TTPAL	1480
  	ZNF335	1481
  	ELMO2	1482
  	B4GALT5	1483
  	DPM1	1484
  	ZFP64	1485
  	ZNF217	1486
  	CTSZ	1487
  	SYCP2	1488
  	PSMA7	1489
  	DIDO1	1490
  	YTHDF1	1491
  	CHODL	1492
  	BACH1	1493
  	C21orf41//BACH1	1494
  	IL10RB	1495
  	IFNAR1	1496
  	IFNGR2	1497
  	SON	1498
  	MORC3//DOPEY2	1499
  	DYRK1A	1500
  	KCNJ15	1501
  	ETS2	1502
  	RRP1B	1503
  	PFKL	1504
  	TRPM2	1505
  	ADARB1	1506
  	SAMSN1//LOC388813	1507
  	N6AMT1	1508
  	SYNJ1	1509
  	TMEM50B	1510
  	KCNE1	1511
  	PRDM15	1512
  	C2CD2	1513
  	WDR4	1514
  	U2AF1	1515
  	CSTB	1516
  	UBE2G2//SUMO3	1517
  	PTTG1IP	1518
  	POFUT2	1519
  	MCM3AP	1520
  	IL17RA//CECR7	1521
  	C22orf37	1522
  	LZTR1	1523
  	PPIL2//YPEL1	1524
  	CYTSA	1525
  	SNRPD3//C22orf13	1526
  	NF2	1527
  	LIMK2	1528
  	SLC5A1	1529
  	MCM5	1530
  	NCF4	1531
  	GGA1	1532
  	SH3BP1//PDXP	1533
  	POLR2F//LOC100131530	1534
  	APOBEC3A//APOBEC3B	1535
  	APOBEC3D	1536
  	ATF4	1537
  	CACNA1I	1538
  	ZC3H7B	1539
  	CCDC134	1540
  	TSPO	1541
  	NUP50	1542
  	TBC1D22A//LOC100289878	1543
  	RP3-402G11.5	1544
  	SAPS2	1545
  	NCAPH2	1546
  	BID	1547
  	SLC25A1	1548
  	KLHL22//KRT18	1549
  	PI4KA//PI4KAP1//	1550
  	PI4KAP2//LOC100293141
  	MAPK1	1551
  	ZNF70	1552
  	TPST2	1553
  	SF3A1//CCDC157	1554
  	PES1	1555
  	PIK3IP1	1556
  	PATZ1	1557
  	C22orf30	1558
  	IL2RB	1559
  	CSNK1E//LOC400927	1560
  	UNC84B	1561
  	CBX7//LOC100128400	1562
  	RPS19BP1	1563
  	MKL1//KIAA1659	1564
  	RANGAP1	1565
  	TCF20	1566
  	LDOC1L	1567
  	UNQ6126	1568
  	TUBGCP6	1569
  	SBF1//SBF1P1	1570
  	MSL3	1571
  	MOSPD2	1572
  	BMX//HNRPDL	1573
  	PDHA1	1574
  	YY2	1575
  	PDK3	1576
  	GK//GK3P//FTL//	1577
  	LOC652904
  	CXorf59	1578
  	ATP6AP2	1579
  	USP9X//USP9Y	1580
  	RP2	1581
  	USP11	1582
  	RBM3	1583
  	FTSJ1	1584
  	WAS	1585
  	PLP2	1586
  	TSPYL2//GPR173	1587
  	MAGED2	1588
  	UBQLN2	1589
  	NLGN3	1590
  	ACRC	1591
  	UPRT	1592
  	CXorf26	1593
  	ATP7A	1594
  	DIAPH2	1595
  	CSTF2//RAD21	1596
  	ARMCX3	1597
  	ARMCX5	1598
  	GPRASP1	1599
  	TMEM31	1600
  	TBC1D8B	1601
  	MID2	1602
  	DOCK11	1603
  	LONRF3	1604
  	UBE2A	1605
  	SH2D1A	1606
  	OCRL	1607
  	SLC25A14	1608
  	HPRT1	1609
  	CD40LG	1610
  	AFF2	1611
  	SSR4//IDH3G	1612
  	FAM50A	1613
  	DKC1//SNORA36A//	1614
  	SNORA56
  	ARSD	1615
  	KAL1	1616
  	CTPS2	1617
  	RPS6KA3	1618
  	BCOR	1619
  	MAOB//NAT13	1620
  	ZNF41	1621
  	OTUD5	1622
  	KCND1	1623
  	ZMYM3	1624
  	MAGT1	1625
  	BRWD3	1626
  	TRMT2B	1627
  	GLA	1628
  	MORF4L2	1629
  	PSMD10	1630
  	ACSL4	1631
  	LAMP2	1632
  	CUL4B	1633
  	ODZ1	1634
  	ELF4	1635
  	RAP2C	1636
  	FAM127B//FAM127C//	1637
  	FAM127A
  	TMEM185A	1638
  	ARD1A	1639
  	IRAK1	1640
  	DNASE1L1//RPL10	1641
  	SH3KBP1	1642
  	Mitochondrial1	1643
  	Mitochondrial2	1644
  	CCNL2	1645
  	INPP5B	1646
  	TLR5	1647
  	ADRB3//GOT1L1	1648
  	NOC2L//SAMD11//	1649
  	LOC401010
  	SHFM1	1650
  	§ HUGO Gene Nomenclature Committee
  	¶ Synonymous with SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332
  	* Synonymous with SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@

