US20040097460A1 - Diagnosis of sepsis or SIRS using biomarker profiles - Google Patents

Diagnosis of sepsis or SIRS using biomarker profiles Download PDF

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US20040097460A1
US20040097460A1 US10/704,661 US70466103A US2004097460A1 US 20040097460 A1 US20040097460 A1 US 20040097460A1 US 70466103 A US70466103 A US 70466103A US 2004097460 A1 US2004097460 A1 US 2004097460A1
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sepsis
biomarker
biomarkers
sirs
nucleic acid
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Richard Ivey
Thomas Gentle
Richard Moore
Michael Towns
Nicholas Bachur
Robert Rosenstein
Paul Goldenbaum
Song Shi
Donald Copertino
James Garrett
Gregory Tice
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Becton Dickinson and Co
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Assigned to BECTON, DICKINSON AND COMPANY reassignment BECTON, DICKINSON AND COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GARRETT, JAMES, TOWNS, MICHAEL L., COPERTINO, DONALD, IVEY, RICHARD M., MOORE, RICHARD L., ROSENSTEIN, ROBERT W., TICE, GREGORY, BACHUR, JR., NICHOLAS, GENTLE, JR., THOMAS M., GOLDENBAUM, PAUL E., SHI, SONG
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6834—Enzymatic or biochemical coupling of nucleic acids to a solid phase
    • C12Q1/6837—Enzymatic or biochemical coupling of nucleic acids to a solid phase using probe arrays or probe chips
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/26—Infectious diseases, e.g. generalised sepsis

Definitions

  • the present invention relates to methods of diagnosing or predicting sepsis or its stages of progression in an individual.
  • the present invention also relates to methods of diagnosing systemic inflammatory response syndrome in an individual.
  • Sepsis follows a well-described time course, progressing from systemic inflammatory response syndrome (“SIRS”)-negative to SIRS-positive to sepsis, which may then progress to severe sepsis, septic shock, multiple organ dysfunction (“MOD”), and ultimately death. Sepsis also may arise in an infected individual when the individual subsequently develops SIRS. “SIRS” is commonly defined as the presence of two or more of the following parameters: body temperature greater than 38° C.
  • Sepsis is commonly defined as SIRS with a confirmed infectious process.
  • severe sepsis is associated with MOD, hypotension, disseminated intravascular coagulation (“DIC”) or hypoperfusion abnormalities, including lactic acidosis, oliguria, and changes in mental status.
  • DIC disseminated intravascular coagulation
  • Septic shock is commonly defined as sepsis-induced hypotension that is resistant to fluid resuscitation with the additional presence of hypoperfusion abnormalities.
  • Most existing sepsis scoring systems or predictive models predict only the risk of late-stage complications, including death, in patients who already are considered septic. Such systems and models, however, do not predict the development of sepsis itself. What is particularly needed is a way to categorize those patients with SIRS who will or will not develop sepsis.
  • researchers will typically define a single biomarker that is expressed at a different level in a group of septic patients versus a normal (i.e., non-septic) control group of patients.
  • diagnosis would be made by a technique that accurately, rapidly, and simultaneously measures a plurality of biomarkers at a single point in time, thereby minimizing disease progression during the time required for diagnosis.
  • the present invention allows for accurate, rapid, and sensitive prediction and diagnosis of sepsis through a measurement of more than one biomarker taken from a biological sample at a single point in time. This is accomplished by a molecular diagnostics approach, in which a biomarker profile is obtained at a single point in time from an individual, particularly an individual at risk of developing sepsis, having sepsis, or suspected of having sepsis, and comparing the biomarker profile from the individual to a reference biomarker profile.
  • the reference biomarker profile may be obtained from a population of individuals (a “reference population”) who are, for example, afflicted with sepsis or who are suffering from either the onset of sepsis or a particular stage in the progression of sepsis. If the biomarker profile from the individual contains appropriately characteristic features of the biomarker profile from the reference population, then the individual is diagnosed as having a more likely chance of becoming septic, as being afflicted with sepsis or as being at the particular stage in the progression of sepsis as the reference population.
  • the reference biomarker profile may also be obtained from various populations of individuals including those who are suffering from SIRS or those who are suffering from an infection but who are not suffering from SIRS.
  • the present invention allows the clinician to determine, inter alia, those patients who do not have SIRS, who have SIRS but are not likely to develop sepsis within the time frame of the investigation, who have sepsis, or who are at risk of eventually becoming septic.
  • the methods of the present invention are particularly useful for detecting or predicting the onset of sepsis in SIRS patients, one of ordinary skill in the art will understand that the present methods may be used for any patient including, but not limited to, patients suspected of having SIRS or of being at any stage of sepsis.
  • a biological sample could be taken from a patient, and a profile of biomarkers in the sample could be compared to several different reference biomarker profiles, each profile derived from individuals such as, for example, those having SIRS or being at a particular stage of sepsis. Classification of the patient's biomarker profile as corresponding to the profile derived from a particular reference population is predictive that the patient falls within the reference population. Based on the diagnosis resulting from the methods of the present invention, an appropriate treatment regimen could then be initiated.
  • the present invention provides various nucleic acid biomarkers or combinations of nucleic acid biomarkers.
  • the nucleic acid biomarkers are identified from a biological sample (e.g., a blood sample) obtained from an individual.
  • a biological sample e.g., a blood sample
  • the levels of expression of certain mRNAs that encode for proteins involved in the host's response to SIRS or various stages of sepsis, including the onset of sepsis prior to clinical suspicion of sepsis using conventional techniques are predictive or diagnostic of the stage of sepsis or are diagnostic of SIRS.
  • Nucleic acid biomarkers of the invention include, but are not limited to, mRNAs, nucleic acids that are made from these mRNAs, and nucleic acids capable of forming a duplex with mRNAs.
  • the nucleic acid biomarkers of the present invention are termed “host response biomarkers.”
  • the present invention also provides a kit comprising at least one, two, three, four, five, 10 or more host response biomarkers selected from the group consisting of nucleic acids listed in any one of TABLES 2-10.
  • the nucleic acids listed in TABLES 2-10 are identified by the protein that they encode.
  • a host response biomarker for the purpose of the invention includes, but is not limited to, an mRNA that encodes the identified protein, a cDNA made from the mRNA, and a DNA or other molecule that is able to form a specific complex (e.g., a hybridized duplex) with an mRNA that encodes the identified protein.
  • the kit comprises a DNA probe (e.g., a cDNA or oligonucleotide) capable of forming a duplex with an mRNA that corresponds to a biomarker listed in TABLES 2-10.
  • a DNA probe e.g., a cDNA or oligonucleotide
  • the present invention provides a method comprising using DNA probes to construct a nucleic acid array capable of determining the status of sepsis or of diagnosing SIRS in the individual by detecting mRNA biomarkers present in a biological sample from the individual.
