# Patent

## Title
Methods, kits and devices for identifying biomarkers of treatment response and use thereof to predict treatment efficacy

## Abstract

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

## Background
FIELD OF THE INVENTION
The invention features methods, kits, and devices for identifying biomarkers of patient sensitivity to medical treatments, e.g., sensitivity to chemotherapeutic agents, and predicting treatment efficacy using the biomarkers.
BACKGROUND OF THE INVENTION
DNA microarrays have been used to measure gene expression in tumor samples from patients and to facilitate diagnosis. Gene expression can reveal the presence of cancer in a patient, its type, stage, and origin, and whether genetic mutations are involved. Gene expression may even have a role in predicting the efficacy of chemotherapy. Over recent decades, the National Cancer Institute (NCI) has tested compounds, including chemotherapy agents, for their effect in limiting the growth of 60 human cancer cell lines. The NCI has also measured gene expression in these 60 cancer cell lines using DNA microarrays. Various studies have explored the relationship between gene expression and compound effect using the NCI datasets. Critical time is often lost due to a trial and error approach to finding an effective chemotherapy for patients with cancer. In addition, cancer cells often develop resistance to a previously effective therapy. In such situations, patient outcome could be greatly improved by early detection of such resistance.
There remains a need for proven methods and devices that predict the sensitivity or resistance of cancer patients to a medical treatment.
SUMMARY OF THE INVENTION
The invention features methods, kits, and devices for determining the sensitivity or resistance of a patient, e.g., a cancer patient, to a treatment, e.g., treatment with a compound, such as a chemotherapeutic agent, or radiation. In particular, the methods, kits, and devices can be used to determine the sensitivity or resistance of a cancer patient to any medical treatment, including, e.g., treatment with a compound, drug, or radiation. The methods, kits, and devices of the invention have been used to accurately determine treatment efficacy in cancer patients (e.g., patients with lung, lymphoma, and brain cancer) and can be used to determine treatment efficacy in patients diagnosed with any cancer.
Methods, kits, and devices for detecting the level of expression of biomarkers (e.g., genes and microRNAs) that indicate sensitivity or resistance to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), Rituximab, PXD101, (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin are also provided. The methods, kits, and devices can be used to predict the sensitivity or resistance of a subject (e.g., a cancer patient) diagnosed with a disease condition, e.g., cancer (e.g., cancers of the breast, prostate, lung and bronchus, colon and rectum, urinary bladder, skin, kidney, pancreas, oral cavity and pharynx, ovary, thyroid, parathyroid, stomach, brain, esophagus, liver and intrahepatic bile duct, cervix larynx, heart, testis, small and large intestine, anus, anal canal and anorectum, vulva, gallbladder, pleura, bones and joints, hypopharynx, eye and orbit, nose, nasal cavity and middle ear, nasopharynx, ureter, peritoneum, omentum and mesentery, or gastrointestines, as well as any form of cancer including, e.g., chronic myeloid leukemia, acute lymphocytic leukemia, non-Hodgkin's lymphoma, melanoma, carcinoma, basal cell carcinoma, malignant mesothelioma, neuroblastoma, multiple myeloma, leukemia, retinoblastoma, acute myeloid leukemia, chronic lymphocytic leukemia, Hodgkin's lymphoma, carcinoid tumors, acute tumor, or soft tissue sarcoma) to a treatment, e.g., treatment with a compound or drug, e.g., a chemotherapeutic agent, or radiation.
In a first aspect, the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression 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, ADAMTS1, ADD1, AF1Q, AIF1, AKAP1, AKAP13, AKR1C1, AKT1, ALDH2, ALDOC, ALG5, ALMS1, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXA1, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCAT1, BCHE, BCL11B, BDNF, BHLHB2, BIN2, BLMH, BMI1, BNIP3, BRDT, BRRN1, BTN3A3, C11orf2, C14orf139, C15orf25, C18orf10, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C6orf32, CACNA1G, CACNB3, CALM1, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNB1IP1, CCND3, CCR7, CCR9, CD1A, CD1C, CD1D, CD1E, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDC10, CDC14B, CDH11, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-1, CNP, COL4A1, COL5A2, COL6A1, CORO1C, CRABP1, CRK, CRY1, CSDA, CTBP1, CTSC, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDX18, DDX5, DGKA, DIAPH1, DKC1, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAJC7, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DXS9879E, EEF1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHL1, FHOD1, FKBP1A, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOT1, FMNL1, FNBP1, FOLH1, FOXF2, FSCN1, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPT1, GIMAP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GZMA, GZMB, H1F0, H1FX, H2AFX, H3F3A, HA-1, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HRMT1L1, HSA9761, HSPA5, HSU79274, HTATSF1, ICAM1, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIG1, IQGAP1, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K-ALPHA-1, KHDRBS1, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1128, KIAA1393, KIFC1, LAIR1, LAMB1, LAMB3, LAT, LBR, LCK, LCP1, LCP2, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAP1B, MAP1LC3B, MAP4K1, MAPK1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCM7, MDH2, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPP1, MPZL1, MRP63, MRPS2, MT1E, MT1K, MUF1, MVP, MYB, MYL9, MYO1B, NAP1L1, NAP1L2, NARF, NASP, NCOR2, NDN, NDUFAB1, NDUFS6, NFKBIA, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCH1, NPC1, NQO1, NR1D2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OXA1L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFN1, PFN2, PGAM1, PHEMX, PHLDA1, PIM2, PITPNC1, PLACE, PLAGL1, PLAUR, PLCB1, PLEK2, PLEKHC1, PLOD2, PLSCR1, PNAS-4, PNMA2, POLR2F, PPAP2B, PRF1, PRG1, PRIM1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOV1, PTP4A3, PTPN7, PTPNS1, PTRF, PURA, PWP1, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAP1B, RASGRP2, RBPMS, RCN1, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOK3, RIPK2, RIS1, RNASE6, RNF144, RPL10, RPL10A, RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLP0, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A4, SART3, SATB1, SCAP1, SCARB1, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT10, SEPT6, SERPINA1, SERPINB1, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHD1, SHMT2, SIAT1, SKB1, SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPI1, SRF, SRM, SSA2, SSBP2, SSRP1, SSSCA1, STAG3, STAT1, STAT4, STAT5A, STC1, STC2, STOML2, T3JAM, TACC1, TACC3, TAF5, TAL1, TAP1, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB10, TMSNB, TNFAIP3, TNFAIP8, TNFRSF10B, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAV1, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFN1A1, and ZYX; in which change in the level of expression of the gene indicates the cell is sensitive or resistant to the treatment.
In an embodiment, the method further includes determining a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXD101 (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin by measuring the level of expression of one or more of the genes known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., 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 or decreases relative to the level of expression of the gene(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the gene(s) indicates sensitivity to the treatment, and vice versa). Alternatively, a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents listed above can be determined by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) known to change (e.g., the level of expression is increased or decreased) in a patient sensitive to a treatment with these agents, in which the microRNA is selected from the group consisting of ath-MIR180aNo2, Hcd102 left, Hcd111 left, Hcd115 left, Hcd120 left, Hcd142 right, Hcd145 left, Hcd148_HPR225 left, Hcd181 left, Hcd181 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, Hcd28_HPR39left, Hcd28_HPR39 right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd350 right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd438 right, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd511 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left, Hcd760 left, Hcd763 right, Hcd768 left, Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPR100 right, HPR129 left, HPR154 left, HPR159 left, HPR163 left, HPR169 right, HPR172 right, HPR181 left, HPR187 left, HPR199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left, HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir—490_Hcd20 right, HSHELA01, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRV1A, let-7f-2-prec2, mir-001b-1-prec1, mir-001b-2-prec, mir-007-1-prec, mir-007-2-precNo2, mir-010a-precNo2, mir-015b-precNo2, mir-016a-chr13, mir-016b-chr3, mir-017-precNo1, mir-017-precNo2, mir-018-prec, mir-019a-prec, mir-019b-1-prec, mir-019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2-prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNo1, mir-034-precNo2, mir-092-prec-13=092-1No2, mir-092-prec-X=092-2, mir-093-prec-7.1=093-1, mir-095-prec-4, mir-096-prec-7No1, mir-096-prec-7No2, mir-098-prec-X, mir-099b-prec-19No1, mir-100-1/2-prec, mir-100No1, mir-101-prec-9, mir-102-prec-1, mir-103-2-prec, mir-103-prec-5=103-1, mir-106aNo1, mir-106-prec-X, mir-107No1, mir-107-prec-10, mir-122a-prec, mir-123-precNo1, mir-123-precNo2, mir-124a-1-prec1, mir-124a-2-prec, mir-124a-3-prec, mir-125b-1, mir-125b-2-precNo2, mir-127-prec, mir-128b-precNo1, mir-128b-precNo2, mir-133a-1, mir-135-2-prec, mir-136-precNo2, mir-138-1-prec, mir-140No2, mir-142-prec, mir-143-prec, mir-144-precNo2, mir-145-prec, mir-146bNo1, mir-146-prec, mir-147-prec, mir-148aNo1, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-1-prec1, mir-154-prec1No1, mir-155-prec, mir-15aNo1, mir-16-1No1, mir-16-2No1, mir-181a-precNo1, mir-181b-1No1, mir-181b-2No1, mir-181b-precNo1, mir-181b-precNo2, mir-181c-precNo1, mir-181dNo1, mir-188-prec, mir-18bNo2, mir-191-prec, mir-192No2, mir-193bNo2, mir-194-2No1, mir-195-prec, mir-196-2-precNo2, mir-197-prec, mir-198-prec, mir-199a-1-prec, mir-199a-2-prec, mir-199b-precNo1, mir-200a-prec, mir-200bNo1, mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir-208-prec, mir-20bNo1, mir-212-precNo1, mir-212-precNo2, mir-213-precNo1, mir-214-prec, mir-215-precNo2, mir-216-precNo1, mir-219-2No1, mir-219-prec, mir-223-prec, mir-29b-1No1, mir-29b-2=102prec7.1=7.2, mir-321No1, mir-321No2, mir-324No1, mir-324No2, mir-328No1, mir-342No1, mir-361No1, mir-367No1, mir-370No1, mir-371No1, miR-373*No1, mir-375, mir-376aNo1, mir-379No1, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423No1, mir-424No2, mir-429No1, mir-429No2, mir-4323p, mir-4325p, mir-449No1, mir-450-1, mir-450-2No1, mir-483No1, mir-484, mir-487No1, mir-495No1, mir-499No2, mir-501No2, mir-503No1, mir-509No1, mir-514-1No2, mir-515-15p, mir-515-23p, mir-516-33p, mir-516-43p, mir-518e/526c, mir-519a-1/52, mir-519a-2No2, mir-519b, mir-519c/52, mir-520c/52, mir-526a-2No1, mir-526a-2No2, MPR103 right, MPR121 left, MPR121 left, MPR130 left, MPR130 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPR174 right, MPR185 right, MPR197 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, and MPR95 left.
In an embodiment, the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes. In another embodiment, the change in the level of gene or microRNA expression (e.g., an increase or decrease) is determined relative to the level of gene or microRNA expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of gene or microRNA expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment. In another embodiment, the change in the level of gene or microRNA expression (e.g., an increase or decrease) is determined relative to the level of gene or microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of gene or microRNA expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.
In a second aspect, the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) of the patient, in which the microRNA is selected from the group set forth in the first aspect of the invention. In an embodiment, the method further includes determining a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents set forth in the first aspect of the invention by measuring the level of expression of one or more of the microRNAs known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., 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 microRNA(s) increases or decreases relative to the level of expression of the microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the microRNA(s) indicates sensitivity to the treatment, and vice versa). In an embodiment, the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes. In another embodiment, the change in the level of microRNA expression (e.g., an increase or decrease) is determined relative to the level of microRNA expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of microRNA expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment. In another embodiment, the change in the level of microRNA expression (e.g., an increase or decrease) is determined relative to the level of microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of microRNA expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.
In another embodiment, the invention features a method for determining the development of resistance by a patient (e.g., resistance of a cell, such as a cancer cell, in the patient) to a treatment to which the patient was previously sensitive. The method includes measuring the level of expression of one or more of the microRNAs set forth in the first aspect of the invention, such that the level of expression of a microRNA which is decreased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment. Alternatively, a decrease in the expression level of a microRNA 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.
In a third aspect, the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity 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 (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more of the genes) set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the gene(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and a nucleic acid molecule encoded by the gene, or a complement thereof. Alternatively, the kit includes one or more single-stranded nucleic acid molecules that are substantially complementary to or substantially identical to at least 5 consecutive nucleotides of at least one of the microRNAs set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the microRNA(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and the microRNA, or a complement thereof. The kit further includes instructions for applying nucleic acid molecules 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) or microRNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of the gene(s) or microRNA(s) changes (e.g., increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention). In an embodiment, the instructions further indicate that a change in the level of expression of the gene(s) or microRNA(s) relative to the expression of the gene(s) or microRNA(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 (e.g., a decrease in the level of expression of a gene or microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
In another embodiment, the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXD101 (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin by measuring the level of expression of one or more of the genes or microRNAs set forth in the first aspect of the invention and known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., 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) or microRNA(s) increases or decreases relative to the level of expression of the gene(s) or microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the gene(s) or microRNA(s) indicates sensitivity to the treatment, and vice versa).
In another embodiment, the nucleic acid molecules are characterized by their ability to specifically identify nucleic acid molecules complementary to the genes or microRNAs in a sample collected from a cancer patient.
In a fourth aspect, the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity 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 microRNAs (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more of the microRNAs) set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the microRNA(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and a microRNA, or a complement thereof. The kit further includes instructions for applying nucleic acid molecules collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the microRNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of microRNA(s) changes (e.g., increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention). In an embodiment, the instructions further indicate that a change in the level of expression of microRNA(s) relative to the expression of microRNA(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 (e.g., a decrease in the level of expression of a microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
In another embodiment, the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXD101 (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin by measuring the level of expression of one or more of the microRNAs set forth in the first aspect of the invention and known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., 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 microRNA(s) increases or decreases relative to the level of expression of the microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the or microRNA(s) indicates sensitivity to the treatment, and vice versa).
In another embodiment, the nucleic acid molecules are characterized by their ability to specifically identify nucleic acid molecules complementary to the microRNAs in a sample collected from a cancer patient.
In a fifth aspect, the invention features a method of identifying biomarkers (e.g., genes and microRNAs) indicative of sensitivity of a cancer patient to a treatment for cancer by obtaining pluralities of measurements of the expression level of a gene or microRNA (e.g., by detection of the expression of a gene or microRNA using a single probe or by using multiple probes directed to a single gene or microRNA) 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 or microRNA in cells with the growth of the cells to obtain a correlation coefficient; selecting the median correlation coefficient calculated for the gene or microRNA; and identifying the gene or microRNA 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 or microRNA is identified as a biomarker for a patient's sensitivity to a treatment if the correlation coefficient exceeds 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 or more). In an embodiment, the method is performed in the presence of a second treatment.
In a sixth aspect, the invention features a method of determining sensitivity of a patient (e.g., a cancer patient) to a treatment for cancer by obtaining a measurement of the level of expression of a gene or microRNA in 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 determining whether or not the patient will be responsive to the treatment for cancer. In an embodiment, the measurement is obtained by measuring the level of expression of any of the genes or microRNAs set forth in the first aspect of the invention in a cell known to be sensitive or resistant to the treatment. In another embodiment, the method is performed in the presence of a second treatment. In another embodiment, the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements. In another embodiment, treatment, e.g., a compound, has previously failed to show efficacy in a patient. In several embodiments, the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker gene expression. In another embodiment, the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis. In another embodiment, the invention features a kit, apparatus, and software used to implement the method of the sixth aspect of the invention.
In several embodiments of all aspects of the invention, the level of expression of the gene(s) is determined by measuring the level of mRNA transcribed from the gene(s), by detecting the level of a protein product of the gene(s), or by detecting the level of the biological activity of a protein product of the gene(s). In further embodiments of all aspects of the invention, an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue sensitive to the treatment, indicates increased sensitivity of the cancer patient to the treatment. Alternatively, an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue resistant to the treatment, indicates increased resistance of the cancer patient to the treatment. In another embodiment of all aspects of the invention, the cell is a cancer cell. In another embodiment of all aspects of the invention, the expression level of the gene(s) is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In an embodiment of all aspects of the invention, the level of expression of two of the listed genes or microRNAs is measured, more preferably the level of expression of three, four, five, six, seven, eight, nine, or ten of the listed genes or microRNAs is measured, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes or microRNAs is measured. In another embodiment of all aspects of the invention, the expression level of the gene(s) or microRNA(s) is determined using the kit of the third or fourth aspects of the invention.
In another embodiment of all aspects of the invention, the treatment is radiation therapy or a compound, such as a chemotherapy 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, PXD101 (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin. In another embodiment of all aspects of the invention, the treatment has previously failed to show effect in a subject (e.g., a subject selected from a subpopulation determined to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
In another embodiment of all aspects of the invention, the treatment is, e.g., administration of a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient. In an embodiment of all aspects of the invention, the treatment is, e.g., a chemotherapeutic agent, such as, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), a histone deacetylase (HDAC) inhibitor such as PXD101, 5-Aza-2′-deoxycytidine (Decitabine), alpha emitters such as astatine-211, bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-11, nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71, arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-110, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetium-99m, and technetium-99, gamma emitters such as cobalt-60, cesium-137, and technetium-99m, Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERATM), Trastuzumab (e.g., HERCEPTINTM), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-t1, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201, G-250, unconjugated, ACA-125, Onyvax-105, CDP-860, BrevaRex MAb, AR54, IMC-1C11, GlioMAb-H, ING-1, Anti-LCG MAbs, MT-103, KSB-303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb, CAT, Prostate cancer antibody, H22×Ki-4, ABX-MA1, Imuteran, Monopharm-C, Acivicin, Aclarubicin, Acodazole Hydrochloride, Acronine, Adozelesin, Adriamycin, Aldesleukin, Altretamine, Ambomycin, A. metantrone Acetate, Aminoglutethimide, Amsacrine, Anastrozole, Anthramycin, Asparaginase, Asperlin, Azacitidine, Azetepa, Azotomycin, Batimastat, Benzodepa, Bicalutamide, Bisantrene Hydrochloride, Bisnafide Dimesylate, Bizelesin, Bleomycin Sulfate, Brequinar Sodium, Bropirimine, Busulfan, Cactinomycin, Calusterone, Camptothecin, Caracemide, Carbetimer, Carboplatin, Carmustine, Carubicin Hydrochloride, Carzelesin, Cedefingol, Chlorambucil, Cirolemycin, Cisplatin, Cladribine, Combretestatin A-4, Crisnatol Mesylate, Cyclophosphamide, Cytarabine, Dacarbazine, DACA (N-[2-(Dimethyl-amino) ethyl]acridine-4-carboxamide), Dactinomycin, Daunorubicin Hydrochloride, Daunomycin, Decitabine, Dexormaplatin, Dezaguanine, Dezaguanine Mesylate, Diaziquone, Docetaxel, Dolasatins, Doxorubicin, Doxorubicin Hydrochloride, Droloxifene, Droloxifene Citrate, Dromostanolone Propionate, Duazomycin, Edatrexate, Eflornithine Hydrochloride, Ellipticine, Elsamitrucin, Enloplatin, Enpromate, Epipropidine, Epirubicin Hydrochloride, Erbulozole, Esorubicin Hydrochloride, Estramustine, Estramustine Phosphate Sodium, Etanidazole, Ethiodized Oil I 131, Etoposide, Etoposide Phosphate, Etoprine, Fadrozole Hydrochloride, Fazarabine, Fenretinide, Floxuridine, Fludarabine Phosphate, Fluorouracil, 5-FdUMP, Fluorocitabine, Fosquidone, Fostriecin Sodium, Gemcitabine, Gemcitabine Hydrochloride, Gold Au 198, Homocamptothecin, Hydroxyurea, Idarubicin Hydrochloride, Ifosfamide, Ilmofosine, Interferon Alfa-2a, Interferon Alfa-2b, Interferon Alfa-n1, Interferon Alfa-n3, Interferon Beta-I a, Interferon Gamma-I b, Iproplatin, Irinotecan Hydrochloride, Lanreotide Acetate, Letrozole, Leuprolide Acetate, Liarozole Hydrochloride, Lometrexol Sodium, Lomustine, Losoxantrone Hydrochloride, Masoprocol, Maytansine, Mechlorethamine Hydrochloride, Megestrol Acetate, Melengestrol Acetate, Melphalan, Menogaril, Mercaptopurine, Methotrexate, Methotrexate Sodium, Metoprine, Meturedepa, Mitindomide, Mitocarcin, Mitocromin, Mitogillin, Mitomalcin, Mitomycin, Mitosper, Mitotane, Mitoxantrone Hydrochloride, Mycophenolic Acid, Nocodazole, Nogalamycin, Ormaplatin, Oxisuran, Paclitaxel, Pegaspargase, Peliomycin, Pentamustine, PeploycinSulfate, Perfosfamide, Pipobroman, Piposulfan, Piroxantrone Hydrochloride, Plicamycin, Plomestane, Porfimer Sodium, Porfiromycin, Prednimustine, Procarbazine Hydrochloride, Puromycin, Puromycin Hydrochloride, Pyrazofurin, Rhizoxin, Rhizoxin D, Riboprine, Rogletimide, Safingol, Safingol Hydrochloride, Semustine, Simtrazene, Sparfosate Sodium, Sparsomycin, Spirogermanium Hydrochloride, Spiromustine, Spiroplatin, Streptonigrin, Streptozocin, Strontium Chloride Sr 89, Sulofenur, Talisomycin, Taxane, Taxoid, Tecogalan Sodium, Tegafur, Teloxantrone Hydrochloride, Temoporfin, Teniposide, Teroxirone, Testolactone, Thiamiprine, Thioguanine, Thiotepa, Thymitaq, Tiazofurin, Tirapazamine, Tomudex, TOP53, Topotecan Hydrochloride, Toremifene Citrate, Trestolone Acetate, Triciribine Phosphate, Trimetrexate, Trimetrexate Glucuronate, Triptorelin, Tubulozole Hydrochloride, Uracil Mustard, Uredepa, Vapreotide, Verteporfin, Vinblastine, Vinblastine Sulfate, Vincristine, Vincristine Sulfate, Vindesine, Vindesine Sulfate, Vinepidine Sulfate, Vinglycinate Sulfate, Vinleurosine Sulfate, Vinorelbine Tartrate, Vinrosidine Sulfate, Vinzolidine Sulfate, Vorozole, Zeniplatin, Zinostatin, Zorubicin Hydrochloride, 2-Chlorodeoxyadenosine, 2′ Deoxyformycin, 9-aminocamptothecin, raltitrexed, N-propargyl-5,8-dideazafolic acid, 2-chloro-2′-arabino-fluoro-2′-deoxyadenosine, 2-chloro-2′-deoxyadenosine, anisomycin, trichostatin A, hPRL-G129R, CEP-751, linomide, sulfur mustard, nitrogen mustard (mechlor ethamine), cyclophosphamide, melphalan, chlorambucil, ifosfamide, busulfan, N-methyl-Nnitrosourea (MNU), N, N′-Bis(2-chloroethyl)-N-nitrosourea (BCNU), N-(2-chloroethyl)-N′ cyclohexyl-N-nitrosourea (CCNU), N-(2-chloroethyl)-N′-(trans-4-methylcyclohexyl-N-nitrosourea (MeCCNU), N-(2-chloroethyl)-N′-(diethyl)ethylphosphonate-N-nitrosourea (fotemustine), streptozotocin, diacarbazine (DTIC), mitozolomide, temozolomide, thiotepa, mitomycin C, AZQ, adozelesin, Cisplatin, Carboplatin, Ormaplatin, Oxaliplatin, C1-973, DWA 2114R, JM216, JM335, Bis(platinum), tomudex, azacitidine, cytarabine, gemcitabine, 6-Mercaptopurine, 6-Thioguanine, Hypoxanthine, teniposide 9-amino camptothecin, Topotecan, CPT-11, Doxorubicin, Daunomycin, Epirubicin, darubicin, mitoxantrone, losoxantrone, Dactinomycin (Actinomycin D), amsacrine, pyrazoloacridine, all-trans retinol, 14-hydroxy-retro-retinol, all-trans retinoic acid, N-(4-Hydroxyphenyl) retinamide, 13-cis retinoic acid, 3-Methyl TTNEB, 9-cis retinoic acid, fludarabine (2-F-ara-AMP), 2-chlorodeoxyadenosine (2-Cda), 20-pi-1,25 dihydroxyvitamin D3,5-ethynyluracil, abiraterone, aclarubicin, acylfulvene, adecypenol, adozelesin, aldesleukin, ALL-TK antagonists, altretamine, ambamustine, amidox, amifostine, aminolevulinic acid, amrubicin, amsacrine, anagrelide, anastrozole, andrographolide, angiogenesis inhibitors, antagonist D, antagonist G, antarelix, anti-dorsalizing morphogenetic protein-1, antiandrogen, prostatic carcinoma, antiestrogen, antineoplaston, antisense oligonucleotides, aphidicolin glycinate, apoptosis gene modulators, apoptosis regulators, apurinic acid, ara-CDP-DL-PTBA, argininedeaminase, asulacrine, atamestane, atrimustine, axinastatin 1, axinastatin 2, axinastatin 3, azasetron, azatoxin, azatyrosine, baccatin III derivatives, balanol, batimastat, BCR/ABL antagonists, benzochlorins, benzoylstaurosporine, beta lactam derivatives, beta-alethine, betaclamycin B, betulinic acid, bFGF inhibitor, bicalutamide, bisantrene, bisaziridinylspermine, bisnafide, bistratene A, bizelesin, breflate, bleomycin A2, bleomycin B2, bropirimine, budotitane, buthionine sulfoximine, calcipotriol, calphostin C, camptothecin derivatives (e.g., 10-hydroxy-camptothecin), canarypox IL-2, capecitabine, carboxamide-amino-triazole, carboxyamidotriazole, CaRest M3, CARN 700, cartilage derived inhibitor, carzelesin, casein kinase inhibitors (ICOS), castanospermine, cecropin B, cetrorelix, chlorins, chloroquinoxaline sulfonamide, cicaprost, cis-porphyrin, cladribine, clomifene analogues, clotrimazole, collismycin A, collismycin B, combretastatin A4, combretastatin analogue, conagenin, crambescidin 816, crisnatol, cryptophycin 8, cryptophycin A derivatives, curacin A, cyclopentanthraquinones, cycloplatam, cypemycin, cytarabine ocfosfate, cytolytic factor, cytostatin, dacliximab, decitabine, dehydrodidemnin B, 2′ deoxycoformycin (DCF), deslorelin, dexifosfamide, dexrazoxane, dexverapamil, diaziquone, didemnin B, didox, diethylnorspermine, dihydro-5-azacytidine, 9-dihydrotaxol, dioxamycin, diphenyl spiromustine, discodermolide, docosanol, dolasetron, doxifluridine, droloxifene, dronabinol, duocarmycin SA, ebselen, ecomustine, edelfosine, edrecolomab, eflornithine, elemene, emitefur, epirubicin, epothilones (A, R═H, B, R=Me), epithilones, epristeride, estramustine analogue, estrogen agonists, estrogen antagonists, etanidazole, etoposide, etoposide 4′-phosphate (etopofos), exemestane, fadrozole, fazarabine, fenretinide, filgrastim, finasteride, flavopiridol, flezelastine, fluasterone, fludarabine, fluorodaunorunicin hydrochloride, forfenimex, formestane, fostriecin, fotemustine, gadolinium texaphyrin, gallium nitrate, galocitabine, ganirelix, gelatinase inhibitors, gemcitabine, glutathione inhibitors, hepsulfam, heregulin, hexamethylene bisacetamide, homoharringtonine (HHT), hypericin, ibandronic acid, idarubicin, idoxifene, idramantone, ilmofosine, ilomastat, imidazoacridones, imiquimod, immunostimulant peptides, insulin-like growth factor-1 receptor inhibitor, interferon agonists, interferons, interleukins, iobenguane, iododoxorubicin, 4-ipomeanol, irinotecan, iroplact, irsogladine, isobengazole, isohomohalicondrin B, itasetron, jasplakinolide, kahalalide F, lamellarin-N triacetate, lanreotide, leinamycin, lenograstim, lentinan sulfate, leptolstatin, letrozole, leukemia inhibiting factor, leukocyte alpha interferon, leuprolide, estrogen, and progesterone combinations, leuprorelin, levamisole, liarozole, linear polyamine analogue, lipophilic disaccharide peptide, lipophilic platinum compounds, lissoclinamide 7, lobaplatin, lombricine, lometrexol, lonidamine, losoxantrone, lovastatin, loxoribine, lurtotecan, lutetium texaphyrin, lysofylline, lytic peptides, maytansine, mannostatin A, marimastat, masoprocol, maspin, matrilysin inhibitors, matrix metalloproteinase inhibitors, menogaril, merbarone, meterelin, methioninase, metoclopramide, MIF inhibitor, ifepristone, miltefosine, mirimostim, mismatched double stranded RNA, mithracin, mitoguazone, mitolactol, mitomycin analogues, mitonafide, mitotoxin fibroblast growth factor-saporin, mitoxantrone, mofarotene, molgramostim, monoclonal antibody, human chorionic gonadotrophin, monophosphoryl lipid A and myobacterium cell wall skeleton combinations, mopidamol, multiple drug resistance gene inhibitor, multiple tumor suppressor 1-based therapy, mustard anticancer agent, mycaperoxide B, mycobacterial cell wall extract, myriaporone, N-acetyldinaline, N-substituted benzamides, nafarelin, nagrestip, naloxone and pentazocine combinations, napavin, naphterpin, nartograstim, nedaplatin, nemorubicin, neridronic acid, neutral endopeptidase, nilutamide, nisamycin, nitric oxide modulators, nitroxide antioxidant, nitrullyn, 06-benzylguanine, octreotide, okicenone, oligonucleotides, onapristone, ondansetron, ondansetron, oracin, oral cytokine inducer, ormaplatin, osaterone, oxaliplatin, oxaunomycin, paclitaxel analogues, paclitaxel derivatives, palauamine, palmitoylrhizoxin, pamidronic acid, panaxytriol, panomifene, parabactin, pazelliptine, pegaspargase, peldesine, pentosan polysulfate sodium, pentostatin, pentrozole, perflubron, perfosfamide, perillyl alcohol, phenazinomycin, phenylacetate, phosphatase inhibitors, picibanil, pilocarpine hydrochloride, pirarubicin, piritrexim, placetin A, placetin B, plasminogen activator inhibitor, platinum complex, platinum compounds, platinum-triamine complex, podophyllotoxin, porfimer sodium, porfiromycin, propyl bis-acridone, prostaglandin J2, proteasome inhibitors, protein A-based immune modulator, protein kinase C inhibitor, protein kinase C inhibitors, microalgal, protein tyrosine phosphatase inhibitors, purine nucleoside phosphorylase inhibitors, purpurins, pyrazoloacridine, pyridoxylated hemoglobin polyoxyethylene conjugate, raf antagonists, raltitrexed, ramosetron, ras farnesyl protein transferase inhibitors, ras inhibitors, ras-GAP inhibitor, retelliptine demethylated, rhenium Re 186 etidronate, rhizoxin, ribozymes, RII retinamide, rogletimide, rohitukine, romurtide, roquinimex, rubiginone B 1, ruboxyl, safingol, saintopin, SarCNU, sarcophytol A, sargramostim, Sdi 1 mimetics, semustine, senescence derived inhibitor 1, sense oligonucleotides, signal transduction inhibitors, signal transduction modulators, single chain antigen binding protein, sizofuran, sobuzoxane, sodium borocaptate, sodium phenylacetate, solverol, somatomedin binding protein, sonermin, sparfosic acid, spicamycin D, spiromustine, splenopentin, spongistatin 1, squalamine, stem cell inhibitor, stem-cell division inhibitors, stipiamide, stromelysin inhibitors, sulfinosine, superactive vasoactive intestinal peptide antagonist, suradista, suramin, swainsonine, synthetic glycosaminoglycans, tallimustine, tamoxifen methiodide, tauromustine, tazarotene, tecogalan sodium, tegafur, tellurapyrylium, telomerase inhibitors, temoporfin, temozolomide, teniposide, tetrachlorodecaoxide, tetrazomine, thaliblastine, thalidomide, thiocoraline, thrombopoietin, thrombopoietin mimetic, thymalfasin, thymopoietin receptor agonist, thymotrinan, thyroid stimulating hormone, tin ethyl etiopurpurin, tirapazamine, titanocene dichloride, topotecan, topsentin, toremifene, totipotent stem cell factor, translation inhibitors, tretinoin, triacetyluridine, triciribine, trimetrexate, triptorelin, tropisetron, turosteride, tyrosine kinase inhibitors, tyrphostins, UBC inhibitors, ubenimex, urogenital sinus-derived growth inhibitory factor, urokinase receptor antagonists, vapreotide, variolin B, vector system, erythrocyte gene therapy, velaresol, veramine, verdins, verteporfin, vinorelbine, vinxaltine, vitaxin, vorozole, zanoterone, zeniplatin, zilascorb, or zinostatin stimalamer. In another embodiment of all aspects of the invention, a second treatment is utilized to determine gene expression in a sample from the patient.
In another embodiment of all aspects of the invention, the gene is selected from the group consisting of ABL1, ACTB, ACTN1, ACTN4, ACTR2, ADA, ADAM9, ADAMTS1, ADD1, ADORA2A, AF1Q, AIF1, AKAP1, AKAP13, AKR1B1, AKR1C1, AKT1, ALDH2, ALDH3A1, ALDOC, ALG5, ALMS1, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANPEP, ANXA1, ANXA2, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATOX1, ATP1B3, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, B2M, BASP1, BAX, BC008967, BCAT1, BCHE, BCL11B, BDNF, BHLHB2, BIN2, BLM, BLMH, BLVRA, BMI1, BNIP3, BRDT, BRRN1, BTN3A2, BTN3A3, Cllorf2, C14orf139, C15orf25, C18orf10, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C5orf13, C6orf32, CACNA1G, CACNB3, CALD1, CALM1, CALML4, CALU, CAP350, CAPG, CAPN2, CAPN3, CASP2, CASP6, CASP7, CAST, CBFB, CBLB, CBR1, CBX3, CCL2, CCL21, CCNA2, CCNB1IP1, CCND3, CCR7, CCR9, CCT5, CD151, CD1A, CD1B, CD1C, CD1D, CD1E, CD2, CD28, CD37, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD53, CD59, CD6, CD63, CD81, CD8A, CD8B1, CD99, CDC10, CDCl4B, CDH11, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-1, CNN3, CNP, COL1A1, COL4A1, COL4A2, COL5A2, COL6A1, COL6A2, COPA, COPEB, CORO1A, CORO1C, COX7B, CPSF1, CRABP1, CREB3L1, CRIP2, CRK, CRY1, CSDA, CSPG2, CSRP1, CST3, CTBP1, CTGF, CTNNA1, CTSB, CTSC, CTSD, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDOST, DDX18, DDX5, DGKA, DIAPH1, DIPA, DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DUSP3, DXS9879E, DYRK2, E2F4, ECE1, ECM1, EEF1A1, EEF1B2, EEF1G, EFNB2, EHD2, EIF2S2, EIF3S2, EIF4B, EIF4G3, EIF5A, ELA2B, ELK3, EMP3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD104, FAM46A, FARSLA, FAT, FAU, FBL, FCGR2A, FCGR2C, FER1L3, FGFR1, FHL1, FHOD1, FKBP1A, FKBP9, FLII, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOT1, FMNL1, FN1, FNBP1, FOLH1, FOXF2, FSCN1, FSTL1, FTH1, FTL, FYB, FYN, GOS2, G6PD, GALIG, GALNT6, GAPD, GAS7, GATA2, GATA3, GFPT1, GIMP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GMFG, GNA15, GNAI2, GNAQ, GNB2, GNB5, GOT2, GPNMB, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GYPC, GZMA, GZMB, H1F0, H1FX, H2AFX, H3F3A, HA-1, HCLS1, HEM1, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGB2, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HPRT1, HRMT1L1, HSA9761, HSPA5, HSU79274, HTATSF1, HU6800, ICAM1, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGFBP3, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIG1 IQGAP1, IQGAP2, IRS2, ITGA3, ITGA5, ITGB2, ITK, ITM2A, JAK1, JARID2, JUNB, K-ALPHA-1, KHDRBS1, KIAA0220, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1128, KIAA1393, KIFC1, KPNB1, LAIR1, LAMB1, LAMB3, LAMR1, LAPTM5, LAT, LBR, LCK, LCP1, LCP2, LDHB, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LMNB1, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, M6PRBP1, MAD2L1BP, MAGEB2, MAL, MAN1A1, MAP1B, MAP1LC3B, MAP4K1, MAPK1, MAPRE1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCMI, MDH2, MDK, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MIA, MICA, MLPH, MME, MMP2, MPHOSPH6, MPP1, MPZL1, MRP63, MRPL12, MRPS2, MSN, MT1E, MT1K, MUF1, MVP, MYB, MYC, MYL6, MYL9, MYO1B, NAP1L1, NAP1L2, NARF, NARS, NASP, NBL1, NCL, NCOR2, NDN, NDUFAB1, NDUFS6, NFIL3, NFKBIA, NID2, NIPA2, NK4, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCH1, NPC1, NQO1, NR1D2, NUCB2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OK/SW-c1.56, OPTN, OXA1L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PBEF1, PCBP2, PCCB, PEA15, PFDN5, PFN1, PFN2, PGAM1, PGK1, PHEMX, PHLDA1, PIM2, PITPNC1, PKM2, PLACE, PLAGL1, PLAU, PLAUR, PLCB1, PLEK2, PLEKHCl, PLOD2, PLSCR1, PNAS-4, PNMA2, POLR2F, PON2, PPAP2B, PPIA, PPIF, PPP1R11, PPP2CB, PRF1, PRG1, PRIM1, PRKCA, PRKCB1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRPS1, PRSS11, PRSS23, PSCDBP, PSMB9, PSMC3, PSMC5, PSME2, PTGER4, PTGES2, PTMA, PTOV1, PTP4A3, PTPN7, PTPNS1, PTPRC, PTPRCAP, PTRF, PTS, PURA, PWP1, PYGL, QKI, RAB31, RAB3GAP, RAB7, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RALY, RAP1B, RASGRP2, RBMX, RBPMS, RCN1, REA, RFC3, RFC5, RGC32, RGS3, RHOC, RHOH, RIMS3, RIOK3, RIPK2, RIS1, RNASE6, RNF144, RNPS1, RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL17, RPL18, RPL18A, RPL24, RPL3, RPL32, RPL36A, RPL39, RPL7, RPL9, RPLP0, RPLP2, RPS10, RPS11, RPS15, RPS15A, RPS19, RPS2, RPS23, RPS24, RPS25, RPS27, RPS28, RPS4X, RPS4Y1, RPS6, RPS7, RPS9, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A13, S100A4, SART3, SATB1, SCAP1, SCARB1, SCARB2, SCN3A, SCTR, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT6, SEPT10, SEPW1, SERPINA1, SERPINB1, SERPINB6, SFRS3, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHD1, SHFM1, SHMT2, SIAT1, SKB1, SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMA3, SMAD3, SMARCD3, SMOX, SMS, SND1, SNRPA, SNRPB, SNRPB2, SNRPE, SNRPF, SOD2, SOX4, SP140, SPANXC, SPARC, SPI1, SRF, SRM, SRRM1, SSA2, SSBP2, SSRP1, SSSCA1, STAG3, STAT1, STAT4, STAT5A, STC1, STC2, STMN1, STOML2, SUI1, T3JAM, TACC1, TACC3, TAF5, TAGLN, TALE TAP1, TARP, TBCA, TCF12, TCF4, TCF7, TFDP2, TFPI, TFRC, TGFB1, TIMM17A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB10, TMSNB, TNFAIP3, TNFAIP8, TNFRSF10B, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOP2A, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBA3, TUBGCP3, TUFM, TUSC3, TXN, TXNDC5, UBASH3A, UBB, UBC, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGCG, UGDH, UGT2B17, ULK2, UMPS, UNG, UROD, USP34, USP4, USP7, VASP, VAV1, VIM, VLDLR, VWF, WARS, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, XPO1, ZAP128, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFN1A1, or ZYX.
The nucleic acid sequence of each listed genes is publicly available through the GenBank or RefSeq database. The gene sequences are also included as part of the HG-U133A GeneChip from Affymetrix, Inc.
“Resistant” or “resistance” as used herein means that a cell, a tumor, a patient (e.g., a human), 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, patient, or living organism by less than 10%, 20%, 30%, 40%, 50%, 60%, or 70% relative to the growth of a similar cell not exposed to the treatment. Resistance to treatment can 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 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 patient (e.g., a human), 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, patient, 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 reduced cell growth, and thus, sensitivity to the treatment. A greater reduction in growth indicates more sensitivity to the treatment. By “chemosensitive” or “chemosensitivity” is meant sensitivity to a compound.
“Complement” of a nucleic acid sequence or a “complementary” nucleic acid sequence as used herein refers to an oligonucleotide which is in “antiparallel association” when it is aligned with the nucleic acid sequence such that the 5′ end of one sequence is paired with the 3′ end of the other. Nucleotides and other bases can have complements and may be present in complementary nucleic acids. Bases not commonly found in natural nucleic acids that can 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 can contain mismatched base pairs or unmatched bases. Skilled artisans 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. Typically, complementarity is determined by comparing contiguous nucleic acid sequences.
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, and pseudoisocytosine with G. Anti-sense RNA can be complementary to other oligonucleotides, e.g., mRNA.
“Biomarker” as used herein indicates a transcription product (e.g., RNA, such as an RNA primary transcript, mRNA, tRNA, rRNA, microRNA (miRNA), or complementary RNA or DNA (e.g., cDNA) strands thereof) or a translation product (e.g., a polypeptide or metabolite thereof) of a biomarker gene, as defined herein, whose level of expression indicates the sensitivity or resistance of a cell (e.g., a cancer cell), tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
“Compound” as used herein means a chemical or biological substance, e.g., a drug, a protein, an antibody, or an oligonucleotide, which can be used to treat a disease or which has biological activity in vivo or in vitro. Compounds may or may not be approved by the U.S. Food and Drug Administration (FDA). Preferred compounds include, e.g., chemotherapy agents that can inhibit cancer growth. Preferred chemotherapy agents include, 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), Rituximab (e.g., MABTHERATM), histone deacetylase (HDAC) inhibitors, and 5-Aza-2′-deoxycytidine (Decitabine). Exemplary radioactive chemotherapeutic agents include compounds containing alpha emitters such as astatine-211, bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-11, nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71, arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-110, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetium-99m, and technetium-99, and gamma emitters such as cobalt-60, cesium-137, and technetium-99m. Exemplary chemotherapeutic agents also include antibodies such as Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERATM), Trastuzumab (e.g., HERCEPTINTM), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-t1, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201, G-250, ACA-125, Onyvax-105, CDP-860, BrevaRex MAb, AR54, IMC-1C11, GlioMAb-H, ING-1, Anti-LCG MAbs, MT-103, KSB-303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb, CAT, Prostate cancer antibody, H22xKi-4, ABX-MA1, Imuteran, and Monopharm-C. Exemplary chemotherapeutic agents also include Acivicin; Aclarubicin; Acodazole Hydrochloride; Acronine; Adozelesin; Adriamycin; Aldesleukin; Altretamine; Ambomycin; A. metantrone Acetate; Aminoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Camptothecin; Caracemide; Carbetimer; Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirolemycin; Cisplatin; Cladribine; Combretestatin A-4; Crisnatol Mesylate; Cyclophosphamide; Cytarabine; Dacarbazine; DACA (N-[2-(Dimethyl-amino) ethyl]acridine-4-carboxamide); Dactinomycin; Daunorubicin Hydrochloride; Daunomycin; Decitabine; Dexormaplatin; Dezaguanine; Dezaguanine Mesylate; Diaziquone; Docetaxel; Dolasatins; Doxorubicin; Doxorubicin Hydrochloride; Droloxifene; Droloxifene Citrate; Dromostanolone Propionate; Duazomycin; Edatrexate; Eflornithine Hydrochloride; Ellipticine; Elsamitrucin; Enloplatin; Enpromate; Epipropidine; Epirubicin Hydrochloride; Erbulozole; Esorubicin Hydrochloride; Estramustine; Estramustine Phosphate Sodium; Etanidazole; Ethiodized Oil I 131; Etoposide; Etoposide Phosphate; Etoprine; Fadrozole Hydrochloride; Fazarabine; Fenretinide; Floxuridine; Fludarabine Phosphate; Fluorouracil; 5-FdUMP; Fluorocitabine; Fosquidone; Fostriecin Sodium; Gemcitabine; Gemcitabine Hydrochloride; Gold Au 198; Homocamptothecin; Hydroxyurea; Idarubicin Hydrochloride; Ifosfamide; Ilmofosine; Interferon Alfa-2a; Interferon Alfa-2b; Interferon Alfa-n1; Interferon Alfa-n3; Interferon Beta-I a; Interferon Gamma-I b; Iproplatin; Irinotecan Hydrochloride; Lanreotide Acetate; Letrozole; Leuprolide Acetate; Liarozole Hydrochloride; Lometrexol Sodium; Lomustine; Losoxantrone Hydrochloride; Masoprocol; Maytansine; Mechlorethamine Hydrochloride; Megestrol Acetate; Melengestrol Acetate; Melphalan; Menogaril; Mercaptopurine; Methotrexate; Methotrexate Sodium; Metoprine; Meturedepa; Mitindomide; Mitocarcin; Mitocromin; Mitogillin; Mitomalcin; Mitomycin; Mitosper; Mitotane; Mitoxantrone Hydrochloride; Mycophenolic Acid; Nocodazole; Nogalamycin; Ormaplatin; Oxisuran; Paclitaxel; Pegaspargase; Peliomycin; Pentamustine; PeploycinSulfate; Perfosfamide; Pipobroman; Piposulfan; Piroxantrone Hydrochloride; Plicamycin; Plomestane; Porfimer Sodium; Porfiromycin; Prednimustine; Procarbazine Hydrochloride; Puromycin; Puromycin Hydrochloride; Pyrazofurin; Rhizoxin; Rhizoxin D; Riboprine; Rogletimide; Safingol; Safingol Hydrochloride; Semustine; Simtrazene; Sparfosate Sodium; Sparsomycin; Spirogermanium Hydrochloride; Spiromustine; Spiroplatin; Streptonigrin; Streptozocin; Strontium Chloride Sr 89; Sulofenur; Talisomycin; Taxane; Taxoid; Tecogalan Sodium; Tegafur; Teloxantrone Hydrochloride; Temoporfin; Teniposide; Teroxirone; Testolactone; Thiamiprine; Thioguanine; Thiotepa; Thymitaq; Tiazofurin; Tirapazamine; Tomudex; TOP53; Topotecan Hydrochloride; Toremifene Citrate; Trestolone Acetate; Triciribine Phosphate; Trimetrexate; Trimetrexate Glucuronate; Triptorelin; Tubulozole Hydrochloride; Uracil Mustard; Uredepa; Vapreotide; Verteporfin; Vinblastine; Vinblastine Sulfate; Vincristine; Vincristine Sulfate; Vindesine; Vindesine Sulfate; Vinepidine Sulfate; Vinglycinate Sulfate; Vinleurosine Sulfate; Vinorelbine Tartrate; Vinrosidine Sulfate; Vinzolidine Sulfate; Vorozole; Zeniplatin; Zinostatin; Zorubicin Hydrochloride; 2-Chlorodeoxyadenosine; 2′ Deoxyformycin; 9-aminocamptothecin; raltitrexed; N-propargyl-5,8-dideazafolic acid; 2-chloro-2′-arabino-fluoro-2′-deoxyadenosine; 2-chloro-2′-deoxyadenosine; anisomycin; trichostatin A; hPRL-G129R; CEP-751; linomide; sulfur mustard; nitrogen mustard (mechlor ethamine); cyclophosphamide; melphalan; chlorambucil; ifosfamide; busulfan; N-methyl-Nnitrosourea (MNU); N,N′-Bis(2-chloroethyl)-N-nitrosourea (BCNU); N-(2-chloroethyl)-N′ cyclohexyl-N-nitrosourea (CCNU); N-(2-chloroethyl)-N′-(trans-4-methylcyclohexyl-N-nitrosourea (MeCCNU); N-(2-chloroethyl)-N′-(diethyl)ethylphosphonate-N-nitrosourea (fotemustine); streptozotocin; diacarbazine (DTIC); mitozolomide; temozolomide; thiotepa; mitomycin C; AZQ; adozelesin; Cisplatin; Carboplatin; Ormaplatin; Oxaliplatin; C1-973; DWA 2114R; JM216; JM335; Bis(platinum); tomudex; azacitidine; cytarabine; gemcitabine; 6-Mercaptopurine; 6-Thioguanine; Hypoxanthine; teniposide 9-amino camptothecin; Topotecan; CPT-11; Doxorubicin; Daunomycin; Epirubicin; darubicin; mitoxantrone; losoxantrone; Dactinomycin (Actinomycin D); amsacrine; pyrazoloacridine; all-trans retinol; 14-hydroxy-retro-retinol; all-trans retinoic acid; N-(4-Hydroxyphenyl) retinamide; 13-cis retinoic acid; 3-Methyl TTNEB; 9-cis retinoic acid; fludarabine (2-F-ara-AMP); and 2-chlorodeoxyadenosine (2-Cda).
Other chemotherapeutic agents include, but are not limited to, 20-pi-1,25 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; antiestrogen; antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA; argininedeaminase; asulacrine; atamestane; atrimustine; axinastatin 1; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin III derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bleomycin A2; bleomycin B2; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives (e.g., 10-hydroxy-camptothecin); canarypox IL-2; capecitabine; carboxamide-amino-triazole; carboxyamidotriazole; CaRest M3; CARN 700; cartilage derived inhibitor; carzelesin; casein kinase inhibitors (ICOS); castanospermine; cecropin B; cetrorelix; chlorins; chloroquinoxaline sulfonamide; cicaprost; cis-porphyrin; cladribine; clomifene analogues; clotrimazole; collismycin A; collismycin B; combretastatin A4; combretastatin analogue; conagenin; crambescidin 816; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam; cypemycin; cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; 2′ deoxycoformycin (DCF); deslorelin; dexifosfamide; dexrazoxane; dexverapamil; diaziquone; didemnin B; didox; diethylnorspermine; dihydro-5-azacytidine; 9-dihydrotaxol; dioxamycin; diphenyl spiromustine; discodermolide; docosanol; dolasetron; doxifluridine; droloxifene; dronabinol; duocarmycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; emitefur; epirubicin; epothilones (A, R═H; B, R=Me); epithilones; epristeride; estramustine analogue; estrogen agonists; estrogen antagonists; etanidazole; etoposide; etoposide 4′-phosphate (etopofos); exemestane; fadrozole; fazarabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfenimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene bisacetamide; homoharringtonine (HHT); hypericin; ibandronic acid; idarubicin; idoxifene; idramantone; ilmofosine; ilomastat; imidazoacridones; imiquimod; immunostimulant peptides; insulin-like growth factor-1 receptor inhibitor; interferon agonists; interferons; interleukins; iobenguane; iododoxorubicin; 4-ipomeanol; irinotecan; iroplact; irsogladine; isobengazole; isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F; lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate; leptolstatin; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide, estrogen, and progesterone combinations; leuprorelin; levamisole; liarozole; linear polyamine analogue; lipophilic disaccharide peptide; lipophilic platinum compounds; lissoclinamide 7; lobaplatin; lombricine; lometrexol; lonidamine; losoxantrone; lovastatin; loxoribine; lurtotecan; lutetium texaphyrin; lysofylline; lytic peptides; maytansine; mannostatin A; marimastat; masoprocol; maspin; matrilysin inhibitors; matrix metalloproteinase inhibitors; menogaril; merbarone; meterelin; methioninase; metoclopramide; MIF inhibitor; ifepristone; miltefosine; mirimostim; mismatched double stranded RNA; mithracin; mitoguazone; mitolactol; mitomycin analogues; mitonafide; mitotoxin fibroblast growth factor-saporin; mitoxantrone; mofarotene; molgramostim; monoclonal antibody, human chorionic gonadotrophin; monophosphoryl lipid A and myobacterium cell wall skeleton combinations; mopidamol; multiple drug resistance gene inhibitor; multiple tumor suppressor 1-based therapy; mustard anticancer agent; mycaperoxide B; mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone and pentazocine combinations; napavin; naphterpin; nartograstim; nedaplatin; nemorubicin; neridronic acid; neutral endopeptidase; nilutamide; nisamycin; nitric oxide modulators; nitroxide antioxidant; nitrullyn; 06-benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin; oxaunomycin; paclitaxel analogues; paclitaxel derivatives; palauamine; palmitoylrhizoxin; pamidronic acid; panaxytriol; panomifene; parabactin; pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin; phenylacetate; phosphatase inhibitors; picibanil; pilocarpine hydrochloride; pirarubicin; piritrexim; placetin A; placetin B; plasminogen activator inhibitor; platinum complex; platinum compounds; platinum-triamine complex; podophyllotoxin; porfimer sodium; porfiromycin; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide; rogletimide; rohitukine; romurtide; roquinimex; rubiginone B 1; ruboxyl; safingol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofuran; sobuzoxane; sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1; squalamine; stem cell inhibitor; stem-cell division inhibitors; stipiamide; stromelysin inhibitors; sulfinosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfin; temozolomide; teniposide; tetrachlorodecaoxide; tetrazomine; thaliblastine; thalidomide; thiocoraline; thrombopoietin; thrombopoietin mimetic; thymalfasin; thymopoietin receptor agonist; thymotrinan; thyroid stimulating hormone; tin ethyl etiopurpurin; tirapazamine; titanocene dichloride; topotecan; topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex; urogenital sinus-derived growth inhibitory factor; urokinase receptor antagonists; vapreotide; variolin B; vector system, erythrocyte gene therapy; velaresol; veramine; verdins; verteporfin; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; and zinostatin stimalamer.
To “inhibit growth” as used herein means causing a reduction in cell growth in vivo or in vitro by, e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% or more, as evident by a reduction in the size or number of cells exposed to a treatment (e.g., exposure to a compound), relative to the size or number of cells in the absence of the treatment. Growth inhibition can 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.
“Biomarker gene” as used herein means a gene in a cell (e.g., a cancer cell) the expression of which, as measured by, e.g., detecting the level of one or more biomarkers produced from the gene, correlates to sensitivity or resistance of the cell, tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
“Microarray” as used herein means a device employed by any method that quantifies one or more subject oligonucleotides, e.g., DNA or RNA, or analogues thereof, at a time. One exemplary class of microarrays consists of DNA probes attached to a glass or quartz surface. Many microarrays, e.g., those made by Affymetrix, use several probes for determining the expression of a single gene. The DNA microarray can 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 can 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(1)(suppl.):1-60 (1999); and Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000). Additionally, exemplary nucleic acid microarrays can include a substrate-bound plurality of nucleic acids in which the plurality of nucleic acids is disposed on a plurality of beads, rather than on a unitary planar substrate, as is described, inter alia, in Brenner et al., Proc. Natl. Acad. Sci. USA 97(4):1665-1670 (2000). Examples of nucleic acid microarrays may be found in U.S. Pat. Nos. 6,391,623, 6,383,754, 6,383,749, 6,380,377, 6,379,897, 6,376,191, 6,372,431, 6,351,712 6,344,316, 6,316,193, 6,312,906, 6,309,828, 6,309,824, 6,306,643, 6,300,063, 6,287,850, 6,284,497, 6,284,465, 6,280,954, 6,262,216, 6,251,601, 6,245,518, 6,263,287, 6,251,601, 6,238,866, 6,228,575, 6,214,587, 6,203,989, 6,171,797, 6,103,474, 6,083,726, 6,054,274, 6,040,138, 6,083,726, 6,004,755, 6,001,309, 5,958,342, 5,952,180, 5,936,731, 5,843,655, 5,814,454, 5,837,196, 5,436,327, 5,412,087, and 5,405,783, herein incorporated by reference.
Exemplary microarrays can 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 the plurality of bound polypeptides being separately detectable. Alternatively, the peptide microarray, can have a plurality of binders, including, but not limited to, monoclonal antibodies, polyclonal antibodies, phage display binders, yeast 2 hybrid binders, aptamers, that can specifically detect the binding of specific oligonucleotides, peptides, or proteins. Examples of peptide arrays may be found in International Patent Publication Nos. 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, and WO 97/42507, and in U.S. Pat. Nos. 6,268,210, 5,766,960, and 5,143,854, herein incorporated by reference.
“Gene expression” as used herein means the level of expression of a biomarker gene (e.g., the level of a transcription product, such as an mRNA, tRNA, or microRNA, or its complement (e.g., a cDNA complement of the transcription product), or a translation product, such as a polypeptide or metabolite thereof) in a cell, tissue, organism, or patient (e.g., a human). Gene expression can be measured by detecting the presence, quantity, or activity of a DNA, RNA, or polypeptide, or modifications thereof (e.g., splicing, phosphorylation, and acetylation) 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_NCIH522, NSCLC_A549ATCC, NSCLC_EKVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC_H460, NSCLC_HOP62, NSCLC_HOP92, COLON_HT29, COLON_HCC-2998, COLON_HCT116, COLON_SW620, COLON_COLO205, COLON_HCT15, COLON_KM12, BREAST_MCF7, BREAST_MCF7ADRr, BREAST_MDAMB231, BREAST_HS578T, BREAST_MDAMB435, BREAST_MDN, BREAST_BT549, BREAST_T47D, OVAR_OVCAR3, OVAR_OVCAR4, OVAR_OVCAR5, OVAR_OVCAR8, OVAR_IGROV1, OVAR_SKOV3, LEUK_CCRFCEM, LEUK_K562, LEUK_MOLT4, LEUK_HL60, LEUK_RPMI8266, LEUK_SR, RENAL_UO31, RENAL_SN12C, RENAL_A498, RENAL_CAKI1, RENAL_RXF393, RENAL—7860, RENAL_ACHN, RENAL_TK10, MELAN_LOXIMVI, MELAN_MALME3M, MELAN_SKMEL2, MELAN_SKMEL5, MELAN_SKMEL28, MELAN_M14, MELAN_UACC62, MELAN_UACC257, PROSTATE_PC3, PROSTATE_DU145, CNS_SNB19, CNS_SNB75, CNS_U251, CNS_SF268, CNS_SF295, and CNS_SF539.
“Treatment” or “medical treatment” means administering to a patient (e.g., a human) 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 patient or living organism.
Other features and advantages of the invention will be apparent from the following description, drawings, and claims.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts an illustration of the method of identifying biomarkers and predicting patient sensitivity to a medical treatment. The method has an in vitro component where the growth inhibition of a compound or medical treatment is measured on cell lines (6 of the 60 cell lines tested are shown). The gene expression is measured on the same cell lines without compound treatment. Those genes that have a correlation above a certain cutoff (e.g., a preffered cutoff of 0.3, in which a correlation coefficient equal to or greater than the cutoff of 0.3 is deemed statistcally significant by, e.g., cross-validation) to the growth inhibition are termed marker genes and the expression of those genes in vivo, e.g., may predict the sensitivity or resistance of a patient's cancer to a compound or other medical treatment. The in vivo component is applied to a patient to determine whether or not the treatment will be effective in treating disease in the patient. Here, the gene expression in cells of a sample of the suspected disease tissue (e.g., a tumor) in the patient is measured before or after treatment. The activity of the marker genes in the sample is compared to a reference population of patients known to be sensitive or resistant to the treatment. The expression of marker genes in the cells of the patient known to be expressed in the cells of reference patients sensitive to the treatment indicates that the patient to be treated is sensitive to the treatment and vice versa. Based on this comparison the patient is predicted to be sensitive or resistant to treatment with the compound.
FIG. 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.
FIG. 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 chemotheraphydrugs. The subject received Vincristine and Cisplatin, but passed away 9 months later.
FIG. 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.
FIG. 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.
FIG. 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.
FIG. 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.
FIG. 8 depicts the predictions of sensitivity or resistance to treatment of a patient diagnosed with DLBCL. Various drug combinations and radiation therapy are considered. The drug combinations (indicated by abbreviations) are those commonly used to treat DLBCL.
FIG. 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.
FIG. 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 FIG. 9.
FIG. 11 depicts the predicted sensitivity of cancer patients to sunitinib. The cancer patients are grouped according to cancer type or origin and cancer types with predicted high sensitivity are labeled.