• 	TABLE 2
  	HGNC § Gene	SEQ
  	Name	ID NO
  	PRKCZ	1651
  	SKI	1652
  	RER1	1653
  	TAS1R1	1654
  	VAMP3	1655
  	AGTRAP	1656
  	VPS13D	1657
  	KLHDC7A	1658
  	NBL1//C1orf151	1659
  	MDS2	1660
  	RCAN3	1661
  	LDLRAP1	1662
  	MAN1C1	1663
  	SH3BGRL3	1664
  	DHDDS	1665
  	HCRTR1	1666
  	CCDC28B	1667
  	LCK	1668
  	ZNF362	1669
  	THRAP3	1670
  	PPIE//CCDC25	1671
  	CAP1	1672
  	CTPS	1673
  	C1orf84	1674
  	FAAH	1675
  	DMBX1	1676
  	CYP4B1	1677
  	BTF3L4	1678
  	LRRC42	1679
  	C1orf175//TTC4	1680
  	TMEM61	1681
  	FPGT//TNNI3K	1682
  	ACADM	1683
  	SPATA1	1684
  	EPHX4	1685
  	RPAP2	1686
  	RPL5//SNORA66//	1687
  	SNORD21//FAM69A
  	RTCD1	1688
  	SLC30A7	1689
  	RNPC3//AMY2B	1690
  	CELSR2	1691
  	AHCYL1	1692
  	CEPT1//DRAM2	1693
  	CHIA	1694
  	LIX1L	1695
  	UPF0627	1696
  	MRPS21	1697
  	TNFAIP8L2	1698
  	SMCP	1699
  	DCST1	1700
  	RAG1AP1	1701
  	C1orf182	1702
  	HAPLN2	1703
  	NTRK1	1704
  	CD1E	1705
  	TOMM40L//NR1I3	1706
  	POU2F1	1707
  	TIPRL	1708
  	SFT2D2	1709
  	CACNA1E	1710
  	SMG7	1711
  	OCLM	1712
  	RGS2	1713
  	ZC3H11A//RP11-74E24.2	1714
  	MFSD4	1715
  	IL20	1716
  	RPS6KC1	1717
  	C1orf95	1718
  	ARF1	1719
  	GALNT2	1720
  	TNFRSF4	1721
  	NADK	1722
  	FLJ14100//C1orf86	1723
  	GPR153	1724
  	RERE	1725
  	SLC2A7	1726
  	SDHB	1727
  	RNF186	1728
  	DDOST	1729
  	GPN2	1730
  	RPA2	1731
  	PEF1	1732
  	PTP4A2	1733
  	TRIM62	1734
  	PHC2	1735
  	LSM10	1736
  	MRPS15	1737
  	RRAGC	1738
  	COL9A2	1739
  	TESK2	1740
  	NRD1	1741
  	KTI12	1742
  	CC2D1B	1743
  	YIPF1	1744
  	JAK1	1745
  	SLC35D1	1746
  	DIRAS3	1747
  	ZZZ3	1748
  	GNG5	1749
  	ZNHIT6	1750
  	ODF2L	1751
  	SEP15	1752
  	BARHL2	1753
  	GCLM	1754
  	CLCC1//GPSM2//C1orf62	1755
  	SORT1	1756
  	SLC16A4	1757
  	PHTF1	1758
  	RSBN1	1759
  	DENND2C//BCAS2	1760
  	CD58	1761
  	SPAG17//WDR3	1762
  	REG4//NBPF7	1763
  	RP11-94I2.2//NBPF16//	1764
  	NBPF11//NBPF15//NBPF8//
  	NBPF20//NBPF10//NBPF14//
  	NBPF1//LOC100288142//
  	NBPF12//KIAA1245//
  	LOC100290137
  	APH1A	1765
  	POGZ	1766
  	TDRKH	1767
  	THEM4	1768
  	S100A11	1769
  	CRNN	1770
  	SPRR2C
  	S100A12	1771
  	S100A8	1772
  	GATAD2B//PLIN2	1773
  	DENND4B	1774
  	PBXIP1	1775
  	PYGO2	1776
  	SHC1	1777
  	DCST2	1778
  	GBA//GBAP	1779
  	ASH1L	1780
  	RIT1	1781
  	MEF2D	1782
  	AIM2	1783
  	COPA	1784
  	DEDD	1785
  	TADA1L	1786
  	GPA33	1787
  	CD247	1788
  	F5	1789
  	PIGC	1790
  	KIAA0040	1791
  	TOR1AIP2//TOR1AIP1//IFRG15	1792
  	STX6//KIAA1614	1793
  	EDEM3	1794
  	UCHL5	1795
  	DENND1B	1796
  	DDX59	1797
  	KIF21B	1798
  	ARL8A	1799
  	CYB5R1	1800
  	MYBPH	1801
  	CHI3L1	1802
  	PIK3C2B//LOC100130573	1803
  	NUAK2	1804
  	NUCKS1	1805
  	FAIM3	1806
  	PLXNA2	1807
  	SLC30A1	1808
  	LPGAT1	1809
  	ANGEL2	1810
  	RAB3GAP2//AURKAPS1//	1811
  	AURKA//SNORA36B
  	TP53BP2	1812
  	NVL	1813
  	TMEM63A	1814
  	PARP1	1815
  	ITPKB	1816
  	TARBP1	1817
  	CHML	1818
  	AKT3	1819
  	SMYD3	1820
  	AHCTF1	1821
  	OR1C1	1822
  	NCOA1	1823
  	HADHB	1824
  	ABHD1//PREB	1825
  	SPAST	1826
  	SLC30A6//DDX50	1827
  	CRIPT	1828
  	MSH2	1829
  	FOXN2	1830
  	CCDC104	1831
  	VRK2	1832
  	AHSA2//USP34	1833
  	OTX1	1834
  	AFTPH	1835
  	CEP68	1836
  	PLEK	1837
  	ANXA4	1838
  	MXD1	1839
  	NAGK	1840
  	SMYD5//NOTO	1841
  	MTHFD2	1842
  	TTC31	1843
  	SEMA4F	1844
  	TMSB10	1845
  	SH2D6	1846
  	GNLY	1847
  	KCNIP3	1848
  	CNNM4	1849
  	CNNM3	1850
  	ZAP70	1851
  	LIPT1//MRPL30	1852
  	MAP4K4	1853
  	IL1R2	1854
  	IL1R1	1855
  	IL18R1	1856
  	POLR1B	1857
  	CHCHD5	1858
  	IL1RN	1859
  	PSD4	1860
  	DDX18	1861
  	INSIG2	1862
  	TMEM177//LOC100125918	1863
  	RALB	1864
  	PROC	1865
  	GPR17//LOC100291428//LIMS2	1866
  	IMP4	1867
  	FAM123C	1868
  	ACVR2A	1869
  	MBD5	1870
  	LYPD6B	1871
  	SLC4A10	1872
  	UBR3	1873
  	HAT1	1874
  	ITGA6	1875
  	ZAK	1876
  	OSBPL6	1877
  	PLEKHA3	1878
  	ZC3H15	1879
  	COL3A1	1880
  	GLS	1881
  	OBFC2A	1882
  	COQ10B	1883
  	MARS2	1884
  	CFLAR	1885
  	NOP58	1886
  	FAM117B	1887
  	CYP20A1	1888
  	FASTKD2	1889
  	PIKFYVE	1890
  	C2orf62	1891
  	SLC11A1	1892
  	AGFG1	1893
  	CHRNG	1894
  	EIF4E2	1895
  	TRPM8	1896
  	LRRFIP1	1897
  	GAL3ST2	1898
  	TMEM18	1899
  	LAPTM4A	1900
  	SF3B14	1901
  	TP53I3	1902
  	UNQ2999	1903
  	GPR113//SELI	1904
  	MPV17	1905
  	PPM1G	1906
  	NLRC4	1907
  	CDC42EP3	1908
  	HNRPLL	1909
  	COX7A2L	1910
  	KCNG3	1911
  	CALM2//C2orf61	1912
  	BCL11A	1913
  	XPO1	1914
  	NAT8B	1915
  	DUSP11	1916
  	MOGS	1917
  	SNRNP200	1918
  	SEMA4C	1919
  	MITD1	1920
  	IL1A	1921
  	SLC35F5	1922
  	CCDC93	1923
  	CLASP1	1924
  	SAP130	1925
  	YSK4	1926
  	GTDC1	1927
  	ORC4L	1928
  	NR4A2//FLJ46875	1929
