WO2007072225A2 - Methods and devices for identifying biomarkers of treatment response and use thereof to predict treatment efficacy - Google Patents

Methods and devices for identifying biomarkers of treatment response and use thereof to predict treatment efficacy Download PDF

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Publication number
WO2007072225A2
WO2007072225A2 PCT/IB2006/004048 IB2006004048W WO2007072225A2 WO 2007072225 A2 WO2007072225 A2 WO 2007072225A2 IB 2006004048 W IB2006004048 W IB 2006004048W WO 2007072225 A2 WO2007072225 A2 WO 2007072225A2
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Prior art keywords
gene
treatment
expression
patient
level
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PCT/IB2006/004048
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French (fr)
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WO2007072225A8 (en
WO2007072225A3 (en
Inventor
Steen Knudsen
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Medical Prognosis Institute
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Priority to CN200680052220.2A priority Critical patent/CN101365806B/en
Priority to CA2631236A priority patent/CA2631236C/en
Priority to EP06848658A priority patent/EP1960551A2/en
Priority to JP2008542865A priority patent/JP5984324B2/en
Publication of WO2007072225A2 publication Critical patent/WO2007072225A2/en
Priority to US12/151,949 priority patent/US8445198B2/en
Publication of WO2007072225A3 publication Critical patent/WO2007072225A3/en
Publication of WO2007072225A8 publication Critical patent/WO2007072225A8/en

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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
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • 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
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • 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

Definitions

  • the invention features methods and devices for identifying biomarkers of patient sensitivity to medical treatments, e.g., sensitivity to chemotherapeutic agents, and predicting treatment efficacy using the biomarkers.
  • DNA microarrays have been used to measure gene expression in tumor samples from patients and to facilitate diagnosis. Gene expression can reveal the presence of cancer in a patient, its type, stage, and origin, and whether genetic mutations are involved. Gene expression may even have a role in predicting the efficacy of chemotherapy.
  • NCI National Cancer Institute
  • the NCI has also measured gene expression in those 60 cancer cell lines using DNA microarrays.
  • Various studies have explored the relationship between gene expression and compound effect using the NCI datasets.
  • the invention features methods and devices for predicting the sensitivity or resistance of a patient, e.g., a cancer patient, to a treatment, e.g., treatment with a compound, such as a / chemotherapeutic agent, or radiation.
  • a treatment e.g., treatment with a compound, such as a / chemotherapeutic agent, or radiation.
  • the methods and devices can be used to predict the sensitivity or resistance of a cancer patient to any medical treatment, including, e.g., treatment with a compound, drug, or radiation.
  • the devices and methods of the invention have been used to accurately predict treatment efficacy in cancer patients (e.g., patients with lung, lymphoma, and brain cancer) and can be used to predict treatment efficacy in patients diagnosed with any cancer.
  • Vincristine, Cisplatin Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara- C, Methy
  • the methods and devices can be used to predict the sensitivity or resistance of a subject (e.g., a cancer patient) diagnosed with a disease condition, e.g., cancer (e.g., cancers of the breast, prostate, lung and bronchus, colon and rectum, urinary bladder, skin, kidney, pancreas, oral cavity and pharynx, ovary, thyroid, parathyroid, stomach, brain, esophagus, liver and intrahepatic bile duct, cervix larynx, heart, testis, small and large intestine, anus, anal canal and anorectum, vulva, gallbladder, pleura, bones and joints, hypopharynx, eye and orbit, nose, nasal cavity and middle ear, nasopharynx, ureter, peritoneum, omentum and mesentery, or gastrointestines, as well as any form of cancer including, e.g., chronic myeloid leukemia, acute lymphoc
  • the invention features a method of predicting sensitivity of a cancer patient to a treatment for cancer by determining the expression level of at least one gene in a cell (e.g., a cancer cell) of the patient, in which the gene is selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADAMTSl, ADDl, AFlQ, AIFl, AKAPl, AKAP13, AKRlCl, AKTl, ALDH2, ALDOC, ALG5, ALMSl, ALOX15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP 15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7.
  • a cell e.g., a cancer cell
  • the gene is selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADA
  • the method includes determining the expression of two of the listed genes, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes.
  • the change in the level of gene expression is determined relative to the level of gene expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of gene expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment
  • the change in the level of gene expression is determined relative to the level of gene expression in a cell or tissue known to be resistant to the treatment, such that a similar level of gene expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.
  • the at least one gene is selected from the group consisting of RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, BLMH, CTBPl, TBCA, MDH2, and DXS9879E, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Vincristine.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of UBB, B2M, MANlAl, and SUIl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Vincristine.
  • the at least one gene is selected from the group consisting of ClQRl, SLA, PTPN7, ZNFNlAl, CENTBl, IFI16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl, ARHGAPl 5, TM6SF1, and TCF4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Cisplatin.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of HCLSl, CD53, PTPRCAP 5 and PTPRC, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Cisplatin.
  • the at least one gene is selected from the group consisting of SRM 5 SCARBl, SIATl, CUGBP2, ICAMl 5 WASPIP, ITM2A, PALM2-AKAP2, PTPNSl 3 MPPl, LNEC 5 FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl 5 IFI16, MCAM, MEF2C, SLC1A4, FYN, Clorf38, CHSl, FCGR2C, TNDC, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl, and ADAMTS 1, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Azaguanine.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN, SPARC, VIM 5 GAS7, ANPEP, EMP3, BTN3A2, FNl, and CAPN3, wherein an increase in expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Azaguanine.
  • the at least one gene is selected from the group consisting of CD99, ENSIGl, PRGl, MUFl, SLA, SSBP2, GNB5, MFNG 5 PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFNlAl, CENTBl, NAPlLl, HLA-DRA, IFIl 6, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAP15, NOTCHl, and UBASH3A, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Etoposide.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, HCLSl 5 CD53, GMFG, PTPRCAP, PTPRC, COROlA, and ITK, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Etoposide.
  • the at least one gene is selected from the group consisting of CD99, ALDOC, SLA 5 SSBP2, EL2RG, CXorf9, RHOH, ZNFNlAl, CENTBl, CDlC, MAP4K1, CD3G, CCR9, CXCR4, AJRHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AlFl, ARHGAPl 5, NARF, and PACAP, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Adriamycin.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, HCLSl, CD53, GMFG, PTPRCAP, TCF7, CDlB, PTPRC, COROlA, HEMl, and ITK, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Adriamycin.
  • the at least one gene is selected from the group consisting of RPL12, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Aclarubicin.
  • the method further includes measuring the expression level of at . least one gene selected from the group consisting of RPL32, FBL, and PTPRC, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Aclarubicin.
  • the at least one gene is selected from the group consisting of PGAMl, DPYSL3, INSIGl 5 GJAl, BNTP3, PRGl, G6PD, PLOD2, LOXL2, SSBP2, Clorf29, TOX, STCl 3 TNFRSFlA, NCOR2, NAPlLl, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ, MAPlB, TRIM22, CD3D, BCATl, IFI44, CUTC 5 NAP1L2, NME7, FLJ21159, and COL5A2, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitoxanthrone.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of BASPl, COL6A2, PTPRC, PRKCA, CCL2, and RAB31, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitoxantrone.
  • the at least one gene is selected from the group consisting of STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitomycin.
  • the at least one gene is selected from the group consisting of RPLlO, RPS4X, NUDC 5 DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS 5 CEPl 3 IL13RA2, MAGEB2, HMGN2 5 ALMSl 5 GPR65, FLJ10774, NOL8, DAZAPl 5 SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC 5 and KIAA1393, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Paclitaxel.
  • the method further includes measuring the expression level of RALY, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Paclitaxel.
  • the at least one gene is selected from the group consisting of PFNl, PGAMl, K-ALPHA-I 5 CSDA 5 UCHLl.
  • the at least one gene is selected from the group consisting of ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRIP 13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Taxotere.
  • the at least one gene is selected from the group consisting of IFITM2, UBE2L6, USP4, ITM2A, EL2RG, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, ZNFNlAl 5 CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP 5 LAIRl, SH2D1A, SEPT6, HA-I 5 ERCC2, CDSD 5 LSTl, AIFl, ADA, DATFl, ARHGAP 15, PLAC8, CECRl 5 LOC81558, and EHD2, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Dexamethasone.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, ITGB2, ANPEP 5 CD53, CD37, AD0RA2A, GNA15, PTPRC, COROlA, HEMl, FLH, and CREB3L1, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Dexamethasone.
  • at least one gene selected from the group consisting of LAPTM5, ITGB2, ANPEP 5 CD53, CD37, AD0RA2A, GNA15, PTPRC, COROlA, HEMl, FLH, and CREB3L1, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Dexamethasone.
  • the at least one gene is selected from the group consisting of ITM2A, RHOH.
  • PRIMl 5 CENTBl, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA 3 and FHODl such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Ara-C.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of GNAl 5, PTPRC, and RPLl 3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Ara-C.
  • the at least one gene is selected from the group consisting of CD99, ARHGDIB 5 VWF, ITM2A, LGALS9, INPP5D, SATBl, TFDP2, SLA 5 IL2RG, MFNG, SELL 5 CDW52, LRMP, ICAM2 5 RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl 5 CENTBl 5 LCP2, SPIl, GZMA 5 CEPl, CD8A, SCAPl 5 CD2, CDlC 5 TNFRSF7, VAVl, MAP4K1, CCR7, C6orf32 5 ALOXl 5B, BRDT, CD3G, LTB 3 ATP2A3, NVL, RASGRP2, LCPl, CXCR4, PRKD2, GATA3, TRA@ 5 KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDl
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of SRRMl, LAPTM5, ITGB2, CD53, CD37, GMFG, PTPRCAP, GNA15, BLM, PTPRC 5 COROlA 5 PRKCBl 5 HEMl 5 and UGT2B17, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methylprednisolone.
  • the at least one gene is selected from the group consisting of PRPF8, RPLl 8, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBSl, SNRPA 5 IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRTMl, PFDN5, OXAlL 5 H3F3A, ATIC 3 CIAPlNl 3 RPS2, PCCB 3 SHMT2, RPLPO 3 HNRPAl, STOML2, SKBl 3 GLTSCR2, CCNBlIPl 3 MRPS2, FLJ20859 3 and FLJ12270, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methotrexate.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of RNPSl, RPL32, EEFlG, PTMA 3 RPLl 3, FBL 5 RBMX, and RP S 9, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methotrexate.
  • the at least one gene is selected from the group consisting of PFNl, HKl 3 MCLl 3 ZYX, RAPlB 3 GNB2, EPASl 3 PGAMl 3 CKAP4, DUSPl 3 MYL9, K- ALPHA-I, LGALSl 3 CSDA 3 IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMBl, FHLl, INSIGl, TIMPl, GJAl 3 PSME2, PRGl 3 EXTl, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAPl, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STCI 3 PTGER4, EL6, SMAD3, WNT5A, BDNF, TNFRSFlA, FLNC 3 DKF
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN 3 ACTR2.
  • AKRlBl VIM 3 ITGA3, OPTN, M6PRBP1, COLlAl, BASPl, ANPEP, TGFBl 3 NFIL3, NK4, CSPG2, PLAU 3 COL6A2, UBC, FGFRl, BAX 3 COL4A2, and RAB31, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Bleomycin.
  • the at least one gene is selected from the group consisting of SSRPl, NUDC, CTSC 3 AP1G2, PSME2, LBR, EFNB2, SERPINAl 3 SSSCAl 3 EZH2, MYB, PRJ-Ml, H2AFX, HMGAl 3 HMMR 3 TK2 3 WHSCl, DIAPHl 3 LAMB3, DPAGTl, UCK2, SERPHSBl, MDNl 5 BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, BDSTRPD, and PNAS-4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methyl-GAG.
  • the method further includes measuring the expression level of PTMA, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methyl-GAG.
  • the at least one gene is selected from the group consisting of ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, BFIl 6, LRRN3, DOCKlO, LEPREl 5 COL5A2, and ADAMTSl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Carboplatin.
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN, VIM, CSPG2, and FGFRl 5 such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Carboplatin.
  • the at least one gene is selected from the group consisting of RPL18, RPLlOA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl 3 APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPLl 7, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Fluorouracil (5-FU).
  • 5-Fluorouracil 5-Fluorouracil
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of RNPSl, RPL13, RPS6, and RPL3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Fluorouracil (5-FU).
  • RNPSl 5-Fluorouracil
  • the at least one gene is selected from the group consisting of KIFCl, VLDLR, RUNXl, PAFAH1B3, HlFX, RNF144, TMSNB 5 CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, CDlD, ADA, CD99, CD2, CNP, ERG, CD3E, CDlA, PSMC3, RPS4Y1, AKTl, TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl, AKRlCl, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ,
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of ITK 5 RALY, PSMC5, MYL6, CDlB, STMNl, GNA15, MDK, CAPG, ACTNl, CTNNAl, FARSLA, E2F4, CPSFl 5 SEPWl 5 TFRC, ABLl 5 TCF7, FGFRl 5 NUCB2, SMA3, FAT, VIM, and ATP2A3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Rituximab.
  • at least one gene selected from the group consisting of ITK 5 RALY, PSMC5, MYL6, CDlB, STMNl, GNA15, MDK, CAPG, ACTNl, CTNNAl, FARSLA, E2F4, CPSFl 5 SEPWl 5 TFRC, ABLl 5 TCF7, FGFRl 5 NUCB2, SMA3, FAT, VIM, and ATP
  • the at least one gene is selected from the group consisting of TRAl 5 ACTN4, CALMl 5 CD63, FKBPlA, CALU, IQGAPl 5 MGC8721, STATl, TACCl 5 TM4SF8, CD59, CKAP4, DUSPl 5 RCNl 5 MGC8902, LGALSl 5 BHLHB2, RRBPl 5 PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl 5 PRSS23, PLOD2, NPCl 3 TOBl, GFPTl 5 IL8, PYGL, LOXL2, KIAA0355, UGDH 5 PURA, ULK2, CENTG2, NID2, CAP350, CXCLl 5 BTN3A3, IL6, WNT5A, FOXF
  • the method further includes measuring the expression level of at least one gene selected from the group consisting of WARS, CD81 , CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAKl, EIF4G3, COLlAl, DYRK2, NF1L3, ACTNl, CAPN2, BTN3A2, IGFBP3, FNl, COL4A2, and KPNBl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with, radiation therapy.
  • at least one gene selected from the group consisting of WARS, CD81 , CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAKl, EIF4G3, COLlAl, DYRK2, NF1L3, ACTNl, CAPN2, BTN3A2, IGFBP3, FNl, COL4A2, and KPNBl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with,
  • the at least one gene is selected from the group consisting of FAU, N0L5A, ANP32A, ARHGDIB 5 LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG 3 GPSM3, PIM2.
  • the at least one gene is selected from the group consisting of CD99, SNRPA 5 CUGBP2, STAT5A, SLA 5 IL2RG, GTSEl 5 MYB 5 PTPN7, CXorf9, RHOH, ZNFNlAl, CENTBl 5 LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCPl, SELPLG, CD3Z, PRKCQ 5 GZMB 5 SCN3A, LAIRl, SH2D1A, SEPT6, CG018, CD3D, C18orflO, PRFl, AIFl, MCM5, LPXN, C22orfl 8, ARHGAP15, and LEFl , such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Aza-2'- deoxycytidine (Dec ⁇ tabine).
  • a second aspect of the invention features a method for determining the development of resistance by a patient (i.e,. a cell, such as a cancer cell, in the patient) to a treatment that the patient was previously sensitive to.
  • the method includes determining the level of expression of one or more of the genes set forth in the first aspect of the invention, such that an increase in the expression level of a gene(s) which is decreased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.
  • an decrease in the expression level of a gene(s) which is increased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.
  • a third aspect of the invention features a kit that includes a single-stranded nucleic acid (e.g., deoxyribonucleic acid or ribonucleic acid) that is complementary to or identical to at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the genes set forth in the first aspect of the invention, such that the single stranded nucleic acid is sufficient for the detection of expression of the gene(s) by allowing specific hybridization between the single stranded nucleic acid and a nucleic acid encoded by the gene, or a complement thereof.
  • a single-stranded nucleic acid e.g., deoxyribonucleic acid or
  • the kit further includes instructions for applying nucleic acids collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the gene(s) hybridized to the single stranded nucleic acid, and predicting the patient's sensitivity to a treatment for cancer when use of the kit establishes that the expression level of the gene(s) is changes (i.e., increased or decreased relative to a control sample (i.e., tissue or cell) known to be sensitive or resitant to the treatment, as is discussed above in connection with the first aspect of the invention).
  • a control sample i.e., tissue or cell
  • the instructions further indicate that an alteration in the expression level of the gene(s) relative to the expression of the gene(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (i.e., a decrease in the level of expression of a gene known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
  • a control sample e.g., a cell or tissue known to be sensitive or resistant to the treatment
  • the kit can be utilized to determine a patient's resistance or sensitivity to Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Riruximab, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine) by determining the expression level of one or more of the genes set forth in the first aspect of the invention and known to be increased in a patient sensitive to treatment with these agents (i.e., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the gene(s) increases relative to the level of expression of the gene(s) in a control sample (i.e.
  • the nucleic acids are characterized by their ability to specifically identify nucleic acids complementary to the genes in a sample collected from a cancer patient.
  • a fourth aspect of the invention features a method of identifying biomarkers indicative of sensitivity of a cancer patient to a treatment for cancer by obtaining pluralities of measurements of the expression level of a gene (e.g., by detection of the expression of a gene using a single gene probe or by using multiple gene probes directed to a single gene) in different cell types and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of the cell types in the absence of the treatment for cancer; correlating each plurality of measurements of the expression level of the gene in cells with the growth of the cells to obtain a correlation coefficient; selecting the median correlation coefficient calculated for the gene; and identifying the gene as a biomarker for use in determining the sensitivity of a cancer patient to said treatment for cancer if said median correlation coefficient exceeds 0.3 (preferably the gene is identified as a biomarker for a patient's sensitivity to a treatment if the correlation coefficient exceeds 0.4, 0.5, 0.6, 0.7, 0.8. 0.9, 0.95, or 0.99 or
  • a fifth aspect of the invention features a method of predicting sensitivity of a patient (e.g., a cancer patient) to a treatment for cancer by obtaining a measurement of a biomarker gene expression from a sample (e.g., a cell or tissue) from the patient; applying a model predictive of sensitivity to a treatment for cancer to the measurement, in which the model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks; and predicting whether or not the patient will be responsive to the treatment for cancer.
  • the measurement is obtained by assaying gene expression of the biomarker in a cell known to be sensitive or resistant to the treatment.
  • the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements.
  • treatment e.g., a compound
  • the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker gene expression.
  • the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis.
  • a sixth aspect of the invention features a kit, apparatus, and software used to implement the method of the fifth aspect of the invention.
  • the expression level of the gene(s) is determined by detecting the level of mRNA transcribed from the gene(s), by detecting the level of a protein product of the gene(s), or by detecting the level of the biological activity of a protein product of the gene(s).
  • an increase or decrease in the expression level of the gene(s), relative to the expression level of the gene(s) in a cell or tissue sensitive to the treatment indicates increased sensitivity of the cancer patient to the treatment.
  • an increase or decrease in the expression level of the gene(s), relative to the expression level of the gene(s) in a cell or tissue resistant to the treatment indicates increased resistance of the cancer patient to the treatment.
  • the cell is a cancer cell.
  • the expression level of the gene(s) is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
  • qRT-PCR quantitative reverse transcription-polymerase chain reaction
  • the level of expression of two of the listed genes is performed, more preferably the level of expression of three, four, five, six, seven, eight, nine, or ten of the listed genes is performed, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes is performed.
  • the expression level of the gene(s) is determined using the kit of the third aspect of the invention.
  • the treatment is a compound, such as a chemotherapteutic agent selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine).
  • a chemotherapteutic agent selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C
  • the compound has previously failed to show effect in a subject (e.g., a subject selected from a subpopulation predicted to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
  • a subject e.g., a subject selected from a subpopulation predicted to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
  • the treatment is, e.g., administration of a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient.
  • the treatment is, e.g., a chemotherapeutic agent, such as, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), MABTHERATM (Rituximab), histone deacetylase (HDAC) inhibitors, 5-Aza-2'-deoxycy
  • a chemotherapeutic agent such as, e.g., Vincristine
  • Combretestatin A-4 Crisnatol Mesylate, Cyclophosphamide, Cytarabine, dacarbazine, DACA (N- [2- (Dimethyl- amino) ethyl] acridine-4-carboxamide), Dact ⁇ nornycin, Daunorubicin Hydrochloride, Daunomycin, Decitabine, Dexormaplatin, Dezaguanine, Dezaguanine Mesylate, Diaziquone, Docetaxel, Dolasati ⁇ s, Doxorubicin, Doxorubicin Hydrochloride, Droloxifene, Droloxifene Citrate, Dromostanolone Propionate, Duazomycin.
  • Edatrexate Eflornithine Hydrochloride, Ellipticine, Elsamitrucin, Enloplatin, Enpromate, Epipropidine, Epirubicin Hydrochloride, Erbulozole, Esorubicin Hydrochloride, Estramustine, Estramustine Phosphate Sodium, Etanidazole, Ethiodized Oil 1 131, Etoposide, Etoposide Phosphate, Etoprine, Fadrozole Hydrochloride, Fazarabine, Fenretinide, Floxuridine, Fludarabine Phosphate.
  • cryptophycin 8 cryptophycin A derivatives, curacin A, cyclopentanthraquinones, cycloplatam, cypemycin, cytarabine ocfosfate, cytolytic factor, cytostatin.
  • nedaplatin nemorubicin, neridronic acid, neutral endopeptidase, nilutamide, nisamycin, nitric oxide modulators, nitroxide antioxidant, nitrullyn, 06-benzylguani ⁇ e, octreotide, okicenone, oligonucleotides, onapristone, ondansetron, ondansetron, oracin, oral cytokine inducer, ormaplatin, osaterone, oxaliplatin, oxaunomycin, paclitaxel analogues, paclitaxel derivatives, palauamine, palmitoylrhizoxin, pamidronic acid, panaxytriol, panomifene, parabactin, pazelliptine, pegaspargase, peldesine, pentosan polysulfate sodium, pentostatin, pentrozole, perflubron, perfosf
  • sarcophytol A sargramostim, Sdi 1 mimetics, semustine, senescence derived inhibitor 1, sense oligonucleotides, signal transduction inhibitors, signal transduction modulators, single chain antigen binding protein, sizofiran, sobuzoxane, sodium borocaptate, sodium phenylacetate, solverol, somatomedin binding protein, sonermin, sparfosic acid, spicamycin D, spiromustine, splenopentin, spongistatin 1 , squalamine, stem cell inhibitor, stem-cell division inhibitors, stipiamide, stromelysin inhibitors, sulfinosine, superactive vasoactive intestinal peptide antagonist, suradista, suramin, swainsonine, synthetic glycosaminoglycans, tallimustine, tamoxifen methiodide, tauromustine, tazarotene, tecogalan
  • the gene is selected from the group consisting of ABLl, ACTB, ACTNl, ACTN4, ACTR2, ADA, ADAM9, ADAMTSl, ADDl, ADORA2A, AFlQ, AIFl, AKAPl, AKAP13, AKRlBl, AKRlCl, AKTl, ALDH2, ALDH3A1, ALDOC, ALG5, ALMSl, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANPEP 3 ANXAl, ANXA2, AP1G2, APOBEC3B, APRT, ARHE, ARHGAPl 5, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC 5 ATOXl 5 ATP1B3, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1
  • nucleic acid sequence of each of the listed genes is publicly available through the Genba ⁇ k database.
  • the gene sequences are also included as part of the HG-U133A GeneChip from Affymetrix, Inc.
  • Resistance means that a cell, a tumor, a person, or a living organism is able to withstand treatment, e.g., with a compound, such as a chemotherapeutic agent or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, person, or living organism by less than 10%, 20%, 30%, 40%, 50%, 60%, or 70% relative to the growth of a cell not exposed to the treatment Resistance to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, greater absorbance indicates greater cell growth, and thus, resistance to the treatment. A smaller reduction in growth indicates more resistance to a treatment. By “chemoresistant” or “chemoresistance” is meant resistance to a compound.
  • Sensitive or “sensitivity” as used herein means that a cell, a tumor, a person, or a living organism is responsive to treatment, e.g., with a compound, such as a chemotherapeutic agent or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, person, or living organism by 70%, 80%, 90%, 95%, 99% or 100%.
  • Sensitivity to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, lesser absorbance indicates lesser cell growth, and thus, .
  • sensitivity to the treatment. A greater reduction in growth indicates more sensitivity to the treatment.
  • chemosensitivity is meant sensitivity to a compound.
  • “Complement” of a nucleic acid sequence or a “complementary” nucleic acid sequence as used herein refers to an oligonucleotide which is in "antiparallel association" when it is aligned with the nucleic acid sequence such that the 5' end of one sequence is paired with the 3' end of the other. Nucleotides and other bases may have complements and may be present in complementary nucleic acids. Bases not commonly found in natural nucleic acids that may be included in the nucleic acids of the present invention include, for example, inosine and 7- deazaguanine. "Complementarity" may not be perfect; stable duplexes of complementary nucleic acids may contain mismatched base pairs or unmatched bases.
  • nucleic acid technology can determine duplex stability empirically considering a number of variables including, for example, the length of the oligonucleotide, percent concentration of cytosine and guanine bases in the oligonucleotide, ionic strength, and incidence of mismatched base pairs.
  • nucleic acids When complementary nucleic acid sequences form a stable duplex, they are said to be “hybridized” or to “hybridize” to each other or it is said that “hybridization” has occurred.
  • Nucleic acids are referred to as being “complementary” if they contain nucleotides or nucleotide homologues that can form hydrogen bonds according to Watson-Crick base-pairing rules (e.g., G with C, A with T or A with U) or other hydrogen bonding motifs such as for example diaminopurine with T, 5-methyl C with G, 2-thiothymidine with A, inosine with C, pseudoisocytosine with G, etc.
  • Anti-sense RNA may be complementary to other oligonucleotides, e.g., mRNA.
  • Biomarker indicates a gene whose expression indicates sensitivity or resistance to a treatment.
  • Compound as used herein means a chemical or biological substance, e.g., a drug, a protein, an antibody, or an oligonucleotide, which may be used to treat a disease or which has biological activity in vivo or in vitro. Preferred compounds may or may not be approved by the U.S. Food and Drug Administration (FDA). Preferred compounds include, e.g., chemotherapy agents that may inhibit cancer growth. Preferred chemotherapy agents include, e.g., Vincristine, .
  • radioactive chemotherapeutic agents include compounds containing alpha emitters such as astatine-211, bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium- 137, carbon- 11, nitrogen- 13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, cop ⁇ er-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71, arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-110, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon- 122, technetium-94m, technetium-94, technetium-99m
  • Exemplary chemotherapeutic agents also include antibodies such as Alemtuzumab, Daclizumab, Rituximab (MABTHERATM), Trastuzumab (HERCEPTINTM), Gemtuzumab, Ibrirumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME 5 ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-tl, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL 5 Visilizumab.
  • antibodies such as Alemtuzumab, Daclizumab, Rituximab (MABTHERATM
  • TriGem TriAb, R3, MT-201, G-250, unconjugated, ACA-125, Onyvax-105, CDP- 860, BrevaRex MAb 5 AR54, IMC-ICl 1, GlioMAb-H, ING-I, Anti-LCG MAbs, MT-103, KSB- 303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb 3 CAT, Prostate cancer antibody, H22xKi-4, ABX-MAl, Imuteran, and Monopharm-C.
  • chemotherapeutic agents also include Acivicin; Aclarubicin; Acodazole Hydrochloride; Acronine; Adozelesin; Adriamycin; Aldesleukin; Altretarnine; Ambomycin; A. metantrone Acetate; Arnmoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Camptothecin; Caracemide; Carbetimer; Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirole
  • chemotherapeutic agents include, but are not limited to, 20-pi-l,25 dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene; adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide; anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein-1; antiandrogen, prostatic carcinoma; antiestrogen; antineoplaston; antisense _
  • oligonucleotides oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA; argininedeaminase; asulacrine; atamestane; atrimustine; axinastatin 1; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin DI derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate
  • inhibit growth means causing a reduction in cell growth in vivo or in vitro by, e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% or more, as evident by a reduction in the size or number of cells exposed to a treatment (e.g., exposure to a compound), relative to the size or number of cells in the absence of the treatment.
  • Growth inhibition may be the result of a treatment that induces apoptosis in a cell, induces necrosis in a cell, slows cell cycle progression, disrupts cellular metabolism, induces cell lysis, or induces some other mechanism that reduces the size or number of cells.
  • Marker gene or “biomarker gene” as used herein means a gene in a cell the expression of which correlates to sensitivity or resistance of the cell (and thus the patient from which the cell was obtained) to a treatment (e.g., exposure to a compound).
  • oligonucleotide as used herein means a device employed by any method that quantifies one or more subject oligonucleotides, e.g., DNA or RNA, or analogues thereof, at a time.
  • One exemplary class of microarray s consists of DNA probes attached to a glass or quartz surface.
  • the DNA microarray may contain oligonucleotide probes that may be, e.g., full-length cDNAs complementary to an RNA or cDNA fragments that hybridize to part of an RNA.
  • Exemplary RNAs include mRNA, miRNA, and miRNA precursors.
  • Exemplary microarrays also include a "nucleic acid microarray" having a substrate- bound plurality of nucleic acids, hybridization to each of the plurality of bound nucleic acids being separately detectable.
  • the substrate may be solid or porous, planar or non-planar, unitary or distributed.
  • Exemplary nucleic acid microarrays include all of the devices so called in Schena (ed.), DNA Microarrays: A Practical Approach (Practical Approach Series), Oxford University Press (1999); Nature Genet. 21(l)(suppl.):l-60 (1999); Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000).
  • exemplary nucleic acid microarrays include substrate-bound plurality of nucleic acids in which the plurality of nucleic acids are disposed on a plurality of beads, rather than on a unitary planar substrate, as is described, inter alia, in Brenner et al., Proc. Natl. Acad. Sci. USA 97(4):1665-1670 (2000). Examples of nucleic acid microarrays may be found in U.S. Pat. Nos.
  • Exemplary rnicroarrays may also include "peptide microarrays" or “protein microarrays” having a substrate-bound plurality of polypeptides, the binding of a oligonucleotide, a peptide, or a protein to each of the plurality of bound polypeptides being separately detectable.
  • the peptide microarray may have a plurality of binders, including but not limited to monoclonal antibodies, polyclonal antibodies, phage display binders, yeast 2 hybrid binders, aptamers, which can specifically detect the binding of specific oligonucleotides, peptides, or proteins.
  • peptide arrays may be found in WO 02/31463, WO 02/25288, WO 01/94946, WO 01/88162, WO 01/68671, WO 01/57259, WO 00/61806, WO 00/54046, WO 00/47774, WO 99/40434, WO 99/39210, WO 97/42507 and U.S. Pat. Nos. 6,268,210, 5,766,960, 5,143,854, the disclosures of which are incorporated herein by reference in then * entireties.
  • Gene expression means the amount of a gene product in a cell, tissue, organism, or subject, e.g., amounts of DNA, RNA, or proteins, amounts of modifications of DNA, RNA, or protein, such as splicing, phosphorylation, acetylation, or methylation, or amounts of activity of DNA, RNA, or proteins associated with a given gene.
  • NCI60 as used herein means a panel of 60 cancer cell lines from lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate and brain cancers including the following cancer cell lines: NSCLC_NCIH23, NSCLC_NC1H522, NSCLC_A549ATCC, NSCLCJ ⁇ KVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC_H460, NSCLC_HOP62, NSCLC_HOP92, COLON_HT29, COLON_HCC-2998, COLON_HCT116, COLON_SW620, COLON_COLO205, COLON_HCT15, COLONJCM12, BREAST_MCF7, BREAST_MCF7ADRr, BREAST_MDAMB231, BREAST_HS578T, BREAST_MDAMB435, BREAST_MDN, BREAST_BT549, BR£AST_T
  • Treatment means administering to a subject or living organism or exposing to a cell or tumor a compound (e.g., a drug., a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, and a radioactive agent) or some other form of medical intervention used to treat or prevent cancer or the symptoms of cancer (e.g., cryotherapy and radiation therapy).
  • Radiation therapy includes the administration to a patient of radiation generated from sources such as particle accelerators and related medical devices that emit X- radiation, gamma radiation, or electron (Beta radiation) beams.
  • a treatment may further include surgery, e.g., to remove a tumor from a subject or living organism.
  • Figure 1 depicts an illustration of the method of identifying biomarkers and predicting patient sensitivity to a medical treatment.
  • the method has an in vitro component where the growth inhibition of a compound or medical treatment is measured on cell lines (6 of the 60 cell lines tested are shown). The gene expression is measured on the same cell lines without compound treatment.
  • Those genes that have a correlation above a certain cutoff e.g., a preffered cutoff of 0.3, in which a correlation coefficient equal to or greater than the cutoff of 0.3 is deemed statistcally significant by, e.g., cross-validation
  • marker genes e.g., may predict the sensitivity or resistance of a patient's cancer to a compound or other medical treatment.
  • the in vivo component is applied to a patient to determine whether or not the treatment will be effective in treating disease in the patient.
  • the gene expression in cells of a sample of the suspected disease tissue (e.g., a tumor) in the patient is measured before or after treatment.
  • the activity of the marker genes in the sample is compared to a reference population of patients known to be sensitive or resistant to the treatment.
  • the expression of marker genes in the cells of the patient known to be expressed in the cells of reference patients sensitive to the treatment indicates that the patient to be treated is sensitive to the treatment and vice versa. Based on this comparison the patient is predicted to be sensitive or resistant to treatment with the compound.
  • Figure 2 depicts the treatment sensitivity predictions for a 5-year-old American boy with a brain tumor.
  • the subject had surgery to. remove the tumor and, based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemosensitive to ten chemotherapy drugs.
  • the subject received Vincristine and Cisplatin and survived.
  • Figure 3 depicts the treatment sensitivity predictions for a 7-month-old American girl with a brain tumor.
  • the subject had surgery to remove the tumor, and based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemoresistant to twelve chemotheraphy drugs.
  • the subject received Vincristine and Cisplatin, but passed away 9 months later.
  • Figure 4 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant to Cisplatin. All patients received Cisplatin after surgery.
  • Figure 5 depicts the survival rate of 56 lymphoma patients divided into a group predicted to be chemosensitive to Vincristine and Adriamycin and a group predicted to be chemoresistant. All patients received Vincristine and Adriamycin.
  • Figure 6 depicts the survial rate of 19 lung cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant. All patients received Cisplatin.
  • Figure 7 depicts the survival rate of 14 diffuse large-B-cell lymphoma (DLBCL) patients divided into a group predicted to be chemosensitive to the drug combination R-CHOP and a group predicted to be chemoresistant. All patients were treated with R-CHOP.
  • DLBCL diffuse large-B-cell lymphoma
  • Figure 8 depicts the predictions of sensitivity or resistance to treatment of a patient diagnosed with DLBCL.
  • Various drug combinations and radiation therapy are considered.
  • The' drug combinations are those commonly used to treat DLBCL.
  • Figure 9 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation.
  • Figure 10 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation. Gene biomarkers used in predicting radiation sensitivity or resistance were obtained using the correlation of the median gene expression measurement to cancer cell growth as opposed to the median of the correlations as employed in Figure 9.
  • kits of the invention include microarrays having oligonucleotide probes that are biomarkers of sensitivity or resistance to treatment (e.g., treatment with a chemotherapeutic agent) that hybridize to nucleic acids derived from or obtained from a subject and instructions for using the device to predict the sensitivity or resistance of the subject to the treatment.
  • the invention also features methods of using the microarrays to determine whether a subject, e.g., a cancer patient, will be sensitive or resistant to treatment with, e.g., a chemotherapy agent. Also featured are methods of identifying gene biomarkers of sensitivity or resistance to a medical treatment based on the correlation of gene biomarker expression to treatment efficacy, e.g., the growth inhibition of cancer cells. Gene biomarkers that identify subjects as sensitive or resistant to a treatment may also be identified within patient populations already thought to be sensitive or resistant to that treatment. Thus, the methods, devices, and kits of the invention can be used to identify patient subpopulations that are responsive to a treatment thought to be ineffective for treating disease (e.g., cancer) in the genera] population.
  • disease e.g., cancer
  • cancer patient sensitivity to a compound or other medical treatment may be predicted using biomarker gene expression regardless of prior knowledge about patient responsiveness to treatment.
  • the method according to the present invention can be implemented using software that is run on an apparatus for measuring gene expression in connection with a microarray.
  • the microarray e.g. a DNA microarray
  • included in a kit for processing a tumor sample from a subject, and the apparatus for reading the microarray and turning the result into a chemosensitivity profile for the subject may be used to implement the methods of the invention.
  • the microarrays of the invention include one or more oligonucleotide probes that have nucleotide sequences that are identical to or complementary to, e.g., at least 5, 8, 12, 20, 30, 40, 60, 80, 100, 150, or 200 consecutive nucleotides (or nucleotide analogues) of the biomarker genes listed below.
  • the oligonucleotide probes may be, e.g., 5-20, 25, 5-50, 50-100, or over 100 nucleotides long.
  • the oligonucleotide probes may be deoxyribonucleic acids (DNA) or ribonucleic acids (RNA).
  • Consecutive nucleotides within the oligonucleotide probes may also appear as consecutive nucleotides in one or more of the genes described herein beginning at or near, e.g., the first, tenth, twentieth, thirtieth, fortieth, fiftieth, sixtieth, seventieth, eightieth, ninetieth, hundredth, hundred-fiftieth, two-hundredth, five- hundredth, or one-thousandth nucleotide of the genes listed in Tables 1-21 or below.
  • Tables 1-21 indicates the preferred gene biomarkers for the compound lists.
  • Column List_Preferred of Tables 1-21 indicates the most preferred gene biomarkers.
  • Column List_2005 of Tables 1-21 indicates additional biomarkers employed in Examples 1-8.
  • Column Correlation of Tables 1-21 indicates the correlation coefficient of the biomarker gene expression to cancer cell growth inhibition. The following combinations of gene biomarkers have been used to detect a subject's sensitivity to the indicated treatment:
  • Gene sequences B2M, MYC 5 CD99, RPS24, PPIF, PBEFl , and ANP32B preferably gene sequences CD99, INSIGl, LAPTM5, PRGl, MlIFl, HCLSl 5 CD53, SLA, SSBP2, GNB5, MFNG, GMFG 5 PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP 5 ZNFNlAl, CENTBl, PTPRC, NAPlLl, HLA-DRA, IFIl 6, COROlA, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAP15, NOTCHl, and UBASH3A, and most preferably gene sequences CD99, INSIGl, LAP
  • HMGN2 PRPSl 5 DDX5, PRGl 5 PPIA 5 G6PD, PSMB9, SNRPF 5 and MAPlB 5 preferably gene sequences PGAMl 5 DPYSL3, INSIGl 5 GJAl 5 BNIP3, PRGl 5 G6PD, BASPl 5 PLOD2, LOXL2, SSBP2, Clorf29, TOX 5 STCl 5 TNFRSFlA 5 NCOR2, NAPlLl 5 LOC94105, COL6A2, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ 5 MAPlB, PTPRC, PRKCA, TRIM22, CD3D, BCATl 5 IFI44, CCL2, RAB31, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, and most preferably gene sequences PGAMl 5 DPYSL3, INSIGl, GJAl, BNTP3, PRGl, G6PD, PLOD
  • One or more of the gene sequences GAPD 5 GAPD 5 GAPD 5 TOP2A, SUIl 5 TOP2A, FTL, HNRPC, TNFRSFlA, SHCl, CCT7, P4HB, CTSL, DDX5, G6PD, and SNRPF 5 preferably gene sequences STCl 5 GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, and most preferably gene sequences STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, whose expression indicates chemosensitivity to Mitomycin.