DETAILED DESCRIPTION
The invention features methods for identifying biomarkers of treatment sensitivity, e.g., chemosensitivity to compounds, or resistance, devices that include the biomarkers, kits that include the devices, and methods for predicting treatment efficacy in a patient (e.g., a human diagnosed with cancer). The kits of the invention include microarrays having oligonucleotide probes that are biomarkers of sensitivity or resistance to treatment (e.g., treatment with a chemotherapeutic agent) that hybridize to nucleic acids derived from or obtained from a subject and instructions for using the device to predict the sensitivity or resistance of the subject to the treatment. The invention also features methods of using the microarrays to determine whether a subject, e.g., a cancer patient, will be sensitive or resistant to treatment with, e.g., a chemotherapy agent. Also featured are methods of identifying biomarkers of sensitivity or resistance to a medical treatment based on the correlation of gene or microRNA expression to treatment efficacy, e.g., the growth inhibition of cancer cells. Gene or microRNA biomarkers that identify subjects as sensitive or resistant to a treatment can 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 general population. More generally, cancer patient sensitivity to a compound or other medical treatment can be predicted using biomarker 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 (e.g., a computer) for measuring biomarker expression in connection with a microarray. The microarray (e.g., a DNA microarray), included in a kit for processing a tumor sample from a patient, and the apparatus for reading the microarray and turning the result into a chemosensitivity profile for the patient may be used to implement the methods of the invention.
Microarrays Containing Oligonucleotide Probes
The microarrays of the invention include one or more oligonucleotide probes that have nucleotide sequences that are substantially identical to or substantially 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 or biomarker gene products (e.g., transcription or translation gene products, such as microRNAs) listed below. The oligonucleotide probes may be, e.g., 5-20, 25, 5-50, 50-100, or over 100 nucleotides long. The oligonucleotide probes may be deoxyribonucleic acids (DNA) or ribonucleic acids (RNA). Consecutive nucleotides within the oligonucleotide probes (e.g., 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides), which are used as biomarkers of chemosensitivity, may also appear as consecutive nucleotides in one or more of the genes described herein beginning at or near, e.g., the first, tenth, twentieth, thirtieth, fortieth, fiftieth, sixtieth, seventieth, eightieth, ninetieth, hundredth, hundred-fiftieth, two-hundredth, five-hundredth, or one-thousandth nucleotide of the genes or microRNAs listed in Tables 1-136 below. Column List—2006 of Tables 1-21 indicates the preferred biomarker genes for the compound lists. Column List_Preferred of Tables 1-21 indicates the most preferred biomarker genes. 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. Tables 80-136 indicate microRNA biomarkers that can be used to determine a patient's (e.g., a human's) sensitivity to a treatment. The following combinations of biomarkers have been used to detect a patient's sensitivity to the indicated treatment:
a) One or more of the gene sequences SFRS3, CCT5, RPL39, SLC25A5, UBE2S, EEF1A1, RPLP2, RPL24, RPS23, RPL39, RPL18, NCL, RPL9, RPL10A, RPS10, EIF3S2, SHFM1, RPS28, REA, RPL36A, GAPD, HNRPA1, RPS11, HNRPA1, LDHB, RPL3, RPL11, MRPL12, RPL18A, COX7B, and RPS7, preferably gene sequences UBB, RPS4X, S100A4, NDUFS6, B2M, C14orf139, MAN1A1, SLC25A5, RPL10, RPL12, EIF5A, RPL36A, SUI1, BLMH, CTBP1, TBCA, MDH2, and DXS9879E, and most preferably gene sequences RPS4X, S100A4, NDUFS6, C14orf139, SLC25A5, RPL10, RPL12, EIF5A, RPL36A, BLMH, CTBP1, TBCA, MDH2, and DXS9879E, whose expression indicates chemosensitivity to Vincristine.

b) One or more of the gene sequences B2M, ARHGDIB, FTL, NCL, MSN, SNRPF, XPO1, LDHB, SNRPF, GAPD, PTPN7, ARHGDIB, RPS27, IFI16, C5orf13, and HCLS1, preferably gene sequences C1QR1, HCLS1, CD53, SLA, PTPN7, PTPRCAP, ZNFN1A1, CENTB1, PTPRC, 1E116, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRF1, ARHGAP15, TM6SF1, and TCF4, and most preferably gene sequences C1QR1, SLA, PTPN7, ZNFN1A1, CENTB1, 1E116, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRF1, ARHGAP15, TM6SF1, and TCF4, whose expression indicates chemosensitivity to Cisplatin.

c) One or more of the gene sequences PRPS1, DDOST, B2M, SPARC, LGALS1, CBFB, SNRPB2, MCAM, MCAM, EIF2S2, HPRT1, SRM, FKBP1A, GYPC, UROD, MSN, HNRPA1, SND1, COPA, MAPRE1, EIF3S2, ATP1B3, EMP3, ECM1, ATOX1, NARS, PGK1, OK/SW-c1.56, FN1, EEF1A1, GNAI2, PRPS1, RPL7, PSMB9, GPNMB, PPP1R11, MIA, RAB7, VIM, and SMS, preferably gene sequences MSN, SPARC, VIM, SRM, SCARB1, SIAT1, CUGBP2, GAS7, ICAM1, WASPIP, ITM2A, PALM2-AKAP2, ANPEP, PTPNS1, MPP1, LNK, FCGR2A, EMP3, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCP1, 1E116, MCAM, MEF2C, SLC1A4, BTN3A2, FYN, FN1, C1orf38, CHS1, CAPN3, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-1, FLJ10539, FLJ35036, DOCK10, TRPV2, IFRG28, LEF1, and ADAMTS1, and most preferably gene sequences SRM, SCARB1, SIAT1, CUGBP2, ICAM1, WASPIP, ITM2A, PALM2-AKAP2, PTPNS1, MPP1, LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCP1, IFI16, MCAM, MEF2C, SLC1A4, FYN, C1orf38, CHS1, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-1, FLJ10539, FLJ35036, DOCK10, TRPV2, IFRG28, LEF1, and ADAMTS1, whose expression indicates chemosensitivity to Azaguanine.

d) One or more of the gene sequences B2M, MYC, CD99, RPS24, PPIF, PBEF1, and ANP32B, preferably gene sequences CD99, INSIG1, LAPTM5, PRG1, MUF1, HCLS1, CD53, SLA, SSBP2, GNB5, MFNG, GMFG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP, ZNFN1A1, CENTB1, PTPRC, NAP1L1, HLA-DRA, IFI16, CORO1A, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRF1, ARHGAP15, NOTCH1, and UBASH3A, and most preferably gene sequences CD99, INSIGE PRG1, MUF1, SLA, SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFN1A1, CENTB1, NAP1L1, HLA-DRA, IFI16, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRF1, ARHGAP15, NOTCH1, and UBASH3A, whose expression indicates chemosensitivity to Etoposide.

e) One or more of the gene sequences KIAA0220, B2M, TOP2A, CD99, SNRPE, RPS27, HNRPA1, CBX3, ANP32B, HNRPA1, DDX5, PPIA, SNRPF, and USP7, preferably gene sequences CD99, LAPTM5, ALDOC, HCLS1, CD53, SLA, SSBP2, IL2RG, GMFG, CXorf9, RHOH, PTPRCAP, ZNFN1A1, CENTB1, TCF7, CD1C, MAP4K1, CD1B, CD3G, PTPRC, CCR9, CORO1A, CXCR4, ARHGEF6, HEM1, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CD1A, LAIR1, ITK, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIF1, ARHGAP15, NARF, and PACAP, and most preferably gene sequences CD99, ALDOC, SLA, SSBP2, IL2RG, CXorf9, RHOH, ZNFN1A1, CENTB1, CD1C, MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CD1A, LAIR1, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIF1, ARHGAP15, NARF, and PACAP, whose expression indicates chemosensitivity to Adriamycin.

f) One or more of the gene sequences RPLP2, LAMR1, RPS25, EIF5A, TUFM, HNRPA1, RPS9, MYB, LAMR1, ANP32B, HNRPA1, HNRPA1, EIF4B, HMGB2, RPS15A, and RPS7, preferably gene sequences RPL12, RPL32, RPLP2, MYB, ZNFN1A1, SCAP1, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, FBL, RPS2, PTPRC, DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD, and most preferably gene sequences RPL12, RPLP2, MYB, ZNFN1A1, SCAP1, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD, whose expression indicates chemosensitivity to Aclarubicin.

g) One or more of the gene sequences ARHGEF6, B2M, TOP2A, TOP2A, ELA2B, PTMA, LMNB1, TNFRSF1A, NAP1L1, B2M, HNRPA1, RPL9, C5orf13, NCOR2, ANP32B, OK/SW-c1.56, TUBA3, HMGN2, PRPS1, DDX5, PRG1, PPIA, G6PD, PSMB9, SNRPF, and MAP1B, preferably gene sequences PGAM1, DPYSL3, INSIG1, GJA1, BNIP3, PRG1, G6PD, BASP1, PLOD2, LOXL2, SSBP2, C1orf29, TOX, STC1, TNFRSF1A, NCOR2, NAP1L1, LOC94105, COL6A2, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AF1Q, MAP1B, PTPRC, PRKCA, TRIM22, CD3D, BCAT1, IFI44, CCL2, RAB31, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, and most preferably gene sequences PGAM1, DPYSL3, INSIG1, GJA1, BNIP3, PRG1, G6PD, PLOD2, LOXL2, SSBP2, C1orf29, TOX, STC1, TNFRSF1A, NCOR2, NAP1L1, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AF1Q, MAP1B, TRIM22, CD3D, BCAT1, IFI44, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, whose expression indicates chemosensitivity to Mitoxantrone.

h) One or more of the gene sequences GAPD, GAPD, GAPD, TOP2A, SUI1, TOP2A, FTL, HNRPC, TNFRSF1A, SHCl, CCT7, P4HB, CTSL, DDX5, G6PD, and SNRPF, preferably gene sequences STC1, GPR65, DOCK10, COL5A2, FAM46A, and LOC54103, and most preferably gene sequences STC1, GPR65, DOCK10, COL5A2, FAM46A, and LOC54103, whose expression indicates chemosensitivity to Mitomycin.

i) One or more of the gene sequences RPS23, SFRS3, KIAA0114, RPL39, SFRS3, LOC51035, RPS6, EXOSC2, RPL35, IFRD2, SMN2, EEF1A1, RPS3, RPS18, and RPS7, preferably gene sequences RPL10, RPS4X, NUDC, RALY, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEP1, IL13RA2, MAGEB2, HMGN2, ALMS1, GPR65, FLJ10774, NOL8, DAZAP1, SLC25A15, PAF53, DXS9879E, PITPNC1, SPANXC, and KIAA1393, and most preferably RPL10, RPS4X, NUDC, DKCl, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEP1, IL13RA2, MAGEB2, HMGN2, ALMS1, GPR65, FLJ10774, NOL8, DAZAP1, SLC25A15, PAF53, DXS9879E, PITPNC1, SPANXC, and KIAA1393, whose expression indicates chemosensitivity to Paclitaxel.

j) One or more of the gene sequences CSDA, LAMR1, and TUBA3, preferably gene sequences PFN1, PGAM1, K-ALPHA-1, CSDA, UCHL1, PWP1, PALM2-AKAP2, TNFRSF1A, ATP5G2, AF1Q, NME4, and FHOD1, and most preferably gene sequences PFN1, PGAM1, K-ALPHA-1, CSDA, UCHL1, PWP1, PALM2-AKAP2, TNFRSF1A, ATP5G2, AF1Q, NME4, and FHOD1, whose expression indicates chemosensitivity to Gemcitabine.

k) One or more of the gene sequences RPS23, SFRS3, KIAA0114, SFRS3, RPS6, DDX39, and RPS7, preferably gene sequences ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFC1, EPB41L4B, and CALML4, and most preferably gene sequences ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFC1, EPB41L4B, and CALML4, whose expression indicates chemosensitivity to Taxotere.

l) One or more of the gene sequences IL2RG, H1FX, RDBP, ZAP70, CXCR4, TM4SF2, ARHGDIB, CDA, CD3E, STMN1, GNA15, AXL, CCND3, SATB1, EIF5A, LCK, NKX2-5, LAPTM5, IQGAP2, FLII, EIF3S5, TRB, CD3D, HOXB2, GATA3, HMGB2, PSMB9, ATP5G2, CORO1A, ARHGDIB, DRAP1, PTPRCAP, RHOH, and ATP2A3, preferably gene sequences IFITM2, UBE2L6, LAPTM5, USP4, ITM2A, ITGB2, ANPEP, CD53, IL2RG, CD37, GPRASP1, PTPN7, CXorf9, RHOH, GIT2, ADORA2A, ZNFN1A1, GNA15, CEP1, TNFRSF7, MAP4K1, CCR7, CD3G, PTPRC, ATP2A3, UCP2, CORO1A, GATA3, CDKN2A, HEM1, TARP, LAIR1, SH2D1A, FLII, SEPT6, HA-1, CREB3L1, ERCC2, CD3D, LST1, A1F1, ADA, DATF1, ARHGAP15, PLAC8, CECR1, LOC81558, and EHD2, and most preferably gene sequences IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASP1, PTPN7, CXorf9, RHOH, GIT2, ZNFN1A1, CEP1, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIR1, SH2D1A, SEPT6, HA-1, ERCC2, CD3D, LST1, A1F1, ADA, DATF1, ARHGAP15, PLAC8, CECR1, LOC81558, and EHD2, whose expression indicates chemosensitivity to Dexamethasone.

m) One or more of the gene sequences TM4SF2, ARHGDIB, ADA, H2AFZ, NAP1L1, CCND3, FABP5, LAMR1, REA, MCM5, SNRPF, and USP7, preferably gene sequences ITM2A, RHOH, PRIM1, CENTB1, GNA15, NAP1L1, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CD1E, ADA, and FHOD1, and most preferably gene sequences ITM2A, RHOH, PRIM1, CENTB1, NAP1L1, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CD1E, ADA, and FHOD1, whose expression indicates chemosensitivity to Ara-C.

n) One or more of the gene sequences LGALS9, CD7, IL2RG, PTPN7, ARHGEF6, CENTB1, SEPT6, SLA, LCP1, IFITM1, ZAP70, CXCR4, TM4SF2, ZNF91, ARHGDIB, TFDP2, ADA, CD99, CD3E, CD1C, STMN1, CD53, CD7, GNA15, CCND3, MAZ, SATB1, ZNF22, AES, AIF1, MYB, LCK, C5orf13, NKX2-5, ZNFN1A1, STAT5A, CHI3L2, LAPTM5, MAP4K1, DDX11, GPSM3, TRB, CD3D, CD3G, PRKCB1, CD1E, HCLS1, GATA3, TCF7, RHOG, CDW52, HMGB2, DGKA, ITGB2, PSMB9, IDH2, AES, MCM5, NUCB2, CORO1A, ARHGDIB, PTPRCAP, CD47, RHOH, LGALS9, and ATP2A3, preferably gene sequences CD99, SRRM1, ARHGDIB, LAPTM5, VWF, ITM2A, ITGB2, LGALS9, INPPSD, SATB1, CD53, TFDP2, SLA, IL2RG, MFNG, CD37, GMFG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, PTPRCAP, GIT2, ZNFN1A1, CENTB1, LCP2, SPI1, GNA15, GZMA, CEP1, BLM, CD8A, SCAP1, CD2, CD1C, TNFRSF7, VAV1, MAP4K1, CCR7, C6orf32, ALOX15B, BRDT, CD3G, PTPRC, LTB, ATP2A3, NVL, RASGRP2, LCP1, CORO1A, CXCR4, PRKD2, GATA3, TRA@, PRKCB1, HEM1, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CD1A, LST1, LAIR1, CACNA1G, TRB@, SEPT6, HA-1, DOCK2, CD3D, TRD@, T3JAM, ENBP1, CD6, AIF1, FOLH1, CD1E, LY9, UGT2B17, ADA, CDKL5, TRIM, EVL, DATF1, RGC32, PRKCH, ARHGAP15, NOTCH1, BIN2, SEMA4G, DPEP2, CECR1, BCL11B, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEF1, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, and most preferably gene sequences CD99, ARHGDIB, VWF, ITM2A, LGALS9, INPP5D, SATB1, TFDP2, SLA, IL2RG, MFNG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNEN1A1, CENTB1, LCP2, SPI1, GZMA, CEP1, CD8A, SCAP1, CD2, CD1C, TNFRSF7, VAV1, MAP4K1, CCR7, C6orf32, ALOX15B, BRDT, CD3G, LTB, ATP2A3, NVL, RASGRP2, LCP1, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CD1A, LST1, LAIR1, CACNA1G, TRB@, SEPT6, HA-1, DOCK2, CD3D, TRD@, T3JAM, ENBP1, CD6, AIF1, FOLH1, CD1E, LY9, ADA, CDKL5, TRIM, EVL, DATF1, RGC32, PRKCH, ARHGAP15, NOTCH1, BIN2, SEMA4G, DPEP2, CECR1, BCL11B, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEF1, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, whose expression indicates chemosensitivity to Methylprednisolone.

