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1. WO2020163639 - MÉTHODES DE TRAITEMENT ET DE PRÉDICTION CIBLÉES DE LA SURVIE D'UN PATIENT DANS LE CANCER

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

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WHAT IS CLAIMED IS:

1. A method for detecting poor prognosis in a subj ect with cancer comprising measuring the expression of MYBL2 in a sample from the subject with cancer and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject with cancer relative to the healthy reference sample is an indicator of poor prognosis.

2. A method for detecting poor prognosis in a subject with cancer comprising measuring the expression of one or more of MYBL2, PTTG1, FOXM1, E2F7, and CDK1 in a sample from the subject with cancer and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject with cancer relative to the healthy reference sample is an indicator of poor prognosis.

3. A method for detecting poor prognosis in a subject with cancer comprising measuring the expression of one or more of UHRF1, TRIP13, TRIM29, HDAC7, ARNTL2, AEBP1, or ACTL6A in a sample from the subject with cancer and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject with cancer relative to the healthy reference sample is an indicator of poor prognosis.

4. A method for detecting poor prognosis in a subj ect with lung adenocarcinoma comprising measuring the expression of ARNTL2 in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

5. The method of claim 4, further comprising measuring the expression of at least one of LOXL2, FOXM1, MAFK, MMP14, TRIM29, FOSL1, CDK1, E2F7, ZNF697, SNAI2, PLSCR1, NPAS2, PLK4, BCL9L, TDG, SMAD3, HOXA13, MYBL2, or BRIP1 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

6. A method for detecting poor prognosis in a subj ect with lung squamous cell carcinoma comprising measuring the expression of TCF21 in a sample from

the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

7. The method of claim 6, further comprising measuring the expression of at least one of ATOH8, SMAD7, ELANE, NCOR2, CALCOCOl, HICl, NACC2, PKNOX2, SNAI1, RARA, PBX4, MAFK, CSRNP1, HNF1B, SPI1, HDAC7, SKI LDB2, or SOX18 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

8. A method for detecting poor prognosis in a subject with breast invasive carcinoma comprising measuring the expression of CLOCK in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

9. The method of claim 8, further comprising measuring the expression of at least one of AFF4, PGK1, STON1 GTF2A1L, MTDH, MED 13, NCOA2, YWHAB, TAF13, REST, ZNF623, ZFHX3, PDE3A, KIAA0754, MED23, SMAD5, XRCC4, CCNT1, ADAMTS12, or ZNF699 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

10. A method for detecting poor prognosis in a subject with prostate adenocarcinoma comprising measuring the expression of MBD1 in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

11. The method of claim 10, further comprising measuring the expression of at least one of U2AF2, GAS2, POLE3, DBF4, NUP62, ZNF274, KIAA0319, GGA3, ZNF57, NCBP2, QTRTD1, KCNC3, TIALl, SRC, RAB14, POP1, CIZ1, SLC12A5, or DDX27 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

12. A method for detecting poor prognosis in a subject with colon and/or rectum adenocarcinoma comprising measuring the expression of HLX in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

13. The method of claim 10, further comprising measuring the expression of at least one of AEBP1, ZEB1, GLI3, MEIS1, MEIS3, TCF7L1, MAFB, TSHZ3,

TGFB1I1, ZNF676, HAND2, ZNF154, MECP2, ZNF521, HDAC7, GLIS2, LZTS1, HICl, or ZNF512B in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

14. A method for detecting poor prognosis in a subject with pancreatic adenocarcinoma comprising measuring the expression of GRHL2 in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

15. The method of claim 14, further comprising measuring the expression of at least one of ACTL6A, YAP1, TRIM29, ARNTL2, KLF5, ZFP36L1, AHR, NMI, NFE2L3, E2F8, SP100, RBMS1, KLF3, MSLN, E2F7, UHRFl, POU2F3, YY1, or PTPN14 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