  • LOC51035, RPS6, EXOSC2, RPL35, IFRD2, SMN2, EEFlAl 5 RPS3, RPS18, and RPS7 preferably gene sequences RPLlO 5 RPS4X, NUDC, RALY 5 DKCl, DKFZP564C186, PRP19, RAB9P40, HS A9761 , GMDS, CEP 1, ELl 3RA2, MAGEB2, HMGN2, ALMS 1 , GPR65, FLJ10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC 3 and KIAA1393, and most preferably RPLlO, RPS4X, NUDC, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEPl, IL13RA2, MAGEB2, HMGN2, ALMSl, GPR65, FLJ10774, NOL
  • One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, SFRS3, RPS6, DDX39, and RPS7 preferably gene sequences ANP32B, GTF3A, RRM2, TRIMl 4, SKP2, TRIP13, . RFC3, CASP7, TXN 3 MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, and most preferably gene sequences ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRDP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, whose expression indicates chemosensitivity to Taxotere.
  • TM4SF2, ARHGDIB, ADA, H2AFZ, NAPlLl, CCND3, FABP5, LAMRl, REA, MCM5, SNRPF, and USP7 preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl, GNA15, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CDlE, ADA 5 and FHODl, and most preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA, and FHODl 5 whose expression indicates chemosensitivity to Ara-C.
  • ACTB One or more of the gene sequences ACTB, COL5A1, MTlE, CSDA, COL4A2, MMP2, COLlAl, TNFRSFlA, CFHLl, TGFBI, FSCNl, NNMT, PLAUR, CSPG2, NFEL3, C5orfl3, NCOR2, TUBB4, MYLK, TUBA3, PLAU, COL4A2, COL6A2, COL6A3, JJPITM2, PSMB9, CSDA, and COLlAl, preferably gene sequences MSN, PFNl, HKl, ACTR2, MCLl, ZYX, RAPlB, GNB2, EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I 5 LGALSl, CSDA, AKRlBl, IFITM2, ITGA5, VIM 5 DPYSL3, JUNB, ITGA3, NFKBIA, LAMBl, F
  • One or more of the gene sequences NOS2A, MUCl, TFF3, GPlBB, IGLLl, BATF, MYB, PTPRS, NEFL, AIP, CEL, DGKA, RUNXl, ACTRlA, and CLCNKA preferably gene sequences PTMA, SSRPl 5 NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl, SSSCAl, EZH2, MYB, PRIMl 5 H2AFX, HMGAl, HMMR 5 TK2, WHSCl, DIAPHl, LAMB3, DPAGTl, UCK2, SERPINBl, MDNl, BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, and most preferably gene sequences SSRPl 5 NUDC, CTSC, AP1G2, PSME2, LBR 5 EFNB2, SERPINAl, S
  • Probes that may be employed on microarrays of the invention include oligonucleotide probes having sequences complementary to any of the biomarker gene sequences described above. Additionally, probes employed on microarrays of the invention may also include proteins, peptides, or antibodies that selectively bind any of the oligonucleotide probe sequences or their complementary sequences. Exemplary probes are listed in Tables 22-44, wherein for each treatment listed, the gene biomarkers indicative of treatment sensitivity, the correlation of biomarker gene expression to growth inhibition, and the sequence of an exemplary probe (Tables 22-44) to detect the biomarker genes' (Tables 1-21) expression are shown.
  • the gene expression measurements of the NCI60 cancer cell lines were obtained from the National Cancer Institute and the Massachusetts Institute of Technology (MIT). Each dataset was normalized so that sample expression measured by different chips could be compared.
  • GI50 Growth inhibition data
  • the correlation between the logit-transformed.expression level of each gene in each cell line and the logarithm of GI50 can be calculated, e.g., using the Pearson correlation coefficient or the Spearman Rank-Order correlation coefficient.
  • any other measure of patient sensitivity to a given compound may be correlated to the patient's gene expression. Since a plurality of measurements may be available for a single gene, the most accurate determination of correlation coefficient was found to be the median of the correlation coefficients calculated for all probes measuring expression of the same gene.
  • the median correlation coefficient of gene expression measured on a probe to growth inhibition or patient sensitivity is calculated for all genes, and genes that have a median correlation above 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 are retained as biomarker genes.
  • the correlation coefficient of biomarker genes will exceed 0.3. This is repeated for all the compounds to be tested. The result is a list of marker genes that correlates to sensitivity for each compound tested.
  • the biomarker genes whose expression, has been shown to correlate to chemosensitivity can be used to classify a patient, e.g., a cancer patient, as-sensitive to a medical treatment, e.g., administration of a chemotherapeutic agent or radiation.
  • a medical treatment e.g., administration of a chemotherapeutic agent or radiation.
  • a tumor sample or a blood sample e.g., in case of leukemia or lymphoma
  • expression of the biomarker genes in the cells of the patient in the presence of the treatment agent is determined (using, for example,- an RNA extraction kit, a DNA microarray and a DNA microarray scanner).
  • the gene expression measurements are then logit transformed as described above.
  • the sum of the expression measurements of the marker genes is then compared to the median of the sums derived from a training set population of patients having the same rumor. If the sum of gene expression in the patient is closest to the median of the sums of expression in the surviving members of the training set, the patient is predicted to be sensitive to the compound or other medical treatment. If the sum of expression in the patient is closest to the median of the sums of expression in the non-surviving members of the training set, the patient is predicted to be resistant to the compound.
  • Machine learning techniques such as Neural Networks, Support Vector Machines, K Nearest Neighbor, and Nearest Centroids may also be employed to develop models that discriminate patients sensitive to treatment from those resistant to treatment using biomarker gene expression as model variables which assign each patient a classification as resistant or sensitive.
  • Machine learning techniques used to classify patients using various measurements are described in U.S. Patent No. 5,822,715; U.S. Patent Application Publication Nos. 2003/0073083, 2005/0227266, 2005/0208512, 2005/0123945, 2003/0129629, and 2002/0006613; and in Vapnik V N.
  • a more compact microarray may be designed using only the oligonucleotide probes having measurements yielding the median correlation coefficients with cancer cell growth inhibition. Thus, in this embodiment, only one probe needs to be used to measure expression of each gene.
  • the invention may also be used to identify a subpopulation of patients, e.g., cancer patients, that are sensitive to a compound or other medical treatment previously thought to be ineffective for the treatment of cancer.
  • biomarker genes whose expression correlates to sensitivity to a compound or other treatment, may be identified so that patients sensitive to a compound or other treatment may be identified.
  • gene expression within cell lines may be correlated to the growth of those cell lines in the presence of the same compound or other treatment.
  • genes whose expression correlates to cell growth with a correlation coefficient exceeding 0.3 may be considered possible biomarkers.
  • genes may be identified as biomarkers according to their ability to discriminate patients known to be sensitive to a treatment from those known to be resistant. The significance of the differences in gene expression between the sensitive and resistant patients may be measured using, e.g., t-tests.
  • na ⁇ ve Bayes ⁇ an classifiers may be used to identify gene biomarkers that discriminate sensitive and resistant patient subpopulations given the gene expressions of the sensitive and resistant subpopulations within a treated patient population.
  • the patient subpopulations considered may be further divided into patients predicted to survive without treatment, patients predicted to die without treatment, and patients predicted to have symptoms without treatment.
  • the above methodology may be similarly applied to any of these further defined patient subpopulations to identify gene biomarkers able to predict a subject's sensitivity to compounds or other treatments for the treatment of cancer.
  • the invention is particularly useful for recovering compounds or other treatments that failed in clinical trials by identifying sensitive patient subpopulations using the gene expression methodology disclosed herein to identify gene biomarkers that can be used to predict clinical outcome.
  • This invention may also be used to predict patients who are resistant or sensitive to a particular treatment by using a kit that includes a kit for RNA extraction from tumors (e.g., Trizol from Invitrogen Inc), a kit for RNA amplification (e.g., MessageAmp from Ambion Inc), a microarray for measuring gene expression (e.g., HG-Ul 33 A GeneCbip from Affymetrix Inc), a microarray hybridization station and scanner (e.g., GeneChip System 3000Dx from Affymetrix Inc), and software for analyzing the expression of marker genes as described in herein (e.g., implemented in R from R-Project or S-Plus from Insightful Corp.).
  • a kit for RNA extraction from tumors e.g., Trizol from Invitrogen Inc
  • a kit for RNA amplification e.g., MessageAmp from Ambion Inc
  • a microarray for measuring gene expression e.g., HG-Ul
  • the human tumor cell lines of the cancer screening panel are grown in RPMI 1640 medium containing 5% fetal bovine serum and 2 mM L-glutamine. Cells are inoculated into 96 well microliter plates in 100 ⁇ L at plating densities ranging from 5,000 to 40,000 cells/well depending on the doubling time of individual cell lines. After cell inoculation, the microliter plates are incubated at 37 0 C, 5% CO2, 95% air and 100% relative humidity for 24 hours prior to addition of experimental compounds.
  • the plates axe incubated for an additional 48 h at 37°C, 5% CO2, 95% air, and 100% relative humidity.
  • the assay is terminated by the addition of cold TCA.
  • Cells are fixed in situ by the gentle addition of 50 ⁇ l of cold 50% (w/v) TCA (final concentration, 10% TCA) and incubated for 60 minutes at 4°C. The supernatant is discarded, and the plates are washed five times with tap water and air-dried.
  • Sulforhodamine B (SRB) solution 100 ⁇ l) at 0.4% (w/v) in 1% acetic acid is added to each well, and plates are incubated for 10 minutes at room temperature.
  • GI50 Growth inhibition of 50%
  • C-Tz C-Tz
  • TGI total growth inhibition
  • the LC50 concentration of compound resulting in a 50% reduction in the measured protein at the end of the compound treatment as compared to that at the beginning
  • RNA is amplified using e.g. MessageAmp kit from Ambion following manufacturers instructions- Amplified RNA is quantified using e.g. HG-Ul 33 A GeneChip from Affymetrix Lie and compatible apparatus e.g. GCS3000Dx from Affymetrix, using manufacturers instructions.
  • the resulting gene expression measurements are further processed as described in this document. The procedures described can be implemented using R software available from R- Project and supplemented with packages available from Bioconductor.
  • qRT-PCR quantitative reverse transcriptase polymerase chain reaction
  • Example 1 Identification of gene biomarkers for chemosensitivity to common chemotherapy drugs.
  • DNA chip measurements of the 60 cancer cell lines of the NCI60 data set were downloaded from the Broad Institute and logit normalized. Growth inhibition data of thousands of compounds against the same cell lines were downloaded from the National Cancer Institute. Compounds where the difference concentration to achieve 50% in growth inhibition (GI50) was less than 1 log were deemed uninformative and rejected. Each gene's expression in each cell line was correlated to its growth (-log(GI50)) in those cell lines in the presence of a given compound. The median Pearson correlation coefficient was used when multiple expression measurements were available for a given gene, and genes having a median correlation coefficient greater than 0.3 were identified as biomarkers for a given compound.
  • Example 2 Prediction of treatment sensitivity for brain cancer patients.
  • DNA chip measurements of gene expression in tumors from 60 brain cancer patients were downloaded from the Broad Institute. All data files were logit normalized. For each of the common chemotherapy drugs Cisplatin, Vincristine, Adriamycine, Etoposide, Aclarubicine, Mitoxantrone and Azaguanine, the gene expression for the marker genes was summed. The sum was normalized by dividing by the standard deviation of all patients and compared to the median of the sums of patients who survived and the median of the sums of patients who died:
  • NormalizedSum(compound) sum(marker genes for compound)/sd(sums of all patients)
  • Sensitivity(compound) [NormalizedSum(compound)- median(NormalizedSumdeadpatients(compound))] 2
  • Figures 2 and 3 show the resulting treatment sensitivity predictions for two of the 60 patients. All patients received Cisplatin and the prediction of survival amongst the 60 patients based on their Cisplatin chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 4.
  • fastICA Independent Component Analysis
  • Chemosensitivity or sensitivity to radiation treatment was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive/treatment sensitive prediction resulted in a prediction of chemosensitive/treatment sensitive. All other predictions resulted in a prediction of chemoresistant/treatment resistant.
  • the performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups shown in Figure 4. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 3 Prediction of chemosensitivity for lymphoma (DLBCL) patients.
  • Chemosensitivity was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive prediction resulted in a prediction of chemosensitive. All other predictions resulted in a prediction of chemoresistant.
  • the performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups is shown in Figure 5. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 4 Prediction of chemosensitivity for lung cancer patients.
  • DNA chip measurements of gene expression in the tumors from 86 lung cancer (adenocarcinoma) patients was downloaded from the University of Michigan, Ann Arbor. Of the 86 patients, 19 had Stage EI of the disease and received adjuvant chemotherapy. Raw data was logit normalized. Instead of the combined classifier described for the brain cancer and lymphoma examples above, the sum of biomarker gene expression was calculated for each patient and used to discriminate chemosensitive and chemoresistant patients. For each patient, the gene expression of the 16 marker genes for Cisplatin sensitivity (all Stage IQ patients received Cisplatin aiter surgery) was summed. If the sum was closer to the median of the sums of the surviving patients, the patient was predicted to be sensitive to Cisplatin.
  • the patient was predicted to be resistant to Cisplatin.
  • the survival rates of the two predicted groups are shown in Figure 6.
  • the survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 5 Prediction of Rituximab sensitivity for lymphoma (DLBCL) patients.
  • the method is not limited to cytotoxic chemicals. It is also applicable to predicting the efficacy of protein therapeutics, such as monoclonal antibodies, approved for treating cancer.
  • protein therapeutics such as monoclonal antibodies
  • MABTHERATM Renidourab, RITUXANTM ⁇ was examined. Data for cytotoxicity of Rituximab in cell lines in vitro were obtained from published reports (Ghetie et al. 5 Blood, 97(5):1392-1398, 2001). This cytotoxicity in each cell line was correlated to the expression of genes in these cell lines (downloaded from the NCBI Gene Expression Omnibus database using accession numbers GSE2350, GSE1880, GDS181).
  • the identified marker genes were used to predict the sensitivity of DLBCL to Rituximab in a small set of 14 patients treated with Rituximab and CHOP (R-CHOP) (downloaded from NCBI Gene Expression Omnibus under accession number GSE4475). Conversion between different chip types was performed using matching tables available through Affymetrix.
  • the survival of patients predicted to be sensitive to be R-CHOP is compared to the survival of patients predicted to be resistant to R-CHOP in Figure 7.
  • the survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • R-CHOP contains Rituximab (MABTHERATM), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone
  • R-ICE contains Riruximab, Ifosfamide, Carboplatin, and Etoposide
  • R-MIME contains Rituximab, Mitoguazone, Ifosfamide, Methotrexate, and Etoposide
  • CHOEP contains Cyclophosphamide, Doxorubicin, Etoposide, Vincristine and Prednisone
  • DHAP contains
  • Example 6 Prediction of radiosensitivity for brain tumor (medulloblastoma) patients.
  • the method of identifying biomarkers can also be applied to other forms of treatment such as radiation therapy.
  • sensitivity to radiation therapy was predicted for brain tumor patients.
  • Radiation therapy in the form of craniospinal irradiation yielding 2,400-3,600 centiGray (cGy) with a tumor dose of 5,300-7,200 cGy was administered to the brain tumor patients using a medical device that emits beams of radiation.
  • Sensitivity of the 60 cancer cell lines used in the NCI60 dataset to radiation treatment was obtained from published reports. This sensitivity was correlated to the expression of genes in the cell lines as described above to identify marker genes.
  • DNA microarray measurements of gene expression in brain tumors obtained from patients subsequently treated with radiation therapy were obtained from the Broad Institute.
  • the identified gene biomarkers were used to classify the patients as sensitive or resistant to radiation therapy.
  • the survival of the patients in the two predicted categories is shown in Figure 9.
  • the survival rate of the patients predicted to be sensitive to radiation therapy was higher than the patients predicted to be resistant to radiation
  • Every member of a population may not be equally responsive to a particular treatment. For example, new compounds often fail in late clinical trials because of lack of efficacy in the population tested. WMIe such compounds may not be effective in the overall population, there may be subpopulations sensitive to those, failed compounds due to various reasons, including inherent differences in gene expression.
  • the method as described herein can be used to rescue failed compounds by identifying a patient subpopulation sensitive to a compound using then- gene expression as an indicator. Subsequent- clinical trials restricted to a sensitive patient subpopulation may demonstrate efficacy of a previously failed compound within that particular patient subpopulation, advancing the compound towards approval for use in that subpopulation.
  • in vitro measurements of the inhibitory effects of a compound on various cancer cell samples from the responsive patient subpopulation collected as described above or measures of clinical response of a treated patient are compared to the gene expression of cells from those patients.
  • the growth of the cancer cell samples can be correlated to gene expression measurements as described above.
  • marker genes that can be used to predict patient sensitivity to the failed compound.
  • biomarker genes will be identified within the patient population previously shown to be sensitive to the failed compound.
  • the expression of biomarker genes in patients can be measured according to the procedure detailed above. The patients are predicted to be responsive or non-responsive to compound treatment according to their gene biomarker expression. Clinical effect must then be demonstrated in the group of patients that are predicted to be sensitive to the failed compound.
  • the method may be further refined if patients responsive to the compound treatment are further subdivided into those predicted to survive without the compound and those predicted to die or suffer a relapse without the compound.
  • Clinical efficacy in the subpopulation that is predicted to die or suffer relapse can be further demonstrated. Briefly, the gene expression at the time of diagnosis of patients who later die from their disease is compared to gene expression at the time of diagnosis of patients who are still alive after 5 years. Genes differentially expressed between the two groups are identified as prospective biomarkers and a model is built using those gene biomarkers to predict treatment efficacy.
  • Examples of compounds that have failed in clinical trials include Iressa (Gefinitib, AstraZeneca) in refractory, advanced non-small-cell lung cancer (NSCLC), Avastin (Bevacizumab, Genentech) in first-line treatment for advanced pancreatic cancer, Avastin (Bevacizumab, Genentech) in relapsed metastatic breast cancer patients, and Tarceva (Erlotinib, Genentech) in metastatic non-small cell lung cancer (NSCLC).
  • the method of the invention may be applied to these compounds, among others, so that sensitive patient subpopulations responsive to those compounds may be identified. ⁇
  • Example 8 Median of the correlations versus correlation of the median.
  • the median of the correlation to measured radiosensitivity of cell lines in vitro is 0.3 ' 2.
  • the correlation of the median is 0.39. Adjusting the cutoff from 0.3 to 0.4 to compensate for the difference does not improve on Figure 10, however. . . . .
  • CDGBP2 0.37 TGGACCCCACTGGCTGAGAATCTGG
  • RPL12 0.3 A AAAAATTGGTTTTTTCCCCTTTTGGTTCGCCTGCTCCTG
  • Table 33 Taxotere (docetaxel) biomarkers.
  • IFITM2 0.38 ATATATGGACCTAGCTTGAGGCAAT [2,] UBE2L6 0.32 AAGCCTATACGTTTCTGTGGAGTAA [3,] ITM2A 0.38 CACCCAGCTGGTCCTGTGGATGGGA
  • CDKL5 0.44 GCCCCACTGGACAACACTGATTCCT
  • PALM2-AKAP2 0.33 TGGACCCCACTGGCTGAGAATCTGG
  • Methyl-GAG (meth biomarkers .
  • CTSC 0.35 CACCCAGCTGGTCCTGTGGATGGGA
  • IRS2 0.35 TGGACCCCACTGGCTGAGAATCTGG
  • DNAPTP6 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [120,] ADAMTSl 0.37 TTGGACATCTCTAGTGTAGCTGCCA

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Abstract

The present invention features methods and devices for predicting the sensitivity of a patient to a compound or medical treatment. The invention also features methods for identifying gene biornarkers whose expression correlates to treatment sensitivity or resistance within a patient population or subpopulation.

Description

METHODS AND DEVICES FOR IDENTIFYING BIOMARKERS OF TREATMENT RESPONSE AND USE THEREOF TO PREDICT TREATMENT EFFICACY
FIELD OF THE INVENTION
The invention features methods and devices for identifying biomarkers of patient sensitivity to medical treatments, e.g., sensitivity to chemotherapeutic agents, and predicting treatment efficacy using the biomarkers.
BACKGROUND OF THE INVENTION
DNA microarrays have been used to measure gene expression in tumor samples from patients and to facilitate diagnosis. Gene expression can reveal the presence of cancer in a patient, its type, stage, and origin, and whether genetic mutations are involved. Gene expression may even have a role in predicting the efficacy of chemotherapy. Over recent decades, the National Cancer Institute (NCI) has tested compounds, including chemotherapy agents, for their effect in limiting the growth of 60 human cancer cell lines. The NCI has also measured gene expression in those 60 cancer cell lines using DNA microarrays. Various studies have explored the relationship between gene expression and compound effect using the NCI datasets.
During chemotherapy for cancers critical time is often lost due to a trial and error approach to finding an effective therapy. In addition, cancer cells often develop resistance to a previously effective therapy. In such situations, patient outcome would be greatly improved by early detection of such resistance.
There remains a need for proven methods and devices that predict the sensitivity or resistance of cancer patients to a medical treatment.
SUMMARY OF THE INVENTION
The invention features methods and devices for predicting the sensitivity or resistance of a patient, e.g., a cancer patient, to a treatment, e.g., treatment with a compound, such as a / chemotherapeutic agent, or radiation. In particular, the methods and devices can be used to predict the sensitivity or resistance of a cancer patient to any medical treatment, including, e.g., treatment with a compound, drug, or radiation. The devices and methods of the invention have been used to accurately predict treatment efficacy in cancer patients (e.g., patients with lung, lymphoma, and brain cancer) and can be used to predict treatment efficacy in patients diagnosed with any cancer.
Devices employing specific chemosensitivity/ chemoresistance biomarkers for the common chemotherapy drugs Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara- C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5- Fluorouracil), rituximab, radiation, histone deacetylase (EtDAC) inhibitors, and 5-Aza-2'- deoxycytidine (Decitabine) are also provided. The methods and devices can be used to predict the sensitivity or resistance of a subject (e.g., a cancer patient) diagnosed with a disease condition, e.g., cancer (e.g., cancers of the breast, prostate, lung and bronchus, colon and rectum, urinary bladder, skin, kidney, pancreas, oral cavity and pharynx, ovary, thyroid, parathyroid, stomach, brain, esophagus, liver and intrahepatic bile duct, cervix larynx, heart, testis, small and large intestine, anus, anal canal and anorectum, vulva, gallbladder, pleura, bones and joints, hypopharynx, eye and orbit, nose, nasal cavity and middle ear, nasopharynx, ureter, peritoneum, omentum and mesentery, or gastrointestines, as well as any form of cancer including, e.g., chronic myeloid leukemia, acute lymphocytic leukemia, non-Hodgkin lymphoma, melanoma, carcinoma, basal cell carcinoma, malignant mesothelioma, neuroblastoma, multiple myeloma, leukemia, retinoblastoma, acute myeloid leukemia, chronic lymphocytic leukemia, Hodgkin lymphoma, carcinoid tumors, acute tumor, or soft tissue sarcoma) to a treatment, e.g., treatment with a compound or drug, e.g., a chemotherapeutic agent, or radiation.
In the first aspect, the invention features a method of predicting sensitivity of a cancer patient to a treatment for cancer by determining the expression level of at least one gene in a cell (e.g., a cancer cell) of the patient, in which the gene is selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADAMTSl, ADDl, AFlQ, AIFl, AKAPl, AKAP13, AKRlCl, AKTl, ALDH2, ALDOC, ALG5, ALMSl, ALOX15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP 15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7. ASAHl, ASPH5 ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl, BCHE, BCLI lB, BDNF, BHLHB2, BIN2, BLMH, BMIl, BNIP3, BRDT, BRRNl, BTN3A3, Cl Iorf2, C14orfl39, C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl8, C6orf32, CACNAlG, CACNB3, CALMl, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB5 CCNA2, CCNBlIPl, CCND3, CCR7, CCR9, CDlA, CDlC, CDlD, CDlE, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDClO, CDC14B, CDHl 1, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl, CENTG2, CEPl, CG018, CHRNA3, CHSl, CIAPINl, CKAP4, CKIP-I, CNP, COL4A1, COL5A2, COL6A1, COROlC, CRABPl, CRK, CRYl5 CSDA, CTBPl, CTSC, CTSL, CUGBP2, CUTC, CXCLl, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATFl, DAZAPl, DBNl, DBT, DCTNl, DDXl 8, DDX5, DGKA, DIAPHl, DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAJC7, DNAPTP6, DOCKlO3 DOCK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl, DXS9879E, EEF 1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPASl, EPB41L4B, ERCC2, ERG, ERP70, EVERl, EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHLl, FHODl, FKBPlA, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl, FMNLl, FNBPl, FOLHl, FOXF2, FSCNl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GZMA5 GZMB, HlFO, HlFX, H2AFX, H3F3A, HA-I, HEXB, HIC5 HIST1H4C, HKl, HLA-A, HLA-B, HLA-DRA, HMGAl, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM, HOXA9, HRMTlLl, HSA9761, HSPA5, HSU79274, HTATSFl, ICAMl, ICAM2, IER3, IFIl 6, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, EL13RA2, JX21R, JJL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl, IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARJD2, JUNB, K- ALPHA-I, KHDRBSl, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, _
KIAAl 128, KIAA1393, KIFCl, LAIRl, LAMBl, LAMB3, LAT, LBR, LCK, LCPl, LCP2, LEFl, LEPREl, LGALSl, LGALS9. LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN3 LRMP, LRP12, LRRC5, LRRN3, LSTl, LTB, LUM5 LY9, LY96, MAGEB2, MAL, MAPlB5 MAP1LC3B, MAP4K1, MAPKl, MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDNl , MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL5 MLPH, MPHOSPH6, MPPl, MPZLl, MRP63, MRPS2, MTlE, MTlK, MUFl, MVP, MYB, MYL9, MYOlB, NAPlLl, NAP1L2, NARF, NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NTD2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCHl, NPCl, NQOl, NR1D2, MIDC, NUP210, NUP88, NVL, NXFl, OBFCl, OCRL, OGT, OXAlL, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFNl, PFN2, PGAMl, PHEMX, PHLDAl, PIM2, PITPNCl, PLAC8, PLAGLl, PLAUR, PLCBl, PLEK2, PLEKHCl, PLOD2, PLSCRl, PNAS-4, PNMA2, P0LR2F, PPAP2B, PRFl, PRGl, PRIMl, PRKCH, PRKCQ, PRKD2, PRNP, PRP 19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl, PTP4A3, PTPN7, PTPNSl, PTRF, PURA, PWPl, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAPlB, RASGRP2, RBPMS, RCNl, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOO, RIPK2, RISl, RNASE6, RNF144, RPLlO, RPLlOA, RPL12, RPL13A, RPL17, RPLl 8, RPL36A, RPLPO, RPLP2, RPS 15, RPS 19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBPl, RRM2. RUNXl, RUNX3, S100A4, SART3, SATBl, SCAPl, SCARBl, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPTlO, SEPT6, SERPINAl, SERPINBl, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHMT2, SIATl, SKBl, SKP2, SLA, SLC 1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, S0D2, S0X4, SP140, SPANXC, SPIl, SRF, SRM, SSA2, SSBP2, SSRPl, SSSCAl, STAG3, STATl, STAT4, STAT5A, STCl, STC2, ST0ML2, T3JAM, TACCl, TACC3, TAF5, TALI, TAPl, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TlMPl, TJPl, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFATP8, TNFRSFlOB, TNFRSFlA, TNFRSF7, TNIK, TNPOl5 TOBl, TOMM20, TOX, TPKl, TPM2, TRA@, TRAl, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHLl5 UCK2, UCP2, UFDlL, UGDH3 ULK2, UMPS, UNG, USP34, USP4, VASP, VAVl, VLDLR5 VWF, WASPIP, WBSCR20A, WBSCR20C, WHSCl, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl, and ZYX; in which change in the level of expression of the gene indicates the patient is sensitive or resistant to the treatment. In an embodiment, the method includes determining the expression of two of the listed genes, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes. In another embodiment, the change in the level of gene expression (e.g., an increase or decrease) is determined relative to the level of gene expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of gene expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment, ha another embodiment, the change in the level of gene expression (e.g., an increase or decrease) is determined relative to the level of gene expression in a cell or tissue known to be resistant to the treatment, such that a similar level of gene expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment. hi another embodiment, the at least one gene is selected from the group consisting of RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, BLMH, CTBPl, TBCA, MDH2, and DXS9879E, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Vincristine. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of UBB, B2M, MANlAl, and SUIl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Vincristine.
In another embodiment, the at least one gene is selected from the group consisting of ClQRl, SLA, PTPN7, ZNFNlAl, CENTBl, IFI16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl, ARHGAPl 5, TM6SF1, and TCF4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Cisplatin. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of HCLSl, CD53, PTPRCAP5 and PTPRC, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Cisplatin.
In another embodiment, the at least one gene is selected from the group consisting of SRM5 SCARBl, SIATl, CUGBP2, ICAMl5 WASPIP, ITM2A, PALM2-AKAP2, PTPNSl3 MPPl, LNEC5 FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl5 IFI16, MCAM, MEF2C, SLC1A4, FYN, Clorf38, CHSl, FCGR2C, TNDC, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl, and ADAMTS 1, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Azaguanine. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN, SPARC, VIM5 GAS7, ANPEP, EMP3, BTN3A2, FNl, and CAPN3, wherein an increase in expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Azaguanine.
In another embodiment, the at least one gene is selected from the group consisting of CD99, ENSIGl, PRGl, MUFl, SLA, SSBP2, GNB5, MFNG5 PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFNlAl, CENTBl, NAPlLl, HLA-DRA, IFIl 6, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAP15, NOTCHl, and UBASH3A, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Etoposide. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, HCLSl5 CD53, GMFG, PTPRCAP, PTPRC, COROlA, and ITK, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Etoposide.
In another embodiment, the at least one gene is selected from the group consisting of CD99, ALDOC, SLA5 SSBP2, EL2RG, CXorf9, RHOH, ZNFNlAl, CENTBl, CDlC, MAP4K1, CD3G, CCR9, CXCR4, AJRHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AlFl, ARHGAPl 5, NARF, and PACAP, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Adriamycin. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, HCLSl, CD53, GMFG, PTPRCAP, TCF7, CDlB, PTPRC, COROlA, HEMl, and ITK, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Adriamycin. hi another embodiment, the at least one gene is selected from the group consisting of RPL12, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Aclarubicin. Alternatively, the method further includes measuring the expression level of at . least one gene selected from the group consisting of RPL32, FBL, and PTPRC, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Aclarubicin.
In another embodiment, the at least one gene is selected from the group consisting of PGAMl, DPYSL3, INSIGl5 GJAl, BNTP3, PRGl, G6PD, PLOD2, LOXL2, SSBP2, Clorf29, TOX, STCl3 TNFRSFlA, NCOR2, NAPlLl, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ, MAPlB, TRIM22, CD3D, BCATl, IFI44, CUTC5 NAP1L2, NME7, FLJ21159, and COL5A2, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitoxanthrone. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of BASPl, COL6A2, PTPRC, PRKCA, CCL2, and RAB31, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitoxantrone.
In another embodiment, the at least one gene is selected from the group consisting of STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Mitomycin.
In another embodiment, the at least one gene is selected from the group consisting of RPLlO, RPS4X, NUDC5 DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS5 CEPl3 IL13RA2, MAGEB2, HMGN25 ALMSl5 GPR65, FLJ10774, NOL8, DAZAPl5 SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC5 and KIAA1393, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Paclitaxel. Alternatively, the method further includes measuring the expression level of RALY, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Paclitaxel.
In another embodiment, the at least one gene is selected from the group consisting of PFNl, PGAMl, K-ALPHA-I5 CSDA5 UCHLl. PWPl5 PALM2-AKAP2, TNFRSFlA, ATP5G2, AFlQ, NME4, and FHODl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Gemcitabine.
In another embodiment, the at least one gene is selected from the group consisting of ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRIP 13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Taxotere.
In another embodiment, the at least one gene is selected from the group consisting of IFITM2, UBE2L6, USP4, ITM2A, EL2RG, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, ZNFNlAl5 CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP5 LAIRl, SH2D1A, SEPT6, HA-I5 ERCC2, CDSD5 LSTl, AIFl, ADA, DATFl, ARHGAP 15, PLAC8, CECRl5 LOC81558, and EHD2, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Dexamethasone. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of LAPTM5, ITGB2, ANPEP5 CD53, CD37, AD0RA2A, GNA15, PTPRC, COROlA, HEMl, FLH, and CREB3L1, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Dexamethasone.
In another embodiment, the at least one gene is selected from the group consisting of ITM2A, RHOH. PRIMl5 CENTBl, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA3 and FHODl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Ara-C. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of GNAl 5, PTPRC, and RPLl 3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Ara-C.
In another embodiment, the at least one gene is selected from the group consisting of CD99, ARHGDIB5 VWF, ITM2A, LGALS9, INPP5D, SATBl, TFDP2, SLA5 IL2RG, MFNG, SELL5 CDW52, LRMP, ICAM25 RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl5 CENTBl5 LCP2, SPIl, GZMA5 CEPl, CD8A, SCAPl5 CD2, CDlC5 TNFRSF7, VAVl, MAP4K1, CCR7, C6orf325 ALOXl 5B, BRDT, CD3G, LTB3 ATP2A3, NVL, RASGRP2, LCPl, CXCR4, PRKD2, GATA3, TRA@5 KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDlA5 LSTl, LAIRl5 CACNAlG, TRB@, SEPT6, HA-I5 DOCK2, CD3D, TRD@, T3JAM, FNBPl, CD6, AIFl5 FOLHl5 CDlE5 LY9, ADA, CDKL5, TRIM5 EVL5 DATFl5 RGC32, PRKCH, ARHGAP15, NOTCHl5 BIN2, SEMA4G, DPEP2, CECRl, BCLI lB5 STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methylprednisolone. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of SRRMl, LAPTM5, ITGB2, CD53, CD37, GMFG, PTPRCAP, GNA15, BLM, PTPRC5 COROlA5 PRKCBl5 HEMl5 and UGT2B17, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methylprednisolone.
In another embodiment, the at least one gene is selected from the group consisting of PRPF8, RPLl 8, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBSl, SNRPA5 IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRTMl, PFDN5, OXAlL5 H3F3A, ATIC3 CIAPlNl3 RPS2, PCCB3 SHMT2, RPLPO3 HNRPAl, STOML2, SKBl3 GLTSCR2, CCNBlIPl3 MRPS2, FLJ208593 and FLJ12270, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methotrexate. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of RNPSl, RPL32, EEFlG, PTMA3 RPLl 3, FBL5 RBMX, and RP S 9, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methotrexate. hi another embodiment, the at least one gene is selected from the group consisting of PFNl, HKl3 MCLl3 ZYX, RAPlB3 GNB2, EPASl3 PGAMl3 CKAP4, DUSPl3 MYL9, K- ALPHA-I, LGALSl3 CSDA3 IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMBl, FHLl, INSIGl, TIMPl, GJAl3 PSME2, PRGl3 EXTl, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAPl, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STCI3 PTGER4, EL6, SMAD3, WNT5A, BDNF, TNFRSFlA, FLNC3 DKFZP564K0822, FLOTl3 PTRF, HLA-B, MGC4083, TNFRSFlOB, PLAGLl, PNMA2, TFPI, LAT, GZMB, CYR.61, PLAUR, FSCNl, ERP70, AFlQ, HIC, COL6A1, IFITM3, MAPlB3 FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MTlE3 CDClO, DOCK2, TRIM22, RISl3 BCATl, PRFl, DBNl, MTlK. TMSBlO3 FLJl 0350, Clorf243 NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Bleomycin. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN3 ACTR2. AKRlBl, VIM3 ITGA3, OPTN, M6PRBP1, COLlAl, BASPl, ANPEP, TGFBl3 NFIL3, NK4, CSPG2, PLAU3 COL6A2, UBC, FGFRl, BAX3 COL4A2, and RAB31, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Bleomycin.
In another embodiment, the at least one gene is selected from the group consisting of SSRPl, NUDC, CTSC3 AP1G2, PSME2, LBR, EFNB2, SERPINAl3 SSSCAl3 EZH2, MYB, PRJ-Ml, H2AFX, HMGAl3 HMMR3 TK23 WHSCl, DIAPHl3 LAMB3, DPAGTl, UCK2, SERPHSBl, MDNl5 BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, BDSTRPD, and PNAS-4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methyl-GAG. Alternatively, the method further includes measuring the expression level of PTMA, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Methyl-GAG.
In another embodiment, the at least one gene is selected from the group consisting of ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, BFIl 6, LRRN3, DOCKlO, LEPREl5 COL5A2, and ADAMTSl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Carboplatin. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of MSN, VIM, CSPG2, and FGFRl5 such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Carboplatin. hi another embodiment, the at least one gene is selected from the group consisting of RPL18, RPLlOA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl3 APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPLl 7, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Fluorouracil (5-FU). Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of RNPSl, RPL13, RPS6, and RPL3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Fluorouracil (5-FU).
In another embodiment, the at least one gene is selected from the group consisting of KIFCl, VLDLR, RUNXl, PAFAH1B3, HlFX, RNF144, TMSNB5 CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, CDlD, ADA, CD99, CD2, CNP, ERG, CD3E, CDlA, PSMC3, RPS4Y1, AKTl, TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl, AKRlCl, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL3 FHLl, EZH2, SMOX, SLC4A2, UFDlL, ZNF32, HTATSFl, SHDl9 PTOVl5 NXFl, FYB, TRJM28, BC008967, TRB@, HlFO, CD3D, CD3G, CENPB5 ALDH2, ANXAl, H2AFX, CDlE5 DDX5, CCNA2, ENO2, SNRPB5 GATA3, RHM2, GLUL5 SOX4, MAL, UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl5 SH3GL3, PLEKHCl5 CD47, POLR2F, RHOH, and ADDl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Rituximab. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of ITK5 RALY, PSMC5, MYL6, CDlB, STMNl, GNA15, MDK, CAPG, ACTNl, CTNNAl, FARSLA, E2F4, CPSFl5 SEPWl5 TFRC, ABLl5 TCF7, FGFRl5 NUCB2, SMA3, FAT, VIM, and ATP2A3, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with Rituximab.
In another embodiment, the at least one gene is selected from the group consisting of TRAl5 ACTN4, CALMl5 CD63, FKBPlA, CALU, IQGAPl5 MGC8721, STATl, TACCl5 TM4SF8, CD59, CKAP4, DUSPl5 RCNl5 MGC8902, LGALSl5 BHLHB2, RRBPl5 PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl5 PRSS23, PLOD2, NPCl3 TOBl, GFPTl5 IL8, PYGL, LOXL2, KIAA0355, UGDH5 PURA, ULK2, CENTG2, NID2, CAP350, CXCLl5 BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDHI l5 P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT5 TNFRSFlOB5 KIAAl 128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A1I, MPZLl5 SSA2, NQOl, ASPH5 ASAHl, MGLL5 SERPINB6, HSP A5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB5 MAPKl, MYOlB, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCBl, KIAA0802, RAB3GAP, SERPINBl, TIMM17A, SOD2, HLA-A5 NOMO2, LOC55831, PHLDAl, TMEM2, MLPH5 FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl5 COROlC, DNAJClO, TNPOl, LONP5 AMIGO2, DNAPTP6, and ADAMTSl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with radiation therapy. Alternatively, the method further includes measuring the expression level of at least one gene selected from the group consisting of WARS, CD81 , CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAKl, EIF4G3, COLlAl, DYRK2, NF1L3, ACTNl, CAPN2, BTN3A2, IGFBP3, FNl, COL4A2, and KPNBl, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with, radiation therapy.
In another embodiment, the at least one gene is selected from the group consisting of FAU, N0L5A, ANP32A, ARHGDIB5 LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG3 GPSM3, PIM2. EVERl9 LRMP, ICAM2, RIMS3, FMNLl, MYB, PTPN7, LCK5 CXorf95 RHOH, ZNFNlAl, CENTBl, LCP2, DBT, CEPl, EL6R, VAVl, MAP4KI, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCPl, CXCR4, PSCDBP, SELPLG5 CD3Z, PRKCQ, CDlA, GATA2, P2RX5, LAIRl, Clorf38, SH2D1A, TRB@, SEPT6, HA-I, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPAl, AIFl5 CYFIP2, GLTSCR2, CllαrG, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP5 and MGC2744, such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with histone deacetylase (HDAC) inhibitor.