  	DPP4	1930
  	GALNT3	1931
  	SCN7A	1932
  	FRZB	1933
  	STK17B	1934
  	CLK1//PPIL3	1935
  	MPP4	1936
  	INO80D	1937
  	KLF7	1938
  	FAM119A	1939
  	NGEF	1940
  	ARL4C	1941
  	RAB17	1942
  	HDLBP	1943
  	LRRN1	1944
  	SETD5	1945
  	IRAK2	1946
  	C3orf42	1947
  	TSEN2	1948
  	NR2C2//MRPS25	1949
  	UBE2E1	1950
  	C3orf35	1951
  	SNRK	1952
  	ZNF197	1953
  	GNAI2	1954
  	ALAS1	1955
  	PRKCD	1956
  	CACNA1D	1957
  	PXK	1958
  	PTPRG	1959
  	ATXN7	1960
  	SLC35A5	1961
  	SLC15A2	1962
  	CCDC48	1963
  	DNAJC13	1964
  	CLDN18	1965
  	GYG1	1966
  	SELT	1967
  	MED12L	1968
  	RAP2B	1969
  	MYNN	1970
  	ABCF3	1971
  	VPS8	1972
  	HRG	1973
  	EIF4A2//SNORA4	1974
  	LPP	1975
  	CCDC50	1976
  	LOC152217	1977
  	TADA3L	1978
  	SEC13	1979
  	TIMP4	1980
  	METTL6	1981
  	DAZL//DAZ4//DAZ3//DAZ2	1982
  	SATB1//TBC1D5	1983
  	SCN10A	1984
  	SEC22C	1985
  	ZDHHC3	1986
  	ZDHHC3	1987
  	SLC6A20	1988
  	UQCRC1	1989
  	PRKAR2A	1990
  	IMPDH2	1991
  	CCDC71	1992
  	UBA7	1993
  	CAMKV	1994
  	WDR82	1995
  	LMOD3	1996
  	FOXP1	1997
  	MORC1	1998
  	ATG3	1999
  	GSK3B//LOC100129275	2000
  	HCLS1	2001
  	KPNA1	2002
  	PTPLB	2003
  	C3orf22	2004
  	RPN1	2005
  	KIAA1257//ACAD9//	2006
  	LOC100132731
  	FOXL2	2007
  	MECOM	2008
  	PLD1	2009
  	GNB4	2010
  	MRPL47	2011
  	KLHL6	2012
  	THPO	2013
  	ETV5	2014
  	BCL6//LOC100131635	2015
  	ATP13A5	2016
  	TMEM44	2017
  	KIAA1530	2018
  	TACC3	2019
  	CNO	2020
  	BST1	2021
  	KLF3	2022
  	TMEM33//DCAF4L1	2023
  	KIT	2024
  	ENAM	2025
  	FAM47E//STBD1	2026
  	ENOPH1	2027
  	PDLIM5	2028
  	CCDC109B//HIGD1A//CCDC13	2029
  	EGF	2030
  	PCDH10	2031
  	RAB33B	2032
  	TMEM184C	2033
  	RBM46	2034
  	GRIA2	2035
  	C4orf39	2036
  	KLHL2	2037
  	TLL1	2038
  	F11	2039
  	SLBP	2040
  	HAUS3//POLN	2041
  	PPARGC1A	2042
  	TLR10	2043
  	C4orf34	2044
  	TXK	2045
  	RPL21P44
  	KDR	2046
  	RCHY1	2047
  	CNOT6L	2048
  	PLAC8	2049
  	HPSE	2050
  	GPRIN3	2051
  	PPA2	2052
  	COL25A1	2053
  	C4orf3	2054
  	QRFPR	2055
  	MFSD8	2056
  	MAP9	2057
  	PDGFC	2058
  	TKTL2	2059
  	ACSL1	2060
  	SUB1//TMEM183A	2061
  	CARD6	2062
  	MCCC2	2063
  	TNPO1	2064
  	PDE8B	2065
  	PAPD4	2066
  	THBS4	2067
  	FAM151B	2068
  	RASGRF2	2069
  	SNX2	2070
  	LMNB1//PCIF1	2071
  	MEGF10	2072
  	LEAP2	2073
  	TCF7	2074
  	KDM3B	2075
  	CXXC5	2076
  	SLC4A9	2077
  	ANKHD1-EIF4EBP3//	2078
  	ANKHD1//EIF4EBP3
  	KIAA0141	2079
  	GRPEL2	2080
  	MFAP3	2081
  	GABRA6	2082
  	GABRA1	2083
  	DOCK2	2084
  	RANBP17//USP12	2085
  	ERGIC1	2086
  	ATP6V0E1//SNORA74B
  	ZNF346	2087
  	NSD1	2088
  	CLPTM1L	2089
  	UGT3A1	2090
  	GDNF	2091
  	TTC33	2092
  	hCG_2039148
  	MOCS2	2093
  	SLC38A9	2094
  	CCDC125	2095
  	ANKRA2	2096
  	HAPLN1	2097
  	CCNH	2098
  	TMEM161B	2099
  	MBLAC2	2100
  	MCTP1	2101
  	TICAM2//TMED7//	2102
  	TMED7-TICAM2
  	KIF3A	2103
  	C5orf15	2104
  	SKP1	2105
  	CXCL14	2106
  	KLHL3	2107
  	CD14	2108
  	YIPF5	2109
  	LARS	2110
  	DCTN4	2111
  	CCDC69	2112
  	ATOX1	2113
  	TIMD4	2114
  	ADAM19	2115
  	SLIT3	2116
  	RNF44	2117
  	DOK3	2118
  	MGAT4B//SQSTM1	2119
  	C5orf45//SQSTM1	2120
  	RASGEF1C	2121
  	MGAT1	2122
  	IRF4	2123
  	HIVEP1	2124
  	E2F3	2125
  	HIST1H4I	2126
  	HIST1H2BM	2127
  	MOG	2128
  	ZNRD1//NCRNA00171	2129
  	TRIM15	2130
  	HCG27	2131
  	BAT2//SNORA38	2132
  	CYP21A2	2133
  	ITPR3	2134
  	MAPK14	2135
  	MAPK13	2136
  	PNPLA1	2137
  	SFRS3	2138
  	CDKN1A	2139
  	FOXP4	2140
  	CUL9	2141
  	RUNX2	2142
  	ZNF451	2143
  	SOBP	2144
  	C6orf182	2145
  	KIAA1919	2146
  	RWDD1	2147
  	KPNA5	2148
  	TPD52L1	2149
  	ARG1	2150
  	RAB32	2151
  	ARID1B	2152
  	SLC22A3	2153
  	SERPINB1	2154
  	C6orf146	2155
  	GCM2	2156
  	ATXN1	2157
  	DCDC2//KAAG1	2158
  	HIST1H3I	2159
  	HIST1H4L	2160
  	GABBR1	2161
  	RNA243
  	DDAH2	2162
  	CLIC1	2163
  	NEU1	2164
  	RXRB	2165
  	VPS52	2166
  	TCP11	2167
  	CLPS	2168
  	PGC	2169
  	ZNF318	2170
  	YIPF3	2171
  	MRPL14	2172
  	PLA2G7	2173
  	PKHD1	2174
  	IL17F	2175
  	HTR1B	2176
  	GABRR2	2177
  	UBE2J1	2178
  	BACH2	2179
  	MCM9	2180
  	VNN1	2181
  	IL20RA	2182
  	FLJ27255	2183
  	T	2184
  	RPS6KA2	2185
  	HGC6.3	2186
  	UNC84A//C7orf20	2187
  	SDK1	2188
  	ZDHHC4	2189
  	C7orf26	2190
  	GLCCI1//tcag7.903	2191
  	GPNMB	2192
  	CCDC126	2193
  	WIPF3//ZNRF2//LOC441208	2194
  	GPR141	2195
  	STARD3NL	2196
  	POU6F2	2197
  	CDC2L5	2198
  	ZMIZ2	2199
  	UPP1	2200
  	ZNF273	2201
  	KCTD7//RABGEF1	2202
  	RABGEF1//tcag7.967//	2203
  	tcag7.951//KCTD7//
  	LOC100293333
  	CCDC132	2204
  	PVRIG//PILRB//STAG3	2205
  	PILRB//PVRIG//STAG3	2206
  	C7orf51	2207
  	GNB2	2208
  	LRRC17	2209
  	LRRN3	2210
  	CFTR	2211
  	LSM8	2212