o) One or more of the gene sequences RPLP2, RPL4, HMGA1, RPL27, IMPDH2, LAMR1, PTMA, ATPSB, NPM1, NCL, RPS25, RPL9, TRAP1, RPL21, LAMR1, REA, HNRPA1, LDHB, RPS2, NME1, PAICS, EEF1B2, RPS15A, RPL19, RPL6, ATP5G2, SNRPF, SNRPG, and RPS7, preferably gene sequences PRPF8, RPL18, RNPS1, RPL32, EEF1G, GOT2, RPL13A, PTMA, RPS15, RPLP2, CSDA, KHDRBS1, SNRPA, IMPDH2, RPS19, NUP88, ATPSD, PCBP2, ZNF593, HSU79274, PRIM1, PFDN5, OXA1L, H3F3A, ATIC, RPL13, CIAPIN1 FBL, RPS2, PCCB, RBMX, SHMT2, RPLP0, HNRPA1, STOML2, RPS9, SKB1, GLTSCR2, CCNB1IP1, MRPS2, FLJ20859, and FLJ12270, and most preferably gene sequences PRPF8, RPL18, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBS1, SNRPA, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIM1, PFDN5, OXA1L, H3F3A, ATIC, CIAPIN1, RPS2, PCCB, SHMT2, RPLP0, HNRPA1, STOML2, SKB1, GLTSCR2, CCNBHP1, MRPS2, FLJ20859, and FLJ12270, whose expression indicates chemosensitivity to Methotrexate.

p) One or more of the gene sequences ACTB, COL5A1, MT1E, CSDA, COL4A2, MMP2, COL1A1, TNFRSF1A, CFHL1, TGFBI, FSCN1, NNMT, PLAUR, CSPG2, NFIL3, C5orf13, NCOR2, TUBB4, MYLK, TUBA3, PLAU, COL4A2, COL6A2, COL6A3, IFITM2, PSMB9, CSDA, and COL1A1, preferably gene sequences MSN, PFN1, HK1, ACTR2, MCL1, ZYX, RAP1B, GNB2, EPAS1, PGAM1, CKAP4, DUSP1, MYL9, K-ALPHA-1, LGALS1, CSDA, AKR1B1, IFITM2, ITGA5, VIM, DPYSL3, JUNB, ITGA3, NFKBIA, LAMB1, FHL1, INSIG1, TIMP1, GJA1, PSME2, PRG1, EXT1, DKFZP434J154, OPTN, M6PRBP1, MVP, VASP, ARL7, NNMT, TAP1, COL1A1, BASP1, PLOD2, ATF3, PALM2-AKAP2, IL8, ANPEP, LOXL2, TGFB1, IL4R, DGKA, STC2, SEC61G, NFIL3, RGS3, NK4, F2R, TPM2, PSMB9, LOX, STC1, CSPG2, PTGER4, IL6, SMAD3, PLAU, WNT5A, BDNF, TNFRSF1A, FLNC, DKFZP564K0822, FLOT1, PTRF, HLA-B, COL6A2, MGC4083, TNFRSF10B, PLAGL1, PNMA2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCN1, ERP70, AF1Q, UBC, FGFR1, HIC, BAX, COL4A2, COL6A1, IFITM3, MAP1B, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MT1E, CDC10, DOCK2, TRIM22, RIS1, BCAT1, PRF1, DBN1, MT1K, TMSB10, RAB31, FLJ10350, C1orf24, NME7, TMEM22, TPK1, COL5A2, ELK3, CYLD, ADAMTS1, EHD2, and ACTB, and most preferably gene sequences PFN1, HK1, MCL1, ZYX, RAP1B, GNB2, EPAS1, PGAM1, CKAP4, DUSP1, MYL9, K-ALPHA-1, LGALS1, CSDA, IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMB1, FHL1, INSIG1, TIMP1, GJA1, PSME2, PRG1, EXT1, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAP1, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STC1, PTGER4, IL6, SMAD3, WNT5A, BDNF, TNFRSF1A, FLNC, DKFZP564K0822, FLOT1, PTRF, HLA-B, MGC4083, TNFRSF10B, PLAGL1, PNMA2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCN1, ERP70, AF1Q, HIC, COL6A1, IFITM3, MAP1B, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MT1E, CDC10, DOCK2, TRIM22, RIS1, BCAT1, PRF1, DBN1, MT1K, TMSB10, FLJ10350, C1orf24, NME7, TMEM22, TPK1, COL5A2, ELK3, CYLD, ADAMTS1, EHD2, and ACTB, whose expression indicates chemosensitivity to Bleomycin.

q) One or more of the gene sequences NOS2A, MUC1, TFF3, GP1BB, IGLL1, BATF, MYB, PTPRS, NEFL, AlP, CEL, DGKA, RUNX1, ACTR1A, and CLCNKA, preferably gene sequences PTMA, SSRP1, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINA1, SSSCA1, EZH2, MYB, PRIM1, H2AFX, HMGA1, HMMR, TK2, WHSC1, DIAPH1, LAMB3, DPAGT1, UCK2, SERPINB1, MDN1, BRRN1, G0S2, RAC2, MGC21654, GTSE1, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, and most preferably gene sequences SSRP1, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINA1, SSSCA1, EZH2, MYB, PRIM1, H2AFX, HMGA1, HMMR, TK2, WHSC1, DIAPH1, LAMB3, DPAGT1, UCK2, SERPINB1, MDN1, BRRN1, G0S2, RAC2, MGC21654, GTSE1, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, whose expression indicates chemosensitivity to Methyl-GAG.

r) One or more of the gene sequences MSN, ITGA5, VIM, TNFAIP3, CSPG2, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, FGFR1, DOCK10, LEPRE1, COL5A2, and ADAMTS1, and most preferably gene sequences ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, DOCK10, LEPRE1, COL5A2, and ADAMTS1, whose expression indicates chemosensitivity to carboplatin.

s) One or more of the gene sequences RPL18, RPL10A, RNPS1, ANAPC5, EEF1B2, RPL13A, RPS15, AKAP1, NDUFAB1, APRT, ZNF593, MRP63, IL6R, RPL13, SART3, RPS6, UCK2, RPL3, RPL17, RPS2, PCCB, TOMM20, SHMT2, RPLP0, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, and most preferably gene sequences RPL18, RPL10A, ANAPC5, EEF1B2, RPL13A, RPS15, AKAP1, NDUFAB1, APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPL17, RPS2, PCCB, TOMM20, SHMT2, RPLP0, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, whose expression indicates chemosensitivity to 5-FU (5-Fluorouracil).

t) One or more of the gene sequences ITK, KIFC1, VLDLR, RUNX1, PAFAH1B3, H1FX, RNF144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, RALY, PSMC5, CD1D, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CD1A, CD1B, STMN1, PSMC3, RPS4Y1, AKT1, TAL1, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTN1, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, CTNNA1, AKR1C1, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSF1, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, SEPW1, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, TFRC, H1F0, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, ABL1, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFR1, SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, VIM, PLEKHCl, CD47, POLR2F, RHOH, ADD1, and ATP2A3, preferably gene sequences ITK, KIFC1, VLDLR, RUNX1, PAFAH1B3, H1FX, RNF144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, RALY, PSMC5, CD1D, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CD1A, CD1B, STMN1, PSMC3, RPS4Y1, AKT1, TAL1, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTN1, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, CTNNA1, AKR1C1, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSF1, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, SEPW1, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, TFRC, H1F0, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, ABL1, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFR1, SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, VIM, PLEKHCl, CD47, POLR2F, RHOH, ADD1, and ATP2A3, and most preferably gene sequences KIFC1, VLDLR, RUNX1, PAFAH1B3, H1FX, RNF144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, CD1D, ADA, CD99, CD2, CNP, ERG, CD3E, CD1A, PSMC3, RPS4Y1, AKT1, TALE UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, AKR1C1, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, H1F0, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, SOX4, MAL, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, PLEKHCl, CD47, POLR2F, RHOH, and ADD1, whose expression indicates chemosensitivity to Rituximab (e.g., MABTHERATM).

u) One or more of the gene sequences CCL21, ANXA2, SCARB2, MAD2L1BP, CAST, PTS, NBL1, ANXA2, CD151, TRAM2, HLA-A, CRIP2, UGCG, PRSS11, MME, CBR1, LGALS1, DUSP3, PFN2, MICA, FTH1, RHOC, ZAP128, PON2, COL5A2, CST3, MCAM, IGFBP3, MMP2, GALIG, CTSD, ALDH3A1, CSRP1, S100A4, CALD1, CTGF, CAPG, HLA-A, ACTN1, TAGLN, FSTL1, SCTR, BLVRA, COPEB, DIPA, SMARCD3, FN1, CTSL, CD63, DUSP1, CKAP4, MVP, PEA15, S100A13, and ECE1, preferably gene sequences TRA1, ACTN4, WARS, CALM1, CD63, CD81, FKBP1A, CALU, IQGAP1, CTSB, MGC8721, STAT1, TACC1, TM4SF8, CD59, CKAP4, DUSP1, RCN1, MGC8902, LGALS1, BHLHB2, RRBP1, PKM2, PRNP, PPP2CB, CNN3, ANXA2, IER3, JAK1, MARCKS, LUM, FER1L3, SLC20A1, EIF4G3, HEXB, EXT1, TJP1, CTSL, SLC39A6, RIOK3, CRK, NNMT, COL1A1, TRAM2, ADAM9, DNAJC7, PLSCR1, PRSS23, PLOD2, NPC1, TOB1, GFPT1, IL8, DYRK2, PYGL, LOXL2, KIAA0355, UGDH, NFIL3, PURA, ULK2, CENTG2, NID2, CAP350, CXCL1, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDH11, P4HA1, GRP58, ACTN1, CAPN2, DSIPI, MAP1LC3B, GALIG, IGSF4, IRK, ATP2A2, OGT, TNFRSF10B, KIAA1128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, BTN3A2, SLC7A11, MPZL1, IGFBP3, SSA2, FN1, NQO1, ASPH, ASAH1, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A2, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAP1B, MAPK1, MYO1B, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCB1, KIAA0802, KPNB1, RAB3GAP, SERPINB1, TIMM17A, SOD2, HLA-A, NOMO2, L0055831, PHLDA1, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCK10, LRP12, TXNDC5, CDCl4B, HRMT1L1, CORO1C, DNAJC10, TNPO1, LONP, AMIGO2, DNAPTP6, and ADAMTS1, and most preferably gene sequences TRA1, ACTN4, CALM1, CD63, FKBP1A, CALU, IQGAP1, MGC8721, STAT1, TACC1, TM4SF8, CD59, CKAP4, DUSP1, RCN1, MGC8902, LGALS1, BHLHB2, RRBP1, PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXT1, TJP1, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCR1, PRSS23, PLOD2, NPC1, TOB1, GFPT1, IL8, PYGL, LOXL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCL1, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDH11, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRK, ATP2A2, OGT, TNFRSF10B, KIAA1128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A11, MPZL1, SSA2, NQO1, ASPH, ASAH1, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAP1B, MAPK1, MYO1B, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCB1, KIAA0802, RAB3GAP, SERPINBE TIMM17A, SOD2, HLA-A, NOMO2, L0055831, PHLDA1, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCK10, LRP12, TXNDC5, CDC14B, HRMT1L1, CORO1C, DNAJC10, TNPO1, LONP, AMIGO2, DNAPTP6, and ADAMTS1, whose expression indicates sensitivity to radiation therapy.

v) One or more of the gene sequences FAU, NOL5A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVER1, LRMP, ICAM2, RIMS3, FMNL1, MYB, PTPN7, LCK, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, DBT, CEP1, IL6R, VAV1, MAP4K1, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCP1, CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CD1A, GATA2, P2RX5, LAIR1, C1orf38, SH2D1A, TRB@, SEPT6, HA-1, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPA1, AIF1, CYFIP2, GLTSCR2, Cllorf2, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP, and MGC2744, whose expression indicates sensitivity to an HDAC inhibitor.

w) One or more of the gene sequences CD99, SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSE1, MYB, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCP1, SELPLG, CD3Z, PRKCQ, GZMB, SCN3A, LAIR1, SH2D1A, SEPT6, CG018, CD3D, C18orf10, PRF1, AIF1, MCM5, LPXN, C22orf18, ARHGAP15, and LEF1, whose expression indicates sensitivity to 5-Aza-2′-deoxycytidine (Decitabine).

Probes that may be employed on microarrays of the invention include oligonucleotide probes having sequences complementary to any of the biomarker gene or microRNA 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 biomarkers indicative of treatment sensitivity, the correlation of biomarker expression to growth inhibition, and the sequence of an exemplary probe (Tables 22-44) to detect biomarker (Tables 1-21) expression are shown.
Identification of Biomarker Genes
The gene expression measurements of the NCI60 cancer cell lines were obtained from the National Cancer Institute and the Massachusetts Institute of Technology (MIT). Each dataset was normalized so that sample expression measured by different chips could be compared. The preferred method of normalization is the logit transformation, which is performed for each gene y on each chip:

logit(y)=log [(y−background)/(saturation−y)],

where background is calculated as the minimum intensity measured on the chip minus 0.1% of the signal intensity range: min-0.001*(max-min), and saturation is calculated as the maximum intensity measured on the chip plus 0.1% of the signal intensity range: max+0.001*(max−min). The resulting logit transformed data is then z-transformed to mean zero and standard deviation 1.

Next, gene expression is correlated to cancer cell growth inhibition. Growth inhibition data (GI50) of the NCI60 cell lines in the presence of any one of thousands of tested compounds was obtained from the NCI. The correlation between the logit-transformed expression level of each gene in each cell line and the logarithm of GI50 (the concentration of a given compound that results in a 50% inhibition of growth) can be calculated, e.g., using the Pearson correlation coefficient or the Spearman Rank-Order correlation coefficient. Instead of using GI50s, any other measure of patient sensitivity to a given compound may be correlated to the patient's gene expression. Since a plurality of measurements may be available for a single gene, the most accurate determination of correlation coefficient was found to be the median of the correlation coefficients calculated for all probes measuring expression of the same gene.
The median correlation coefficient of gene expression measured on a probe to growth inhibition or patient sensitivity is calculated for all genes, and genes that have a median correlation above 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 are retained as biomarker genes. Preferably, the correlation coefficient of biomarker genes will exceed 0.3. This is repeated for all the compounds to be tested. The result is a list of marker genes that correlates to sensitivity for each compound tested.
Predicting Patient Sensitivity or Resistance to Medical Treatment
For a given compound, the biomarker whose expression has been shown to correlate to chemosensitivity can be used to classify a patient, e.g., a cancer patient, as sensitive to a medical treatment, e.g., administration of a chemotherapeutic agent or radiation. Using a tumor sample or a blood sample (e.g., in case of leukemia or lymphoma) from a patient, expression of the biomarker 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 biomarker expression measurements are then logit transformed as described above. The sum of the expression measurements of the biomarkers is then compared to the median of the sums derived from a training set population of patients having the same tumor. If the sum of biomarker 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 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. Pat. No. 5,822,715; U.S. Patent Application Publication Nos. 2003/0073083, 2005/0227266, 2005/0208512, 2005/0123945, 2003/0129629, and 2002/0006613; and in Vapnik V N. Statistical Learning Theory, John Wiley & Sons, New York, 1998; Hastie et al., 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, N.Y.; Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, New York; and V. Tresp et al., “Neural Network Modeling of Physiological Processes”, in Hanson S. J. et al. (Eds.), Computational Learning Theory and Natural Learning Systems 2, MIT Press, 1994, hereby incorporated by reference.
Other variables can be used to determine relative biomarker expression between a patient (e.g., a cancer patient) and a normal subject (e.g., a control subject), including but not limited to, measurement of biomarker DNA copy number and the identification of biomarker genetic mutations.
A more compact microarray can 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 biomarker. Biomarkers include polypeptides and metabolites thereof. A skilled artisan can use employ assays that measure changes in polypeptide biomarker expression (e.g., Western blot, immunofluorescent staining, and flow cytometry) to determine a patient's sensitivity to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
Identifying a Subpopulation of Patients Sensitive to a Treatment for Cancer
The invention can also be used to identify a subpopulation of patients, e.g., cancer patients, that are sensitive to a compound or other medical treatment previously thought to be ineffective for the treatment of cancer. To this end, genes or microRNAs whose expression correlates to sensitivity to a compound or other treatment can be identified so that patients sensitive to a compound or other treatment may be identified. To identify such biomarkers, gene or microRNA expression within cell lines can be correlated to the growth of those cell lines in the presence of the same compound or other treatment. Preferably, genes or microRNAs whose expression correlates to cell growth with a correlation coefficient exceeding 0.3 may be considered possible biomarkers.
Alternatively, genes or microRNAs can 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 or microRNA expression between the sensitive and resistant patients may be measured using, e.g., t-tests. Alternatively, naïve Bayesian 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 can 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 biomarkers able to predict a subject's sensitivity to compounds or other treatments for the treatment of cancer.
Patients with elevated expression of biomarkers correlated to sensitivity to a compound or other medical treatment would be predicted to be sensitive to that compound or other medical treatment.
The invention is particularly useful for recovering compounds or other treatments that failed in clinical trials by identifying sensitive patient subpopulations using the gene or microRNA expression methodology disclosed herein to identify biomarkers that can be used to predict clinical outcome.
Kit, Apparatus, and Software for Clinical Use
This invention can 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 biomarker expression (e.g., HG-U133A GeneChip from Affymetrix Inc.), a microarray hybridization station and scanner (e.g., GeneChip System 3000Dx from Affymetrix Inc.), and software for analyzing the expression of marker genes as described in herein (e.g., implemented in R from R-Project or S-Plus from Insightful Corp.).
Methodology of the In Vitro Cancer Growth Inhibition Screen
The human tumor cell lines of the cancer screening panel are grown in RPMI 1640 medium containing 5% fetal bovine serum and 2 mM L-glutamine. Cells are inoculated into 96 well microtiter plates in 100 μL at plating densities ranging from 5,000 to 40,000 cells/well depending on the doubling time of individual cell lines. After cell inoculation, the microtiter plates are incubated at 37° C., 5% CO2, 95% air, and 100% relative humidity for 24 hrs prior to addition of experimental compounds.
After 24 hrs, two plates of each cell line are fixed in situ with TCA, to represent a measurement of the cell population for each cell line at the time of compound addition (Tz). Experimental compounds are solubilized in dimethyl sulfoxide at 400-fold the desired final maximum test concentration and stored frozen prior to use. At the time of compound addition, an aliquot of frozen concentrate is thawed and diluted to twice the desired final maximum test concentration with complete medium containing 50 μg/mL Gentamicin. Additional four, 10-fold or 1⁄2 log serial dilutions are made to provide a total of five compound concentrations plus control. Aliquots of 100 μL of these different compound dilutions are added to the appropriate microtiter wells already containing 100 μL of medium, resulting in the required final compound concentrations.
Following compound addition, the plates are incubated for an additional 48 hrs at 37° C., 5% CO2, 95% air, and 100% relative humidity. For adherent cells, the assay is terminated by the addition of cold TCA. Cells are fixed in situ by the gentle addition of 50 μL of cold 50% (w/v) TCA (final concentration, 10% TCA) and incubated for 60 min at 4° C. The supernatant is discarded, and the plates are washed five times with tap water and air-dried. Sulforhodamine B (SRB) solution (100 μL) at 0.4% (w/v) in 1% acetic acid is added to each well, and plates are incubated for 10 min at room temperature. After staining, unbound dye is removed by washing five times with 1% acetic acid and the plates are air-dried. Bound stain is subsequently solubilized with 10 mM trizma base, and the absorbance is read on an automated plate reader at a wavelength of 515 nm. For suspension cells, the methodology is the same except that the assay is terminated by fixing settled cells at the bottom of the wells by gently adding 50 μL of 80% TCA (final concentration, 16% TCA). Using the seven absorbance measurements [time zero, (Tz), control growth, (C), and test growth in the presence of compound at the five concentration levels (Ti)], the percentage growth is calculated at each of the compound concentrations levels. Percentage growth inhibition is calculated as:

[(Ti−Tz)/(C−Tz)]×100 for concentrations for which Ti>/=Tz 

[(Ti−Tz)/Tz]×100 for concentrations for which Ti<Tz 

Three dose response parameters are calculated for each experimental agent. Growth inhibition of 50% (GI50) is calculated from [(Ti−Tz)/(C−Tz)]×100=50, which is the compound concentration resulting in a 50% reduction in the net protein increase (as measured by SRB staining) in control cells during the compound incubation. The compound concentration resulting in total growth inhibition (TGI) is calculated from Ti=Tz. The LC50 (concentration of compound resulting in a 50% reduction in the measured protein at the end of the compound treatment as compared to that at the beginning) indicating a net loss of cells following treatment is calculated from [(Ti−Tz)/Tz]×100=−50. Values are calculated for each of these three parameters if the level of activity is reached; however, if the effect is not reached or is exceeded, the value for that parameter is expressed as greater or less than the maximum or minimum concentration tested.
RNA Extraction and Gene Expression Measurement
Cell/tissue samples are snap frozen in liquid nitrogen until processing. RNA is extracted using e.g., Trizol Reagent (Invitrogen) following manufacturers instructions. RNA is amplified using e.g., MessageAmp kit (Ambion) following manufacturers instructions. Amplified RNA is quantified using e.g., HG-U133A GeneChip (Affymetrix) and compatible apparatus e.g., GCS3000Dx (Affymetrix), using manufacturers instructions.
The resulting gene expression measurements are further processed as described in this document. The procedures described can be implemented using R software available from R-Project and supplemented with packages available from Bioconductor.
For many drugs 10-30 biomarkers are sufficient to give an adequate response, thus, given the relatively small number of biomarkers required, procedures, such as quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), can be performed to measure, with greater precision, the amount of biomarker genes expressed in a sample. This will provide an alternative to or a complement to microarrays so that a single companion test, typically more quantitative than microarrays alone, employing biomarkers of the invention can be used to predict sensitivity to a new drug. qRT-PCR can be performed alone or in combination with a microarray described herein. Procedures for performing qRT-PCR are described in, e.g., U.S. Pat. No. 7,101,663 and U.S. Patent Application Nos. 2006/0177837 and 2006/0088856. The methods of the invention are readily applicable to newly discovered drugs as well as drugs described herein.
The following examples are provided so that those of ordinary skill in the art can see how to use the methods and kits of the invention. The examples are not intended to limit the scope of what the inventor regards as their invention.
EXAMPLES
Example 1
Identification of Gene Biomarkers for Chemosensitivity to Common Chemotherapy Drugs
DNA chip measurements of the 60 cancer cell lines of the NCI60 data set were downloaded from the Broad Institute (Cambridge, Mass.) and logit normalized. Growth inhibition data of thousands of compounds against the same cell lines were downloaded from the National Cancer Institute. Compounds where the difference concentration to achieve 50% in growth inhibition (GI50) was less than 1 log were deemed uninformative and rejected. Each gene's expression in each cell line was correlated to its growth (−log(GI50)) in those cell lines in the presence of a given compound. The median Pearson correlation coefficient was used when multiple expression measurements were available for a given gene, and genes having a median correlation coefficient greater than 0.3 were identified as biomarkers for a given compound.
Example 2
Prediction of Treatment Sensitivity for Brain Cancer Patients
DNA chip measurements of gene expression in tumors from 60 brain cancer patients were downloaded from the Broad Institute. All data files were logit normalized. For each of the common chemotherapy drugs Cisplatin, Vincristine, Adriamycine, Etoposide, Aclarubicine, Mitoxantrone and Azaguanine, the gene expression for the marker genes was summed. The sum was normalized by dividing by the standard deviation of all patients and compared to the median of the sums of patients who survived and the median of the sums of patients who died:



  
    
      
    
    ⁢
    
      
        NormalizedSum
        ⁢
        
          
        
        ⁢
        
          (
          compound
          )
        
      
      =
      
        
          sum
          ⁢
          
            
          
          ⁢
          
            (
            
              marker
              ⁢
              
                
              
              ⁢
              genes
              ⁢
              
                
              
              ⁢
              for
              ⁢
              
                
              
              ⁢
              compound
            
            )
          
        
        
          sd
          ⁢
          
            
          
          ⁢
          
            (
            
              sums
              ⁢
              
                
              
              ⁢
              of
              ⁢
              
                
              
              ⁢
              all
              ⁢
              
                
              
              ⁢
              patients
            
            )
          
        
      
    
  




  
    
      Sensitivity
      ⁡
      
        (
        compound
        )
      
    
    =
    
      
        
          [
          
            
              
                
                  
                    NormalizedSum
                    ⁢
                    
                      
                    
                    ⁢
                    
                      (
                      compound
                      )
                    
                  
                  -
                
              
            
            
              
                
                  median
                  ⁡
                  
                    (
                    
                      NormalizedSumdeadpatients
                      ⁢
                      
                        
                      
                      ⁢
                      
                        (
                        compound
                        )
                      
                    
                    )
                  
                
              
            
          
          ]
        
        2
      
      -
      
        
          [
          
            
              
                
                  
                    NormalizedSum
                    ⁢
                    
                      
                    
                    ⁢
                    
                      (
                      compound
                      )
                    
                  
                  -
                
              
            
            
              
                
                  median
                  ⁡
                  
                    (
                    
                      NormalizedSumsurvivingpatients
                      ⁢
                      
                        
                      
                      ⁢
                      
                        (
                        compound
                        )
                      
                    
                    )
                  
                
              
            
          
          ]
        
        2
      
    
  



FIGS. 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 FIG. 4. The expression of the 16 Cisplatin biomarker genes was first reduced to 5 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the five components from the 60 patients: K Nearest Neighbor with K=1, K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network. Chemosensitivity or sensitivity to radiation treatment was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive/treatment sensitive prediction resulted in a prediction of chemosensitive/treatment sensitive. All other predictions resulted in a prediction of chemoresistant/treatment resistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups shown in FIG. 4. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
Example 3
Prediction of Chemosensitivity for Lymphoma (DLBCL) Patients
DNA chip measurements of gene expression in the tumors from 56 DLBCL (diffuse large B-cell lymphoma) patients were downloaded from the Broad Institute. All data files were logit normalized. All patients received Vincristine and Adriamycine and the prediction of survival amongst the 56 patients based on their Vincristine and Adriamycine chemosensitivity yielded the Kaplan-Meier survival curve shown in FIG. 5. The expression of the 33 Vincristine genes and 16 Adriamycine genes was first reduced to 3 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the independent components from the 56 patients: K Nearest Neighbor with K=1, K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network. Chemosensitivity was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive prediction resulted in a prediction of chemosensitive. All other predictions resulted in a prediction of chemoresistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups is shown in FIG. 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 III 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 III patients received Cisplatin after surgery) was summed. If the sum was closer to the median of the sums of the surviving patients, the patient was predicted to be sensitive to Cisplatin. If the sum was closest to the median of the sums of the non-surviving patients, the patient was predicted to be resistant to Cisplatin. The survival rates of the two predicted groups are shown in FIG. 6. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
Example 5
Prediction of Rituximab Sensitivity for Lymphoma (DLBCL) Patients
The method is not limited to cytotoxic chemicals. It is also applicable to predicting the efficacy of protein therapeutics, such as monoclonal antibodies, approved for treating cancer. For example, the monoclonal antibody Rituximab (e.g., MABTHERATM and RITUXANTM) was examined. Data for cytotoxicity of Rituximab in cell lines in vitro were obtained from published reports (Ghetie et al., 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 FIG. 7. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
To predict the sensitivity toward combination therapies, such as those used to treat Diffuse Large B-cell Lymphoma (DLBCL), patient sensitivity to a particular combination therapy is predicted by combining the marker genes for the individual compounds used in the combination. An example of this is shown in FIG. 8, where the predicted sensitivities of one patient towards a number of combination therapies used against DLBCL (identified by their acronyms) are shown: R-CHOP contains Rituximab (e.g., MABTHERATM), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone; R-ICE contains Rituximab, Ifosfamide, Carboplatin, and Etoposide; R-MIME contains Rituximab, Mitoguazone, Ifosfamide, Methotrexate, and Etoposide; CHOEP contains Cyclophosphamide, Doxorubicin, Etoposide, Vincristine and Prednisone; DHAP contains Dexamethasone, Cytarabine (Ara C), and Cisplatin; ESHAP contains Etoposide, Methylprednisolone (Solumedrol), Cytarabine (Ara-C) and Cisplatin; and HOAP-Bleo contains Doxorubicin, Vincristine, Ara C, Prednisone, and Bleomycin.
Example 6
Prediction of Radiosensitivity for Brain Tumor (Medulloblastoma) Patients
The method of identifying biomarkers can also be applied to other forms of treatment such as radiation therapy. For example, sensitivity to radiation therapy was predicted for brain tumor patients. Radiation therapy in the form of craniospinal irradiation yielding 2,400-3,600 centiGray (cGy) with a tumor dose of 5,300-7,200 cGy was administered to the brain tumor patients using a medical device that emits beams of radiation. Sensitivity of the 60 cancer cell lines used in the NCI60 dataset to radiation treatment was obtained from published reports. This sensitivity was correlated to the expression of genes in the cell lines as described above to identify marker genes. DNA microarray measurements of gene expression in brain tumors obtained from patients subsequently treated with radiation therapy were obtained from the Broad Institute. The identified gene biomarkers were used to classify the patients as sensitive or resistant to radiation therapy. The survival of the patients in the two predicted categories is shown in FIG. 9. The survival rate of the patients predicted to be sensitive to radiation therapy was higher than the patients predicted to be resistant to radiation therapy.
Example 7
Drug Rescue
Every member of a population may not be equally responsive to a particular treatment. For example, new compounds often fail in late clinical trials because of lack of efficacy in the population tested. While 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 their gene expression as an indicator. Subsequent clinical trials restricted to a sensitive patient subpopulation may demonstrate efficacy of a previously failed compound within that particular patient subpopulation, advancing the compound towards approval for use in that subpopulation.
To this end, in vitro measurements of the inhibitory effects of a compound on various cancer cell lines are compared to the gene expression of cells. The growth of the cancer cell samples can be correlated to gene expression measurements as described above. This will identify marker genes that can be used to predict patient sensitivity to the failed compound. Once biomarkers are identified, the expression of biomarker genes in cells obtained from 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 profile. 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 a period of time (e.g., 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 Gefinitib (e.g., Iressa, AstraZeneca) in refractory, advanced non-small-cell lung cancer (NSCLC), Bevacizumab (e.g., Avastin, Genentech) in first-line treatment for advanced pancreatic cancer, Bevacizumab (e.g., Avastin, Genentech) in relapsed metastatic breast cancer patients, and Erlotinib (e.g., Tarceva, Genentech) in metastatic non-small cell lung cancer (NSCLC). The method of the invention may be applied to these compounds, among others, so that sensitive patient subpopulations responsive to those compounds may be identified.
Example 8
Median of the Correlations Versus Correlation of the Median
The median of the correlations of the individual probe measurements to cancer cell growth as employed by the invention was compared to the correlation of the median probe measurements: this will determine at which step of the method a median calculation should be performed. In the former, several correlations are calculated for each gene since multiple probes measure a given gene's expression, but only the median of the correlation coefficients is finally retained to identify biomarkers. In the latter, only one correlation is calculated for each gene because only the median gene expression measurement is considered for each gene. FIG. 10 shows the results of using the correlation of the median expression measurements to identify biomarker genes of radiation sensitivity predicting the survival of 60 brain cancer patients. The difference in survival between the group predicted to be radiation sensitive and the group predicted to be radiation resistant in FIG. 10 is much smaller than the difference depicted in FIG. 9 which employed a median correlation coefficient suggesting that the invention's median of the correlations employed in FIG. 9 outperforms the correlation of the median depicted in FIG. 10.
If we look at individual marker genes like OMD, the median of the correlation to measured radiosensitivity of cell lines in vitro is 0.32. The correlation of the median, however, is 0.39. Adjusting the cutoff from 0.3 to 0.4 to compensate for the difference does not improve on FIG. 10, however.
We have also compared median correlation to weighted voting as proposed by Staunton et al., PNAS 98(19):10787-10792, 2001). Weighted voting produced a poor result similar to that of FIG. 10, with a P-value of 0.11.
Example 9
Other Methods of Identifying Biomarkers
The examples shown above all rely on the availability of measurements of inhibition by a compound or treatment of the growth of cell lines in vitro. Such measurements may not always be available or practical. In that case an alternative method of identifying biomarkers can be employed. If the target(s) of the compound is/are known, it is possible to build a model based on the gene expression of the known target(s). One example is the drug sunitinib (SU11248), for which eight targets are known. Sunitinib inhibits at least eight receptor protein-tyrosine kinases including vascular endothelial growth factor receptors 1-3 (VEGFR1-VEGFR3), platelet-derived growth factor receptors (PDGFRA and PDGFRB), stem cell factor receptor (Kit), Flt-3, and colony-stimulating factor-1 receptor (CSF-1R). U.S. Patent Application Publication 2006/0040292 mentions prediction of response measuring just two targets, PDGFRA and KIT. Using the sum of the gene expression of four targets it is possible to predict with more reliability the response to sunitinib. As an example, the predicted sunitinib sensitivity of cell lines HT29, U118, 786, and H226 is 0.24, 2.3, 0.14 and 0.60, respectively, based on the sum of the four targets PDGFRB, KDR, KIT and FLT3. This correlates well with the measured response in mouse xenografts of these cells (correlation coefficient 0.86) as well as with the measured anti-angiogenetic effect measured in mouse xenografts (Potapova et al. Contribution of individual targets to the antitumor efficacy of the multitargeted receptor tyrosine kinase inhibitor SU11248 (Mol. Cancer. Ther. 5(5):1280-9, 2006). This is better than a model based only on two targets PDGFRA and KIT (correlaton coefficient 0.56).
This four-gene predictor of sunitinib response can be applied to a large number of tumor samples from patients with different tumors from which gene expression analysis has been performed in order to get an idea of the range of sensitivities within each cancer type as well as which cancer types are most susceptible to treatment with sunitinib. FIG. 11 shows just a small fraction of the cancer samples available. The comparison is based on normalizing the samples in such a way (e.g., logit normalization) that different cancer types become comparable. Sunitinib is currently approved by the FDA for renal cancer and gastrointestinal cancer. Both kidney and colon show a good response in this plot.
Any other drug response response predictor based on gene expression can be tested in the same manner as shown in FIG. 11.
The approach of identifying biomarkers based on known targets can also be applied to RNA antagonists such as SPC2996 targeted against Bcl-2. A response predictor can be built based on measuring the gene expression of Bcl-2 in samples from cancer patients. The same approach can be used for the targets of all mRNA antagonists or inhibitors.
Example 10
Identifying Candidate Drugs for a Known Target
The methods of the invention described herein can also be used for identifying candidate drugs to a known target. Basically, the method of identifying biomarkers is run backwards in order to identify candidate drugs. If one starts with a known target, the expression of its corresponding gene is determined in the NCI 60 cell lines and correlated to the measured growth inhibition of all the thousands of drugs tested in the NCI 60 cell lines. This provides a list, ranked by correlation coefficient, of candidate drugs for the target. It is even possible to test new drugs and compare their correlation coefficient to the target gene expression to the correlation coefficients of the already tested drugs.
Example 11
Using microRNAs as Biomarkers of Drug Response
In recent years it has become clear that microRNAs (miRNA) play an important role in regulating the translation of mRNAs. As such, microRNAs may contain important information relevant for the prediction of drug sensitivity. This information may be complementary to the information contained in mRNA expression. Shown below is the correlation between predicted and measured chemosensitivity of the NCI 60 cell lines. The prediction is based either on mRNA measurements with DNA microarrays as described herein or predictions based on measurements of microRNA concentration (ArrayExpress accession number E-MEXP-1029) using a microRNA specific microarray (ArrayExpress accession number A-MEXP-620). Whenever more than one probe is used to determine the concentration of a given microRNA, the median correlation procedure is used for calculating correlation between microRNA concentration and −log(GI50).















miRNA
mRNA
Combined





















cisplatin
0.16
0.02
0.21



PXD101
0.44
0.31
0.50



vincristine
0.06
0.11
0.26



etoposide
0.32
0.41
0.44



adriamycine
0.24
0.22
0.28










As the above table shows, the correlation (determined using leave-one-out cross-validation) is highest when using a combination (linear sum) of microRNA and mRNA predictions. These results suggest that a more accurate drug response predictor can be built using a combination of microRNA and mRNA. It is possible to measure both in the same experiment, as long as one takes into consideration that microRNAs in general do not have a polyA tail as mRNA does. Only slight modifications to the amplification and labeling methods used for mRNA may be needed to incorporate microRNAs into the analysis. Commercial kits for microRNA extraction, amplification, and labeling are available from suppliers (e.g., Ambion Inc.).