In another embodiment, the at least one gene is selected from the group consisting of CD99, SNRPA5 CUGBP2, STAT5A, SLA5 IL2RG, GTSEl5 MYB5 PTPN7, CXorf9, RHOH, ZNFNlAl, CENTBl5 LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCPl, SELPLG, CD3Z, PRKCQ5 GZMB5 SCN3A, LAIRl, SH2D1A, SEPT6, CG018, CD3D, C18orflO, PRFl, AIFl, MCM5, LPXN, C22orfl 8, ARHGAP15, and LEFl , such that an increase in the expression level of one or more of these genes indicates that the patient is sensitive to treatment with 5-Aza-2'- deoxycytidine (Decϊtabine).
A second aspect of the invention features a method for determining the development of resistance by a patient (i.e,. a cell, such as a cancer cell, in the patient) to a treatment that the patient was previously sensitive to. The method includes determining the level of expression of one or more of the genes set forth in the first aspect of the invention, such that an increase in the expression level of a gene(s) which is decreased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment. Alternatively, an decrease in the expression level of a gene(s) which is increased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.
A third aspect of the invention features a kit that includes a single-stranded nucleic acid (e.g., deoxyribonucleic acid or ribonucleic acid) that is complementary to or identical to at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the genes set forth in the first aspect of the invention, such that the single stranded nucleic acid is sufficient for the detection of expression of the gene(s) by allowing specific hybridization between the single stranded nucleic acid and a nucleic acid encoded by the gene, or a complement thereof. The kit further includes instructions for applying nucleic acids collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the gene(s) hybridized to the single stranded nucleic acid, and predicting the patient's sensitivity to a treatment for cancer when use of the kit establishes that the expression level of the gene(s) is changes (i.e., increased or decreased relative to a control sample (i.e., tissue or cell) known to be sensitive or resitant to the treatment, as is discussed above in connection with the first aspect of the invention). In an embodiment, the instructions further indicate that an alteration in the expression level of the gene(s) relative to the expression of the gene(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (i.e., a decrease in the level of expression of a gene known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
In an embodiment, the kit can be utilized to determine a patient's resistance or sensitivity to Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Riruximab, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine) by determining the expression level of one or more of the genes set forth in the first aspect of the invention and known to be increased in a patient sensitive to treatment with these agents (i.e., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the gene(s) increases relative to the level of expression of the gene(s) in a control sample (i.e., a cell or tissue) in which increased expression of the gene(s) indicates sensitivity to the treatment, and vice versa).
In an embodiment, the nucleic acids are characterized by their ability to specifically identify nucleic acids complementary to the genes in a sample collected from a cancer patient.
A fourth aspect of the invention features a method of identifying biomarkers indicative of sensitivity of a cancer patient to a treatment for cancer by obtaining pluralities of measurements of the expression level of a gene (e.g., by detection of the expression of a gene using a single gene probe or by using multiple gene probes directed to a single gene) in different cell types and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of the cell types in the absence of the treatment for cancer; correlating each plurality of measurements of the expression level of the gene in cells with the growth of the cells to obtain a correlation coefficient; selecting the median correlation coefficient calculated for the gene; and identifying the gene as a biomarker for use in determining the sensitivity of a cancer patient to said treatment for cancer if said median correlation coefficient exceeds 0.3 (preferably the gene is identified as a biomarker for a patient's sensitivity to a treatment if the correlation coefficient exceeds 0.4, 0.5, 0.6, 0.7, 0.8. 0.9, 0.95, or 0.99 or more). In an embodiment, the method is performed in the presence of a second treatment.
A fifth aspect of the invention features a method of predicting sensitivity of a patient (e.g., a cancer patient) to a treatment for cancer by obtaining a measurement of a biomarker gene expression from a sample (e.g., a cell or tissue) from the patient; applying a model predictive of sensitivity to a treatment for cancer to the measurement, in which the model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks; and predicting whether or not the patient will be responsive to the treatment for cancer. In an embodiment, the measurement is obtained by assaying gene expression of the biomarker in a cell known to be sensitive or resistant to the treatment. In another embodiment, the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements. In another embodiment, treatment, e.g., a compound, has previously failed to show efficacy in a patient. In several embodiments, the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker gene expression. In another embodiment, the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis.
A sixth aspect of the invention features a kit, apparatus, and software used to implement the method of the fifth aspect of the invention.
In several embodiments of all aspects of the invention, the expression level of the gene(s) is determined by detecting the level of mRNA transcribed from the gene(s), by detecting the level of a protein product of the gene(s), or by detecting the level of the biological activity of a protein product of the gene(s). In further embodiments of all aspects of the invention, an increase or decrease in the expression level of the gene(s), relative to the expression level of the gene(s) in a cell or tissue sensitive to the treatment, indicates increased sensitivity of the cancer patient to the treatment. Alternatively, an increase or decrease in the expression level of the gene(s), relative to the expression level of the gene(s) in a cell or tissue resistant to the treatment, indicates increased resistance of the cancer patient to the treatment. In another embodiment of all aspects of the invention, the cell is a cancer cell. In another embodiment of all aspects of the invention, the expression level of the gene(s) is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In an embodiment of all aspects of the invention, the level of expression of two of the listed genes is performed, more preferably the level of expression of three, four, five, six, seven, eight, nine, or ten of the listed genes is performed, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes is performed. Ih another embodiment of all aspects of the invention, the expression level of the gene(s) is determined using the kit of the third aspect of the invention.
In another embodiment of all aspects of the invention, the treatment is a compound, such as a chemotherapteutic agent selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine). In another embodiment of all aspects of the invention, the compound has previously failed to show effect in a subject (e.g., a subject selected from a subpopulation predicted to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
In another embodiment of all aspects of the invention, the treatment is, e.g., administration of a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient. In an emobodiment of all aspects of the invention, the treatment is, e.g., a chemotherapeutic agent, such as, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), MABTHERA™ (Rituximab), histone deacetylase (HDAC) inhibitors, 5-Aza-2'-deoxycytidine (Decitabine), alpha emitters such as astatine-211, bismuth- 212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-11, nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper-62, copper-64, zinc-62, zinc-63. arsenic-70, arsenic-71, . arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-110, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetiurn-99m5 and technetium-99, gamma emitters such as cobalt-60, cesium- 137, and technetium-99m, Alemtuzumab, Daclizumab, Rituxixαab (MABTHERA™), Trastuzumab (HERCEPTIN™), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb5 AME5 ABX-EGF5 EMD 72 000, Apolizumab, Labetuzumab, ior-tl5 MDX-220, MRA5 H-Il scFv, Oregovomab, huJ591 MAb, BZL5 Visilizύmab, TriGem, TriAb, R3, MT-201, G-250, unconjugated, ACA-125, Onyvax-1055 CDP- 860, BrevaRex MAb5 AR54, IMC-ICl I5 GHoMAb-H3 DSTG-I5 Anti-LCG MAbs, MT-103, KSB- 303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb5 CAT, Prostate cancer antibody, H22xKi-4, ABX-MAl5 Imuteraα, Monophaπn-C, Acivicin, Aclarubicin, Acodazole Hydrochloride, Acronine, Adozelesin, Adriamycin, Aldesleukin, Altretamine, Ambomycin, A. metantrone Acetate, AininoglutethicQide, Amsacrine, Aπastrozole, Anthramycin, Asparaginase, Asperlin, Azacitidine, Azetepa, Azotomycin, Batimastat, Benzodepa, Bicalutamide, Bisantrene Hydrochloride, Bisnafide Dimesylate, Bizelesin, Bleomycin Sulfate, Brequinar Sodium, Bropirimine, Busulfan, Cactinomycin, Calusterone, Camptothecin, Caracemide, Carbetimer, Carboplatin, Carmustine, Carubicin Hydrochloride, Carzelesin, Cedefingol, Chlorambucil, Cirolemycin, Cisplatin, Cladribine. Combretestatin A-4, Crisnatol Mesylate, Cyclophosphamide, Cytarabine, Dacarbazine, DACA (N- [2- (Dimethyl- amino) ethyl] acridine-4-carboxamide), Dactϊnornycin, Daunorubicin Hydrochloride, Daunomycin, Decitabine, Dexormaplatin, Dezaguanine, Dezaguanine Mesylate, Diaziquone, Docetaxel, Dolasatiαs, Doxorubicin, Doxorubicin Hydrochloride, Droloxifene, Droloxifene Citrate, Dromostanolone Propionate, Duazomycin. Edatrexate, Eflornithine Hydrochloride, Ellipticine, Elsamitrucin, Enloplatin, Enpromate, Epipropidine, Epirubicin Hydrochloride, Erbulozole, Esorubicin Hydrochloride, Estramustine, Estramustine Phosphate Sodium, Etanidazole, Ethiodized Oil 1 131, Etoposide, Etoposide Phosphate, Etoprine, Fadrozole Hydrochloride, Fazarabine, Fenretinide, Floxuridine, Fludarabine Phosphate. Fluorouracil, 5- FdUMP5 Flurocitabine, Fosquidone, Fostriecin Sodium, Gemcitabine, Gemcitabine Hydrochloride, Gold Au 198, Homocamptothecin, Hydroxyurea, Idarubicin Hydrochloride, Ifosfamide, Ilmofosine, Interferon Alfa-2a, Interferon Alfa-2b, Interferon Alfa-nl, Interferon Alfa-n3, Interferon Beta-I a, Interferon Gamma-I b, Iproplatin, Irinotecan Hydrochloride, Lanreotide Acetate, Letrozole, Leuprolide Acetate, Liarozole Hydrochloride, Lometrexol Sodium, Lomustine, Losoxantrone Hydrochloride, Masoprocol, Maytansine, Mechlorethamine Hydrochloride, Megestrol Acetate, Melengestrol Acetate, Melphalan, Menogaril, Mercaptopurine, Methotrexate, Methotrexate Sodium, Metoprine, Meturedepa, Mitindomide, Mitocarcin, Mitocromin, MitogilHn, Mitomalcin, Mitomycin, Mitosper, Mitotane, Mitoxantrone Hydrochloride, Mycophenolic Acid, Nocodazole, Nogalamycin,Ormaplatin, Oxisuran, Paclitaxel, Pegaspargase, Peliomycin, Pentamustine, PeploycinSulfate, Perfosfamide, Pipobroman, Piposulfan, Piroxantrone Hydrochloride, Plicamycin, Plomestane, Porfimer Sodium, Porfiromycin, Prednimustine, Procarbazine Hydrochloride, Puromycin, Puromycin Hydrochloride, Pyrazofurin, Rhizoxin, Rhizoxhi D, Riboprine, Rogletimide, Safϊngol, Safingol Hydrochloride, Semustine, Skntrazene, Sparfosate Sodium, Sparsomycin, Spirogermanium Hydrochloride, Spiromustine, Spiroplatin, Streptonigrin, Streptozocin, Strontium Chloride Sr 89, Sulofenur, Talisomycin, Taxane, Taxoid, Tecogalan Sodium, Tegafur, Teloxantrone Hydrochloride, Temoporfm, Teniposide, Teroxirone, Testolactone, Thiamiprine, Thioguanine, Thiotepa, Thymitaq, Tiazofurin, Tirapazamine, Tomudex, TOP53, Topotecan Hydrochloride, Toremifene Citrate, Trestolone Acetate, Triciribύie Phosphate, Trimetrexate, Trirnetrexate Glucuronate, Triptorelin, Tubulozole Hydrochloride, Uracil Mustard, Uredepa, Vapreotide, Verteporfin, Vinblastine, Vinblastine Sulfate, Vincristine, Vincristine Sulfate, Vindesine, Vindesine Sulfate, Vinepidine Sulfate, Vinglycinate Sulfate, Vinleurosine Sulfate, Vmorelbine Tartrate, Vinrosidine Sulfate, Vinzolidine Sulfate, Vorozole, Zeniplatin, Zinostatin, Zorubicin Hydrochloride, 2-Chlorodeoxyadenosine, 2' Deoxyformycin, 9-aminocamptothecin, raltitrexed, N-propargyl-5,8-dideazafolic acid, 2chloro-2'-arabino-fluoro-2'-deoxyadenosine, 2-chloro-2'- deoxyadenosine, anisomycin, trichostatin A, hPRL-G129R, CEP-751, linomide, sulfur mustard, nitrogen mustard (mechlor ethamine), cyclophosphamide, melphalan, chlorambucil, ifosfamide, busulfan, N-methyl-Nnitrosourea (MNU), N, N'-Bis (2-chloroethyl)-N-nitrosourea (BCNU), N- (2-chloroethyl)-Np cyclohexyl-N-nitrosourea (CCNU), N- (2-chloroethyl)-Nf- (trans-4- methylcyclohexyl-N-nitrosourea (MeCCNU), N- (2-chloroethyl)-N'- (diethyl) ethylphosphonate- N-nitrosourea (fotemustine), streptozotocin, diacarbazine (DΗC), mitozolomide, temozolomide, thiotepa, mitomycin C, AZQ, adozelesin, Cisplatin, Carboplatin, Ormaplatin, Oxaliplatin,Cl- 973, DWA 2114R, JM216, JM335. Bis (platinum), tomudex, azacitidine, cytarabine, gemcitabine, 6-Mercaptopurine, 6-Thioguanine, Hypoxanthiαe, teniposide 9-arnino camptothecin, Topotecan, CPT-I l, Doxorubicin, Daunomycϊn, Epirubicin, darubicin, mitoxantrone, losoxantrone, Dactinomycin (Actinomycin D), amsacrine, pyrazoloacridine, all- trans retinol, 14-hydroxy-retro-retinol, all-trans retinoic acid, N- (4- Hydroxyphenyl) retinamide, 13-cis retinoic acid, 3-Methyl TTNEB, 9-cis retinoic acid, fludarabine (2-F-ara-AMP), 2- chlorodeoxyadenosine (2-Cda), 20-pi-l,25 dihydroxyvitamin D3, 5-ethynyluracil, abϊraterone, aclarubicin, acylfulvene, adecypenol, adozelesin, aldesleukin, ALL-TK antagonists, altretamine, ambamustine, amidox, amifostine, aminolevulinic acid, amrubicin, amsacrine, anagrelide, anastrozole, andrographolide, angiogenesis inhibitors, antagonist D, antagonist G, antarelix, anti- dorsalizing morphogenetic protein- 1, antiandrogen, prostatic carcinoma, antiestrogen, antineoplaston, antisense oligonucleotides, aphidicolin glycinate, apoptosis gene modulators, apoptosis regulators, apurinic acid, ara-CDP-DL-PTBA, argininedeaminase, asulacrine, atamestane, atrimustine, axinastatin 1, axinastatin 2, axinastatin 3, azasetron, azatoxin, azatyrosine, baccatin HI derivatives, balanol, batimastat, BCPJABL antagonists, benzochlorins, benzoylstaurosporine, beta lactam derivatives, beta-alethine, betaclamycin B, betulinic acid, bFGF inhibitor, bicalutamide, bisantrene, bisaziridinylspermine, bisnafide, bistratene A, bizelesin, breflate, bleomycin A2, bleomycin B2, bropirimine, budotitane, buthionine sulfoximine, calcipotriol, calphostin C, camptothecin derivatives (e.g., 10-hydroxy- camptothecin), canarypox EL-2, capecitabine, carboxamide-amino-triazole, carboxyamidotriazole, CaRest M3, CARN 700, cartilage derived inhibitor, carzelesin, casein kinase inhibitors (ICOS), castanospermine, cecropin B, cetrorelix, chlorins, chloroquinoxaline sulfonamide, cicaprost, cis-porphyrin, cladribine, clomifene analogues, clotrimazole, collismycin A , collismycin B, combretastatin A4, combretastatin analogue, conagenin, crambescidin 816 , crisnatol. cryptophycin 8, cryptophycin A derivatives, curacin A, cyclopentanthraquinones, cycloplatam, cypemycin, cytarabine ocfosfate, cytolytic factor, cytostatin. dacliximab, decitabine, dehydrodidemnin B3 2'deoxycoformyciα (DCF), deslorelin, dexifosfarnide, dexrazoxane, dexverapam.il, diaziquone, didemnin B5 didox, diethylnorspeπnine, dih.ydro-5-azacytidine, dihydrotaxol, 9- , dioxamycin, diphenyl spiromustiαe, discodermolide, docosanol, dolasetron, doxϋluridine, droloxifene, dronabinol, duocarmycin SA, ebselen, ecomustine, edelfosine, edrecolomab, eflomitliine, elemene, emitefur, epirubicrn, epothilones (A, R = H, B, R = Me), epithilones, epristeride, estramustine analogue, estrogen agonists, estrogen antagonists, etanidazole, etoposide, etoposide 4'-phosphate (etopofos), exemestane, fadrozole, fazarabine, fenretinide, filgrastim, finasteride, flavopiridol, flezelastine, fluasterone, fludarabine, fluorodaunorunicin hydrochloride, forfenimex, formestane, fostriecin, fotemustine, gadolinium texaphyrin, gallium nitrate, galocitabine, ganirelix, gelatinase inhibitors, gemcitabine, glutathione inhibitors, hepsulfam, heregulin, hexamethylene bisacetamide, homoharringtonine (HHT), hypericin, ibandronic acid, idarubicin, idoxifene, idramantone, ilmofosine, ilomastat, imidazoacridones, imiquimod, irnrnunostimulant peptides, insulin-like growth factor- 1 receptor inhibitor, interferon agonists, interferons, interleukins, iobenguane, iododoxorubicin, ipomeanol, 4- , irinotecan, iroplact, irsogladine, isobengazole, isohornohalicondrin B, itasetron, jasplakinolide, kahalalide F, lamellarin-N triacetate, lanreotide, leinamycin, lenograstim, lentinan sulfate, leptolstatin, letrozole, leukemia inhibiting factor, leukocyte alpha interferon, leuprolide + estrogen + progesterone, leuprorelin, levamisole, liarozole, linear polyamine analogue, lipophilic disaccharide peptide, lipophilic platinum compounds, lissoclinamide 7, lobaplatin, lombricine, lometrexol, lonidamine, losoxantrone, lovastatin, loxoribine, lurtotecan, lutetium texaphyrin, lysofylline, lytic peptides, maytansine, mannostatin A, marimastat, masoprocol, maspin, matrilysin inhibitors, matrix metalloproteinase inhibitors, menogaril, rnerbarone, meterelin, methioninase, metoclopramide, MIF inhibitor, ifepristone, miltefosine, mirimostim, mismatched double stranded RNA, mithracin, mitoguazone, rnitolactol, mitomycin analogues, mitonafide, mitotoxin fibroblast growth factor-saporin, mitoxantrone, mofarotene, molgramostim, monoclonal antibody, human chorionic gonadotroph^, monophosphoryl lipid A + myobacterium cell wall sk, mopidamol, multiple drug resistance gene inhibitor, multiple tumor suppressor 1- based therapy, mustard anticancer agent, mycaperoxide B, mycobacterial cell wall. extract, myriaporone, N-acetyldinaline, N-substituted benzamides, nafarelin, nagrestip, naloxone + pentazocine, napavin, naphterpin, nartograstim. nedaplatin, nemorubicin, neridronic acid, neutral endopeptidase, nilutamide, nisamycin, nitric oxide modulators, nitroxide antioxidant, nitrullyn, 06-benzylguaniαe, octreotide, okicenone, oligonucleotides, onapristone, ondansetron, ondansetron, oracin, oral cytokine inducer, ormaplatin, osaterone, oxaliplatin, oxaunomycin, paclitaxel analogues, paclitaxel derivatives, palauamine, palmitoylrhizoxin, pamidronic acid, panaxytriol, panomifene, parabactin, pazelliptine, pegaspargase, peldesine, pentosan polysulfate sodium, pentostatin, pentrozole, perflubron, perfosfamide, perillyl alcohol, phenazinomycin, phenylacetate, phosphatase inhibitors, picibanil, pilocarpine hydrochloride, pirarubicin, piritrexim, placetin A5 placetin B, plasminogen activator inhibitor, platinum complex, platinum compounds, platinum-triamine complex, podophyllotoxin, porfϊmer sodium, porfiromycin, propyl bis-acridone, prostaglandin J2, proteasome inhibitors, protein A-based immune modulator, protein kinase C inhibitor, protein kinase C inhibitors, microalgal, protein tyrosine phosphatase inhibitors, purine nucleoside phosphorylase inhibitors, purpurins, pyrazoloacridine, pyridoxylated hemoglobin polyoxyethylene conjugate, raf antagonists, raltitrexed, ramosetron, ras farnesyl protein transferase inhibitors, ras inhibitors, ras-GAP inhibitor, retelliptine demethylated, rhenium Re 186 etidronate, rhizoxin, ribozymes, Ru retinamide, rogletimide, rohitukine, romurtide, roquinirnex, rubiginone B 1, ruboxyl, safϊngol, saintopin, SarCNU. sarcophytol A, sargramostim, Sdi 1 mimetics, semustine, senescence derived inhibitor 1, sense oligonucleotides, signal transduction inhibitors, signal transduction modulators, single chain antigen binding protein, sizofiran, sobuzoxane, sodium borocaptate, sodium phenylacetate, solverol, somatomedin binding protein, sonermin, sparfosic acid, spicamycin D, spiromustine, splenopentin, spongistatin 1 , squalamine, stem cell inhibitor, stem-cell division inhibitors, stipiamide, stromelysin inhibitors, sulfinosine, superactive vasoactive intestinal peptide antagonist, suradista, suramin, swainsonine, synthetic glycosaminoglycans, tallimustine, tamoxifen methiodide, tauromustine, tazarotene, tecogalan sodium, tegafur, tellurapyrylium, telomerase inhibitors, temoporfin, temozolomide, teniposide, tetrachlorodecaoxide, tetrazomine, thaliblastine, thalidomide, thiocoraline, thrombopoietin, thrombopoietin mimetic, mymalfasin, thymopoietin receptor agonist, thymotrinaα, thyroid stimulating hormone, tin ethyl etiopurpurin, tirapazamine, titanocene dichloride, topotecaα, topsentin, toremifene, totipotent stem cell factor, translation inhibitors, tretinoin, triacetyluridine, triciribine, trimetrexate, triptorelin, tropisetron, turosteride, tyrosine kinase inhibitors, tyrphostins, UBC inhibitors, ubenimex, urogenital sinus- derived growth inhibitory factor, urokinase receptor antagonists, vapreotide, variolin B, vector system, erythrocyte gene therapy, velaresol, veramine, verdins, verteporfin, vinorelbine, vinxaltine, vitaxin, vorozole, zanoterone, zeniplatin, zilascorb, or zinostatin stimalamer. In another embodiment of all aspects of the invention, a second treatment is utilized to determine gene expression in a sample from the patient.
In another embodiment of all aspects of the invention, the gene is selected from the group consisting of ABLl, ACTB, ACTNl, ACTN4, ACTR2, ADA, ADAM9, ADAMTSl, ADDl, ADORA2A, AFlQ, AIFl, AKAPl, AKAP13, AKRlBl, AKRlCl, AKTl, ALDH2, ALDH3A1, ALDOC, ALG5, ALMSl, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANPEP3 ANXAl, ANXA2, AP1G2, APOBEC3B, APRT, ARHE, ARHGAPl 5, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC5 ATOXl5 ATP1B3, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, B2M, BASPl, BAX, BC008967, BCATl, BCHE, BCLl IB, BDNF, BHLHB2, BIN2, BLM, BLMH, BLVRA, BMIl, BNIP3, BRDT, BRRNl, BTN3A2, BTN3A3, Cllorf2, C14orfl39, C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl8, C5orfl3, C6orf32, CACNAlG5 CACNB3, CALDl, CALMl, CALML4, CALU, CAP350, CAPG, CAPN2, CAPN3, CASP2, CASP6, CASP7, CAST, CBFB, CBLB, CBRl, CBX3, CCL2, CCL21, CCNA2, CCNBlIPl, CCND3, CCR7, CCR9, CCT5, CD151, CDlA, CDlB5 CDlC, CDlD5 CDlE, CD2, CD28, CD37, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD53, CD59, CD6, CD63, CD81, CD8A, CD8B1, CD99, CDClO, CDC14B, CDHIl, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl, CENTG2. CEPl5 CG018, CHRNA3, CHSl5 CIAPINl5 CKAP4, CKIP-I5 CNN3, CNP, COLlAl5 COL4A1, COL4A2, COL5A2, COL6A1, COL6A2, COPA, COPEB5 COROlA, COROlC, C0X7B, CPSFl5 CRABPl, CREB3L1, CRIP2, CRK, CRYl, CSDA5 CSPG2, CSRPl5 CST3, CTBPl5 CTGF9 CTNNAl5 CTSB, CTSC, CTSD, CTSL, CUGBP2, CUTC, CXCLl, CXCR4, CXorf9, CYFIP2, CYLD3 CYR61, DATFl, DAZAPl, DBNl, DBT, DCTNl, DDOST, DDX18, DDX5, DGKA, DIAPHl, DIPA, DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZρ564J157, DKFZP564K0822, DNAJClO3 DNAPTP6, DOCKlO, DOCK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl, DUSP3, DXS9879E, DYRK2, E2F4, ECEl, ECMl, EEFlAl, EEF1B2, EEFlG, EFNB2, EHD2, EJJF2S2, EIF3S2, EIF4B, EIF4G3, EIF5A, ELA2B, ELK3, EMP3, ENO2, EPASl, EPB41L4B, ERCC2, ERG, ERP70, EVERl, EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD104, FAM46A, FARSLA, FAT, FAU, FBL, FCGR2A, FCGR2C, FER1L3, FGFRl, FHLl, FHODl, FKBPlA, FKBP9, FLH, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl, FMNLl, FNl, FNBPl, FOLHl, FOXF2, FSCNl, FSTLl, FTHl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GAPD, GAS7, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS, GMFG, GNA15, GNAI2, GNAQ, GNB2, GNB5, GOT2, GPNMB, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GYPC, GZMA, GZMB, HlFO, HlFX, H2AFX, H3F3A, HA-I, HCLSl, HEMl, HEXB, HIC, HIST1H4C, HKl, HLA-A, HLA-B, HLA-DRA3 HMGAl, HMGB2, HMGN2, HMMR3 HNRPAl, HNRPD, HNRPM, HOXA9, HPRTl, HRMTlLl, HS A9761, HSP A5, HSU79274, HTATSFl, HU6800, ICAMl, ICAM2, JJER3, IFIl 6, TJFI44, IFITMS, IFITM3, IFRG28, IGFBP2, IGFBP3, IGSF4, EL13RA2, IL21R, IL2RG, EL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl, IQGAPl, IQGAP2, IRS2, ITGA3, ITGA5, ITGB2, ITK, ITM2A, JAKl, JARJJD2, JUNB, K-ALPHA-I, KHDRBSl, KIAA0220, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KJAAl 128, KIAA1393, KIFCl, KPNBl, LAIRl, LAMBl, LAMB3, LAMRl, LAPTM5, LAT, LBR3 LCK, LCPl, LCP2, LDHB, LEFl, LEPREl, LGALSl, LGALS9, LHFPL2, LMNBl, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LSTl, LTB, LUM, LY9, LY96, M6PRBP1, MAD2L1BP, MAGEB2, MAL, MANlAl, MAPlB, MAP1LC3B, MAP4K1, MAPKl, MAPREl, MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDK, MDNl, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MIA, MICA, MLPH, MME, MMP2, MPHOSPH6, MPPl, MPZLl, MRP63, MRPL12, MRPS2, MSN5 MTlE, MTlK, MXJFl, MVP, MYB5 MYC, MYL6, MYL9, MYOlB, NAPlLl, NAP1L2, NARP, NARS, NASP5 NBLl5 NCL5 NCOR2, NDN5 NDUFABl, NDUFS6, NFIL3, NFKBIA5 NID2, NTP A2, NK45 NME45 NME7, NNMT5 NOL5A, NOL8, NOMO2, NOTCHl, NPCl5 NQOl, NR1D2, NUCB2, NUDC, NUP210, NUP88, NVL5 NXFl, OBFCl, OCRL, OGT, OK/SW-cl.56, OPTN, OXAlL5 P2RX5, P4HA1, PACAP5 PAF53, PAFAH1B3, PALM2- AKAP2, PAX6, PBEFl, PCBP2, PCCB, PEA15, PFDN5, PFNl5 PFN2, PGAMl5 PGKl5 PHEMX5 PHLDAl, PIM2, PITPNCl5 PKM2, PLAC8, PLAGLl5 PLAU5 PLAUR5 PLCBl5 PLEK2, PLEKHCl3 PLOD2, PLSCRl, PNAS-4, PNMA2, P0LR2F, PON25 PPAP2B, PPIA, PPIF, PPPlRl I5 PPP2CB, PRFl5 PRGl5 PRIMl5 PRKCA5 PRKCBl5 PRKCH5 PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRPSl, PRSSl I3 PRSS23, PSCDBP5 PSMB9, PSMC3, PSMC5, PSME2, PTGER4, PTGES2, PTMA, PTOVl, PTP4A3, PTPN7, PTPNSl5 PTPRC5 PTPRCAP5 PTRF5 PTS, PURA, PWPl, PYGL3 QKI5 RAB31, RAB3GAP, RAB7, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RALY5 RAPlB5 RASGRP2, RBMX, RBPMS, RCNl5 REA5 RFC3, RFC5, RGC32, RGS3, RHOC, RHOH, RTMS3, RI0K3, RTPK2, RISl, RNASE6, RNF144, RNPSl5 RPLlO5 RPLlOA5 RPLI l5 RPL12. RPL13, RPL13A, RPL17, RPL18, RPL18A, RPL24, RPL3, RPL32, RPL36A, RPL39, RPL7, RPL9, RPLPO, RPLP2, RPSlO5 RPSI l5 RPS15, RPS15A, RPS19, RPS2, RPS23, RPS24, RPS25, RPS27, RPS28, RPS4X, RPS4Y1, RPS6, RPS7, RPS9, RRAS5 RRAS2, RRBPl5 RRM2, RUNXl, RUNX3, S100A13, S100A4, SART3, SATBl5 SCAPl5 SCARBl, SCARB2, SCN3A, SCTR, SEC31L2, SEC61G, SELL5 SELPLG, SEMA4G, SEPT6, SEPTlO, SEPWl5 SERPINAl5 SERPINBl5 SERPINB6, SFRS3, SFRS5, SFRS6. SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl5 SHFMl5 SHMT2, SIATl, SKBl5 SKP2, SLA, SLC1A4, SLC20A1. SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMA3, SMAD3, SMARCD3, SMOX5 SMS, SNDl, SNRPA5 SNRPB, SNRPB2, SNRPE, SNRPF, SOD2, SOX4, SP 140, SPANXC5 SPARC5 SPIl5 SRF, SRM5 SRRMl5 SSA2, SSBP2, SSRPl, SSSCAl, STAG3, STATl5 STAT4, STAT5A, STCl, STC23 STMNl5 STOML2, SUIl5 T3JAM, TACCl, TACC3, TAF5, TAGLN, TALl5 TAPl5 TARP5 TBCA5 TCF12, TCF4, TCF7, TFDP2, TFPI, TFRC, TGFBl, TIMMl 7A, TIMPl, TJPl. TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO3 TMSNB, TNFAEP3, TNFAIP8, TNFRSFlOB5 TNFRSFlA, TNFRSF7, TNIK, TNPOl, TOBl, TOMM20, TOP2A, TOX3 TPKl3 TPM2, TRA@, TRAl, TRAM2, TRB@, TRD@, TRIM, TRJM14, TRIM22, TRIM28, TRIP 13, TRPV2, TUBA3, TUBGCP3, TUFM3 TUSC3, TXN, TXNDC5, UBASH3A, UBB, UBC5 UBE2A, UBE2L6, UBE2S, UCHLl3 UCK2, UCP2, UFDlL, UGCG3 UGDH, UGT2B17, ULK2, UMPS, UNG3 UROD, USP34, USP4, USP7. VASP3 VAVl, VTM, VLDLR, VWF, WARS3 WASPIP3 WBSCR20A, WBSCR20C, WHSCl3 WNT5A, XPOl, ZAP128, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl5 or ZYX.
The nucleic acid sequence of each of the listed genes is publicly available through the Genbaαk database. The gene sequences are also included as part of the HG-U133A GeneChip from Affymetrix, Inc.
"Resistant" or "resistance" as used herein means that a cell, a tumor, a person, or a living organism is able to withstand treatment, e.g., with a compound, such as a chemotherapeutic agent or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, person, or living organism by less than 10%, 20%, 30%, 40%, 50%, 60%, or 70% relative to the growth of a cell not exposed to the treatment Resistance to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, greater absorbance indicates greater cell growth, and thus, resistance to the treatment. A smaller reduction in growth indicates more resistance to a treatment. By "chemoresistant" or "chemoresistance" is meant resistance to a compound.
"Sensitive" or "sensitivity" as used herein means that a cell, a tumor, a person, or a living organism is responsive to treatment, e.g., with a compound, such as a chemotherapeutic agent or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, person, or living organism by 70%, 80%, 90%, 95%, 99% or 100%. Sensitivity to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, lesser absorbance indicates lesser cell growth, and thus, .
sensitivity to the treatment. A greater reduction in growth indicates more sensitivity to the treatment. By "chemosensitϊve" or "chemosensitivity" is meant sensitivity to a compound.
"Complement" of a nucleic acid sequence or a "complementary" nucleic acid sequence as used herein refers to an oligonucleotide which is in "antiparallel association" when it is aligned with the nucleic acid sequence such that the 5' end of one sequence is paired with the 3' end of the other. Nucleotides and other bases may have complements and may be present in complementary nucleic acids. Bases not commonly found in natural nucleic acids that may be included in the nucleic acids of the present invention include, for example, inosine and 7- deazaguanine. "Complementarity" may not be perfect; stable duplexes of complementary nucleic acids may contain mismatched base pairs or unmatched bases. Those skilled in the art of nucleic acid technology can determine duplex stability empirically considering a number of variables including, for example, the length of the oligonucleotide, percent concentration of cytosine and guanine bases in the oligonucleotide, ionic strength, and incidence of mismatched base pairs.
When complementary nucleic acid sequences form a stable duplex, they are said to be "hybridized" or to "hybridize" to each other or it is said that "hybridization" has occurred. Nucleic acids are referred to as being "complementary" if they contain nucleotides or nucleotide homologues that can form hydrogen bonds according to Watson-Crick base-pairing rules (e.g., G with C, A with T or A with U) or other hydrogen bonding motifs such as for example diaminopurine with T, 5-methyl C with G, 2-thiothymidine with A, inosine with C, pseudoisocytosine with G, etc. Anti-sense RNA may be complementary to other oligonucleotides, e.g., mRNA.
"Biomarker" as used herein indicates a gene whose expression indicates sensitivity or resistance to a treatment.
"Compound" as used herein means a chemical or biological substance, e.g., a drug, a protein, an antibody, or an oligonucleotide, which may be used to treat a disease or which has biological activity in vivo or in vitro. Preferred compounds may or may not be approved by the U.S. Food and Drug Administration (FDA). Preferred compounds include, e.g., chemotherapy agents that may inhibit cancer growth. Preferred chemotherapy agents include, e.g., Vincristine, .
Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylpredrdsolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), MABTHERA™ (Rituximab), radiation, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine). Exemplary radioactive chemotherapeutic agents include compounds containing alpha emitters such as astatine-211, bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium- 137, carbon- 11, nitrogen- 13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copρer-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71, arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-110, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon- 122, technetium-94m, technetium-94, technetium-99m, and technetium-99, or gamma emitters such as cobalt-60, cesium- 137, and technetium-99m. Exemplary chemotherapeutic agents also include antibodies such as Alemtuzumab, Daclizumab, Rituximab (MABTHERA™), Trastuzumab (HERCEPTIN™), Gemtuzumab, Ibrirumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME5 ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-tl, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL5 Visilizumab. TriGem, TriAb, R3, MT-201, G-250, unconjugated, ACA-125, Onyvax-105, CDP- 860, BrevaRex MAb5 AR54, IMC-ICl 1, GlioMAb-H, ING-I, Anti-LCG MAbs, MT-103, KSB- 303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb3 CAT, Prostate cancer antibody, H22xKi-4, ABX-MAl, Imuteran, and Monopharm-C. Exemplary chemotherapeutic agents also include Acivicin; Aclarubicin; Acodazole Hydrochloride; Acronine; Adozelesin; Adriamycin; Aldesleukin; Altretarnine; Ambomycin; A. metantrone Acetate; Arnmoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Camptothecin; Caracemide; Carbetimer; Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirolemycin; Cisplatin; Cladribine; Combretestatin A-4; Grisnatol Mesylate; Cyclophosphamide; Cytarabine; Dacarbazine; DACA (N- [2- (Dimethyl-amino) ethyl] acridine-4-carboxamide); Dactinomycin; Daunorubicin Hydrochloride; Daunomycin;. Decitabine; Dexormaplatin; Dezaguanine; Dezaguanine Mesylate; Diaziquone; Docetaxel; Dolasatins; Doxorubicin; Doxorubicin Hydrochloride; Droloxifene; Droloxifene Citrate; Dromostanolone Propionate; Duazomycin; Edatrexate; Eflornithine Hydrochloride; Ellipticine; Elsamitrucin; Enloplatin; Enpromate; Epipropidine; Epirubicin Hydrochloride; Erbulozole; Esorubicin Hydrochloride; Estramustine; Estramustine Phosphate Sodium; Etanidazole; Ethiodized Oil 1 131; Etoposide; Etoposide Phosphate; Etoprine; Fadrozole Hydrochloride; Fazarabine; Fenretinide; Floxuridine; Fludarabine Phosphate; Fluorouracil; 5-FdUMP; Flurocitabine; Fosquidone; Fostriecin Sodium; Gemcitabine; Gemcitabine Hydrochloride; Gold Au 198; Homocamptothecin; Hydroxyurea; Idarubicin Hydrochloride; Ifosfamide; llmofosine; Interferon Alfa-2a; Interferon Alfa-2b; hiterferon Alfa-nl; Interferon Alfa-n3; Interferon Beta-I a; Interferon Gamma-I b; Iproplatin; Irinotecan Hydrochloride; Lanreotide Acetate; Letrozole; Leuprolide Acetate; Lϊarozole Hydrochloride; Lometrexol Sodium; Lomustine; Losoxantrone Hydrochloride; Masoprocol; Maytansine; Mechlorethamine Hydrochloride; Megestrol Acetate; Melengestrol Acetate; Melphalan; Menogaril; Mercaptopurine; Methotrexate; Methotrexate Sodium; Metoprine; Meturedepa; Mitindomide; Mitocarcin; Mitocromin; Mitogillin; Mitomalcin; Mitomycin; Mitosper; Mitotane; Mitoxantrone Hydrochloride; Mycophenolic Acid; Nocodazole; Nogalamycin;Ormaplatin; Oxisuran; Paclitaxel; Pegaspargase; Peliomycin; Pentamustine; PeploycinSulfate; Perfosfamide; Pipobroman; Piposulfan; Piroxantrone Hydrochloride; Plicamycin; Plomestane; Porfimer Sodium; Porfiromycin; Prednimustine; Procarbazine Hydrochloride; Puromycin; Puromycin Hydrochloride; Pyrazofurin; Rhizoxin; Rhizoxin D; Riboprine; Rogletimide; Safingol; Safingol Hydrochloride; Semustine; Simtrazene; Sparfosate Sodium; Sparsomycin; Spirogermanium Hydrochloride; Spiromustine; Spiroplatin; Streptonigrin; Streptozocin; Strontium Chloride Sr 89; Sulofenur; Talisomycin; Taxane; Taxoid; Tecogalan Sodium; Tegafur; Teloxantrone Hydrochloride; Temoporfin; Ten poside; Teroxirone; Testolactone; Thiamiprine; Thioguanine; Thiotepa; Thymitaq; Tiazofurin; Tirapazamine; Tomudex; TOP53; Topotecan Hydrochloride; Toremifene Citrate; Trestolone Acetate; Triciribine Phosphate; Trimetrexate; Trimetrexate Glucuronate; Triptorelin; Tubulozole Hydrochloride; Uracil Mustard; Uredepa; Vapreotide; Verteporfin; Vinblastine; Vinblastine Sulfate; Vincristine; Vincristine Sulfate; Vindesine; Vindesine Sulfate; Vinepidine Sulfate; ■ Vinglycinate Sulfate; Vinleurosine Sulfate; Vinorelbine Tartrate; Vinrosidine Sulfate; Vinzolidine Sulfate; Vorozole; Zeniplatin; Zinostatin; Zorubicin Hydrochloride; 2- Chlorodeoxyadenosine; 2' Deoxyformycin; 9-aminocamptothecin; raltitrexed; N-propargyl-5,8- dideazafolic acid; 2chloro-2'-arabino-fluoro-2'-deoxyadenosine; 2-chloro-2'-deoxyadenosine; anisomycin; trichostatin A; hPRL-G129R; CEP-751; linomide; sulfur mustard; nitrogen mustard (mechlor ethamine); cyclophosphamide; melphalan; chlorambucil; ifosfamide; busulfan; N- methyl-Nnitrosourea (MNU); N, N'-Bis (2-chloroethyl)-N-nitrosourea (BCNU); N- (2- chloroethyl)-N' cyclohexyl-N-nitrosourea (CCNU); N- (2-chloroethyl)-N'- (trans-4- methylcyclohexyl-N-nitrosourea (MeCCNU); N- (2-chloroethyl)-N'- (diethyl) ethylphosphonate- N-nitrosourea (fotemustine); streptozotocin; diacarbazine (DTIC); mitozolomide; temozolomide; tbiotepa; mitomycin C; AZQ; adozelesin; Cisplatin; Carboplatin; Ormaplatin; Oxaliplatin;Cl- 973; DWA 2114R; JM216; JM335; Bis (platinum); tomudex; azacitidine; cytarabine; gemcitabine; 6-Mercaptopurine; 6-Thioguanine; Hypoxanthine; teniposide 9-amino camptothecin; Topotecan; CPT-I l; Doxorubicin; Daunomycin; Epirubicin; darubicin; mitoxantrone; losoxantrone; Dactinomycin (Actinomycin D); amsacrine; pyrazoloacridine; all- trans retinol; 14-hydroxy-retro-retinol; all-trans retinoic acid; N- (4- Hydroxyphenyl) retinamide; 13-cis retinoic acid; 3-Methyl TTNEB; 9-cis retinoic acid; fludarabine (2-F-ara-AMP); or 2- chlorodeoxyadenosine (2-Cda).