  • the present invention further provides a profile of host response biomarkers comprising at least two, three, four, five, 10 or 20 or more features that are measurable characteristics of nucleic acids and that contribute to the classification of an individual in a reference population with an accuracy of at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, or about 100%.
  • the reference population is selected from the group consisting of a normal reference population, a SIRS-positive reference population, an infected/SIRS-negative reference population, a sepsis-positive reference population, a reference population at a particular stage in the progression of sepsis, a SIRS-positive reference population confirmed as having sepsis by conventional techniques after about 0-36 hours, a SIRS-positive reference population confirmed as having sepsis by conventional techniques after about 36-60 hours, and a SIRS-positive reference population confirmed as having sepsis by conventional techniques after about 60-84 hours.
  • the present invention further provides a method of isolating a host response biomarker, where the biomarker is capable of being used in a method of determining the status of sepsis or diagnosing SIRS in an individual.
  • This method comprises obtaining a reference biomarker profile of host response biomarkers from biological samples obtained from a population of individuals and identifying a feature of the reference biomarker profile capable of determining the status of sepsis or diagnosing SIRS in the individual.
  • a host response biomarker that corresponds to the feature is then identified and isolated.
  • FIG. 1 illustrates the progression of SIRS to sepsis.
  • the condition of sepsis consists of at least three stages, with a septic patient progressing from severe sepsis to septic shock to multiple organ dysfunction.
  • FIG. 2 shows the relationship between sepsis and SIRS.
  • the various sets shown in the Venn diagram correspond to populations of individuals having the indicated condition.
  • the present invention allows for the rapid, sensitive, and accurate diagnosis or prediction of sepsis using one or more biological samples obtained from an individual at a single time point (“snapshot”) or during the course of disease progression.
  • sepsis may be diagnosed or predicted prior to the onset of clinical symptoms, thereby allowing for more effective therapeutic intervention.
  • Systemic inflammatory response syndrome refers to a clinical response to a variety of severe clinical insults, as manifested by two or more of the following conditions within a 24-hour period:
  • body temperature greater than 38° C. (100.4° F.) or less than 36° C. (96.8° F.);
  • HR heart rate
  • RR respiratory rate greater than 20 breaths/minute, or P CO2 less than 32 mm Hg, or requiring mechanical ventilation
  • WBC white blood cell count
  • a patient with SIRS has a clinical presentation that is classified as SIRS, as defined above, but is not clinically deemed to be septic.
  • Individuals who are at risk of developing sepsis include patients in an ICU and those who have otherwise suffered from a physiological trauma, such as a burn or other insult.
  • “Sepsis” refers to a SIRS-positive condition that is associated with a confirmed infectious process. Clinical suspicion of sepsis arises from the suspicion that the SIRS-positive condition of a SIRS patient is a result of an infectious process.
  • “sepsis” includes all stages of sepsis including, but not limited to, the onset of sepsis, severe sepsis and MOD associated with the end stages of sepsis.
  • the “onset of sepsis” refers to an early stage of sepsis, i.e., prior to a stage when the clinical manifestations are sufficient to support a clinical suspicion of sepsis. Because the methods of the present invention are used to detect sepsis prior to a time that sepsis would be suspected using conventional techniques, the patient's disease status at early sepsis can only be confirmed retrospectively, when the manifestation of sepsis is more clinically obvious. The exact mechanism by which a patient becomes septic is not a critical aspect of the invention. The methods of the present invention can detect changes in the biomarker profile independent of the origin of the infectious process. Regardless of how sepsis arises, the methods of the present invention allow for determining the status of a patient having, or suspected of having, sepsis or SIRS, as classified by previously used criteria.
  • “Severe sepsis” refers to sepsis associated with organ dysfunction, hypoperfusion abnormalities, or sepsis-induced hypotension. Hypoperfusion abnormalities include, but are not limited to, lactic acidosis, oliguria, or an acute alteration in mental status.
  • “Septic shock” refers to sepsis-induced hypotension that is not responsive to adequate intravenous fluid challenge and with manifestations of peripheral hypoperfusion.
  • a “converter patient” refers to a SIRS-positive patient who progresses to clinical suspicion of sepsis during the period the patient is monitored, typically during an ICU stay.
  • a “non-converter patient” refers to a SIRS-positive patient who does not progress to clinical suspicion of sepsis during the period the patient is monitored, typically during an ICU stay.
  • a “biomarker” is virtually any biological compound, such as a protein and a fragment thereof, a peptide, a polypeptide, a proteoglycan, a glycoprotein, a lipoprotein, a carbohydrate, a lipid, a nucleic acid, an organic or inorganic chemical, a natural polymer, and a small molecule, that is present in the biological sample and that may be isolated from, or measured in, the biological sample.
  • a biomarker can be the entire intact molecule, or it can be a portion thereof that may be partially functional or recognized, for example, by an antibody or other specific binding protein.
  • a biomarker is considered to be informative if a measurable aspect of the biomarker is associated with a given state of the patient, such as a particular stage of sepsis.
  • a measurable aspect may include, for example, the presence, absence, or concentration of the biomarker in the biological sample from the individual and/or its presence as part of a profile of biomarkers.
  • Such a measurable aspect of a biomarker is defined herein as a “feature.”
  • a feature may also be a ratio of two or more measurable aspects of biomarkers, which biomarkers may or may not be of known identity, for example.
  • a “biomarker profile” comprises at least two such features, where the features can correspond to the same or different classes of biomarkers such as, for example, a nucleic acid and a carbohydrate.
  • a biomarker profile may also comprise at least three, four, five, 10, 20, 30 or more features.
  • a biomarker profile comprises hundreds, or even thousands, of features.
  • the biomarker profile comprises at least one measurable aspect of at least one internal standard.
  • a “phenotypic change” is a detectable change in a parameter associated with a given state of the patient.
  • a phenotypic change may include an increase or decrease of a biomarker in a bodily fluid, where the change is associated with sepsis or the onset of sepsis.
  • a phenotypic change may further include a change in a detectable aspect of a given state of the patient that is not a change in a measurable aspect of a biomarker.
  • a change in phenotype may include a detectable change in body temperature, respiration rate, pulse, blood pressure, or other physiological parameter. Such changes can be determined via clinical observation and measurement using conventional techniques that are well-known to the skilled artisan.
  • “conventional techniques” are those techniques that classify an individual based on phenotypic changes without obtaining a biomarker profile according to the present invention.
  • a “decision rule” is a method used to classify patients. This rule can take on one or more forms that are known in the art, as exemplified in Hastie et al., in “The Elements of Statistical Learning,” Springer-Verlag (Springer, N.Y. (2001)), herein incorporated by reference in its entirety. Analysis of biomarkers in the complex mixture of molecules within the sample generates features in a data set. A decision rule may be used to act on a data set of features to, inter alia, predict the onset of sepsis, to determine the progression of sepsis, to diagnose sepsis, or to diagnose SIRS.