Tables 22A-76A list the microRNA probes that are useful for detection of sensitivity to individual drugs, as determined by their median correlation to −log(GI50) for the indicated drug.
Other Embodiments
All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each independent publication or patent application was specifically and individually indicated to be incorporated by reference. While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure that come within known or customary practice within the art to which the invention pertains and may be applied to the essential features hereinbefore set forth.
Legend:
List—2006: biomarkers identified in 2006 using the new U133A chip measurements
List—2005: biomarkers listed in 2005 patent filing
HU6800: biomarkers obtained with old HU6800 chip measurements
List_Prior: matching biomakrers in prior art
List_Preferr: Prederred list of biomarkers
Correlation: The correlation of the biomarker to sensitivity to the compound







TABLE 1







Vincristine biomarkers













List_2006
List_2005
List_Prior
List_Preferr
Correlation
















 [1,]
UBB

UBB

0.39


 [2,]
RPS4X


RPS4X
0.34


 [3,]
S100A4


S100A4
0.32


 [4,]
NDUFS6


NDUFS6
0.31


 [5,]
B2M

B2M

0.35


 [6,]
C14orf139


C14orf139
0.3


 [7,]
MAN1A1

MAN1A1

0.33


 [8,]
SLC25A5
SLC25A5

SLC25A5
0.32


 [9,]
RPL10


RPL10
0.38


[10,]
RPL12


RPL12
0.31


[11,]
EIF5A


EIF5A
0.31


[12,]
RPL36A
RPL36A

RPL36A
0.3


[13,]
SUI1

SUI1

0.33


[14,]
BLMH


BLMH
0.32


[15,]
CTBP1


CTBP1
0.32


[16,]
TBCA


TBCA
0.3


[17,]
MDH2


MDH2
0.34


[18,]
DXS9879E


DXS9879E
0.35


[19,]

SFRS3


[20,]

CCT5


[21,]

RPL39


[22,]

UBE2S


[23,]

EEF1A1


[24,]

COX7B


[25,]

RPLP2


[26,]

RPL24


[27,]

RPS23


[28,]

RPL18


[29,]

NCL


[30,]

RPL9


[31,]

RPL10A


[32,]

RPS10


[33,]

EIF3S2


[34,]

SHFM1


[35,]

RPS28


[36,]

REA


[37,]

GAPD


[38,]

HNRPA1


[39,]

RPS11


[40,]

LDHB


[41,]

RPL3


[42,]

RPL11


[43,]

MRPL12


[44,]

RPL18A


[45,]

RPS7
















TABLE 2







Cisplatin biomarkers

















Cor-



List_2006
List_2005
List_Prior
List_Preferr
relation
















 [1,]
C1QR1


C1QR1
0.3


 [2,]
HCLS1
HCLS1
HCLS1

0.33


 [3,]
CD53

CD53

0.35


 [4,]
SLA


SLA
0.37


 [5,]
PTPN7
PTPN7

PTPN7
0.31


 [6,]
PTPRCAP

PTPRCAP

0.32


 [7,]
ZNFN1A1


ZNFN1A1
0.33


 [8,]
CENTB1


CENTB1
0.37


 [9,]
PTPRC

PTPRC

0.36


[10,]
IFI16
IFI16

IFI16
0.31


[11,]
ARHGEF6


ARHGEF6
0.35


[12,]
SEC31L2


SEC31L2
0.32


[13,]
CD3Z


CD3Z
0.32


[14,]
GZMB


GZMB
0.3


[15,]
CD3D


CD3D
0.34


[16,]
MAP4K1


MAP4K1
0.32


[17,]
GPR65


GPR65
0.39


[18,]
PRF1


PRF1
0.31


[19,]
ARHGAP15


ARHGAP15
0.35


[20,]
TM6SF1


TM6SF1
0.41


[21,]
TCF4


TCF4
0.4


[22,]

GAPD


[23,]

ARHGDIB


[24,]

RPS27


[25,]

C5orf13


[26,]

LDHB


[27,]

SNRPF


[28,]

B2M


[29,]

FTL


[30,]

NCL


[31,]

MSN


[32,]

XPO1
















TABLE 3







Azaguanine biomarkers













List_2006
List_2005
List_Prior
List_Preferr
Correlation
















 [1,]
MSN
MSN
MSN

0.36


 [2,]
SPARC
SPARC
SPARC

0.48


 [3,]
VIM
VIM
VIM

0.47


 [4,]
SRM
SRM

SRM
0.32


 [5,]
SCARB1


SCARB1
0.4


 [6,]
SIAT1


SIAT1
0.31


 [7,]
CUGBP2


CUGBP2
0.37


 [8,]
GAS7

GAS7

0.34


 [9,]
ICAM1


ICAM1
0.43


[10,]
WASPIP


WASPIP
0.44


[11,]
ITM2A


ITM2A
0.31


[12,]
PALM2-AKAP2


PALM2-AKAP2
0.31


[13,]
ANPEP

ANPEP

0.33


[14,]
PTPNS1


PTPNS1
0.39


[15,]
MPP1


MPP1
0.32


[16,]
LNK


LNK
0.43


[17,]
FCGR2A


FCGR2A
0.3


[18,]
EMP3
EMP3
EMP3

0.33


[19,]
RUNX3


RUNX3
0.43


[20,]
EVI2A


EVI2A
0.4


[21,]
BTN3A3


BTN3A3
0.4


[22,]
LCP2


LCP2
0.34


[23,]
BCHE


BCHE
0.35


[24,]
LY96


LY96
0.47


[25,]
LCP1


LCP1
0.42


[26,]
IFI16


IFI16
0.33


[27,]
MCAM
MCAM

MCAM
0.37


[28,]
MEF2C


MEF2C
0.41


[29,]
SLC1A4


SLC1A4
0.49


[30,]
BTN3A2

BTN3A2

0.43


[31,]
FYN


FYN
0.31


[32,]
FN1
FN1
FN1

0.33


[33,]
C1orf38


C1orf38
0.37


[34,]
CHS1


CHS1
0.33


[35,]
CAPN3

CAPN3

0.5


[36,]
FCGR2C


FCGR2C
0.34


[37,]
TNIK


TNIK
0.35


[38,]
AMPD2


AMPD2
0.3


[39,]
SEPT6


SEPT6
0.41


[40,]
RAFTLIN


RAFTLIN
0.39


[41,]
SLC43A3


SLC43A3
0.52


[42,]
RAC2


RAC2
0.33


[43,]
LPXN


LPXN
0.54


[44,]
CKIP-1


CKIP-1
0.33


[45,]
FLJ10539


FLJ10539
0.33


[46,]
FLJ35036


FLJ35036
0.36


[47,]
DOCK10


DOCK10
0.3


[48,]
TRPV2


TRPV2
0.31


[49,]
IFRG28


IFRG28
0.3


[50,]
LEF1


LEF1
0.31


[51,]
ADAMTS1


ADAMTS1
0.36


[52,]

PRPS1


[53,]

DDOST


[54,]

B2M


[55,]

LGALS1


[56,]

CBFB


[57,]

SNRPB2


[58,]

EIF2S2


[59,]

HPRT1


[60,]

FKBP1A


[61,]

GYPC


[62,]

UROD


[63,]

HNRPA1


[64,]

SND1


[65,]

COPA


[66,]

MAPRE1


[67,]

EIF3S2


[68,]

ATP1B3


[69,]

ECM1


[70,]

ATOX1


[71,]

NARS


[72,]

PGK1


[73,]

OK/SW-cl.56


[74,]

EEF1A1


[75,]

GNAI2


[76,]

RPL7


[77,]

PSMB9


[78,]

GPNMB


[79,]

PPP1R11


[80,]

MIA


[81,]

RAB7


[82,]

SMS
















TABLE 4







Etoposide biomarkers













List_2006
List_2005
List_Prior
List_Preferr
Correlation
















 [1,]
CD99
CD99

CD99
0.3


 [2,]
INSIG1


INSIG1
0.35


 [3,]
LAPTM5

LAPTM5

0.32


 [4,]
PRG1


PRG1
0.34


 [5,]
MUF1


MUF1
0.35


 [6,]
HCLS1

HCLS1

0.33


 [7,]
CD53

CD53

0.32


 [8,]
SLA


SLA
0.37


 [9,]
SSBP2


SSBP2
0.37


[10,]
GNB5


GNB5
0.35


[11,]
MFNG


MFNG
0.33


[12,]
GMFG

GMFG

0.32


[13,]
PSMB9


PSMB9
0.31


[14,]
EVI2A


EVI2A
0.41


[15,]
PTPN7


PTPN7
0.3


[16,]
PTGER4


PTGER4
0.3


[17,]
CXorf9


CXorf9
0.3


[18,]
PTPRCAP

PTPRCAP

0.3


[19,]
ZNFN1A1


ZNFN1A1
0.35


[20,]
CENTB1


CENTB1
0.3


[21,]
PTPRC

PTPRC

0.31


[22,]
NAP1L1


NAP1L1
0.31


[23,]
HLA-DRA


HLA-DRA
0.34


[24,]
IFI16


IFI16
0.38


[25,]
CORO1A

CORO1A

0.3


[26,]
ARHGEF6


ARHGEF6
0.33


[27,]
PSCDBP


PSCDBP
0.4


[28,]
SELPLG


SELPLG
0.35


[29,]
LAT


LAT
0.3


[30,]
SEC31L2


SEC31L2
0.42


[31,]
CD3Z


CD3Z
0.36


[32,]
SH2D1A


SH2D1A
0.33


[33,]
GZMB


GZMB
0.34


[34,]
SCN3A


SCN3A
0.3


[35,]
ITK

ITK

0.35


[36,]
RAFTLIN


RAFTLIN
0.39


[37,]
DOCK2


DOCK2
0.33


[38,]
CD3D


CD3D
0.31


[39,]
RAC2


RAC2
0.34


[40,]
ZAP70


ZAP70
0.35


[41,]
GPR65


GPR65
0.35


[42,]
PRF1


PRF1
0.32


[43,]
ARHGAP15


ARHGAP15
0.32


[44,]
NOTCH1


NOTCH1
0.31


[45,]
UBASH3A


UBASH3A
0.32


[46,]

B2M


[47,]

MYC


[48,]

RPS24


[49,]

PPIF


[50,]

PBEF1


[51,]

ANP32B
















TABLE 5







Adriamycin biomarkers

















Cor-



List_2006
List_2005
List_Prior
List_Preferr
relation
















 [1,]
CD99
CD99

CD99
0.41


 [2,]
LAPTM5

LAPTM5

0.39


 [3,]
ALDOC


ALDOC
0.31


 [4,]
HCLS1

HCLS1

0.32


 [5,]
CD53

CD53

0.31


 [6,]
SLA


SLA
0.35


 [7,]
SSBP2


SSBP2
0.34


 [8,]
IL2RG


IL2RG
0.38


 [9,]
GMFG

GMFG

0.32


[10,]
CXorf9


CXorf9
0.32


[11,]
RHOH


RHOH
0.31


[12,]
PTPRCAP

PTPRCAP

0.32


[13,]
ZNFN1A1


ZNFN1A1
0.43


[14,]
CENTB1


CENTB1
0.36


[15,]
TCF7

TCF7

0.32


[16,]
CD1C


CD1C
0.3


[17,]
MAP4K1


MAP4K1
0.35


[18,]
CD1B

CD1B

0.39


[19,]
CD3G


CD3G
0.31


[20,]
PTPRC

PTPRC

0.38


[21,]
CCR9


CCR9
0.34


[22,]
CORO1A

CORO1A

0.38


[23,]
CXCR4


CXCR4
0.3


[24,]
ARHGEF6


ARHGEF6
0.31


[25,]
HEM1

HEM1

0.32


[26,]
SELPLG


SELPLG
0.31


[27,]
LAT


LAT
0.31


[28,]
SEC31L2


SEC31L2
0.33


[29,]
CD3Z


CD3Z
0.37


[30,]
SH2D1A


SH2D1A
0.37


[31,]
CD1A


CD1A
0.4


[32,]
LAIR1


LAIR1
0.39


[33,]
ITK

ITK

0.3


[34,]
TRB@


TRB@
0.34


[35,]
CD3D


CD3D
0.33


[36,]
WBSCR20C


WBSCR20C
0.34


[37,]
ZAP70


ZAP70
0.33


[38,]
IFI44


IFI44
0.32


[39,]
GPR65


GPR65
0.31


[40,]
AIF1


AIF1
0.3


[41,]
ARHGAP15


ARHGAP15
0.37


[42,]
NARF


NARF
0.3


[43,]
PACAP


PACAP
0.32


[44,]

KIAA0220


[45,]

B2M


[46,]

TOP2A


[47,]

SNRPE


[48,]

RPS27


[49,]

HNRPA1


[50,]

CBX3


[51,]

ANP32B


[52,]

DDX5


[53,]

PPIA


[54,]

SNRPF


[55,]

USP7
















TABLE 6







Aclarubicin biomarkers













List_2006
List_2005
List_Prior
List_Preferr
Correlation
















 [1,]
RPL12


RPL12
0.3


 [2,]
RPL32

RPL32

0.37


 [3,]
RPLP2
RPLP2

RPLP2
0.37


 [4,]
MYB
MYB

MYB
0.31


 [5,]
ZNFN1A1


ZNFN1A1
0.34


 [6,]
SCAP1


SCAP1
0.33


 [7,]
STAT4


STAT4
0.31


 [8,]
SP140


SP140
0.4


 [9,]
AMPD3


AMPD3
0.3


[10,]
TNFAIP8


TNFAIP8
0.4


[11,]
DDX18


DDX18
0.31


[12,]
TAF5


TAF5
0.3


[13,]
FBL

FBL

0.41


[14,]
RPS2


RPS2
0.34


[15,]
PTPRC

PTPRC

0.37


[16,]
DOCK2


DOCK2
0.32


[17,]
GPR65


GPR65
0.35


[18,]
HOXA9


HOXA9
0.33


[19,]
FLJ12270


FLJ12270
0.31


[20,]
HNRPD


HNRPD
0.4


[21,]

LAMR1


[22,]

RPS25


[23,]

EIF5A


[24,]

TUFM


[25,]

HNRPA1


[26,]

RPS9


[27,]

ANP32B


[28,]

EIF4B


[29,]

HMGB2


[30,]

RPS15A


[31,]

RPS7
















TABLE 7







Mitoxantrone biomarkers

















Cor-



List_2006
List_2005
List_Prior
List_Preferr
relation
















 [1,]
PGAM1


PGAM1
0.32


 [2,]
DPYSL3


DPYSL3
0.36


 [3,]
INSIG1


INSIG1
0.32


 [4,]
GJA1


GJA1
0.31


 [5,]
BNIP3


BNIP3
0.31


 [6,]
PRG1
PRG1

PRG1
0.39


 [7,]
G6PD
G6PD

G6PD
0.34


 [8,]
BASP1

BASP1

0.31


 [9,]
PLOD2


PLOD2
0.34


[10,]
LOXL2


LOXL2
0.31


[11,]
SSBP2


SSBP2
0.36


[12,]
C1orf29


C1orf29
0.35


[13,]
TOX


TOX
0.35


[14,]
STC1


STC1
0.39


[15,]
TNFRSF1A
TNFRSF1A

TNFRSF1A
0.34


[16,]
NCOR2
NCOR2

NCOR2
0.3


[17,]
NAP1L1
NAP1L1

NAP1L1
0.32


[18,]
LOC94105


LOC94105
0.34


[19,]
COL6A2

COL6A2

0.3


[20,]
ARHGEF6
ARHGEF6

ARHGEF6
0.34


[21,]
GATA3


GATA3
0.35


[22,]
TFPI


TFPI
0.31


[23,]
LAT


LAT
0.31


[24,]
CD3Z


CD3Z
0.37


[25,]
AF1Q


AF1Q
0.33


[26,]
MAP1B
MAP1B

MAP1B
0.34


[27,]
PTPRC

PTPRC

0.31


[28,]
PRKCA

PRKCA

0.35


[29,]
TRIM22


TRIM22
0.3


[30,]
CD3D


CD3D
0.31


[31,]
BCAT1


BCAT1
0.32


[32,]
IFI44


IFI44
0.33


[33,]
CCL2

CCL2

0.37


[34,]
RAB31

RAB31

0.31


[35,]
CUTC


CUTC
0.33


[36,]
NAP1L2


NAP1L2
0.33


[37,]
NME7


NME7
0.35


[38,]
FLJ21159


FLJ21159
0.33


[39,]
COL5A2


COL5A2
0.38


[40,]

B2M


[41,]

OK/SW-cl.56


[42,]

TOP2A


[43,]

ELA2B


[44,]

PTMA


[45,]

LMNB1


[46,]

HNRPA1


[47,]

RPL9


[48,]

C5orf13


[49,]

ANP32B


[50,]

TUBA3


[51,]

HMGN2


[52,]

PRPS1


[53,]

DDX5


[54,]

PPIA


[55,]

PSMB9


[56,]

SNRPF
















TABLE 8







Mitomycin biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















[1,]
STC1


STC1
0.34


[2,]
GPR65


GPR65
0.32


[3,]
DOCK10


DOCK10
0.35


[4,]
COL5A2


COL5A2
0.33


[5,]
FAM46A


FAM46A
0.36


[6,]
LOC54103


LOC54103
0.39
















TABLE 9







Paclitaxel (Taxol) biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
RPL10


RPL10
0.31


 [2,]
RPS4X


RPS4X
0.31


 [3,]
NUDC


NUDC
0.3


 [4,]
RALY

RALY

0.31


 [5,]
DKC1


DKC1
0.3


 [6,]
DKFZP564C186


DKFZP564C186
0.32


 [7,]
PRP19


PRP19
0.31


 [8,]
RAB9P40


RAB9P40
0.33


 [9,]
HSA9761


HSA9761
0.37


[10,]
GMDS


GMDS
0.3


[11,]
CEP1


CEP1
0.3


[12,]
IL13RA2


IL13RA2
0.34


[13,]
MAGEB2


MAGEB2
0.41


[14,]
HMGN2


HMGN2
0.35


[15,]
ALMS1


ALMS1
0.3


[16,]
GPR65


GPR65
0.31


[17,]
FLJ10774


FLJ10774
0.31


[18,]
NOL8


NOL8
0.31


[19,]
DAZAP1


DAZAP1
0.32


[20,]
SLC25A15


SLC25A15
0.31


[21,]
PAF53


PAF53
0.36


[22,]
DXS9879E


DXS9879E
0.31


[23,]
PITPNC1


PITPNC1
0.33


[24,]
SPANXC


SPANXC
0.3


[25,]
KIAA1393


KIAA1393
0.33
















TABLE 10







Gemcitabine (Gemzar) biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
PFN1


PFN1
0.37


 [2,]
PGAM1


PGAM1
0.35


 [3,]
K-ALPHA-1


K-ALPHA-1
0.34


 [4,]
CSDA


CSDA
0.31


 [5,]
UCHL1


UCHL1
0.36


 [6,]
PWP1


PWP1
0.37


 [7,]
PALM2-


PALM2-
0.31



AKAP2


AKAP2


 [8,]
TNFRSF1A


TNFRSF1A
0.31


 [9,]
ATP5G2


ATP5G2
0.36


[10,]
AF1Q


AF1Q
0.31


[11,]
NME4


NME4
0.31


[12,]
FHOD1


FHOD1
0.32
















TABLE 11







Taxotere (docetaxel) biomarkers













List_2006
List_2005
List_Prior
List_Preferr
Correlation
















 [1,]
ANP32B


ANP32B
0.45


 [2,]
GTF3A


GTF3A
0.31


 [3,]
RRM2


RRM2
0.31


 [4,]
TRIM14


TRIM14
0.31


 [5,]
SKP2


SKP2
0.33


 [6,]
TRIP13


TRIP13
0.36


 [7,]
RFC3


RFC3
0.45


 [8,]
CASP7


CASP7
0.32


 [9,]
TXN


TXN
0.36


[10,]
MCM5


MCM5
0.34


[11,]
PTGES2


PTGES2
0.39


[12,]
OBFC1


OBFC1
0.37


[13,]
EPB41L4B


EPB41L4B
0.32


[14,]
CALML4


CALML4
0.31
















TABLE 12







Dexamethasone biomarkers

















Cor-



List_2006
HU6800
List_Prior
List_Preferr
relation
















 [1,]
IFITM2


IFITM2
0.38


 [2,]
UBE2L6


UBE2L6
0.32


 [3,]
LAPTM5
LAPTM5
LAPTM5

0.36


 [4,]
USP4


USP4
0.33


 [5,]
ITM2A


ITM2A
0.38


 [6,]
ITGB2

ITGB2

0.42


 [7,]
ANPEP

ANPEP

0.31


 [8,]
CD53

CD53

0.34


 [9,]
IL2RG
IL2RG

IL2RG
0.36


[10,]
CD37

CD37

0.34


[11,]
GPRASP1


GPRASP1
0.36


[12,]
PTPN7


PTPN7
0.31


[13,]
CXorf9


CXorf9
0.36


[14,]
RHOH
RHOH

RHOH
0.33


[15,]
GIT2


GIT2
0.31


[16,]
ADORA2A

ADORA2A

0.31


[17,]
ZNFN1A1


ZNFN1A1
0.35


[18,]
GNA15
GNA15
GNA15

0.33


[19,]
CEP1


CEP1
0.31


[20,]
TNFRSF7


TNFRSF7
0.46


[21,]
MAP4K1


MAP4K1
0.3


[22,]
CCR7


CCR7
0.33


[23,]
CD3G


CD3G
0.35


[24,]
PTPRC

PTPRC

0.41


[25,]
ATP2A3
ATP2A3

ATP2A3
0.4


[26,]
UCP2


UCP2
0.3


[27,]
CORO1A
CORO1A
CORO1A

0.39


[28,]
GATA3
GATA3

GATA3
0.37


[29,]
CDKN2A


CDKN2A
0.32


[30,]
HEM1

HEM1

0.3


[31,]
TARP


TARP
0.3


[32,]
LAIR1


LAIR1
0.34


[33,]
SH2D1A


SH2D1A
0.34


[34,]
FLII
FLII
FLII

0.33


[35,]
SEPT6


SEPT6
0.34


[36,]
HA-1


HA-1
0.34


[37,]
CREB3L1

CREB3L1

0.31


[38,]
ERCC2


ERCC2
0.65


[39,]
CD3D
CD3D

CD3D
0.32


[40,]
LST1


LST1
0.39


[41,]
AIF1


AIF1
0.35


[42,]
ADA


ADA
0.33


[43,]
DATF1


DATF1
0.41


[44,]
ARHGAP15


ARHGAP15
0.3


[45,]
PLAC8


PLAC8
0.31


[46,]
CECR1


CECR1
0.31


[47,]
LOC81558


LOC81558
0.33


[48,]
EHD2


EHD2
0.37
















TABLE 13







Ara-C (Cytarabine hydrochloride) biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
ITM2A


ITM2A
0.32


 [2,]
RHOH


RHOH
0.31


 [3,]
PRIM1


PRIM1
0.3


 [4,]
CENTB1


CENTB1
0.31


 [5,]
GNA15

GNA15

0.32


 [6,]
NAP1L1
NAP1L1

NAP1L1
0.31


 [7,]
ATP5G2


ATP5G2
0.31


 [8,]
GATA3


GATA3
0.33


 [9,]
PRKCQ


PRKCQ
0.32


[10,]
SH2D1A


SH2D1A
0.3


[11,]
SEPT6


SEPT6
0.42


[12,]
PTPRC

PTPRC

0.35


[13,]
NME4


NME4
0.33


[14,]
RPL13

RPL13

0.3


[15,]
CD3D


CD3D
0.31


[16,]
CD1E


CD1E
0.32


[17,]
ADA
ADA

ADA
0.34


[18,]
FHOD1


FHOD1
0.31
















TABLE 14







Methylprednisolone biomarkers

















Cor-



List_2006
HU6800
List_Prior
List_Preferr
relation
















 [1,]
CD99
CD99

CD99
0.31


 [2,]
SRRM1

SRRM1

0.31


 [3,]
ARHGDIB
ARHGDIB

ARHGDIB
0.31


 [4,]
LAPTM5
LAPTM5
LAPTM5

0.37


 [5,]
VWF


VWF
0.45


 [6,]
ITM2A


ITM2A
0.35


 [7,]
ITGB2
ITGB2
ITGB2

0.43


 [8,]
LGALS9
LGALS9

LGALS9
0.43


 [9,]
INPP5D


INPP5D
0.34


[10,]
SATB1
SATB1

SATB1
0.32


[11,]
CD53
CD53
CD53

0.33


[12,]
TFDP2
TFDP2

TFDP2
0.4


[13,]
SLA
SLA

SLA
0.31


[14,]
IL2RG
IL2RG

IL2RG
0.3


[15,]
MFNG


MFNG
0.3


[16,]
CD37

CD37

0.37


[17,]
GMFG

GMFG

0.4


[18,]
SELL


SELL
0.33


[19,]
CDW52
CDW52

CDW52
0.33


[20,]
LRMP


LRMP
0.32


[21,]
ICAM2


ICAM2
0.38


[22,]
RIMS3


RIMS3
0.36


[23,]
PTPN7
PTPN7

PTPN7
0.39


[24,]
ARHGAP25


ARHGAP25
0.37


[25,]
LCK
LCK

LCK
0.3


[26,]
CXorf9


CXorf9
0.3


[27,]
RHOH
RHOH

RHOH
0.51


[28,]
PTPRCAP
PTPRCAP
PTPRCAP

0.5


[29,]
GIT2


GIT2
0.33


[30,]
ZNFN1A1
ZNFN1A1

ZNFN1A1
0.53


[31,]
CENTB1
CENTB1

CENTB1
0.36


[32,]
LCP2


LCP2
0.34


[33,]
SPI1


SPI1
0.3


[34,]
GNA15
GNA15
GNA15

0.39


[35,]
GZMA


GZMA
0.31


[36,]
CEP1


CEP1
0.37


[37,]
BLM

BLM

0.33


[38,]
CD8A


CD8A
0.38


[39,]
SCAP1


SCAP1
0.32


[40,]
CD2


CD2
0.48


[41,]
CD1C
CD1C

CD1C
0.37


[42,]
TNFRSF7


TNFRSF7
0.31


[43,]
VAV1


VAV1
0.41


[44,]
MAP4K1
MAP4K1

MAP4K1
0.36


[45,]
CCR7


CCR7
0.37


[46,]
C6orf32


C6orf32
0.38


[47,]
ALOX15B


ALOX15B
0.43


[48,]
BRDT


BRDT
0.33


[49,]
CD3G
CD3G

CD3G
0.51


[50,]
PTPRC

PTPRC

0.37


[51,]
LTB


LTB
0.32


[52,]
ATP2A3
ATP2A3

ATP2A3
0.3


[53,]
NVL


NVL
0.31


[54,]
RASGRP2


RASGRP2
0.35


[55,]
LCP1
LCP1

LCP1
0.34


[56,]
CORO1A
CORO1A
CORO1A

0.41


[57,]
CXCR4
CXCR4

CXCR4
0.3


[58,]
PRKD2


PRKD2
0.33


[59,]
GATA3
GATA3

GATA3
0.39


[60,]
TRA@


TRA@
0.4


[61,]
PRKCB1
PRKCB1
PRKCB1

0.35


[62,]
HEM1

HEM1

0.32


[63,]
KIAA0922


KIAA0922
0.36


[64,]
TARP


TARP
0.49


[65,]
SEC31L2


SEC31L2
0.32


[66,]
PRKCQ


PRKCQ
0.37


[67,]
SH2D1A


SH2D1A
0.33


[68,]
CHRNA3


CHRNA3
0.5


[69,]
CD1A


CD1A
0.44


[70,]
LST1


LST1
0.36


[71,]
LAIR1


LAIR1
0.47


[72,]
CACNA1G


CACNA1G
0.33


[73,]
TRB@
TRB@

TRB@
0.31


[74,]
SEPT6
SEPT6

SEPT6
0.33


[75,]
HA-1


HA-1
0.42


[76,]
DOCK2


DOCK2
0.32


[77,]
CD3D
CD3D

CD3D
0.41


[78,]
TRD@


TRD@
0.38


[79,]
T3JAM


T3JAM
0.37


[80,]
FNBP1


FNBP1
0.37


[81,]
CD6


CD6
0.4


[82,]
AIF1
AIF1

AIF1
0.31


[83,]
FOLH1


FOLH1
0.45


[84,]
CD1E
CD1E

CD1E
0.58


[85,]
LY9


LY9
0.39


[86,]
UGT2B17

UGT2B17

0.47


[87,]
ADA
ADA

ADA
0.39


[88,]
CDKL5


CDKL5
0.44


[89,]
TRIM


TRIM
0.38


[90,]
EVL


EVL
0.39


[91,]
DATF1


DATF1
0.31


[92,]
RGC32


RGC32
0.51


[93,]
PRKCH


PRKCH
0.3


[94,]
ARHGAP15


ARHGAP15
0.34


[95,]
NOTCH1


NOTCH1
0.36


[96,]
BIN2


BIN2
0.31


[97,]
SEMA4G


SEMA4G
0.35


[98,]
DPEP2


DPEP2
0.33


[99,]
CECR1


CECR1
0.36


[100,] 
BCL11B


BCL11B
0.33


[101,] 
STAG3


STAG3
0.41


[102,] 
GALNT6


GALNT6
0.32


[103,] 
UBASH3A


UBASH3A
0.3


[104,] 
PHEMX


PHEMX
0.38


[105,] 
FLJ13373


FLJ13373
0.34


[106,] 
LEF1


LEF1
0.49


[107,] 
IL21R


IL21R
0.42


[108,] 
MGC17330


MGC17330
0.33


[109,] 
AKAP13


AKAP13
0.53


[110,] 
ZNF335


ZNF335
0.3


[111,] 
GIMAP5


GIMAP5
0.34
















TABLE 15







Methotrexate biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
PRPF8


PRPF8
0.34


 [2,]
RPL18


RPL18
0.34


 [3,]
RNPS1

RNPS1

0.36


 [4,]
RPL32

RPL32

0.39


 [5,]
EEF1G

EEF1G

0.34


 [6,]
GOT2


GOT2
0.31


 [7,]
RPL13A


RPL13A
0.31


 [8,]
PTMA
PTMA
PTMA

0.41


 [9,]
RPS15


RPS15
0.39


[10,]
RPLP2
RPLP2

RPLP2
0.32


[11,]
CSDA


CSDA
0.39


[12,]
KHDRBS1


KHDRBS1
0.32


[13,]
SNRPA


SNRPA
0.31


[14,]
IMPDH2
IMPDH2

IMPDH2
0.39


[15,]
RPS19


RPS19
0.47


[16,]
NUP88


NUP88
0.36


[17,]
ATP5D


ATP5D
0.33


[18,]
PCBP2


PCBP2
0.32


[19,]
ZNF593


ZNF593
0.4


[20,]
HSU79274


HSU79274
0.32


[21,]
PRIM1


PRIM1
0.3


[22,]
PFDN5


PFDN5
0.33


[23,]
OXA1L


OXA1L
0.37


[24,]
H3F3A


H3F3A
0.42


[25,]
ATIC


ATIC
0.31


[26,]
RPL13

RPL13

0.36


[27,]
CIAPIN1


CIAPIN1
0.34


[28,]
FBL

FBL

0.33


[29,]
RPS2
RPS2

RPS2
0.32


[30,]
PCCB


PCCB
0.36


[31,]
RBMX

RBMX

0.33


[32,]
SHMT2


SHMT2
0.34


[33,]
RPLP0


RPLP0
0.35


[34,]
HNRPA1
HNRPA1

HNRPA1
0.35


[35,]
STOML2


STOML2
0.32


[36,]
RPS9

RPS9

0.36


[37,]
SKB1


SKB1
0.33


[38,]
GLTSCR2


GLTSCR2
0.37


[39,]
CCNB1IP1


CCNB1IP1
0.3


[40,]
MRPS2


MRPS2
0.33


[41,]
FLJ20859


FLJ20859
0.34


[42,]
FLJ12270


FLJ12270
0.3
















TABLE 16







Bleomycin biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
MSN

MSN

0.3


 [2,]
PFN1


PFN1
0.45


 [3,]
HK1


HK1
0.33


 [4,]
ACTR2

ACTR2

0.31


 [5,]
MCL1


MCL1
0.31


 [6,]
ZYX


ZYX
0.32


 [7,]
RAP1B


RAP1B
0.34


 [8,]
GNB2


GNB2
0.32


 [9,]
EPAS1


EPAS1
0.31


[10,]
PGAM1


PGAM1
0.42


[11,]
CKAP4


CKAP4
0.31


[12,]
DUSP1


DUSP1
0.4


[13,]
MYL9


MYL9
0.4


[14,]
K-ALPHA-1


K-ALPHA-1
0.37


[15,]
LGALS1


LGALS1
0.38


[16,]
CSDA
CSDA

CSDA
0.3


[17,]
AKR1B1

AKR1B1

0.32


[18,]
IFITM2
IFITM2

IFITM2
0.36


[19,]
ITGA5


ITGA5
0.43


[20,]
VIM

VIM

0.39


[21,]
DPYSL3


DPYSL3
0.44


[22,]
JUNB


JUNB
0.32


[23,]
ITGA3

ITGA3

0.38


[24,]
NFKBIA


NFKBIA
0.32


[25,]
LAMB1


LAMB1
0.37


[26,]
FHL1


FHL1
0.31


[27,]
INSIG1


INSIG1
0.31


[28,]
TIMP1


TIMP1
0.48


[29,]
GJA1


GJA1
0.54


[30,]
PSME2


PSME2
0.34


[31,]
PRG1


PRG1
0.46


[32,]
EXT1


EXT1
0.35


[33,]
DKFZP434J154


DKFZP434J154
0.31


[34,]
OPTN

OPTN

0.31


[35,]
M6PRBP1

M6PRBP1

0.52


[36,]
MVP


MVP
0.34


[37,]
VASP


VASP
0.31


[38,]
ARL7


ARL7
0.39


[39,]
NNMT
NNMT

NNMT
0.34


[40,]
TAP1


TAP1
0.3


[41,]
COL1A1
COL1A1
COL1A1

0.33


[42,]
BASP1

BASP1

0.35


[43,]
PLOD2


PLOD2
0.37


[44,]
ATF3


ATF3
0.42


[45,]
PALM2-AKAP2


PALM2-AKAP2
0.33


[46,]
IL8


IL8
0.34


[47,]
ANPEP

ANPEP

0.35


[48,]
LOXL2


LOXL2
0.32


[49,]
TGFB1

TGFB1

0.31


[50,]
IL4R


IL4R
0.31


[51,]
DGKA


DGKA
0.32


[52,]
STC2


STC2
0.31


[53,]
SEC61G


SEC61G
0.41


[54,]
NFIL3
NFIL3
NFIL3

0.47


[55,]
RGS3


RGS3
0.37


[56,]
NK4

NK4

0.34


[57,]
F2R


F2R
0.34


[58,]
TPM2


TPM2
0.35


[59,]
PSMB9
PSMB9

PSMB9
0.34


[60,]
LOX


LOX
0.37


[61,]
STC1


STC1
0.35


[62,]
CSPG2
CSPG2
CSPG2

0.35


[63,]
PTGER4


PTGER4
0.31


[64,]
IL6


IL6
0.34


[65,]
SMAD3


SMAD3
0.38


[66,]
PLAU
PLAU
PLAU

0.35


[67,]
WNT5A


WNT5A
0.44


[68,]
BDNF


BDNF
0.34


[69,]
TNFRSF1A
TNFRSF1A

TNFRSF1A
0.46


[70,]
FLNC


FLNC
0.34


[71,]
DKFZP564K0822


DKFZP564K0822
0.34


[72,]
FLOT1


FLOT1
0.38


[73,]
PTRF


PTRF
0.39


[74,]
HLA-B


HLA-B
0.36


[75,]
COL6A2
COL6A2
COL6A2

0.32


[76,]
MGC4083


MGC4083
0.32


[77,]
TNFRSF10B


TNFRSF10B
0.34


[78,]
PLAGL1


PLAGL1
0.31


[79,]
PNMA2


PNMA2
0.38


[80,]
TFPI


TFPI
0.38


[81,]
LAT


LAT
0.46


[82,]
GZMB


GZMB
0.51


[83,]
CYR61


CYR61
0.37


[84,]
PLAUR
PLAUR

PLAUR
0.35


[85,]
FSCN1
FSCN1

FSCN1
0.32


[86,]
ERP70


ERP70
0.32


[87,]
AF1Q


AF1Q
0.3


[88,]
UBC

UBC

0.37


[89,]
FGFR1

FGFR1

0.33


[90,]
HIC


HIC
0.33


[91,]
BAX

BAX

0.35


[92,]
COL4A2
COL4A2
COL4A2

0.32


[93,]
COL6A1


COL6A1
0.32


[94,]
IFITM3


IFITM3
0.3


[95,]
MAP1B


MAP1B
0.38


[96,]
FLJ46603


FLJ46603
0.37


[97,]
RAFTLIN


RAFTLIN
0.34


[98,]
RRAS


RRAS
0.31


[99,]
FTL


FTL
0.3


[100,] 
KIAA0877


KIAA0877
0.31


[101,] 
MT1E
MT1E

MT1E
0.31


[102,] 
CDC10


CDC10
0.51


[103,] 
DOCK2


DOCK2
0.32


[104,] 
TRIM22


TRIM22
0.36


[105,] 
RIS1


RIS1
0.37


[106,] 
BCAT1


BCAT1
0.42


[107,] 
PRF1


PRF1
0.34


[108,] 
DBN1


DBN1
0.36


[109,] 
MT1K


MT1K
0.3


[110,] 
TMSB10


TMSB10
0.42


[111,] 
RAB31

RAB31

0.45


[112,] 
FLJ10350


FLJ10350
0.4


[113,] 
C1orf24


C1orf24
0.34


[114,] 
NME7


NME7
0.46


[115,] 
TMEM22


TMEM22
0.3


[116,] 
TPK1


TPK1
0.37


[117,] 
COL5A2


COL5A2
0.34


[118,] 
ELK3


ELK3
0.38


[119,] 
CYLD


CYLD
0.4


[120,] 
ADAMTS1


ADAMTS1
0.31


[121,] 
EHD2


EHD2
0.41


[122,] 
ACTB
ACTB

ACTB
0.33
















TABLE 17







Methyl-GAG (Methyl glyoxal bis(amidinohydrazone)


dihydrochloride)













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
PTMA

PTMA

0.32


 [2,]
SSRP1


SSRP1
0.37


 [3,]
NUDC


NUDC
0.35


 [4,]
CTSC


CTSC
0.35


 [5,]
AP1G2


AP1G2
0.33


 [6,]
PSME2


PSME2
0.3


 [7,]
LBR


LBR
0.38


 [8,]
EFNB2


EFNB2
0.31


 [9,]
SERPINA1


SERPINA1
0.34


[10,]
SSSCA1


SSSCA1
0.32


[11,]
EZH2


EZH2
0.36


[12,]
MYB
MYB

MYB
0.33


[13,]
PRIM1


PRIM1
0.39


[14,]
H2AFX


H2AFX
0.33


[15,]
HMGA1


HMGA1
0.35


[16,]
HMMR


HMMR
0.33


[17,]
TK2


TK2
0.42


[18,]
WHSC1


WHSC1
0.35


[19,]
DIAPH1


DIAPH1
0.34


[20,]
LAMB3


LAMB3
0.31


[21,]
DPAGT1


DPAGT1
0.42


[22,]
UCK2


UCK2
0.31


[23,]
SERPINB1


SERPINB1
0.31


[24,]
MDN1


MDN1
0.35


[25,]
BRRN1


BRRN1
0.33


[26,]
G0S2


G0S2
0.43


[27,]
RAC2


RAC2
0.35


[28,]
MGC21654


MGC21654
0.36


[29,]
GTSE1


GTSE1
0.35


[30,]
TACC3


TACC3
0.31


[31,]
PLEK2


PLEK2
0.32


[32,]
PLAC8


PLAC8
0.31


[33,]
HNRPD


HNRPD
0.35


[34,]
PNAS-4


PNAS-4
0.3
















TABLE 18







Carboplatin biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
MSN

MSN

0.31


 [2,]
ITGA5


ITGA5
0.43


 [3,]
VIM

VIM

0.34


 [4,]
TNFAIP3


TNFAIP3
0.4


 [5,]
CSPG2

CSPG2

0.35


 [6,]
WNT5A


WNT5A
0.34


 [7,]
FOXF2


FOXF2
0.36


 [8,]
LOC94105


LOC94105
0.32


 [9,]
IFI16


IFI16
0.38


[10,]
LRRN3


LRRN3
0.33


[11,]
FGFR1

FGFR1

0.37


[12,]
DOCK10


DOCK10
0.4


[13,]
LEPRE1


LEPRE1
0.32


[14,]
COL5A2


COL5A2
0.3


[15,]
ADAMTS1


ADAMTS1
0.34
















TABLE 19







5-FU (5-Fluorouracil) biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
RPL18