16. A method for detecting poor prognosis in a subject with liver hepatocellular carcinoma comprising measuring the expression of TRIP 13 in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

17. The method of claim 16, further comprising measuring the expression of at least one of MYBL2, HDAC2, PTTG1, SMARCD1, RAN, PITX2, HMGA1, ENOl, YBX1, NPM1, CDK1, FUBP1, ACTL6A, SSRP1, MAFG, ZNF207, KDM1A, E2F6, or SOX11 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

18. A method for detecting poor prognosis in a subj ect with acute myeloid leukemia comprising measuring the expression of TFEB in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

19. The method of claim 18, further comprising measuring the expression of at least one of VDR, DAXX, HTATIP2, ETS2, HDAC7, STAT6, SREBF1, NFKB2, CC2D1A, PLA2G4A, HOXA7, PPP1R13L, HOXAIO, BATF, HOXA6, TCF15, ZNF532, LPIN1, or TRIM32 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

20. A method for detecting poor prognosis in a subject with ovarian serous cystadenocarcinoma comprising measuring the expression of NFKBIB in a sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

21. The method of claim 20, further comprising measuring the expression of at least one of SNORE) 15 A, PERI, STAC2, HTR3C, SOCS5, TEX261, SLC1A6, HCG22, CDSN, PFDN5, ZNF781, KDM1A, BNC1, TCF7L1, ZNF90, PAX3, HIF3A, ARID IB, or BUD31 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

22. A method for detecting poor prognosis in a subject with glioblastoma multiforme comprising measuring the expression of VDR in a sample from the subject and comparing the expression with a reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

23. The method of claim 22, further comprising measuring the expression of at least one of MMP14, HDAC7, AEBP1, BHLHE40, FOSL1, LRRFIP1, LZTS1, BCL3, SLC2A4RG, PLEKHN1, MAFK, ETV4, EVC2, MICAL2, C90RF64, TSHZ2, CD300E, RELB, or EPS8L2 in the sample from the subject and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the subject relative to the healthy reference sample is an indicator of poor prognosis.

24. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one of 409 genes for master regulators of poor prognosis which are selected from the group consisting of: MYBL2, FOXM1, CDK1, PTTG1, E2F7, UHRFl, TRIP13, AEBP1, HDAC7, ACTL6A, ARNTL2, TRIM29, HMGA2, TCF3, LOXL2, MEIS3, TGFB1I1, HICl, SPI1, FOSL1, MMP14, VDR, MAFK, SLC2A4RG, NPM1, CCNE1, CDK2, HTATIP2, NFE2L3, PLSCR1, KDM1A, GRHL2, FOXD1, EZH2, PLK4, DNMT1, ETV4, PCGF6, PPRC1, ATF6, HEYL, OTX1, SSRPl, BNC1, ZNF521, ZNF532, REST, KLF17, LIF, NCOR2, SALL2, HAND2, LZTS1, TCF7L1, TSHZ3, ZNF512B, MAFB, DEK, SNAI2, TDG, BASP1, ZNF280C, TSHZ2, LMX1B, SMARCD3, RAD9A, DBF4, RBMSl, TRIM32, MEOX2, SP100,

HDAC2, RAN, SOX11, ZNF697, SNAI1, PKNOX2, E2F1, E2F8, EHF, NOC2L, ZBTB9, POU3F1, FOSL2, FLU, HOXA11, ZIC2, PITX1, PSMC3IP, HOXC11, SNAPC4, PRMT5, RCOR1, TEAD4, WWTR1, BARX2, CALU, CD109, NFIC, SOX7, TCF4, ZHX3, PDE3A, CCNT1, CLOCK, KIAA0754, NCOA2, TAF13, AFF4, MED 13, MED23, MTDH, PGK1, SMAD5, STON1 GTF2A1L, XRCC4, YWHAB, ZFHX3, ZNF623, ATF2, ITGB1, PDIA6, TUBB3, ELK3, FNDC3B, ITGA5, KIRREL, SPRY4, FNDC3A, HSP90AB1, KLF7,