  • a classification may be made with at least about 90% certainty, or even more, in one embodiment. In other embodiments, the certainty is at least about 80%, at least about 70%, or at least about 60%. The useful degree of certainty may vary, depending on the particular method of the present invention. “Certainty” is defined as the total number of accurately classified individuals divided by the total number of individuals subjected to classification. As used herein, “certainty” means “accuracy.” Classification may also be characterized by its “sensitivity.” The “sensitivity” of classification relates to the percentage of sepsis patients who were correctly identified as having sepsis.
  • “Sensitivity” is defined in the art as the number of true positives divided by the sum of true positives and false negatives.
  • the “specificity” of the method is defined as the percentage of patients who were correctly identified as not having sepsis. That is, “specificity” relates to the number of true negatives divided by the sum of true negatives and false positives.
  • the sensitivity and/or specificity is at least about 90%, at least about 80%, at least about 70% or at least about 60%.
  • the number of features that may be used to classify an individual with adequate certainty is typically about four. Depending on the degree of certainty sought, however, the number of features may be more or less, but in all cases is at least one. In one embodiment, the number of features that may be used to classify an individual is optimized to allow a classification of an individual with high certainty.
  • Determining the status” of sepsis or SIRS in a patient encompasses classification of a patient's biomarker profile to (1) detect the presence of sepsis or SIRS in the patient, (2) predict the onset of sepsis or SIRS in the patient, or (3) measure the progression of sepsis in a patient.
  • “Diagnosing” sepsis or SIRS means to identify or detect sepsis or SIRS in the patient. Because of the greater sensitivity of the present invention to detect sepsis before an overtly observable clinical manifestation, the identification or detection of sepsis includes the detection of the onset of sepsis, as defined above.
  • “predicting the onset of sepsis” means to classify the patient's biomarker profile as corresponding to the profile derived from individuals who are progressing from a particular stage of SIRS to sepsis or from a state of being infected to sepsis (i.e., from infection to infection with concomitant SIRS).
  • “Detecting the progression” or “determining the progression” of sepsis or SIRS means to classify the biomarker profile of a patient who is already diagnosed as having sepsis or SIRS. For instance, classifying the biomarker profile of a patient who has been diagnosed as having sepsis can encompass detecting or determining the progression of the patient from sepsis to severe sepsis or to sepsis with MOD.
  • sepsis may be diagnosed or predicted by obtaining a profile of biomarkers from a sample obtained from an individual.
  • “obtain” means “to come into possession of.”
  • the present invention is particularly useful in predicting and diagnosing sepsis in an individual who has an infection, or even sepsis, but who has not yet been diagnosed as having sepsis, who is suspected of having sepsis, or who is at risk of developing sepsis.
  • the present invention may be used to detect and diagnose SIRS in an individual. That is, the present invention may be used to confirm a clinical suspicion of SIRS.
  • the present invention also may be used to detect various stages of the sepsis process such as infection, bacteremia, sepsis, severe sepsis, septic shock and the like.
  • the profile of biomarkers obtained from an individual is compared to a reference biomarker profile.
  • the reference biomarker profile can be generated from one individual or a population of two or more individuals. The population, for example, may comprise three, four, five, ten, 15, 20, 30, 40, 50 or more individuals.
  • the reference biomarker profile and the individual's (test) biomarker profile that are compared in the methods of the present invention may be generated from the same individual, provided that the test and reference biomarker profiles are generated from biological samples taken at different time points and compared to one another. For example, a sample may be obtained from an individual at the start of a study period. A reference biomarker profile taken from that sample may then be compared to biomarker profiles generated from subsequent samples from the same individual. Such a comparison may be used, for example, to determine the status of sepsis in the individual by repeated classifications over time.
  • the reference populations may be chosen from individuals who do not have SIRS (“SIRS-negative”), from individuals who do not have SIRS but who are suffering from an infectious process, from individuals who are suffering from SIRS without the presence of sepsis (“SIRS-positive”), from individuals who are suffering from the onset of sepsis, from individuals who are sepsis-positive and suffering from one of the stages in the progression of sepsis, or from individuals with a physiological trauma that increases the risk of developing sepsis. Furthermore, the reference populations may be SIRS-positiveand are then subsequently diagnosed with sepsis using conventional techniques.
  • a population of SIRS-positive patients used to generate the reference profile may be diagnosed with sepsis about 24, 48, 72, 96 or more hours after biological samples were taken from them for the purposes of generating a reference biomarker profile.
  • the population of SIRS-positive individuals is diagnosed with sepsis using conventional techniques about 0-36 hours, about 0-36 hours, about 60-84 hours, or about 84-108 hours after the biological samples were taken. If the biomarker profile is indicative of sepsis or one of its stages of progression, a clinician may begin treatment prior to the manifestation of clinical symptoms of sepsis. Treatment typically will involve examining the patient to determine the source of the infection.
  • the clinician typically will obtain cultures from the site of the infection, preferably before beginning relevant empirical antimicrobial therapy and perhaps additional adjunctive therapeutic measures, such as draining an abscess or removing an infected catheter.
  • additional adjunctive therapeutic measures such as draining an abscess or removing an infected catheter.
  • the methods of the present invention comprise comparing an individual's biomarker profile with a reference biomarker profile.
  • “comparison” includes any means to discern at least one difference in the individual's and the reference biomarker profiles.
  • a comparison may include a visual inspection of chromatographic spectra, and a comparison may include arithmetical or statistical comparisons of values assigned to the features of the profiles. Such statistical comparisons include, but are not limited to, applying a decision rule.
  • the biomarker profiles comprise at least one internal standard
  • the comparison to discern a difference in the biomarker profiles may also include features of these internal standards, such that features of the biomarker are correlated to features of the internal standards.
  • the comparison can predict, inter alia, the chances of acquiring sepsis or SIRS; or the comparison can confirm the presence or absence of sepsis or SIRS; or the comparison can indicate the stage of sepsis at which an individual may be.
  • the present invention therefore, obviates the need to conduct time-intensive assays over a monitoring period, as well as the need to identify each biomarker.
  • the invention does not require a monitoring period to classify an individual, it will be understood that repeated classifications of the individual, i.e., repeated snapshots, may be taken over time until the individual is no longer at risk.
  • a profile of biomarkers obtained from the individual may be compared to one or more profiles of biomarkers obtained from the same individual at different points in time.
  • each comparison made in the process of repeated classifications is capable of classifying the individual as belonging to or not belonging to the reference population.
  • an “individual” is an animal, preferably a mammal, more preferably a human or non-human primate.
  • the terms “individual,” “subject” and “patient” are used interchangeably herein.