  	LUC7L2	2213
  	MGAM//LOC100124692	2214
  	GIMAP7	2215
  	INSIG1	2216
  	RBM33	2217
  	ICA1	2218
  	FAM126A	2219
  	HIBADH	2220
  	TRIL	2221
  	SCRN1	2222
  	ELMO1	2223
  	INHBA	2224
  	CAMK2B	2225
  	NPC1L1	2226
  	DDC//LOC100129427	2227
  	NSUN5//NSUN5B//NSUN5C	2228
  	CLDN3	2229
  	C7orf23//DMTF1	2230
  	SRI	2231
  	BET1	2232
  	MCM7	2233
  	GATS	2234
  	ATXN7L1//RINT1//EFCAB10	2235
  	KIAA1549	2236
  	SLC37A3	2237
  	SMARCD3	2238
  	MLL3//BAGE2	2239
  	CLN8	2240
  	MSRA	2241
  	PIWIL2	2242
  	NEFM//LOC100129717	2243
  	EPHX2	2244
  	LEPROTL1	2245
  	MAK16//C8orf41	2246
  	AP3M2	2247
  	FNTA	2248
  	SGK196	2249
  	UBE2V2	2250
  	FLJ46365	N/A
  	SNTG1	2251
  	TRIM55	2252
  	C8orf45	2253
  	PREX2	2254
  	PLEKHF2	2255
  	BAALC//FLJ10489	2256
  	TTC35	2257
  	MTBP	2258
  	ZHX2	2259
  	RNF139	2260
  	TG	2261
  	DENND3//C8orf60	2262
  	TNFRSF10D	2263
  	TRIM35	2264
  	GSR	2265
  	WHSC1L1	2266
  	PCMTD1//PXDNL	2267
  	NCOA2	2268
  	TRAM1//LOC286190	2269
  	RUNX1T1	2270
  	EXT1	2271
  	DDEF1IT1	2272
  	CDC37L1	2273
  	UBE2R2	2274
  	UBAP1//KIF24	2275
  	GALT	2276
  	RGP1//GBA2	2277
  	TGFBR1	2278
  	C9orf6//IKBKAP	2279
  	IMAGE5303689	N/A
  	ATP6V1G1	2280
  	TLR4	2281
  	SET	2282
  	MRPL41	2283
  	C9orf68	2284
  	HAUS6//SCARNA8	2285
  	KLHL9	2286
  	C9orf82	2287
  	NDUFB6//DFFB	2288
  	SIT1	2289
  	FAM108B1	2290
  	TRPM6	2291
  	FRMD3	2292
  	SLC28A3	2293
  	BICD2	2294
  	C9orf84	2295
  	AKNA	2296
  	MEGF9	2297
  	C5	2298
  	GOLGA1//SCAI	2299
  	SH2D3C	2300
  	FAM102A	2301
  	FLJ10232	N/A
  	ASB6	2302
  	BAT2L	2303
  	EDF1	2304
  	FBXW5	2305
  	C10orf18	2306
  	FBXO18	2307
  	GATA3	2308
  	CUGBP2	2309
  	VIM	2310
  	STAM	2311
  	WAC	2312
  	BAMBI	2313
  	ZNF487//LOC439911	2314
  	ALOX5	2315
  	WDFY4	2316
  	SRGN	2317
  	CCDC109A	2318
  	FAM149B1//FAM149B2	2319
  	MINPP1	2320
  	PTEN//PTENP1	2321
  	ENTPD1//C10orf131	2322
  	ABCC2	2323
  	SFXN2	2324
  	SHOC2	2325
  	ACSL5	2326
  	BCCIP//DHX32	2327
  	FAM188A	2328
  	CUBN	2329
  	SVIL//hCG_1783494	2330
  	FAM13C//PHYHIPL	2331
  	ATAD1	2332
  	ANKRD22	2333
  	FLJ34077	N/A
  	COX15	2334
  	ERLIN1	2335
  	ACTR1A	2336
  	ABLIM1	2337
  	RAB11FIP2	2338
  	C10orf84	2339
  	PRDX3	2340
  	C10orf119	2341
  	NSMCE4A	2342
  	TALDO1//INTS8	2343
  	TNNT3	2344
  	FXC1	2345
  	PDE3B	2346
  	DNAJC24	2347
  	PTPRJ//OR4B1	2348
  	C11orf31	2349
  	TMEM109	2350
  	CD6	2351
  	CD5	2352
  	TMEM138	2353
  	POLR2G	2354
  	TMEM179B	2355
  	NAT11	2356
  	OTUB1	2357
  	RBM14//RBM4	2358
  	AIP	2359
  	PPFIA1	2360
  	IL18BP//NUMA1	2361
  	C11orf30	2362
  	C11orf82	2363
  	TMEM126B	2364
  	C11orf73	2365
  	PIWIL4	2366
  	LOC100132686	2367
  	PAFAH1B2	2368
  	UBE4A	2369
  	TRAPPC4	2370
  	SC5DL	2371
  	VWA5A//OR10D1P	2372
  	STT3A	2373
  	VPS26B	2374
  	TRIM21	2375
  	ZBED5	2376
  	SAAL1	2377
  	FANCF	2378
  	LIN7C	2379
  	PHF21A	2380
  	CUGBP1	2381
  	OSBP	2382
  	CYBASC3	2383
  	TUT1	2384
  	SLC25A45	2385
  	LTBP3	2386
  	EIF1AD	2387
  	GAB2	2388
  	CREBZF	2389
  	PICALM	2390
  	SLC36A4	2391
  	CCDC82	2392
  	KIAA1826	2393
  	MPZL3	2394
  	MPZL2	2395
  	H2AFX	2396
  	SIAE	2397
  	ZBTB44	2398
  	HSN2	2399
  	ADIPOR2	2400
  	NCAPD2//SCARNA10//FADS1	2401
  	PTPN6	2402
  	CLEC4D	2403
  	CDKN1B	2404
  	GOLT1B	2405
  	FAR2	2406
  	FGD4	2407
  	TMEM106C	2408
  	TMBIM6	2409
  	C12orf62	2410
  	PRR13//PCBP2	2411
  	DGKA	2412
  	COQ10A	2413
  	TSPAN31	2414
  	CDK4/MARCH9/C3HC4	2415
  	LEMD3	2416
  	IRAK3	2417
  	TMTC3	2418
  	ACTR6	2419
  	TCTN1	2420
  	PXMP2//PGAM5	2421
  	DCP1B	2422
  	SLC2A3//SLC2A14	2423
  	C3AR1	2424
  	PLBD1	2425
  	TM7SF3	2426
  	ASB8//PHB	2427
  	LMBR1L	2428
  	FMNL3//PRPF40B	2429
  	AAAS	2430
  	NFE2	2431
  	GPR84	2432
  	CD63	2433
  	SARNP//DNAJC14	2434
  	NACA	2435
  	CDK4//TSPAN31	2436
  	TMBIM4//LOC100133322	2437
  	IL22	2438
  	LIN7A	2439
  	HAL	2440
  	APPL2	2441
  	GLTP	2442
  	GIT2	2443
  	VPS29	2444
  	PPTC7	2445
  	DDX54//CCDC42B	2446
  	SLC24A6	2447
  	SDS	2448
  	RBM19	2449
  	MED13L	2450
  	C12orf49	2451
  	FBXO21	2452
  	WSB2	2453
  	TAOK3	2454
  	CIT	2455
  	RAB35	2456
  	RPLP0	2457
  	PXN	2458
  	TRIAP1	2459
  	SFRS9	2460
  	POP5	2461
  	UNQ1887	2462
  	C12orf43	2463
  	ANAPC5	2464
  	KDM2B	2465
  	MORN3	2466
  	TMEM120B//RHOF	2467
  	LOC338799
  	DIABLO//B3GNT4	2468
  	VPS33A	2469
  	CLIP1	2470
  	PITPNM2	2471
  	EIF2B1	2472
  	CCDC92	2473
  	NCOR2	2474
  	DHX37	2475
  	DDX51	2476
  	POLE	2477
  	GOLGA3	2478
  	ZMYM2	2479
  	SPATA13//C1QTNF9	2480
  	NUPL1	2481
  	PAN3//EEF1A1//CHCHD2	2482
  	ALOX5AP	2483
  	EEF1DP3	2484
  	KL	2485
  	UFM1	2486
  	NARG1L	2487
  	ITM2B	2488
  	FNDC3A	2489