PEAR1, ZNF281, GLI3, GLIS2, ZEB1, MECP2, HLX, MEIS1, ZNF154, ZNF676, HEY1, YAF2, HSF2, TAF9B, MAF, TP63, AEBP2, DMTF1, HSA MIR 30E, HSA MIR 3653, MICAL2, RELB, C90RF64, EVC2, CD300E, PLEKHN1, BCL3, BHLHE40, EPS8L2, LRRFIPl, DDN, FHL2, NFE2L1, ZFP42, POLR2C, HOXA1, MSX2, PCGF2, SMYD1, CCND1, E2F4, LHX1, MLXIPL, PERINEURAL INVASION, DLX4, ETV6, LBX2, STAT2, ZGLP1, KAT2A, IFI16, RUNX1, RBCKl, ZNF335, IRF3, TAF10, TFAP2E, ZNF488, AATF, PRRX1, AHCTF1, FOXD2, ELF4, HOXAIO, SREBF1, HOXA6,

PLA2G4A, BATF, NFKB2, TCF15, LPIN1, STAT6, CC2D1A, DAXX, ETS2, HOXA7, PPP1R13L, TFEB, NR2E1, OTP, PHTF1, TGIF1, ZNF217, DMRTA2, TEAD3, MYCBP, E2F6, HMGA1, PITX2, SMARCD1, YBX1, ZNF207, ENOl, FUBP1, MAFG, NPAS2, SMAD3, BCL9L, HOXA13, LDB2, ELANE, SKI, NACC2, TCF21, RARA, SMAD7,

CALCOCOl, PBX4, SOX18, HNF1B, ATOH8, CSRNP1, BRCA1, BRIP1, DNMT3B, MYBL1, BEND6, NRG1, ZNF90, HCG22, ARID1B, TEX261, SLC1A6, SOCS5, ZNF781, HTR3C, PAX3, STAC2, BUD31, NFKBIB, CDSN, HIF3A, PERI, PFDN5, SNORD15A, KLF5, POU2F3, PTPN14, YAP1, MSLN, KLF3, AHR, ZFP36L1, NMI, YY1, BRCA2, CASC5, COP A, LHX4, RFX5, ZBTB37, BLZF1, C110RF42, IRF6, TAF2, ZNF157, ZNF195, S100A5, TTTY14, TSG101, PAX5, TFAP2B, PATE2, CIZ1, NUP62, POLE3, POP1, RAB14, TIAL1, KIAA0319, QTRTD1, ZNF57, MBD1, U2AF2, GAS2, KCNC3, NCBP2, DDX27, SLC12A5, GGA3, SRC, ZNF274, GMEB1, MEX3A, SERBP1, TARDBP, LHX8, MYBBP1A, MAGED1, C1QBP, HES6, MED 15, OVOL1, PA2G4, GATAD2A, SOX15, TFAP2A, ZNF750, SLC38A8, OVOL2, ERG, PTGER3, RUNX1T1, ZFPM2, FOXC2, FOXD3, HOXD11, LIMS3, TREX2, ZSCAN10, HSA MIR 483, IGF2, SOX2, TNFRSF1A, TFE3, ZFP57, CDX4, DPPA2, LOC100287704, ZNF679, ANTXR1, DCAF17, SIX2, UCHL5, PIAS2, SMAD1, ZFHX4, PEG3, SMAD9, GZF1, ZFP41, SIX4, MED13L, NR0B2, PPARGC1A, PRDM12, ZNF462, FXN, JUN, HDAC9, PBX3, LPIN3, ZNF80, EOMES, BATF2, CUT A, PRDMl, ZBTB7B, ZNF768, SPIC, FOXN4, MED8, TRIB3, DDX41, HGS, DRAPl, CCDC137, GMEB2, RFX2, THRB, DMAP1, RBPJL, GLI2, TSC22D1, GATA6, GLIS3, FOXF1, NR5A2, BATF3, IRF1, SNCAIP, CITED1, CEBPG, IRF5, BCL1 IB, XBP1, ZNF576, and SAP30 and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

25. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one of 85 genes for master regulators of poor prognosis which are selected from the group consisting of: MYBL2, FOXM1, CDK1, PTTG1, E2F7, UHRFl, TRIP13, AEBP1, HDAC7, ACTL6A, ARNTL2, TRIM29, HMGA2, TCF3, LOXL2, MEIS3, TGFB1I1, HICl, SPI1, FOSL1, MMP14, VDR, MAFK, SLC2A4RG, NPM1, CCNE1, CDK2, HTATIP2, NFE2L3, PLSCR1, KDM1A, GRHL2, FOXD1, EZH2, PLK4, DNMT1, ETV4, PCGF6, PPRC1, ATF6, HEYL, OTX1, SSRPl, BNC1, ZNF521, ZNF532, REST, KLF17, LIF, NCOR2, SALL2, HAND2, LZTS1, TCF7L1, TSHZ3, ZNF512B, MAFB, DEK, SNAI2, TDG, BASP1, ZNF280C, TSHZ2, LMX1B, SMARCD3, RAD9A, DBF4, RBMSl, TRIM32, MEOX2, SP100,

HDAC2, RAN, SOX11, ZNF697, SNAI1, PKNOX2, E2F1, E2F8, EHF, NOC2L, ZBTB9, POU3F1, FOSL2, and FLIl and comparing the expression with a healthy reference sample,

wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

26. The method of claim 25, further comprising calculating cell cycle, epigenetic/chromosome remodeling, Epithelial Mesenchymal Transitions (EMT), immune/development risk scores.

27. The method of claim 26, wherein calculating the cell cycle risk score comprises measuring the expression of at least one of CDK2, CCNE1,F0XM1, UHRFI, CDK1, PTTG1, MYBL2, or TRIP 13.

28. The method of claim 26, wherein calculating the epigenetic risk score comprises measuring the expression of at least one of RAN, ACTL6A, NPMI, HDAC2, S0X11,KDM1A, NOC2L, ZBTB9, ZNF697, TRIM32, PPRC1, POU3F1, BNC1, ATF6, OTX1, SSRPl, ETV4, EZH2, DNMT1, PLK4, E2F8, E2F1, DBF4, RAD9A, ZNF280C, DEK, PCGF6, or TDG.

29. The method of claim 26, wherein calculating the EMT risk score comprises measuring the expression of at least one of SNAI2, E2F7, ARNTL2, LOXL2, HMGA4, MMP14, FOSL1, LIF, FOXD1, LMX1B, TSHZ2, ZNF512B, SNAI1, MEOX2, C2A4RG, MAFK, NCOR2, ZNF532, HADC7, VDR, HTATIP2, NFE2L3, SP100, REST, PLSCR1, FOSL2, TRIM29, or GRHL2.

30. The method of claim 26, wherein calculating the

immune/developmental risk score comprises measuring the expression of at least one of EHF, RBMSl, FLIl, MAFB, SPI1, BASP1, SMARCD3, HAND2, TCFL1, TSHZ3, ZNF521, HEYL, PKNOX2, HICl, SALL2, KLF17, MEIS3, TGFB1I1, LZTS1, or AEBP1.

31. A method for treating a subj ect with cancer comprising administering an immunotherapy composition or small molecule that targets a master regulator of poor prognosis.

32. The method of claim 31, wherein the immunotherapy composition comprises a peptide formulation derived from a master regulator of poor prognosis or a nanoparticle or dendritic cell containing peptides derived from a master regulator of poor prognosis.