RPL18
0.39


 [2,]
RPL10A


RPL10A
0.36


 [3,]
RNPS1

RNPS1

0.3


 [4,]
ANAPC5


ANAPC5
0.5


 [5,]
EEF1B2


EEF1B2
0.4


 [6,]
RPL13A


RPL13A
0.38


 [7,]
RPS15


RPS15
0.34


 [8,]
AKAP1


AKAP1
0.37


 [9,]
NDUFAB1


NDUFAB1
0.3


[10,]
APRT


APRT
0.32


[11,]
ZNF593


ZNF593
0.37


[12,]
MRP63


MRP63
0.31


[13,]
IL6R


IL6R
0.31


[14,]
RPL13

RPL13

0.31


[15,]
SART3


SART3
0.35


[16,]
RPS6

RPS6

0.49


[17,]
UCK2


UCK2
0.38


[18,]
RPL3

RPL3

0.32


[19,]
RPL17


RPL17
0.34


[20,]
RPS2


RPS2
0.32


[21,]
PCCB


PCCB
0.31


[22,]
TOMM20


TOMM20
0.39


[23,]
SHMT2


SHMT2
0.36


[24,]
RPLP0


RPLP0
0.3


[25,]
GTF3A


GTF3A
0.5


[26,]
STOML2


STOML2
0.4


[27,]
DKFZp564J157


DKFZp564J157
0.38


[28,]
MRPS2


MRPS2
0.34


[29,]
ALG5


ALG5
0.37


[30,]
CALML4


CALML4
0.3
















TABLE 20







Rituximab (e.g., Mabthera) biomarkers












List_2006
List_Prior
List_Preferr
Correlation















 [1,]
ITK
ITK

0.36


 [2,]
KIFC1

KIFC1
0.36


 [3,]
VLDLR

VLDLR
0.39


 [4,]
RUNX1

RUNX1
0.32


 [5,]
PAFAH1B3

PAFAH1B3
0.32


 [6,]
H1FX

H1FX
0.43


 [7,]
RNF144

RNF144
0.38


 [8,]
TMSNB

TMSNB
0.47


 [9,]
CRY1

CRY1
0.37


[10,]
MAZ

MAZ
0.33


[11,]
SLA

SLA
0.35


[12,]
SRF

SRF
0.37


[13,]
UMPS

UMPS
0.41


[14,]
CD3Z

CD3Z
0.33


[15,]
PRKCQ

PRKCQ
0.31


[16,]
HNRPM

HNRPM
0.45


[17,]
ZAP70

ZAP70
0.38


[18,]
ADD1

ADD1
0.31


[19,]
RFC5

RFC5
0.35


[20,]
TM4SF2

TM4SF2
0.33


[21,]
PFN2

PFN2
0.3


[22,]
BMI1

BMI1
0.31


[23,]
TUBGCP3

TUBGCP3
0.33


[24,]
ATP6V1B2

ATP6V1B2
0.42


[25,]
RALY
RALY

0.31


[26,]
PSMC5
PSMC5

0.36


[27,]
CD1D

CD1D
0.32


[28,]
ADA

ADA
0.34


[29,]
CD99

CD99
0.33


[30,]
CD2

CD2
0.43


[31,]
CNP

CNP
0.48


[32,]
ERG

ERG
0.47


[33,]
MYL6
MYL6

0.41


[34,]
CD3E

CD3E
0.36


[35,]
CD1A

CD1A
0.46


[36,]
CD1B
CD1B

0.47


[37,]
STMN1
STMN1

0.32


[38,]
PSMC3

PSMC3
0.38


[39,]
RPS4Y1

RPS4Y1
0.36


[40,]
AKT1

AKT1
0.38


[41,]
TAL1

TAL1
0.37


[42,]
GNA15
GNA15

0.37


[43,]
UBE2A

UBE2A
0.35


[44,]
TCF12

TCF12
0.35


[45,]
UBE2S

UBE2S
0.52


[46,]
CCND3

CCND3
0.38


[47,]
PAX6

PAX6
0.35


[48,]
MDK
MDK

0.3


[49,]
CAPG
CAPG

0.36


[50,]
RAG2

RAG2
0.39


[51,]
ACTN1
ACTN1

0.37


[52,]
GSTM2

GSTM2
0.47


[53,]
SATB1

SATB1
0.36


[54,]
NASP

NASP
0.3


[55,]
IGFBP2

IGFBP2
0.46


[56,]
CDH2

CDH2
0.49


[57,]
CRABP1

CRABP1
0.36


[58,]
DBN1

DBN1
0.49


[59,]
CTNNA1
CTNNA1

0.53


[60,]
AKR1C1

AKR1C1
0.32


[61,]
CACNB3

CACNB3
0.37


[62,]
FARSLA
FARSLA

0.35


[63,]
CASP2

CASP2
0.42


[64,]
CASP2

CASP2
0.31


[65,]
E2F4
E2F4

0.36


[66,]
LCP2

LCP2
0.35


[67,]
CASP6

CASP6
0.32


[68,]
MYB

MYB
0.3


[69,]
SFRS6

SFRS6
0.44


[70,]
GLRB

GLRB
0.34


[71,]
NDN

NDN
0.39


[72,]
CPSF1
CPSF1

0.33


[73,]
GNAQ

GNAQ
0.44


[74,]
TUSC3

TUSC3
0.41


[75,]
GNAQ

GNAQ
0.54


[76,]
JARID2

JARID2
0.44


[77,]
OCRL

OCRL
0.5


[78,]
FHL1

FHL1
0.36


[79,]
EZH2

EZH2
0.4


[80,]
SMOX

SMOX
0.35


[81,]
SLC4A2

SLC4A2
0.35


[82,]
UFD1L

UFD1L
0.3


[83,]
SEPW1
SEPW1

0.31


[84,]
ZNF32

ZNF32
0.35


[85,]
HTATSF1

HTATSF1
0.35


[86,]
SHD1

SHD1
0.43


[87,]
PTOV1

PTOV1
0.42


[88,]
NXF1

NXF1
0.46


[89,]
FYB

FYB
0.47


[90,]
TRIM28

TRIM28
0.38


[91,]
BC008967

BC008967
0.4


[92,]
TRB@

TRB@
0.3


[93,]
TFRC
TFRC

0.31


[94,]
H1F0

H1F0
0.36


[95,]
CD3D

CD3D
0.32


[96,]
CD3G

CD3G
0.4


[97,]
CENPB

CENPB
0.36


[98,]
ALDH2

ALDH2
0.33


[99,]
ANXA1

ANXA1
0.35


[100,] 
H2AFX

H2AFX
0.51


[101,] 
CD1E

CD1E
0.33


[102,] 
DDX5

DDX5
0.39


[103,] 
ABL1
ABL1

0.3


[104,] 
CCNA2

CCNA2
0.3


[105,] 
ENO2

ENO2
0.35


[106,] 
SNRPB

SNRPB
0.38


[107,] 
GATA3

GATA3
0.36


[108,] 
RRM2

RRM2
0.48


[109,] 
GLUL

GLUL
0.4


[110,] 
TCF7
TCF7

0.39


[111,] 
FGFR1
FGFR1

0.33


[112,] 
SOX4

SOX4
0.3


[113,] 
MAL

MAL
0.3


[114,] 
NUCB2
NUCB2

0.38


[115,] 
SMA3
SMA3

0.31


[116,] 
FAT
FAT

0.52


[117,] 
UNG

UNG
0.31


[118,] 
ARHGDIB

ARHGDIB
0.36


[119,] 
RUNX1

RUNX1
0.38


[120,] 
MPHOSPH6

MPHOSPH6
0.5


[121,] 
DCTN1

DCTN1
0.34


[122,] 
SH3GL3

SH3GL3
0.38


[123,] 
VIM
VIM

0.41


[124,] 
PLEKHC1

PLEKHC1
0.3


[125,] 
CD47

CD47
0.32


[126,] 
POLR2F

POLR2F
0.37


[127,] 
RHOH

RHOH
0.43


[128,] 
ADD1

ADD1
0.46


[129,] 
ATP2A3
ATP2A3

0.38
















TABLE 21







Radiation sensitivity biomarkers













List_2006
HU6800
List_Prior
List_Preferr
Correlation
















 [1,]
TRA1


TRA1
0.36


 [2,]
ACTN4


ACTN4
0.36


 [3,]
WARS

WARS

0.39


 [4,]
CALM1


CALM1
0.32


 [5,]
CD63
CD63

CD63
0.32


 [6,]
CD81

CD81

0.43


 [7,]
FKBP1A


FKBP1A
0.38


 [8,]
CALU


CALU
0.47


 [9,]
IQGAP1


IQGAP1
0.37


[10,]
CTSB

CTSB

0.33


[11,]
MGC8721


MGC8721
0.35


[12,]
STAT1


STAT1
0.37


[13,]
TACC1


TACC1
0.41


[14,]
TM4SF8


TM4SF8
0.33


[15,]
CD59


CD59
0.31


[16,]
CKAP4
CKAP4

CKAP4
0.45


[17,]
DUSP1
DUSP1

DUSP1
0.38


[18,]
RCN1


RCN1
0.31


[19,]
MGC8902


MGC8902
0.35


[20,]
LGALS1
LGALS1

LGALS1
0.33


[21,]
BHLHB2


BHLHB2
0.3


[22,]
RRBP1


RRBP1
0.31


[23,]
PKM2

PKM2

0.33


[24,]
PRNP


PRNP
0.42


[25,]
PPP2CB

PPP2CB

0.31


[26,]
CNN3

CNN3

0.36


[27,]
ANXA2
ANXA2
ANXA2

0.32


[28,]
IER3


IER3
0.34


[29,]
JAK1

JAK1

0.33


[30,]
MARCKS


MARCKS
0.43


[31,]
LUM


LUM
0.48


[32,]
FER1L3


FER1L3
0.47


[33,]
SLC20A1


SLC20A1
0.41


[34,]
EIF4G3

EIF4G3

0.36


[35,]
HEXB


HEXB
0.46


[36,]
EXT1


EXT1
0.47


[37,]
TJP1


TJP1
0.32


[38,]
CTSL
CTSL

CTSL
0.38


[39,]
SLC39A6


SLC39A6
0.36


[40,]
RIOK3


RIOK3
0.38


[41,]
CRK


CRK
0.37


[42,]
NNMT


NNMT
0.37


[43,]
COL1A1

COL1A1

0.35


[44,]
TRAM2
TRAM2

TRAM2
0.35


[45,]
ADAM9


ADAM9
0.52


[46,]
DNAJC7


DNAJC7
0.38


[47,]
PLSCR1


PLSCR1
0.35


[48,]
PRSS23


PRSS23
0.3


[49,]
PLOD2


PLOD2
0.36


[50,]
NPC1


NPC1
0.39


[51,]
TOB1


TOB1
0.37


[52,]
GFPT1


GFPT1
0.47


[53,]
IL8


IL8
0.36


[54,]
DYRK2

DYRK2

0.3


[55,]
PYGL


PYGL
0.46


[56,]
LOXL2


LOXL2
0.49


[57,]
KIAA0355


KIAA0355
0.36


[58,]
UGDH


UGDH
0.49


[59,]
NFIL3

NFIL3

0.53


[60,]
PURA


PURA
0.32


[61,]
ULK2


ULK2
0.37


[62,]
CENTG2


CENTG2
0.35


[63,]
NID2


NID2
0.42


[64,]
CAP350


CAP350
0.31


[65,]
CXCL1


CXCL1
0.36


[66,]
BTN3A3


BTN3A3
0.35


[67,]
IL6


IL6
0.32


[68,]
WNT5A


WNT5A
0.3


[69,]
FOXF2


FOXF2
0.44


[70,]
LPHN2


LPHN2
0.34


[71,]
CDH11


CDH11
0.39


[72,]
P4HA1


P4HA1
0.33


[73,]
GRP58


GRP58
0.44


[74,]
ACTN1
ACTN1
ACTN1

0.41


[75,]
CAPN2

CAPN2

0.54


[76,]
DSIPI


DSIPI
0.44


[77,]
MAP1LC3B


MAP1LC3B
0.5


[78,]
GALIG
GALIG

GALIG
0.36


[79,]
IGSF4


IGSF4
0.4


[80,]
IRS2


IRS2
0.35


[81,]
ATP2A2


ATP2A2
0.35


[82,]
OGT


OGT
0.3


[83,]
TNFRSF10B


TNFRSF10B
0.31


[84,]
KIAA1128


KIAA1128
0.35


[85,]
TM4SF1


TM4SF1
0.35


[86,]
RBPMS


RBPMS
0.43


[87,]
RIPK2


RIPK2
0.42


[88,]
CBLB


CBLB
0.46


[89,]
NR1D2


NR1D2
0.47


[90,]
BTN3A2

BTN3A2

0.38


[91,]
SLC7A11


SLC7A11
0.4


[92,]
MPZL1


MPZL1
0.3


[93,]
IGFBP3
IGFBP3
IGFBP3

0.31


[94,]
SSA2


SSA2
0.36


[95,]
FN1
FN1
FN1

0.32


[96,]
NQO1


NQO1
0.4


[97,]
ASPH


ASPH
0.36


[98,]
ASAH1


ASAH1
0.33


[99,]
MGLL


MGLL
0.35


[100,] 
SERPINB6


SERPINB6
0.51


[101,] 
HSPA5


HSPA5
0.33


[102,] 
ZFP36L1


ZFP36L1
0.39


[103,] 
COL4A2

COL4A2

0.3


[104,] 
COL4A1


COL4A1
0.3


[105,] 
CD44


CD44
0.35


[106,] 
SLC39A14


SLC39A14
0.38


[107,] 
NIPA2


NIPA2
0.36


[108,] 
FKBP9


FKBP9
0.48


[109,] 
IL6ST


IL6ST
0.4


[110,] 
DKFZP564G2022


DKFZP564G2022
0.39


[111,] 
PPAP2B


PPAP2B
0.33


[112,] 
MAP1B


MAP1B
0.3


[113,] 
MAPK1


MAPK1
0.3


[114,] 
MYO1B


MYO1B
0.38


[115,] 
CAST
CAST

CAST
0.31


[116,] 
RRAS2


RRAS2
0.52


[117,] 
QKI


QKI
0.31


[118,] 
LHFPL2


LHFPL2
0.36


[119,] 
SEPT10


SEPT10
0.38


[120,] 
ARHE


ARHE
0.5


[121,] 
KIAA1078


KIAA1078
0.34


[122,] 
FTL


FTL
0.38


[123,] 
KIAA0877


KIAA0877
0.41


[124,] 
PLCB1


PLCB1
0.3


[125,] 
KIAA0802


KIAA0802
0.32


[126,] 
KPNB1

KPNB1

0.37


[127,] 
RAB3GAP


RAB3GAP
0.43


[128,] 
SERPINB1


SERPINB1
0.46


[129,] 
TIMM17A


TIMM17A
0.38


[130,] 
SOD2


SOD2
0.35


[131,] 
HLA-A
HLA-A

HLA-A
0.33


[132,] 
NOMO2


NOMO2
0.43


[133,] 
LOC55831


LOC55831
0.32


[134,] 
PHLDA1


PHLDA1
0.32


[135,] 
TMEM2


TMEM2
0.47


[136,] 
MLPH


MLPH
0.35


[137,] 
FAD104


FAD104
0.34


[138,] 
LRRC5


LRRC5
0.42


[139,] 
RAB7L1


RAB7L1
0.41


[140,] 
FLJ35036


FLJ35036
0.36


[141,] 
DOCK10


DOCK10
0.41


[142,] 
LRP12


LRP12
0.36


[143,] 
TXNDC5


TXNDC5
0.4


[144,] 
CDC14B


CDC14B
0.39


[145,] 
HRMT1L1


HRMT1L1
0.38


[146,] 
CORO1C


CORO1C
0.38


[147,] 
DNAJC10


DNAJC10
0.31


[148,] 
TNPO1


TNPO1
0.33


[149,] 
LONP


LONP
0.32


[150,] 
AMIGO2


AMIGO2
0.38


[151,] 
DNAPTP6


DNAPTP6
0.31


[152,] 
ADAMTS1


ADAMTS1
0.37


[153,] 

CCL21


[154,] 

SCARB2


[155,] 

MAD2L1BP


[156,] 

PTS


[157,] 

NBL1


[158,] 

CD151


[159,] 

CRIP2


[160,] 

UGCG


[161,] 

PRSS11


[162,] 

MME


[163,] 

CBR1


[164,] 

DUSP3


[165,] 

PFN2


[166,] 

MICA


[167,] 

FTH1


[168,] 

RHOC


[169,] 

ZAP128


[170,] 

PON2


[171,] 

COL5A2


[172,] 

CST3


[173,] 

MCAM


[174,] 

MMP2


[175,] 

CTSD


[176,] 

ALDH3A1


[177,] 

CSRP1


[178,] 

S100A4


[179,] 

CALD1


[180,] 

CTGF


[181,] 

CAPG


[182,] 

TAGLN


[183,] 

FSTL1


[184,] 

SCTR


[185,] 

BLVRA


[186,] 

COPEB


[187,] 

DIPA


[188,] 

SMARCD3


[189,] 

MVP


[190,] 

PEA15


[191,] 

S100A13


[192,] 

ECE1
















TABLE 22







Vincristine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













 1
SLC25A5
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


 2
RPL10
0.38
GCCCCACTGGACAACACTGATTCCT


 


 3
RPL12
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


 4
RPS4X
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


 5
EIF5A
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


 6
BLMH
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


 7
TBCA
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


 1
MDH2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


 9
S100A4
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


10
C14orf139
0.3
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 23







Cisplatin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













11
C1QR1
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


3
SLA
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
PTPN7
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
ZNFN1A1
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


10
CENTB1
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


16
IFI16
0.31
TCCTCCATCACCTGAAACACTGGAC


 


3
ARHGEF6
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
SEC31L2
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CD3Z
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


16
GZMB
0.3
TCCTCCATCACCTGAAACACTGGAC


 


16
CD3D
0.34
TCCTCCATCACCTGAAACACTGGAC


 


11
MAP4K1
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


11
GPR65
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


24
PRF1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
ARHGAP15
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


3
TM6SF1
0.41
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
TCF4
0.4
AAATGTTTCCTTGTGCCTGCTCCTG
















TABLE 24







Etoposide biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
CD99
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


24
INSIG1
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
PRG1
0.34
GCCCCACTGGACAACACTGATTCCT


 


6
MUF1
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


11
SLA
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


9
SSBP2
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


24
GNB5
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
MFNG
0.33
GCCCCACTGGACAACACTGATTCCT


 


6
PSMB9
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


16
EVI2A
0.41
TCCTCCATCACCTGAAACACTGGAC


 


6
PTPN7
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


3
PTGER4
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
CXorf9
0.3
GCCCCACTGGACAACACTGATTCCT


 


7
ZNFN1A1
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
CENTB1
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


16
NAP1L1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


3
HLA-DRA
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
IFI16
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


9
ARHGEF6
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


6
PSCDBP
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


10
SELPLG
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


4
SEC31L2
0.42
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
CD3Z
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
SH2D1A
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


9
GZMB
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


2
SCN3A
0.3
GCCCCACTGGACAACACTGATTCCT


 


16
RAFTLIN
0.39
TCCTCCATCACCTGAAACACTGGAC


 


3
DOCK2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD3D
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
ZAP70
0.35
TCCTCCATCACCTGAAACACTGGAC


 


9
GPR65
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


9
PRF1
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


7
ARHGAP15
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
NOTCH1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
UBASH3A
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 25







Azaguanine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
SRM
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
SCARB1
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


4
SIAT1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
CUGBP2
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


1
WASPIP
0.44
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
ITM2A
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


7
PALM2-AKAP2
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
LNK
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


3
FCGR2A
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
RUNX3
0.43
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
EVI2A
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
BTN3A3
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
LCP2
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
BCHE
0.35
TCCTCCATCACCTGAAACACTGGAC


 


3
LY96
0.47
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
LCP1
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
IFI16
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


10
MCAM
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


11
MEF2C
0.41
CACCCAGCTGGTCCTGTGGATGGGA


 


1
FYN
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
C1orf38
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


3
FCGR2C
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
TNIK
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


1
AMPD2
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
SEPT6
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
RAFTLIN
0.39
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
SLC43A3
0.52
CACCCAGCTGGTCCTGTGGATGGGA


 


6
LPXN
0.54
AAGCCTATACGTTTCTGTGGAGTAA


 


1
CKIP-1
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
FLJ10539
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
FLJ35036
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


2
DOCK10
0.3
GCCCCACTGGACAACACTGATTCCT


 


7
TRPV2
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
IFRG28
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
LEF1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
ADAMTS1
0.36
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 26







Carboplatin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













 4
ITGA5
0.43
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
TNFAIP3
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
WNT5A
0.34
TCCTCCATCACCTGAAACACTGGAC


 


3
FOXF2
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
LOC94105
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
IFI16
0.38
TCCTCCATCACCTGAAACACTGGAC


 


10
LRRN3
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


1
DOCK10
0.4
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
LEPRE1
0.32
GCCCCACTGGACAACACTCATTCCT


 


9
ADAMTS1
0.34
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 27







Adriamycin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
CD99
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


24
ALDOC
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
SLA
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
SSBP2
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
IL2RG
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
CXorf9
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


7
RHOH
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
ZNFN1A1
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


6
CENTB1
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


16
MAP4K1
0.35
TCCTCCATCACCTGAAACACTGGAC


 


4
CD3G
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
CCR9
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


24
CXCR4
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
ARHGEF6
0.31
TCCTCCATCACCTGAAACACTGGAC


 


9
SELPLG
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


3
SEC31L2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD3Z
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
SH2D1A
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


6
CD1A
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


6
LAIR1
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


16
TRB@
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
CD3D
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
WBSCR20C
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
ZAP70
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
IFI44
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
GPR65
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


11
AIF1
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


1
ARHGAP15
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
NARF
0.3
TCCTCCATCACCTGAAACACTGGAC


 


11
PACAP
0.32
CACCCAGCTGGTCCTGTGGATGGGA
















TABLE 28







Aclarubicin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













4
RPL12
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
RPLP2
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


24
MYB
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ZNFN1A1
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
SCAP1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
STAT4
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
SP140
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


3
AMPD3
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
TNFAIP8
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


24
DDX18
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
TAF5
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
RPS2
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


6
DOCK2
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


6
GPR65
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


24
HOXA9
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
FLJ12270
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
HNRPD
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 29







Mitoxantrone biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
PGAM1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
DPYSL3
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
INSIG1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
GJA1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


10
BNIP3
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


2
PRG1
0.39
GCCCCACTGGACAACACTGATTCCT


 


3
G6PD
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
PLOD2
0.34
GCCCCACTGGACAACACTGATTCCT


 


24
LOXL2
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
SSBP2
0.36
TCCTCCATCACCTGAAACACTGGAC


 


24
C1orf29
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
TOX
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
STC1
0.39
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
TNFRSF1A
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
NCOR2
0.3
TCCTCCATCACCTGAAACACTGGAC


 


24
NAP1L1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
LOC94105
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


16
ARHGEF6
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
GATA3
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
TFPI
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
CD3Z
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


2
AF1Q
0.33
GCCCCACTGGACAACACTGATTCCT


 


3
MAP1B
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
CD3D
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
BCAT1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
IFI44
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


4
CUTC
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
NAP1L2
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


4
NME7
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
FLJ21159
0.33
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 30







Mitomycin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe





3
STC1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
GPR65
0.32
GCCCCACTGGACAACACTGATTCCT


 


7
DOCK10
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
FAM46A
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
LOC54103
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 31







Paclitaxel (Taxol) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
RPL10
0.31
TCCTCCATCACCTGAAACACTGGAC


 


16
RPS4X
0.31
TCCTCCATCACCTGAAACACTGGAC


 


24
DKC1
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
DKFZP564C186
0.32
ACTTGTCCTCAGCTTGGGGTTCTTC


 


3
PRP19
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
PAB9P40
0.33
GCCCCACTGGACAACACTGATTCCT


 


4
HSA9761
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
GMDS
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
CEP1
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
IL13RA2
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
MAGEB2
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
HMGN2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


16
ALMS1
0.3
TCCTCCATCACCTGAAACACTGGAC


 


3
GPR65
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
FLJ10774
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


3
NOL8
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
DAZAP1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
SLC25A15
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


16
PAF53
0.36
TCCTCCATCACCTGAAACACTGGAC


 


16
PITPNC1
0.33
TCCTCCATCACCTGAAACACTGGAC


 


9
SPANXC
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


11
KIAA1393
0.33
CACCCAGCTGGTCCTGTGGATGGGA
















TABLE 32







Gemcitabine (Gemzar) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













11
UBE2L6
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


11
TAP1
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


1
F2R
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
PSMB9
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
IL7R
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
TNFAIP8
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


9
HLA-C
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


9
IFI44
0.31
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 33







Taxotere (docetaxel) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
ANP32B
0.45
GCCCCACTGGACAACACTGATTCCT


 


10
GTF3A
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


7
TRIM14
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
SKP2
0.33
GCCCCACTGGACAACACTGATTCCT


 


1
TRIP13
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
RFC3
0.45
GCCCCACTGGACAACACTGATTCCT


 


3
CASP7
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
TXN
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


4
MCM5
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
PTGES2
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
OBFC1
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


2
EPB41L4B
0.32
GCCCCACTGGACAACACTGATTCCT


 


16
CALML4
0.31
TCCTCCATCACCTGAAACACTGGAC
















TABLE 34







Dexamethasone biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













234
IFITM2
0.38
ATATATGGACCTAGCTTGAGGCAAT


 


6
UBE2L6
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


11
ITM2A
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


16
IL2RG
0.36
TCCTCCATCACCTGAAACACTGGAC


 


1
GPRASP1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
PTPN7
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
CXorf9
0.36
GCCCCACTGGACAACACTGATTCCT


 


3
RHOH
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
GIT2
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ZNFN1A1
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
CEP1
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


6
MAP4K1
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


4
CCR7
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
CD3G
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


6
UCP2
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


9
GATA3
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


1
CDKN2A
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
TARP
0.3
GCCCCACTGGACAACACTGATTCCT


 


10
LAIR1
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


24
SH2D1A
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
SEPT6
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
HA-1
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
CD3D
0.32
TCCTCCATCACCTGAAACACTGGAC


 


11
LST1
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


6
AIF1
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


3
ADA
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
DATF1
0.41
CACCCAGCTGGTCCTGTGGATGGGA


 


1
ARHGAP15
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
PLAC8
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


2
CECR1
0.31
GCCCCACTGGACAACACTGATTCCT


 


9
LOC81558
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


7
EHD2
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 35







Ara-C (Cytarabine hydrochloride) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













9
ITM2A
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


4
RHOH
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
PRIM1
0.3
TCCTCCATCACCTGAAACACTGGAC


 


24
CENTB1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
NAP1L1
0.31
GCCCCACTGGACAACACTGATTCCT


 


16
ATP5G2
0.31
TCCTCCATCACCTGAAACACTGGAC


 


4
GATA3
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
PRKCQ
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


2
SH2D1A
0.3
GCCCCACTGGACAACACTGATTCCT


 


7
SEPT6
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
NME4
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
CD3D
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
CD1E
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
ADA
0.34
GCCCCACTGGACAACACTGATTCCT


 


11
FHOD1
0.31
CACCCAGCTGGTCCTGTGGATGGGA
















TABLE 36







Methylprednisolone biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
CD99
0.31
GCCCCACTGGACAACACTGATTCCT


 


3
ARHGDIB
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
ITM2A
0.35
GCCCCACTGGACAACACTGATTCCT


 


16
LGALS9
0.43
TCCTCCATCACCTGAAACACTGGAC


 


9
INPP5D
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


24
SATB1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
TFDP2
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
SLA
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


3
IL2RG
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
MFNG
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
SELL
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
CDW52
0.33
TCCTCCATCACCTGAAACACTGGAC


 


1
LRMP
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
ICAM2
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


3
RIMS3
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
PTPN7
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


1
ARHGAP25
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
LCK
0.3
TCCTCCATCACCTGAAACACTGGAC


 


10
CXorf9
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


6
RHOH
0.51
AAGCCTATACGTTTCTGTGGAGTAA


 


7
GIT2
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ZNFN1A1
0.53
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
CENTB1
0.36
TCCTCCATCACCTGAAACACTGGAC


 


1
LCP2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
SPI1
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
GZMA
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CEP1
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


9
CD8A
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


16
SCAP1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


2
CD2
0.48
GCCCCACTGGACAACACTGATTCCT


 


7
VAV1
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
MAP4K1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
CCR7
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
C6orf32
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
ALOX15B
0.43
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
BRDT
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CD3G
0.51
AAGCCTATACGTTTCTGTGGAGTAA


 


7
LTB
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
NVL
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


3
RASGRP2
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
LCP1
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
CXCR4
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


11
PRKD2
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


1
GATA3
0.39
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
KIAA0922
0.36
GCCCCACTGGACAACACTGATTCCT


 


16
TARP
0.49
TCCTCCATCACCTGAAACACTGGAC


 


7
SEC31L2
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
PRKCQ
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


6
SH2D1A
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CHRNA3
0.5
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CD1A
0.44
AAGCCTATACGTTTCTGTGGAGTAA


 


11
LST1
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


11
LAIR1
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


2
CACNA1G
0.33
GCCCCACTGGACAACACTGATTCCT


 


7
TRB@
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
SEPT6
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
HA-1
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


1
DOCK2
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CD3D
0.41
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
TRD@
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
T3JAM
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
FNBP1
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
CD6
0.4
CACCCAGCTGGTCCTGTGGATGGGA


 


3
AIF1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
FOLH1
0.45
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
CD1E
0.58
CACCCAGCTGGTCCTGTGGATGGGA


 


24
LY9
0.39
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ADA
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
CDKL5
0.44
GCCCCACTGGACAACACTGATTCCT


 


6
TRIM
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


7
DATF1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
RGC32
0.51
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
ARHGAP15
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


24
NOTCH1
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
BIN2
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
SEMA4G
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


11
DPEP2
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


1
CECR1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
BCL11B
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
STAG3
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


3
GALNT6
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
UBASH3A
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
PHEMX
0.38
TCCTCCATCACCTGAAACACTGGAC


 


24
FLJ13373
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
LEF1
0.49
TCCTCCATCACCTGAAACACTGGAC


 


10
IL21R
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


24
MGC17330
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
AKAP13
0.53
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
GIMAP5
0.34
AAATGTTTCCTTGTGCCTGCTCCTG
















TABLE 37







Methotrexate biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
PRPF8
0.34
TCCTCCATCACCTGAAACACTGGAC


 


6
RPL18
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


11
GOT2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


1
RPL13A
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
RPS15
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


2
RPLP2
0.32
GCCCCACTGGACAACACTGATTCCT


 


2
CSDA
0.39
GCCCCACTGGACAACACTGATTCCT


 


16
KHDRBS1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


1
SNRPA
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
IMPDH2
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
RPS19
0.47
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
NUP88
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


3
ATP5D
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
PCBP2
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
ZNF593
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
HSU79274
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


11
PRIM1
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


16
PFDN5
0.33
TCCTCCATCACCTGAAACACTGGAC


 


11
OXA1L
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


7
ATIC
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
CIAPIN1
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
RPS2
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


2
PCCB
0.36
GCCCCACTGGACAACACTGATTCCT


 


7
SHMT2
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
RPLP0
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


9
HNRPA1
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


3
STOML2
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
SKB1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
GLTSCR2
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


24
CCNB1IP1
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
MRPS2
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


3
FLJ20859
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
FLJ12270
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 38







Bleomycin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
PFN1
0.45
GCCCCACTGGACAACACTGATTCCT


 


10
HK1
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


9
MCL1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


9
ZYX
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


7
RAP1B
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
GNB2
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


7
EPAS1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
PGAM1
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CKAP4
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
DUSP1
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
MYL9
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


10
K-ALPHA-1
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


24
CSDA
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
IFITM2
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


2
ITGA5
0.43
GCCCCACTGGACAACACTGATTCCT


 


9
DPYSL3
0.44
TGGACCCCACTGGCTGAGAATCTGG


 


1
JUNB
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
NFKBIA
0.32
TCCTCCATCACCTGAAACACTGGAC


 


4
LAMB1
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
FHL1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


9
INSIG1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


9
TIMP1
0.48
TGGACCCCACTGGCTGAGAATCTGG


 


6
GJA1
0.54
AAGCCTATACGTTTCTGTGGAGTAA


 


24
PRG1
0.46
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
EXT1
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
DKFZP434J154
0.31
GCCCCACTGGACAACACTGATTCCT


 


11
MVP
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


16
VASP
0.31
TCCTCCATCACCTGAAACACTGGAC


 


9
ARL7
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


1
NNMT
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
TAP1
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
PLOD2
0.37
GCCCCACTGGACAACACTGATTCCT


 


11
ATF3
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


9
PALM2-AKAP2
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


2
IL8
0.34
GCCCCACTGGACAACACTGATTCCT


 


2
LOXL2
0.32
GCCCCACTGGACAACACTGATTCCT


 


7
IL4R
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
DGKA
0.32
GCCCCACTGGACAACACTGATTCCT


 


11
SEC61G
0.41
CACCCAGCTGGTCCTGTGGATGGGA


 


9
RGS3
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


11
F2R
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


11
TPM2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


11
PSMB9
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


1
LOX
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
STC1
0.35
TCCTCCATCACCTGAAACACTGGAC


 


11
PTGER4
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


10
SMAD3
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


9
WNT5A
0.44
TGGACCCCAGTGGCTGAGAATCTGG


 


16
BDNF
0.34
TCCTCCATCACCTGAAACACTGGAC


 


16
TNFRSF1A
0.46
TCCTCCATCACCTGAAACACTGGAC


 


7
FLNC
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
DKFZP564K0822
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


10
FLOT1
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


9
PTRF
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


10
HLA-B
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


2
MGC4083
0.32
GCCCCACTGGACAACACTGATTCCT


 


3
TNFRSF10B
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
PLAGL1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
PNMA2
0.38
GCCCCACTGGACAACACTGATTCCT


 


1
TFPI
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
GZMB
0.51
TCCTCCATCACCTGAAACACTGGAC


 


6
PLAUR
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


7
FSCN1
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
ERP70
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
AF1Q
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


3
HIC
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
COL6A1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


2
IFITM3
0.3
GCCCCACTGGACAACACTGATTCCT


 


11
MAP1B
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


16
FLJ46603
0.37
TCCTCCATCACCTGAAACACTGGAC


 


9
RAFTLIN
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


1
RRAS
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
FTL
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


11
KIAA0877
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


24
MT1E
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
CDC10
0.51
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
DOCK2
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


7
RIS1
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
BCAT1
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


16
PRF1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


2
DBN1
0.36
GCCCCACTGGACAACACTGATTCCT


 


3
MT1K
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
TMSB10
0.42
GCCCCACTGGACAACACTGATTCCT


 


4
FLJ10350
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
C1orf24
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
NME7
0.46
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
TMEM22
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
TPK1
0.37
TCCTCCATCACCTGAAACACTGGAC


 


3
ELK3
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
CYLD
0.4
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
ADAMTS1
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


16
EHD2
0.41
TCCTCCATCACCTGAAACACTGGAC


 


24
ACTB
0.33
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 39







Methyl-GAG (methyl glyoxal bis amidinohydrazone


dihydrochloride) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
SSRP1
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
CTSC
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


7
LBR
0.38
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
EFNB2
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
SERPINA1
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
SSSCA1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
EZH2
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


2
MYB
0.33
GCCCCACTGGACAACACTGATTCCT


 


16
PRIM1
0.39
TCCTCCATCACCTGAAACACTGGAC


 


24
H2AFX
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
HMGA1
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


24
HMMR
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
TK2
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


4
WHSC1
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
DIAPH1
0.34
GCCCCACTGGACAACACTGATTCCT


 


2
LAMB3
0.31
GCCCCACTGGACAACACTGATTCCT


 


3
DPAGT1
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
UCK2
0.31
GCCCCACTGGACAACACTGATTCCT


 


24
SERPINB1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
MDN1
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
G0S2
0.43
CACCCAGCTGGTCCTGTGGATGGGA


 


9
MGC21654
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
GTSE1
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
TACC3
0.31
TCCTCCATCACCTGAAACACTGGAC


 


11
PLAC8
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


10
HNRPD
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


10
PNAS-4
0.3
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 40







HDAC inhibitors biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













10
FAU
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


9
NOL5A
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


11
ANP32A
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


7
ARHGDIB
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
LBR
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
FABP5
0.33
TCCTCCATCACCTGAAACACTGGAC


 


10
ITM2A
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


16
SFRS5
0.34
TCCTCCATCACCTGAAACACTGGAC


 


11
IQGAP2
0.4
CACCCAGCTGGTCCTGTGGATGGGA


 


6
SLC7A6
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


3
SLA
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
IL2RG
0.31
TCCTCCATCACCTGAAACACTGGAC


 


1
MFNG
0.39
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
GPSM3
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


10
PIM2
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


2
EVER1
0.35
GCCCCACTGGACAACACTGATTCCT


 


3
LRMP
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
ICAM2
0.44
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
RIMS3
0.43
TGGACCCCACTGGCTGAGAATCTGG


 


10
FMNL1
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


3
MYB
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
PTPN7
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
LCK
0.48
CACCCAGCTGGTCCTGTGGATGGGA


 


7
CXorf9
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
RHOH
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ZNFN1A1
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
CENTB1
0.45
CACCCAGCTGGTCCTGTGGATGGGA


 


3
LCP2
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
DBT
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
CEP1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


9
IL6R
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


24
VAV1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
MAP4K1
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


24
CD28
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
PTP4A3
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
CD3G
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