  	CDADC1	2490
  	ARL11	2491
  	LMO7	2492
  	DNAJC3	2493
  	TM9SF2	2494
  	CLYBL	2495
  	PCCA	2496
  	ABHD13	2497
  	LAMP1	2498
  	TMCO3	2499
  	UPF3A	2500
  	ZMYM5//ZMYM2	2501
  	ZDHHC20//LOC728099	2502
  	PARP4	2503
  	MTMR6//LOC646482	2504
  	HSPH1	2505
  	N4BP2L2//CG030	2506
  	ELF1	2507
  	LCP1	2508
  	KPNA3	2509
  	C13orf1	2510
  	DLEU2//DLEU2L	2511
  	GUCY1B2	2512
  	INTS6	2513
  	DACH1	2514
  	TBC1D4	2515
  	EDNRB	2516
  	UGGT2	2517
  	GPR183	2518
  	LIG4	2519
  	ANKRD10	2520
  	RASA3	2521
  	RNASE2//LOC643332	2522
  	RPGRIP1	2523
  	IRF9	2524
  	TSSK4	2525
  	C14orf21	2526
  	SCFD1	2527
  	FANCM	2528
  	ABHD12B	2529
  	PTGDR	2530
  	FBXO34//KIAA0831	2531
  	C14orf101	2532
  	ACTR10	2533
  	ARID4A	2534
  	JKAMP	2535
  	HIF1A	2536
  	SYNE2	2537
  	EXD2	2538
  	SLC39A9	2539
  	SFRS5	2540
  	PCNX	2541
  	SIPA1L1//SNORD56B//	2542
  	LOC145474//LOC283567
  	YLPM1	2543
  	BATF	2544
  	FLVCR2//RPS24	2545
  	GPR65	2546
  	TDP1	2547
  	EVL	2548
  	ZNF839	2549
  	TDRD9	2550
  	INF2	2551
  	PLD4	2552
  	MTA1//LOC647310//	2553
  	LOC100128343
  	NDRG2	2554
  	DAD1//OR6J1	2555
  	SLC7A8	2556
  	IPO4	2557
  	TM9SF1	2558
  	ADCY4	2559
  	RIPK3	2560
  	EAPP	2561
  	BAZ1A	2562
  	NFKBIA	2563
  	SEC23A	2564
  	C14orf104	2565
  	C14orf138	2566
  	SOS2	2567
  	NIN	2568
  	PYGL	2569
  	CNIH	2570
  	DHRS7	2571
  	WDR89	2572
  	ACTN1	2573
  	NUMB	2574
  	C14orf43	2575
  	ABCD4	2576
  	KIAA0317	2577
  	NEK9	2578
  	ANGEL1	2579
  	SPTLC2	2580
  	SERPINA6	2581
  	DICER1	2582
  	BCL11B	2583
  	ANKRD9	2584
  	PPP1R13B	2585
  	AKT1	2586
  	BRF1	2587
  	TUBGCP5	2588
  	SNRPN *	2589
  	APBA2	2590
  	MTMR15//MTMR10	2591
  	RYR3	2592
  	BAHD1	2593
  	CHP	2594
  	JMJD7-PLA2G4B//	2595
  	JMJD7//PLA2G4B
  	HAUS2	2596
  	C15orf63//SERF2	2597
  	B2M	2598
  	TRIM69	2599
  	PLDN	2600
  	SQRDL	2601
  	GALK2	2602
  	USP8	2603
  	GLDN	2604
  	MAPK6	2605
  	LACTB	2606
  	RAB8B	2607
  	APH1B	2608
  	USP3//LOC100130855	2609
  	SNX1	2610
  	LBXCOR1//PIAS1//CALML4	2611
  	NEO1	2612
  	MPI	2613
  	FBXO22//FBXO22OS	2614
  	RCN2	2615
  	FAH	2616
  	IL16	2617
  	ABHD2	2618
  	SLCO3A1	2619
  	MCTP2	2620
  	MEF2A//LYSMD4	2621
  	NIPA2//CYFIP1	2622
  	HERC2//HERC2P2//	2623
  	HERC2P3//LOC440248
  	MTMR10//MTMR15	2624
  	C15orf24	2625
  	SLC12A6	2626
  	LPCAT4	2627
  	INO80	2628
  	OIP5	2629
  	ZFP106	2630
  	CDAN1	2631
  	SPG11//ISLR	2632
  	SPPL2A	2633
  	GNB5//LOC100129973	2634
  	MYO5A	2635
  	ARPP19	2636
  	RAB27A	2637
  	CCPG1//PIGB//DYX1C1	2638
  	BNIP2	2639
  	CA12	2640
  	FAM96A	2641
  	KIAA0101//CSNK1G1	2642
  	TLE3	2643
  	PARP6	2644
  	NPTN	2645
  	MAN2C1	2646
  	IMP3	2647
  	MTHFS	2648
  	ST20//C15orf37	2649
  	TMC3	2650
  	AP3B2	2651
  	C15orf40	2652
  	WDR73	2653
  	NTRK3	2654
  	DET1	2655
  	TM2D3	2656
  	WDR90	2657
  	RHOT2//FBXL16	2658
  	TMEM204	2659
  	CRAMP1L//HN1L	2660
  	MAPK8IP3	2661
  	TBL3	2662
  	TSC2	2663
  	KCTD5//PRO0461//PDPK1	2664
  	CLUAP1	2665
  	DNASE1	2666
  	DNAJA3	2667
  	CP110	2668
  	C16orf62	2669
  	LYRM1	2670
  	METTL9	2671
  	EEF2K	2672
  	POLR3E	2673
  	PLK1	2674
  	PRKCB	2675
  	IL21R//LOC283888	2676
  	SULT1A2//SULT1A1	2677
  	ATXN2L	2678
  	LAT ¶	2679
  	KIF22	2680
  	MAZ	2681
  	CORO1A//LOC606724	2682
  	ITGAL	2683
  	SRCAP//SNORA30	2684
  	ZNF646//ZNF668	2685
  	C16orf67	2686
  	TMEM188	2687
  	LPCAT2	2688
  	CETP	2689
  	CKLF	2690
  	CMTM1//CKLF	2691
  	TMEM208	2692
  	CTCF	2693
  	THAP11	2694
  	NUTF2	2695
  	EDC4	2696
  	SLC7A6//SLC7A6OS	2697
  	PRMT7	2698
  	SNTB2	2699
  	VPS4A	2700
  	DDX19B//DDX19A	2701
  	CHST4	2702
  	HP//HPR	2703
  	PLCG2	2704
  	KLHL36	2705
  	KIAA0182	2706
  	BANP//RUNDC2C	2707
  	TRAPPC2L	2708
  	SPG7	2709
  	CDK10	2710
  	TCF25	2711
  	AFG3L1	2712
  	LUC7L	2713
  	AXIN1	2714
  	JMJD8	2715
  	LMF1	2716
  	UNKL	2717
  	UNKL	2718
  	CLCN7	2719
  	MRPS34	2720
  	RNPS1	2721
  	NLRC3	2722
  	TRAP1//DNASE1	2723
  	ADCY9	2724
  	CORO7	2725
  	C16orf72	2726
  	RRN3//LOC653390//	2727
  	LOC730092//LOC100131998
  	XYLT1//LYRM2//ZC3H11A	2728
  	DCUN1D3//LYRM1	2729
  	IGSF6//METTL9	2730
  	CDR2//RRN3//	2731
  	LOC100131998//LOC653390
  	COG7	2732
  	GGA2	2733
  	NSMCE1	2734
  	GTF3C1	2735
  	CCDC101//LOC388242	2736
  	C16orf54	2737
  	KCTD13	2738
  	SEPT1	2739
  	ZNF764//ZNF747	2740
  	C16orf58//LOC100128371	2741
  	ITFG1	2742
  	ABCC11//LONP2	2743
  	NUDT21	2744
  	BBS2//OGFOD1	2745
  	CSNK2A2	2746
  	GOT2	2747
  	FAM96B	2748
  	FHOD1//SLC9A5	2749
  	ATP6V0D1//LOC100132855	2750
  	GFOD2	2751
  	SLC12A4	2752
  	DPEP3	2753
  	DPEP2	2754
  	CHTF8//HAS3	2755
  	COG8//PDF	2756
  	TERF2	2757
  	AARS	2758
  	ST3GAL2	2759
  	VAC14//LOC100130894	2760