33. The method of claim 31, wherein the immunotherapy composition comprises a nanoparticle or dendritic cells containing RNA which codes for a master regulator of poor prognosis.

34. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: MYBL2, FOXM1, CDK1,

PTTG1, E2F7, UHRF1, TRIP13, AEBP1, HDAC7, ACTL6A, ARNTL2, TRIM29, HMGA2, TCF3, LOXL2, MEIS3, TGFB1I1, HICl, SPI1, FOSL1, MMP14, VDR, MAFK,

SLC2A4RG, NPM1, CCNE1, CDK2, HTATIP2, NFE2L3, PLSCR1, KDM1A, GRHL2, FOXD1, EZH2, PLK4, DNMT1, ETV4, PCGF6, PPRC1, ATF6, HEYL, OTX1, SSRP1, BNC1, ZNF521, ZNF532, REST, KLF17, LIF, NCOR2, SALL2, HAND2, LZTS1, TCF7L1, TSHZ3, ZNF512B, MAFB, DEK, SNAI2, TDG, BASP1, ZNF280C, TSHZ2, LMX1B, SMARCD3, RAD9A, DBF4, RBMS1, TRIM32, MEOX2, SP100, HDAC2, RAN, SOX11, ZNF697, SNAI1, PKNOX2, E2F1, E2F8, EHF, NOC2L, ZBTB9, POU3F1, FOSL2, FLU, HOXAl l, ZIC2, PITX1, PSMC3IP, HOXC11, SNAPC4, PRMT5, RCOR1, TEAD4, WWTR1, BARX2, CALU, CD 109, NFIC, SOX7, TCF4, ZHX3, PDE3A, CCNT1, CLOCK, KIAA0754, NCOA2, TAF13, AFF4, MED 13, MED23, MTDH, PGK1, SMAD5,

STON1 GTF2A1L, XRCC4, YWHAB, ZFHX3, ZNF623, ATF2, ITGB1, PDIA6, TUBB3, ELK3, FNDC3B, ITGA5, KIRREL, SPRY4, FNDC3A, HSP90AB1, KLF7, PEAR1, ZNF281, GLI3, GLIS2, ZEB1, MECP2, HLX, MEIS1, ZNF154, ZNF676, HEY1, YAF2, HSF2, TAF9B, MAF, TP63, AEBP2, DMTF1, HSA MIR 30E, HSA MIR 3653, MICAL2, RELB, C90RF64, EVC2, CD300E, PLEKHN1, BCL3, BHLHE40, EPS8L2, LRRFIPl, DDN, FHL2, NFE2L1, ZFP42, POLR2C, HOXA1, MSX2, PCGF2, SMYD1, CCND1,

E2F4, LHX1, MLXIPL, PERINEURAL INVASION, DLX4, ETV6, LBX2, STAT2,

ZGLP1, KAT2A, IFI16, RUNX1, RBCK1, ZNF335, IRF3, TAF10, TFAP2E, ZNF488, AATF, PRRX1, AHCTF1, FOXD2, ELF4, HOXAIO, SREBF1, HOXA6, PLA2G4A, BATF, NFKB2, TCF15, LPIN1, STAT6, CC2D1A, DAXX, ETS2, HOXA7, PPP1R13L, TFEB, NR2E1, OTP, PHTF1, TGIF1, ZNF217, DMRTA2, TEAD3, MYCBP, E2F6, HMGA1, PITX2, SMARCD1, YBX1, ZNF207, ENOl, FUBP1, MAFG, NPAS2, SMAD3, BCL9L, HOXA13, LDB2, ELANE, SKI, NACC2, TCF21, RARA, SMAD7, CALCOCOl, PBX4, SOX18, HNF1B, ATOH8, CSRNP1, BRCA1, BRIP1, DNMT3B, MYBL1, BEND6, NRG1, ZNF90, HCG22, ARID1B, TEX261, SLC1A6, SOCS5, ZNF781, HTR3C, PAX3, STAC2, BUD31, NFKBIB, CDSN, HIF3A, PERI, PFDN5, SNORD15A, KLF5, POU2F3, PTPN14, YAP1, MSLN, KLF3, AHR, ZFP36L1, NMI, YY1, BRCA2, CASC5, COP A, LHX4, RFX5,