  • the individual can be normal, suspected of having SIRS or sepsis, at risk of developing SIRS or sepsis, or confirmed as having SIRS or sepsis. While there are many known biomarkers that have been implicated in the progression of sepsis, not all of these markers appear in the initial, pre-clinical stages.
  • the subset of biomarkers characteristic of early-stage sepsis may, in fact, be determined only by a retrospective analysis of samples obtained from individuals who ultimately manifest clinical symptoms of sepsis. Without being bound by theory, even an initial pathologic infection that results in sepsis may provoke physiological changes that are reflected in particular changes in biomarker expression.
  • the profile of biomarkers from a biological sample obtained from an individual may be compared to this reference profile to determine whether the test subject is also at that particular stage of sepsis.
  • one of the advantages of the present invention is the capability of classifying an individual, using a biomarker profile from a single biological sample, as having membership in a particular population.
  • the determination of whether a particular physiological response is becoming established or is subsiding may be facilitated by a subsequent classification of the individual.
  • the present invention provides numerous biomarkers that both increase and decrease in level of expression as a physiological response to sepsis or SIRS is established or subsides.
  • an investigator can select a feature of an individual's biomarker profile that is known to change in intensity as a physiological response to sepsis becomes established.
  • a comparison of the same feature in a profile from a subsequent biological sample from the individual can establish whether the individual is progressing toward more severe sepsis or is progressing toward normalcy.
  • biomarkers are not essential to the invention. Indeed, the present invention should not be limited to biomarkers that have previously been identified. (See, e.g., U.S. patent application Ser. No. 10/400,275, filed Mar. 26, 2003.) It is, therefore, expected that novel biomarkers will be identified that are characteristic of a given population of individuals, especially a population in one of the early stages of sepsis.
  • a biomarker is identified and isolated. It then may be used to raise a specifically-binding antibody, which can facilitate biomarker detection in a variety of diagnostic assays.
  • any immunoassay may use any antibodies, antibody fragment or derivative capable of binding the biomarker molecules (e.g., Fab, Fv, or scFv fragments). Such immunoassays are well-known in the art. If the biomarker is a protein, it may be sequenced and its encoding gene may be cloned using well-established techniques.
  • the methods of the present invention may be employed to screen, for example, patients admitted to an ICU.
  • a biological sample such as, for example, blood
  • the complex mixture of proteins and other molecules within the blood is resolved as a profile of biomarkers. This may be accomplished through the use of any technique or combination of techniques that reproducibly distinguishes these molecules on the basis of some physical or chemical property.
  • the molecules are immobilized on a matrix and then are separated and distinguished by laser desorption/ionization time-of-flight mass spectrometry.
  • a spectrum is created by the characteristic desorption pattern that reflects the mass/charge ratio of each molecule or its fragments.
  • biomarkers are selected from the various mRNA species obtained from a cellular extract, and a biomarker profile is obtained by hybridizing the individual's mRNA species to an array of cDNAs.
  • the diagnostic use of cDNA arrays is well-known in the art. (See, e.g., Zou, et. al., Oncogene 21: 4855-4862 (2002).)
  • a profile may be obtained using a combination of protein and nucleic acid separation methods.
  • kits that are useful in determining the status of sepsis or diagnosing SIRS in an individual.
  • the kits of the present invention comprise at least one biomarker. Specific biomarkers useful in the present invention are set forth herein.
  • the biomarkers of the kit can be used to generate biomarker profiles according to the present invention. Generally, the biomarkers of the kit will bind, with at least some specificity, to the biomarker molecules contained in the biological sample from which the biomarker profile is generated.
  • kits of the kit include, but are not limited to, proteins, and fragments thereof, peptides, polypeptides, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids, organic and inorganic chemicals, and natural and synthetic polymers.
  • the biomarker(s) may be part of an array, or the biomarker(s) may be packaged separately and/or individually.
  • the kit may also comprise at least one internal standard to be used in generating the biomarker profiles of the present invention. Likewise, the internal standards can be any of the classes of compounds described above.
  • the kits of the present invention also may contain reagents that can be used to detectably label biomarkers contained in the biological samples from which the biomarker profiles are generated.
  • the kit may comprise a set of antibodies or functional fragments thereof that specifically bind at least two, three, four, five, 10, 20 or more of the biomarkers set forth in any one of the following TABLES that list biomarkers.
  • the antibodies themselves may be detectably labeled.
  • the kit also may comprise a specific biomarker binding component, such as an aptamer. If the biomarkers comprise a nucleic acid, the kit may provide an oligonucleotide probe that is capable of forming a duplex with the biomarker or with a complementary strand of a biomarker. The oligonucleotide probe may be detectably labeled.
  • kits of the present invention may also include pharmaceutical excipients, diluents and/or adjuvants when the biomarker is to be used to raise an antibody.
  • pharmaceutical adjuvants include, but are not limited to, preservatives, wetting agents, emulsifying agents, and dispersing agents. Prevention of the action of microorganisms can be ensured by the inclusion of various antibacterial and antifungal agents, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents such as sugars, sodium chloride, and the like. Prolonged absorption of an injectable pharmaceutical form can be brought about by the inclusion of agents which delay absorption such as aluminum monostearate and gelatin.
  • the methods of the present invention comprise obtaining a profile of biomarkers from a biological sample taken from an individual.
  • the biological sample may be blood, plamsa, serum, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue sample, a tissue biopsy, a stool sample and the like.
  • the reference biomarker profile may be obtained, for example, from a population of individuals selected from the group consisting of SIRS-negative individuals, SIRS-positive individuals, individuals who are suffering from the onset of sepsis and individuals who already have sepsis.
  • the reference biomarker profile from individuals who already have sepsis may be obtained at any stage in the progression of sepsis, such as infection, bacteremia, severe sepsis, septic shock or MOD.
  • a separation method may be used to create a profile of biomarkers, such that only a subset of biomarkers within the sample is analyzed.
  • the biomarkers that are analyzed in a sample may consist of mRNA species from a cellular extract, which has been fractionated to obtain only the nucleic acid biomarkers within the sample, or the biomarkers may consist of a fraction of the total complement of proteins within the sample, which have been fractionated by chromatographic techniques.
  • a profile of biomarkers may be created without employing a separation method.
  • a biological sample may be interrogated with a labeled compound that forms a specific complex with a biomarker in the sample, where the intensity of the label in the specific complex is a measurable characteristic of the biomarker.
  • a suitable compound for forming such a specific complex is a labeled antibody.
  • a biomarker is measured using an antibody with an amplifiable nucleic acid as a label.
  • the nucleic acid label becomes amplifiable when two antibodies, each conjugated to one strand of a nucleic acid label, interact with the biomarker, such that the two nucleic acid strands form an amplifiable nucleic acid.
  • the biomarker profile may be derived from an assay, such as an array, of nucleic acids, where the biomarkers are the nucleic acids or complements thereof.