  	AP1G1	2761
  	WDR59	2762
  	CTRB2//CTRB1	2763
  	TAF1C//ADAD2	2764
  	FBXO31	2765
  	ZCCHC14	2766
  	FAM38A	2767
  	CENPBD1	2768
  	TIMM22	2769
  	RPA1	2770
  	DPH1//OVCA2	2771
  	SGSM2	2772
  	ARRB2	2773
  	LOC100130950	2774
  	DNAH2	2775
  	PIGL	2776
  	TRPV2	2777
  	MPRIP	2778
  	DRG2	2779
  	ALKBH5//FLJ13773	2780
  	SMCR7	2781
  	WSB1	2782
  	TAOK1	2783
  	CPD	2784
  	SUZ12P	2785
  	RNF135	2786
  	ZNF830	2787
  	TAF15	2788
  	GGNBP2	2789
  	LASP1	2790
  	PSMD3	2791
  	CDC6	2792
  	NBR2	2793
  	TMUB2	2794
  	MGC57346//C17orf69	2795
  	NSF//LOC728806	2796
  	GOSR2	2797
  	NPEPPS//TBC1D3F//LOC440434	2798
  	KPNB1	2799
  	CDK5RAP3	2800
  	ATP5G1	2801
  	UBE2Z	2802
  	XYLT2//LOC100130580	2803
  	NOG	2804
  	DGKE	2805
  	AKAP1	2806
  	TMEM49//CLTC//MIR21	2807
  	CLTC	2808
  	CA4	2809
  	C17orf64	2810
  	DCAF7	2811
  	PITPNC1	2812
  	NOL11//SNORA38B	2813
  	MAP2K6	2814
  	COG1	2815
  	CD300A	2816
  	TMEM104	2817
  	MRPS7	2818
  	KIAA0195	2819
  	TSEN54	2820
  	LLGL2	2821
  	LOC100134934//CDK3	2822
  	MFSD11	2823
  	SEPT9	2824
  	TNRC6C	2825
  	TMC8	2826
  	ENGASE	2827
  	RPTOR	2828
  	GPS1	2829
  	FN3KRP	2830
  	TBCD	2831
  	GEMIN4	2832
  	GLOD4	2833
  	SLC43A2	2834
  	PRPF8	2835
  	SMG6//C17orf6	2836
  	METT10D//LOC284009	2837
  	SHPK	2838
  	TAX1BP3	2839
  	P2RX5	2840
  	MYBBP1A//SPNS2	2841
  	PELP1	2842
  	PFN1	2843
  	ZNF232	2844
  	DHX33	2845
  	DERL2	2846
  	NLRP1//LOC728392	2847
  	ASGR2	2848
  	NEURL4//GPS2//D4S234E	2849
  	ZBTB4	2850
  	TP53	2851
  	VAMP2	2852
  	PIK3R5	2853
  	ELAC2	2854
  	NCOR1//C20orf191//	2855
  	LOC100131704
  	ZNF287	2856
  	TOM1L2//LOC246315	2857
  	GRAP//SNORD3B-1//	2858
  	SNORD3B-2//LOC400581
  	ALDOC	2859
  	SDF2	2860
  	RAB34	2861
  	PHF12	2862
  	NUFIP2	2863
  	OMG	2864
  	EVI2B	2865
  	C17orf66//RSL24D1	2866
  	SYNRG//LOC100131822	2867
  	PLXDC1	2868
  	CACNB1	2869
  	PGAP3	2870
  	MED24	2871
  	NR1D1//THRA	2872
  	CCR7	2873
  	STAT5B//STAT5A	2874
  	FAM134C	2875
  	VAT1	2876
  	DUSP3	2877
  	C17orf65//ASB16	2878
  	UBTF	2879
  	GPATCH8	2880
  	MAP3K14//LOC100133991	2881
  	OSBPL7	2882
  	SLC35B1	2883
  	TOB1	2884
  	COX11//TOM1L1	2885
  	VEZF1	2886
  	SFRS1//FLJ44342	2887
  	SEPT4	2888
  	MED13//LOC100129112	2889
  	LIMD2//MAP3K3	2890
  	STRADA	2891
  	FTSJ3	2892
  	CD79B	2893
  	ICAM2	2894
  	ERN1	2895
  	TEX2	2896
  	LRRC37A3//LRRC37A2//	2897
  	LRRC37A//ARL17P1//
  	LRRC37A4//LOC100294335//
  	LOC644397
  	GNA13	2898
  	WIPI1//ARSG	2899
  	FAM20A	2900
  	NAT9	2901
  	GGA3	2902
  	H3F3B//H3F3C	2903
  	EXOC7	2904
  	SFRS2	2905
  	TMC6//LOC100131096	2906
  	USP36	2907
  	CD7	2908
  	RAB31	2909
  	VAPA	2910
  	SEH1L	2911
  	HQ0644/PRO0644	2912
  	RNMT	2913
  	RNF138	2914
  	GALNT1	2915
  	ELP2	2916
  	PIK3C3	2917
  	SLC14A2	2918
  	ME2	2919
  	SERPINB2//SERPINB10	2920
  	ZNF407	2921
  	ZNF236	2922
  	NFATC1//LOC100127994	2923
  	ENOSF1//TYMS	2924
  	MYOM1	2925
  	AFG3L2	2926
  	ABHD3	2927
  	OSBPL1A	2928
  	CDH2	2929
  	DSC1	2930
  	PSTPIP2	2931
  	C18orf32	2932
  	MBD2//SNORA37	2933
  	PIGN	2934
  	TMX3	2935
  	PQLC1	2936
  	GZMM	2937
  	ARID3A	2938
  	CIRBP	2939
  	DAZAP1	2940
  	SPPL2B	2941
  	NFIC	2942
  	VAV1	2943
  	ARHGEF18//LOC100128573	2944
  	STXBP2//LOC554363//	2945
  	LOC100131801
  	C19orf59	2946
  	ZNF317	2947
  	ILF3	2948
  	SMARCA4	2949
  	PRKCSH	2950
  	IER2	2951
  	CCDC130	2952
  	DCAF15	2953
  	IL27RA	2954
  	KLF2	2955
  	SIN3B	2956
  	DDA1	2957
  	GTPBP3	2958
  	FAM129C	2959
  	FCHO1	2960
  	ARRDC2	2961
  	IFI30	2962
  	C19orf60	2963
  	CRTC1//MAML2	2964
  	RFXANK//MEF2B//LOC729991	2965
  	ZNF101	2966
  	ZNF738	2967
  	ZNF257//ZNF492//ZNF99//	2968
  	ZNF98//LOC646864
  	C19orf2	2969
  	KIAA0355//FLJ21369	2970
  	USF2	2971
  	TMEM147	2972
  	LIN37//PSENEN	2973
  	C19orf55	2974
  	TBCB//POLR2I	2975
  	ZNF382	2976
  	ZNF568	2977
  	ZNF420	2978
  	ZNF383	2979
  	CCDC97	2980
  	ZNF574	2981
  	CD177	2982
  	ZNF230//ZNF222	2983
  	VASP	2984
  	GRWD1	2985
  	FLT3LG	2986
  	ZNF175	2987
  	NCRNA00085
  	PPP2R1A
  	ZNF808//ZNF578//ZNF611	2988
  	LENG8	2989
  	FCAR	2990
  	RPL28	2991
  	U2AF2	2992
  	LOC100288114//MGC9913
  	ZFP28	2993
  	ZNF460	2994
  	ZNF549	2995
  	ZNF211	2996
  	ZNF587//ZNF417	2997
  	ZNF274	2998
  	ZNF544	2999
  	ZNF8	3000
  	TRIM28	3001
  	C19orf6	3002
  	C19orf34	3003
  	GNG7	3004
  	AES	3005
  	EEF2//SNORD37	3006
  	PLIN5//LRG1	3007
  	PLIN3	3008
  	PTPRS	3009
  	SAFB2//SAFB	3010
  	RANBP3	3011
  	GTF2F1//LOC100130856	3012
  	XAB2	3013
  	ELAVL1	3014
  	ADAMTS10	3015
  	FBXL12	3016
  	DNMT1	3017
  	TYK2	3018
  	KEAP1	3019
  	KRI1	3020
  	TMEM205//hCG_29977	3021