1
LTB
0.4
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
USP34
0.44
GCCCCACTGGACAACACTGATTCCT


 


24
NVL
0.41
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
CD8B1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
SFRS6
0.31
GCCCCACTGGACAACACTGATTCCT


 


1
LCP1
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
CXCR4
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
PSCDBP
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


10
SELPLG
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


24
CD3Z
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
PRKCQ
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


2
CD1A
0.34
GCCCCACTGGACAACACTGATTCCT


 


10
GATA2
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


3
P2RX5
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
LAIR1
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


2
C1orf38
0.4
GCCCCACTGGACAACACTGATTCCT


 


24
SH2D1A
0.44
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
TRB@
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


2
SEPT6
0.34
GCCCCACTGGACAACACTGATTCCT


 


6
HA-1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


24
DOCK2
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
WBSCR20C
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD3D
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
RNASE6
0.31
GCCCCACTGGACAACACTGATTCCT


 


4
SFRS7
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
WBSCR20A
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


10
NUP210
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


24
CD6
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
HNRPA1
0.3
GCCCCACTGGACAACACTGATTCCT


 


6
AIF1
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


9
CYFIP2
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


24
GLTSCR2
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
C11orf2
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
ARHGAP15
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


10
BIN2
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


7
SH3TC1
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
STAG3
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
TM6SF1
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
C15orf25
0.33
TCCTCCATCACCTGAAACACTGGAC


 


4
FLJ22457
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
PACAP
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
MGC2744
0.31
GCCCCACTGGACAACACTGATTCCT
















TABLE 41







5-Fluorouracil biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













4
RPL18
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
RPL10A
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


7
ANAPC5
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
EEF1B2
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
RPL13A
0.5
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
RPS15
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
NDUFAB1
0.38
GCCCCACTGGACAACACTGATTCCT


 


4
APRT
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
ZNF593
0.34
TCCTCCATCACCTGAAACACTGGAC


 


4
MRP63
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
IL6R
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


16
SART3
0.37
TCCTCCATCACCTGAAACACTGGAC


 


2
UCK2
0.32
GCCCCACTGGACAACACTGATTCCT


 


6
RPL17
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


11
RPS2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


24
PCCB
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
TOMM20
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


10
SHMT2
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


24
RPLP0
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
GTF3A
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


9
STOML2
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


4
DKFZp564J157
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
MRPS2
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
ALG5
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
CALML4
0.33
CACCCAGCTGGTCCTGTGGATGGGA
















TABLE 42







Radiation sensitivity biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













9626
TRA1
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
ACTN4
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
CALM1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


1
CD63
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
FKBP1A
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


7
CALU
0.47
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
IQGAP1
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


16
MGC8721
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
STAT1
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


1
TACC1
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
TM4SF8
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


16
CD59
0.31
TCCTCCATCACCTGAAACACTGGAC


 


24
CKAP4
0.45
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
DUSP1
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
RCN1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
MGC8902
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
RRBP1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
PRNP
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


2
IER3
0.34
GCCCCACTGGACAACACTGATTCCT


 


2
MARCKS
0.43
GCCCCACTGGACAACACTGATTCCT


 


3
FER1L3
0.47
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
SLC20A1
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
HEXB
0.46
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
EXT1
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


4
TJP1
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
CTSL
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
SLC39A6
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
RIOK3
0.38
TCCTCCATCACCTGAAACACTGGAC


 


3
CRK
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
NNMT
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
TRAM2
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


1
ADAM9
0.52
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
PLSCR1
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


3
PRSS23
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
PLOD2
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
NPC1
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
TOB1
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


11
GFPT1
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


4
IL8
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
PYGL
0.46
TCCTCCATCACCTGAAACACTGGAC


 


10
LOXL2
0.49
TTGGACATCTCTAGTGTAGCTGCCA


 


24
KIAA0355
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
UGDH
0.49
TTGGACATCTCTAGTGTAGCTGCCA


 


3
PURA
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
ULK2
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


2
CENTG2
0.35
GCCCCACTGGACAACACTGATTCCT


 


2
CAP350
0.31
GCCCCACTGGACAACACTGATTCCT


 


1
CXCL1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
BTN3A3
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


6
WNT5A
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


4
FOXF2
0.44
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
LPHN2
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
CDH11
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


16
P4HA1
0.33
TCCTCCATCACCTGAAACACTGGAC


 


11
GRP58
0.44
CACCCAGCTGGTCCTGTGGATGGGA


 


9
DSIPI
0.44
TGGACCCCACTGGCTGAGAATCTGG


 


6
MAP1LC3B
0.5
AAGCCTATACGTTTCTGTGGAGTAA


 


4
GALIG
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
IGSF4
0.4
TCCTCCATCACCTGAAACACTGGAC


 


9
IRS2
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


11
ATP2A2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


1
OGT
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
TNFRSF10B
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


11
KIAA1128
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


11
TM4SF1
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


3
RIPK2
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
NR1D2
0.47
TTGGACATCTCTAGTGTAGCTGCCA


 


10
SSA2
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


6
NQO1
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


3
ASPH
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
ASAH1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
MGLL
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


6
SERPINB6
0.51
AAGCCTATACGTTTCTGTGGAGTAA


 


24
HSPA5
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
ZFP36L1
0.39
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
COL4A1
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
NIPA2
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
FKBP9
0.48
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
IL6ST
0.4
GCCCCACTGGACAACACTGATTCCT


 


10
DKFZP564G2022
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


9
PPAP2B
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


11
MAP1B
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


9
MAPK1
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


7
MYO1B
0.38
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
CAST
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
RRAS2
0.52
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
QKI
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
LHFPL2
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
SEPT10
0.38
GCCCCACTGGACAACACTGATTCCT


 


6
ARHE
0.5
AAGCCTATACGTTTCTGTGGAGTAA


 


6
KIAA1078
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


1
FTL
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
KIAA0877
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
PLCB1
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


3
KIAA0802
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
RAB3GAP
0.43
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
SERPINB1
0.46
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
TIMM17A
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
SOD2
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


10
HLA-A
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


11
NOMO2
0.43
CACCCAGCTGGTCCTGTGGATGGGA


 


1
LOC55831
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
PHLDA1
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


9
TMEM2
0.47
TGGACCCCACTGGCTGAGAATCTGG


 


7
MLPH
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
FAD104
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
LRRC5
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


10
RAB7L1
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


1
FLJ35036
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
DOCK10
0.41
TCCTCCATCACCTGAAACACTGGAC


 


6
LRP12
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


7
TXNDC5
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
CDC14B
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
HRMT1L1
0.38
CACCCAGCTGGTCCTCTGGATGGGA


 


10
DNAJC10
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


2
TNPO1
0.33
GCCCCACTGGACAACACTGATTCCT


 


4
LONP
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
AMIGO2
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


3
DNAPTP6
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
ADAMTS1
0.37
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 43







Rituximab (e.g., Mabthera) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
PSMB2
0.89
TCCTCCATCACCTGAAACACTGGAC


 


6
BAT1
0.88
AAGCCTATACGTTTCTGTGGAGTAA


 


24
ASCC3L1
0.89
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
SET
0.94
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
YWHAZ
0.83
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
GLUL
0.8
TGGACCCCACTGGCTGAGAATCTGG


 


24
LDHA
0.8
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
HMGB1
0.84
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
SFRS2
0.87
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
DPYSL2
0.82
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
MGC8721
0.82
CACCCAGCTGGTCCTGTGGATGGGA


 


3
NOL5A
0.86
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
SFRS10
0.88
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SF3B1
0.82
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
K-ALPHA-1
0.86
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
TXNRD1
0.86
TGGACCCCACTGGCTGAGAATCTGG


 


11
ARHGDIB
0.83
CACCCAGCTGGTCCTGTGGATGGGA


 


10
ZFP36L2
0.92
TTGGACATCTCTAGTGTAGCTGCCA


 


9
DHX15
0.81
TGGACCCCACTGGCTGAGAATCTGG


 


11
SOX4
0.85
CACCCAGCTGGTCCTGTGGATGGGA


 


766
GRSF1
0.81
TGGACCCCACTGGCTGAGAATCTGG


 


2
MCM3
0.85
GCCCCACTGGACAACACTGATTCCT


 


16
IFITM1
0.82
TCCTCCATCACCTGAAACACTGGAC


 


16
RPA2
0.86
TCCTCCATCACCTGAAACACTGGAC


 


7
LBR
0.87
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
CKS1B
0.85
AAGCCTATACGTTTCTGTGGAGTAA


 


9
NASP
0.82
TGGACCCCACTGGCTGAGAATCTGG


 


16
HNRPDL
0.81
TCCTCCATCACCTGAAACACTGGAC


 


3
CUGBP2
0.81
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
PTBP1
0.87
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
ARL7
0.83
TTGGACATCTCTAGTGTAGCTGCCA


 


7
CTCF
0.83
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
HMGCR
0.86
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ITM2A
0.88
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
SFRS3
0.93
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
SRPK2
0.82
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
JARID2
0.92
CACCCAGCTGGTCCTGTGGATGGGA


 


1
M96
0.84
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
MAD2L1
0.87
TCCTCCATCACCTGAAACACTGGAC


 


7
SATB1
0.81
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
TMPO
0.9
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
SIVA
0.84
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
SEMA4D
0.9
TCCTCCATCACCTGAAACACTGGAC


 


24
TFDP2
0.87
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
SKP2
0.86
AAGCCTATACGTTTCTGTGGAGTAA


 


2
SH3YL1
0.88
GCCCCACTGGACAACACTGATTCCT


 


16
RFC4
0.87
TCCTCCATCACCTGAAACACTGGAC


 


6
PCBP2
0.83
AAGCCTATACGTTTCTGTGGAGTAA


 


2
IL2RG
0.84
GCCCCACTGGACAACACTGATTCCT


 


1
CDC45L
0.89
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
GTSE1
0.83
TTGGACATCTCTAGTGTAGCTGCCA


 


6
KIF11
0.85
AAGCCTATACGTTTCTGTGGAGTAA


 


10
FEN1
0.88
TTGGACATCTCTAGTGTAGCTGCCA


 


9
MYB
0.9
TGGACCCCACTGGCTGAGAATCTGG


 


16
LCK
0.87
TCCTCCATCACCTGAAACACTGGAC


 


2
CENPA
0.84
GCCCCACTGGACAACACTGATTCCT


 


2
CCNE2
0.84
GCCCCACTGGACAACACTGATTCCT


 


10
H2AFX
0.88
TTGGACATCTCTAGTGTAGCTGCCA


 


16
SNRPG
0.84
TCCTCCATCACCTGAAACACTGGAC


 


24
CD3G
0.94
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
STK6
0.9
ACTTGTCCTCAGCTTGGGGTTCTTC


 


3
PTP4A2
0.81
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
FDFT1
0.91
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
HSPA8
0.84
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
HNRPR
0.94
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
MCM7
0.92
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
SFRS6
0.85
TGGACCCCACTGGCTGAGAATCTGG


 


11
PAK2
0.8
CACCCAGCTGGTCCTGTGGATGGGA


 


1
LCP1
0.85
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
STAT3
0.81
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
OK/SW-cl.56
0.8
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
WHSC1
0.81
TGGACCCCACTGGCTGAGAATCTGG


 


6
DIAPH1
0.88
AAGCCTATACGTTTCTGTGGAGTAA


 


1
KIF2C
0.88
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
HDGFRP3
0.89
CACCCAGCTGGTCCTGTGGATGGGA


 


10
PNMA2
0.93
TTGGACATCTCTAGTGTAGCTGCCA


 


1
GATA3
0.93
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
BUB1
0.88
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
TPX2
0.8
CACCCAGCTGGTCCTGTGGATGGGA


 


24
SH2D1A
0.86
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
TNFAIP8
0.9
TCCTCCATCACCTGAAACACTGGAC


 


4
CSE1L
0.83
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
MCAM
0.8
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
AF1Q
0.83
GCCCCACTGGACAACACTGATTCCT


 


11
CD47
0.86
CACCCAGCTGGTCCTGTGGATGGGA


 


6
SFRS1
0.85
AAGCCTATACGTTTCTGTGGAGTAA


 


1
FYB
0.92
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
TRB@
0.84
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
CXCR4
0.94
GCCCCACTGGACAACACTGATTCCT


 


16
H3F3B
0.84
TCCTCCATCACCTGAAACACTGGAC


 


7
MKI67
0.83
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
MAC30
0.82
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
ARID5B
0.88
AAGCCTATACGTTTCTGTGGAGTAA


 


6
LOC339287
0.81
AAGCCTATACGTTTCTGTGGAGTAA


 


24
CD3D
0.82
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
ZAP70
0.87
AAGCCTATACGTTTCTGTGGAGTAA


 


16
LAPTM4B
0.83
TCCTCCATCACCTGAAACACTGGAC


 


24
SFRS7
0.87
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
HNRPA1
0.9
AAGCCTATACGTTTCTGTGGAGTAA


 


6
HSPCA
0.88
AAGCCTATACGTTTCTGTGGAGTAA


 


24
AIF1
0.82
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
GTF3A
0.87
AAGCCTATACGTTTCTGTGGAGTAA


 


10
MCM5
0.91
TTGGACATCTCTAGTGTAGCTGCCA


 


6
GTL3
0.85
AAGCCTATACGTTTCTGTGGAGTAA


 


3
ZNF22
0.89
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
FLJ22794
0.83
GCCCCACTGGACAACACTGATTCCT


 


7
LZTFL1
0.89
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
e(y)2
0.87
TCCTCCATCACCTGAAACACTGGAC


 


16
FLJ20152
0.92
TCCTCCATCACCTGAAACACTGGAC


 


7
C10orf3
0.86
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
NRN1
0.86
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
FLJ10858
0.81
GCCCCACTGGACAACACTGATTCCT


 


2
BCL11B
0.89
GCCCCACTGGACAACACTGATTCCT


 


6
ASPM
0.91
AAGCCTATACGTTTCTGTGGAGTAA


 


10
LEF1
0.9
TTGGACATCTCTAGTGTAGCTGCCA


 


7
LOC146909
0.83
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 44







5-Aza-2′-deoxycytidine (decitabine) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













10
CD99
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


1
SNRPA
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CUGBP2
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
STAT5A
0.32
GCCCCACTGGACAACACTGATTCCT


 


10
SLA
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


9
IL2RG
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


7
GTSE1
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
MYB
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


1
PTPN7
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CXorf9
0.42
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
RHOH
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
ZNFN1A1
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


11
CENTB1
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


4
LCP2
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
HIST1H4C
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


3
CCR7
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
APOBEC3B
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
MCM7
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


6
LCP1
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
SELPLG
0.4
TGGACCCCACTGGCTGAGAATCTGG


 


1
CD3Z
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
PRKCQ
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
GZMB
0.32
GCCCCACTGGACAACACTGATTCCT


 


6
SCN3A
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


3
LAIR1
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
SH2D1A
0.35
GCCCCACTGGACAACACTGATTCCT


 


7
SEPT6
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
CG018
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
CD3D
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


24
C18orf10
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
PRF1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
AIF1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


7
MCM5
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
LPXN
0.35
TCCTCCATCACCTGAAACACTGGAC


 


4
C22orf18
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
ARHGAP15
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
LEF1
0.43
GCCCCACTGGACAACACTGATTCCT
















TABLE 45







Idarubicin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













9
SLC9A3R1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


9
RPS19
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


1
ITM2A
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
SSBP2
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


1
CXorf9
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
RHOH
0.32
TCCTCCATCACCTGAAACACTGGAC


 


4
ZNFN1A1
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
FXYD2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


9
CCR9
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


10
NAP1L1
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


4
CXCR4
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SH2D1A
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
CD1A
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


4
TRB@
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
SEPT6
0.32
GCCCCACTGGACAACACTGATTCCT


 


3
RPS2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
DOCK2
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD3D
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
CD6
0.3
GCCCCACTGGACAACACTGATTCCT


 


7
ZAP70
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
AIF1
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
CD1E
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
CYFIP2
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


1
ADA
0.41
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
TRIM
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
GLTSCR2
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
FLJ10858
0.35
GCCCCACTGGACAACACTGATTCCT


 


1
BCL11B
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
GIMAP6
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
STAG3
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


7
UBASH3A
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 46







Melphalan biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













 9
CD99
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


 3
HLA-DPB1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


 3
ARHGDIB
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


 11
IFITM1
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


 24
UBE2L6
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


 24
ITM2A
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


 4
SERPINA1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


 4
STAT5A
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


 24
INPP5D
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


 3
DGKA
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


 3
SATB1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


 4
SEMA4D
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


 11
TFDP2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


 16
SLA
0.49
TCCTCCATCACCTGAAACACTGGAC


 


 11
IL2RG
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


 24
CD48
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


 7
MFNG
0.48
ACTTGTCCTCAGCTTGGGCTTCTTC


 


 11
ALOX5AP
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


 6
GPSM3
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


 2
PSMB9
0.34
GCCCCACTGGACAACACTGATTCCT


 


 9
KIAA0711
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


 4
SELL
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


 3
ADA
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


 10
EDG1
0.49
TTGGACATCTCTAGTGTAGCTGCCA


 


 11
RIMS3
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


 6
FMNL1
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


 2
MYB
0.3
GCCCCACTGGACAACACTGATTCCT


 


 4
PTPN7
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


 4
LCK
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


 11
CXorf9
0.55
CACCCAGCTGGTCCTGTGGATGGGA


 


 9
RHOH
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


 7
ZNFN1A1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


 3
CENTB1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


 24
LCP2
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


 11
FXYD2
0.55
CACCCAGCTGGTCCTGTGGATGGGA


 


 6
CD1D
0.44
AAGCCTATACGTTTCTGTGGAGTAA


 


 3
BATF
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


 16
STAT4
0.33
TCCTCCATCACCTGAAACACTGGAC


 


 16
VAV1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


 11
MAP4K1
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


 1
CCR7
0.44
TCCTGTACTTGTCCTCAGCTTGGGC


 


 1
PDE4C
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


 6
CD3G
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


 10
CCR9
0.36
TTGGACATCTCTAGTGTAGGTGCCA


 


 1
SP110
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


 4
LCP1
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


 2
IFI16
0.32
GCCCCACTGGACAACACTGATTCCT


 


 7
CXCR4
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


 6
ARHGEF6
0.47
AAGCCTATACGTTTCTGTGGAGTAA


 


 10
GATA3
0.55
TTGGACATCTCTAGTGTAGCTGCCA


 


 10
SELPLG
0.47
TTGGACATCTCTAGTGTAGCTGCCA


 


 9
SEG31L2
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


 10
CD3Z
0.5
TTGGACATCTCTAGTGTAGCTGCCA


 


 2
PRKCQ
0.56
GCCGCACTGGACAACACTGATTCCT


 


 16
SH2D1A
0.33
TCCTCCATCACCTGAAACACTGGAC


 


 3
GZMB
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


 3
CD1A
0.55
TGCCTGCTCCTGTACTTGTCCTCAG


 


 11
SCN3A
0.64
CACCCAGCTGGTCCTGTGGATGGGA


 


 11
LAIR1
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


 10
FYB
0.49
TTGGACATCTCTAGTGTAGCTGCCA


 


 10
TRB@
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


 2
SEPT6
0.32
GCCCCACTGGACAACACTGATTCCT


 


 2
HA-1
0.48
GCCCCACTGGACAACACTGATTCCT


 


 10
DOCK2
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


 4
CG018
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


 16
CD3D
0.32
TCCTCCATCACCTGAAACACTGGAC


 


 3
T3JAM
0.41
TGCCTGCTCCTGTACTTGTCCTCAG


 


 1
FNBP1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


 6
CD6
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


 9
ZAP70
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
LST1
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
GPR65
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


2
PRF1
0.41
GCCCCACTGGACAACACTGATTCCT


 


2
AIF1
0.32
GCCCCACTGGACAACACTGATTCCT


 


16
FLJ20331
0.42
TCCTCCATCACCTGAAACACTGGAC


 


11
RAG2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


1
WDR45
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CD1E
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
CYFIP2
0.4
TCCTCCATCACCTGAAACACTGGAC


 


11
TARP
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


7
TRIM
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
RPL10L
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
GLTSCR2
0.46
CACCCAGCTGGTCCTGTGGATGGGA


 


6
GIMAP5
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


3
ARHGAP15
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
NOTCH1
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
BIN2
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


24
C13orf18
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
CECR1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
BCL11B
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
GIMAP6
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
STAG3
0.58
TTGGACATCTCTAGTGTAGCTGCCA


 


1
TM6SF1
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
HSD17B7
0.32
GCCCCACTGGACAACACTGATTCCT


 


7
UBASH3A
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
MGC5566
0.45
TCCTCCATCACCTGAAACACTGGAC


 


6
FLJ22457
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


3
TPK1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
PHF11
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
DKFZP434B0335
0.4
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 47







IL4-PR38 fusion protein biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
MCL1
0.3
TCCTGCATCACCTGAAACACTGGAC


 


11
DDX23
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


3
JUNB
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
ZFP36
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


7
IFITM1
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
CKS1B
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


2
SERPINA1
0.31
GCCCCACTGGACAACACTGATTCCT


 


7
IL4R
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
CLDN3
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
ARL4A
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
HMMR
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
FLJ12671
0.42
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
ANKHD1
0.42
GCCCCACTGGACAACACTGATTCCT


 


7
KIF2C
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
RPA3
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


3
MCCC2
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
CDH17
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
LSM5
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


2
PRF1
0.32
GCCCCACTGGACAACACTGATTCCT


 


16
ROD1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


16
FLJ12666
0.37
TCCTCCATCACCTGAAACACTGGAC


 


10
SUV420H1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


16
MUC13
0.36
TCCTCCATCACCTGAAACACTGGAC


 


2
C13orf18
0.35
GCCCCACTGGACAACACTGATTCCT


 


3
CDCA8
0.35
TGCCTGCTCCTGTACTTGTCCTCAG
















TABLE 48







Valproic acid (VPA) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













4
STOM
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
TNFAIP3
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
ASNS
0.31
GCCCCACTGGACAACACTGATTCCT


 


3
GARS
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
CXCR4
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


9
EGLN3
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


1
LBH
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
GDF15
0.3
TGCCTGCTCCTGTACTTGTCCTCAG
















TABLE 49







All-trans retinoic acid (ATRA) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
PPIB
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
ZFP36L2
0.48
AAGCCTATACGTTTCTGTGGAGTAA


 


7
IFI30
0.46
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
USP7
0.35
TCCTCCATCACCTGAAACACTGGAC


 


16
SRM
0.43
TCCTCCATCACCTGAAACACTGGAC


 


3
SH3BP5
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
ALDOC
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


7
FADS2
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
GUSB
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


1
PSCD1
0.48
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
IQGAP2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
STS
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
MFNG
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
FLI1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
PIM2
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


1
INPP4A
0.54
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
LRMP
0.51
GCCCCACTGGACAACACTGATTCCT


 


4
ICAM2
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
EVI2A
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


4
MAL
0.46
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
BTN3A3
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


10
PTPN7
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


10
IL10RA
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


6
SPI1
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


3
TRAF1
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
ITGB7
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
ARHGAP6
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
MAP4K1
0.52
GCCCCACTGGACAACACTGATTCCT


 


6
CD28
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


16
PTP4A3
0.3
TCCTCCATCACCTGAAACACTGGAC


 


7
LTB
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
Clorf38
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
WBSCR22
0.53
TCCTCCATCACCTGAAACACTGGAC


 


16
CD8B1
0.35
TCCTCCATCACCTGAAACACTGGAC


 


7
LCP1
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
FLJ13052
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
MEF2C
0.71
TTGGACATCTCTAGTGTAGCTGCCA


 


4
PSCDBP
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
IL16
0.51
TGGACCCCACTGGCTGAGAATCTGG


 


3
SELPLG
0.53
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
MAGEA9
0.6
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
LAIR1
0.43
TCCTCCATCACCTGAAACACTGGAC


 


16
TNFRSF25
0.53
TCCTCCATCACCTGAAACACTGGAC


 


7
EVI2B
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
IGJ
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
PDCD4
0.47
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
RASA4
0.52
CACCCAGCTGGTCCTGTGGATGGGA


 


6
HA-1
0.73
AAGCCTATACGTTTCTGTGGAGTAA


 


1
PLCL2
0.47
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
RNASE6
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


10
WBSCR20C
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


6
NUP210
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


7
RPL10L
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
C11orf2
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


3
CABC1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
ARHGEF3
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
TAPBPL
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
CHST12
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
FKBP11
0.54
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
FLJ35036
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


11
MYLIP
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


7
TXNDC5
0.31
ACTTGTCCTCAGCTTGGGCTTGTTC


 


16
PACAP
0.3
TCCTCCATCACCTGAAACACTGGAC


 


1
TOSO
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
PNAS-4
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


6
IL21R
0.57
AAGCCTATACGTTTCTGTGGAGTAA


 


24
TCF4
0.64
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 50







Cytoxan biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
C6orf29
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


4
TRIM31
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
CD69
0.37
GCCCCACTGGACAACACTGATTCCT


 


7
LRRN3
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
GPR35
0.41
TCCTCCATCACCTGAAACACTGGAC


 


10
CDW52
0.48
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 51







Topotecan (Hycamtin) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
K-ALPHA-1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CSDA
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


10
UCHL1
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


16
NAP1L1
0.3
TCCTCCATCACCTGAAACACTGGAC


 


1
ATP5G2
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
HDGFRP3
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


2
IFI44
0.3
GCCCCACTGGACAACACTGATTCCT
















TABLE 52







Suberoylanilide hydroxamic acid (SAHA,


vorinostat, Zolinza) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













24
NOL5A
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
STOM
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
SIAT1
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
CUGBP2
0.39
GCCCCACTGGACAACACTGATTCCT


 


9
GUSB
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


24
ITM2A
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
JARID2
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
RUNX3
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


3
ICAM2
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
PTPN7
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


10
VAV1
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


6
PTP4A3
0.42
AAGCCTATACGTTTCTGTGGAGTAA


 


7
MCAM
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
MEF2C
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
IDH3B
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
RFP
0.31
TCCTCCATCACCTGAAACACTGGAC


 


1
SEPT6
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
SLC43A3
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
WBSCR20C
0.46
TGGACCCCACTGGCTGAGAATCTGG


 


1
SHMT2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
GLTSCR2
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CABC1
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
FLJ20859
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
FLJ20010
0.51
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
MGC10993
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
FKBP11
0.31
TCCTCCATCACCTGAAACACTGGAC
















TABLE 53







Depsipeptide (FR901228) biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













4
ZFP36L2
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
TRIB2
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
LCP2
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
C6orf32
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
IL16
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


6
CACNA1G
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


2
SPDEF
0.31
GCCCCACTGGACAACACTGATTCCT


 


16
HAB1
0.39
TCCTCCATCACCTGAAACACTGGAC


 


9
TOSO
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


6
ARHGAP25
0.33
AAGCCTATACGTTTCTGTGGAGTAA
















TABLE 54







Bortezomib biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













4
PLEKHB2
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
ARPC1B
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


24
MX1
0.39
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
CUGBP2
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


6
IFI16
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


4
TNFRSF14
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
SP110
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


9
ELF1
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


1
LPXN
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
IFRG28
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
LEF1
0.33
GCCCCACTGGACAACACTGATTCCT


 


1
PYCARD
0.31
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 55







Leukeran biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
SSRP1
0.31
GCCCCACTGGACAACACTGATTCCT


 


4
ALDOC
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
C1QR1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
TTF1
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
PRIM1
0.31
GCCCCACTGGACAACACTGATTCCT


 


16
USP34
0.38
TCCTCCATCACCTGAAACACTGGAC


 


1
TK2
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
GOLGIN-67
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
NPD014
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
KIAA0220
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
SLC43A3
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
WBSCR20C
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


3
ICAM2
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
TEX10
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


7
CHD7
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
SAMSN1
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


7
TPRT
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 56







Fludarabine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













7
HLA-E
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
BAT3
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
ENO2
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


1
UBE2L6
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
CUGBP2
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
ITM2A
0.32
GCCCCACTGGACAACACTGATTCCT


 


2
PALM2-AKAP2
0.41
GCCCCACTGGACAACACTGATTCCT


 


2
JARID2
0.33
GCCCCACTGGACAACACTGATTCCT


 


9
DGKA
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


6
SLC7A6
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


4
TFDP2
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
ADA
0.41
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
EDG1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
ICAM2
0.46
AAGCCTATACGTTTCTGTGGAGTAA


 


16
PTPN7
0.33
TCCTCCATCACCTGAAACACTGGAC


 


6
CXorf9
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


11
RHOH
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


4
MX2
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
ZNFN1A1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


9
COCH
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


9
LCP2
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


16
CLGN
0.31
TCCTCCATCACCTGAAACACTGGAC


 


2
BNC1
0.38
GCCCCACTGGACAACACTGATTCCT


 


1
FLNC
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
HLA-DRB3
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
UCP2
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


2
HLA-DRB1
0.3
GCCCCACTGGACAACACTGATTCCT


 


24
GATA3
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
PRKCQ
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
SH2D1A
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
NFATC3
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
TRB@
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
FNBP1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


7
SEPT6
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
NME4
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


24
DKFZP434C171
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
ZC3HAV1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
SLC43A3
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
CD3D
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
AIF1
0.35
TCCTCCATCACCTGAAACACTGGAC


 


16
SPTAN1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
CD1E
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
TRIM
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


24
DATF1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
FHOD1
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
ARHGAP15
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


6
STAG3
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


1
SAP130
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
CYLD
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 57







Vinblastine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe





7
CD99
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 58







Busulfan biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
RPLP2
0.37
TCCTCCATCACCTGAAACACTGGAC


 


7
BTG1
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
CSDA
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
ARHGDIB
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


16
INSIG1
0.41
TCCTCCATCACCTGAAACACTGGAC


 


10
ALDOC
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


16
WASPIP
0.31
TCCTCCATCACCTGAAACACTGGAC


 


1
C1QR1
0.46
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
EDEM1
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


24
SLA
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
MFNG
0.4
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
GPSM3
0.75
GCCCCACTGGACAACACTGATTCCT


 


7
ADA
0.53
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
LRMP
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
EVI2A
0.52
TCCTCCATCACCTGAAACACTGGAC


 


7
FMNL1
0.45
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
PTPN7
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
RHOH
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
ZNFN1A1
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CENTB1
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


9
MAP4K1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


1
CD28
0.51
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
SP110
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
NAP1L1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
IFI16
0.35
TCCTCCATCACCTGAAACACTGGAC


 


4
ARHGEF6
0.42
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SELPLG
0.45
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
CD3Z
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


11
SH2D1A
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


3
LAIR1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
RAFTLIN
0.36
GCCCCACTGGACAACACTGATTCCT


 


7
HA-1
0.61
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
DOCK2
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
CD3D
0.31
GCCCCACTGGACAACACTGATTCCT


 


7
T3JAM
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
ZAP70
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


16
GPR65
0.32
TCCTCCATCACCTGAAACACTGGAC


 


11
CYFIP2
0.58
CACCCAGCTGGTCCTGTGGATGGGA


 


10
LPXN
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


1
RPL10L
0.41
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
GLTSCR2
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
ARHGAP15
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


3
BCL11B
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
TM6SF1
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


7
PACAP
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
TCF4
0.32
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 59







Dacarbazine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













24
ARHGDIB
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
ITM2A
0.4
TCCTCCATCACCTGAAACACTGGAC


 


11
SSBP2
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


2
PIM2
0.39
GCCCCACTGGACAACACTGATTCCT


 


2
SELL
0.31
GCCCCACTGGACAACACTGATTCCT


 


1
ICAM2
0.43
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
EVI2A
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


10
MAL
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


7
PTPN7
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ZNFN1A1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
LCP2
0.3
GCCCCACTGGACAACACTGATTCCT


 


9
ARHGAP6
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


7
CD28
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
CD8B1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


24
LCP1
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
NPD014
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
CD69
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


6
NFATC3
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


4
TRB@
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
IGJ
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


10
SLC43A3
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


16
DOCK2
0.36
TCCTCCATCACCTGAAACACTGGAC


 


9
FHOD1
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


6
PACAP
0.31
AAGCCTATACGTTTCTGTGGAGTAA
















TABLE 60







Oxaliplatin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













10
RPL18
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


4
RPL10A
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
RPS3A
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


11
EEF1B2
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


6
GOT2
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


4
RPL13A
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
RPS15
0.41
GCCCCACTGGACAACACTGATTCCT


 


3
NOL5A
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
RPLP2
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
SLC9A3R1
0.43
TGGACCCCACTGGCTGAGAATCTGG


 


2
E1F3S3
0.43
GCCCCACTGGACAACACTGATTCCT


 


3
MTHFD2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
IMPDH2
0.34
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
ALDOC
0.44
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
FABP5
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


24
ITM2A
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
PCK2
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
MFNG
0.33
GCCCCACTGGACAACACTGATTCCT


 


9
GCH1
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


11
PIM2
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


24
ADA
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
ICAM2
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
TTF1
0.47
TTGGACATCTCTAGTGTAGCTGCCA


 


3
MYB
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
PTPN7
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


16
RHOH
0.42
TCCTCCATCACCTGAAACACTGGAC


 


7
ZNFN1A1
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
PRIM1
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
FHIT
0.48
TCCTCCATCACCTGAAACACTGGAC


 


9
ASS
0.45
TGGACCCCACTGGCTGAGAATCTGG


 


3
SYK
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
OXA1L
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


3
LCP1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
DDX18
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


4
NOLA2
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
KIAA0922
0.41
TCCTCCATCACCTGAAACACTGGAC


 


24
PRKCQ
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
NFATC3
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
ANAPC5
0.34
TCCTCCATCACCTGAAACACTGGAC


 


9
TRB@
0.4
TGGACCCCACTGGCTGAGAATCTGG


 


24
CXCR4
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
FNBP4
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
SEPT6
0.53
TTGGACATCTCTAGTGTAGCTGCCA


 


16
RPS2
0.35
TCCTCCATCACCTGAAACACTGGAC


 


7
MDN1
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
PCCB
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


9
RASA4
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


11
WBSCR20C
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


10
SFRS7
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


3
WBSCR20A
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
NUP210
0.43
TGGACCCCACTGGCTGAGAATCTGG


 


24
SHMT2
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
RPLP0
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


11
MAP4K1
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


16
HNRPA1
0.37
TCCTCCATCACCTGAAACACTGGAC


 


2
CYFIP2
0.3
GCCCCACTGGACAACACTGATTCCT


 


16
RPL10L
0.32
TCGTCCATCACCTGAAACACTGGAC


 


9
GLTSCR2
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


1
MRPL16
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
MRPS2
0.34
GCCCCACTGGACAACACTGATTCCT


 


6
FLJ12270
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CDK5RAP3
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


1
ARHGAP15
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
CUTC
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
FKBP11
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
ADPGK
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


2
FLJ22457
0.32
GCCCCACTGGACAACACTGATTCCT


 


24
PUS3
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
PACAP
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
CALML4
0.31
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 61







Hydroxyurea biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













16
CSDA
0.31
TCCTCCATCACCTGAAACACTGGAC


 


6
INSIG1
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


11
UBE2L6
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


2
PRG1
0.36
GCCCCACTGGACAACACTGATTCCT


 


7
ITM2A
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
DGKA
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


11
SLA
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


9
PCBP2
0.51
TGGACCCCACTGGCTGAGAATCTGG


 


7
IL2RG
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
ALOX5AP
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


2
PSMB9
0.33
GCCCCACTGGACAACACTGATTCCT


 


10
LRMP
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


9
ICAM2
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


16
PTPN7
0.36
TCCTCCATCACCTGAAACACTGGAC


 


24
CXorf9
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
RHOH
0.41
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
ZNFN1A1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
CENTB1
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


11
LCP2
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


2
STAT4
0.32
GCCCCACTGGACAACACTGATTCCT


 


24
CCR7
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
CD3G
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SP110
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
TNFAIP8
0.31
TCCTCCATCACCTGAAACACTGGAC


 


9
IFI16
0.4
TGGACCCCACTGGCTGAGAATCTGG


 


7
CXCR4
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
ARHGEF6
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


16
SELPLG
0.3
TCCTCCATCACCTGAAACACTGGAC


 


16
CD3Z
0.38
TCCTCCATCACCTGAAACACTGGAC


 


3
PRKCQ
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
SH2D1A
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
CD1A
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


9
NFATC3
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


16
LAIR1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


11
TRB@
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


11
SEPT6
0.34
CAGCCAGCTGGTCCTGTGGATGGGA


 


24
RAFTLIN
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
DOCK2
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


3
CD3D
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
CD6
0.42
AAGCCTATACGTTTCTGTGGAGTAA


 


3
AIF1
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
CD1E
0.41
GCCCCACTGGACAACACTGATTCCT


 


1
CYFIP2
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
TARP
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
ADA
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


9
ARHGAP15
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
GIMAP6
0.34
GCCCCACTGGACAACACTGATTCCT


 


7
STAG3
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
FLJ22457
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
PACAP
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


1
TCF4
0.4
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 62







Tegafur biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
RPL11
0.31
GCCCCACTGGACAACACTGATTCCT


 


3
RPL17
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
ANAPC5
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


1
RPL13A
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
STOM
0.37
TCCTCCATCACCTGAAACACTGGAC


 


2
TUFM
0.38
GCCCCACTGGACAACACTGATTCCT


 


1
SCARB1
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
FABP5
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


24
KIAA0711
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
ILGR
0.33
TCCTCCATCACCTGAAACACTGGAC


 


4
WBSCR22
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
UCK2
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
GZMB
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


11
Clorf38
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


1
PCBP2
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
GPR65
0.44
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
GLTSCR2
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
FKBP11
0.38
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 63







Daunorubicin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
ALDOC
0.41
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
ITM2A
0.32
GCCCCACTGGACAACACTGATTCCT


 


24
SLA
0.41
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
SSBP2
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
IL2RG
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


10
MFNG
0.47
TTGGACATCTCTAGTGTAGCTGCCA


 


16
SELL
0.33
TCCTCCATCACCTGAAACACTGGAC


 


4
STC1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
LRMP
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


2
MYB
0.41
GCCCCACTGGACAACACTGATTCCT


 


4
PTPN7
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
CXorf9
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


4
RHOH
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
ZNFN1A1
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


9
CENTB1
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


9
MAP4K1
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


1
CCR7
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
CD3G
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
CCR9
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


11
CBFA2T3
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


4
CXGR4
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
ARHGEF6
0.4
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
SELPLG
0.45
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
SEC31L2
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
CD3Z
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


1
SH2D1A
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
CD1A
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


11
SCN3A
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


3
LAIR1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
TRB@
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
DOCK2
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