  • the biomarkers may be ribonucleic acids.
  • the biomarker profile also may be obtained using a method selected from the group consisting of nuclear magnetic resonance, nucleic acid arrays, dot blotting, slot blotting, reverse transcription amplification and Northern analysis.
  • the biomarker profile is detected immunologically by reacting antibodies, or functional fragments thereof, specific to the biomarkers.
  • a functional fragment of an antibody is a portion of an antibody that retains at least some ability to bind to the antigen to which the complete antibody binds.
  • the fragments which include, but are not limited to, scFv fragments, Fab fragments and F(ab) 2 fragments, can be recombinantly produced or enzymatically produced.
  • specific binding molecules other than antibodies, such as aptamers may be used to bind the biomarkers.
  • the biomarker profile may comprise a measurable aspect of an infectious agent or a component thereof.
  • the biomarker profile may comprise measurable aspects of small molecules, which may include fragments of proteins or nucleic acids, or which may include metabolites.
  • Biomarker profiles may be generated by the use of one or more separation methods.
  • suitable separation methods may include a mass spectrometry method, such as electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS) n (n is an integer greater than zero), matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS) n , atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS), APP
  • mass spectrometry methods may include, inter alia, quadrupole, fourier transform mass spectrometry (FTMS) and ion trap.
  • suitable separation methods may include chemical extraction partitioning, column chromatography, ion exchange chromatography, hydrophobic (reverse phase) liquid chromatography, isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) or other chromatography, such as thin-layer, gas or liquid chromatography, or any combination thereof.
  • the biological sample may be fractionated prior to application of the separation method.
  • Biomarker profiles also may be generated by methods that do not require physical separation of the biomarkers themselves.
  • nuclear magnetic resonance (NMR) spectroscopy may be used to resolve a profile of biomarkers from a complex mixture of molecules.
  • NMR nuclear magnetic resonance
  • Additional procedures include nucleic acid amplification technologies, which may be used to generate a profile of biomarkers without physical separation of individual biomarkers. (See Stordeur et al., J. Immunol. Methods 259: 55-64 (2002) and Tan et al., Proc. Nat'l Acad. Sci. USA 99: 11387-11392 (2002), for example.)
  • laser desorption/ionization time-of-flight mass spectrometry is used to create a profile of biomarkers where the biomarkers are proteins or protein fragments that have been ionized and vaporized off an immobilizing support by incident laser radiation. A profile is then created by the characteristic time-of-flight for each protein, which depends on its mass-to-charge (“m/z”) ratio.
  • m/z mass-to-charge ratio
  • Laser desorption/ionization time-of-flight mass spectrometry allows the generation of large amounts of information in a relatively short period of time.
  • a biological sample is applied to one of several varieties of a support that binds all of the biomarkers, or a subset thereof, in the sample.
  • Cell lysates or samples are directly applied to these surfaces in volumes as small as 0.5 ⁇ L, with or without prior purification or fractionation.
  • the lysates or sample can be concentrated or diluted prior to application onto the support surface.
  • Laser desorption/ionization is then used to generate mass spectra of the sample, or samples, in as little as three hours.
  • the total mRNA from a cellular extract of the individual is assayed, and the various mRNA species that are obtained from the biological sample are used as biomarkers.
  • Profiles may be obtained, for example, by hybridizing these mRNAs to an array of probes, which may comprise oligonucleotides or cDNAs, using standard methods known in the art.
  • the mRNAs may be subjected to gel electrophoresis or blotting methods such as dot blots, slot blots or Northern analysis, all of which are known in the art. (See, e.g., Sambrook et al.
  • mRNA profiles also may be obtained by reverse transcription followed by amplification and detection of the resulting cDNAs, as disclosed by Stordeur et al., supra, for example.
  • the profile may be obtained by using a combination of methods, such as a nucleic acid array combined with mass spectroscopy.
  • comparison of the individual's biomarker profile to a reference biomarker profile comprises applying a decision rule.
  • the decision rule can comprise a data analysis algorithm, such as a computer pattern recognition algorithm. Other suitable algorithms include, but are not limited to, logistic regression or a nonparametric algorithm that detects differences in the distribution of feature values (e.g., a Wilcoxon Signed Rank Test).
  • the decision rule may be based upon one, two, three, four, five, 10, 20 or more features. In one embodiment, the decision rule is based on hundreds or more of features. Applying the decision rule may also comprise using a classification tree algorithm.
  • the reference biomarker profile may comprise at least three features, where the features are predictors in a classification tree algorithm.
  • the data analysis algorithm predicts membership within a population (or class) with an accuracy of at least about 60%, at least about 70%, at least about 80% and at least about 90%.
  • Suitable algorithms are known in the art, some of which are reviewed in Hastie et al., supra. Such algorithms classify complex spectra from biological materials, such as a blood sample, to distinguish individuals as normal or as possessing biomarker expression levels characteristic of a particular disease state. While such algorithms may be used to increase the speed and efficiency of the application of the decision rule and to avoid investigator bias, one of ordinary skill in the art will realize that computer-based algorithms are not required to carry out the methods of the present invention.
  • Algorithms may be applied to the comparison of biomarker profiles, regardless of the method that was used to generate the biomarker profile.
  • suitable algorithms can be applied to biomarker profiles generated using gas chromatography, as discussed in Harper, “Pyrolysis and GC in Polymer Analysis,” Dekker, New York (1985).
  • Wagner et al., Anal. Chem 74: 1824-35 (2002) disclose an algorithm that improves the ability to classify individuals based on spectra obtained by static time-of-flight secondary ion mass spectrometry (TOF-SIMS). Additionally, Bright et al., J. Microbiol.
  • Methods 48: 127-38 disclose a method of distinguishing between bacterial strains with high certainty (79-89% correct classification rates) by analysis of MALDI-TOF-MS spectra. Dalluge, Fresenius J. Anal. Chem. 366: 701-11 (2000) discusses the use of MALDI-TOF-MS and liquid chromatography-electrospray ionization mass spectrometry (LC/ESI-MS) to classify profiles of biomarkers in complex biological samples.
  • LC/ESI-MS liquid chromatography-electrospray ionization mass spectrometry
  • the methods of the present invention can be carried out by generation of a biomarker profile that is diagnostic or predictive of sepsis or SIRS. Because profile generation is sufficient to carry out the invention, the biomarkers that constitute the profile need not be known or subsequently identified. profiles of the present invention may include those known to be informative of the state of the immune system in response to infection; however, not all of these biomarkers may be equally informative. These biomarkers can include hormones, autoantibodies, soluble and insoluble receptors, growth factors, transcription factors, cell surface markers and soluble markers from the host or from the pathogen itself, such as coat proteins, lipopolysaccharides (endotoxin), lipoteichoic acids, etc.