  	ZNF563	3022
  	MAN2B1//MORG1	3023
  	C19orf56	3024
  	DHPS	3025
  	TNPO2//SNORD41	3026
  	LPHN1	3027
  	NDUFB7	3028
  	AKAP8	3029
  	AKAP8L	3030
  	CHERP//C19orf44//CALR3	3031
  	INSL3//JAK3	3032
  	IL12RB1	3033
  	UPK1A	3034
  	TYROBP	3035
  	ZNF529	3036
  	ZNF461	3037
  	ZNF607	3038
  	YIF1B	3039
  	PRR13	3040
  	CEACAM4	3041
  	PLAUR	3042
  	TRAPPC6A	3043
  	ERCC1//CD3EAP	3044
  	RTN2	3045
  	SYMPK	3046
  	PGLYRP1	3047
  	NOSIP	3048
  	PNKP	3049
  	NKG7	3050
  	FPR1	3051
  	ZNF28	3052
  	OSCAR	3053
  	MBOAT7	3054
  	LILRA5	3055
  	LILRA4	3056
  	ZNF550//ZNF549	3057
  	ZNF416	3058
  	ZNF256	3059
  	ZNF329	3060
  	FAM110A	3061
  	ITPA	3062
  	CDC25B	3063
  	CDS2	3064
  	CRLS1	3065
  	CSRP2BP	3066
  	SEC23B	3067
  	SLC24A3	3068
  	HCK	3069
  	ASXL1	3070
  	ACSS2	3071
  	C20orf4	3072
  	TGIF2	3073
  	C20orf24//SLA2	3074
  	RPN2//EEF1A2	3075
  	CTNNBL1	3076
  	ACTR5	3077
  	PPP1R16B	3078
  	DHX35	3079
  	PLCG1	3080
  	MYBL2	3081
  	SYS1//SYS1-DBNDD2//DBNDD2	3082
  	DNTTIP1	3083
  	CTSA	3084
  	MMP9//LOC100128028	3085
  	DDX27	3086
  	SLC9A8	3087
  	RNF114	3088
  	PTPN1	3089
  	TSHZ2	3090
  	PFDN4	3091
  	CSTF1	3092
  	CASS4	3093
  	GNAS	3094
  	C20orf177	3095
  	CDH26	3096
  	C20orf197	3097
  	LOC284757
  	ARFGAP1	3098
  	PRPF6	3099
  	NSFL1C	3100
  	SIRPD	3101
  	SIRPG//SIRPA	3102
  	RNF24	3103
  	RASSF2	3104
  	TMX4	3105
  	JAG1	3106
  	C20orf74	3107
  	C20orf3	3108
  	C20orf112	3109
  	CDK5RAP1	3110
  	AHCY	3111
  	GGT7	3112
  	EDEM2	3113
  	RBM39//LOC643167	3114
  	BLCAP	3115
  	SERINC3//TTPAL	3116
  	ZNF335	3117
  	ELMO2	3118
  	B4GALT5	3119
  	DPM1	3120
  	ZFP64	3121
  	ZNF217	3122
  	CTSZ	3123
  	SYCP2	3124
  	PSMA7	3125
  	DIDO1	3126
  	YTHDF1	3127
  	CHODL	3128
  	BACH1	3129
  	C21orf41//BACH1	3130
  	IL10RB	3131
  	IFNAR1	3132
  	IFNGR2	3133
  	SON	3134
  	MORC3//DOPEY2	3135
  	DYRK1A	3136
  	KCNJ15	3137
  	ETS2	3138
  	RRP1B	3139
  	PFKL	3140
  	TRPM2	3141
  	ADARB1	3142
  	SAMSN1//LOC388813	3143
  	N6AMT1	3144
  	SYNJ1	3145
  	TMEM50B	3146
  	KCNE1	3147
  	PRDM15	3148
  	C2CD2	3149
  	WDR4	3150
  	U2AF1	3151
  	CSTB	3152
  	UBE2G2//SUMO3	3153
  	PTTG1IP	3154
  	POFUT2	3155
  	MCM3AP	3156
  	IL17RA//CECR7	3157
  	C22orf37	3158
  	LZTR1	3159
  	PPIL2//YPEL1	3160
  	CYTSA	3161
  	SNRPD3//C22orf13	3162
  	NF2	3163
  	LIMK2	3164
  	SLC5A1	3165
  	MCM5	3166
  	NCF4	3167
  	GGA1	3168
  	SH3BP1//PDXP	3169
  	POLR2F//LOC100131530	3170
  	APOBEC3A//APOBEC3B	3171
  	APOBEC3D	3172
  	ATF4	3173
  	CACNA1I	3174
  	ZC3H7B	3175
  	CCDC134	3176
  	TSPO	3177
  	NUP50	3178
  	TBC1D22A//LOC100289878	3179
  	RP3-402G11.5	3180
  	SAPS2	3181
  	NCAPH2	3182
  	BID	3183
  	SLC25A1	3184
  	KLHL22//KRT18	3185
  	PI4KA//PI4KAP1//	3186
  	PI4KAP2//LOC100293141
  	MAPK1	3187
  	ZNF70	3188
  	TPST2	3189
  	SF3A1//CCDC157	3190
  	PES1	3191
  	PIK3IP1	3192
  	PATZ1	3193
  	C22orf30	3194
  	IL2RB	3195
  	CSNK1E//LOC400927	3196
  	UNC84B	3197
  	CBX7//LOC100128400	3198
  	RPS19BP1	3199
  	MKL1//KIAA1659	3200
  	RANGAP1	3201
  	TCF20	3202
  	LDOC1L	3203
  	UNQ6126	3204
  	TUBGCP6	3205
  	SBF1//SBF1P1	3206
  	MSL3	3207
  	MOSPD2	3208
  	BMX//HNRPDL	3209
  	PDHA1	3210
  	YY2	3211
  	PDK3	3212
  	GK//GK3P//FTL//LOC652904	3213
  	CXorf59	3214
  	ATP6AP2	3215
  	USP9X//USP9Y	3216
  	RP2	3217
  	USP11	3218
  	RBM3	3219
  	FTSJ1	3220
  	WAS	3221
  	PLP2	3222
  	TSPYL2//GPR173	3223
  	MAGED2	3224
  	UBQLN2	3225
  	NLGN3	3226
  	ACRC	3227
  	UPRT	3228
  	CXorf26	3229
  	ATP7A	3230
  	DIAPH2	3231
  	CSTF2//RAD21	3232
  	ARMCX3	3233
  	ARMCX5	3234
  	GPRASP1	3235
  	TMEM31	3236
  	TBC1D8B	3237
  	MID2	3238
  	DOCK11	3239
  	LONRF3	3240
  	UBE2A	3241
  	SH2D1A	3242
  	OCRL	3243
  	SLC25A14	3244
  	HPRT1	3245
  	CD40LG	3246
  	AFF2	3247
  	SSR4//IDH3G	3248
  	FAM50A	3249
  	DKC1//SNORA36A//SNORA56	3250
  	ARSD	3251
  	KAL1	3252
  	CTPS2	3253
  	RPS6KA3	3254
  	BCOR	3255
  	MAOB//NAT13	3256
  	ZNF41	3257
  	OTUD5	3258
  	KCND1	3259
  	ZMYM3	3260
  	MAGT1	3261
  	BRWD3	3262
  	TRMT2B	3263
  	GLA	3264
  	MORF4L2	3265
  	PSMD10	3266
  	ACSL4	3267
  	LAMP2	3268
  	CUL4B	3269
  	ODZ1	3270
  	ELF4	3271
  	RAP2C	3272
  	FAM127B//FAM127C//FAM127A	3273
  	TMEM185A	3274
  	ARD1A	3275
  	IRAK1	3276
  	DNASE1L1//RPL10	3277
  	SH3KBP1	3278
  	Mitochondrial	N/A
  	Mitochondrial	N/A
  	CCNL2	3279
  	INPP5B	3280
  	TLR5	3281
  	ADRB3//GOT1L1	3282
  	NOC2L//SAMD11//	3283
  	LOC401010
  	SHFM1	3284
  	§ HUGO Gene Nomenclature Committee
  	¶ Synonymous with SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332
  	* Synonymous with SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@

