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EP1960551A2 - Méthodes et appareils pour identifier des biomarqueurs de réponse à un traitement et leur utilisation pour prédire l'efficacité d un traitement - Google Patents

Méthodes et appareils pour identifier des biomarqueurs de réponse à un traitement et leur utilisation pour prédire l'efficacité d un traitement

Info

Publication number
EP1960551A2
EP1960551A2 EP06848658A EP06848658A EP1960551A2 EP 1960551 A2 EP1960551 A2 EP 1960551A2 EP 06848658 A EP06848658 A EP 06848658A EP 06848658 A EP06848658 A EP 06848658A EP 1960551 A2 EP1960551 A2 EP 1960551A2
Authority
EP
European Patent Office
Prior art keywords
gene
treatment
expression
patient
level
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06848658A
Other languages
German (de)
English (en)
Inventor
Steen Knudsen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Allarity Therapeutics AS
Original Assignee
Medical Prognosis Institute AS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Medical Prognosis Institute AS filed Critical Medical Prognosis Institute AS
Publication of EP1960551A2 publication Critical patent/EP1960551A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • 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

La présente invention a pour objet des méthodes et des appareils pour prédire la sensibilité d'un patient à un composé ou à un traitement médical. L'invention concerne aussi des méthodes pour identifier des biomarqueurs de gène dont l'expression correspond à une sensibilité ou une résistance à un traitement dans une population ou sous-population de patients.
EP06848658A 2005-12-01 2006-12-01 Méthodes et appareils pour identifier des biomarqueurs de réponse à un traitement et leur utilisation pour prédire l'efficacité d un traitement Withdrawn EP1960551A2 (fr)

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CA2631236A1 (fr) 2007-06-28
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