Other chemotherapeutic agents include, but are not limited to, 20-pi-l,25 dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene; adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide; anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein-1; antiandrogen, prostatic carcinoma; antiestrogen; antineoplaston; antisense _
oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA; argininedeaminase; asulacrine; atamestane; atrimustine; axinastatin 1; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin DI derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bleomycin A2; bleomycin B2; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives (e.g., lO-hydroxy-camptothecin); canarypox HL-2; capecitabine; carboxamide-amino-triazole; carboxyamidotriazole; CaRest M3; CARN 700; cartilage derived inhibitor; carzelesin; casein kinase inhibitors (ICOS); castanospermine; cecropin B; cetrorelix; chlorins; chloroquinoxaline sulfonamide; cicaprost; cis-porphyrin; cladribine; clomifene analogues; clotrimazole; collismycin A ; collismycin B; combretastatin A4; combretastatin analogue; conagenin; crambescidin 816 ; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam; cypemycin; cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; 2'deoxycoformycin (DCF); deslorelin; dexifosfamide; dexrazoxane; dexverapamii; diaziquone; didernnin B; didox; diethylnorspermine; dihydro-5-azacytidine; dihydrotaxol, 9- ; dioxamycin; diphenyl spiromustine; discodermolide; docosanol; dolasetron; doxifluridine; droloxifene; dronabinol; duocarmycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; ernitefur; epirubicin; epothilones (A, R = H; B3 R = Me); epithilones; epristeride; estramustine analogue; estrogen agonists; estrogen antagonists; etanidazole; etoposide; etoposide 4'-phosphate (etopofos); exemestane; fadrozole; fazarabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfeπimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene bisacetamide; homoharringtonine (HHT); hypericin; ibandronic acid; idarubicin; idoxifene; idramantone; ilmofosine; ilomastat; imidazoacridones; imiquimod; immunostimulant peptides; insulin-like growth factor-1 receptor inhibitor; interferon agonists; interferons; interleukins; iobenguane; iododoxorubicin; ipomeanol, 4- ; irinotecan; iroplact; irsogladine; isobengazole; isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F; lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate; Ieptolstatin; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide + estrogen + progesterone; leuprorelin; levamisole; liarozole; linear polyamine analogue; lipophilic disaccharide peptide; lipophilic platinum compounds; lissoclϊnamide 7; lobaplatin; lombricine; lometrexol; lonidamine; losoxantrone; lovastatin; loxoribine; lurtotecan; lutetium texaphyrin; lysofylline; lytic peptides; maytansine; mannostatin A; marimastat; masoprocol; maspin; matrilysiα inhibitors; matrix metalloproteinase inhibitors; menogaril; rnerbarone; meterelin; methioninase; metoclopramide; IvHF inhibitor; ifepristone; miltefosine; mirimostim; mismatched double stranded RNA; mithracin; mitoguazone; mitolactol; mitomycin analogues; mitonafide; mitotoxin fibroblast growth factor-saporin; mitoxantrone; mofarotene; molgramostim; monoclonal antibody, human chorionic gonadotrophin; monophosphoryl lipid A + myobacteriurn cell wall sk; mopidamol; multiple drug resistance gene inhibitor; multiple tumor suppressor 1- based therapy; mustard anticancer agent; mycaperoxide B; mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone + pentazocine; napavin; naphterpin; nartograstim; nedaplatin; nemorubicin; neridronic acid; neutral endopeptidase; nilutamide; nisamycin; nitric oxide modulators; nitroxide antioxidant; nitruUyn; 06-benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin; oxaunomycin; paclitaxel analogues; paclitaxel derivatives; palauamine; palmitoylrhizoxin; pamidronic acid; panaxytriol; panomifene; parabactin; pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin; phenylacetate; phosphatase inhibitors; picibanil; pilocarpine hydrochloride; pirarubicin; piritrexim; placetin A; placetin B; plasminogen activator inhibitor; platinum complex; platinum compounds; platinum-triamine complex; podophyllotoxin; porfirner sodium; porfiromycin; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin, polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide; rogletimide; roMtukine; romurtide; roquinimex; rubiginone B 1; ruboxyl; safϊngol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1 ; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofiran; sobuzoxane; sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1 ; squalamine; stem cell inhibitor; stem-cell division inhibitors; stipiamide; stromelysin inhibitors; sulfinosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfϊn; temozolomide; teniposide; tetrachlorodecaoxide; tetrazomine; thaliblastine; thalidomide; thiocoraline; thrombopoietin; thrombopoietin mimetic; thyrnalfasin; thymopoietin receptor agonist; thymotrinan; thyroid stimulating hormone; tin ethyl etiopurpurin; tirapazamine; titanocene dichloride; topotecan; topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex; urogenital sinus- derived growth inhibitory factor; urokinase receptor antagonists; vapreotide; variolin B; vector system, erythrocyte gene therapy; velaresol; veramine; verdins; verteporfln; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; and zinostatin stimalamer.
To "inhibit growth" as used herein means causing a reduction in cell growth in vivo or in vitro by, e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% or more, as evident by a reduction in the size or number of cells exposed to a treatment (e.g., exposure to a compound), relative to the size or number of cells in the absence of the treatment. Growth inhibition may be the result of a treatment that induces apoptosis in a cell, induces necrosis in a cell, slows cell cycle progression, disrupts cellular metabolism, induces cell lysis, or induces some other mechanism that reduces the size or number of cells.
"Marker gene" or "biomarker gene" as used herein means a gene in a cell the expression of which correlates to sensitivity or resistance of the cell (and thus the patient from which the cell was obtained) to a treatment (e.g., exposure to a compound).
"Microarray" as used herein means a device employed by any method that quantifies one or more subject oligonucleotides, e.g., DNA or RNA, or analogues thereof, at a time. One exemplary class of microarray s consists of DNA probes attached to a glass or quartz surface. For example, many microarrays, including those made by Afrymetrix, use several probes for determining the expression of a single gene. The DNA microarray may contain oligonucleotide probes that may be, e.g., full-length cDNAs complementary to an RNA or cDNA fragments that hybridize to part of an RNA. Exemplary RNAs include mRNA, miRNA, and miRNA precursors. Exemplary microarrays also include a "nucleic acid microarray" having a substrate- bound plurality of nucleic acids, hybridization to each of the plurality of bound nucleic acids being separately detectable. The substrate may be solid or porous, planar or non-planar, unitary or distributed. Exemplary nucleic acid microarrays include all of the devices so called in Schena (ed.), DNA Microarrays: A Practical Approach (Practical Approach Series), Oxford University Press (1999); Nature Genet. 21(l)(suppl.):l-60 (1999); Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000). Additionally, exemplary nucleic acid microarrays include substrate-bound plurality of nucleic acids in which the plurality of nucleic acids are disposed on a plurality of beads, rather than on a unitary planar substrate, as is described, inter alia, in Brenner et al., Proc. Natl. Acad. Sci. USA 97(4):1665-1670 (2000). Examples of nucleic acid microarrays may be found in U.S. Pat. Nos. 6,391,623, 6,383,754, 6,383,749, 6,380,377, 6,379,897, 6,376,191, 6,372,431, 6,351,712 6,344,316, 6,316,193, 6,312,906, 6,309,828, 6,309,824, 6,306,643, 6,300,063, 6,287,850, 6,284,497, 6,284,465, 6,280,954, 6,262,216, 6,251,601, 6,245,518, 6,263,287, 6,251,601, 6,238,866, 6,228,575, 6,214,587, 6,203,989, 6,171,797, 6,103,474, 6,083,726, 6,054,274, 6,040,138, 6,083,726, 6,004,755, 6,001,309, 5,958,342, 5,952,180, 5,936,731, 5,843,655, 5,814,454, 5,837,196, 5,436,327, 5,412,087, 5,405,783, the disclosures of which are incorporated herein by reference in their entireties.
Exemplary rnicroarrays may also include "peptide microarrays" or "protein microarrays" having a substrate-bound plurality of polypeptides, the binding of a oligonucleotide, a peptide, or a protein to each of the plurality of bound polypeptides being separately detectable. Alternatively, the peptide microarray, may have a plurality of binders, including but not limited to monoclonal antibodies, polyclonal antibodies, phage display binders, yeast 2 hybrid binders, aptamers, which can specifically detect the binding of specific oligonucleotides, peptides, or proteins. Examples of peptide arrays may be found in WO 02/31463, WO 02/25288, WO 01/94946, WO 01/88162, WO 01/68671, WO 01/57259, WO 00/61806, WO 00/54046, WO 00/47774, WO 99/40434, WO 99/39210, WO 97/42507 and U.S. Pat. Nos. 6,268,210, 5,766,960, 5,143,854, the disclosures of which are incorporated herein by reference in then* entireties.
"Gene expression" as used herein means the amount of a gene product in a cell, tissue, organism, or subject, e.g., amounts of DNA, RNA, or proteins, amounts of modifications of DNA, RNA, or protein, such as splicing, phosphorylation, acetylation, or methylation, or amounts of activity of DNA, RNA, or proteins associated with a given gene.
"NCI60" as used herein means a panel of 60 cancer cell lines from lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate and brain cancers including the following cancer cell lines: NSCLC_NCIH23, NSCLC_NC1H522, NSCLC_A549ATCC, NSCLCJΞKVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC_H460, NSCLC_HOP62, NSCLC_HOP92, COLON_HT29, COLON_HCC-2998, COLON_HCT116, COLON_SW620, COLON_COLO205, COLON_HCT15, COLONJCM12, BREAST_MCF7, BREAST_MCF7ADRr, BREAST_MDAMB231, BREAST_HS578T, BREAST_MDAMB435, BREAST_MDN, BREAST_BT549, BR£AST_T47D, OVAR_OVCAR3, OVAR_OVCAR4, OVAR_OVCAR5, OVAR_OVCAR8, OVARJGROV1, OVAR_SKOV3, LEUK_CCRPCEM, LEUKJC562, LEUKJVIOLT4, LEUK__HL60, LEUK_RPMI8266, LEUK_SR, RENAL_UO31, KENAL_SN12C, RENAL_A498, RENAL_CAKI1, RENALJRXF393, RENAL_7860, RENAL_ACHN, RENALJTKl 0, MELAN_LOXIMVI, MELAN_MALME3M, MELAN_SKMEL2, MELAN_SKMEL5, MELAN_SKMEL28, MELAN_M14, MELANJJACC62, MELAN_UACC257, PROSTATEJPC3, PROSTATEJDU145, CNS_SNB19, CNS_SNB75, CNS_U251, CNS_SF268, CNS_SF295, and CNS_SF539.
"Treatment" or "medical treatment" means administering to a subject or living organism or exposing to a cell or tumor a compound (e.g., a drug., a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, and a radioactive agent) or some other form of medical intervention used to treat or prevent cancer or the symptoms of cancer (e.g., cryotherapy and radiation therapy). Radiation therapy includes the administration to a patient of radiation generated from sources such as particle accelerators and related medical devices that emit X- radiation, gamma radiation, or electron (Beta radiation) beams. A treatment may further include surgery, e.g., to remove a tumor from a subject or living organism.
Other features and advantages of the invention will be apparent from the following Detailed Description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 depicts an illustration of the method of identifying biomarkers and predicting patient sensitivity to a medical treatment. The method has an in vitro component where the growth inhibition of a compound or medical treatment is measured on cell lines (6 of the 60 cell lines tested are shown). The gene expression is measured on the same cell lines without compound treatment. Those genes that have a correlation above a certain cutoff (e.g., a preffered cutoff of 0.3, in which a correlation coefficient equal to or greater than the cutoff of 0.3 is deemed statistcally significant by, e.g., cross-validation) to the growth inhibition are termed marker genes and the expressipn of those genes in vivo, e.g., may predict the sensitivity or resistance of a patient's cancer to a compound or other medical treatment. The in vivo component is applied to a patient to determine whether or not the treatment will be effective in treating disease in the patient. Here, the gene expression in cells of a sample of the suspected disease tissue (e.g., a tumor) in the patient is measured before or after treatment. The activity of the marker genes in the sample is compared to a reference population of patients known to be sensitive or resistant to the treatment. The expression of marker genes in the cells of the patient known to be expressed in the cells of reference patients sensitive to the treatment indicates that the patient to be treated is sensitive to the treatment and vice versa. Based on this comparison the patient is predicted to be sensitive or resistant to treatment with the compound.
Figure 2 depicts the treatment sensitivity predictions for a 5-year-old American boy with a brain tumor. The subject had surgery to. remove the tumor and, based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemosensitive to ten chemotherapy drugs. The subject received Vincristine and Cisplatin and survived.
Figure 3 depicts the treatment sensitivity predictions for a 7-month-old American girl with a brain tumor. The subject had surgery to remove the tumor, and based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemoresistant to twelve chemotheraphy drugs. The subject received Vincristine and Cisplatin, but passed away 9 months later.
Figure 4 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant to Cisplatin. All patients received Cisplatin after surgery.
Figure 5 depicts the survival rate of 56 lymphoma patients divided into a group predicted to be chemosensitive to Vincristine and Adriamycin and a group predicted to be chemoresistant. All patients received Vincristine and Adriamycin.
Figure 6 depicts the survial rate of 19 lung cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant. All patients received Cisplatin.
Figure 7 depicts the survival rate of 14 diffuse large-B-cell lymphoma (DLBCL) patients divided into a group predicted to be chemosensitive to the drug combination R-CHOP and a group predicted to be chemoresistant. All patients were treated with R-CHOP.
Figure 8 depicts the predictions of sensitivity or resistance to treatment of a patient diagnosed with DLBCL. Various drug combinations and radiation therapy are considered. The' drug combinations (indicated by abbreviations) are those commonly used to treat DLBCL.
Figure 9 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation.
Figure 10 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation. Gene biomarkers used in predicting radiation sensitivity or resistance were obtained using the correlation of the median gene expression measurement to cancer cell growth as opposed to the median of the correlations as employed in Figure 9.
DETAILED DESCRIPTION
The invention features methods for identifying biomarkers of treatment sensitivity, e.g., chemosensitiviry to compounds, or resistance, devices that include the biomarkers, kits that include the devices, and methods for predicting treatment efficacy in a patient (e.g., a patient diagnosed with cancer). The kits of the invention include microarrays having oligonucleotide probes that are biomarkers of sensitivity or resistance to treatment (e.g., treatment with a chemotherapeutic agent) that hybridize to nucleic acids derived from or obtained from a subject and instructions for using the device to predict the sensitivity or resistance of the subject to the treatment. The invention also features methods of using the microarrays to determine whether a subject, e.g., a cancer patient, will be sensitive or resistant to treatment with, e.g., a chemotherapy agent. Also featured are methods of identifying gene biomarkers of sensitivity or resistance to a medical treatment based on the correlation of gene biomarker expression to treatment efficacy, e.g., the growth inhibition of cancer cells. Gene biomarkers that identify subjects as sensitive or resistant to a treatment may also be identified within patient populations already thought to be sensitive or resistant to that treatment. Thus, the methods, devices, and kits of the invention can be used to identify patient subpopulations that are responsive to a treatment thought to be ineffective for treating disease (e.g., cancer) in the genera] population. More generally, cancer patient sensitivity to a compound or other medical treatment may be predicted using biomarker gene expression regardless of prior knowledge about patient responsiveness to treatment. The method according to the present invention can be implemented using software that is run on an apparatus for measuring gene expression in connection with a microarray. The microarray (e.g. a DNA microarray) , included in a kit for processing a tumor sample from a subject, and the apparatus for reading the microarray and turning the result into a chemosensitivity profile for the subject may be used to implement the methods of the invention.
Microarrays Containing Oligonucleotide Probes
The microarrays of the invention include one or more oligonucleotide probes that have nucleotide sequences that are identical to or complementary to, e.g., at least 5, 8, 12, 20, 30, 40, 60, 80, 100, 150, or 200 consecutive nucleotides (or nucleotide analogues) of the biomarker genes listed below. The oligonucleotide probes may be, e.g., 5-20, 25, 5-50, 50-100, or over 100 nucleotides long. The oligonucleotide probes may be deoxyribonucleic acids (DNA) or ribonucleic acids (RNA). Consecutive nucleotides within the oligonucleotide probes (e.g., 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides), which are used as gene biomarkers of chemosensitivity, may also appear as consecutive nucleotides in one or more of the genes described herein beginning at or near, e.g., the first, tenth, twentieth, thirtieth, fortieth, fiftieth, sixtieth, seventieth, eightieth, ninetieth, hundredth, hundred-fiftieth, two-hundredth, five- hundredth, or one-thousandth nucleotide of the genes listed in Tables 1-21 or below. Column List_2006 of Tables 1-21 indicates the preferred gene biomarkers for the compound lists. Column List_Preferred of Tables 1-21 indicates the most preferred gene biomarkers. Column List_2005 of Tables 1-21 indicates additional biomarkers employed in Examples 1-8. Column Correlation of Tables 1-21 indicates the correlation coefficient of the biomarker gene expression to cancer cell growth inhibition. The following combinations of gene biomarkers have been used to detect a subject's sensitivity to the indicated treatment:
a) One or more of the gene sequences SFRS3, CCT5, RPL39, SLC25A5, UBE2S, EEFl Al , RPLP2, RPL24, RPS23, RPL39, RPL18, NCL, RPL9, RPLlOA, RPSlO, EEF3S2, SHFMl, RPS28, REA, RPL36A, GAPD, HNRPAl, RPSl 1, HNRPAl, LDHB, RPL3, RPLl 1, MRPL12, RPLl 8A5 COX7B, and RPS7, preferably gene sequences UBB, RPS4X, S100A4, NDUFS6, B2M, C14orfl39, MANlAl, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, SUIl, BLMH, CTBPl- TBCA, MDH2, and DXS9879E, and most preferably gene sequences RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO5 RPL12, EEF5A, RPL36A, BLMH, CTBPl, TBCA, MDH2, and DXS9879E, whose expression indicates chemosensitivity to Vincristine.
b) One or more of the gene sequences B2M, ARHGDIB, FTL5 NCL, MSN, SNRPF, XPOl , LDHB, SNRPF, GAPD, PTPN7, AJRHGDIB, RPS27, IFIl 6, C5orfl3, and HCLS 1 , preferably gene sequences ClQRl, HCLSl, CD53, SLA5 PTPN7, PTPRCAP, ZNFNlAl5 CENTBl, PTPRC, IFIl 6, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl, ARHGAP 15, TM6SF1, and TCF4, and most preferably gene sequences ClQRl, SLA, PTPN7, ZNFNlAl5 CENTBl, IFΪ16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl5 ARHGAP 15, TM6SF1, and TCF4, whose expression indicates chemosensitivity to Cisplatin.
c) One or more of the gene sequences PRPS 1 , DDOST, B2M, SPARC, LGALS 1 , CBFB, SNRPB2, MCAM, MCAM, EIF2S2, HPRTl5 SRM, FKBPlA, GYPC, UROD5 MSN, HNRPAl, SNDl, COPA, MAPREl, EIF3S2, ATP1B3, EMP3, ECMl, ATOXl, NARS, PGKl, OK/SW- cl.56, FNl, EEFlAl5 GNAI2, PRPSl, RPL7, PSMB9, GPNMB, PPPlRl I5 MIA, RAB7, VIM5 and SMS, preferably gene sequences MSN, SPARC5 VIM, SRM, SCARBl, SIATl5 CUGBP2, GAS7, ICAMl, WASPIP, ITM2A, PALM2-AKAP2, ANPEP, PTPNSl, MPPl5 LNK, FCGR2A, EMP3, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl, IFI16, MCAM5 MEF2C, SLC1A4, BTN3A2, FYN, FNl, Clorf38, CHSl, CAPN3, FCGR2C, TNDC5 AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJl 0539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl, and ADAMTSl, and most preferably gene sequences SRM5 SCARBl5 SIATl, CUGBP2, ICAMl, WASPIP, ITM2A, PALM2-AKAP2, PTPNSl5 MPPl, LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl, IFI16, MCAM, MEF2C, SLC1A4, FYN, Clorf38, CHSl, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN", CKIP-I5 FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl5 and ADAMTSl, whose expression indicates chemosensitivity to Azaguanine.
d) One or more of the gene sequences B2M, MYC5 CD99, RPS24, PPIF, PBEFl , and ANP32B, preferably gene sequences CD99, INSIGl, LAPTM5, PRGl, MlIFl, HCLSl5 CD53, SLA, SSBP2, GNB5, MFNG, GMFG5 PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP5 ZNFNlAl, CENTBl, PTPRC, NAPlLl, HLA-DRA, IFIl 6, COROlA, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAP15, NOTCHl, and UBASH3A, and most preferably gene sequences CD99, INSIGl, PRGl, MUFl, SLA, SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFNlAl, CENTBl, NAPlLl, HLA-DRA, IFIl 6, ARHGEF6, PSCDBP3 SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, D0CK2, CD3D, RAC2, ZAP70, GPR65. PRFl, ARHGAP15, NOTCHl, and UBASH3A, whose expression indicates chemosensitivity to Etoposide.
e) One or more of the gene sequences KIAA0220, B2M, TOP2A, CD99, SNRPE, RPS27, HNRPAl, CBX3, ANP32B, HNRPAl, DDX5, PPIA, SNRPF, and USP7, preferably gene sequences CD99, LAPTM5, ALDOC, HCLSl, CD53, SLA, SSBP2, IL2RG, GMFG, CXorf9, RHOH, PTPRCAP, ZNFNlAl, CENTBl, TCF7, CDlC. MAP4K1, CDlB, CD3G, PTPRC, CCR9, COROlA, CXCR4, ARHGEF6, HEMl, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, ITK, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIFl, ARHGAP15, NARF, and PACAP, and most preferably gene sequences CD99, ALDOC, SLA, SSBP2, IL2RG, CXorf9, RHOH, ZNFNlAl, CENTBl, CDlC, MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIFl , ARHGAP 15, NARF, and PACAP, whose expression indicates chemosensitivity to Adriamycin.
f) One or more of the gene sequences RPLP2, LAMRl3 RPS25, EIF5A, TUFM, HNRPAl , RPS9, MYB, LAMRl, ANP32B, HNRPAl, HNRPAl, EIF4B, HMGB2, RPS15A, and RPS7, preferably gene sequences RPLl 2, RPL32, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4, SP 140, AMPD3, TOTAIPS, DDXl 8, TAF5, FBL3 RPS2, PTPRC5 DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD5 and most preferably gene sequences RPL12, RPLP2, IVfYB5 ZNFNlAl5 SCAPl, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ 12270, and HNRPD5 whose expression indicates chemosensitivity to Aclarubicin.
g) One or more of the gene sequences ARHGEF6, B2M, TOP2A, TOP2A, ELA2B, PTMA3 LMNBl, TNFRSFlA, NAPlLl5 B2M, HNRPAl5 RPL9, C5orfl3, NCOR2, ANP32B, OK/SW- cl.56, TUBA3. HMGN2, PRPSl5 DDX5, PRGl5 PPIA5 G6PD, PSMB9, SNRPF5 and MAPlB5 preferably gene sequences PGAMl5 DPYSL3, INSIGl5 GJAl5 BNIP3, PRGl5 G6PD, BASPl5 PLOD2, LOXL2, SSBP2, Clorf29, TOX5 STCl5 TNFRSFlA5 NCOR2, NAPlLl5 LOC94105, COL6A2, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ5 MAPlB, PTPRC, PRKCA, TRIM22, CD3D, BCATl5 IFI44, CCL2, RAB31, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, and most preferably gene sequences PGAMl5 DPYSL3, INSIGl, GJAl, BNTP3, PRGl, G6PD, PLOD2, LOXL2, SSBP2, Clorf295 TOX, STCl, TNFRSFlA5 NCOR2, NAPlLl5 LOC94105, ARHGEF6, GATA3, TFPI5 LAT5 CD3Z, AFlQ5 MAPlB5 TRTM22, CD3D, BCATl5 IFI44, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, whose expression indicates chemosensitivity to Mitoxantrone.
' h) One or more of the gene sequences GAPD5 GAPD5 GAPD5 TOP2A, SUIl5 TOP2A, FTL, HNRPC, TNFRSFlA, SHCl, CCT7, P4HB, CTSL, DDX5, G6PD, and SNRPF5 preferably gene sequences STCl5 GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, and most preferably gene sequences STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, whose expression indicates chemosensitivity to Mitomycin.
i) One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, RPL39, SFRS3,
LOC51035, RPS6, EXOSC2, RPL35, IFRD2, SMN2, EEFlAl5 RPS3, RPS18, and RPS7, preferably gene sequences RPLlO5 RPS4X, NUDC, RALY5 DKCl, DKFZP564C186, PRP19, RAB9P40, HS A9761 , GMDS, CEP 1, ELl 3RA2, MAGEB2, HMGN2, ALMS 1 , GPR65, FLJ10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC3 and KIAA1393, and most preferably RPLlO, RPS4X, NUDC, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEPl, IL13RA2, MAGEB2, HMGN2, ALMSl, GPR65, FLJ10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC5 and KIAA1393, whose expression indicates chemosensitivity to Paclitaxel.
j) One or more of the gene sequences CSDA, LAMRl, and TUBA3, preferably gene sequences PFNl, PGAMl, K-ALPHA-I. CSDA, UCHLl, PWPl, PALM2-AKAP2, TNFRSFlA, ATP5G2, AFlQ, NME4, and FHODl, and most preferably gene sequences PFNl, PGAMl, K-ALPHA-I, CSDA, UCHLl, PWPl, PALM2-AKAP2, TNFRSFlA3 ATP5G2, AFlQ, NME4, FHODl, whose expression indicates chemosensitivity to Gemcitabine.
k) One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, SFRS3, RPS6, DDX39, and RPS7, preferably gene sequences ANP32B, GTF3A, RRM2, TRIMl 4, SKP2, TRIP13, . RFC3, CASP7, TXN3 MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, and most preferably gene sequences ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRDP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, whose expression indicates chemosensitivity to Taxotere.
1) One or more of the gene sequences IL2RG, HlFX3 RDBP, ZAP70, CXCR4, TM4SF2,
ARHGDIB, CDA, CD3E, STMNl, GNA15, AXL, CCND3, SATBl, EIF5A, LCK, NKX2-5, LAPTM5, IQGAP2, FLII3 EIF3S5, TRB, CD3D, HOXB2, GATA3, HMGB2, PSMB9, ATP5G2, COROlA, ARHGDIB, DRAPI, PTPRCAP, RHOH, and ATP2A3, preferably gene sequences IFITM2, UBE2L6, LAPTM5, USP4, ITM2A, ITGB2, ANPEP3 CD53, IL2RG, CD37, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, ADORA2A, ZNFNlAl3 GNA15, CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, PTPRC, ATP2A3, UCP2, COROlA, GATA3, CDKN2A, HEMl; TARP, LAIRl, SH2D1 A, FLH, SEPT6, HA-I, CREB3L1, ERCC2, CD3D, LSTl, AIFl, ADA, DATFl, ARHGAP15, PLAC8, CECRl5 LOC81558, and EHD2, and most preferably gene sequences IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASPl, PTPN7, CXorfP, RHOH5 GIT2, ZNFNlAl5 CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIRl, SH2D1A, SEPT6, HA-I5 ERCC2, CD3D, LSTl, AIFl5 ADA5 DATFl5 ARHGAP15, PLAC8, CECRl, LDC81558, and EHD2, whose expression indicates chemosensitivity to Dexamethasone.
m) One or more of the gene sequences TM4SF2, ARHGDIB, ADA, H2AFZ, NAPlLl, CCND3, FABP5, LAMRl, REA, MCM5, SNRPF, and USP7, preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl, GNA15, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CDlE, ADA5 and FHODl, and most preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA, and FHODl5 whose expression indicates chemosensitivity to Ara-C.
n) One or more of the gene sequences LGALS9, CD75 IL2RG, PTPN7, ARHGEF6, CENTBl, SEPT6, SLA, LCPl, IFITMl, ZAP70, CXCR4, TM4SF2, ZNF91, ARHGD]B, TFDP2, ADA5 CD99, CD3E, CDlC, STMNl, CD53, CD75 GNA15, CCND3, MAZ, SATBl, ZNF22, AES, AIFl, MYB5 LCK, C5orfl3, NKX2-5, ZNFNlAl, STAT5A, CHI3L2, LAPTM5, MAP4K1, DDXl I5 GPSM3, TRB5 CD3D, CD3G, PRKCBl, CDlE, HCLSl, GATA3, TCF7, RHOG, CDW52, HMGB2, DGKA, ITGB2, PSMB9, IDH2, AES, MCM5, NUCB2, COROlA5 ARHGDIB, PTPRCAP, CD47, RHOH, LGALS9, and ATP2A3, preferably gene sequences CD99, SRRMl, ARHGDIB, LAPTM5, VWF, ITM2A, ITGB2, LGALS9, INPP5D, SATBl, CD53, TFDP2, SLA, IL2RG, MFNG, CD37, GMFG, SELL5 CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, PTPRCAP, GIT2, ZNFNlAl5 CENTBl, LCP2, SPIl, GNA15, GZMA5 CEPl5 BLM, CD8A, SCAPl, CD2, CDlC, TNFRSF7, VAVl5 MAP4K1, CCR7, C6orf32, ALOXl 5B, BRDT5 CD3G, PTPRC, LTB, ATP2A3, NVL5 RASGRP2, LCP 1, COROlA, CXCR4, PRKD2, GATA3, TRA@, PRKCB 1 , HEMl , KIAA0922, TARP, SEC31L2, PRKCQ5 SH2D1A, CHRNA3, CDlA, LSTl3 LAIRl, CACNAlG, TRB@, SEPT6, HA-I, DOCK2, CD3D, TRD@, T3JAM, FNBPl, CD6, AIFl, FOLHl, CDlE, LY9, UGT2B17, ADA, CDKL5, TRIM, EVL, DATFl, RGC32, PRElCH, ARHGAP15, NOTCHl5 BIN2, SEMA4G, DPEP2, CECRl, BCLI lB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, and most preferably gene sequences CD99, ARHGDIB, VWF, ITM2A, LGALS9, 1NPP5D, SATBl5 TFDP2, SLA, IL2RG, MFNG, SELL3 CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl, CENTBl5 LCP2, SPIl, GZMA, CEPl3 CD8A, SCAPl, CD2, CDlC, TNFRSF7, VAVl, MAP4K1, CCR7, C6orf32, AL0X15B, BRDT, CD3G, LTB, ATP2A3, NVL, RASGRP2, LCPl, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDlA, LSTl, LAIRl, CACNAlG, TRB@, SEPT6, HA-I, DOCK2, CD3D, TRX>@, T3JAM, FNBPl3 CD6, AIFl, FOLHl, CDlE, LY9, ADA, CDKL5, TRIM3 EVL, DATFl, RGC32, PRKCH3 ARHGAP15, NOTCHl, BDST2, SEMA4G, DPEP2, CECRl, BCLIlB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, whose expression indicates chemosensitivity to Methylprednisolone.
o) One or more of the gene sequences RPLP2, RPL4, HMGAl, RPL27, IMPDH2, LAMRl , PTMA, ATP5B, NPMl, NCL3 RPS25, RPL9, TRAPl, RPL21, LAMRl, REA, HNRPAl3 LDHB, RPS2, NMEl3 PAICS, EEF1B2, RPS15A, RPL19, RPL6, ATP5G2, SNRPF, SNRPG, and RPS7, preferably gene sequences PRPF8, RPLl 8, RNPSl, RPL32, EEFlG, GOT2, RPL13A, PTMA, RPS15, RPLP2, CSDA, KHDRBSl, SNRP A, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl3 PFDN5, OXAlL, H3F3A, ATIC3 RPL13, CIAPINl5 FBL3 RPS2, PCCB, RBMX, SHMT2, RPLPO, HNRPAl, STOML2, RPS9, SKBl, GLTSCR2, CCNBlIPl3 MRPS2, FLJ20859, and FLJ12270, and most preferably gene sequences PRPF8, RPLl 8, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBSl, SNRPA3 IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl3 PFDN5, OXAlL, H3F3A, ATIC, CIAPINl3 RPS2, PCCB3 SHMT2, RPLPO, HNRPAl, STOML2, SKBl, GLTSCR2, CCNBlIPl, MRPS2, FLJ20859, and FLJ12270, whose expression indicates chemosensitivity to Methotrexate.
p) One or more of the gene sequences ACTB, COL5A1, MTlE, CSDA, COL4A2, MMP2, COLlAl, TNFRSFlA, CFHLl, TGFBI, FSCNl, NNMT, PLAUR, CSPG2, NFEL3, C5orfl3, NCOR2, TUBB4, MYLK, TUBA3, PLAU, COL4A2, COL6A2, COL6A3, JJPITM2, PSMB9, CSDA, and COLlAl, preferably gene sequences MSN, PFNl, HKl, ACTR2, MCLl, ZYX, RAPlB, GNB2, EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I5 LGALSl, CSDA, AKRlBl, IFITM2, ITGA5, VIM5 DPYSL3, JUNB, ITGA3, NFKBIA, LAMBl, FHLl, INSIGl, TIMPl, GJAl, PSME2, PRGl, EXTl5 DKFZP434J154, OPTN, M6PRBP1, MVP, VASP, ARL7, NNMT, TAPl, COLlAl, BASPl, PLOD2, ATF3, PALM2-AKAP2, IL8, ANPEP, LOXL2, TGFBl, IL4R, DGKA5 ' STC2, SEC61G, NFIL3, RGS3, NK4, F2R, TPM2, PSMB9, LOX5 STCl5 CSPG2, PTGER4, EL6, SMAD3, PLAU, WNT5A, BDNF, TNFRSFlA5 FLNC, DKFZP564K0822, FLOTl, PTRF, HLA-B, COL6A2, MGC4083, TNFRSFlOB, PLAGLl, PNMA2, TFPI, LAT5 GZMB, CYR61, PLAUR5 FSCNl, ERP70, AFlQ, UBC, FGFRl, HIC, BAX, COL4A2, COL6A1, IFITM3, MAPlB, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MTlE, CDClO, DOCK2, TRIM22, RISl, BCATl, PRFl5 DBNl, MTlK, TMSBlO, RAB31, FLJl 0350, Clorf24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB5 and most preferably gene sequences PFNl, HKl, MCLl5 ZYX5 RAPlB, GNB2, EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I5 LGALSl, CSDA5 IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA5 LAMBl5 FHLl, INSIGl, TMPl5 GJAl, PSME2, PRGl5 EXTl, DKFZP434J154, MVP5 VASP, ARL7, NNMT, TAPl, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA5 STC2, SEC61G, RGS3, F2R5 TPM2, PSMB9, LOX, STCl, PTGER4, IL6, SMAD3, WNT5A, BDNF5 TNFRSFlA5 FLNC, DKFZP564K0822, FLOTl, PTRF, HLA-B, MGC4083, TNFRSFlOB, PLAGLl5 PNMA2, TFPI5 LAT5 GZMB, CYR61, PLAUR5 FSCNl5 ERP70, AFlQ, HIC5 COL6A1, IFITM3, MAPlB, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MTlE, CDClO, DOCK2, TRIM22, RISl, BCATl, PRFl, DBNl, MTlK, TMSBlO5 FLJ10350, Clorf24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB, whose expression indicates chemosensitivity to Bleomycin.
q) One or more of the gene sequences NOS2A, MUCl, TFF3, GPlBB, IGLLl, BATF, MYB, PTPRS, NEFL, AIP, CEL, DGKA, RUNXl, ACTRlA, and CLCNKA, preferably gene sequences PTMA, SSRPl5 NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl, SSSCAl, EZH2, MYB, PRIMl5 H2AFX, HMGAl, HMMR5 TK2, WHSCl, DIAPHl, LAMB3, DPAGTl, UCK2, SERPINBl, MDNl, BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, and most preferably gene sequences SSRPl5 NUDC, CTSC, AP1G2, PSME2, LBR5 EFNB2, SERPINAl5 SSSCAl, EZH2, MYB, PRIMl, H2AFX, HMGAl, HMMR, TK2, WHSCl, DIAPHl, LAMB3, DPAGTl, UCK2, SERPlNBl5 MDNl, BRRNl5 G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, whose expression indicates chemosensitivity to Methyl-GAG.
r) One or more of the gene sequences MSN5 ITGA5, VIM, TNFAIP3 , CSPG2, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, FGFRl, DOCKlO, LEPREl, COL5A2, and ADAMTSl, and most preferably gene sequences ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, IFIl 6, LRRN3, DOCKlO5 LEPREl, COL5A2, and ADAMTSl, whose expression indicates chemosensitivity to carboplatin.