Claims (30)

1. A composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a first pair and a second pair of reverse transcribed mRNAs, wherein the first pair comprises a PLAC8 reverse transcribed mRNA and a PLA2G7 reverse transcribed mRNA and wherein the second pair comprises a CEACAM4 reverse transcribed mRNA and a LAMP1 reverse transcribed mRNA.

2. The composition according to claim 1, wherein the at least one oligonucleotide primer or probe is hybridized to an individual one of the reverse transcribed mRNAs.

3. The composition according to claim 1, wherein the reverse transcribed mRNAs are derived from components of the immune system.

4. The composition according to claim 1, wherein the reverse transcribed mRNAs are derived from leukocytes.

5. The composition according to claim 1, wherein the reverse transcribed mRNAs are derived from blood cells.

6. The composition according to claim 1, wherein the reverse transcribed mRNAs are derived from peripheral blood cells.

7. The composition according to claim 1, further comprising a labeled reagent for detecting the reverse transcribed mRNAs.

8. The composition according to claim 6, wherein the labeled reagent is a labeled said at least one oligonucleotide primer or probe.

9. The composition according to claim 6, wherein the labeled reagent is a labeled said reverse transcribed mRNA.

10. A method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:

a) obtaining a sample taken from a biological subject showing a clinical sign of SIRS, the sample including polynucleotide expression products;

b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values, the pair of biomarker values being selected from the group consisting of:

i) a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene;

ii) a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene;

c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values;

d) comparing the indicator to first and second indicator references, the first and second indicator references being indicative of inSIRS and ipSIRS, respectively; and,

e) determining a likelihood of the subject having inSIRS or ipSIRS in accordance with the results of the comparison.

11. The method according to claim 10, wherein the method includes:

a) determining a first derived biomarker value using the first pair of biomarker values;

b) determining a second derived biomarker value using the second pair of biomarker values; and,

c) determining the indicator by combining the first and second derived biomarker values.

12. The method according to claim 11, wherein the method includes combining the derived biomarker values using a combining function, the combining function being at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) a nearest neighbour model; and,

k) a probabilistic model.

13. The method according to claim 10, wherein the method is performed at least in part using an electronic processing device.

14. The method according to claim 11, wherein the method includes, in at least one electronic processing device:

a) obtaining the pairs of biomarker values;

b) determining the first derived biomarker value;

c) determining the second derived biomarker value; and,

d) determining the indicator by adding the first and second derived biomarker values.

15. The method according to claim 10, wherein the method includes, in at least one processing device, generating a representation of the indicator.

16. The method according to claim 15, wherein the representation includes:

a) an alphanumeric indication of the indicator;

b) a graphical indication of a comparison of the indicator to one or more indicator references;

c) an alphanumeric indication of a likelihood of the subject having at least one medical condition.

17. The method according to claim 10, wherein the indicator references are based on at least one of:

a) an indicator threshold range;

b) an indicator threshold; and,

c) an indicator distribution.

18. The method according to claim 10, wherein the indicator references are derived from indicators determined for a number of individuals in a reference population.

19. The method according to claim 18, wherein the indicator reference is based on a distribution of indicators determined for a group of a reference population, the group consisting of individuals diagnosed as having the medical condition or lacking the medical condition.

20. The method according to claim 18, wherein the reference population includes at least one of:

a) a plurality of individuals of different sexes;

b) a plurality of individuals of different ethnicities;

c) a plurality of healthy individuals;

d) a plurality of individuals suffering from at least one of inSIRS and ipSIRS;

e) a plurality of individuals lacking at least one of inSIRS and ipSIRS;

f) a plurality of individuals showing clinical signs of at least one of inSIRS and ipSIRS;

g) first and second groups of individuals, each group of individuals suffering from one of inSIRS and ipSIRS; and,

h) first and second groups of individuals, the first group of individuals suffering from at least one of inSIRS and ipSIRS, and the second group lacking one or both of inSIRS and ipSIRS.

21. The method according to claim 10, wherein the likelihood is based on a probability generated using the results of the comparison.

22. The method according to claim 10, wherein the method includes:

a) determining first and second indicator probabilities using the results of the comparisons; and,

b) combining the first and second indicator probabilities to determine a condition probability indicative of the likelihood.

23. The method according to claim 10, wherein the method includes:

a) quantifying polynucleotide expression products by:

i) amplifying at least some polynucleotide expression products in the sample; and,

ii) determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and,

b) determining the indicator by determining a difference between the amplification amounts.

24. The method according to claim 23, wherein the amplification amount is at least one of:

a) a cycle time;

b) a number of cycles;

c) a cycle threshold;

d) an amplification time; and,

e) relative to an amplification amount of another amplified product.

25. The method according to claim 23, wherein the method includes determining:

a) a first derived biomarker value by determining a difference between the amplification amounts of a first pair of polynucleotide expression products;

b) a second derived biomarker value by determining a difference between the amplification amounts of a second pair of polynucleotide expression products;

c) determining the indicator by adding the first and second derived biomarker values.

26. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of the PLA2G7 gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of the PLAC8 gene, wherein the second pair of reagents comprises: (iii) a reagent that allows quantification of a polynucleotide expression product of the CEACAM4 gene; and (iv) a reagent that allows quantification of a polynucleotide expression product of the LAMP1 gene.

27. A method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the pair of biomarker values comprises at least one of:

a) a first pair of biomarker values comprising first and second biomarker values corresponding to first and second biomarkers, wherein the first immune system biomarker represents a polynucleotide expression product of the PLA2G7 gene and wherein the second immune system biomarker representing a polynucleotide expression product of the PLAC8 gene, and

b) a second pair of biomarker values comprises third and fourth biomarker values corresponding to third and fourth immune system biomarkers, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of the CEACAM4 gene and wherein the fourth immune system biomarker represents a polynucleotide expression product of the LAMP1 gene.

28. The method according to claim 27, wherein the indicator-determining method comprises: determining the first pair and second pair of biomarker values and determining a first derived biomarker value calculated using the first pair of biomarker values and a second derived biomarker value calculated using the second pair of biomarker values; and determining the indicator based on a combination of the first and second derived biomarker values.

29. The method according to claim 27, comprising: sending the sample taken from the subject to a laboratory at which the indicator is determined.

30. The method according to claim 27, wherein the sample comprises cells obtained from the subject or a nucleic acid sample thereof.

Images