ZBTB37, BLZF1, C110RF42, IRF6, TAF2, ZNF157, ZNF195, S100A5, TTTY14, TSG101, PAX5, TFAP2B, PATE2, CIZ1, NUP62, POLE3, POP1, RAB14, TIAL1, KIAA0319, QTRTD1, ZNF57, MBD1, U2AF2, GAS2, KCNC3, NCBP2, DDX27, SLC12A5, GGA3, SRC, ZNF274, GMEB1, MEX3A, SERBP1, TARDBP, LHX8, MYBBP1A, MAGED1, C1QBP, HES6, MED 15, OVOL1, PA2G4, GATAD2A, SOX15, TFAP2A, ZNF750, SLC38A8, OVOL2, ERG, PTGER3, RUNX1T1, ZFPM2, FOXC2, FOXD3, HOXD11, LIMS3, TREX2, ZSCAN10, HSA MIR 483, IGF2, SOX2, TNFRSF1A, TFE3, ZFP57, CDX4, DPPA2, LOC100287704, ZNF679, ANTXR1, DCAF17, SIX2, UCHL5, PIAS2, SMAD1, ZFHX4, PEG3, SMAD9, GZF1, ZFP41, SIX4, MED13L, NR0B2, PPARGC1A, PRDM12, ZNF462, FXN, JUN, HDAC9, PBX3, LPIN3, ZNF80, EOMES, BATF2, CUT A, PRDMl, ZBTB7B, ZNF768, SPIC, FOXN4, MED8, TRIB3, DDX41, HGS, DRAP1, CCDC137, GMEB2, RFX2, THRB, DMAP1, RBPJL, GLI2, TSC22D1, GATA6, GLIS3, FOXF1, NR5A2, BATF3, IRF1, SNCAIP, CITED1, CEBPG, IRF5, BCL11B, XBP1, ZNF576, and SAP30.

35. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: CDK2, CCNE1, FOXM1, UHRFI, CDK1, PTTG1, MYBL2, and TRIP13.

36. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: RAN, ACTL6A, NPMI, HDAC2, S0X11,KDM1A, NOC2L, ZBTB9, ZNF697, TRIM32, PPRC1, POU3F1, BNC1, ATF6, OTX1, SSRPl, ETV4, EZH2, DNMT1, PLK4, E2F8, E2F1, DBF4, RAD9A, ZNF280C, DEK, PCGF6, and TDG.

37. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: SNAI2, E2F7, ARNTL2, LOXL2, HMGA4, MMP14, FOSL1, LIF, FOXD1, LMX1B, TSHZ2, ZNF512B, SNAI1, MEOX2, C2A4RG, MAFK, NCOR2, ZNF532, HADC7, VDR, HTATIP2, NFE2L3, SP100, REST, PLSCR1, FOSL2, TRIM29, and GRHL2.

38. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: EHF, RBMSl, FLIl, MAFB,

SPI1, BASP1, SMARCD3, HAND2, TCFL1, TSHZ3, ZNF521, HEYL, PKNOX2, HICl, SALL2, KLF17, MEIS3, TGFB1I1, LZTS1, and AEBP1.

39. The method of any one of claims 31-33, wherein the master regulator of poor prognosis is selected from the group consisting of: MYBL2, PTTG1, FOXM1, E2F7, CDK1, UHRF1, TRIP13, TRIM29, HDAC7, ARNTL2, AEBP1, or ACTL6A.