11
WBSCR20C
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


3
CD3D
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
T3JAM
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


7
CD6
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
ZAP70
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
GPR65
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
AIF1
0.3
GCCCCACTGGACAACACTGATTCCT


 


16
WDR45
0.3
TCCTCCATCACCTGAAACACTGGAC


 


24
CD1E
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
CYFIP2
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


10
TARP
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


1
TRIM
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
ARHGAP15
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
NOTCH1
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


6
STAG3
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


3
UBASH3A
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
MGC5566
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
PACAP
0.33
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 64







Bleomycin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













11
PFN1
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


7
CALU
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
ZYX
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


2
PSMD2
0.36
GCCCCACTGGACAACACTGATTCCT


 


1
RAP1B
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
EPAS1
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
PGAM1
0.36
GCCCCACTGGACAACACTGATTCCT


 


3
STAT1
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
CKAP4
0.38
GCCCCACTGGACAACACTGATTCCT


 


1
DUSP1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
RCN1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


9
UCHL1
0.44
TGGACCCCACTGGCTGAGAATCTGG


 


4
ITGA5
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
NFKBIA
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
LAMB1
0.4
GCCCCACTGGACAACACTGATTCCT


 


10
TGFBI
0.37
TTGGACATCTCTAGTGTAGCTGCCA


 


2
FHL1
0.31
GCCCCACTGGACAACACTGATTCCT


 


16
GJA1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


11
PRG1
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


11
EXT1
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


1
MVP
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
NNMT
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


16
TAP1
0.37
TCCTCCATCACCTGAAACACTGGAC


 


9
CRIM1
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


2
PLOD2
0.36
GCCCCACTGGACAACACTGATTCCT


 


1
RPS19
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
AXL
0.43
GCCCCACTGGACAACACTGATTCCT


 


16
PALM2-AKAP2
0.42
TCCTCCATCACCTGAAACACTGGAC


 


1
IL8
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
LOXL2
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
PAPSS2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


24
CAV1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


7
F2R
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
PSMB9
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


9
LOX
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


1
Clorf29
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
STC1
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


24
LIF
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
KCNJ8
0.46
GCCCCACTGGACAACACTGATTCCT


 


3
SMAD3
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
HPCAL1
0.45
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
WNT5A
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
BDNF
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
TNFRSF1A
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
NCOR2
0.45
CACCCAGCTGGTCCTGTGGATGGGA


 


10
FLNC
0.44
TTGGACATCTCTAGTGTAGCTGCCA


 


4
HMGA2
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
HLA-B
0.42
AAGCCTATACGTTTCTGTGGAGTAA


 


4
FLOT1
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
PTRF
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


24
IFI16
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
MGC4083
0.34
TCCTCCATCACCTGAAACACTGGAC


 


7
TNFRSF10B
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
PNMA2
0.38
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
TFPI
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
CLECSF2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
SP110
0.34
GCCCCACTGGACAACACTGATTCCT


 


7
PLAUR
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ASPH
0.42
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
FSCN1
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
HIC
0.46
TCCTCCATCACCTGAAACACTGGAC


 


9
HLA-C
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


16
COL6A1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


4
IL6ST
0.45
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
IFITM3
0.36
GCCCCACTGGACAACACTGATTCCT


 


16
MAP1B
0.31
TCCTCCATCACCTGAAACACTGGAC


 


7
FLJ46603
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
RAFTLIN
0.32
GCCCCACTGGACAACACTGATTCCT


 


11
FTL
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


16
KIAA0877
0.43
TCCTCCATCACCTGAAACACTGGAC


 


9
MT1E
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


3
CDC10
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
ZNF258
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
BCAT1
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


4
IFI44
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
SOD2
0.36
GCCCCACTGGACAACACTGATTCCT


 


16
TMSB10
0.33
TCCTCCATCACCTGAAACACTGGAC


 


10
FLJ10350
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
Clorf24
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


4
EFHD2
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
RPS27L
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


11
TNFRSF12A
0.43
CACCCAGCTGGTCCTGTGGATGGGA


 


10
FAD104
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


7
RAB7L1
0.58
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
NME7
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


10
TMEM22
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


2
TPK1
0.31
GCCCCACTGGACAACACTGATTCCT


 


9
ELK3
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


6
CYLD
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


7
AMIGO2
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
ADAMTS1
0.43
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
ACTB
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 65







Estramustine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe





7
HSPCB
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
LDHA
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
TM4SF7
0.32
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 66







Chlorambucil biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













9
CSDA
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


24
INSIG1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
UBE2L6
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


24
PRG1
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
ITM2A
0.3
GCCCCACTGGACAACACTGATTCCT


 


24
DGKA
0.38
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
TFDP2
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
SLA
0.32
TCCTCCATCACCTGAAACACTGGAC


 


6
IL2RG
0.44
AAGCCTATACGTTTCTGTGGAGTAA


 


2
ALOX5AP
0.45
GCCCCACTGGACAACACTGATTCCT


 


10
GPSM3
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


7
PSMB9
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
SELL
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
ADA
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
EDG1
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
FMNL1
0.3
TCCTCCATCACCTGAAACACTGGAC


 


24
PTPN7
0.5
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
CXorf9
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


10
RHOH
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


24
ZNFN1A1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
CENTB1
0.47
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
LCP2
0.37
TCCTCCATCACCTGAAACACTGGAC


 


6
CD1D
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


4
STAT4
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
VAV1
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
MAP4K1
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


1
CCR7
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
PDE4C
0.42
GCCCCACTGGACAACACTGATTCCT


 


6
CD3G
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


6
CCR9
0.43
AAGCCTATACGTTTCTGTGGAGTAA


 


10
SP110
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


10
TNFAIP8
0.48
TTGGACATCTCTAGTGTAGCTGCCA


 


4
LCP1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
IFI16
0.5
TCCTCCATCACCTGAAACACTGGAC


 


7
CXCR4
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
ARHGEF6
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
SELPLG
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


9
SEC31L2
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


6
CD3Z
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


2
PRKCQ
0.31
GGCCCACTGGACAACACTGATTCCT


 


7
SH2D1A
0.47
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
GZMB
0.48
TGGACCCCACTGGCTGAGAATCTGG


 


6
CD1A
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


10
LAIR1
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


10
AF1Q
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


16
TRB@
0.35
TCCTCCATCACCTGAAACACTGGAC


 


9
SEPT6
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


6
DOCK2
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


10
RPS19
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


10
CD3D
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


9
T3JAM
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
FNBP1
0.31
GCCCCACTGGACAACACTGATTCCT


 


9
CD6
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


11
ZAP70
0.52
CACCCAGCTGGTCCTGTGGATGGGA


 


4
LST1
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
BCAT1
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


6
PRF1
0.4
AAGCCTATACGTTTCTGTGGAGTAA


 


10
AIF1
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


9
RAG2
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


7
CD1E
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
CYFIP2
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


9
TARP
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


11
TRIM
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


16
GLTSCR2
0.37
TCCTCCATCACCTGAAACACTGGAC


 


7
GIMAP5
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
ARHGAP15
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


11
NOTCH1
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


24
BCL11B
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
GIMAP6
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


1
STAG3
0.4
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
TM6SF1
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


1
UBASH3A
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
MGC5566
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


16
FLJ22457
0.31
TCCTCCATCACCTGAAACACTGGAC


 


4
TPK1
0.33
AAATGTTTCCTTGTGCCTGCTCCTG
















TABLE 67







Mechlorethamine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
PRG1
0.37
GCCCCACTGGACAACACTGATTCCT


 


7
SLC2A3
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
RPS19
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
PSMB10
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
ITM2A
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
DGKA
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
SEMA4D
0.34
TCCTCCATCACCTGAAACACTGGAC


 


3
SLA
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
IL2RG
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


6
MFNG
0.42
AAGCCTATACGTTTCTGTGGAGTAA


 


6
ALOX5AP
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
GPSM3
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


7
PSMB9
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
SELL
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


4
ADA
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
FMNL1
0.4
CACCCAGCTGGTCCTGTGGATGGGA


 


9
MYB
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


6
PTPN7
0.43
AAGCCTATACGTTTCTGTGGAGTAA


 


3
CXorf9
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
RHOH
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


16
ZNFN1A1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


16
CENTB1
0.43
TCCTCCATCACCTGAAACACTGGAC


 


10
FXYD2
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


10
CD1D
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


10
STAT4
0.44
TTGGACATCTCTAGTGTAGCTGCCA


 


2
MAP4K1
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
CCR7
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


24
PDE4C
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
CD3G
0.4
GCCCCACTGGACAACACTGATTCCT


 


9
CCR9
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


4
SP110
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
TK2
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
TNFAIP8
0.34
GCCCCACTGGACAACACTGATTCCT


 


1
NAP1L1
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
SELPLG
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
SEC31L2
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
CD3Z
0.44
TTGGACATCTCTAGTGTAGCTGCCA


 


1
PRKCQ
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
SH2D1A
0.41
GCCCCACTGGACAACACTGATTCCT


 


9
GZMB
0.43
TGGACCCCACTGGCTGAGAATCTGG


 


9
CD1A
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


9
LAIR1
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


10
TRB@
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


11
SEPT6
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


9
DOCK2
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


9
CG018
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


1
WBSCR20C
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
CD3D
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
CD6
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


10
LST1
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


6
GPR65
0.42
AAGCCTATACGTTTCTGTGGAGTAA


 


11
PRF1
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


1
ALMS1
0.41
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
AIF1
0.31
GCCCCACTGGACAACACTGATTCCT


 


11
CD1E
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


2
CYFIP2
0.33
GCCCCACTGGACAACACTGATTCCT


 


4
TARP
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
GLTSCR2
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
FLJ12270
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


2
ARHGAP15
0.33
GCCCCACTGGACAACACTGATTCCT


 


2
NAP1L2
0.32
GCCCCACTGGACAACACTGATTCCT


 


24
CECR1
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
GIMAP6
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
STAG3
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


11
TM6SF1
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


10
C15orf25
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


1
MGC5566
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
FLJ22457
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
ET
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


11
TPK1
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


10
PHF11
0.36
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 68







Streptozocin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
PGK1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
SCD
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


3
INSIG1
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
IGBP1
0.39
TCCTCCATCACCTGAAACACTGGAC


 


16
TNFAIP3
0.31
TCCTCCATCACCTGAAACACTGGAC


 


11
TNFSF10
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


3
ABCA1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
AGA
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


11
ABCA8
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


3
DBC1
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
PTGER2
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
UGT1A3
0.32
TCCTCCATCACCTGAAACACTGGAC


 


11
C10orf10
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


9
TM4SF13
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


24
CGI-90
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
LXN
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


10
DNAJC12
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


11
HIPK2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


7
C9orf95
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 69







Carmustine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
RPLP2
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD99
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
IFITM1
0.36
TCCTCCATCACCTGAAACACTGGAC


 


16
INSIG1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


3
ALDOC
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
ITM2A
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
SERPINA1
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


6
C1QR1
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


10
STAT5A
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


24
INPP5D
0.44
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
SATB1
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
VPS16
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SLA
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
IL2RG
0.45
TCCTCCATCACCTGAAACACTGGAC


 


16
MFNG
0.33
TCCTCCATCACCTGAAACACTGGAC


 


6
SELL
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


2
LRMP
0.41
GCCCCACTGGACAACACTGATTCCT


 


16
ICAM2
0.54
TCCTCCATCACCTGAAACACTGGAC


 


7
MYB
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
PTPN7
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
ARHGAP25
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


9
LCK
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


9
CXorf9
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


4
RHOH
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
ZNFN1A1
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CENTB1
0.59
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ADD2
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
LCP2
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


3
SFI1
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
DBT
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
GZMA
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


9
CD2
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
BATF
0.38
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
HIST1H4C
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
ARHGAP6
0.4
TCCTCCATCACCTGAAACACTGGAC


 


9
VAV1
0.42
TGGACCCCACTGGCTGAGAATCTGG


 


24
MAP4K1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
CCR7
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
PDE4C
0.57
TCCTCCATCACCTGAAACACTGGAC


 


6
CD3G
0.44
AAGCCTATACGTTTCTGTGGAGTAA


 


4
CCR9
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
SP140
0.48
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
TK2
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


16
LCP1
0.38
TCCTCCATCACCTGAAACACTGGAC


 


2
IFI16
0.34
GCCCCACTGGACAACACTGATTCCT


 


2
CXCR4
0.42
GCCCCACTGGACAACACTGATTCCT


 


4
ARHGEF6
0.45
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
PSCDBP
0.42
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
SELPLG
0.52
TGGACCCCACTGGCTGAGAATCTGG


 


1
SEC31L2
0.42
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
CD3Z
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
PRKCQ
0.46
TCCTTGTGCCTGCTCCTGTACTTGT


 


3
SH2D1A
0.46
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
GZMB
0.55
TTGGACATCTCTAGTGTAGCTGCCA


 


24
CD1A
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
GATA2
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


16
LY9
0.54
TCCTCCATCACCTGAAACACTGGAC


 


10
LAIR1
0.3
TTGGACATGTCTAGTGTAGCTGCCA


 


16
TRB@
0.33
TCCTCCATCACCTGAAACACTGGAC


 


7
SEPT6
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
HA-1
0.32
TCCTCCATCACCTGAAACACTGGAC


 


1
SLC43A3
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
DOCK2
0.31
TCCTCCATCACCTGAAACACTGGAC


 


7
CG018
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
MLC1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
CD3D
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
T3JAM
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


1
CD6
0.43
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
ZAP70
0.43
GCCCCACTGGACAACACTGATTCCT


 


1
DOK2
0.3
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
LST1
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
GPR65
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
PRF1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
ALMS1
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


24
AIF1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
PRDX2
0.48
GCCCCACTGGACAACACTGATTCCT


 


4
FLJ12151
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
FBXW12
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
CD1E
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


24
CYFIP2
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
TARP
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
TRIM
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


4
RPL10L
0.43
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
GLTSCR2
0.43
CACCCAGCTGGTCCTGTGGATGGGA


 


24
CKIP-1
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
NRN1
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
ARHGAP15
0.4
TCCTCCATCACCTGAAACACTGGAC


 


3
NOTCH1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
PSCD4
0.4
CACCCAGCTGGTCCTGTGGATGGGA


 


6
C13orf18
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


7
BCL11B
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
GIMAP6
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
STAG3
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
NARF
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
TM6SF1
0.48
TCCTCCATCACCTGAAACACTGGAC


 


1
C15orf25
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
FLJ11795
0.35
GCCCCACTGGACAACACTGATTCCT


 


2
SAMSN1
0.37
GCCCCACTGGACAACACTGATTCCT


 


1
UBASH3A
0.4
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
PACAP
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


11
LEF1
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


6
IL21R
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


2
TCF4
0.41
GCCCCACTGGACAACACTGATTCCT


 


1
DKFZP434B0335
0.33
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 70







Lomustine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













1
RPS15
0.43
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
INSIG1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


3
ALDOC
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
ITM2A
0.32
TCCTCCATCACCTGAAACACTGGAC


 


3
C1QR1
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
STAT5A
0.37
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
INPP5D
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
VPS16
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
SLA
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
USP20
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
IL2RG
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
MFNG
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
LRMP
0.43
GCCCCACTGGACAACACTGATTCCT


 


7
EVI2A
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
PTPN7
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
ARHGAP25
0.39
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
RHOH
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


1
ZNFN1A1
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
CENTB1
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
LCP2
0.41
TGGACCCCACTGGCTGAGAATCTGG


 


3
SPI1
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
ARHGAP6
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


11
MAP4K1
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


16
CCR7
0.35
TCCTCCATCACCTGAAACACTGGAC


 


2
LY96
0.35
GCCCCACTGGACAACACTGATTCCT


 


7
C6orf32
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
MAGEA1
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
SP140
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


16
LCP1
0.36
TCCTCCATCACCTGAAACACTGGAC


 


3
IFI16
0.39
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
ARHGEF6
0.33
TCCTCCATCACCTGAAACACTGGAC


 


6
PSCDBP
0.43
AAGCCTATACGTTTCTGTGGAGTAA


 


7
SELPLG
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
CD3Z
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


2
PRKCQ
0.4
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
GZMB
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
LAIR1
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


1
SH2D1A
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
TRB@
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


3
RFP
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
SEPT6
0.41
TCCTCCATCACCTGAAACACTGGAC


 


1
HA-1
0.43
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
SLC43A3
0.4
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
CD3D
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
T3JAM
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


2
GPR65
0.34
GCCCCACTGGACAACACTGATTCCT


 


3
PRF1
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
AIF1
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


4
LPXN
0.38
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
RPL10L
0.3
TGGACCCCACTGGCTGAGAATCTGG


 


11
SITPEC
0.36
CACCCAGCTGGTCCTGTGGATGGGA


 


9
ARHGAP15
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


1
C13orf18
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
NARF
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


24
TM6SF1
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
PACAP
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


24
TCF4
0.33
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 71







Mercaptopurine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
SSRP1
0.31
GCCCCACTGGACAACACTGATTCCT


 


4
ALDOC
0.36
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
C1QRl
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
TTF1
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
PRIM1
0.31
GCCCCACTGGACAACACTGATTCCT


 


16
USP34
0.38
TCCTCCATCACCTGAAACACTGGAC


 


1
TK2
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
GOLGIN-67
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
N2D014
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
KIAA0220
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
SLC43A3
0.3
TTGGACATCTCTAGTGTAGCTGCCA


 


11
WBSCR20C
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


3
ICAM2
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
TEX10
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


7
CHD7
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
SAMSN1
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


7
TPRT
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 72







Teniposide biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
CD99
0.35
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
INSIG1
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


3
PRG1
0.36
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
ALDOC
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
ITM2A
0.33
AAGCCTATACGTTTCTGTGGAGTAA


 


2
SLA
0.43
GCCCCACTGGACAACACTGATTCCT


 


24
SSBP2
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
IL2RG
0.37
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
MFNG
0.32
TTGGACATCTCTAGTGTAGCTGCCA


 


16
ALOX5AP
0.32
TCCTCCATCACCTGAAACACTGGAC


 


16
C1orf29
0.3
TCCTCCATCACCTGAAACACTGGAC


 


11
SELL
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


9
STC1
0.47
TGGACCCCACTGGCTGAGAATCTGG


 


16
LRMP
0.33
TCCTCCATCACCTGAAACACTGGAC


 


3
MYB
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


6
PTPN7
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CXorf9
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


6
RHOH
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


11
ZNFN1A1
0.34
CACCCAGCTGGTCCTGTGGATGGGA


 


9
CENTB1
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


3
ADD2
0.31
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD1D
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
BATF
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
MAP4K1
0.3
GCCCCACTGGACAACACTGATTCCT


 


1
CCR7
0.48
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
PDE4C
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


10
CD3G
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


7
CCR9
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
SP110
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
TNFAIP8
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
NAP1L1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


7
CXCR4
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


1
ARHGEF6
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
GATA3
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
SELPLG
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


9
SEC31L2
0.46
TGGACCCCACTGGCTGAGAATCTGG


 


2
CD3Z
0.35
GCCCCACTGGACAACACTGATTCCT


 


4
SH2D1A
0.45
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
GZMB
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
CD1A
0.45
GCCCCACTGGACAACACTGATTCCT


 


24
SCN3A
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


24
LAIR1
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
AF1Q
0.3
TCCTCCATCACCTGAAACACTGGAC


 


6
TRB@
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


1
DOCK2
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
MLC1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


9
CD3D
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


7
T3JAM
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
CD6
0.38
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
ZAP70
0.34
TCCTCCATCACCTGAAACACTGGAC


 


24
IFI44
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
GPR65
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
PRF1
0.34
TCCTCCATCACCTGAAACACTGGAC


 


7
AIF1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
WDR45
0.41
GCCCCACTGGACAACACTGATTCCT


 


6
CD1E
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


9
CYFIP2
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


11
TARP
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


9
TRIM
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


6
ARHGAP15
0.38
AAGCCTATACGTTTCTGTGGAGTAA


 


3
NOTCH1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
STAG3
0.32
GCCCCACTGGACAACACTGATTCCT


 


6
NARF
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


2
TM6SF1
0.33
GCCCCACTGGACAACACTGATTCCT


 


1
UBASH3A
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


7
MGC5566
0.31
AGTTGTCCTCAGCTTGGGCTTCTTC
















TABLE 73







Dactinomycin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













2
ALDOC
0.37
GCCCCACTGGACAACACTGATTCCT


 


9
C1QR1
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


1
SLA
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
WBSCR20A
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


7
MFNG
0.3
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
SELL
0.3
GCCCCACTGGACAACACTGATTCCT


 


10
MYB
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


24
RHOH
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ZNFN1A1
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
LCP2
0.3
CACCCAGCTGGTCCTGTGGATGGGA


 


6
MAP4K1
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


24
CBFA2T3
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


2
LCP1
0.32
GCCCCACTGGACAACACTGATTCCT


 


7
SELPLG
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


2
CD3Z
0.35
GCCCCACTGGACAACACTGATTCCT


 


9
LAIR1
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


6
WBSCR20C
0.3
AAGCCTATACGTTTCTGTGGAGTAA


 


11
CD3D
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


4
GPR65
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
ARHGAP15
0.32
TCCTCCATCACCTGAAACACTGGAC


 


24
FLJ10178
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


16
NARF
0.35
TCCTCCATCACCTGAAACACTGGAC


 


1
PUS3
0.32
TCCTGTACTTGTCCTCAGCTTGGGC
















TABLE 74







Tretinoin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













6
PPIB
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


6
ZFP36L2
0.48
AAGCCTATACGTTTCTGTGGAGTAA


 


7
IFI30
0.46
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
USP7
0.35
TCCTCCATCACCTGAAACACTGGAC


 


16
SRM
0.43
TCCTCCATCACCTGAAACACTGGAC


 


3
SH3BP5
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
ALDOC
0.41
TTGGACATCTCTAGTGTAGCTGCCA


 


7
FADS2
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
GUSB
0.38
TTGGACATCTCTAGTGTAGCTGCCA


 


1
PSCD1
0.48
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
IQGAP2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
STS
0.34
GCCCCACTGGACAACACTGATTCCT


 


9
MFNG
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


7
FLI1
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
PIM2
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


1
INPP4A
0.54
TCCTGTACTTGTCCTCAGCTTGGGC


 


2
LRMP
0.51
GCCCCACTGGACAACACTGATTCCT


 


4
ICAM2
0.3
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
EVI2A
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


4
MAL
0.46
AAATGTTTCCTTGTGCCTGCTCCTG


 


10
BTN3A3
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


10
PTPN7
0.4
TTGGACATCTCTAGTGTAGCTGCCA


 


10
IL10RA
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


6
SPI1
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


3
TRAF1
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


24
ITGB7
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
ARHGAP6
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


2
MAP4K1
0.52
GCCCCACTGGACAACACTGATTCCT


 


6
CD28
0.34
AAGCCTATACGTTTCTGTGGAGTAA


 


16
PTP4A3
0.3
TCCTCCATCACCTGAAACACTGGAC


 


7
LTB
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
C1orf38
0.4
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
WBSCR22
0.53
TCCTCCATCACCTGAAACACTGGAC


 


16
CD8B1
0.35
TCCTCCATCACCTGAAACACTGGAC


 


7
LCP1
0.35
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
FLJ13052
0.31
TCCTCCATCACCTGAAACACTGGAC


 


10
MEF2C
0.71
TTGGACATCTCTAGTGTAGCTGCCA


 


4
PSCDBP
0.41
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
IL16
0.51
TGGACCCCACTGGCTGAGAATCTGG


 


3
SELPLG
0.53
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
MAGEA9
0.6
AAATGTTTCCTTGTGCCTGCTCCTG


 


16
LAIR1
0.43
TCCTCCATCACCTGAAACACTGGAC


 


16
TNFRSF25
0.53
TCCTCCATCACCTGAAACACTGGAC


 


7
EVI2B
0.42
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
IGJ
0.37
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
PDCD4
0.47
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
RASA4
0.52
CACCCAGCTGGTCCTGTGGATGGGA


 


6
HA-1
0.73
AAGCCTATACGTTTCTGTGGAGTAA


 


1
PLCL2
0.47
TCCTGTACTTGTCCTCAGCTTGGGC


 


6
RNASE6
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


10
WBSCR20C
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


6
NUP210
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


7
RPL10L
0.39
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
C11orf2
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


3
CABC1
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
ARHGEF3
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
TAPBPL
0.42
TGCCTGCTCCTGTACTTGTCCTCAG


 


4
CHST12
0.35
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
FKBP11
0.54
TGCCTGCTCCTGTACTTGTCCTCAG


 


10
FLJ35036
0.42
TTGGACATCTCTAGTGTAGCTGCCA


 


11
MYLIP
0.38
CACCCAGCTGGTCCTGTGGATGGGA


 


7
TXNDC5
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


16
PACAP
0.3
TCCTCCATCACCTGAAACACTGGAC


 


1
TOSO
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


9
PNAS-4
0.37
TGGACCCCACTGGCTGAGAATCTGG


 


6
IL21R
0.57
AAGCCTATACGTTTCTGTGGAGTAA


 


24
TCF4
0.64
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 75







Ifosfamide biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













9
ARHGDIB
0.36
TGGACCCCACTGGCTGAGAATCTGG


 


9
ZFP36L2
0.45
TGGACCCCACTGGCTGAGAATCTGG


 


6
ITM2A
0.39
AAGCCTATACGTTTCTGTGGAGTAA


 


4
LGALS9
0.54
AAATGTTTCCTTGTGCCTGCTCCTG


 


1
INPP5D
0.53
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
SATB1
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


6
TFDP2
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


3
IL2RG
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
CD48
0.5
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
SELL
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
ADA
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
LRMP
0.34
GCCCCACTGGACAACACTGATTCCT


 


6
RIMS3
0.37
AAGCCTATACGTTTCTGTGGAGTAA


 


1
LCK
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


11
CXorf9
0.4
CACCCAGCTGGTCCTGTGGATGGGA


 


2
RHOH
0.3
GCCCCACTGGACAACACTGATTCCT


 


10
ZNFN1A1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


1
LCP2
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
CD1D
0.49
TCCTCCATCACCTGAAACACTGGAC


 


11
CD2
0.42
CACCCAGCTGGTCCTGTGGATGGGA


 


4
ZNF91
0.45
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
MAP4K1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


10
CCR7
0.44
TTGGACATCTCTAGTGTAGCTGCCA


 


3
IGLL1
0.43
TGCCTGCTCCTGTACTTGTCCTCAG


 


16
CD3G
0.3
TCCTCCATCACCTGAAACACTGGAC


 


7
ZNF430
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
CCR9
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


7
CXCR4
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


6
KIAA0922
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


2
TARP
0.31
GCCCCACTGGACAACACTGATTCCT


 


1
FYN
0.35
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
SH2D1A
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


6
CD1A
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


10
LST1
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


7
LAIR1
0.36
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
TRB@
0.34
TGGACCCCACTGGCTGAGAATCTGG


 


10
SEPT6
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


16
CD3D
0.37
TCCTCCATCACCTGAAACACTGGAC


 


4
CD6
0.32
AAATGTTTCCTTGTGCCTGCTCCTG


 


3
AIF1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


1
CD1E
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
TRIM
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
GLTSCR2
0.34
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
ARHGAP15
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
BIN2
0.33
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
SH3TC1
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


16
CECR1
0.36
TCCTCCATCACCTGAAACACTGGAC


 


16
BCL11B
0.38
TCCTCCATCACCTGAAACACTGGAC


 


2
GIMAP6
0.32
GCCCCACTGGACAACACTGATTCCT


 


10
STAG3
0.46
TTGGACATCTCTAGTGTAGCTGCCA


 


7
GALNT6
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
MGC5566
0.49
TCCTTGTGCCTGCTCCTGTACTTGT


 


1
PACAP
0.48
TCCTGTACTTGTCCTCAGCTTGGGC


 


3
LEF1
0.4
TGCCTGCTCCTGTACTTGTCCTCAG
















TABLE 76







Tamoxifen biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













1
MLP
0.33
TCCTGTACTTGTCCTCAGCTTGGGC


 


24
GLUL
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
SLC9A3R1
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


10
ZFP36L2
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


16
INSIG1
0.31
TCCTCCATCACCTGAAACACTGGAC


 


1
TBL1X
0.36
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
NDUFAB1
0.43
AAATGTTTCCTTGTGCCTGCTCCTG


 


9
EBP
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


10
TRIM14
0.43
TTGGACATCTCTAGTGTAGCTGCCA


 


2
SRPK2
0.41
GCCCCACTGGACAACACTGATTCCT


 


4
PMM2
0.4
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
CLDN3
0.41
AAGCCTATACGTTTCTGTGGAGTAA


 


10
GCH1
0.34
TTGGACATCTCTAGTGTAGCTGCCA


 


4
IDI1
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


24
TTF1
0.46
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
MYB
0.39
CACCCAGCTGGTCCTGTGGATGGGA


 


11
RASGRP1
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


9
HIST1H3H
0.38
TGGACCCCACTGGCTGAGAATCTGG


 


4
CBFA2T3
0.34
AAATGTTTCCTTGTGCCTGCTCCTG


 


2
SRRM2
0.43
GCCCCACTGGACAACACTGATTCCT


 


1
ANAPC5
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


1
MBD4
0.5
TCCTGTACTTGTCCTCAGCTTGGGC


 


16
GATA3
0.32
TCCTCCATCACCTGAAACACTGGAC


 


6
HIST1H2BG
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


9
RAB14
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


6
PIK3R1
0.36
AAGCCTATACGTTTCTGTGGAGTAA


 


11
MGC50853
0.37
CACCCAGCTGGTCCTGTGGATGGGA


 


2
ELF1
0.35
GCCCCACTGGACAACACTGATTCCT


 


24
ZRF1
0.32
TCCTTGTGCCTGCTCCTGTACTTGT


 


4
ZNF394
0.31
AAATGTTTCCTTGTGCCTGCTCCTG


 


4
S100A14
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


11
SLC6A14
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


16
GALNT6
0.37
TCCTCCATCACCTGAAACACTGGAC


 


4
SPDEF
0.44
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
TPRT
0.5
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CALML4
0.31
TTGGACATCTCTAGTGTAGCTGCCA
















TABLE 77







Floxuridine biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













7
CSDA
0.33
ACTTGTCCTCAGCTTGGGCTTCTTC


 


9
F8A1
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


9
KYNU
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


6
PHF14
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


16
SERPINB2
0.34
TCCTCCATCACCTGAAACACTGGAC


 


2
OPHN1
0.31
GCCCCACTGGACAACACTGATTCCT


 


6
HRMT1L2
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


2
TNFRSF1A
0.3
GCCCCACTGGACAACACTGATTCCT


 


6
PPP4C
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


16
CES1
0.3
TCCTCCATCACCTGAAACACTGGAC


 


2
TP53AP1
0.3
GCCCCACTGGACAACACTGATTCCT


 


2
TM4SF4
0.32
GCCCCACTGGACAACACTGATTCCT


 


3
RPL5
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


9
BC008967
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


10
TLK2
0.35
TTGGACATCTCTAGTGTAGCTGCCA


 


24
COL4A6
0.31
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
PAK3
0.32
CACCCAGCTGGTCCTGTGGATGGGA


 


24
RECK
0.34
TCCTTGTGCCTGCTCCTGTACTTGT


 


6
LOC51321
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


16
MST4
0.36
TCCTCCATCACCTGAAACACTGGAC


 


9
DERP6
0.32
TGGACCCCACTGGCTGAGAATCTGG


 


24
SCD4
0.33
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
FLJ22800
0.31
TGGACCCCACTGGCTGAGAATCTGG
















TABLE 78







Irinotecan biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













3
CSDA
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
UBE2L6
0.32
GCCCCACTGGACAACACTGATTCCT


 


9
TAP1
0.44
TGGACCCCACTGGCTGAGAATCTGG


 


3
RPS19
0.32
TGCCTGCTCCTGTACTTGTCCTCAG


 


7
SERPINA1
0.32
ACTTGTCCTCAGCTTGGGCTTCTTC


 


10
C1QR1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


11
SLA
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


3
GPSM3
0.46
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
PSMB9
0.3
TGCCTGCTCCTGTACTTGTCCTCAG


 


3
EDG1
0.34
TGCCTGCTCCTGTACTTGTCCTCAG


 


2
FMNL1
0.4
GCCCCACTGGACAACACTGATTCCT


 


10
PTPN7
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


6
ZNFN1A1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


10
CENTB1
0.33
TTGGACATCTCTAGTGTAGCTGCCA


 


7
BATF
0.41
ACTTGTCCTCAGCTTGGGCTTCTTC


 


4
MAP4K1
0.39
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
PDE4C
0.31
AAGCCTATACGTTTCTGTGGAGTAA


 


24
SP110
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
HLA-DRA
0.31
TGGACCCCACTGGCTGAGAATCTGG


 


10
IFI16
0.36
TTGGACATCTCTAGTGTAGCTGCCA


 


6
HLA-DRB1
0.32
AAGCCTATACGTTTCTGTGGAGTAA


 


7
ARHGEF6
0.43
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
SELPLG
0.35
TCCTTGTGCCTGCTCCTGTACTTGT


 


11
SEC31L2
0.35
CACCCAGCTGGTCCTGTGGATGGGA


 


16
CD3Z
0.51
TCCTCCATCACCTGAAACACTGGAC


 


10
PRKCQ
0.39
TTGGACATCTCTAGTGTAGCTGCCA


 


6
SH2D1A
0.43
AAGCCTATACGTTTCTGTGGAGTAA


 


16
GZMB
0.49
TCCTCCATCACCTGAAACACTGGAC


 


7
TRB@
0.43
ACTTGTCCTCAGCTTGGGCTTCTTC


 


7
HLA-DPA1
0.47
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
AIM1
0.36
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
DOCK2
0.39
TGGACCCCACTGGCTGAGAATCTGG


 


1
CD3D
0.31
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
IFITM1
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


2
ZAP70
0.31
GCCCCACTGGACAACACTGATTCCT


 


11
PRF1
0.47
CACCCAGCTGGTCCTGTGGATGGGA


 


2
Clorf24
0.39
GCCCCACTGGACAACACTGATTCCT


 


16
ARHGAP15
0.48
TCCTCCATCACCTGAAACACTGGAC


 


11
C13orf18
0.33
CACCCAGCTGGTCCTGTGGATGGGA


 


24
TM6SF1
0.37
TCCTTGTGCCTGCTCCTGTACTTGT
















TABLE 79







Satraplatin biomarkers.