s) One or more of the gene sequences RPL18, RPLlOA5 RNPSl, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl5 NDUFABl, APRT, ZNF593, MRP63, IL6R, RPL13, SART3, RPS6, UCK2, RPL3, RPLl 7, RPS2, PCCB5 TOMM20, SHMT2, RPLPO5 GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, and most preferably gene sequences RPLl 8, RPLlOA5 ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl, APRT, ZNF593, MRP63, EL6R, SART3, UCK2, RPL17, RPS2, PCCB5 TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, whose expression indicates chemosensitivity to 5-FU(5-Fluorouracil). t) One or more of the gene sequences ITK, KIFCl , VLDLR, RUNXl , PAFAH1B3, HlFX3 RNF144, TMSNB, CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ5 HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, RALY, PSMC5, CDlD3 ADA, CD99. CD2, CNP, ERG, MYL6, CD3E, CDlA, CDlB3 STMNl3 PSMC3, RPS4Y1, AKTl, TALI, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTNl5 GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl3 CTNNAl, AKRlCl, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6. GLRB, NDN5 CPSFl5 GNAQ5 TUSC3, GNAQ3 JARID2, OCRL, FHLl5 EZH2, SMOX5 SLC4A2, UFDlL, SEPWl, ZNF32, HTATSFl, SHDl5 PTOVl5 NXFl3 FYB5 TRIM28, BC008967, TRB@5 TFRC, HlFO, CD3D, CD3G, CENPB3 ALDH2, ANXAl5 H2AFX, CDlE5 DDX5, ABLl, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFRl, SOX4, MAL3 NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl5 SH3GL3, VIM, PLEKHCl3 CD47, POLR2F, RHOH, ADDl, and ATP2A3, preferably gene sequences ITK, KIFCl, VLDLR, RUNXl3 PAFAH1B3, HlFX, RNF144, TMSNB3 CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl3 RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, RALY, PSMC5, CDlD, ADA, CD99, CD2, CNP, ERG5 MYL6, CD3E, CDlA, CDlB, STMNl3 PSMC3, RPS4Y1, AKTl, TALI, GNAl 5, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTNl, GSTM2, SATBl3 NASP5 IGFBP2, CDH2, CRABPl5 DBNl, CTNNAl, AKRlCl, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB5 NDN, CPSFl5 GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHLl, EZH2, SMOX, SLC4A2, UFDlL5 SEPWl5 ZNF32, HTATSFl5 SHDl5 PTOVl, NXFl5 FYB, TRIM28, BC008967, TRB@, TFRC5 HlFO3 CD3D, CD3G, CENPB5 ALDH2, ANXAl3 H2AFX, CDlE, DDX5, ABLl3 CCNA2, ENO2, SNRPB3 GATA3, RRM2, GLUL5 TCF7, FGFRl5 SOX4, MAL, NUCB2, SMA3, FAT5 UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl5 SH3GL3, VIM3 PLEKHCl5 CD47, POLR2F, RHOH, ADDl5 and ATP2A3, and most preferably gene sequences KIFCl3 VLDLR, RUNXl5 PAFAH1B3, HlFX5 RNF144, TMSNB, CRYl, MAZ, SLA, SRF5 UMPS, CD3Z, PRKCQ5 HNRPM5 ZAP70, ADDl3 RFC5, TM4SF2, PFN2, BMIl5 TUBGCP3, ATP6V1B2, CDlD, ADA5 CD99, CD2, CNP, ERG, CD3E, CDlA, PSMC3, RPS4Y1, AKTl5 TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl5 AKRlCl5 CACNB3, CASP2, CASP2, LCP2, CASP6, MYB5 SFRS6, GLRB5 NDN5 GNAQ, TUSC3, GNAQ5 JARID2, OCRL5 FHLl5 EZH2, SMOX, SLC4A2, UFDlL, ZNF32, HTATSFl5 SHDl, PTOVl5 NXFl5 FYB5 TRJM28, BC008967, TRB@, HlFO5 CD3D, CD3G, CENPB5 ALDH2, ANXAl5 H2AFX, CDlE5 DDX5, CCNA2, ENO2, SNRPB5 GATA3, RRM25 GLUL5 SOX4, MAL5 UNG5 ARHGDB5 RUNXl5 MPHOSPH6, DCTNl5 SH3GL3, PLEKHCl5 CD47, POLR2F, RHOH, and ADDl, whose expression indicates chemosensitivity to MABTHERA™ (Riruxinrab).
u) One or more of the gene sequences CCL21, ANXA2, SCARB2, MAD2L1BP, CAST5 PTS5 NBLl5 ANXA2, CD151, TRAM2, HLA-A5 CRIP2, UGCG, PRSSl I5 MME, CBRl5 LGALSl5 DUSP3, PFN2, MICA5 FTHl, RHOC, ZAP128, PON2, COL5A2, CST3, MCAM, IGFBP3, MMP2, GALIG, CTSD, ALDH3A1, CSRPl5 S100A4, CALDl, CTGF5 CAPG5 HLA- A, ACTNl, TAGLN5 FSTLl, SCTR5 BLVRA, COPEB, DIPA, SMARCD3, FNl, CTSL, CD63, DUSPl5 CKAP4, MVP, PEA15, S100A13, and ECEl, preferably gene sequences TRAl5 ACTN4, WARS, CALMl5 CD63, CD81, FKBPlA5 CALU5 IQGAPl5 CTSB, MGC8721, STATl, TACCl, TM4SF8, CD59, CKAP4, DUSPl, RCNl5 MGC8902, LGALSl, BHLHB2, RRBPl5 PKM2, PRNP, PPP2CB, CNN3, ANXA2, IER3, JAKl, MARCKS, LUM5 FER1L3, SLC20A1, EIF4G3, HEXB5 EXTl, TJPl, CTSL5 SLC39A6, RIOK3, CRK, NNMT, COLlAl, TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PLOD2, NPCl, TOBl, GFPTl, JX8, DYRK2, PYGL5 LOXL2, KIAA0355, UGDH, NFIL3, PURA, ULK2, CENTG2, NDD2, CAP350, CXCLl, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDHI l, P4HA1, GRP58, ACTNl, CAPN2, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT5 TNFRSFlOB5 KIAAl 128, TM4SF1, RBPMS5 RIPK2, CBLB5 NR1D2, BTN3A2, SLC7A11, MPZLl, IGFBP3, SSA2, FNl, NQOl5 ASPH, ASAHl5 MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A2, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB5 MAPKl5 MY1OlB5 CAST5 RRAS2, QKI5 LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCBl, KIAA0802, KPNBl, RAB3GAP, SERPINBl, TIMM17A, SOD2, HLA-A, NOMO2, LOC55831, PHLDAl, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl, COROlC5 DNAJClO, TNPOl, LONP, AMIGO2, DNAPTP6, and ADAMTSl, and most preferably gene sequences TRAl3 ACTN4, CALMl, CD63, FKBPlA, CALU, IQGAPl, MGC8721, STATl, TACCl, TM4SF8, CD59, CKAP4, DUSPl5 RCNl, MGC8902, LGALSl, BHLHB2, RRBPl, PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PL0D2, NPCl, TOBl, GFPTl5 IL8, PYGL, LOXL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP35O, CXCLl, BTN3A3, EL6, WNT5A, F0XF2, LPHN2, CDHl I5 P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSFlOB5 KIAAl 128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A11, MPZLl, SSA2, NQOl, ASPH, ASAHl, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB, MAPKl, MYOlB, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCBl, KIAA0802, RAB3GAP, SERPINBl3 TIMM17A, SOD2, HLA-A5 NOMO2, LOC55831, PHLDAl, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl, COROlC, DNAJClO, TNPOl, LONP, AMIGO2, DNAPTP6, and ADAMTSl, whose expression indicates sensitivity to radiation therapy.
v) One or more of the gene sequences FAU3 NOL5A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVERl, LRMP5 ICAM2, RIMS3, FMNLl, MYB, PTPN7, LCK, CXorf9, RHOH3 ZNFNlAl5 CENTBl3 LCP2, DBT, CEPl5 IL6R, VAVl, MAP4K1, CD28, PTP4A3, CD3G, LTB5 USP34, NVL, CDSBl5 SFRS6, LCPl, CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CDlA, GATA2, P2RX5, LAIRl, Clorf38, SH2D1A, TRB@, SEPT6, HA-I, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPAl, AIFl, CYFIP2, GLTSCR2, Cl Iorf2, ARHGAP15, B1N2, SH3TC1, STAG3, TM6SF1, C15orf255 FLJ22457, PACAP, and MGC2744, whose expression indicates sensitivity to an HDAC inhibitor.
w) One or more of the gene sequences CD993 SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSEl, MYB, PTPN7, CXorf9, RHOH5 ZNFNlAl5 CENTBl, LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCPl, SELPLG3 CD3Z, PRKCQ, GZMB, SCN3A, LATRl5 SH2D1A, SEPT6, CG018, CD3D, C18orflO, PRFl5 AIFl5 MCM5, LPXN5 C22orfl8, ARHGAP15, and LEFl, whose expression indicates sensitivity to 5-Aza-2'-deoxycytidine (Decitabine).
Probes that may be employed on microarrays of the invention, include oligonucleotide probes having sequences complementary to any of the biomarker gene sequences described above. Additionally, probes employed on microarrays of the invention may also include proteins, peptides, or antibodies that selectively bind any of the oligonucleotide probe sequences or their complementary sequences. Exemplary probes are listed in Tables 22-44, wherein for each treatment listed, the gene biomarkers indicative of treatment sensitivity, the correlation of biomarker gene expression to growth inhibition, and the sequence of an exemplary probe (Tables 22-44) to detect the biomarker genes' (Tables 1-21) expression are shown.
Identification of Biomarker Genes
The gene expression measurements of the NCI60 cancer cell lines were obtained from the National Cancer Institute and the Massachusetts Institute of Technology (MIT). Each dataset was normalized so that sample expression measured by different chips could be compared. The preferred method of normalization is the logit transformation, which is performed for each gene y on each chip: logit(y) = log \(y-background) I {saturation -y)],
where background is calculated as the rrύnirnum intensity measured on the chip minus 0.1% of the signal intensity range: mϊn-0.001 *(max-min), and saturation is calculated as the maximum intensity measured on the chip plus 0.1% of the signal intensity range: max+0.001*(max-min). The resulting logit transformed data is then z-transformed to mean zero and standard deviation 1.
Next, gene expression is correlated to cancer cell growth inhibition. Growth inhibition data (GI50) of the NCI60 cell lines in the presence of any one of thousands of tested compounds was obtained from the NCI. The correlation between the logit-transformed.expression level of each gene in each cell line and the logarithm of GI50 (the concentration of a given compound that results in a 50% inhibition of growth) can be calculated, e.g., using the Pearson correlation coefficient or the Spearman Rank-Order correlation coefficient. Instead of using GI50s, any other measure of patient sensitivity to a given compound may be correlated to the patient's gene expression. Since a plurality of measurements may be available for a single gene, the most accurate determination of correlation coefficient was found to be the median of the correlation coefficients calculated for all probes measuring expression of the same gene.
The median correlation coefficient of gene expression measured on a probe to growth inhibition or patient sensitivity is calculated for all genes, and genes that have a median correlation above 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 are retained as biomarker genes. Preferably, the correlation coefficient of biomarker genes will exceed 0.3. This is repeated for all the compounds to be tested. The result is a list of marker genes that correlates to sensitivity for each compound tested.
Predicting Patient Sensitivity or Resistance to Medical Treatment
For a given compound, the biomarker genes whose expression, has been shown to correlate to chemosensitivity can be used to classify a patient, e.g., a cancer patient, as-sensitive to a medical treatment, e.g., administration of a chemotherapeutic agent or radiation. Using a tumor sample or a blood sample (e.g., in case of leukemia or lymphoma) from a patient, expression of the biomarker genes in the cells of the patient in the presence of the treatment agent is determined (using, for example,- an RNA extraction kit, a DNA microarray and a DNA microarray scanner). The gene expression measurements are then logit transformed as described above. The sum of the expression measurements of the marker genes is then compared to the median of the sums derived from a training set population of patients having the same rumor. If the sum of gene expression in the patient is closest to the median of the sums of expression in the surviving members of the training set, the patient is predicted to be sensitive to the compound or other medical treatment. If the sum of expression in the patient is closest to the median of the sums of expression in the non-surviving members of the training set, the patient is predicted to be resistant to the compound.
Machine learning techniques such as Neural Networks, Support Vector Machines, K Nearest Neighbor, and Nearest Centroids may also be employed to develop models that discriminate patients sensitive to treatment from those resistant to treatment using biomarker gene expression as model variables which assign each patient a classification as resistant or sensitive. Machine learning techniques used to classify patients using various measurements are described in U.S. Patent No. 5,822,715; U.S. Patent Application Publication Nos. 2003/0073083, 2005/0227266, 2005/0208512, 2005/0123945, 2003/0129629, and 2002/0006613; and in Vapnik V N. Statistical Learning Theory, John Wiley & Sons, New York, 1998; Hastie et al., 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, N. Y.; Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, New York; V. Tresp et al., "Neural Network Modeling of Physiological Processes", in Hanson S. J. et al. (Eds.), Computational Learning Theory and Natural Learning Systems 2, MET Press, 1994, each of which are hereby incorporated by reference in their entirety.
A more compact microarray may be designed using only the oligonucleotide probes having measurements yielding the median correlation coefficients with cancer cell growth inhibition. Thus, in this embodiment, only one probe needs to be used to measure expression of each gene.
Identifying a Subpopulation of Patients Sensitive to a Treatment for Cancer
The invention may also be used to identify a subpopulation of patients, e.g., cancer patients, that are sensitive to a compound or other medical treatment previously thought to be ineffective for the treatment of cancer. To this end, biomarker genes, whose expression correlates to sensitivity to a compound or other treatment, may be identified so that patients sensitive to a compound or other treatment may be identified. To identify such gene biomarkers, gene expression within cell lines may be correlated to the growth of those cell lines in the presence of the same compound or other treatment. Preferably, genes whose expression correlates to cell growth with a correlation coefficient exceeding 0.3 may be considered possible biomarkers.
Alternatively, genes may be identified as biomarkers according to their ability to discriminate patients known to be sensitive to a treatment from those known to be resistant. The significance of the differences in gene expression between the sensitive and resistant patients may be measured using, e.g., t-tests. Alternatively, naϊve Bayesϊan classifiers may be used to identify gene biomarkers that discriminate sensitive and resistant patient subpopulations given the gene expressions of the sensitive and resistant subpopulations within a treated patient population.
The patient subpopulations considered may be further divided into patients predicted to survive without treatment, patients predicted to die without treatment, and patients predicted to have symptoms without treatment. The above methodology may be similarly applied to any of these further defined patient subpopulations to identify gene biomarkers able to predict a subject's sensitivity to compounds or other treatments for the treatment of cancer.
Patients with elevated expression of biomarker genes correlated to sensitivity to a compound or other medical treatment would be predicted to be sensitive to that compound or other medical treatment.
The invention is particularly useful for recovering compounds or other treatments that failed in clinical trials by identifying sensitive patient subpopulations using the gene expression methodology disclosed herein to identify gene biomarkers that can be used to predict clinical outcome.
Kit, Apparatus, and Software for Clinical Use
This invention may also be used to predict patients who are resistant or sensitive to a particular treatment by using a kit that includes a kit for RNA extraction from tumors (e.g., Trizol from Invitrogen Inc), a kit for RNA amplification (e.g., MessageAmp from Ambion Inc), a microarray for measuring gene expression (e.g., HG-Ul 33 A GeneCbip from Affymetrix Inc), a microarray hybridization station and scanner (e.g., GeneChip System 3000Dx from Affymetrix Inc), and software for analyzing the expression of marker genes as described in herein (e.g., implemented in R from R-Project or S-Plus from Insightful Corp.).
Methodology of the In Vitro Cancer Growth Inhibition Screen
The human tumor cell lines of the cancer screening panel are grown in RPMI 1640 medium containing 5% fetal bovine serum and 2 mM L-glutamine. Cells are inoculated into 96 well microliter plates in 100 μL at plating densities ranging from 5,000 to 40,000 cells/well depending on the doubling time of individual cell lines. After cell inoculation, the microliter plates are incubated at 370C, 5% CO2, 95% air and 100% relative humidity for 24 hours prior to addition of experimental compounds.
After 24 hours, two plates of each cell line are fixed in situ with TCA, to represent a measurement of the cell population for each cell line at the time of compound addition (Tz). Experimental compounds are solubilized in dimethyl sulfoxide at 400-fold the desired final maximum test concentration and stored frozen prior to use. At the time of compound addition, an aliquot of frozen concentrate is thawed and diluted to twice the desired final maximum test concentration with complete medium containing 50 μg/ml Gentamicin. Additional four, 10-fold or 1A log serial dilutions are made to provide a total of five compound concentrations plus control. Aliquots of 100 μl of these different compound dilutions are added to the appropriate micro titer wells already containing 100 μl of medium, resulting in the required final compound concentrations.
Following compound addition, the plates axe incubated for an additional 48 h at 37°C, 5% CO2, 95% air, and 100% relative humidity. For adherent cells, the assay is terminated by the addition of cold TCA. Cells are fixed in situ by the gentle addition of 50 μl of cold 50% (w/v) TCA (final concentration, 10% TCA) and incubated for 60 minutes at 4°C. The supernatant is discarded, and the plates are washed five times with tap water and air-dried. Sulforhodamine B (SRB) solution (100 μl) at 0.4% (w/v) in 1% acetic acid is added to each well, and plates are incubated for 10 minutes at room temperature. After staining, unbound dye is removed by washing five times with 1 % acetic acid and the plates are air-dried. Bound stain is subsequently solubilized with 10 mM trizma base, and the absorbance is read on an automated plate reader at a wavelength of 515 nm. For suspension cells, the methodology is the same except that the assay is terminated by fixing settled cells at the bottom of the wells by gently adding 50 μl of 80% TCA (final concentration, 16 % TCA). Using the seven absorbance measurements [time zero, (Tz)3 control growth, (C), and test growth in the presence of compound at the five concentration levels (Ti)], the percentage growth is calculated at each of the compound concentrations levels. Percentage growth inhibition is calculated as:
[(Ti-Tz)/(C-Tz)] x 100 for concentrations for which Ti>/=Tz ' [(Ti-Tz)/Tz] x 100 for concentrations for which Ti<Tz
Three dose response parameters are calculated for each experimental agent. Growth inhibition of 50% (GI50) is calculated from [(Ti-Tz)/(C-Tz)] x 100 = 50, which is the compound concentration resulting in a 50% reduction in the net protein increase (as measured by SRB staining) in control cells during the compound incubation. The compound concentration resulting in total growth inhibition (TGI) is calculated from Ti = Tz. The LC50 (concentration of compound resulting in a 50% reduction in the measured protein at the end of the compound treatment as compared to that at the beginning) indicating a net loss of cells following treatment is calculated from [(Ti-Tz)ZTz] x 100 = -50. Values are calculated for each of these three parameters if the level of activity is reached; however, if the effect is not reached or is exceeded, the value for that parameter is expressed as greater or less than the maximum or minimum concentration tested.
RNA Extraction and Gene Expression Measurement
Cell/tissue samples are snap frozen in liquid nitrogen until processing. KNA is-extracted using e.g. Trizol Reagent from Invitrogen following manufacturers instructions. RNA is amplified using e.g. MessageAmp kit from Ambion following manufacturers instructions- Amplified RNA is quantified using e.g. HG-Ul 33 A GeneChip from Affymetrix Lie and compatible apparatus e.g. GCS3000Dx from Affymetrix, using manufacturers instructions. The resulting gene expression measurements are further processed as described in this document. The procedures described can be implemented using R software available from R- Project and supplemented with packages available from Bioconductor.
For many drugs 10-30 biomarkers are sufficient to give an adequate response, thus, given the relatively small number of biomarkers required, procedures, such as quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), may be performed to measure, with greater precision, the amount of biomarker genes expressed in a sample. This will provide an alternative to or a complement to microarrays so that a single companion test, perhaps more quantitative than microarrays alone, employing biomarkers of the invention can be used to predict sensitivity to a new drug. qRT-PCR may be performed alone or in combination with a microarray described herein. Procedures for performing qRT-PCR are described in, e.g., U.S. Patent No. 7,101,663 and U.S. Patent Application Nos. 2006/0177S37 and 2006/0088856. The methods of the invention are readily applicable to newly discovered drugs as well as drugs described herein.
The following examples are provided so that those of ordinary skill in the art can see how to use the methods and kits of the invention. The examples are not intended to limit the scope of what the inventor regards as their invention.
EXAMPLES
Example 1: Identification of gene biomarkers for chemosensitivity to common chemotherapy drugs.
DNA chip measurements of the 60 cancer cell lines of the NCI60 data set were downloaded from the Broad Institute and logit normalized. Growth inhibition data of thousands of compounds against the same cell lines were downloaded from the National Cancer Institute. Compounds where the difference concentration to achieve 50% in growth inhibition (GI50) was less than 1 log were deemed uninformative and rejected. Each gene's expression in each cell line was correlated to its growth (-log(GI50)) in those cell lines in the presence of a given compound. The median Pearson correlation coefficient was used when multiple expression measurements were available for a given gene, and genes having a median correlation coefficient greater than 0.3 were identified as biomarkers for a given compound.
Example 2: Prediction of treatment sensitivity for brain cancer patients.
DNA chip measurements of gene expression in tumors from 60 brain cancer patients were downloaded from the Broad Institute. All data files were logit normalized. For each of the common chemotherapy drugs Cisplatin, Vincristine, Adriamycine, Etoposide, Aclarubicine, Mitoxantrone and Azaguanine, the gene expression for the marker genes was summed. The sum was normalized by dividing by the standard deviation of all patients and compared to the median of the sums of patients who survived and the median of the sums of patients who died:
NormalizedSum(compound) = sum(marker genes for compound)/sd(sums of all patients)
Sensitivity(compound) = [NormalizedSum(compound)- median(NormalizedSumdeadpatients(compound))]2
[NormalizedSum(compound) - medianCNormalizedSumsurvivingpatientsζcompound))]2
Figures 2 and 3 show the resulting treatment sensitivity predictions for two of the 60 patients. All patients received Cisplatin and the prediction of survival amongst the 60 patients based on their Cisplatin chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 4. The expression of the 16 Cisplatin bϊomarker genes was first reduced to 5 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the five components from the 60 patients: K Nearest Neighbor with K=I, K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network. Chemosensitivity or sensitivity to radiation treatment was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive/treatment sensitive prediction resulted in a prediction of chemosensitive/treatment sensitive. All other predictions resulted in a prediction of chemoresistant/treatment resistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups shown in Figure 4. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
Example 3: Prediction of chemosensitivity for lymphoma (DLBCL) patients.
DNA chip measurements of gene expression in the tumors from 56 DLBCL (diffuse large B-cell lymphoma) patients were downloaded from the Broad Institute. All data files were logit normalized. All patients received Vincristine and Adriamycine and the prediction of survival ■ amongst the 56 patients based on their Vincristine and Adriamycine chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 5. The expression of the 33 Vincristine genes and 16 Adriamycine genes was first reduced to 3 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the independent components from the 56 patients: K Nearest Neighbor with K=I, K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network. Chemosensitivity was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive prediction resulted in a prediction of chemosensitive. All other predictions resulted in a prediction of chemoresistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups is shown in Figure 5. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
Example 4: Prediction of chemosensitivity for lung cancer patients.
DNA chip measurements of gene expression in the tumors from 86 lung cancer (adenocarcinoma) patients was downloaded from the University of Michigan, Ann Arbor. Of the 86 patients, 19 had Stage EI of the disease and received adjuvant chemotherapy. Raw data was logit normalized. Instead of the combined classifier described for the brain cancer and lymphoma examples above, the sum of biomarker gene expression was calculated for each patient and used to discriminate chemosensitive and chemoresistant patients. For each patient, the gene expression of the 16 marker genes for Cisplatin sensitivity (all Stage IQ patients received Cisplatin aiter surgery) was summed. If the sum was closer to the median of the sums of the surviving patients, the patient was predicted to be sensitive to Cisplatin. If the sum was closest to the median of the sums of the non-surviving patients, the patient was predicted to be resistant to Cisplatin. The survival rates of the two predicted groups are shown in Figure 6. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
Example 5: Prediction of Rituximab sensitivity for lymphoma (DLBCL) patients.
The method is not limited to cytotoxic chemicals. It is also applicable to predicting the efficacy of protein therapeutics, such as monoclonal antibodies, approved for treating cancer. For example, the monoclonal antibody MABTHERA™ (Rituximab, RITUXAN™} was examined. Data for cytotoxicity of Rituximab in cell lines in vitro were obtained from published reports (Ghetie et al.5 Blood, 97(5):1392-1398, 2001). This cytotoxicity in each cell line was correlated to the expression of genes in these cell lines (downloaded from the NCBI Gene Expression Omnibus database using accession numbers GSE2350, GSE1880, GDS181). The identified marker genes were used to predict the sensitivity of DLBCL to Rituximab in a small set of 14 patients treated with Rituximab and CHOP (R-CHOP) (downloaded from NCBI Gene Expression Omnibus under accession number GSE4475). Conversion between different chip types was performed using matching tables available through Affymetrix.
The survival of patients predicted to be sensitive to be R-CHOP is compared to the survival of patients predicted to be resistant to R-CHOP in Figure 7. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
To predict the sensitivity toward combination therapies, such as those used to treat Diffuse Large B-cell Lymphoma (DLBCL), patient sensitivity to a particular combination therapy is predicted by combining the marker genes for the individual compounds used in the combination. An example of this is shown in Figure 8, where the predicted sensitivities of one patient towards a number of combination therapies used against DLBCL (identified by then- acronyms) are shown: R-CHOP contains Rituximab (MABTHERA™), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone; R-ICE contains Riruximab, Ifosfamide, Carboplatin, and Etoposide; R-MIME contains Rituximab, Mitoguazone, Ifosfamide, Methotrexate, and Etoposide; CHOEP contains Cyclophosphamide, Doxorubicin, Etoposide, Vincristine and Prednisone; DHAP contains Dexamethasone, Cytarabine (Ara C), and Cisplatin; ESHAP contains Etoposide, Methylprednisolone (Solumedrol), Cytarabine (Ara-C) and Cisplatin; and HOAP-Bleo contains Doxorubicin, Vincristine, Ara C, Prednisone, and Bleomycin.
Example 6: Prediction of radiosensitivity for brain tumor (medulloblastoma) patients.
The method of identifying biomarkers can also be applied to other forms of treatment such as radiation therapy. For example, sensitivity to radiation therapy was predicted for brain tumor patients. Radiation therapy in the form of craniospinal irradiation yielding 2,400-3,600 centiGray (cGy) with a tumor dose of 5,300-7,200 cGy was administered to the brain tumor patients using a medical device that emits beams of radiation. Sensitivity of the 60 cancer cell lines used in the NCI60 dataset to radiation treatment was obtained from published reports. This sensitivity was correlated to the expression of genes in the cell lines as described above to identify marker genes. DNA microarray measurements of gene expression in brain tumors obtained from patients subsequently treated with radiation therapy were obtained from the Broad Institute. The identified gene biomarkers were used to classify the patients as sensitive or resistant to radiation therapy. The survival of the patients in the two predicted categories is shown in Figure 9. The survival rate of the patients predicted to be sensitive to radiation therapy was higher than the patients predicted to be resistant to radiation therapy.
Example 7: Drug rescue.
Every member of a population may not be equally responsive to a particular treatment. For example, new compounds often fail in late clinical trials because of lack of efficacy in the population tested. WMIe such compounds may not be effective in the overall population, there may be subpopulations sensitive to those, failed compounds due to various reasons, including inherent differences in gene expression. The method as described herein can be used to rescue failed compounds by identifying a patient subpopulation sensitive to a compound using then- gene expression as an indicator. Subsequent- clinical trials restricted to a sensitive patient subpopulation may demonstrate efficacy of a previously failed compound within that particular patient subpopulation, advancing the compound towards approval for use in that subpopulation.
To this end, in vitro measurements of the inhibitory effects of a compound on various cancer cell samples from the responsive patient subpopulation collected as described above or measures of clinical response of a treated patient are compared to the gene expression of cells from those patients. The growth of the cancer cell samples can be correlated to gene expression measurements as described above. This will identify marker genes that can be used to predict patient sensitivity to the failed compound. Preferably, biomarker genes will be identified within the patient population previously shown to be sensitive to the failed compound. Once biomarkers are identified, the expression of biomarker genes in patients can be measured according to the procedure detailed above. The patients are predicted to be responsive or non-responsive to compound treatment according to their gene biomarker expression. Clinical effect must then be demonstrated in the group of patients that are predicted to be sensitive to the failed compound.
The method may be further refined if patients responsive to the compound treatment are further subdivided into those predicted to survive without the compound and those predicted to die or suffer a relapse without the compound. Clinical efficacy in the subpopulation that is predicted to die or suffer relapse can be further demonstrated. Briefly, the gene expression at the time of diagnosis of patients who later die from their disease is compared to gene expression at the time of diagnosis of patients who are still alive after 5 years. Genes differentially expressed between the two groups are identified as prospective biomarkers and a model is built using those gene biomarkers to predict treatment efficacy.
Examples of compounds that have failed in clinical trials include Iressa (Gefinitib, AstraZeneca) in refractory, advanced non-small-cell lung cancer (NSCLC), Avastin (Bevacizumab, Genentech) in first-line treatment for advanced pancreatic cancer, Avastin (Bevacizumab, Genentech) in relapsed metastatic breast cancer patients, and Tarceva (Erlotinib, Genentech) in metastatic non-small cell lung cancer (NSCLC). The method of the invention may be applied to these compounds, among others, so that sensitive patient subpopulations responsive to those compounds may be identified. ■
Example 8: Median of the correlations versus correlation of the median.
The median of the correlations of the individual probe measurements to cancer cell growth as employed by the invention was compared to the correlation of the median probe measurements: this will determine at which step of the method amedian calculation should be performed. In the former, several correlations are calculated for each gene since multiple probes measure a given gene's expression, but only the median of the correlation coefficients is finally retained to identify biomarkers. In the latter, only one correlation is calculated for each gene because only the median gene expression measurement is considered for each gene. Figure 10 shows the results of using the correlation of the median expression measurements to identify biomarker genes of radiation sensitivity predicting the survival of 60 brain cancer patients. The difference in survival between the group predicted to be radiation sensitive and the group predicted to be radiation resistant in Figure 10 is much smaller than the difference depicted in Figure 9 which employed a median correlation coefficient suggesting that the invention's median of the correlations employed in Figure 9 outperforms the correlation of the median depicted in Figure 10.
If we look at individual marker genes like OMD, the median of the correlation to measured radiosensitivity of cell lines in vitro is 0.3'2.The correlation of the median, however, is 0.39. Adjusting the cutoff from 0.3 to 0.4 to compensate for the difference does not improve on Figure 10, however. . . . .
We have also compared median correlation to .weighted voting as proposed by Staunton et al., PNAS 98(19):10787-10792, (2001). Weighted voting produced a poor result similar to that of Figure 10, with a P- value of 0.11.
Other Embodiments
All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each independent publication or patent application was specifically and individually indicated to be incorporated by reference.
While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and mcluding such departures from the present disclosure that come within known or customary practice within the art to which the invention pertains and may be applied to the essential features hereinbefore set forth.
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Table 22. Vincristine biomarkers.
Gene Correlation Probe Sequence
[1,] SLC25A5 0.32 TCCTGTACTTGTCCTCAGCTTGGGC [2,] RPLlO 0.38 GCCCCACTGGACAACACTGATTCCT [3,] RPL12 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [4,1 RPS4X 0.39 AAATGTTTCCTTGTGCCTGCTCCTG [5,} EIF5A 0.31 TCCTGTACTTGTCCTCAGCTTGGGC [6,] BLMH 0.32 AAGCCTATACGTTTCTGTGGAGTAA
[7,] TBCA 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC
[8,] MDH2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC
O,] S100A4 0.32 TGGACCCCACTGGCTGAGAATCTGG
[10,] C14orfl39 0.3 TTGGACATCTCTAGTGTAGCTGCCA
Table 23. Cisplatin biomarkers.
Gene Correlation Probe Sequence
[1,] ClQRl 0. CACCCAGCTGGTCCTGTGGATGGGA
[2,] SLA 0.37 TGCCTGCTCCTGTACTTGTCCTCAG
[3, J PTPN7 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[4,] ZNFNlAl 0.33 CACCCAGCTGGTCCTGTGGATGGGA
[5,] CENTBl 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[6,] IFI16 0.31 TCCTCCATCACCTGAAACACTGGAC
[7,] ARHGEF6 0.35 TGCCTGCTCCTGTACTTGTCCTCAG
[8,] SEC31L2 0.32 AAGCCTATACGTTTCTGTGGAGTAA
[9,] CD3Z 0.32 TTGGACATCTCTAGTGTAGCTGCCA
[10,] GZMB 0.3 TCCTCCATCACCTGAAACACTGGAC
[11,] CD3D 0.34 TCCTCCATCACCTGAAACACTGGAC
[12,] MAP4Kl 0.32 CACCCAGCTGGTCCTGTGGATGGGA
[13,] GPR65 0.39 CACCCAGCTGGTCCTGTGGATGGGA
[14,] PRFl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[15,] ARHGAP15 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[16,] TM6SF1 0.41 TGCCTGCTCCTGTACTTGTCCTCAG
[17,] TCF4 0.4 AAATGTTTCCTTGTGCCTGCTCCTG
Table 24. Etoposide biomarkers.
Gene Correlation Probe Sequence
[1,] CD99 0.3 AAGCCTATACGTTTCTGTGGAGTAA [2,] INSIGl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT [3,] PRGl 0.34 GCCCCACTGGACAACACTGATTCCT [4,] MUFl 0.35 AAGCCTATACGTTTCTGTGGAGTAA [5, ] SLA 0.37 CACCCAGCTGGTCCTGTGGATGGGA [6,] SSBP2 0.37 TGGACCCCACTGGCTGAGAATCTGG [7,] GNB5 0.35 TCCTTGTGCCTGCTCCTGTACTTGT [8,] MFNG 0.33 GCCCCACTGGACAACACTGATTCCT [9,] PSMB9 0.31 AAGCCTATACGTTTCTGTGGAGTAA [10,] EVI2A 0.41 TCCTCCATCACCTGAAACACTGGAC
[H/] PTPN7 0.3 AAGCCTATACGTTTCTGTGGAGTAA [12, ] PTGER4 0.3 TGCCTGCTCCTGTACTTGTCCTCAG [13,] CXorf9 0.3 GCCCCACTGGACAACACTGATTCCT [14, ] ZNFNlAl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC [15,] CENTBl 0.3 TGGACCCCACTGGCTGAGAATCTGG [16,] NAPlLl 0.31 TCCTCCATCACCTGAAACACTGGAC [17,] HLA-DRA 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [18,] IFI16 0.38 CACCCAGCTGGTCCTGTGGATGGGA [19,] ARHGEF6 0.33 TGGACCCCACTGGCTGAGAATCTGG [20,] PSCDBP 0.4 AAGCCTATACGTTTCTGTGGAGTAA [21, ] SELPLG 0.35 TTGGACATCTCTAGTGTAGCTGCCA [22,] SEC31L2 0.42 AAATGTTTCCTTGTGCCTGCTCCTG [23 , ] CD3Z 0.36 TGCCTGCTCCTGTACTTGTCCTCAG [24 , ] SH2D1A 0.33 CACCCAGCTGGTCCTGTGGATGGGA [25, ] GZMB 0.34 TGGACCCCACTGGCTGAGAATCTGG [26, ] SCN3A 0.3 GCCCCACTGGACAACACTGATTCCT [27 , ] RAFTLIN 0.39 TCCTCCATCACCTGAAACACTGGAC [28 , ] DOCK2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG [29, ] CD3D 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC [ 30, ] ZAP70 0.35 TCCTCCATCACCTGAAACACTGGAC [ 31, ] GPR65 0.35 TGGACCCCACTGGCTGAGAATCTGG [32 , ] PRFl 0.32 TGGACCCCACTGGCTGAGAATCTGG [33 , ] ARHGAP15 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC [34 , ] NOTCHl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [35 , ] UBASH3A 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 25. Azaguanine biomarkers .
Gene Correlation Probe Sequence
[Ir] SRM 0.32 TGCCTGCTCCTGTACTTGTCCTCAG
[2,] SCARBl 0.4 TTGGACATCTCTAGTGTAGCTGCCA
[3,] SIATl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[4,] CDGBP2 0.37 TGGACCCCACTGGCTGAGAATCTGG
[5,] WASPIP 0.44 TCCTGTACTTGTCCTCAGCTTGGGC
[6,] ITM2A 31 AAGCCTATACGTTTCTGTGGAGTAA
[7,] PALM2-AKAP2 31 ACTTGTCCTCAGCTTGGGCTTCTTC
[8,] LNK 43 TTGGACATCTCTAGTGTAGCTGCCA
[9,] FCGR2A 3 TGCCTGCTCCTGTACTTGTCCTCAG
[10,] RUNX3 43 TCCTGTACTTGTCCTCAGCTTGGGC
[11,] EVI2A 4 AAATGTTTCCTTGTGCCTGCTCCTG
[12,] BTN3A3 4 ACTTGTCCTCAGCTTGGGCTTCTTC
[13,] LCP2 34 TCCTTGTGCCTGCTCCTGTACTTGT
[14,] BCHE 35 TCCTCCATCACCTGAAACACTGGAC
E15,] LY96 0.47 TGCCTGCTCCTGTACTTGTCCTCAG
[16,] LCPl 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC
[17,] IFI16 0.33 CACCCAGCTGGTCCTGTGGATGGGA
[18,] MCAM 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[19,] MEF2C 0.41 CACCCAGCTGGTCCTGTGGATGGGA
[20,] FYN 0.31 TCCTGTACTTGTCCTCAGCTTGGGC
[21,] Clorf38 0.37 AAGCCTATACGTTTCTGTGGAGTAA
[22,] FCGR2C 0.34 TGCCTGCTCCTGTACTTGTCCTCAG
[23,] TNIK 35 AAGCCTATACGTTTCTGTGGAGTAA
[24,] AMPD2 3 TCCTGTACTTGTCCTCAGCTTGGGC
[25,] SEPT6 41 AAATGTTTCCTTGTGCCTGCTCCTG
[26,] RAFTLIN 39 TCCTTGTGCCTGCTCCTGTACTTGT
[27,] SLC43A3 52 CACCCAGCTGGTCCTGTGGATGGGA
[28,] LPXN 54 AAGCCTATACGTTTCTGTGGAGTAA
[29,] CKIP-I 33 TCCTGTACTTGTCCTCAGCTTGGGC
[30,] FLJ10539 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[31,] FLJ35036 0.36 AAGCCTATACGTTTCTGTGGAGTAA
[32,] DOCKlO 0. .3 GCCCCACTGGACAACACTGATTCCT
[33,] TRPV2 31 ACTTGTCCTCAGCTTGGGCTTCTTC
[34,] IFRG28 3 TCCTTGTGCCTGCTCCTGTACTTGT
[35,] LEFl ,31 ACTTGTCCTCAGCTTGGGCTTCTTC
[36,] ADAMTSl 0.36 TGGACCCCACTGGCTGAGAATCTGG
Table 26. Carboplatin biomarkers.
Gene Correlation Probe Sequence
[1,] ITGA5 0.43 AAATGTTTCCTTGTGCCTGCTCCTG [2,] TNFAIP3 0.4 TGCCTGCTCCTGTACTTGTCCTCAG [3,] WNT5A 0.34 TCCTCCATCACCTGAAACACTGGAC
[4,] FOXF2 0.36 TGCCTGCTCCTGTACTTGTCCTCAG
[5,] LOC94105 0.32 AAATGTTTCCTTGTGCCTGCTCCTG
[6,] IFI16 0.38 TCCTCCATCACCTGAAACACTGGAC
[7,] LRRN3 0.33 TTGGACATCTCTAGTGTAGCTGCCA
[8,] DOCKlO 0.4 TCCTGTACTTGTCCTCAGCTTGGGC
[9,] LEPREl 0.32 GCCCCACTGGACAACACTGATTCCT
[10,] ADAMTSl 0.34 TGGACCCCACTGGCTGAGAATCTGG
Table 27. Adriarαycin biomarkers .