  • biomarkers include, but are not limited to, cell-surface proteins such as CD64 proteins; CD11b proteins; HLA Class II molecules, including HLA-DR proteins and HLA-DQ proteins; CD54 proteins; CD71 proteins; CD86 proteins; surface-bound tumor necrosis factor receptor (TNF-R); pattern-recognition receptors such as Toll-like receptors; soluble markers such as interleukins IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IL-11, IL-12, IL-13, and IL-18; tumor necrosis factor alpha (TNF- ⁇ ); neopterin; C-reactive protein (CRP); procalcitonin (PCT); 6-keto Fl ⁇ ; thromboxane B 2 ; leukotrienes B4, C3, C4, C5, D4 and E4; interferon gamma (IFN ⁇ ); interferon alpha/beta (IFN ⁇ / ⁇ ); lymphotoxin alpha (LT ⁇ ); complement
  • Biomarkers commonly and clinically associated with bacteremia are also candidates for biomarkers useful for the present invention, given the common and frequent occurrence of such biomarkers in biological samples.
  • Biomarkers can include low molecular weight compounds, which can be fragments of proteins or nucleic acids, or they may include metabolites. The presence or concentration of the low molecular weight compounds, such as metabolites, may reflect a phenotypic change that is associated with sepsis and/or SIRS.
  • changes in the concentration of small molecule biomarkers may be associated with changes in cellular metabolism that result from any of the physiological changes in response to SIRS and/or sepsis, such as hypothermia or hyperthermia, increased heart rate or rate of respiration, tissue hypoxia, metabolic acidosis or MOD.
  • Biomarkers may also include RNA and DNA molecules that encode protein biomarkers. rate of respiration, tissue hypoxia, metabolic acidosis or MOD.
  • Biomarkers may also include RNA and DNA molecules that encode protein biomarkers.
  • Biomarkers can also include at least one molecule involved in leukocyte modulation, such as neutrophil activation or monocyte deactivation. Increased expression of CD64 and CD11b is recognized as a sign of neutrophil and monocyte activation.
  • those biomarkers that can be useful in the present invention are those that are associated with macrophage lysis products, as can markers of changes in cytokine metabolism. (See Gagnon et al., Cell 110: 119-31 (2002); Oberholzer, et. al., supra; Vincent, et. al., supra.)
  • Biomarkers can also include signaling factors known to be involved or discovered to be involved in the inflammatory process. Signaling factors may initiate an intracellular cascade of events, including receptor binding, receptor activation, activation of intracellular kinases, activation of transcription factors, changes in the level of gene transcription and/or translation, and changes in metabolic processes, etc.
  • the signaling molecules and the processes activated by these molecules collectively are defined for the purposes of the present invention as “biomolecules involved in the sepsis pathway.”
  • the relevant predictive biomarkers can include biomolecules involved in the sepsis pathway.
  • biomarkers from a biological sample are contacted with an array that can be used to measure the amount of various biomarkers through direct and specific interaction with the biomarkers (e.g., an antibody array or a nucleic acid array).
  • an array that can be used to measure the amount of various biomarkers through direct and specific interaction with the biomarkers (e.g., an antibody array or a nucleic acid array).
  • the choice of the components of the array may be based on a suggestion that a particular pathway is relevant to the determination of the status of sepsis or SIRS in an individual.
  • the indication that a particular biomolecule has a feature that is predictive or diagnostic of sepsis or SIRS may give rise to an expectation that other biomolecules that are physiologically regulated in a concerted fashion likewise may provide a predictive or diagnostic feature.
  • the artisan will appreciate, however, that such an expectation may not be realized because of the complexity of biological systems. For example, if the amount of a specific mRNA biomarker were a predictive feature, a concerted change in mRNA expression of another biomarker might not be measurable, if the expression of the other biomarker was regulated at a post-translational level. Further, the mRNA expression level of a biomarker may be affected by multiple converging pathways that may or may not be involved in a physiological response to sepsis.
  • Biomarkers can be obtained from any biological sample, which can be, by way of example and not of limitation, blood, plasma, saliva, serum, urine, cerebral spinal fluid, sputum, stool, cells and cellular extracts, or other biological fluid sample, tissue sample or tissue biopsy from a host or patient.
  • biological sample can be, by way of example and not of limitation, blood, plasma, saliva, serum, urine, cerebral spinal fluid, sputum, stool, cells and cellular extracts, or other biological fluid sample, tissue sample or tissue biopsy from a host or patient.
  • the precise biological sample that is taken from the individual may vary, but the sampling preferably is minimally invasive and is easily performed by conventional techniques.
  • Measurement of a phenotypic change may be carried out by any conventional technique. Measurement of body temperature, respiration rate, pulse, blood pressure, or other physiological parameters can be achieved via clinical observation and measurement. Measurements of biomarker molecules may include, for example, measurements that indicate the presence, concentration, expression level, or any other value associated with a biomarker molecule. The form of detection of biomarker molecules typically depends on the method used to form a profile of these biomarkers from a biological sample. For instance, biomarkers separated by 2D-PAGE are detected by Coomassie Blue staining or by silver staining, which are well-established in the art.
  • useful biomarkers will include biomarkers that have not yet been identified or associated with a relevant physiological state.
  • useful biomarkers are identified as components of a biomarker profile from a biological sample. Such an identification may be made by any well-known procedure in the art, including immunoassay or automated microsequencing.
  • the biomarker may be isolated by one of many well-known isolation procedures.
  • the invention accordingly provides a method of isolating a biomarker that is diagnostic or predictive of sepsis comprising obtaining a reference biomarker profile obtained from a population of individuals, identifying a feature of the reference biomarker profile that is predictive or diagnostic of sepsis or one of the stages in the progression of sepsis, identifying a biomarker that corresponds with that feature, and isolating the biomarker.
  • the biomarker may be used to raise antibodies that bind the biomarker if it is a protein, or it may be used to develop a specific oligonucleotide probe, if it is a nucleic acid, for example.
  • biomarker The skilled artisan will readily appreciate that useful features can be further characterized to determine the molecular structure of the biomarker. Methods for characterizing biomolecules in this fashion are well-known in the art and include high-resolution mass spectrometry, infrared spectrometry, ultraviolet spectrometry and nuclear magnetic resonance. Methods for determining the nucleotide sequence of nucleic acid biomarkers, the amino acid sequence of polypeptide biomarkers, and the composition and sequence of carbohydrate biomarkers also are well-known in the art.
  • the presently described methods are used to screen SIRS patients who are particularly at risk for developing sepsis.
  • a biological sample is taken from a SIRS-positive patient, and a profile of biomarkers in the sample is compared to a reference profile from SIRS-positive individuals who eventually progressed to sepsis.
  • Classification of the patient's biomarker profile as corresponding to the reference profile of a SIRS-positive population that progressed to sepsis is diagnostic that the SIRS-positive patient will likewise progress to sepsis.