40. The method of any one of claims 31-33, wherein cancer is a cancer type selected from Tables 2 and 3 and the master regulator of poor prognosis is selected from a gene in Tables 2 and 3 and identified as being one of the top 20 master regulators of the cancer type.

41. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis in the hallmark of epithelial mesenchymal transition pathway selected from the group consisting of: ZNF469, PRRX1, AEBP1, MEIS3, SNAI1, MMP14, ADAMTS12, ITGA5, TGFB1I1, and CREB3L1 and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

42. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis in the reactome cell cycle pathway selected from the group consisting of: MYBL2, CDK1, TRIP13, EZH2, FOXM1, UHRF1, PTTG1, E2F7, BRCA1, and E2F8 and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

43. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis in the angiogenesis pathway selected from the group consisting of: HEYL, LZTS1, COL4A1, ERG, SOX18, LDB2, GJC1, HLX, SOX17, and PDE3A and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

44. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis in the immune response pathway selected from the

group consisting of: SPI1, IRF1, GAT A3, IL2RB, BCL3, FOXP3, ACAP1, GBP1, CXCL13, and WWTR1 and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

45. A method for predicting patient survival in multiple cancer types comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis in the inflammatory response pathway selected from the group consisting of: SPI1, MS4A4A, CIITA, MAFB, VDR, BCL3, LILRB2, IRF5, WWTR1, and CALU and comparing the expression with a healthy reference sample, wherein increased expression in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

46. The method for predicting patient survival comprising measuring in a sample from a cancer patient the expression of at least one gene of a master regulator of poor prognosis and comparing the expression with a healthy reference sample, wherein the cancer is a cancer type as in Tables 2 and 3 and the at least one gene of a master regulator of poor prognosis is selected from a gene in Tables 2 and 3 and identified as being one of the top 20 master regulators of the cancer type, wherein increased expression of the at least one gene of a master regulator of poor prognosis in the sample from the patient relative to the healthy reference sample is an indicator of reduced predicted survival time.

47. A method for treating cancer in a subject with cancer comprising:

(a) obtaining or having obtained a sample from the subject;

(b) measuring or having measured the expression level in the sample of one or more master regulator genes selected from the groups consisting of VDR, CDK1, HDAC7, YAP1, HDAC2, and SMAD7;

(c) comparing the expression level of the one or more master regulators in the sample with the expression level of the one or more master regulators a healthy reference sample, wherein

(i) if VDR expression level is increased in the sample from the subject cancer relative to the healthy reference sample and the subject has GBM, glioma, or AML, administering vitamin D to the subject,

(ii) if CDK1 expression level is increased in the sample from the subject with cancer relative to the healthy reference sample and the subject has lung adenocarcinoma, administering a CDKl/2 inhibitor to the subject,

(iii) if HDAC7 expression level is increased in the sample from the subject with cancer relative to the healthy reference sample and the subject has lung squamous cell carcinoma, colon and/or rectal adenocarcinoma, GBM, or AML, administering an HD AC inhibitor to the subject,

(iv) if YAP 1 expression level is increased in the sample from the subject with cancer relative to the healthy reference sample and the subject has pancreatic adenocarcinoma, administering a Yapl inhibitor to the subject,

(v) if HDAC2 expression level is increased in the sample from the subject with cancer relative to the healthy reference sample and the subject has hepatocellular carcinoma, administering an HD AC inhibitor to the subject, and/or

(vi) if SMAD7 expression level is increased in the sample from the subject with cancer relative to the healthy reference sample and the subject has lung squamous cell carcinoma, administering mongersen and/or a TGFbeta pathway inhibitor to the subject.

48. The method of claim 47, wherein the CDKl/2 inhibitor is flavopiridol; the HD AC inhibitor is vorinostat, romidepsin, belinostat, panobinostat, entinostat, or valproic acid; the Yapl inhibitor is vereporfm, CA3, trametinib, dasatinib, or metformin; and the TGFbeta pathway inhibitors is galunisertib or AVID200.