Gene Correlation Probe Sequence
[1,] CD99 0.41 AAGCCTATACGTTTCTGTGGAGTAA
12,] ALDOC 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[3,] SLA 0.35 TGCCTGCTCCTGTACTTGTCCTCAG
[4,] SSBP2 0.34 TCCTCCATCACCTGAAACACTGGAC
[5,] IL2RG 0.38 TCCTTGTGCCTGCTCCTGTACTTGT
[6,] CXorf9 0.32 TGGACCCCACTGGCTGAGAATCTGG
[7,] RHOH 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[8,] ZNFNlAl 0.43 TTGGACATCTCTAGTGTAGCTGCCA
[9,] CENTBl 0.36 AAGCCTATACGTTTCTGTGGAGTAA
[10,] MAP4Kl 0.35 TCCTCCATCACCTGAAACACTGGAC
[H,] CD3G 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[12,] CCR9 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[13,] CXCR4 0.3 TCCTTGTGCCTGCTCCTGTACTTGT
[14,] ARHGEF6 0.31 TCCTCCATCACCTGAAACACTGGAC
[15,] SELPLG 0.31 TGGACCCCACTGGCTGAGAATCTGG
[16,] SEC31L2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG
[17,] CD3Z 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC
[18,] S SHH22DD11AA 0. 3377 TTGGACATCTCTAGTGTAGCTGCCA
[19,] C CDDllAA 0. 44 AAGCCTATACGTTTCTGTGGAGTAA
[20,] L LAAIIRRll 0.39 AAGCCTATACGTTTCTGTGGAGTAA
[21,] T TRRBBii 0.34 TCCTCCATCACCTGAAACACTGGAC
[22,] C CDD33DD 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[23,] WBSCR20C 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC
[24,] ZAP70 0.33 TCCTGTACTTGTCCTCAGCTTGGGC
[25,] IFI44 0.32 TGCCTGCTCCTGTACTTGTCCTCAG
[26,] GPR65 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[27,] AIFl 0.3 CACCCAGCTGGTCCTGTGGATGGGA
[28,] ARHGAP15 0.37 TCCTGTACTTGTCCTCAGCTTGGGC
[29,] NARF 0.3 TCCTCCATCACCTGAAACACTGGAC
[30,] PACAP 0.32 CACCCAGCTGGTCCTGTGGATGGGA
Table 28. Aclarubicin biomarkers.
Gene Correlation Probe Sequence
[1,] RPL12 0.3 A AAAAATTGGTTTTTTCCCCTTTTGGTTCGCCTGCTCCTG
[2,] RPLP2 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[3,} MYB 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[4,] ZNFNlAl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG
[5,] SCAPl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG
[6,] STAT4 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[7,] SP140 0.4 AAGCCTATACGTTTCTGTGGAGTAA
[8,] AMPD3 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[9,] TNFAIP8 0.4 AAGCCTATACGTTTCTGTGGAGTAA
[10,] DDX18 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[11,] TAF5 0.3 TCCTTGTGCCTGCTCCTGTACTTGT
[12,] RPS2 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[13,] DOCK2 0.32 AAGCCTATACGTTTCTGTGGAGTAA [14,] GPR65 0.35 AAGCCTATACGTTTCTGTGGAGTAA
[15,] HOXA9 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[16, ] FLJ12270 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[17,] HNRPD 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 29. Mitoxantrone biomarkers.
Gene Correlation Probe Sequence
[1,] PGAMl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG [2,] DPYSL3 0.36 AAATGTTTCCTTGTGCCTGCTCCTG [3,] INSIGl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT [4,] GJAl 0.31 TTGGACATCTCTAGTGTAGCTGCCA [5,] BNIP3 0.31 TTGGACATCTCTAGTGTAGCTGCCA [6,] PRGl 0.39 GCCCCACTGGACAACACTGATTCCT [7,] G6PD 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [8,] PLOD2 0.34 GCCCCACTGGACAACACTGATTCCT
[9,] LOXL2 0.31 TCCTTGTGCCTGCTCCTGTACTTGT [10,] SSBP2 0.36 TCCTCCATCACCTGAAACACTGGAC
[H,] Clorf29 0.35 TCCTTGTGCCTGCTCCTGTACTTGT [12,] TOX 0.35 TCCTTGTGCCTGCTCCTGTACTTGT [13,] STCl 0.39 TCCTGTACTTGTCCTCAGCTTGGGC [14,] TNFRSFlA 0.34 AAATGTTTCCTTGTGCCTGCTCCTG [15,] NCOR2 0.3 TCCTCCATCACCTGAAACACTGGAC [16,] NAPlLl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT [17,] LOC94105 0.34 AAGCCTATACGTTTCTGTGGAGTAA [18,] ARHGEF6 0.34 TCCTCCATCACCTGAAACACTGGAC
[19,] GATA3 0.35 TCCTTGTGCCTGCTCCTGTACTTGT [20,] TFPI 0.31 TCCTGTACTTGTCCTCAGCTTGGGC [21,] CD3Z 0.37 AAGCCTATACGTTTCTGTGGAGTAA [22,] AFlQ 0.33 GCCCCACTGGACAACACTGATTCCT [23,] MAPlB 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [24,] CD3D 0.31 TCCTTGTGCCTGCTCCTGTACTTGT [25,] BCATl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC [26,] IFI44 0.33 TGGACCCCACTGGCTGAGAATCTGG [27,] CUTC 0.33 AAATGTTTCCTTGTGCCTGCTCCTG [28,] NAP1L2 0.33 AAGCCTATACGTTTCTGTGGAGTAA [29,] NME7 0.35 AAATGTTTCCTTGTGCCTGCTCCTG [30, ] FLJ21159 0.33 TCCTGTACTTGTCCTCAGCTTGGGC
Table 30. Mitomycin, biomarkers.
Gene Correlation Probe Sequence
[1,] STCl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [2,] GPR65 0.32 GCCCCACTGGACAACACTGATTCCT [3,] DOCKlO 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC [4,] FAM46A 0.36 TCCTTGTGCCTGCTCCTGTACTTGT
[5, ] LOC54103 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 31. Paclitaxel (Taxol) biomarkers.
Gene Correlation Probe Sequence
[1,] RPLlO 0.31 TCCTCCATCACCTGAAACACTGGAC
[2,] RPS4X 0.31 TCCTCCATCACCTGAAACACTGGAC
[3,] DKCl 0.3 TCCTTGTGCCTGCTCCTGTACTTGT
[4,] DKFZP564C186 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
[5,] PRPl9 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[6,] RAB9P40 0.33 GCCCCACTGGACAACACTGATTCCT
[7,] HSA9761 0.37 AAATGTTTCCTTGTGCCTGCTCCTG
[8,] GMDS 0.3 AAATGTTTCCTTGTGCCTGCTCCTG [ 9, 3 CEPl 0.3 AAATGTTTCCTTGTGCCTGCTCCTG [ 10, ] IL13RA2 0.34 AAATGTTTCCTTGTGCCTGCTCCTG [ 11/ 3 MAGEB2 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC [12 , ] HMGN2 0.35 CACCCAGCTGGTCCTGTGGATGGGA [ 13 , ] ALMSl 0.3 TCCTCCATCACCTGAAACACTGGAC [ 14 , ] GPR65 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [15 , ] FLJl0774 0.31 TGGACCCCACTGGCTGAGAATCTGG [ 16, ] NOL8 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [ 17 , ] DAZAPl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG [ 18 , ] SLC25A15 0.31 TTGGACATCTCTAGTGTAGCTGCCA [ 19 , ] PAF53 0.36 TCCTCCATCACCTGAAACACTGGAC [ 20 , 3 PITPNCl 0.33 TCCTCCATCACCTGAAACACTGGAC [21 , ] SPANXC 0.3 TGGACCCCACTGGCTGAGAATCTGG [22 , 3 KIAA1393 0.33 CACCCAGCTGGTCCTGTGGATGGGA
Table 32. Gemc±tabine (Gemzar) biomarkers .
Gene Correlation Probe Sequence
[1,] UBE2L6 0.38 CACCCAGCTGGTCCTGTGGATGGGA
[2,] TAPl 0.33 CACCCAGCTGGTCCTGTGGATGGGA
[3,] F2R 0.3 TCCTGTACTTGTCCTCAGCTTGGGC
[4,] PSMB9 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[5,] IL7R 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[6,] TNFAIP8 0.33 AAGCCTATACGTTTCTGTGGAGTAA
[7,] HLA-C 0.33 TGGACCCCACTGGCTGAGAATCTGG
[8,] IFI44 0.31 TGGACCCCACTGGCTGAGAATCTGG
Table 33. Taxotere (docetaxel) biomarkers.
Gene Correlation Probe Sequence
[1,1 ANP32B 0.45 GCCCCACTGGACAAGACTGATTCCT
[2,] GTF3A 0.31 TTGGACATCTCTAGTGTAGCTGCCA
[3,] TRIMl4 0 31 ACTTGTCCTCAGCTTGGGCTTCTTC
[4,] SKP2 33 GCCCCACTGGACAACACTGATTCCT
[5,] TRIP13 36 TCCTGTACTTGTCCTCAGCTTGGGC
[6,] RFC3 45 GCCCCACTGGACAACACTGATTCCT
[7,] CASP7 32 TGCCTGCTCCTGTACTTGTCCTCAG
[8,] TXN 36 AAGCCTATACGTTTCTGTGGAGTAA
[9,3 MCM5 34 AAATGTTTCCTTGTGCCTGCTCCTG
[10,] PTGES2 39 AAATGTTTCCTTGTGCCTGCTCCTG
[11,] OBFCl 37 TGGACCCCACTGGCTGAGAATCTGG
[12,] EPB41L4B 32 GCCCCACTGGACAACACTGATTCCT
[13,3 CALML4 0.31 TCCTCCATCACCTGAAACACTGGAC
Table 34. Dexamethasone biomarkers.
Gene Correlation Probe Sequence
[1,] IFITM2 0.38 ATATATGGACCTAGCTTGAGGCAAT [2,] UBE2L6 0.32 AAGCCTATACGTTTCTGTGGAGTAA [3,] ITM2A 0.38 CACCCAGCTGGTCCTGTGGATGGGA
[4,] IL2RG 0.36 TCCTCCATCACCTGAAACACTGGAC
[5,] GPRASPl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC
[6,] PTPN7 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[7,] CXorf9 0.36 GCCCCACTGGACAACACTGATTCCT
[8,] RHOH 0.33 TGCCTGCTCCTGTACTTGTCCTCAG
[9,] GIT2 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[10,] ZNFNlAl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT
[11,] CEPl 0.31 CACCCAGCTGGTCCTGTGGATGGGA
[12,] MAP4K1 0.3 AAGCCTATACGTTTCTGTGGAGTAA
[13,] CCR7 0.33 AAATGTTTCCTTGTGCCTGCTCCTG [14,] CD3G 0.35 CACCCAGCTGGTCCTGTGGATGGGA [15,] UCP2 0.3 AAGCCTATACGTTTCTGTGGAGTAA [16,] GATA3 0.37 TGGACCCCACTGGCTGAGAATCTGG [17,] CDKN2A 0.32 TCCTGTACTTGTCCTCAGCTTGGGC [18,] TARP 0.3 'GCCCCACTGGACAACACTGATTCCT [19,] LAIRl 0.34 TTGGACATCTCTAGTGTAGCTGCCA [20,] SH2D1A 0.34 TCCTTGTGCCTGCTCCTGTACTTGT [21,] SEPT6 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [22,] HA-I 0.34 TCCTTGTGCCTGCTCCTGTACTTGT [23,] CD3D 0.32 TCCTCCATCACCTGAAACACTGGAC [24,] LSTl 0.39 CACCCAGCTGGTCCTGTGGATGGGA [25,] AIFl 0.35 AAGCCTATACGTTTCTGTGGAGTAA [26,] ADA 0.33 TGCCTGCTCCTGTACTTGTCCTCAG [27,] DATFl 0.41 CACCCAGCTGGTCCTGTGGATGGGA [28,] ARHGAP 15 0.3 TCCTGTACTTGTCCTCAGCTTGGGC [29,] PLAC 8 0.31 CACCCAGCTGGTCCTGTGGATGGGA [30,] CECRl 0.31 GCCCCACTGGACAACACTGATTCCT [31,] LOC81558 0.33 TGGACCCCACTGGCTGAGAATCTGG [32,] EHD2 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 35. Ara-C (Cytarabine hydrochloride) biomarkers. Gene Correlation Probe Sequence
[1,] ITM2A 0.32 TGGACCCCACTGGCTGAGAATCTGG
[2,] RHOH 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[3,] PRIMl 0.3 TCCTCCATCACCTGAAACACTGGAC
[4,3 CENTBl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[5,] NAPlLl 0.31 GCCCCACTGGACAACACTGATTCCT
[6,] ATP5G2 0.31 TCCTCCATCACCTGAAACACTGGAC
[7,] GATA3 0.33 AAATGTTTCCTTGTGCCTGCTCCTG
[8,] PRKCQ 0.32 AAGCCTATACGTTTCTGTGGAGTAA
[9,] SH2D1A 0.3 GCCCCACTGGACAACACTGATTCCT
[10,] SEPT6 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC
[H,] NME4 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC
[12,] CD3D 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[13,] CDlE 0.32 TGGACCCCACTGGCTGAGAATCTGG
[14,] ADA 0.34 GCCCCACTGGACAACACTGATTCCT
[15,] FHODl 0.31 CACCCAGCTGGTCCTGTGGATGGGA
Table 36. Methylprednisolone biomarkers.
Gene Correlation Probe Sequence
[1,1 CD99 0.31 GCCCCACTGGACAACACTGATTCCT
[2,] ARHGDIB 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[3,] ITM2A 0.35 GCCCCACTGGACAACACTGATTCCT
[4,] LGALS9 0.43 TCCTCCATCACCTGAAACACTGGAC
[5,] INPP5D 0.34 TGGACCCCACTGGCTGAGAATCTGG
[6,] SATBl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT
[7,] TFDP2 0.4 AAATGTTTCCTTGTGCCTGCTCCTG
[8,] SLA 0.31 TGGACCCCACTGGCTGAGAATCTGG
[9,] IL2RG 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[10,] MFNG 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[11,] SELL 0.33 AAATGTTTCCTTGTGCCTGCTCCTG
[12,] CDW52 0.33 TCCTCCATCACCTGAAACACTGGAC
[13,] LRMP 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[14,] ICAM2 0.38 CACCCAGCTGGTCCTGTGGATGGGA
[15,] RIMS3 0.36 TGCCTGCTCCTGTACTTGTCCTCAG
[16,] PTPN7 0.39 TGGACCCCACTGGCTGAGAATCTGG
[17,] ARHGAP25 0.37 TCCTGTACTTGTCCTCAGCTTGGGC [18,] LCK 0.3 TCCTCCATCACCTGAAACACTGGAC
[19,1 CXorf9 0.3 TTGGACATCTCTAGTGTAGCTGCCA
[20,] RHOH 0.51 AAGCCTATACGTTTCTGTGGAGTAA
[21,] GIT2 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC
[22,3 ZNFNlAl 0.53 TCCTTGTGCCTGCTCCTGTACTTGT
[23,] CENTBl 0.36 TCCTCCATCACCTGAAACACTGGAC
[24,] LCP2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC
[25,] SPIl 0.3 TCCTGTACTTGTCCTCAGCTTGGGC
[26,] GZMA 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[27,] CEPl 0.37 AAGCCTATACGTTTCTGTGGAGTAA
[28,] CD8A 0.38 TGGACCCCACTGGCTGAGAATCTGG
[29,] SCAPl 0.32 TCCTCCATCACCTGAAACACTGGAC
[30,] CD2 0.48 GCCCCACTGGACAACACTGATTCCT
[31,] VAVl 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC
[32,3 MAP 4 Kl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC
[33,3 CCR7 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC
[34,] C6orf32 0.38 TCCTTGTGCCTGCTCCTGTACTTGT
[35,] ALOX15B 0.43 TGCCTGCTCCTGTACTTGTCCTCAG
[36,3 BRDT 0.33 AAGCCTATACGTTTCTGTGGAGTAA
[37,3 CD3G 0.51 AAGCCTATACGTTTCTGTGGAGTAA
[38,] LTB 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
[39,] NVL 0.31 TTGGACATCTCTAGTGTAGCTGCCA
[40,]. RASGRP2 0.35 TGCCTGCTCCTGTACTTGTCCTCAG
[41,] LCPl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG
[42,] CXCR 4 0.3 AAGCCTATACGTTTCTGTGGAGTAA
[43,] PRKD2 0.33 CACCCAGCTGGTCCTGTGGATGGGA
[44,3 GATA3 0.39 TCCTGTACTTGTCCTCAGCTTGGGC
[45,3 KIAAO 922 0.36 GCCCCACTGGACAACACTGATTCCT
[46,] TARP 0.49 TCCTCCATCACCTGAAACACTGGAC
[47,] SEC31L2 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
[48,3 PRKCQ 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[49,] SH2D1A 0.33 AAGCCTATACGTTTCTGTGGAGTAA
[50,3 CHRNA3 0.5 AAGCCTATACGTTTCTGTGGAGTAA
[51,] CDlA 0.44 AAGCCTATACGTTTCTGTGGAGTAA
[52,3 LSTl 0.36 CACCCAGCTGGTCCTGTGGATGGGA
[53,] LAIRl 0.47 CACCCAGCTGGTCCTGTGGATGGGA
[54,] CACNAlG 0.33 GCCCCACTGGACAACACTGATTCCT
[55,] TRB @ 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[56,3 SEPT6 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[57,] HA-I 0.42 CACCCAGCTGGTCCTGTGGATGGGA
[58,] DOCK2 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[59,3 CD3D 0.41 TCCTGTACTTGTCCTCAGCTTGGGC
[60,] TRD@ 0.38 TGCCTGCTCCTGTACTTGTCCTCAG
[61,] T3JAM 0.37 TGCCTGCTCCTGTACTTGTCCTCAG
[62,] FNBPl 0.37 TCCTGTACTTGTCCTCAGCTTGGGC
[63,3 'CD6 0.4 CACCCAGCTGGTCCTGTGGATGGGA
[64,3 AIFl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[65,] FOLHl 0.45 TCCTGTACTTGTCCTCAGCTTGGGC
[66,] CDlE 0.58 CACCCAGCTGGTCCTGTGGATGGGA
[67,] LY9 0.39 TCCTTGTGCCTGCTCCTGTACTTGT
[68,] ADA 0.39 AAATGTTTCCTTGTGCCTGCTCCTG
[69,] CDKL5 0.44 GCCCCACTGGACAACACTGATTCCT
[70,] TRIM 0.38 AAGCCTATACGTTTCTGTGGAGTAA
[71,3 DATFl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[72,] RGC32 0.51 TCCTTGTGCCTGCTCCTGTACTTGT
[73,] ARHGAPl 5 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[74,] NOTCHl 0.36 TCCTTGTGCCTGCTCCTGTACTTGT [75 , ] BIN2 0.31 AAATGTTTCCTTGTGCCTGCTCCTG [76, ] SEMA4G 0.35 AAGCCTATACGTTTCTGTGGAGTAA [77 , ] DPEP2 0.33 CACCCAGCTGGTCCTGTGGATGGGA [78 , ] CECRl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC [79 , ] BCLIlB 0.33 TGCCTGCTCCTGTACTTGTCCTCAG [ 80 , ] STAG3 0.41 TTGGACATCTCTAGTGTAGCTGCCA [81 , ] GALNT6 0.32 TGCCTGCTCCTGTACTTGTCCTCAG [ 82 , ] UBASH3A 0.3 AAATGTTTCCTTGTGCCTGCTCCTG [83, ] PHEMX 0.38 TCCTCCATCACCTGAAACACTGGAC [84 , ] FLJ13373 0.34 TCCTTGTGCCTGCTCCTGTACTTGT [ 85 , ] LEFl 0.49 TCCTCCATCACCTGAAACACTGGAC [86, ] IL21R 0.42 TTGGACATCTCTAGTGTAGCTGCCA [ 87 , ] MGC17330 0.33 TCCTTGTGCCTGCTCCTGTACTTGT [ 88 , ] AKAPl3 0.53 TCCTTGTGCCTGCTCCTGTACTTGT [89, ] GIMAP5 0.34 AAATGTTTCCTTGTGCCTGCTCCTG
Table 37. Methotrexate biomarkers .
Gene C Coorrrrelation Probe Sequence
[1,] PRPF8 0 0..3344 TCCTCCATCACCTGAAACACTGGAC
[2,3 RPL18 0 0..3344 AAGCCTATACGTTTCTGTGGAGTAA
[3,] GOT2 0 0..3311 CACCCAGCTGGTCCTGTGGATGGGA
[4,] RPLl3A 0 0..3311 TCCTGTACTTGTCCTCAGCTTGGGC
[5,3 RPS15 0 0..3399 CACCCAGCTGGTCCTGTGGATGGGA
[6,] RPLP2 0 0..3322 • • GCCCCACTGGACAACACTGATTCCT
[7,] CSDA 0 0..3399 GCCCCACTGGACAACACTGATTCCT
[8,3 KHDRBSl 0 0..3322 TCCTCCATCACCTGAAACACTGGAC
[9,] SNRPA 0 0..3311 TCCTGTACTTGTCCTCAGCTTGGGC
[10,] IMPDH2 0 0..3399 AAATGTTTCCTTGTGCCTGCTCCTG
[H,] RPS19 0 0..4477 AAATGTTTCCTTGTGCCTGCTCCTG
[12,3 NUP88 0 0..3366. CACCCAGCTGGTCCTGTGGATGGGA
[13,] ATP5D 0 0..3333 TGCCTGCTCCTGTACTTGTCCTCAG
[14,3 PCBP2 0 0..3322 AAATGTTTCCTTGTGCCTGCTCCTG
[15,3 ZNF593 0 0..44 AAATGTTTCCTTGTGCCTGCTCCTG
[16,] HSO79274 0.32 TGGACCCCACTGGCTGAGAATCTGG
[17,] PRIMl 0.3 CACCCAGCTGGTCCTGTGGATGGGA
[18,] PFDN5 0.33 ' TCCTCCATCACCTGAAACACTGGAC
[19,3 OXAlL 0.37 CACCCAGCTGGTCCTGTGGATGGGA [20,] ATIC 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC [21,] CIAPINl 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC [22,3 RPS2 0.32 CACCCAGCTGGTCCTGTGGATGGGA [23,3 PCCB 0.36 GCCCCACTGGACAACACTGATTCCT [24,] SHMT2 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC [25,] RPLPO 0.35 AAGCCTATACGTTTCTGTGGAGTAA [26,] HNRPAl 0.35 TGGACCCCACTGGCTGAGAATCTGG [27,] STOML2 0.32 TGCCTGCTCCTGTACTTGTCCTCAG [28,] SKBl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC [29,3 GLTSCR2 0.37 AAGCCTATACGTTTCTGTGGAGTAA [30,3 CCNBlIPl 0.3 TCCTTGTGCCTGCTCCTGTACTTGT [31,] MRPS2 0.33 TTGGACATCTCTAGTGTAGCTGCCA [32,] FLJ20859 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [33,3 FLJ12270 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 38. Bleomycin biomarkers .
Gene Correlation Probe Sequence
[1,3 PFNl 0.45 •• GCCCCACTGGACAACACTGATTCCT [2,] HKl 0.33 TTGGACATCTCTAGTGTAGCTGCCA [3,] MCLl 0.31 TGGACCCCACTGGCTGAGAATCTGG [4,] ZYX 0.32 TGGACCCCACTGGCTGAGAATCTGG
[5,] ElAPlB 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC
[6,] GNB2 0.32 CACCCAGCTGGTCCTGTGGATGGGA
[7,] EPASl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[8,] PGAMl 0.42 TGCCTGCTCCTGTACTTGTCCTCAG
[9,] CKAP4 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[10,] DUSPl 0.4 AAATGTTTCCTTGTGCCTGCTCCTG
[11/] MYL 9 0.4 TTGGACATCTCTAGTGTAGCTGCCA
[12,] K-ALPHA-I 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[13,] CSDA 0.3 TCCTTGTGCCTGCTCCTGTACTTGT
[14,3 IFITM2 0.36 TTGGACATCTCTAGTGTAGCTGCCA
[15,] ITGA5 0.43 GCCCCACTGGACAACACTGATTCCT
[16,] DPYSL3 0.44 TGGACCCCACTGGCTGAGAATCTGG
[17,] JUNB 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[18,] NFKBIA 0.32 TCCTCCATCACCTGAAACACTGGAC
[19,] LAMBl 0.37 AAATGTTTCCTTGTGCCTGCTCCTG
[20,] FHLl 0.31 TGGACCCCACTGGCTGAGAATCTGG
[21,] INSIGl 0.31 TGGACCCCACTGGCTGAGAATCTGG
[22,] TIMPl 0.48 TGGACCCCACTGGCTGAGAATCTGG
[23,] GJAl 0.54 AAGCCTATACGTTTCTGTGGAGTAA
[24,] PRGl 0.46 TCCTTGTGCCTGCTCCTGTACTTGT
[25,] EXTl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT
[26,] DKFZP434J154 0.31 GCCCCACTGGACAACACTGATTCCT
[27,] MVP 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[28,] VASP 0.31 TCCTCCATCACCTGAAACACTGGAC
[29,] ARL7 0.39 TGGACCCCACTGGCTGAGAATCTGG
[30,] NNMT 0.34 TCCTGTACTTGTCCTCAGCTTGGGC
[31,] TAPl 0.3 TCCTGTACTTGTCCTCAGCTTGGGC
[32,] PLOD2 0.37 GCCCCACTGGACAACACTGATTCCT
[33,] ATF3 0.42 CACCCAGCTGGTCCTGTGGATGGGA
[34,] PALM2-AKAP2 0.33 TGGACCCCACTGGCTGAGAATCTGG
[35,] IL8 0.34 GCCCCACTGGACAACACTGATTCCT
[36,] LOXL2 0.32 GCCCCACTGGACAACACTGATTCCT
[37,] IL4R 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[38,] DGKA 0.32 GCCCCACTGGACAACACTGATTCCT
[39,] SEC61G 0.41 CACCCAGCTGGTCCTGTGGATGGGA
[40,] RGS 3 0.37 TGGACCCCACTGGCTGAGAATCTGG
[41,] F2R 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[42,] TPM2 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[43,] PSMB 9 0.34 CACCCAGCTGGTCCTGTGGATGGGA
[44,] LOX 0.37 TCCTGTACTTGTCCTCAGCTTGGGC
[45,] STCl 0.35 TCCTCCATCACCTGAAACACTGGAC
[46,] PTGER4 0.31 CACCCAGCTGGTCCTGTGGATGGGA
[47,3 SMAD3 0.38 TTGGACATCTCTAGTGTAGCTGCCA
[48,] WNT5A 0.44 TGGACCCCACTGGCTGAGAATCTGG
[49,] BDNF 0.34 TCCTCCATCACCTGAAACACTGGAC
[50, ] TNFRSFlA 0.46 TCCTCCATCACCTGAAACACTGGAC
[51,] FLNC 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC
[52,] DKF2P564K0822 0.34 TTGGACATCTCTAGTGTAGCTGCCA
[53,] FLOTl 0.38 TTGGACATCTCTAGTGTAGCTGCCA
[54,] PTRF 0.39 TGGACCCCACTGGCTGAGAATCTGG
[55,] HLA-B 0.36 TTGGACATCTCTAGTGTAGCTGCCA
[56, ] MGC4083 0.32 GCCCCACTGGACAACACTGATTCCT
[57,] TNFRSFlOB 0.34 TGCCTGCTCCTGTACTTGTCCTCAG
[58,] PLAGLl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[59,3 PNMA2 0.38 GCCCCACTGGACAACACTGATTCCT
[60,] TFPI 0.38 TCCTGTACTTGTCCTCAGCTTGGGC [61,] GZMB 0.51 TCCTCCATCACCTGAAACACTGGAC
[62,] PLAUR 0.35 AAGCCTATACGTTTCTGTGGAGTAA
[63,] FSCNl 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
[64,] ERP70 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC
[65,] AFlQ 0.3 TTGGACATCTCTAGTGTAGCTGCCA
[66,] HIC 0.33 TGCCTGCTCCTGTACTTGTCCTCAG
[67,] COL6Al 0.32 AAGCCTATACGTTTCTGTGGAGTAA
[68,] IFITM3 0.3 GCCCCACTGGACAACACTGATTCCT
[69,] MAPlB 0.38 CACCCAGCTGGTCCTGTGGATGGGA
[70,] FLJ46603 0.37 TCCTCCATCACCTGAAACACTGGAC
[71,] RAFTLIN 0.34 TGGACCCCACTGGCTGAGAATCTGG
[72,] RRAS 0.31 TCCTGTACTTGTCCTCAGCTTGGGC
[73,] FTL 0.3 CACCCAGCTGGTCCTGTGGATGGGA
[74,] KIAA0877 0.31 CACCCAGCTGGTCCTGTGGATGGGA
[75,] MTlE 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[76,] CDClO 0.51 AAATGTTTCCTTGTGCCTGCTCCTG
[77,] DOCK2 0.32 AAGCCTATACGTTTCTGTGGAGTAA
[78,] RISl 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC
[79,] BCATl 0.42 TTGGACATCTCTAGTGTAGCTGCCA
[80,] PRFl 0.34 TCCTCCATCACCTGAAACACTGGAC
[81,] DBNl 0.36 GCCCCACTGGACAACACTGATTCCT
[82,] MTlK 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[83,] TMSBlO 0.42 GCCCCACTGGACAACACTGATTCCT
[84,] FLJ10350 0.4 AAATGTTTCCTTGTGCCTGCTCCTG
[85, J Clorf24 0.34 TGCCTGCTCCTGTACTTGTCCTCAG
[86,] NME7 0.46 TCCTGTACTTGTCCTCAGCTTGGGC
[87,] TMEM22 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[88,] TPKl 0.37 TCCTCCATCACCTGAAACACTGGAC
[89, ] ELK3 0.38 TGCCTGCTCCTGTACTTGTCCTCAG
[90,] CYLD 0.4 TCCTTGTGCCTGCTCCTGTACTTGT
[91,] ADAMTSl 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[92,] EHD2 0.41 TCCTCCATCACCTGAAACACTGGAC
[93,] ACTB 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
Table 39. Methyl-GAG (meth biomarkers .
Gene Correlation Probe Sequence
[1,] SSRPl 0.37 TGCCTGCTCCTGTACTTGTCCTCAG
[2,] CTSC 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[3,] LBR 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC
[4,] EFNB2 0.31 AAATGTTTCCTTGTGCCTGCTCCTG
[5,] SERPINAl 0.34 TCCTTGTGCCTGCTCCTGTACTTGT
[6,] SSSCAl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[7,] EZH2 0.36 TTGGACATCTCTAGTGTAGCTGCCA
[8,] MYB 0.33 GCCCCACTGGACAACACTGATTCCT
[9,] PRIMl 0.39 TCCTCCATCACCTGAAACACTGGAC
[10,] H2AFX 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[H,] HMGAl 0.35 TTGGACATCTCTAGTGTAGCTGCCA
[12,] HMMR 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[13,] TK2 0.42 CACCCAGCTGGTCCTGTGGATGGGA
[14,] WHSCl 0.35 AAATGTTTCCTTGTGCCTGCTCCTG
[15,] DIAPHl 0.34 GCCCCACTGGACAACACTGATTCCT
[16,] LAMB3 0.31 GCCCCACTGGACAACACTGATTCCT
[17,] DPAGTl 0.42 TGCCTGCTCCTGTACTTGTCCTCAG
[18,] UCK2 0.31 GCCCCACTGGACAACACTGATTCCT
[19,] SERPINBl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT
[20,] MDNl 0.35 TGCCTGCTCCTGTACTTGTCCTCAG [21,] G0S2 0.43 CACCCAGCTGGTCCTGTGGATGGGA [22,] MGC21654 0.36 TGGACCCCACTGGCTGAGAATCTGG [23,] GTSEl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC [24,] TACC3 0.31 TCCTCCATCACCTGAAACACTGGAC [25,] PLAC8 0.31 CACCCAGCTGGTCCTGTGGATGGGA [26,] HNRPD 0.35 TTGGACATCTCTAGTGTAGCTGCCA [27,] PNAS-4 0.3 TTGGACATCTCTAGTGTAGCTGCCA
Table 40. HDAC inhibitors biomarkers.
Gene Correlation Probe Sequence
[1,] FAU 0.33 TTGGACATCTCTAGTGTAGCTGCCA [2,] NOL5A 0.33 TGGACCCCACTGGCTGAGAATCTGG [3,] ANP32A 0.32 CACCCAGCTGGTCCTGTGGATGGGA [4,] ARHGDIB 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC [5,] LBR 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC [6,] FABP5 0.33 TCCTCCATCACCTGAAACACTGGAC [7,] ITM2A 0.32 TTGGACATCTCTAGTGTAGCTGCCA [8,] SFRS5 0.34 TCCTCCATCACCTGAAACACTGGAC [9,3 IQGAP2 0.4 CACCCAGCTGGTCCTGTGGATGGGA [10,] SLC7A6 0.35 AAGCCTATACGTTTCTGTGGAGTAA [H,] SLA 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [12,] IL2RG 0.31 TCCTCCATCACCTGAAACACTGGAC [13,] MFNG 0.39 TCCTGTACTTGTCCTCAGCTTGGGC [14,] GPSM3 0.32 TTGGACATCTCTAGTGTAGCTGCCA [15,] PIM2 0.3 TTGGACATCTCTAGTGTAGCTGCCA [16,] EVERl 0.35 GCCCCACTGGACAACACTGATTCCT [17,] LRMP 0.35 TGCCTGCTCCTGTACTTGTCCTCAG [18,] ICAM2 0.44 TCCTGTACTTGTCCTCAGCTTGGGC [19,] RIMS3 0.43 TGGACCCCACTGGCTGAGAATCTGG [20,] FMNLl 0.35 TTGGACATCTCTAGTGTAGCTGCCA [21,] MYB 0.37 TGCCTGCTCCTGTACTTGTCCTCAG [22,] PTPN7 0.36 TCCTTGTGCCTGCTCCTGTACTTGT [23,] LCK 0.48 CACCCAGCTGGTCCTGTGGATGGGA [24,] CXorf9 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC [25,] RHOH 0.31 TCCTTGTGCCTGCTCCTGTACTTGT [26,] ZNFNlAl 0.33 AAATGTTTCCTTGTGCCTGCTCCTG [27, ] CENTBl 0.45 CACCCAGCTGGTCCTGTGGATGGGA [28,] LCP2 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [29, ] DBT 0.32 TCCTGTACTTGTCCTCAGCTTGGGC [30,] CEPl 0.31 TTGGACATCTCTAGTGTAGCTGCCA [31,] IL6R 0.31 TGGACCCCACTGGCTGAGAATCTGG [32,] VAVl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT■ [33,] MAP4K1 0.3 AAGCCTATACGTTTCTGTGGAGTAA [34,] CD28 0.36 TCCTTGTGCCTGCTCCTGTACTTGT [35,] PTP4A3 0.3 TTGGACATCTCTAGTGTAGCTGCCA [36,] CD3G 0.33 CACCCAGCTGGTCCTGTGGATGGGA [37,] LTB 0.4 TCCTGTACTTGTCCTCAGCTTGGGC [38,] USP34 0.44 GCCCCACTGGACAACACTGATTCCT [39,] NVL 0.41 TCCTTGTGCCTGCTCCTGTACTTGT [40,] CD8B1 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC [41,] SFRS 6 0.31 GCCCCACTGGACAACACTGATTCCT [42,] LCPl 0.34 TCCTGTACTTGTCCTCAGCTTGGGC [43,] CXCR4 0.36 TGCCTGCTCCTGTACTTGTCCTCAG [44,] PSCDBP 0.33 TGGACCCCACTGGCTGAGAATCTGG [45,] SELPLG 0.33 TTGGACATCTCTAGTGTAGCTGCCA [46,3 CD3Z 0.3 TCCTTGTGCCTGCTCCTGTACTTGT [47,] PRKCQ 0.33 TTGGACATCTCTAGTGTAGCTGCCA [48,] CDlA 0.34 GCCCCACTGGACAACACTGATTCCT [49,] GATA2 0.31 TTGGACATCTCTAGTGTAGCTGCCA [50,] P2RX5 ■ 0.32 TGCCTGCTCCTGTACTTGTCCTCAG [51,] LAIRl 0.35 TGGACCCCACTGGCTGAGAATCTGG [52,] Clorf38 0.4 GCCCCACTGGACAACACTGATTCCT [53,] SH2D1A 0.44 TCCTTGTGCCTGCTCCTGTACTTGT [54,] TRB@ 0.33 CACCCAGCTGGTCCTGTGGATGGGA [55,] SEPT6 0.34 GCCCCACTGGACAACACTGATTCCT [56,] HA-I 0.32 AAGCCTATACGTTTCTGTGGAGTAA [57,] DOCK2 0.3 TCCTTGTGCCTGCTCCTGTACTTGT [58,] WBSCR20C 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [59,] CD3D 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC [60,] RNASE6 0.31 GCCCCACTGGACAACACTGATTCCT
[61,] SFRS7 0.32 AAATGTTTCCTTGTGCCTGCTCCTG
[62,] WBSCR20A 0.3 AAGCCTATACGTTTCTGTGGAGTAA [63,] NUP210 0.31 TTGGACATCTCTAGTGTAGCTGCCA ' [64,] CD6 0.34 TCCTTGTGCCTGCTCCTGTACTTGT [65,] HNRPAl 0.3 GCCCCACTGGACAACACTGATTCCT [66,] AIFl 0.34 AAGCCTATACGTTTCTGTGGAGTAA [67,] CYFIP2 0.38 TGGACCCCACTGGCTGAGAATCTGG [68,] GLTSCR2 0.38 TCCTTGTGCCTGCTCCTGTACTTGT
[69,] Cllorf2 0.31 AAGCCTATACGTTTCTGTGGAGTAA [70,] ARHGAPl5 0.33 TGGACCCCACTGGCTGAGAATCTGG [71,] BIN2 0.35 TTGGACATCTCTAGTGTAGCTGCCA [72,] SH3TC1 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC [73,] STAG3 0.32 AAATGTTTCCTTGTGCCTGCTCCTG [74,] TM6SF1 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC [75,] C15orf25 0.33 TCCTCCATCACCTGAAACACTGGAC [76,] FLJ22457 0.36 AAATGTTTCCTTGTGCCTGCTCCTG [77,] PACAP 0.34 TGCCTGCTCCTGTACTTGTCCTCAG [78,] MGC2744 0.31 GCCCCACTGGACAACACTGATTCCT
Table 41. 5-Fluorouracil biomarkers.