  • a treatment regimen may then be initiated to forestall or prevent the progression of sepsis.
  • the presently described methods are used to confirm a clinical suspicion that a patient has SIRS.
  • a profile of biomarkers in a sample is compared to reference populations of individuals who have SIRS or who do not have SIRS. Classification of the patient's biomarker profile as corresponding to one population or the other then can be used to diagnose the individual as having SIRS or not having SIRS.
  • the first population (“the SIRS group”) represents patients who developed SIRS and who entered into the present study at “Day 1” but who did not progress to sepsis during their hospital stay.
  • the second population (“the sepsis group”) represents patients who likewise developed SIRS and entered into the present study at Day 1 but who progressed to sepsis typically at least several days after entering the study. Blood samples were taken about every 24 hours from each study group. Clinical suspicion of sepsis in the sepsis group occurred at “time 0.” “Time—24 hours” and “time—48 hours” represent samples taken 24 hours and 48 hours, respectively, preceding the day of clinical suspicion of the onset of sepsis in the sepsis group.
  • the samples from the sepsis group included those taken on the day of entry into the study (Day 1), 48 hours prior to clinical suspicion of sepsis (time—48 hours), 24 hours prior to clinical suspicion of sepsis (time—24 hours), and on the day of clinical suspicion of the onset of sepsis (time 0).
  • RNA M ETHODOLOGIES A L ABORATORY G UIDE FOR I SOLATION AND C HARACTERIZATION , 2 nd ed., R. E. Farrell, Jr., ed., Academic Press (1998) at pp. 55-104.
  • a filter-based total RNA isolation approach may be used, as described for the use with the RNAqueousTM system (Phenol-Free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Tex.). The procedures used for RNA isolation and subsequent manipulations are further described in WO 03/040404, which is assigned to Source Precision Medicine.
  • RNA species were amplified using message specific primers or random primers.
  • Specific primers were designed from data obtained from public databases, such as Unigene (National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.). Primers were designed to amplify specific RNA sequences present in the sample using RT-PCR, using principles generally known by one of ordinary skill in the art.
  • RNA sequences may be amplified using either an isothermic or thermic cycler, such as with an ABI9600, 9700, or 7700 cycler (Applied Biosystems, Foster City, Calif.) Amplified RNAs may then be detected using, for example, fluorescent-tagged detection primers, as described in the TaqManTM system (PCR Reagent Kit, Protocol, part number 402823, revision A (1996), Applied Biosystems, Foster City, Calif.). RNA detection and quantification may be performed by techniques well-known in the art. (See, e.g., WO 03/040404 at page 23, lines 5-13.)
  • RNA isolation from a patient's blood and purification of the RNA Following RNA isolation from a patient's blood and purification of the RNA, the following protocol was used to amplify the RNA and react it with a 72-member gene expression array having the nucleic acid probes set forth in TABLE 1:
  • Kit Components 10 ⁇ TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTP mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL), and RNase/DNase free water (DEPC-Treated Water from Ambion (product number 9915G), or equivalent).
  • RNA samples were removed from the ⁇ 80° C. freezer, thawed at room temperature and placed immediately on ice.
  • RNA sample was brought up to a total volume of 20 mL in a 1.5 mL microcentrifuge tube and 80 mL RT reaction mix were added. The solution was mixed by pipetting up and down.
  • the plate was sealed with Applied Biosystems Optical Caps, or optical-clear film.
  • the number of amplification cycles required to produce a threshold level of fluorescence provides a semi-quantitative estimate of the amount of mRNA corresponding to each gene that was in the sample.
  • the average coefficient of variation (SD/average ⁇ 100) of measurement is typically less than 5%, and may be less than 2%.
  • a method of quantifying real-time PCR amplification is described in Hirayama et al., Blood 92: 46-52 (1998), for example. Additional aspects of quantitative real-time amplification are described in WO 03/040404 at pages 20-23, for example, and variations of such methods are well-known in the art.
  • each sample the concentrations of the mRNAs that bound 72 different cDNA probes on the expression array were measured.
  • each mRNA is a biomarker, and the concentration of each in the sample can be a feature of that biomarker.
  • the concentration of the biomarkers was determined by their ability to form specific duplexes with the various cDNA probes of the array.
  • the cDNA probes of the array correspond to genes that encode proteins that include various cytokines, chemokines or growth factors, cell markers, proteinases or proteinase inhibitors, receptors, transcription regulators, and enzymes, as shown in TABLE 1.
  • the probes indicated with the asterisks are present on the array described at pages 46-52 of WO 03/040404.
  • a selection bias can affect the identification of features that inform a decision rule, when the decision rule is based on a large number of features from relatively few biomarker profiles. (See Ambroise et al., Proc. Nat'l Acad. Sci. USA 99: 6562-66 (2002).) Selection bias may occur when data are used to select features, and performance then is estimated conditioned on the selected features with no consideration made for the variability in the selection process. The result is an overestimation of the classification accuracy. Without compensation for selection bias, classification accuracies may reach 100%, even when the decision rule is based on random input parameters.
  • Selection bias may be avoided by including feature selection in the performance estimation process, whether that performance estimation process is 10-fold cross-validation or a type of bootstrap procedure. (See, e.g., Hastie et al., supra, at 7.10-7.11, herein incorporated by reference.)
  • model performance is measured by ten-fold cross-validation.
  • Ten-fold cross-validation proceeds by randomly partitioning the data into ten exclusive groups. Each group in turn is excluded, and a model is fitted to the remaining nine groups. The fitted model is applied to the excluded group, and predicted class probabilities are generated. The predicted class probabilities can be compared to the actual class memberships by simply generating predicted classes. For example, if the probability of sepsis is, say, greater than 0.5, the predicted class is sepsis.
  • Deviance is a measure comparing probabilities with actual outcomes.
  • “deviance” is defined as: - 2 ⁇ ⁇ ⁇ sepsis ⁇ ⁇ cases ⁇ ⁇ ln ⁇ ( P ⁇ ( sepsis ) ) + ⁇ SIRS ⁇ ⁇ cases ⁇ ⁇ ln ⁇ ( P ⁇ ( SIRS ) ) ⁇
  • a “classification tree” is a recursive partition to classify a particular patient into a specific class (e.g., sepsis or SIRS) using a series of questions that are designed to accurately place the patient into one of the classes. Each question asks whether a patient's condition satisfies a given predictor, with each answer being used to guide the user down the classification tree until a class into which the patient falls can be determined.
  • a “predictor” is a range of values for a feature. In this Example, the feature is a concentration of a nucleic acid biomarker.
  • the nucleic acid biomarkers may be selected from those listed in any one of TABLES 2-10, but the skilled artisan will understand that other nucleic acid biomarkers may be useful for the invention.