Gene Correlation Probe Sequence
[Ir] RPL18 .38 AAATGTTTCCTTGTGCCTGCTCCTG [2,] RPLlOA .39 TGGACCCCACTGGCTGAGAATCTGG
[3,] ANAPC5 .37 ACTTGTCCTCAGCTTGGGCTTCTTC [4,] EΞF1B2 .3 TCCTGTACTTGTCCTCAGCTTGGGC
[5,] RPL13A .5 TGCCTGCTCCTGTACTTGTCCTCAG [6, ] RPS15 .4 ACTTGTCCTCAGCTTGGGCTTCTTC [7,] NDUFABl .38 GCCCCACTGGACAACACTGATTCCT [8,] APRT .32 AAATGTTTCCTTGTGCCTGCTCCTG [9,] ZNF593 .34 TCCTCCATCACCTGAAACACTGGAC [10,] MRP63 .32 AAATGTTTCCTTGTGCCTGCTCCTG
[Hr] IL6R .41 TGGACCCCACTGGCTGAGAATCTGG
[12,] SART3 .37 TCCTCCATCACCTGAAACACTGGAC [13,] UCK2 .32 GCCCCACTGGACAACACTGATTCCT [14,] RPL17 .31 AAGCCTATACGTTTCTGTGGAGTAA [15,] RPS2 0.35 CACCCAGCTGGTCCTGTGGATGGGA [16,] PCCB 0.38 TCCTTGTGCCTGCTCCTGTACTTGT [17,] TOMM20 0.32 TGGACCCCACTGGCTGAGAATCTGG [18,] SHMT2 0.32 TTGGACATCTCTAGTGTAGCTGCCA [19,] RPLPO 0.31 TCCTTGTGCCTGCTCCTGTACTTGT [20,] GTF3A 0. 32 CACCCAGCTGGTCCTGTGGATGGGA [21,] STOML2 0.33 TGGACCCCACTGGCTGAGAATCTGG [22,] DKFZp564J157 0.4 AAATGTTTCCTTGTGCCTGCTCCTG [23,] MRPS2 0.32 TCCTGTACTTGTCCTCAGCTTGGGC [24 , ] ALG5 0.3 TTGGACATCTCTAGTGTAGCTGCCA [25 , ] CALML4 0.33 CACCCAGCTGGTCCTGTGGATGGGA
Table 42. Radiation sensitivity biomarkers. Gene Correlation Probe Sequence
[1,] TElAl 0.36 TGGACCCCACTGGCTGAGAATCTGG
[2,] ACTN4 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC
[3, ] CALMl 0.32 TCCTCCATCACCTGAAACACTGGAC
[4,] CD63 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[5,] FKBPlA 0.38 TGGACCCCACTGGCTGAGAATCTGG
[6,] CALU 0.47 ACTTGTCCTCAGCTTGGGCTTCTTC
[7,] IQGAPl 0.37 TTGGACATCTCTAGTGTAGCTGCCA
[8,] MGC8721 0.35 AAATGTTTCCTTGTGCCTGCTCCTG
[9,] STATl 0.37 TGGACCCCACTGGCTGAGAATCTGG
[10,] TACCl 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC
[11,] TM4SF8 0.33 AAGCCTATACGTTTCTGTGGAGTAA
[12,] CD59 0.31 TCCTCCATCACCTGAAACACTGGAC
[13,] CKAP4 0.45 TCCTTGTGCCTGCTCCTGTACTTGT
[14,] DUSPl 0.38 TCCTGTACTTGTCCTCAGCTTGGGC
[15,] RCNl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[16,] MGC8902 0.35 TGCCTGCTCCTGTACTTGTCCTCAG
[17,] RRBPl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[18,] PRNP 0.42 TTGGACATCTCTAGTGTAGCTGCCA
[19,] IER3 0.34 GCCCCACTGGACAACACTGATTCCT
[20,] MARCKS 0.43 GCCCCACTGGACAACACTGATTCCT
[21,] FER1L3 0.47 TGCCTGCTCCTGTACTTGTCCTCAG
[22,] SLC20A1 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC
[23,] HEXB 0.46 AAATGTTTCCTTGTGCCTGCTCCTG
[24,] EXTl 0.47 CACCCAGCTGGTCCTGTGGATGGGA
[25,] TJPl 0.32 AAATGTTTCCTTGTGCCTGCTCCTG
[26,] CTSL 0.38 TCCTGTACTTGTCCTCAGCTTGGGC
[27,] SLC39A6 0.36 TCCTGTACTTGTCCTCAGCTTGGGC
[28,] RI0K3 0.38 TCCTCCATCACCTGAAACACTGGAC
[29,] CRK 0.37 TGCCTGCTCCTGTACTTGTCCTCAG
[30,] NNMT 0.37 TGCCTGCTCCTGTACTTGTCCTCAG
[31,] TRAM2 0.35 TTGGACATCTCTAGTGTAGCTGCCA
[32, ] ADAM9 0.52 TCCTGTACTTGTCCTCAGCTTGGGC
[33, ] PLSCRl 0.35 TGGACCCCACTGGCTGAGAATCTGG
[34,] PRSS23 0.3 TGCCTGCTCCTGTACTTGTCCTCAG
[35,] PLOD2 0.36 TGCCTGCTCCTGTACTTGTCCTCAG
[36,] NPCl 0.39 TGCCTGCTCCTGTACTTGTCCTCAG
[37,] TOBl 0.37 CACCCAGCTGGTCCTGTGGATGGGA
[38,] GFPTl 0.47 CACCCAGCTGGTCCTGTGGATGGGA
[39,] IL8 0.36 AAATGTTTCCTTGTGCCTGCTCCTG
[40, ] PYGL 0.46 TCCTCCATCACCTGAAACACTGGAC
[41,] LOXL2 0.49 TTGGACATCTCTAGTGTAGCTGCCA
[42,] KIAA0355 0.36 TCCTTGTGCCTGCTCCTGTACTTGT
[43,] UGDH 0.49 TTGGACATCTCTAGTGTAGCTGCCA
[44,] PURA 0.32 TGCCTGCTCCTGTACTTGTCCTCAG
[45, ] 0LK2 0.37 AAGCCTATACGTTTCTGTGGAGTAA
[46,] CENTG2 0.35 GCCCCACTGGACAACACTGATTCCT
[47, ] CAP350 0.31 GCCCCACTGGACAACACTGATTCCT
[48,] CXCLl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC
[49,] BTN3A3 0.35 AAGCCTATACGTTTCTGTGGAGTAA
[50,] WNT5A 0.3 AAGCCTATACGTTTCTGTGGAGTAA
[51,] FOXF2 0.44 AAATGTTTCCTTGTGCCTGCTCCTG
[52,] LPHN2 0.34 GCCCCACTGGACAACACTGATTCCT [53,] CDHIl 0.39 TGGACCCCACTGGCTGAGAATCTGG
[54,] P4HA1 0.33 TCCTCCATCACCTGAAACACTGGAC
[55,] GRP58 0.44 CACCCAGCTGGTCCTGTGGATGGGA
[56,] DSIPI 0.44 TGGACCCCACTGGCTGAGAATCTGG
[57,] MAP1LC3B 0.5 AAGCCTATACGTTTCTGTGGAGTAA
[58,] GALIG 0.36 AAATGTTTCCTTGTGCCTGCTCCTG
[59,] IGSF4 0.4 TCCTCCATCACCTGAAACACTGGAC
[60,] IRS2 0.35 TGGACCCCACTGGCTGAGAATCTGG
[61,] ATP2A2 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[62,] OGT 0.3 TCCTGTACTTGTCCTCAGCTTGGGC
[63,] TNFRSFlOB 0.31 AAGCCTATACGTTTCTGTGGAGTAA
[64,] KIAA1128 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[65,] TM4SF1 0.35 CACCCAGCTGGTCCTGTGGATGGGA
[66,] RIPK2 0.42 TGCCTGCTCCTGTACTTGTCCTCAG
[67,] NR1D2 0.47 TTGGACATCTCTAGTGTAGCTGCCA
[68,] SSA2 0.36 TTGGACATCTCTAGTGTAGCTGCCA
[69,] NQOl 0.4 AAGCCTATACGTTTCTGTGGAGTAA
[70,] ASPH 0.36 TGCCTGCTCCTGTACTTGTCCTCAG
[71,] ASAHl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC
[72,] MGLL 0.35 TGGACCCCACTGGCTGAGAATCTGG
[73,] SERPINB6 0.51 AAGCCTATACGTTTCTGTGGAGTAA
[74,] HSPA5 0.33 TCCTTGTGCCTGCTCCTGTACTTGT
[75,] ZFP36L1 0.39 TCCTTGTGCCTGCTCCTGTACTTGT
[76,] COL4A1 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC
[77,] NIPA2 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC
[78,] FKBP9 0.48 AAATGTTTCCTTGTGCCTGCTCCTG
[79,] ILSST 0.4 GCCCCACTGGACAACACTGATTCCT
[80,] DKFZP564G2022 0.39 TTGGACATCTCTAGTGTAGCTGCCA
[81,] PPAP2B 0.33 TGGACCCCACTGGCTGAGAATCTGG
[82,] MAPlB 0.3 CACCCAGCTGGTCCTGTGGATGGGA
[83,] MAPKl 0.3 TGGACCCCACTGGCTGAGAATCTGG
[84,] MYOlB 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC
[85,] CAST 0.31 TGCCTGCTCCTGTACTTGTCCTCAG
[86,] RRAS2 0.52 AAATGTTTCCTTGTGCCTGCTCCTG
[87,] QKI 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC
[88,] LHFPL2 0.36 TCCTTGTGCCTGCTCCTGTACTTGT
[89,] SEPTlO 0.38 GCCCCACTGGACAACACTGATTCCT
[90,] ARHE 0.5 AAGCCTATACGTTTCTGTGGAGTAA
[91,] KIAAl078 0.34 AAGCCTATACGTTTCTGTGGAGTAA
[92,] FTL 0.38 TCCTGTACTTGTCCTCAGCTTGGGC
[93,] KIAA0877 0.41 AAATGTTTCCTTGTGCCTGCTCCTG
[94, ] PLCBl 0.3 AAGCCTATACGTTTCTGTGGAGTAA
[95,] KIAAO802 0.32 TGCCTGCTCCTGTACTTGTCCTCAG
[96,] RAB3GAP 0.43 TGCCTGCTCCTGTACTTGTCCTCAG
[97,] SERPINBl 0.46 TGCCTGCTCCTGTACTTGTCCTCAG
[98,] TIMM17A 0.38 AAATGTTTCCTTGTGCCTGCTCCTG
[99,] SOD2 0.35 TTGGACATCTCTAGTGTAGCTGCCA
[100,] HLA-A 0.33 TTGGACATCTCTAGTGTAGCTGCCA
[101,] NOMO2 0.43 CACCCAGCTGGTCCTGTGGATGGGA
[102,] LOC55831 0.32 TCCTGTACTTGTCCTCAGCTTGGGC
[103,] PHLDAl 0.32 CACCCAGCTGGTCCTGTGGATGGGA
[104,] TMBM2 0.47 TGGACCCCACTGGCTGAGAATCTGG
[105,] MLPH 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC
[106,] FAD104 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC
[107,] LRRC5 0.42 CACCCAGCTGGTCCTGTGGATGGGA
[108,] RAB7L1 0.41 TTGGACATCTCTAGTGTAGCTGCCA
[109,] FLJ35036 0.36 TCCTGTACTTGTCCTCAGCTTGGGC [110,] DOCKlO 0.41 TCCTCCATCACCTGAAACACTGGAC [111,] LRP12 0.36 AAGCCTATACGTTTCTGTGGAGTAA [112,] TXNDC5 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC [113,] CDC14B 0.39 TGCCTGCTCCTGTACTTGTCCTCAG [114,] HRMTlLl 0.38 CACCCAGCTGGTCCTGTGGATGGGA [115,] DNAJClO 0.31 TTGGACATCTCTAGTGTAGCTGCCA [116,] TNPOl 0.33 GCCCCACTGGACAACACTGATTCCT [117,] LONP. 0.32 AAATGTTTCCTTGTGCCTGCTCCTG [118,] AMIGO2 0.38 AAGCCTATACGTTTCTGTGGAGTAA
[119,] DNAPTP6 0.31 TGCCTGCTCCTGTACTTGTCCTCAG [120,] ADAMTSl 0.37 TTGGACATCTCTAGTGTAGCTGCCA
Table 43. Mabthera (rituximab) biomarkers . Gene Correlation Probe Sequence
[If ] PSMB2 0.89 TCCTCCATCACCTGAAACACTGGAC [2,] BATl 0.88 AAGCCTATACGTTTCTGTGGAGTAA. [3,] ASCC3L1 0.89 TCCTTGTGCCTGCTCCTGTACTTGT [4,] SET 0.94 AAATGTTTCCTTGTGCCTGCTCCTG [5,] YWHAZ 0.83 TCCTTGTGCCTGCTCCTGTACTTGT [6,] GLUL 0.8 TGGACCCCACTGGCTGAGAATCTGG [7,] LDHA 0.8 TCCTTGTGCCTGCTCCTGTACTTGT [8,] HMGBl 0.84 AAATGTTTCCTTGTGCCTGCTCCTG [9,] SFRS2 0.87 AAATGTTTCCTTGTGCCTGCTCCTG [10,] DPYSL2 0.82 TCCTGTACTTGTCCTCAGCTTGGGC [11,] MGC8721 0.82. CACCCAGCTGGTCCTGTGGATGGGA [12,] NOL5A 0.86 TGCCTGCTCCTGTACTTGTCCTCAG [13,] SFRSlO 0.88 AAATGTTTCCTTGTGCCTGCTCCTG [14,] SF3B1 0.82 TCCTGTACTTGTCCTCAGCTTGGGC [15,] K-ALPHA-I 0.86 TGCCTGCTCCTGTACTTGTCCTCAG [16,] TXNRDl 0.86 TGGACCCCACTGGCTGAGAATCTGG [17,] ARHGDIB 0.83 CACCCAGCTGGTCCTGTGGATGGGA [18,] ZFP36L2 0.92 TTGGACATCTCTAGTGTAGCTGCCA [19,] DHXl5 0.81 TGGACCCCACTGGCTGAGAATCTGG [20,] SOX4 0.85 CACCCAGCTGGTCCTGTGGATGGGA [21,] GRSFl 0.81 TGGACCCCACTGGCTGAGAATCTGG [22,] MCM3 0.85 GCCCCACTGGACAACACTGATTCCT [23,] IFITMl 0.82 TCCTCCATCACCTGAAACACTGGAC [24,] RPA2 0.86 TCCTCCATCACCTGAAACACTGGAC [25,] LBR 0.87 ACTTGTCCTCAGCTTGGGCTTCTTC [26,] CKSlB 0.85 AAGCCTATACGTTTCTGTGGAGTAA [27,] NASP 0.82 TGGACCCCACTGGCTGAGAATCTGG [28,] HNRPDL 0.81 TCCTCCATCACCTGAAACACTGGAC [29,] CUGBP2 0.81 TGCCTGCTCCTGTACTTGTCCTCAG [30,] PTBPl 0.87 TCCTTGTGCCTGCTCCTGTACTTGT [31,] ARL7 0.83 TTGGACATCTCTAGTGTAGCTGCCA [32,] CTCF 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC [33,] HMGCR 0.86 TCCTTGTGCCTGCTCCTGTACTTGT [34,] ITM2A 0.88 AAATGTTTCCTTGTGCCTGCTCCTG [35,] SFRS3 0.93 TCCTTGTGCCTGCTCCTGTACTTGT [36,] SRPK2 0.82 TCCTTGTGCCTGCTCCTGTACTTGT [37,] JARID2 0.92 CACCCAGCTGGTCCTGTGGATGGGA [38,] M96 0.84 TCCTGTACTTGTCCTCAGCTTGGGC [39,] MAD2L1 0.87 TCCTCCATCACCTGAAACACTGGAC [40,] SATBl 0.81 ACTTGTCCTCAGCTTGGGCTTCTTC [41,] TMPO 0.9 ACTTGTCCTCAGCTTGGGCTTCTTC [42,] SIVA 0.84 ACTTGTCCTCAGCTTGGGCTTCTTC [43,] SEMA4D 0.9 TCCTCCATCACCTGAAACACTGGAC
[44,] TFDP2 0.87 TCCTTGTGCCTGCTCCTGTACTTGT
[45,] SKP2 0.86 AAGCCTATACGTTTCTGTGGAGTAA
[46,] SH3YL1 0.88 GCCCCACTGGACAACACTGATTCCT
[47,] RFC4 0.87 TCCTCCATCACCTGAAACACTGGAC
[48,] PCBP2 0.83 AAGCCTATACGTTTCTGTGGAGTAA
[49,] IL2RG 0.84 GCCCCACTGGACAACACTGATTCCT
[50,] CDC45L 0.89 TCCTGTACTTGTCCTCAGCTTGGGC
[51,] GTSEl 0.83 TTGGACATCTCTAGTGTAGCTGCCA
[52,] KIFIl 0.85 AAGCCTATACGTTTCTGTGGAGTAA
[53,] FENl 0.88 TTGGACATCTCTAGTGTAGCTGCCA
[54,] MYB 0.9 TGGACCCCACTGGCTGAGAATCTGG
[55,] LCK 0.87 TCCTCCATCACCTGAAACACTGGAC
[56,] CENPA 0.84 GCCCCACTGGACAACACTGATTCCT
[57,] CCNE2 0.84 GCCCCACTGGACAACACTGATTCCT
[58,] H2AFX 0.88 TTGGACATCTCTAGTGTAGCTGCCA
[59,] SNRPG 0.84 TCCTCCATCACCTGAAACACTGGAC
[60,] CD3G 0.94 TCCTTGTGCCTGCTCCTGTACTTGT
[61,] STK6 0.9 ACTTGTCCTCAGCTTGGGCTTCTTC
[62,] PTP4A2 0.81 TGCCTGCTCCTGTACTTGTCCTCAG
[63,] FDFTl 0.91 AAATGTTTCCTTGTGCCTGCTCCTG
[64,] HSPA8 0.84 AAATGTTTCCTTGTGCCTGCTCCTG
[65,] HNRPR 0.94 TCCTTGTGCCTGCTCCTGTACTTGT
[66,] MCM7 0.92 AAATGTTTCCTTGTGCCTGCTCCTG
[67,] SFRS6 0.85 TGGACCCCACTGGCTGAGAATCTGG
[68,] PAK2 0.8 CACCCAGCTGGTCCTGTGGATGGGA
[69,] LCPl 0.85 TCCTGTACTTGTCCTCAGCTTGGGC
[70,] STAT3 0.81 ACTTGTCCTCAGCTTGGGCTTCTTC
[71,] OK/SW-cl.56 0.8 TCCTTGTGCCTGCTCCTGTACTTGT
[72,] WHSCl 0.81 TGGACCCCACTGGCTGAGAATCTGG
[73,] DIAPHl 0.88 AAGCCTATACGTTTCTGTGGAGTAA
[74,] KIF2C 0.88 TCCTGTACTTGTCCTCAGCTTGGGC
[75,] HDGFRP3 0.89 CACCCAGCTGGTCCTGTGGATGGGA
[76,] PNMA2 0.93 TTGGACATCTCTAGTGTAGCTGCCA
[77,] GATA3 0.93 TCCTGTACTTGTCCTCAGCTTGGGC
[78,] BUBl 0.88 AAATGTTTCCTTGTGCCTGCTCCTG
[79,] TPX2 0.8 CACCCAGCTGGTCCTGTGGATGGGA
[80,] SH2D1A 0.86 TCCTTGTGCCTGCTCCTGTACTTGT
[81,] TNFAIP8 0.9 TCCTCCATCACCTGAAACACTGGAC
[82,] CSElL 0.83 AAATGTTTCCTTGTGCCTGCTCCTG
[83,] MCAM 0.8 TCCTGTACTTGTCCTCAGCTTGGGC
[84,] AFlQ 0.83 GCCCCACTGGACAACACTGATTCCT
[85,] CD47 0.86 CACCCAGCTGGTCCTGTGGATGGGA
[86,] SFRSl 0.85 AAGCCTATACGTTTCTGTGGAGTAA
[87,] FYB 0.92 TCCTGTACTTGTCCTCAGCTTGGGC
[88,] TRBg 0.84 ACTTGTCCTCAGCTTGGGCTTCTTC
[89,] CXCR4 0.94 GCCCCACTGGACAACACTGATTCCT
[90,] H3F3B 0.84 TCCTCCATCACCTGAAACACTGGAC
[91,] MKI67 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC
[92,] MAC30 0.82 TCCTTGTGCCTGCTCCTGTACTTGT
[93,] ARID5B 0.88 AAGCCTATACGTTTCTGTGGAGTAA
[94,] LOC339287 0.81 AAGCCTATACGTTTCTGTGGAGTAA
[95,] CD3D 0.82 TCCTTGTGCCTGCTCCTGTACTTGT
[96,] ZAP70 0.87 AAGCCTATACGTTTCTGTGGAGTAA
[97,] LAPTM4B 0.83 TCCTCCATCACCTGAAACACTGGAC
[98,] SFRS7 0.87 TCCTTGTGCCTGCTCCTGTACTTGT
[99,] HNRPAl 0.9 AAGCCTATACGTTTCTGTGGAGTAA [100,] HSPCA • 0.88 i=AGCCTATACGTTTCTGTGGAGTAA [101,] AIFl 0..82 TCCTTGTGCCTGCTCCTGTACTTGT [102,] GTF3A 0.87 AAGCCTATACGTTTCTGTGGAGTAA [103,] MCM5 0.91 TTGGACATCTCTAGTGTAGCTGCCA [104,] GTL3 0.85 AAGCCTATACGTTTCTGTGGAGTAA [105,] ZNF22 0.89 TGCCTGCTCCTGTACTTGTCCTCAG [106,] FLJ22794 0.83 GCCCCACTGGACAACACTGATTCCT [107,] LZTFLl 0.89 ACTTGTCCTCAGCTTGGGCTTCTTC [108,] e(y)2 0.87 TCCTCCATCACCTGAAACACTGGAC [109,] FLJ20152 0.92 TCCTCCATCACCTGAAACACTGGAC [110,] C10orf3 0.86 ACTTGTCCTCAGCTTGGGCTTCTTC
[111,] NRNl ' 0.86 AAATGTTTCCTTGTGCCTGCTCCTG [112,] FLJ10858 0.81 GCCCCACTGGACAACACTGATTCCT [113,] BCLIlB 0.89 GCCCCACTGGACAACACTGATTCCT [114,] ASPM 0.91 AAGCCTATACGTTTCTGTGGAGTAA [115,] LEFl 0.9 TTGGACATCTCTAGTGTAGCTGCCA [116,] LOC146909 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC
Table 44. 5-Aza-2 ' -deoxycytidine (deoitabine) biomarkers .
Gene Correlation Probe Sequence
[1,] CD99 31 TTGGACATCTCTAGTGTAGCTGCCA [2,] SNRPA 32 TCCTGTACTTGTCCTCAGCTTGGGC [3,] CUGBP2 32 TCCTGTACTTGTCCTCAGCTTGGGC [4,] STAT5A 32 GCCCCACTGGACAACACTGATTCCT [5,] SLA 38 TTGGACATCTCTAGTGTAGCTGCCA [6,] ' IL2RG 33 TGGACCCCACTGGCTGAGAATCTGG [7,] GTSEl 32 ACTTGTCCTCAGCTTGGGCTTCTTC [8, ] MYB 36 .TGGACCCCACTGGCTGAGAATCTGG [9,] PTPN7 33 TCCTGTACTTGTCCTCAGCTTGGGC [10,] CXorf9 42 TCCTGTACTTGTCCTCAGCTTGGGC
[H,] RHOH 0.38 AAATGTTTCCTTGTGCCTGCTCCTG [12,] ZNFNlAl 0.33 AAGCCTATACGTTTCTGTGGAGTAA [13,] CENTBl 0.35 CACCCAGCTGGTCCTGTGGATGGGA [14, ] LCP2 0.3 AAATGTTTCCTTGTGCCTGCTCCTG [15,] HIST1H4C 0.33 TGGACCCCACTGGCTGAGAATCTGG [16,] CCR7 0.37 TGCCTGCTCCTGTACTTGTCCTCAG [17,] APOBEC3B 0.31 TCCTTGTGCCTGCTCCTGTACTTGT [18,] MCM7 0.31 TGGACCCCACTGGCTGAGAATCTGG [19, ] LCPl 0.31 AAGCCTATACGTTTCTGTGGAGTAA [20,] SELPLG 0.4 TGGACCCCACTGGCTGAGAATCTGG [21,] CD3Z 0.35 TCCTGTACTTGTCCTCAGCTTGGGC [22,] PRKCQ 0.39 TGCCTGCTCCTGTACTTGTCCTCAG. [23,] GZMB 0.32 GCCCCACTGGACAACACTGATTCCT [24,] SCN3A 4 AAGCCTATACGTTTCTGTGGAGTAA [25,] LAIRl 35 TGCCTGCTCCTGTACTTGTCCTCAG [26,] SH2D1A 35 GCCCCACTGGACAACACTGATTCCT [27,] SEPT6 35 ACTTGTCCTCAGCTTGGGCTTCTTC [28,] CG018 32 ACTTGTCCTCAGCTTGGGCTTCTTC [29,] CD3D 31 TGGACCCCACTGGCTGAGAATCTGG [30,] C18orflO 33 .TCCTTGTGCCTGCTCCTGTACTTGT [31, ] PRFl 31 TCCTCCATCACCTGAAACACTGGAC [32,] AIFl 31 TTGGACATCTCTAGTGTAGCTGCCA [33,] MCM5 31 ACTTGTCCTCAGCTTGGGCTTCTTC [34,] LPXN 35 TCCTCCATCACCTGAAACACTGGAC [35,] C22orfl8 33 AAATGTTTCCTTGTGCCTGCTCCTG [36,] ARHGAPl5 31 AAATGTTTCCTTGTGCCTGCTCCTG [37,] LEFl 0.43 GCCCCACTGGACAACACTGATTCCT What is claimed is:

Claims

1. A method of predicting sensitivity of a cancer patient to a treatment for cancer comprising determining a level of expression of at least one gene in a cell of said patient, said gene selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADAMTSl, ADDl5 AFlQ, AIFl3 AKAPl5 AKAP13, AKRlCl, AKTl5 ALDH2, ALDOC, ALG5, ALMSl, AL0X15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE5 ARHGAP 15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl3 BCHE, BCLl IB, BDNF, BHLHB2, BIN2, BLMH, BMIl, BNIP3, BRDT, BRRNl, BTN3A3, Cl lor£2, C14orfl39, C15or£25, ClδorflO, Clor£24, Clorf29, Clorf38, ClQRl5 C22orfl 8, C6orf32, CACNAlG, CACNB3, CALMl5 CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNBlIPl, CCND3, CCR7, CCR9, CDlA, CDlC, CDlD, CDlE, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDClO, CDC14B, CDHl 1, CDH2, CDKL5, CDKN2A, CDW52, CECRl5 CENPB, CENTBl, CENTG2, CEPl, CG018, CHRNA3, CHSl, CIAPINl, CKAP4, CKIP-I, CNP5 COL4A1, COL5A2, COL6A1, COROlC, CRABPl, CRK, CRYl, CSDA, CTBPl, CTSC, CTSL, CUGBP2, CUTC, CXCLl, CXCR4. CXorf9, CYFIP2, CYLD5 CYR61, DATFl, DAZAPl, DBNl, DBT, DCTNl, DDX18, DDX5, DGKA, DIAPHl, DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAJC7, DNAPTP6, DOCKlO5 DOCK2, DPAGTl5 DPEP2, DPYSL3, DSIPI, DUSPl, DXS9879E, EEF1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPASl, EPB41L4B, ERCC2, ERG, ERP70, EVERl, EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHLl, FHODl, FKBPlA5 FKBP9, FLJ10350, FLJl 0539, FLJl 0774, FLJ12270, FLJ13373, FLJ20859, FLJ211595 FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl5 FMNLl, FNBPl, FOLHl5 FOXF2, FSCNl, FTL, FYB, FYN5 G0S2, G6PD, GALIG, GALNT65 GATA2, GATA3, GFPTl3 GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS5 GNAQ5 GNB2, GNB5, GOT2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GZMA, GZMB, HlFO3 HlFX, H2AFX, H3F3A, HA-I, HEXB5 HIC, HIST1H4C, HKl, HLA-A5 HLA-B, HLA-DRA5 HMGAl5 HMGN2, HMMR, HNRPAl, HNRPD, HNRPM5 HOXA9, HRMTlLl, HSA9761, HSP A5, HSU79274, HTATSFl, ICAMl5 ICAM2, IEEG, IFIl 6, IFI44, BFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, EL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl3 IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K- ALPHA-I, KHDRBSl3 KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAAl 128, KIAA1393, KIFCl, LAIRl, LAMBl, LAMB3, LAT3 LBR, LCK5 LCPl, LCP2, LEFl, LEPREl, LGALSl5 LGALS9, LHFPL2, LNK5 LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX3 LOXL2, LPHN2, LPXN3 LRMP3 LRP12, LRRC5, LRRN3, LSTl, . LTB5 LUM, LY9, LY96, MAGEB2, MAL5 MAPlB, MAP1LC3B, MAP4K1, MAPKl, MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDNI3 MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL5 MLPH, MPHOSPH6, MPPl, MPZLl, MRP63, MRPS2, MTlE, MTlK3 MUFl, MVP, MYB, MYL9, MYOlB, NAPlLl, NAP1L2, NARF, NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCHl, NPCl, NQOl3 NR1D2, NUDC, NUP210, NUP88, NVL3 NXFl3 OBFCl, OCRL5 OGT, OXAlL, P2RX5, P4HA1, PACAP3 PAF53, PAFAH1B3, PALM2-AKAP2. PAX6, PCBP2, PCCB3 PFDN5, PFNl5 PFN2, PGAMl3 PHEMX3 PHLDAl5 PIM2, PITPNCl, PLAC8, PLAGLl5 PLAUR, PLCBl, PLEK2, PLEKHCl, PLOD2, PLSCRl, PNAS-4, PNMA2, P0LR2F, PPAP2B, PRFl5 PRGl3 PRIMl3 PRKCH3 PRKCQ3 PRKD2, PRNP, PRP 19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl3 PTP4A3, PTPN7, PTPNSl5 PTRF, PURA, PWPl, PYGL5 QKI5 RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAPlB3 RASGRP2, RBPMS, RCNl", RFC3, RFC5, RGC32, RGS3, RHOH5 RIMS3, RI0K3, RIPK2, RISl, RNASE6, RNF144, RPLlO, RPLlOA, RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLPO, RPLP2, RPS 15, RPS 19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBPl, RRM2, RUNXl, RUNX3, S100A4, SART3, SATBl5 SCAPl, SCARBl, SCN3A, SEC31L2, SEC61G, SELL3 SELPLG3 SEMA4G, SEPTlO, SEPT6, SERPINAl, SERPlNBl3 SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHMT2, SIATl, SKBl, SKP2, SLA3 SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX3 SNRPA, SNRPB5 SOD2, SOX4, SP 140, SPANXC, SPIl5 SRP, SRM5 SSA2, SSBP2, SSRPl5 SSSCAl5 STAG3, STATl5 STAT4, STAT5A, STCl5 STC2, STOML2, T3JAM, TACCl5 TACC3, TAF5, TALI, TAPl, TARP5 TBCA3 TCF12, TCF4, TFDP2, TFPI5 TIMM17A, TIMPl5 TJPl3 TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO5 TMSNB5 TNFAIP3. TNFAIP8, TNFRSFlOB5 TNFRSFlA3 TNFRSF7, TNLK5 TNPOl5 TOBl5 TOMM20, TOX5 TPKl3 TPM2, TRA@5 TRAl, TRAM2, TRB@5 TRD@5 TRIM3 TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN5 TXNDC5, UBASH3A. UBE2A, UBE2L6, UBE2S, UCHLl3 UCK2, UCP2, UFDlL5 UGDH, ULK2, UMPS5 UNG5 USP34, USP4, VASP5 VAVl, VLDLR5 VWF5 WASPIP, WBSCR20A, WBSCR20C, WHSCl3 WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl, and ZYX; wherein a change in the level of expression of said gene indicates said patient is sensitive to* said treatment.
2. The method of claim I5 wherein said at least one gene is selected from the group consisting of RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, BLMH5 CTBPl5 TBCA3 MDH2, and DXS9879E or wherein the method further comprises measuring a level of expression of at least one gene selected from the group consisting of UBB3 B2M3 MANlAl, and SUIl3 wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Vincristine.
3. The method of claim I5 wherein said at least one gene is selected from the group consisting of ClQRl3 SLA5 PTPN7, ZNFNlAl5 CENTBl, IFIl 6, ARHGEF6, SEC31L2, CD3Z, GZMB5 CD3D, MAP4K1, GPR65, PRFl5 ARHGAP15, TM6SF1, and TCF4 or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of HCLSl , CD53, PTPRCAP, and PTPRC, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Cisplatin.
4. The method of claim 1, wherein said at least one gene is selected from the group consisting of SRM, SCARBl, SIATl, CUGBP2, ICAMl, WASPIP5 ITM2A, PALM2-AKAP2, PTPNSl3 MPPl3 LNK5 FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl, Ml 6, MCAM5 MEF2C, SLC1A4, FYN, Clorf38, CHSl3 FCGR2C, TNHC, AMPD2, SEPT6, RAFTLlN, SLC43A3, RAC2, LPXN5 CKIP-I, FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl, and ADAMTSl or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of MSN, SPARC, VIM, GAS7, ANPEP, EMP3, BTN3A2, FNl, and CAPN3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Azaguanine.
5. The method of claim 1, wherein said at least one gene is selected from the group consisting of CD99, INSIGl, PRGl, MUFl5 SLA, SSBP2, GNB5, MFNG, PSMB9, EVT2A, PTPN7, PTGER4, CXorf95 ZNFNlAl, CENTBl5 NAPlLl, HLA-DRA5 BFIl 6, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAP15, NOTCHl, and UBASH3A or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of LAPTM5, HCLSl3 CD53, GMFG, PTPRCAP3 PTPRC, COROlA, and ITK, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Etoposide.
6. The method of claim I5 wherein said at least one gene is selected from the group consisting of CD99, ALDOC, SLA3 SSBP2, IL2RG, CXorfP, RHOH, ZNFNlAl, CENTBl5 CDlC3 MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT5 SEC31L2, CD3Z, SH2D1A, CDlA5 LAIRl5 TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIFl, ARHGAPl 5, NARF, and PACAP or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of LAPTM5, HCLSl5 CD53, GMFG, PTPRCAP5 TCF7, CDlB, PTPRC, COROlA, HEMl3 and ITK5 wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Adriamycin.
7. The method of claim 1, wherein said at least one gene is selected from the group consisting of RPL12, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4, SP140, AMPD3, TNFAIP8, DDXl 8, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ12270, and HKRPD or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of RPL32, FBL, and PTPRC, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Aclarubicin.
8. The method of claim 1, wherein said at least one gene is selected from the group consisting of PGAMl, DPYSL3, INSIGl5 GJAl, BNIP3, PRGl5 G6PD, PLOD2, LOXL2, SSBP2, Clorf29, TOX3 STCl, TNFRSFlA5 NCOR2, NAPlLl, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ, MAPlB, TRIM22, CD3D, BCATl5 IFI44, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2 or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of BASPl5 COL6A2, PTPRC, PRKCA, CCL2, and RAB31, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Mitoxantrone.
9. The method of claim 1, wherein said at least one gene is selected from the group consisting of STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Mitomycin.
10. The method of claim 1, wherein said at least one gene is selected from the group consisting of RPLlO, RPS4X, NUDC, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS5 CEPl, IL13RA2, MAGEB2, HMGN2, ALMSl5 GPR65, FLJ10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC5 and KIAA1393 or wherein the method further comprises measuring the level of expression of RALY, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Paclitaxel.
11. The method of claim I3 wherein said at least one gene is selected from the group consisting of PFNl, PGAMl, K-ALPHA-I, CSDA, UCHLl, PWPl, PALM2-AKAP2, TNFRSFlA5 ATP5G2, AFlQ5 NME4, and FHODl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Gemcitabine.
12. The method of claim 1 , wherein said at least one gene is selected from the group consisting of ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRJP 13, RFC3, CASP7, TXN5 MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Taxotere.
13. The method of claim 1, wherein said at least one gene is selected from the group consisting of IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, ZNFNlAl3 CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LATRl, SH2D1A, SEPT6, HA-I, ERCC2, CD3D, LSTl5 AIFl5 ADA, DATFl, ARHGAP15, PLAC8, CECRl5 LOC81558, and EHD2 or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of LAPTM5, ITGB2, ANPEP9 CD53, CD37, AD0RA2A, GNA15, PTPRC5 COROlA3 HEMl, FLII, and CREB3L1, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Dexamethasone.
14. The method of claim 1, wherein said at least one gene is selected from the group consisting of ITM2A, RHOH, PRTMl3 CENTBl, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE5 ADA, and FHODl or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of GNAl 5, PTPRC3 and RPLl 3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Ara-C.
15. The method of claim 1, wherein said at least one gene is selected from the group consisting of CD99, ARHGDIB3 VWF5 ITM2A, LGALS9, INPP5D, SATBl, TFDP2, SLA5 IL2RG, MFNG, SELL, CDW52, LRMP5 ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl5 CENTBl, LCP2, SPIl5 GZMA5 CEPl, CD8A, SCAPl5 CD2, CDlC, TNFRSF7, VAVl, MAP4K1, CCR7, C6orf325 ALOXl 5B, BRDT5 CD3G, LTB, ATP2A3, NVL3 RASGRP2, LCPl5 CXCR4, PRKD2, GATA3, TRA@5 KIAA0922, TARP5 SEC31L2, PRKCQ, SH2D1A, CHRNA35 CDlA5 LSTl, LAIRl5 CACNAlG5 TRB@, SEPT6, HA-I5 DOCK2, CD3D, TRD@, T3JAM, FNBPl5 CD6, AIFl5 FOLHl5 CDlE5 LY9, ADA5 CDKL5, TRIM5 EVL5 DATFl, RGC32, PRKCH5 ARHGAP15, NOTCHl5 BENΪ2, SEMA4G, DPEP2, CECRl3 BCLIlB5 STAG3, GALNT6, UBASH3A, PHEMX5 FLJ13373, LEFl5 IL21R, MGC17330, AKAP 13, ZNF335, and G3MAP5 or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of SRRMl5 LAPTM5, ITGB2, CD53, CD375 GMFG5 PTPRCAP3 GNA15, BLM5 PTPRC, COROlA5 PRKCBl, HEMl5 and UGT2B17, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Methylprednisolone.
16. The method of claim I3 wherein said at least one gene is selected from the group consisting of PRPF8, RPL18, GOT2, RPL13A, RPS15, RPLP2, CSDA5 KHDRBSl3 SNRPA, IMPDH2, RPS 19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl5 PFDN5, OXAlL5 H3F3A, ATIC, CIAPDSfI5 RPS2, PCCB5 SHMT2, RPLPO3 HNRPAl, STOML2, SKBl9 GLTSCR2, CCNBlIPl5 MRPS2, FLJ20859, and FLJ12270 or wherein the method further comprises measuring the level, of expression of at least one gene selected from the group consisting of RNPSl5 RPL32, EEFlG, PTMA3 RPL13, FBL5 RBMX5 and RPS9, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Methotrexate.
17. The method of claim I5 wherein said at least one gene is selected from the group consisting of PFNl3 HKl3 MCLl, ZYX, RAPlB5 GNB25 EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I, LGALSl, CSDA5 EFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMBl, FHLl, INSIGl5 TIMPl5 GJAl5 PSME2, PRGl, EXTl5 DKFZP434J154, MVP, VASP, ARL7, NNMT, TAPI5 PLOD2, ATF3, PALM2-AKAP2, IL85 LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3. F2R5 TPM2, PSMB9, LOX, STCl5 PTGER4, IL6, SMAD3, WNT5A, BDNF, TNFRSFlA5 FLNC5 DKFZP564K0822, FLOTl5 PTRF, HLA-B5 MGC4083, TNFRSFlOB, PLAGLl, PNMA2. TFPI5 LAT, GZMB, CYR61, PLAUR, FSCNl, ERP70, AFlQ, HIC5 COL6A1, IFITM3, MAPlB5 FLJ46603, RAFTLIN5 RRAS, FTL5 KIAA0877, MTlE5 CDClO5 DOCK2, TRIM22, RISl, BCATl5 PRFl, DBNl, MTlK, TMSBlO, FLJ10350, Clorf24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting. of MSN5 ACTR2, AKRlBl, VIM5 ITGA3, OPTN, M6PRBP1, COLlAl, BASPl5 ANPEP5 TGFBl, NFIL3, NK4, CSPG2, PLAU, COL6A2, UBC, FGFRl, BAX, COL4A2, and RAB31, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Bleomycin.
18. The method of claim 1, wherein said at least one gene is selected from the group consisting of SSRPl, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl5 SSSCAl, EZH2, MYB, PRJ-Ml5 H2AFX, HMGAl, HMMR, TK2, WHSCl5 DIAPHl, LAMB3, DPAGTl5 UCK2, SERPINBl5 MDNl, BRRNl, G0S2, RAC2, MGC21654, GTSEl5 TACC3, PLEK2, PLAC8, HNRPD5 and PNAS-4 or wherein the method further comprises measuring the level of expression of PTMA5 wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Methyl-GAG.
19. The method of claim I3 wherein said at least one gene is selected from the group consisting of ITGA5, TNFAJP3, WNT5A, FOXF2, LOC94105, IFIl 6, LRRN3, DOCKlO, LEPREl, COL5A2, and ADAMTSl or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of MSN, VIM5 CSPG2, and FGFRl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Carboplatin.
20. The method of claim 1, wherein said at least one gene is selected from the group consisting of RPL18, RPLlOA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl3 APRT, ZNF593, MRP63, EL6R, SART3, UCK2, RPLl 7, RPS2, PCCB5 TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4 or wherein the method further comprises measuring the level of expression of at least one gene selected from, the group consisting of RNPSl, RPLl 3, RPS6, and RPL3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to 5-FU (5-Fluorouracil).