  • the “condition” is the single, specific value of the feature that is measured in the individual's biomarker profile.
  • the “class names” are sepsis and SIRS.
  • the classification tree user first will ask if a first nucleic acid biomarker concentration measured in the individual's biomarker profile falls within a given range of the first feature's predictive range. The answer to the first question may be dispositive in determining if the individual has SIRS or sepsis.
  • the answer to the first question may further direct the user to ask if a second nucleic acid biomarker concentration measured in the individual's biomarker profile falls within a given range of the second feature's predictive range.
  • the answer to the second question may be dispositive or may direct the user further down the classification tree until a patient classification is ultimately determined.
  • MART multiple additive regression trees
  • a MART model uses an initial offset, which specifies a constant that applies to all predictions, followed by a series of regression trees. Its fitting is specified by the number of decision points in each tree, the number of trees to fit, and a “granularity constant” that specifies how radically a particular tree can influence the MART model. For each iteration, a regression tree is fitted to estimate the direction of steepest descent of the fitting criterion. A step having a length specified by the granularity constant is taken in that direction. The MART model then consists of the initial offset plus the step provided by the regression tree. The differences between the observed and predicted values are recalculated, and the cycle proceeds again, leading to a progressive refinement of the prediction. The process continues either for a predetermined number of cycles or until some stopping rule is triggered.
  • the number of splits in each tree is a particularly meaningful fitting parameter. If each tree has only one split, the model looks only at one feature and has no capability for combining two predictors. If each tree has two splits, the model can accommodate two-way interactions among features. With three trees, the model can accommodate three-way interactions, and so forth.
  • MART provides a measure of the contribution or importance of individual features to the classification decision rule. Specifically, the degree to which a single feature contributes to the decision rule upon its selection at a given tree split can be measured to provide a ranking of features by their importance in determining the final decision rule. Repeating the MART analysis on the same data set may yield a slightly different ranking of features, especially with respect to those features that are less important in establishing the decision rule.
  • Sets of predictive features and their corresponding biomarkers useful in the present invention therefore, may vary slightly from those set forth herein.
  • MART MART
  • package for the R statistical programming environment
  • results reported in this document were calculated using R versions 1.7.0 and 1.7.1.
  • the MART algorithm is amenable to ten-fold cross-validation.
  • the granularity parameter was set to 0.05, and the gbm package's internal stopping rule was based on leaving out 20% of the data cases at each marked iteration.
  • the degree of interaction was set to one, so no interactions among features were considered.
  • the gbm package estimates the relative importance of each feature on a percentage basis, which cumulatively equals 100% for all the features of the biomarker profile.
  • the features with highest importance which together account for at least 90% of total importance, are reported as potentially having predictive value.
  • the stopping rule in the fitting of every MART model contributes a stochastic component to model fitting and feature selection. Consequently, multiple MART modeling runs based on the same data choose slightly, or possibly even completely, different sets of features. Such different sets convey the same predictive information; therefore, all the sets are useful in the present invention. Fitting MART models a sufficient number of times is expected to produce all the possible sets of predictive features within a biomarker profile. Accordingly, the disclosed sets of predictors are merely representative of those sets of features that can be used to classify individuals into populations.
  • the fitted model included 23 trees, and the model allowed no interactions among the features.
  • the model correctly classified 19 of 24 SIRS patients and 15 of 23 sepsis patients, giving a model sensitivity of 65% and a specificity of 79%.
  • the biomarkers are ranked in order of importance, as determined by the model, in TABLE 2. All features with zero importance are excluded. Markers indicated with a sign of “ ⁇ 1” were expressed at progressively higher levels in sepsis-positive populations as sepsis progressed, while those biomarkers with a sign of “1” were expressed at progressively lower levels.
  • the fitted model included 12 trees, and the model allowed no interactions among the features. Using ten-fold cross-validation, the model correctly classified 15 of 21 SIRS patients and 17 of 24 sepsis patients, giving a model sensitivity of 71% and a specificity of 71%.
  • the biomarkers are ranked in order of importance, as determined by the model, in TABLE 3.
  • the fitted model included nine trees, and the interactions among the features. Using ten-fold cross-validation, the model correctly classified 8 of 21 SIRS patients and 20 of 24 sepsis patients, giving a model sensitivity of 83%, a specificity of 38%, and an accuracy of 62%. The biomarkers are ranked in order of importance, as determined by the model, in TABLE 4. TABLE 4 feature importance by MART analysis: time -48 hours samples Biomarker Importance Sign 1 CD4 49.232 1 2 ARG2 18.450 ⁇ 1 3 MMP9 13.778 ⁇ 1 4 HSPA1A 10.662 ⁇ 1 5 LBP 7.8786 1
  • Logistic regression provides yet another means of analyzing a data stream from the analysis described above.
  • a nonparametric test such as a Wilcoxon Signed Rank Test can be used to identify individual biomarkers of interest.
  • the features in a biomarker profile are assigned a “p-value,” which indicates the degree of certainty with which the biomarker can be used to classify individuals as belonging to a particular reference population.
  • a p-value having predictive value is lower than about 0.05.
  • Biomarkers having a low p-value can be used by themselves to classify individuals.
  • combinations of two or more biomarkers can be used to classify individuals, where the combinations are chosen on the basis of the relative p-value of a biomarker.
  • biomarkers with lower p-values are preferred for a given combination of biomarkers.
  • Combinations of at least three, four, five, six, 10, 20 or 30 or more biomarkers also can be used to classify individuals in this manner.
  • the artisan will understand that the relative p-value of any given biomarker may vary, depending on the size of the reference population.
  • biomarkers that formed specific duplexes with (i.e., hydridized to) an expression array having the probes listed in TABLE 1 were assigned a p-value by comparison of sepsis and SIRS populations at a given time. That is, p-values were assigned to features from biomarker profiles obtained from biological samples taken at time 0 hours, time—24 hours, and time—48 hours. These p-values are listed in TABLES 5, 6 and 7, respectively.
  • the sepsis and SIRS populations at time 0 (TABLE 5) constituted 23 and 24 patients, respectively; and the sepsis and SIRS populations at time—24 hours and time —48 hours (TABLES 6 and 7) constituted 24 and 21 patients, respectively.
  • a nonparametric test (e.g., a Wilcoxon Signed Rank Test) alternatively can be used to find p-values for features that are based on the progressive appearance or disappearance of the feature in populations that are progressing toward sepsis.
  • a baseline value for a given feature is measured, using the data from the time of entry into the study (Day 1 samples) for the sepsis and SIRS groups.
  • the feature intensity in sepsis and SIRS samples is then compared in, for example, time—48 hour samples, to determine whether the feature intensity has increased or decreased from its baseline value.
  • p-values are assigned to the difference from baseline in a feature intensity in the sepsis populations versus the SIRS populations.

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