21. The method of claim 1, wherein said at least one gene is selected from the group consisting of KIFCl1 VLDLR5 RUNXl, PAFAH1B3, HlFX, RNF144, TMSNB, CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, CDlD, ADA, CD99, CD2, CNP, ERG, CD3E, CDlA, PSMC3, RPS4Y1, AKTl5 TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl, AKRlCl, CACNB3. CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHLl, EZH2, SMOX, SLC4A2, UFDlL, ZNF32, HTATSFl, SHDl, PTOVl, NXFl, FYB, TR1M28, BC008967, TRB@, HlFO, CD3D, CD3G, CENPB, ALDH2, ANXAl, H2AFX, CDlE, DDX5, CCNA2, ENO2, SNRPB5 GATA3, RRM2, GLUL5 SOX4, MAL, UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl, SH3GL3, PLEKHCl, CD47, POLR2F, RHOH, and ADDl or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of ITK5 RALY, PSMC5, MYL6, CDlB, STMNl, GNA15, MDK, CAPG, ACTNl, CTNNAl5 FARSLA, E2F4, CPSFl, SEPWl, TFRC, ABLl5 TCF7, FGFRl, NUCB2, SMA3, FAT, VIM, and ATP2A3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to Rituximab.
22. The method of claim 1 , wherein said at least one gene is selected from the group consisting of TRAl, ACTN4, CALMl, CD63, FKBPlA3 CALU, IQGAPl, MGC8721, STATl, TACCl, TM4SF8, CD59, CKAP4, DUSPl, RCNl, MGC8902, LGALSl, BHLHB2, RRBPl, PRNP, DSR3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PLOD2, NPCl, TOBl, GFPTl, EL8, PYGL, LOXL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCLl, BTN3A3, TL6, WNT5A, FOXF2, LPHN2, CDHIl, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSFlOB, KIAAl 128, TM4SF1, RBPMS, RJPK2, CBLB, NR1D2, SLC7A11, MPZLl, SSA2, NQOl, ASPH, ASAHl, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB, MAPKl, MYOlB, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAAl 078, FTL, KIAA0877, PLCBl, KIAA0802, RAB3GAP, SERPINBl, TTMM17A, SOD2, HLA-A, NOMO2, LOC55831, PHLDAl, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl, COROlC, DNAJClO, TNPOl5 LONP, AMIG02, DNAPTP6, and ADAMTSl or wherein the method further comprises measuring the level of expression of at least one gene selected from the group consisting of WARS, CD81, CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAKl, EIF4G3, COLlAl, DYRK2, NFIL3, ACTNl, CAPN2, BTN3A2, IGFBP3, FNl, COL4A2, and KPNBl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to radiation therapy.
23. The method of claim 1, wherein said at least one gene is selected from the group consisting of FAU, NOL5A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVERl, LRMP, ICAM2, RIMS3, FMNLl, MYB, PTPN7, LCK, CXorf9, RHOH, ZNFNlAl, CENTBl, LCP2, DBT, CEPl, IL6R, VAVl, MAP4K1, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCPl, CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CDlA, GATA2, P2RX5, LAIRl, Clorf38, SH2D1A, TRB@, SEPT6, HA-I, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPAl, AIFl, CYFIP2, GLTSCR2, CllαrG, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP5 and MGC2744, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to histone deacetylase (HDAC) inhibitor.
24. The method of claim 1, wherein said at least one gene is selected from the group consisting of CD99, SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSEl, MYB5 PTPN7, CXorfP, RHOH, ZNFNlAl5 CENTBl5 LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCPl5 SELPLG, CD3Z, PRKCQ, GZMB, SCN3A, LAIRl5 SH2D1A, SEPT6, CGO 18, CD3D, C18orflO, PRFl, AIFl, MCM5, LPXN, C22orfl8, ARHGAP15, and LEFl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to 5-Aza-2'- deoxycytidine (Decitabine).
25. The method of any of claims 1-24, wherein the level of expression of said gene is determined by detecting the level of mRNA transcribed from said gene.
26. The method of any of claims 1 -24, wherein the level of expression of said gene is determined by detecting the level of a protein product of said gene.
27. The method of any of claims 1 -24, wherein said the level of expression of said gene is determined by detecting the level of the biological activity of a protein product of said gene.
28. The method of any of claims 1, wherein an increase in the level of expression of said gene indicates increased sensitivity of said cancer patient to said treatment.
29. The method of any of claims 1-24, wherein said cell is a cancer cell.
30. The method of any of claims 1, wherein a decrease in the level of expression of said gene indicates increased sensitivity of said cancer patient to said treatment.
31. The method of claim 1 , wherein the level of expression of said gene is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
32. A method for determining the development of resistance of a cell in a patient to a treatment to which a cell in said patient has previously been sensitive, said method comprising determining the level of expression of at least one gene of any one of claims 2-24 in said cell, wherein a decrease in the level of expression of said gene in said cell relative to the level of expression of said gene in a control cell sensitive to said treatment indicates resistance or a propensity to develop resistance to the treatment by said patient.
33. A method for deterniining the development of resistance of a cell in a patient to a treatment to which a cell in said patient has previously been sensitive, said method comprising determining the level of expression of at least one gene of any one of claims 2-24 in said cell, wherein an increase in the level of expression of said gene in said cell relative to the level of expression of said gene in a control cell sensitive to said treatment indicates resistance or a propensity to develop resistance to the treatment by said patient.
34. A kit comprising a single-stranded nucleic acid that is complementary to or identical to at least 5 consecutive nucleotides of at least one gene selected from the group consisting of ACTB, ACTN4, ADA3 ADAM9, ADAMTSl, ADDl5 AFlQ, AIFl, AKAPl, AKAP13, AKRlCl, AKTl, ALDH2, ALDOC, ALG5, ALMSl, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP 15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7; ASAHl, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl5 BCHE, BCLl IB5 BDNF, BHLHB2, BIN2, BLMH5 BMIl, BNEP3, BRDT, BRRNl, BTN3A3, Cl loiQ, C14orfl39, C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl8, C6orf32, CACNAlG, CACNB3, CALMl, CALML4, CALU5 CAP350, CASP2, CASP6, CASP7, CAST5 CBLB, CCNA2, CCNBlIPl, CCND3, CCR7, CCR9, CDlA, CDlC3 CDlD5 CDlE5 CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD475 CD59, CD6, CD63, CD8A, CD8B1, CD99, CDClO, CDC14B, CDHIl, CDH2, CDKL5, CDKN2A, CDW52, CECRl5 CENPB, CENTBl5 CENTG2, CEPl, CG018, CHRNA3, CHSl5 CIAPINl5 CKAP4, CKIP-I, CNP5 COL4A1, COL5A2, COL6A1, COROlC, CRABPl, CRK, CRYl, CSDA5 CTBPl, CTSC, CTSL, CUGBP2, CUTC5 CXCLl, CXCR4, CXorf9, CYHP2, CYLD, CYR61, DATFl, DAZAPl5 DBNl3 DBT, DCTNl, DDX18, DDX5, DGKA, DIAPHl5 DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J1575 DKFZP564K0822, DNAJClO, DNAJC7, DNAPTP6, DOCKlO5 DOCK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl3 DXS9879E, EEF1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPASl5 EPB41L4B, ERCC2, ERG3 ERP70, EVERl5 EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHLl5 FHODl, FKBPlA, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl, FMNLl. FNBPl5 FOLHl, F0XF2, FSCNl, FTL, FYB5 FYN, G0S2, G6PD, GALIG5 GALNT6, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL3 GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl3 GZMA, GZMB, HlFO3 HlFX, H2AFX, H3F3A, HA-I, HEXB, HIC, HISTl H4C, HKl3 HLA-A, HLA-B3 HLA-DRA, HMGAl, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM5 HOXA9, HRMTlLl3 HSA9761, HSPA5, HSU79274, HTATSFl, ICAMl, ICAM2, IER3, IFIl 6, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, EL4R, EL6, JJL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl, IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB5 K-ALPHA-I3 KHDRBSl, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAAl 128, KIAA1393, KIFCl, LAIRl, LAMBl5 LAMB3, LAT, LBR5 LCK, LCPl, LCP2, LEFl, LEPREl3 LGALSl3 LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX3 LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LSTl, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAPlB, MAP1LC3B, MAP4K1, MAPKl, MARCKS3 MAZ3 MCAM3 MCLl3 MCM53 MCM7, MDH2, MDNl, MEF2C, MFNG, MGC 1733 O3 MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPPl3 MPZLl3 MRP63, MRPS2, MTlE, MTlK5 MUFl, MVP, MYB, MYL9, MYOlB, NAPlLl, NAP1L2, NARF9 NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NID2, NTPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCHl3 NPCl, NQOl, NR1D2, NUDC, NUP210, NUP88, NVL, NXFl, OBFCl, OCRL, OGT, OXAlL, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFNl, PFN2, PGAMl3 PHEMX3 PHLDAl, PIM2, PITPNCl, PLAC8, PLAGLl, PLAUR, PLCBl, PLEK2, PLEKHCl, PLOD2, PLSCRl3 PNAS-4, PNMA2, POLR2F, PPAP2B, PRFl, PRGl, PRIMl, PRKCH3 PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl, PTP4A3, PTPN7, PTPNSl, PTRF3 PURA, PWPl, PYGL, QKI3 RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAPlB3 RASGRP2, RBPMS, RCNl3 RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RI0K3, RIPK2, RISl3 RNASE6, RNF144, RPLlO, RPLlOA, RPL12, RPL13A, RPL17, RPLl 8, RPL36A, RPLPO, RPLP2, RPSl 5, RPS 19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBPl, RRM2. RUNXl, RUNX3, S100A4, SART3, SATBl, SCAPl, SCARBl3 SCN3A, SEC31L2, SEC61G, SELL3 SELPLG3 SEMA4G, SEPTlO3 SEPT6, SERPINAl, SERPINBl3 SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHMT2, SIATl, SKBl3 SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA3 SNRPB, SOD2, SOX4, SP140, SPANXC, SPIl, SRF3 SRM, SSA2, SSBP2, SSRPl, SSSCAl3 STAG3, STATl, STAT4, STAT5A, STCl, STC2, STOML2, T3JAM, TACCl5 TACC3, TAF5, TALI, TAPl, TARP3 TBCA3 TCF12, TCF4, TFDP2, TFPI, TIMM17A, TEMPI, TJPl, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFAIP8, TNFRSFlOB, TNFRSFlA, TNFRSF7, TNIK, TNPOl3 TOBl, TOMM20, TOX, TPKl3 TPM2, TRA@3 TRAl, TRAM2, TRB@, TRD@, TRTM3 TRTMl 4, TRJM22, TRTM28, TRIP 13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHLl, UCK2, UCP2, UFDlL3 UGDH, ULK2, UMPS5 UNG, USP34, USP4, VASP, VAVl, VLDLR, VWF3 WASPIP3 WBSCR20A, WBSCR20C, WHSCl, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl, and ZYX; wherein said single stranded nucleic acid is sufficient for the detection of the level of expression of said gene and allows specific hybridization between said ' single stranded nucleic acid and a nucleic acid encoded by said gene or a complement thereof, said kit further comprising instructions for applying nucleic acids collected from a sample from a cancer patient, instructions for determining the level of expression of said gene hybridized to said single stranded nucleic acid, and instructions for predicting said patient's sensitivity to a treatment for cancer.
35. . The kit of claim 34, wherein said instructions further indicate that a change in said level of expression of said gene relative to the level of expression of said gene in a control cell sensitive to said treatment indicates a change in sensitivity of said patient to said treatment.
36. The kit of claim 34, wherein said gene is selected from the group consisting of RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, BLMH, CTBPl, TBCA, MDH2, and DXS9879E, or wherein said kit further comprises one or more single- stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of UBB, B2M, MANlAl, and SUIl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Vincristine.
37. The kit of claim 34, wherein said gene is selected from the group consisting of ClQRl, SLA, PTPN7, ZNPNlAl, CENTBl, Ml 6, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl, ARHGAP15, TM6SF1, and TCF4, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of HCLSl, CD53, PTPRCAP, and PTPRC. wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Cisplatin.
38. The kit of claim 34, wherein said gene is selected from the group consisting of SRM, SCARBl, SIATl5 CUGBP2, ICAMl, WASPIP5 ITM2A, PALM2-AKAP2, PTPNSl, MPPl5 LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE5 LΫ96, LCPl5 IFIl 6, MCAM5 MEF2C, SLC1A4, FYN5 Clorf38, CHSl5 FCGR2C, TNIK5 AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJl 0539, FLJ35036, DOCKlO5 TRPV2, IFRG28, LEFl5 and ADAMTS 1 , or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of MSN, SPARC5 VTM5 GAS7, ANPEP5 EMP35 BTN3A2, FNl5 and CAPN3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Azaguanine.
39. The kit of claim 34, wherein said gene is selected from the group consisting of CD99, INSIGl, PRGl, MUFl5 SLA5 SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFNlAl5 CENTBl, NAPlLl5 HLA-DRA5 IFIl 6, ARHGEF6, PSCDBP5 SELPLG, LAT5 SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN5 D0CK2, CD3D, RAC2, ZAP70, GPR65, PRFl5 ARHGAP15, NOTCHl, and UBASH3A, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of LAPTM5, HCLSl5 CD53. GMFG, PTPRCAP, PTPRC, COROlA3 and ITK, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Etoposide.
40. The kit of claim 34, -wherein said gene is selected from the group consisting of CD99, ALDOC5 SLA5 SSBP2, IL2RG5 CXorf9, RHOH5 ZNFNlAl5 CENTBl, CDlC5 MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA5 LAIRl5 TRB@5 CD3D5 WBSCR20C, ZAP70, IFI44, GPR65, AIFl5 ARHGAP15, NARF5 and PACAP, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of LAPTM5, HCLSl3 CD53, GMFG, PTPRCAP5 TCF75 CDlB, PTPRC, COROlA, EDEMl, and ITK, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Adriamycin.
41. The kit of claim 34, wherein said gene is selected from the group consisting of RPL 12, RPLP2, MYB, ZNFNlAl, SCAPl5 STAT4, SP 140, AMPD3, TNFAIP8, DDXl 8, TAF5, RPS2, DOCK2, GPR65, H0XA9, FLJ12270, and HNRPD, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of RPL32, FBL, and PTPRC, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Aclarubicin.
42. The kit of claim 34, wherein said gene is selected from the group consisting of PGAMl, DPYSL3, INSIGl, GJAl, BNIP3, PRGl, G6PD, PLOD2, LOXL2, SSBP2, Clorf29, TOX, STCl, TNFRSFlA, NC0R2, NAPlLl, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ, MAPlB, TRIM22, CD3D, BCATl, EFI44, CUTC, NAP1L2, NME7, FLJ21159, and COL5 A2, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of BASPl3 COL6A2, PTPRC, PRKCA, CCL2, and RAB31, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Mitoxantrone.
43. The kit of claim 34, wherein said gene is selected from the group consisting of STCl, GPR65, DOCKlO, COL5A2', FAM46A, and LOC54103, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Mitomycin.
44. The kit of claim 34, wherein said gene is selected from the group consisting of RPLl 0, RPS4X, NUDC, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEPl, IL13RA2, MAGEB2, HMGN2, ALMSl, GPR65, FLJ10774, NOL8, DAZAPl3 SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC5 and KIAAl 393, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of RALY, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Paclitaxel.
45. The kit of claim 34, wherein said gene is selected from the group consisting of PFNl, PGAMl, K-ALPHA-I, CSDA, UCHLl, PWPl, PALM2-AKAP2, TNFRSFlA, ATP5G2, AFlQ, NME4, and FHODl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Gemcitabine.
46. The kit of claim 34, wherein said gene is selected from the group consisting of ANP32B, GTF3A, RRM2, TRIMl 4, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Taxotere.
47. The kit of claim 34, wherein said gene is selected from the group consisting of IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASPl3 PTPN7, CXorf9, RHOH, GIT2, ZNFNlAl, CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIRl3 SH2D1A. SEPT6, HA-I, ERCC2, CD3D, LSTl3 AIFl, ADA, DATFl, ARHGAP15, PLAC8, CECRl, LOC81558, and EHD2, and COL5A2, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of LAPTM5, ITGB2, ANPEP, CD53, CD37, ADORA2A, GNA15, PTPRC5 COROlA, HEMl3 FLII, and CREB3L1, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Dexamethasone.
48. The kit of claim 34, wherein said gene is selected from the group consisting of ITM2A, RHOH, PRIMl, CENTBl. NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA, and FHODl5 or wherein said kit further comprises one or more single- stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of GNAl 5, PTPRC, and RPLl 3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Ara-C.
49. The kit of claim 34, wherein said gene is selected from the group consisting of CD99, ARHGDIB, VWF, ITM2A, LGALS9, INPP5D, SATBl, TFDP2, SLA, IL2RG, MFNG5 SELL5 GDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl, CENTBl, LCP2, SPIl, GZMA, CEPl, CD8A, SCAPl, CD2, CDlC, TNFRSF7, VAVl, MAP4K1, CCR7, C6orf32, ALOX15B, BRDT, CD3G, LTB5 ATP2A3, NVL, RASGRP2, LCPl, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDlA, LSTl, LAIRl, CACNAlG, TRB@, SEPT6, HA-I, DOCK2, CD3D, TRD@, T3JAM, FNBPl, CD6, AIFl, FOLHl, CDlE, LY9, ADA, CDKL5, TRIM, EVL, DATFl, RGC32, PRKCH, ARHGAP15, NOTCHl, BIN2, SEMA4G, DPEP2, CECRl, BCLI lB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, 3X2 IR, MGC17330, AKAP13, ZNF335, and GIMAP5, or wherein said kit further comprises one or more single- stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of SRRMl3 LAPTM5, ITGB2, CD53, CD37, GMFG, PTPRCAP, GNA15, BLM5 PTPRC5 COROlA, PRKCBl, HEMl3 and UGT2B17, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Methylprednisolone.
50. The kit of claim 34, wherein said gene is selected from the group consisting of PRPF8, RPL18, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBSl, SNRPA, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl5 PFDN5, OXAlL, H3F3A, ATIC5 CIAPINl, RPS2, PCCB, SHMT2, RPLPO5 HNRPAl, STOML2, SKBl, GLTSCR2, CCNBlIPl, MRPS2, FLJ20859, and FLJ12270, or wherein said kit further comprises one or more single- stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of RNPSl5 RPL32, EEFlG3 PTMA5 RPL13, FBL5 RBMX5 and RPS9, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Methotrexate.
51. The kit of claim 34, wherein said gene is selected from the group consisting of PFNl , HKl5 MCLl5 ZYX, RAPlB, GNB2, EPASl5 PGAMl5 CKAP4, DUSPl5 MYL9, K-ALPHA-I. LGALSl5 CSDA5 IFITM2, ITGA5, DPYSL3, JUNB5 NFKBIA5 LAMBl5 FHLl5 INSIGl, TIMPl, GJAl5 PSME2, PRGl, EXTl5 DKFZP434J154, MVP, VASP5 ARL7, NNMT, TAPl, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STCl, PTGER4, TL6, SMAD3, WNT5A, BDNF, TNFRSFlA, FLNC, DKFZP564K0822, FLOTl5 PTRF5 HLA-B5 MGC4083, TNFRSFlOB5 PLAGLl5 PNMA2, TFPI, LAT, GZMB5 CYR61, PLAUR, FSCNl5 ERP70, AFlQ, HIC, COL6A1, IFITM3, MAPlB, FLJ46603, RAFTLIN, RRAS, FTL5 KIAA0877, MTlE5 CDClO5 DOCK2, TRIM22, RISl, BCATl, PRFl, DBNl5 MTlK, TMSBlO5 FLJ10350, Clor£24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of MSN5 ACTR2, AKRlBl, VIM, ITGA3, OPTN, M6PRBP1, COLlAl5 BASPl, ANPEP5 TGFBl5 NFIL3, NK45 CSPG2, PLAU5 COL6A2, UBC, FGFRl, BAX, COL4A2, and RAB31, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Bleomycin.
52. The kit of claim 34, wherein said gene is selected from the group consisting of SSRPl, NUDC5 CTSC, AP1G2, PSME2, LBR3 EFNB2, SERPINAl, SSSCAl3 EZH2, MYB5 PRIMl5 H2AFX, HMGAl5 HMMR3 TK2, WHSCl3 DIAPHl. LAMB33 DPAGTl, UCK2, SERPINBl3 MDNl5 BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of PTMA3 wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Methyl-GAG.
53. The kit of claim 34, wherein said gene is selected from the group consisting of ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, EFTl 6, LRRN3, DOCKlO, LEPREl5 COL5A2, and ADAMTSl, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a. gene selected from the group consisting of MSN, VTM, CSPG2, and FGFRl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Carboplatin.
54. The kit of claim 34, wherein said gene is selected from the group consisting of RPLl 8, RPLlOA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl, APRT3 ZNF593, MRP63, IL6R, SART3, UCK2, RPL 17, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J1575 MRPS2, ALG5, and CALML4, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of RNPSl, RPL13, RPS63 and RPL3, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with 5 -FU (5-Fluorouracil).
55. The kit of claim 34, wherein said gene is selected from the group consisting of KIFCl, VLDLR, RUNXl5 PAFAH1B3, HlFX, RNF144, TMSNB5 CRYl, MAZ, SLA5 SRF3 UMPS3 GD3Z3 PRKCQ, HNRPM5 ZAP70, ADDl5 RFC5, TM4SF2, PFN2, BMIl3 TUBGCP3, ATP6V1B2, CDlD, ADA5 CD99, CD2, CNP5 ERG5 CD3E. CDlA5 PSMC3, RPS4Y1, AKTl, TALl5 UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl5 NASP, IGFBP2, CDH2, CRABPl, DBNl, AKR-ICl5 CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN5 GNAQ, TUSC3, GNAQ5 JARID2, OCRL5 FHLl, EZH2, SMOX, SLC4A2, UFDlL, ZNF32, HTATSFl, SHDl, PTOVl, NXFl, FYB, TRIM28, BC008967, TRB@, HlFO, CD3D, CD3G, CENPB, ALDH2, ANXAl, H2AFX, CDlE, DDX5, CCNA2, ENO2, SNRPB5 GATA3, RRM2, GLUL, SOX4, MAL5 UNG, ARHGDE3, RUNXl, MPHOSPH6, DCTNl5 SH3GL3, PLEKHCl, CD47, POLR2F, RHOH, and ADDl, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of ITK, RALY, PSMC5, MYL6, CDlB, STMNl, GNAl 5, MDK, CAPG, ACTNl, CTNNAl, FARSLA, E2F4, CPSFl5 SEPWl, TFRC, ABLl, TCF7, FGFRl5 NUCB2, SMA3, FAT, VIM, and ATP2A3, wherein an increase in expres the level of expression sion of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with Rituximab.
56. The kit of claim 34, wherein said gene is selected from the group consisting of TRAl , ACTN4, CALMl, CD63, FKBPlA, CALU5 IQGAPl, MGC8721, STATl, TACCl, TM4SF8, CD59, CKAP4, DUSPl, RCNl, MGC8902, LGALSl, BHLHB2, RRBPl, PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RI0K3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PLOD2, NPCl, TOBl, GFPTl, EL8, PYGL, LOXL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCLl, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDHI l, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT5 TNFRSFlOB, KIAAl 128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A1 1, MPZLl, SSA2, NQOl, ASPH, ASAHl, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NEP A25 FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB, MAPKl, MYOlB, CAST5 RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCBl, KIAA0802, RAB3GAP, SERPINBl, TIMM17A, SOD2, ' HLA-A, NOMO2, LOC55831, PHLDAl", TMEM2, MLPH5 FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO5 LRP12, TXNDC5, CDC14B, HRMTlLl, COROlC5 DNAJClO5 TNPOl5 LONP5 AMIGO2, DNAPTP6, and ADAMTSl, or wherein said kit further comprises one or more single-stranded nucleic acids complementary to or identical to at least 5 consecutive nucleotides of a gene selected from the group consisting of WARS5 CD81, CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAKl5 EIF4G3, COLlAl5 DYRK2, NFIL3, ACTNl. CAPN2, BTN3A2, IGFBP3, FNl, COL4A2, and KPNBl5 wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with radiation therapy.
57. The kit of claim 34, wherein said gene is selected from the group consisting of FAU, NOL5A, ANP32A, ARHGDIB5 LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG5 GPSM3, PHM2, EVERl5 LRMP, ICAM2, RIMS3, FMNLl5 MYB, PTPN7, LCK5 CXorf9, RHOH5 ZNFNlAl5 CENTBl, LCP2, DBT, CEPl, ΣL6R, VAVl5 MAP4K1, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCPl5 CXCR4, PSCDBP5 SELPLG, CD3Z, PRKCQ5 CDlA5 GATA2, P2RX5, LATRl, Clorf38, SH2D1A, TRB@, SEPT6, HA-I, D0CK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPAl, AIFl5 CYFJP2, GLTSCR2, Cl lorf2, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP5 and MGC2744, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with histone deacetylase (HDAC) inhibitor.
58. The kit of claim 34, wherein said gene is selected from the group consisting of CD99, SNRPA5 CUGBP2, STAT5A, SLA5 IL2RG, GTSEl5 MYB, PTPN7, CXorf95 RHOH5 ZNFNlAl5 CENTBl5 LCP2, FHST1H4C, CCR7, APOBEC3B, MCM75 LCPl5 SELPLG, CD3Z, PRKCQ, GZMB5 SCN3A, LAIRl5 SH2D1A, SEPT6, CG018, CD3D, ClδorflO, PRFl5 AIFl, MCM5, LPXN, C22orfl8, ARHGAPl 5, and LEFl, wherein an increase in the level of expression of said gene indicates that said patient is sensitive to said treatment and wherein said treatment is treatment with 5-Aza-2'-deoxycytϊdine (Decitabine).
59. The method of any one of claims 1-24, wherein said determining comprises detecting the level of expression of said gene by using a kit of any one of claims 34-58.
60. The kit of claim 34, wherein said treatment comprises administering a chemotherapeutic agent.
61. The kit of claim 34, wherein said single- stranded nucleic acid is characterized by the ability to specifically identify the presence or absence of a nucleic acid complementary to said gene in a sample collected from said cancer patient.
62. The kit of any one of claims 34-58, wherein said single-stranded nucleic acid is at least 15 nucleotides long.
63. The kit of claim 62, wherein said single-stranded nucleic acid is at least 25 nucleotides long.
64. The kit of any one of claims 34-58, wherein said single-stranded nucleic acid is a deoxyribonucleic acid (DNA).
65. A method of identifying biomarkers useful for the determination of sensitivity of a cancer patient to a treatment for cancer comprising: a. obtaining pluralities of measurements of the level of expression of a gene in different cell types and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of said cell types in the absence of said treatment for cancer; b. correlating each plurality of measurements of the level of expression of said gene in said cells with the growth of said cells to obtain a correlation coefficient; c. selecting the median correlation coefficient calculated for said gene; and d. identifying said gene as a biomarker for use in determining the sensitivity of a cancer patient to said treatment for cancer if said median correlation coefficient exceeds 0.3.
66. The method of claim 65, wherein said correlation coefficient exceeds 0.4.
67. The method of claim 65, wherein said gene is selected from the group consisting of ACTB, ACTN4, ADA5 ADAM9, ADAMTSl5 ADDl, AFlQ, AEFl5 AKAPl5 AKAP13, AKRlCl, AKTl5 ALDH2, ALDOC, ALG5, ALMSl5 AL0X15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl5 AP1G2, APOBEC3B, APRT5 ARHE5 ARHGAP 15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl5 ASPH3 ATF3, ATIC5 ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl5 BCHE5 BCLl IB, BDNF3 BHLHB2, BIN2, BLMH5 BMIl, BNIP3, BRDT, BRRNl, BTN3A3, Cl Iorf2, C14orfl39. C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl5 C22orfl8, C6orf32, CACNAlG, CACNB3, CALMl, CALML4, CALU5 CAP350, CASP2, CASP6, CASP7, CAST, CBLB5 CCNA2, CCNBlIPl5 CCND3, CCR7, CCR9, CDlA5 CDlC, CDlD, CDlE5 CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDClO3 CDC14B, CDHI l, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl5 CENTG2, CEPl5 CG018, CHRNA3, CHSl, CIAPINl, CKAP4, CKIP-I5 CNP5 COL4A1, COL5A2, COL6A1, COROlC3 CRABPl, CRK, CRYl, CSDA, CTBPl, CTSC, CTSL, CUGBP2, CUTC, CXCLl, CXCR4, CXorf9, CYFIP2, CYLD5 CYR61, DATFl, DAZAPl5 DBNl, DBT, DCTNl3 DDXl 8, DDX5, DGKA, DIAPHl5 DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAJC7, DNAPTP6, DOCKlO, DOCK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl, DXS9879E, EEF1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPASl5 EPB41L4B, ERCC2, ERG, ERP70, EVERl, EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHLl, FHODl, FKBPlA, FKBP9, FLJ10350, FLJ10539, FLJl 0774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl, FMNLl5 FNBPl, FOLHl5 FOXF2, FSCNl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GZMA5 GZMB, HlFO, HlFX, H2AFX, H3F3A, HA-I, HEXB, HIC, HIST1H4C, HKl, HLA-A5 HLA-B, HLA-DRA, HMGAl, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM3 HOXA9, HRMTlLl, HSA9761, HSPA5, HSU79274, HTATSFl, ICAMl5 ICAM2, B3R3, Ml 6, IFI44, MTM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2. INPP5D, INSIGl, IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K- ALPHA-I, KHDRBSl, KIAA0355, KIAA0802, KIAA0877, KIAA0922. KIAAl 078, KIAA1128, KIAA1393, KJJFCl, LAIRl, LAMBl, LAMB3, LAT, LBR, LCK, LCPl, LCP2, LEFl, LEPREl, LGALSl, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRR.C5, LRRN3, LSTl, LTB, LUM, LY9, LY96, MAGEB2, MAL5 MAPlB, MAP1LC3B, MAP4K1, MAPKl, MARCKS5 MAZ, MCAM5 MCLl5 MCM5, MCM7, MDH2, MDNl, MEF2C, MFNG, MGC 17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPPl, MPZLl, MRP63, MRPS2, MTlE, MTlK5 MUFl, MVP5 MYB, MYL9, MYOlB, NAPlLl, NAP1L2, NARF5 NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NJD2, NTJ? A2, NME4, NME7, NNMT5 NOL5A, NOL8, NOMO2, NOTCHl, NPCl, NQOl, NR1D2, NUDC, NUP210, NUP88, NVL, NXFl, OBFCl, OCRL, OGT, OXAlL, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFNl, PFN2, PGAMl, PHEMX5 PHLDAl, PIM2, PITPNCl, PLAC8, PLAGLl, PLAUR, PLCBl5 PLEK2, PLEKHCl, PLOD2, PLSCRl5 PNAS-4, PNMA2, POLR2F, PPAP2B, PRFl5 PRGl5 PRIMl, PRKCH, PRKCQ5 PRKD2, PRNP, PRPl 9, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl, PTP4A3, PTPN7, PTPNSl, PTRF5 PURA5 PWPl, PYGL, QKI5 RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN5 RAG2, RAPlB5 RASGRP2, RBPMS, RCNl, RFC3, RFC5, RGC32, RGS3, RHOH5 RTMS3, RIOK3, RIPK2, RISl, RNASE6, RNF144, RPLlO, RPLlOA5 RPL12, RPL13A, RPL17, RPLl 8, RPL36A, RPLPO, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBPl5 RRM2, RUNXl5 RUNX3, S100A4, SART3, SATBl, SCAPl5 SCARBl, SCN3A, SEC31L2, SEC61G, SELL, SELPLG3 SEMA4G, SEPTlO, SEPT6, SERPINAl, SERPINBl, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHMT2, SIATl, SKBl9 SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPIl, SRF, SRM, SSA2, SSBP2, SSRPl, SSSCAl, STAG3, STATl, STAT4, STAT5A, STCl, STC2, STOML2, T3JAM, TACCl, TACC3, TAF5, TALl5 TAPl, TARP5 TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TTMPl, TJPl, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFAIP8, TNFRSFlOB, TNFRSFlA5 TNFRSF7, TNTK5 TNPOl, TOBl5 TOMM20, TOX5 TPKl, TPM2, TRA@, TRAl, TRAM2, TRB@, TRD@, TRTM5 TRIM14, TRTM22, TRIM28, TRDP13, TRPV2, TUBGCP3, TUSC3, TXN5 TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHLl, UCK2, UCP2, UFDlL5 UGDH5 ULK2, UMPS, UNG, USP34, USP4, VASP, VAVl5 VLDLR5 VWF5 WASPIP, WBSCR20A, WBSCR20C, WHSCl5 WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl5 and ZYX.
68. The method of claim 65, wherein the measurement of step a) includes measurements of growth in the presence of a second treatment
69. The method of claim 65, wherein said treatment comprises administering a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient.
70. The method of claim 69, wherein said compound is selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, histone deacerylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine).
71. The method of claim 69, wherein said compound has previously failed to show an effect in said cancer patient.
72. The method of claim 65, wherein said cancer patient is selected from a subpopulation predicted to be sensitive to said treatment.
73. The method of claim 65, wherein said cancer patient is selected from a subpopulation predicted to die without treatment.
74. The method of claim 65, wherein said cancer patient is selected from a subpopulation predicted to have disease symptoms without treatment.
75. The method of claim 65, wherein said cancer patient is selected from a subpopulation predicted to be cured without treatment.
76. The methods of claim 1 or 65, wherein said measurements of the level of expression are analyzed using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
77. A method of predicting sensitivity of a cancer patient to a treatment for cancer, comprising: a. obtaining a measurement of the level of expression of a biomarker gene in a sample from said cancer patient; b. applying a model predictive of sensitivity to a treatment for cancer to said measurement, wherein said model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks; and c. predicting whether or not said cancer patient will be responsive to said treatment for cancer. The method of claim 77, wherein said gene is selected from the group consisting of 1B, ACTN4, ADA, ADAM9, ADAMTSl, ADDl, AFlQ, AJPl, AKAPl, AKAP13, LlCl, AKTl, ALDH2, ALDOC, ALG5, ALMSl, AL0X15B, AMIG02, AMPD2, AMPD3, JPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, [GAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl. ASPH, ATF3, ATIC, ATP2A2, 2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl, BCHE, BCLl IB, BDNF, HB2, BIN2, BLMH, BMIl, BNIP3, BRDT, BRRNl, BTN3A3, Cl Iorf2, C14orfl39, τf25, ClSorflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl8, C6orf32, CACNAl G, NB3, CALMl, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, A2, CCNBlIPl, CCND3, CCR7, CCR9, CDlA, CDlC, CDlD, CDlE, CD2, CD28, D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, 10, CDC14B, CDHI l, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl, TG2, CEPl, CG018, CHRNA3, CHSl, CIAPINl, CKAP4, CKIP-I, CNP, COL4A1, >A2, COL6A1, COROlC, CRABPl, CRK, CRYl, CSDA, CTBPl, CTSC, CTSL, 3P2, CUTC, CXCLl, CXCR4, CXorfP, CYFIP2, CYLD, CYR61, DATFl, DAZAPl, I, DBT, DCTNl, DDXl 8, DDX5, DGKA, DIAPHl, DKCl, DKFZP434J154, -P564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAJC7, 3TP6, DOCKlO, DOCK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl5 DXS9879E, B2, EFNB2, EHD2, EEF5A, ELK3, ENO2, EPASl, EPB41L4B, ERCC2, ERG, ERP70, Ll, EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FAD 104, FAM46A, FAU, FCGR2A, 2C, FER1L3, FHLl, FHODl, FKBPlA, FKBP9, FLJ10350, FLJ10539, FLJ10774, 270, FLΪ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl, Λ, FNBPl, FOLHl, FOXF2, FSCNl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, a, GATA3, GFPTl, GIMAP5/GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, , GNB5, GOT2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GZMA, I, HlFO, HlFX, H2AFX, H3F3A, HA-I, HEXB, HIC, HIST1H4C, HKl, HLA-A, HLA-B, DRA, HMGAl, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM, H0XA9, HRMTlLl, HSA9761, HSPA5, HSU79274, HTATSFl5 ICAMl5 ICAM2, IER3, Ml 6, EFT44, BFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, TL6, JL6R, IL6ST, IL83 IMPDH2, 1NPP5D, INSIGl5 IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JLJNB, K- ALPHA-I, KHDRBSl3 KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAAl 128, KIAA1393. KIFCl, LAIRl5 LAMBl5 LAMB3, LAT5 LBR, LCK, LCPl, LCP2, LEFl5 LEPREl5 LGALSl5 LGALS9, LHFPL2, LNK5 LOC54103, LOC55831, LOC81558, LOC94105, LONP5 LOX, LOXL2, LPHN2, LPXN5 LRMP5 LRP12, LRRC5, LRRN3, LSTl, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAPlB5 MAP1LC3B, MAP4K1, MAPKl5 MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDNl, MEF2C, MFNG, MGC 1733O5 MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL5 MLPH, MPHOSPH6, MPPl, MPZLl, MRP63, MRPS2, MTlE5 MTlK, MUFl, MVP, MYB, MYL9, MYOlB, NAPlLl, NAP1L2. NARF, NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NID2, NJDPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCHl, NPCl, NQOl5 NR1D2, NUDC5 NUP210, NUP88, NVL5 NXFl, OBFCl, OCRL, OGT, OXAlL, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFNl, PFN2, PGAMl5 PHEMX, PHLDAl5 PIM2, PITPNCl, PLAC8, PLAGLl, PLAUR, PLCBl, ' PLEK2, PLEKHCI, PLOD2, PLSCRl, PNAS-4, PNMA2, POLR2F, PPAP2B, PRFl5 PRGl, PRIMl5 PRKCH5 PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl, PTP4A3, PTPN7, PTPNSl5 PTRF5 PURA5 PWPl5 PYGL5 QKI5 RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN5 RAG2, RAPlB5 RASGRP2, RBPMS5 RCNl, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOK3, RIPK2, RISl, RNASE6, RNF144, RPLlO, RPLlOA5 RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLPO5 RPLP2, RPS 15, RPS 19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBPl, RRM2, RUNXl, RUNX3, S100A4, SART3, SATBl, SCAPl5 SCARBl, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPTlO, SEPT6, SERPINAl5 SERPINBl, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHMT2, SIATl; SKBl5 SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC5 SPIl, SRP, SRM, SSA2, SSBP2, SSRPl, SSSCAl, STAG3, STATl, STAT4, STAT5A, STCl, STC2, STOML2, T3JAM, TACCl, TACC3, TAF5, TALI, TAPl, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TTMPl, TJPl, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFAIP8, TNFRSFlOB, TNFRSFlA, TNFRSF7, TNIK, TNPOl, TOBl, TOMM20, TOX, TPKl, TPM2. TRA@, TRAl, TRAM2, TRJB@, TRD@, TRIM, TRTM14, TRJM22, TRJM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHLl, UCK2, UCP2, UFDlL, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAVl, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSCl, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl, and ZYX.
79. The method of claim 77, wherein said model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids.
80. The method of claim 77, wherein said model is cross- validated using a random sample of said measurements.
81. The method of claim 77, wherein a second treatment is present.
82. The method of claim 77, wherein said treatment comprises administering a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient.
83. The method of claim 82, wherein said compound is selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine).
84. The method of claim 82, wherein said compound has previously failed to show effect on patients.
85. The method of claim 77, wherein said linear sum is compared to a sum of a reference population with known sensitivity.
86. The method of claim 85, wherein said sum of a reference population is the median of the sums derived from the population members' biomarker gene expression.
87. The method of claim 77, wherein said model is derived from the components of a data set obtained by independent component analysis.
88. The method of claim 77, wherein said model is derived from the components of a data set obtained by principal component analysis.
89. The method of claim 77, wherein said cancer patient is selected from a subpopulation predicted to be sensitive to said treatment.
90. The method of claim 77, wherein said cancer patient is selected from a subpopulation predicted to die without treatment.
91.- The method of claim 77, wherein said cancer patient is selected from a subpopulation predicted to have disease symptoms without treatment.
92. The method of claim 77, wherein said cancer patient is selected from a subpopulation predicted to be cured without treatment.
93. The method of claim 77, wherein said measurements of the level of expression of said biomarker gene are analyzed using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
94. A kit, apparatus, and software used to implement the method of claim 77.
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