SEQ





ID

Corre-



NO
Gene
lation
Medianprobe













1
STAT1
0.32
TCCTGTACTTGTCCTCAGCTTGGGC


 


4
HSBP1
0.33
AAATGTTTCCTTGTGCCTGCTCCTG


 


6
IFI30
0.35
AAGCCTATACGTTTCTGTGGAGTAA


 


16
RIOK3
0.36
TCCTCCATCACCTGAAACACTGGAC


 


7
TNFSF10
0.31
ACTTGTCCTCAGCTTGGGCTTCTTC


 


24
ALOX5AP
0.3
TCCTTGTGCCTGCTCCTGTACTTGT


 


9
ADFP
0.33
TGGACCCCACTGGCTGAGAATCTGG


 


1
IRS2
0.37
TCCTGTACTTGTCCTCAGCTTGGGC


 


10
EFEMP2
0.31
TTGGACATCTCTAGTGTAGCTGCCA


 


9
RTPK2
0.35
TGGACCCCACTGGCTGAGAATCTGG


 


16
DKFZp564I1922
0.33
TCCTCCATCACCTGAAACACTGGAC


 


16
MT1K
0.34
TCCTCCATCACCTGAAACACTGGAC


 


7
RNASET2
0.38
ACTTGTCCTCAGCTTGGGCTTCTTC


 


11
EFHD2
0.31
CACCCAGCTGGTCCTGTGGATGGGA


 


2
TRIB3
0.33
GCCCCACTGGACAACACTGATTCCT


 


4
ACSL5
0.42
AAATGTTTCCTTGTGCCTGCTCCTG


 


7
IFIH1
0.37
ACTTGTCCTCAGCTTGGGCTTCTTC


 


3
DNAPTP6
0.42
TGCCTGCTCCTGTACTTGTCCTCAG
















TABLE 80







Vincristine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2281
Hcd892 left
0.3
GAGGGCTGGAGAGGTTGGGTGCGCTTGTGCGTTTCACTTT


 


2282
Hcd678 right
0.27
GCCCTGAAGCTCCGGACTACAGCTCCCAGGCCTCTCCAAG


 


2283
mir-007-1-prec
0.28
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2284
MPR243 left
0.25
GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG


 


2285
Hcd654 left
0.25
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2286
mir-487No1
0.26
TTATGACGAATCATACAGGGACATCCAGTTTTTCAGTATC


 


2287
Hcd794 right
0.35
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2288
Hcd739 right
0.32
TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT


 


2289
Hcd562 right
0.28
CGCATGTCCTGGCCCTCGTCCTTCCATGGCACTGGCACCG
















TABLE 81







Cisplatin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2290
HUMTRF
0.34
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2291
HPR187 right
0.25
TATTTATTACAAGGTCCTTCTTCCCCGTAAAACTTTGTCC


 


2292
mir-450-1
0.26
AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC


 


2293
mir-155-prec
0.31
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2294
mir-515-15p
0.25
GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT


 


2295
mir-181b-precNo2
0.25
ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC


 


2296
mir-124a-1-prec1
0.26
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2297
mir-450-2No1
0.3
GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG


 


2298
Hcd923 right
0.31
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2299
mir-342No1
0.31
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.27
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.26
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.38
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2303
Hcd213_HPR182 left
0.3
CTGTTTCATACTTGAGGAGAAATTATCCTTGGTGTGTTCG
















TABLE 82







Azaguanine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2304
MPR121 left
0.3
CACCTGGCTCTGAGAACTGAATTCCATAGGCTGTGAGCTC


 


2305
HUMTRS
0.26
TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA


 


2306
mir-213-precNo1
0.26
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2293
mir-155-prec
0.4
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2308
mir-147-prec
0.47
GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT


 


2309
mir-100No1
0.26
CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTATTA


 


2310
mir-138-1-prec
0.29
AGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACG


 


2311
mir-140No2
0.38
TTCTACCACAGGGTAGAACCACGGACAGGATACCGGGGCA


 


2312
mir-146-prec
0.51
TGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAGACCTC


 


2313
mir-509No1
0.25
ATTAAAAATGATTGGTACGTCTGTGGGTAGAGTACTGCAT


 


2314
mir-146bNo1
0.33
CACCTGGCACTGAGAACTGAATTCCATAGGCTGTGAGCTC


 


2315
Hcd514 right
0.26
ATTAGAGACTCGTTAAGAGAAGGTGAGAAGGGCTCAGTAA


 


2316
Hcd397 left
0.34
GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT


 


2317
Hcd731 left
0.27
AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC


 


2318
mir-034-precNo2
0.32
AGTAAGGAAGCAATCAGCAAGTATACTGCCCTAGAAGTGC


 


2319
mir-100-1/2-prec
0.3
TGAGGCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGG
















TABLE 83







Etoposide microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2320
Hcd415 right
0.28
GATGTTTGGGAAACAATGGGAGTGAGAGAATGGGAGAGCT


 


2321
Hcd768 right
0.37
GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT


 


2290
HUMTRF
0.38
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2323
Hcd866 right
0.26
GTCATGCTGCCACCAGCAGGCAGAGAAGAAGCAGAAGAAC


 


2324
Hcd145 left
0.33
AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG


 


2325
HUMTRAB
0.29
ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT


 


2326
Hcd913 right
0.36
CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG


 


2327
HPR163 left
0.29
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2328
Hcd697 right
0.27
GGCCTCATGCTGCCAAGGGCTGGCAAGAAGTCCCTGCTTG


 


2329
Hcd755 left
0.26
GGAAGTGGAGCAAATGGATGGAAAGCAATTTTTGGAAGAT


 


2330
Hcd716 right
0.25
CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG


 


2331
MPR207 right
0.33
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA


 


2332
HSTRNL
0.26
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2333
HPR206 left
0.29
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2284
MPR243 left
0.27
GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG


 


2285
Hcd654 left
0.4
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2336
MPR130 left
0.28
AGGCCAAGGTGACGGGTGCGATTTCTGTGTGAGACAATTC


 


2337
Hcd782 left
0.26
GGAGCCCTGTCTGCAAAGAGTGGTGCGTGTGCGTGTGTGA


 


2287
Hcd794 right
0.26
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2288
Hcd739 right
0.3
TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT


 


2300
mir-142-prec
0.29
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.29
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2342
HUMTRV1A
0.29
ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA


 


2302
Hcd754 left
0.34
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC
















TABLE 84







Carboplatin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2344
Hcd829 right
0.27
AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG


 


2290
HUMTRF
0.26
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2346
HPR187 left
0.29
TGTGTGTTGCGGGGGTGGGGGCCGGTGAAAGTGATTTGAT


 


2347
Hcd210_HPR205 right
0.32
CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC


 


2348
mir-379No1
0.26
TTCCGTGGTTCCTGAAGAGATGGTAGACTATGGAACGTAG


 


2306
mir-213-precNo1
0.26
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2350
mir-4325p
0.29
CCAGGTCTTGGAGTAGGTCATTGGGTGGATCCTCTATTTC


 


2292
mir-450-1
0.3
AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC


 


2293
mir-155-prec
0.25
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2353
Hcd28_HPR39right
0.26
AAGCTCCCAAATTAGCTTTTTAAATAGAAGCTGAGAGTTA


 


2354
MPR244 right
0.27
TAAACATAGAGGAAATTTCACGTTTTCAGTGTCAAATGCT


 


2355
mir-409-3p
0.3
GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT


 


2296
mir-124a-1-prec1
0.28
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2357
mir-154-prec1No1
0.26
GTGGTACTTGAAGATAGGTTATCCGTGTTGCCTTCGCTTT


 


2358
mir-495No1
0.32
GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA


 


2359
mir-515-23p
0.25
CAGAGTGCCTTCTTTTGGAGCGTTACTGTTTGAGAAAAAC


 


2360
Hcd438 right
0.27
GTGTTTATTTGAATCTCACATCGCTCATAAGAATACACGC


 


2361
Hcd770 left
0.3
CCAGTATACAATCCGTTTTTCAGTTTAGCTTGAGATCAGA


 


2362
mir-382
0.32
GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC


 


2301
mir-223-prec
0.3
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.48
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2303
Hcd213_HPR182 left
0.31
CTGTTTCATACTTGAGGAGAAATTATCCTTGGTGTGTTCG
















TABLE 85







Adriamycin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2321
Hcd768 right
0.25
GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT


 


2367
mir-483No1
0.28
ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC


 


2324
Hcd145 left
0.28
AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG


 


2369
mir-197-prec
0.25
TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT


 


2370
mir-212-precNo1
0.27
CCTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC


 


2327
HPR163 left
0.3
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2285
Hcd654 left
0.26
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2299
mir-342No1
0.32
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2287
Hcd794 right
0.32
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2300
mir-142-prec
0.38
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2302
Hcd754 left
0.28
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC
















TABLE 86







Aclarubicin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.32
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2378
mir-096-prec-7No2
0.29
TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT


 


2379
Hcd605 left
0.26
ATTACTAGCAGTTAATGATTGGTTTGTTAGTTAATGGCCC


 


2380
mir-007-2-precNo2
0.34
GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG


 


2381
mir-019b-2-prec
0.28
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2382
MPR216 left
0.26
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2383
mir-019b-1-prec
0.25
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.26
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2332
HSTRNL
0.26
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2386
mir-025-prec
0.31
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2283
mir-007-1-prec
0.4
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2388
mir-019a-prec
0.26
TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC


 


2389
mir-380-5p
0.31
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2390
mir-093-prec-7.1 = 093-1
0.37
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.37
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.32
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2393
mir-018-prec
0.31
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.36
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 87







Mitoxantrone microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence


2395
Hcd768 left
0.26
GATGGTTTAGTGAGGCCCTCGGATCAGCCCGCTGGGTCAG


 


2290
HUMTRF
0.31
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2306
mir-213-precNo1
0.28
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2398
mir-181b-precNo1
0.26
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2354
M2R244 right
0.27
TAAACATAGAGGAAATTTCACGTTTTCAGTGTCAAATGCT


 


2355
mir-409-3p
0.29
GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT


 


2332
HSTRNL
0.33
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2362
mir-382
0.34
GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC


 


2299
mir-342No1
0.3
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.27
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2405
Hcd200 right
0.29
CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT













TABLE 88







Mitomycin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2290
HUMTRF
0.26
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2407
Hcd148_HPR225left
0.27
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2408
Hcd938 right
0.26
ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG


 


2409
MPR174 left
0.32
GAGCCGGTCTCTTTACATCTCAAATACCAGGTATTTAGGT


 


2410
mir-4323p
0.29
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 89







Paclitaxel (Taxol) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.29
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2412
mir-096-prec-7No1
0.36
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2413
mir-101-prec-9
0.38
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2414
mir-20bNo1
0.28
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2381
mir-019b-2-prec
0.28
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2416
mir-032-precNo2
0.29
GGAGATATTGCACATTACTAAGTTGCATGTTGTCACGGCC


 


2417
MPR156 left
0.25
TCCCTCACTTGAACTGACTGCCAGAGTTCACAGACAGCTG


 


2383
mir-019b-1-prec
0.28
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.36
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2386
mir-025-prec
0.36
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2283
mir-007-1-prec
0.27
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2422
mir-361No1
0.29
GGATTTGGGAGCTTATCAGAATCTCCAGGGGTACTTTATA


 


2390
mir-093-prec-7.1 = 093-1
0.37
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.38
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2425
mir-098-prec-X
0.29
TGAGGTAGTAAGTTGTATTGTTGTGGGGTAGGGATATTAG


 


2300
mir-142-prec
0.27
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.26
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2393
mir-018-prec
0.4
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.36
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 90







Gemcitabine (Gemzar) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2430
mir-123-precNo2
0.27
TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG


 


2431
Hcd257 right
0.29
CTTGGTTTTTGCAATAATGCTAGCAGAGTACACACAAGAA


 


2493
mir-155-prec
0.35
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2433
ath-MIR180aNo2
0.26
TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT


 


2434
Hcd448 left
0.33
TGTAATTCCATTGAGGGTTTCTGGTGACTCCAGCTTCGTA


 


2332
HSTRNL
0.31
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2436
MPR174 right
0.29
CATTAGGGACACGTGTGAGTGTGCCAGGCTCATTCCTGAG


 


2405
Hcd200 right
0.29
CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT


 


2410
mir-4323p
0.26
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG


 


2439
HPR244 right
0.3
TAGTTCATGGCGTCCAGCAGCAGCTTCTGGCAGACCGGGT
















TABLE 91







Taxotere (docetaxel) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2412
mir-096-prec-7No1
0.28
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2441
mir-095-prec-4
0.27
CGTTACATTCAACGGGTATTTATTGAGCACCCACTCTGTG


 


2332
HSTRNL
0.26
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2283
mir-007-1-prec
0.37
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG
















TABLE 392







Dexamethasone microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2444
MPR141 left
0.42
CTCAGTCGTGCCCTAGCAGCGGGAACAGTACTGCAGTGAG


 


2445
mir-424No2
0.35
GTTCAAAACGTGAGGCGCTGCTATACCCCCTCGTGGGGAA


 


2446
Hcd690 right
0.26
GGACAAGGGAGGAGACACGCAGAGGTGACAGAAAGGTTAG


 


2447
Hcd783 left
0.26
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2448
mir-150-prec
0.38
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG


 


2449
Hcd266 left
0.37
AAGGTCTTTGGTCTTGGAGGAAGGTGTGCTACTGGAAGAG


 


2450
mir-503No1
0.34
CTCAGCCGTGCCCTAGCAGCGGGAACAGTTCTGCAGTGAG


 


2451
mir-128b-precNo1
0.29
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2316
Hcd397 left
0.26
GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT


 


2453
mir-484
0.38
GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA
















TABLE 93







Ara-C (Cytarabine hydrochloride) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2290
HUMTRF
0.33
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2293
mir-155-prec
0.28
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2294
mir-515-15p
0.27
GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT


 


2408
Hcd938 right
0.26
ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG


 


2458
Hcd642 right
0.25
TCAGGGTTTATGAAGTTATCAAAGCCCCTTGATGGAATTA


 


2459
Hcd120 left
0.26
CTTGGTGTGTTCTCGGTAGCTATGGAAATCCCAGGGTTTC


 


2389
mir-380-5p
0.25
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2299
mir-342No1
0.25
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.27
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.31
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2410
mir-4323p
0.28
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 94







Methylprednisolone microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2465
Hcd544 left
0.26
TTCCAGGTGTCCACCAAGGACGTGCCGCTGGCGCTGATGG


 


2466
mir-181c-precNo1
0.28
TGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGTT


 


2467
Hcd517 left
0.25
TTAAAGCAGGAGAGGTGAGAGGAAGAATTAATGTGTGCTC


 


2468
MPR151 left
0.27
GGGATTAATGACCAGCTGGGGGAGTTGATAGCCCTCAGTG


 


2306
mir-213-precNo1
0.34
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2295
mir-181b-precNo2
0.36
ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC


 


2448
mir-150-prec
0.27
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG


 


2472
mir-153-1-prec1
0.28
CAGTTGCATAGTCACAAAAGTGATCATTGGCAGGTGTGGC


 


2451
mir-128b-precNo1
0.48
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2474
Hcd812 left
0.25
CTGTGGGATCTGGTTCTGTAGCTGAGAGCACATCGCTAAA


 


2475
mir-195-prec
0.3
TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG


 


2299
mir-342No1
0.38
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2477
mir-370No1
0.28
TTACACAGCTCACGAGTGCCTGCTGGGGTGGAACCTGGTC


 


2300
mir-142-prec
0.32
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.36
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2453
mir-484
0.36
GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA
















TABLE 95







Methotrexate microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.37
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2412
mir-096-prec-7No1
0.33
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2483
mir-123-precNo1
0.25
GACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTC


 


2484
Hcd250 left
0.26
GTTCTGTTGCTAAGACAACAGGATGCTAGCAGGCATATGC


 


2485
mir-518e/526c
0.3
TCTCAGGCTGTGACCCTCTAGAGGGAAGCGCTTTCTGTTG


 


2486
HPR232 right
0.3
TGAATTATTGCACAATAAATTCATGCCCTCTTGTGTCTTA


 


2487
Hcd263 left
0.29
GAGCATTAAGATTTCCTATTCTTTGAGGCAAATATTGACC


 


2488
mir-516-33p
0.35
GTGAAAGAAAGTGCTTCCTTTCAGAGGGTTACTCTTTGAG


 


2379
Hcd605 left
0.27
ATTACTAGCAGTTAATGATTGGTTTGTTAGTTAATGGCCC


 


2490
Hcd373 right
0.25
CCTGAAAGGTCTGGTGTTAAGCAAATACTCGGTGACCAGA


 


2491
MPR254 right
0.28
GTTCACAGTGGGAGAAATATGCTTCGTATTACTCTTTCTC


 


2492
MPR215 left
0.3
CAGCTATGTGGACTCTAGCTGCCAAAGGCGCTTCTCCTTC


 


2290
HUMTRF
0.28
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2391
mir-106aNo1
0.27
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2414
mir-20bNo1
0.37
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2496
Hcd361 right
0.28
AACTTGGCTACAAGGCTCTTTCCCTCTCTATGAAGGACAG


 


2497
Hcd412 left
0.25
AGTTGGGGAGAACTTTATGATTATTCTCATGCATCATCTT


 


2498
Hcd781 left
0.26
GAGTGTGGATCTAATCTTCAGCTGATTAAATGTCCCTCAT


 


2381
mir-019b-2-prec
0.33
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2500
HPR214 right
0.29
AGCAAAAGCTATTATTTGCCCTTGATGAGCCAATCAGATG


 


2501
Hcd807 right
0.26
GCGCTGACAAATCTTGCCTGATTCTGTATGATCCATGAGA


 


2502
Hcd817 left
0.37
TAATGAGAATTATGTTTGCACATTGAGGCAGGATAAATCC


 


2503
Hcd788 left
0.25
GACAAACATGCAGGAAAAATTATCCCCTGGGGATTCTACA


 


2504
Hcd970 left
0.31
TTGTGGGTCAGCTGCCCAGCTATCGGCTGGATTAGTGAAT


 


2407
Hcd148_HPR225left
0.26
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2506
Hcd102 left
0.27
ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC


 


2507
Hcd246 right
0.29
TAAAGTGAGTTATGGAGGTTACTCTCCTGTGAGAGGAAAT


 


2508
HPR199 right
0.28
TACACCTAAGGCATGTACTGTATTAATGAACCAATAAAAC


 


2509
HPR233 right
0.27
CATGATGGGGTGGGGTGAGATGGGGAGCGAAGACTATTAC


 


2510
Hcd383 left
0.28
GCCCGGGCATGCATTTTATCTAGCACCATGTGTTTCAGCT


 


2486
MPR224 right
0.29
TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA


 


2512
HPR172 right
0.26
GTTTAAACAGCCAGTGCAAACATTTAGATCTGAGTCAAAA


 


2382
MPR216 left
0.34
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2514
mir-321No2
0.25
CAGGGATTGTGGGTTCGAGTCCCACCCGGGGTAAAGAAAG


 


2515
Hcd586 right
0.28
GAACTGTTTGCTTTGGATGGGCTTGGTCCTCATTGGCTGA


 


2516
Hcd587 right
0.3
AAATAATGACTGGCCATAAGATCAAGACAAGTGTCCAAAG


 


2517
Hcd249 right
0.39
CAGGTACATGTTGATCAGCAGGGGCTGGGAGGCGCCGCTC


 


2518
Hcd279 right
0.27
CTCACGGCGTTGCCATGGAGACAACTCCGGGGCTGGGGCTC


 


2519
HPR159 left
0.3
TCCGTCACTTGAACTGGCTGCCAGCGTTCACAGACAGCTG


 


2520
Hcd689 right
0.28
GTACATCTGGATGTAGTTGTGCTGCAGCTGCTTCTGGTAG


 


2521
Hcd691 right
0.32
CGGCAAAAACCTCTGTCAGAACAAAATTAGGTGATCTATC


 


2383
mir-019b-1-prec
0.32
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2523
Hcd413 right
0.26
CACAAAAAGGCATAAGCAGACATCTTGCCCTTTGGTTTCT


 


2524
Hcd581 right
0.26
AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA


 


2525
Hcd536_HPR104 right
0.28
GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA


 


2526
Hcd230 left
0.28
CATTCTCTACAAGCATATGGCCTTGGGACATTAAGATGGC


 


2527
HPR154 left
0.28
AACATCAAGATCTATTGACCTGAGAGGTAAATATTGACCG


 


2528
Hcd270 right
0.31
AAATGTTGTTATAGTATCCCACCTACCCTGATGTATCTTT


 


2529
Hcd649 right
0.26
GAACAGGCTTCAAGGTTCTTGGCAGGAATATTCCGTGTAG


 


2530
Hcd889 right
0.27
ATGCCTTGTGCTCTGTGCTAATTCAGAAGAATAAGCCTGT


 


2531
Hcd938 left
0.36
CTTGTCGACTAGCCAGTTATGAACAGAGGAGGATGTTCTC


 


2532
HPR266 right
0.32
GGAGATCCCTTCAAGGTACTTAGTTTTAAATGAGTGCTCT


 


2386
mir-025-prec
0.39
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2534
Hcd355_HPR190 left
0.25
TTGTGCACTGCACAACCCTAGTGGCGCCATTCAATTATAG


 


2535
MPR162 left
0.26
CTCTCTTTTTCCTGCTTGATTTGCCTAATGGAAGCTGACA


 


2298
Hcd923 right
0.34
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2537
MPR237 left
0.32
AGCACATCCCATGATCACAGTAATGTTCTTTGGAGATGTA


 


2409
MPR174 left
0.32
GAGCCGGTCTCTTTACATCTCAAATACCAGGTATTTAGGT


 


2388
mir-019a-prec
0.31
TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC


 


2540
hsa_mir_490_Hcd20 right
0.25
ACCAACCTGGAGGACTCCATGCTGTTGAGCTGTTCACAAG


 


2389
mir-380-5p
0.36
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2390
mir-093-prec-7.1 = 093-1
0.38
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.45
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2544
Hcd627 left
0.3
GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT


 


2300
mir-142-prec
0.27
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.29
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2547
mir-001b-2-prec
0.28
TAAGCTATGGAATGTAAAGAAGTATGTATCTCAGGCCGGG


 


2393
mir-018-prec
0.4
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.48
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG


 


2550
Hcd404 left
0.29
TGCTGCTGTTAATGCCATTAGGATGACTATTTATATCACC


 


2551
mir-384
0.25
CATAAGTCATTCCTAGAAATTGTTCATAATGCCTGTAACA


 


2410
mir-4323p
0.4
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 96







Bleomycin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2553
mir-376aNo1
0.27
AATCATAGAGGAAAATCCACGTTTTCAGTATCAAATGCTG


 


2293
mir-155-prec
0.35
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2355
mir-409-3p
0.28
GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT


 


2358
mir-495No1
0.29
GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA


 


2557
Hcd498 right
0.28
CACGAAGAAGTTCAGCAACCAGGAGACCAGGTGGGGGCCG


 


2558
mir-199a-2-prec
0.41
TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT


 


2362
mir-382
0.3
GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC


 


2560
HPR271 right
0.27
AATTGAGCAAACAGTGCAATTTTCTGTAATTATGCCAGTG


 


2561
mir-145-prec
0.31
CCTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTAA


 


2562
mir-199a-1-prec
0.35
GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCA
















TABLE 97







Methyl-GAG (methyl glyoxal bis amidinohydrazone dihydrochloride)


microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.32
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2413
mir-101-prec-9
0.3
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2565
mir-144-precNo2
0.29
CCCTGGCTGGGATATCATCATATACTGTAAGTTTGCGATG


 


2566
mir-519a-1/526c
0.29
TCAGGCTGTGACACTCTAGAGGGAAGCGCTTTCTGTTGTC


 


2567
mir-519b
0.33
GAAAAGAAAGTGCATCCTTTTAGAGGTTTACTGTTTGAGG


 


2568
mir-015b-precNo2
0.26
TGCTACAGTCAAGATGCGAATCATTATTTGCTGCTCTAGA


 


2391
mir-106aNo1
0.27
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2570
mir-16-1No1
0.26
GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTTAAG


 


2571
mir-181dNo1
0.27
GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA


 


2572
mir-017-precNo2
0.31
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.32
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2574
mir-192No2
0.26
TGCCAATTCCATAGGTCACAGGTATGTTCGCCTCAATGCC


 


2306
mir-213-precNo1
0.25
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2576
mir-215-precNo2
0.3
CATTTCTTTAGGCCAATATTCTGTATGACTGTGCTACTTC


 


2577
mir-107No1
0.28
GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC


 


2578
mir-200bNo1
0.28
GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG


 


2579
mir-103-prec-5 = 103-1
0.3
TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCATTG


 


2566
mir-519a-1/526c
0.37
TCAGGCTGTGACACTCTAGAGGGAAGCGCTTTCTGTTGTC


 


2382
MPR216 left
0.28
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2383
mir-019b-1-prec
0.31
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2577
mir-107-prec-10
0.29
GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC


 


2384
mir-135-2-prec
0.39
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2585
mir-103-2-prec
0.29
GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA


 


2586
mir-519a-2No2
0.29
TCTCAGGCTGTGTCCCTCTACAGGGAAGCGCTTTCTGTTG


 


2386
mir-025-prec
0.33
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2588
mir-16-2No1
0.33
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2589
MPR95 left
0.28
TTGTTGGACACTCTTTCCCTGTTGCACTACTGTGGGCCTC


 


2588
mir-016b-chr3
0.29
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2591
Hcd948 right
0.27
TGATATAAATAGTCATCCTAATGGCATTAACAGCAGCACT


 


2475
mir-195-prec
0.35
TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG


 


2390
mir-093-prec-7.1 = 093 - 1
0.38
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.42
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.37
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2596
mir-519c/526c
0.27
TCTCAGCCTGTGACCCTCTAGAGGGAAGCGCTTTCTGTTG


 


2578
mir-200a-prec
0.29
GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG


 


2598
mir-016a-chr13
0.29
CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT


 


2393
mir-018-prec
0.41
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.39
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 98







pXD101 HDAC inhibitors microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.42
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2430
mir-123-precNo2
0.31
TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG


 


2391
mir-106aNo1
0.36
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2414
mir-20bNo1
0.36
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2572
mir-017-precNo2
0.32
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.42
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.3
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir-092-prec-13 = 092-1No2
0.31
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


2609
mir-122a-prec
0.29
CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC


 


2447
Hcd783 left
0.27
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2382
MPR216 left
0.29
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2383
mir-019b-1-prec
0.41
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.46
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2451
mir-128b-precNo1
0.39
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2386
mir-025-prec
0.45
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2616
Hcd511 right
0.26
TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC


 


2390
mir-093-prec-7.1 = 093-1
0.45
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.5
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.5
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.26
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2301
mir-223-prec
0.26
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2393
mir-018-prec
0.48
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.52
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 99







5-Fluorouracil microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2378
mir-096-prec-7No2
0.27
TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT


 


2625
mir-429No1
0.25
CTAATACTGTCTGGTAAAACCGTCCATCCGCTGCCTGATC


 


2626
Hcd693 right
0.25
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


2500
HPR214 right
0.27
AGCAAAAGCTATTATTTGCCCTTGATGAGCCAATCAGATG


 


2628
Hcd586 left
0.26
GTCCTGTCTAAAGGAAGAAGTTTGTTCTACTGTAAACAGT


 


2517
Hcd249 right
0.26
CAGGTACATGTTGATCAGCAGGGGCTGGGAGGCGCCGCTC


 


2520
Hcd689 right
0.27
GTACATCTGGATGTAGTTGTGCTGCAGCTGCTTCTGGTAG


 


2631
mir-194-2No1
0.25
TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG


 


2524
Hcd581 right
0.26
AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA


 


2528
Hcd270 right
0.3
AAATGTTGTTATAGTATCCCACCTACCCTGATGTATCTTT


 


2386
mir-025-prec
0.27
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2635
Hcd340 left
0.27
GGACAATTCAACAGTGGTGAGTCACTTCGCCACTTTTCAG


 


2283
mir-007-1-prec
0.27
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2390
mir-093-prec-7.1 = 093-1
0.25
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.26
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2287
Hcd794 right
0.27
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2394
mir-020-prec
0.26
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG


 


2410
mir-4323p
0.26
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 100







Radiation microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2642
mir-136-precNo2
0.3
TGAGCCCTCGGAGGACTCCATTTGTTTTGATGATGGATTC


 


2643
Hcd570 right
0.26
GCCCAACAGAACAACTTGTTTCTCCAGAGCCTGAGGTTTA


 


2644
Hcd873 left
0.26
TCTTCTGACAATGAAGGTAGGCGGACAACGAGGAGATTGC


 


2645
Hcd282PO right
0.26
GAAGACGGACTTGGTTCCGTTTGACCAGCCAGAGCAGGGG


 


2646
Hcd799 left
0.25
GTCCGGCGCGAGTGGAGCTGTTGTAAAATGGCGGCCGAAG


 


2344
Hcd829 right
0.39
AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG


 


2347
Hcd210_HPR205 right
0.32
CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC


 


2649
mir-219-prec
0.26
ATTGTCCAAACGCAATTCTCGAGTCTATGGCTCCGGCCGA


 


2650
mir-202*
0.31
CCGCCCGCCGTTCCTTTTTCCTATGCATATACTTCTTTGA


 


2651
mir-429No2
0.42
CACCGCCGGCCGATGGGCGTCTTACCAGACATGGTTAGAC


 


2626
Hcd693 right
0.32
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


2653
mir-022-prec
0.34
TGTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGTTG


 


2654
NPR88 right
0.32
CTTACCCTGGTGCGTGGGGCCGCAGGGCTAACACCAAAAA


 


2655
mir-198-prec
0.39
TCATTGGTCCAGAGGGGAGATAGGTTCCTGTGATTTTTCC


 


2656
mir-199b-precNo1
0.29
GTCTGCACATTGGTTAGGCTGGGCTGGGTTAGACCCTCGG


 


2324
Hcd145 left
0.26
AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG


 


2658
mir-124a-2-prec
0.34
TTAAGGCACGCGGTGAATGCCAAGAGCGGAGCCTACGGCT


 


2659
mir-138-2-prec
0.39
AGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCATCCTC


 


2660
Hcd960 left
0.29
CTCAGTCTGCGGGCCCCGAGGAGGGTTGTGGGCCCTTTTT


 


2661
Hcd869 left
0.31
CGAGAGGCACTTTGTACTTCTGCCAGGAGACCATATGATA


 


2662
Hcd384 left
0.41
TTACCCAGCCGGGCCGCCAACACCAGATCCTTCTCCTTCT


 


2663
mir-027b-prec
0.31
CCGCTTTGTTCACAGTGGCTAAGTTCTGCACCTGAAGAGA


 


2664
Hcd444 right
0.31
GTATATGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT


 


2631
mir-194-2No1
0.3
TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG


 


2369
mir-197-prec
0.44
TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT


 


2326
Hcd913 right
0.39
CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG


 


2327
HPR163 left
0.39
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2310
mir-138-1-prec
0.25
AGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACG


 


2670
mir-010a-precNo1
0.25
GTCTGTCTTCTGTATATACCCTGTAGATCCGAATTTGTGT


 


2671
mir-023b-prec
0.34
AATCACATTGCCAGGGATTACCACGCAACCACGACCTTGG


 


2672
mir-193bNo2
0.35
CTGTGGTCTCAGAATCGGGGTTTTGAGGGCGAGATGAGTT


 


2285
Hcd654 left
0.43
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2674
Hcd542 left
0.26
ATCTCAGTAGCCAATATTTTTCTCTGCTGGTATCAAATGA


 


2558
mir-199a-2-prec
0.28
TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT


 


2676
mir-214-prec
0.43
TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG


 


2677
Hcd608 right
0.31
CTTGTGTTTTCACAGCAGCCACAGGCCCTACATCCTTCCT


 


2678
Hcd684 right
0.28
AGAAGGCGCTCCCTGCTAGCCCGGCTCTGTTCTAATTATA


 


2561
mir-145-prec
0.4
CCTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTAA


 


2680
mir-023a-prec
0.37
TCCTGTCACAAATCACATTGCCAGGGATTTCCAACCGACC


 


2681
mir-024-2-prec
0.32
AGTTGGTTTGTGTACACTGGCTCAGTTCAGCAGGAACAGG


 


2562
mir-199a-1-prec
0.29
GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCA
















TABLE 101







5-Aza-2′-deoxycytidine (decitabine) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2412
mir-096-prec-7No1
0.36
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2684
Hcd605 right
0.25
GGTTAAGACTCTAACAAACGAGTTGTGAATTGTAGCAATG


 


2414
mir-20bNo1
0.3
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2686
miR-373*No1
0.26
GGGATACTCAAAATGGGGGCGCTTTCCTTTTTGTCTGTAC


 


2325
HUMTRAB
0.3
ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT


 


2383
mir-019b-1-prec
0.25
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2327
HPR163 left
0.31
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2690
mir-371No1
0.25
ACTTTCTGCTCTCTGGTGAAAGTGCCGCCATCTTTTGAGT


 


2386
mir-025-prec
0.29
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2692
mir-18bNo2
0.27
AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA


 


2390
mir-093-prec-7.1 = 093-1
0.28
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.29
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.29
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2394
mir-020-prec
0.29
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 102







Idarubicin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2290
HUMTRF
0.33
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2367
mir-483No1
0.3
ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC


 


2699
MPR74 left
0.27
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


2609
mir-122a-prec
0.27
CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC


 


2433
ath-MIR180aNo2
0.29
TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT


 


2451
mir-128b-precNo1
0.26
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2703
Hcd923 left
0.25
TGGGAACCTTGTTAAAATGCAGATTCTGATTCTCAGGTCT


 


2391
mir-106-prec-X
0.25
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2299
mir-342No1
0.36
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.34
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.25
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2301
mir-223-prec
0.36
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.26
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.29
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 103







Melphalan microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2711
mir-124a-3-prec
0.32
TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG


 


2712
mir-181a-precNo1
0.28
TCAGAGGACTCCAAGGAACATTCAACGCTGTCGGTGAGTT


 


2713
Hcd773 left
0.26
CTTCCTCCCTGGGCATCTCTAGCACAGGGGATCCCCAAAC


 


2714
Hcd683 left
0.25
CTATGACAGAAGGTACTCTGTGGGAGGGAGGAGATAATAG


 


2715
Hcd796 left
0.29
GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC


 


2290
HUMTRF
0.44
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2305
HUMTRS
0.27
TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA


 


2718
mir-181b-2No1
0.25
CTGATGGCTGCACTCAACATTCATTGCTGTCGGTGGGTTT


 


2719
Hcd294 left
0.26
TTATCATAAAATAATCACAGCCCTCAGGTGCTGTGAGGCA


 


2414
mir-20bNo1
0.27
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2571
mir-181dNo1
0.27
GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA


 


2306
mir-213-precNo1
0.4
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2407
Hcd148_HPR225left
0.29
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2294
mir-515-15p
0.34
GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT


 


2398
mir-181b-precNo1
0.43
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2447
Hcd783 left
0.26
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2325
HUMTRAB
0.29
ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT


 


2728
HUMTRN
0.27
CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT


 


2729
mir-181b-1No1
0.31
TTTAAAAGGTCACAATCAACATTCATTGCTGTCGGTGGGT


 


2296
mir-124a-1-prec1
0.31
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2731
mir-367No1
0.26
TCTGTTGAATATAAATTGGAATTGCACTTTAGCAATGGTG


 


2451
mir-128b-precNo1
0.38
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2360
Hcd43 8right
0.25
GTGTTTATTTGAATCTCACATCGCTCATAAGAATACACGC


 


2386
mir-025-prec
0.3
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2735
mir-216-precNo1
0.35
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2317
Hcd731 left
0.26
AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC


 


2390
mir-093-prec-7.1 = 093-1
0.25
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.27
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2299
mir-342No1
0.36
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.53
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.32
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2342
HUMTRV1A
0.25
ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA


 


2301
mir-223-prec
0.46
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.45
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.3
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 104







IL4-PR3B fusion protein microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2344
Hcd829 right
0.28
AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG


 


2369
mir-197-prec
0.28
TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT


 


2327
HPR163 left
0.28
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2448
mir-150-prec
0.47
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG
















TABLE 105







Valproic acid (VPA) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2750
mir-034precNo1
0.26
GAGTGTTTCTTTGGCAGTGTCTTAGCTGGTTGTTGTGAGC


 


2751
Hcd255 left
0.28
CTAGCTCCGTTCGTGATCCGGGAGCCTGGTGCCAGCGAGA


 


2752
Hcd712 right
0.27
GAAGATCGGTTGTCATCTGGTCTGGTCAGCCCGGCCCCGA


 


2753
Hcd965 left
0.26
TGTTAAGTGGAAAAGCCTCCAGGAACGTGGCAGAAAAAGG


 


2754
Hcd891 right
0.29
GCAACGGCCTGATTCACAACACCAGCTGCCCCACCACACC


 


2347
Hcd210_HPR205 right
0.31
CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC


 


2651
mir-429No2
0.33
CACCGCCGGCCGATGGGCGTCTTACCAGACATGGTTAGAC


 


2757
Hcd753 left
0.27
GACCTGATTCCCATCTTTGTATTTGGCGACCACCCGACTG


 


2626
Hcd693 right
0.38
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


2333
MPR203 left
0.25
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2760
Hcd704 left
0.4
TCTGTATTTAATTTGGCTCAGCCGGGAAGATTTTTGGCTC


 


2761
Hcd863PO right
0.3
TTGCAGAGCCTAAGACACAGGCCCAGAGAGGCAGTGATCG


 


2609
mir-122a-prec
0.29
CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC


 


2763
Hcd760 left
0.35
TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC


 


2764
Hcd338 left
0.35
CTTCTCCTCCTGTTCGCCGCAGGCGCCCGTCCCAGTAGTC


 


2765
HPR213 right
0.33
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA


 


2766
Hcd852 right
0.26
AAAAGTAAACAACAATTTGCCGCTGCCAGCCTCCCATTAG


 


2767
Hcd366 left
0.28
ATACTAGATTAAATTTCAGCCCCGGGCCAATCTGTCAAAG


 


2768
MPR103 right
0.27
GAGGTGTTTGTGCTCCACTCGGCTCCCTTGGTTACATAAC


 


2769
Hcd669 right
0.27
ATGTTTAACAGTCCAGGTTTTGTAGAATATGTGGTGGACC


 


2770
mir-188-prec
0.27
TCACATCCCTTGCATGGTGGAGGGTGAGCTTTCTGAAAAC
















TABLE 106







All-trans retinoic acid (ATRA) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2771
Hcd257 left
0.42
CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG


 


2772
mir-148-prec
0.45
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2773
Hcd512 left
0.28
CTGCGCTCTCGGAAATGACTCGCTCCAATCCCGCTTCGCG


 


2774
HPR227 right
0.25
CAGTGCAATGATATTGTCAAAGCATCTGGGACCAGCCTTG


 


2775
Hcd421 right
0.37
AGTAAACAATGTCGGCTTTCCGCCTCCTCCCCTGCCATCC


 


2333
MPR203 left
0.39
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2777
mir-017-precNo1
0.26
GCATCTACTGCAGTGAAGGCACTTGTAGCATTATGGTGAC


 


2778
mir-219-2No1
0.26
CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG


 


2779
mir-328No1
0.3
GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC


 


2447
Hcd783 left
0.31
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2781
Hcd181 left
0.32
TTGGCGTCCTTGTCTCTCTCTCCCCTGCCCAGTGGCCTCC


 


2765
HPR213 right
0.3
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA


 


2783
mir-191-prec
0.31
CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC


 


2784
mir-375
0.31
TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC


 


2785
mir-212-precNo2
0.26
CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG


 


2326
Hcd913 right
0.34
CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG


 


2330
Hcd716 right
0.48
CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG


 


2331
MPR207 right
0.3
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA


 


2333
HPR206 left
0.26
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2588
mir-016b-chr3
0.29
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2285
Hcd654 left
0.34
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2475
mir-195-prec
0.3
TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG


 


2793
Hcd425 left
0.25
GGTTCTACTCTCTTACCCCTCCCCCACGTGGTTGTTGCTG


 


2772
mir-148aNo1
0.35
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2300
mir-142-prec
0.36
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2598
mir-016a-chr13
0.25
CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT
















TABLE 107







Cytoxan microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2797
Hcd99 right
0.25
CAATCCTGGCTGCAGGCATATTTGCATATTGGATGCTGTG


 


2798
mir-520c/526a
0.32
TCTCAGGCTGTCGTCCTCTAGAGGGAAGCACTTTCTGTTG


 


2783
mir-191-prec
0.32
CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC


 


2800
mir-205-prec
0.35
TCCTTCATTCCACCGGAGTCTGTCTCATACCCAACCAGAT


 


2784
mir-375
0.33
TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC


 


2802
mir-423No1
0.29
CAAAAGCTCGGTCTGAGGCCCCTCAGTCTTGCTTCCTAAC


 


2803
mir-449No1
0.39
TGTGATGAGCTGGCAGTGTATTGTTAGCTGGTTGAATATG


 


2804
mir-196-2-precNo2
0.26
GCTGATCTGTGGCTTAGGTAGTTTCATGTTGTTGGGATTG
















TABLE 108







Topotecan (Hycamtin) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2290
HUMTRF
0.26
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2699
MPR74 left
0.29
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


2306
mir-213-precNo1
0.28
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2293
mir-155-prec
0.31
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2398
mir-181b-precNo1
0.31
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2299
mir-342No1
0.33
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2410
mir-4323p
0.28
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 109







Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza)


microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.38
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2483
mir-123-precNo1
0.31
GACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTC


 


2814
mir-514-1No2
0.29
TGTCTGTGGTACCCTACTCTGGAGAGTGACAATCATGTAT


 


2413
mir-101-prec-9
0.25
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2772
mir-148-prec
0.36
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2391
mir-106aNo1
0.34
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2414
mir-20bNo1
0.41
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2819
Hcd781 right
0.32
AGTTTCTTTAATTAATGAAGTTTTTGGGTCTGCTCCACTT


 


2572
mir-017-precNo2
0.29
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.42
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.27
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir-092prec-13 = 092-1No2
0.28
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


2577
mir-107No1
0.29
GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC


 


2579
mir-103-prec-5 = 103-1
0.32
TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCATTG


 


2382
MPR216 left
0.29
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2827
mir-29b-2 = 102prec7.1 = 7.2
0.27
AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG


 


2383
mir-019b-1-prec
0.4
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2577
mir-107-prec-10
0.3
GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC


 


2384
mir-135-2-prec
0.37
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2524
Hcd581 right
0.28
AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA


 


2585
mir-103-2-prec
0.29
GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA


 


2526
Hcd230 left
0.27
CATTCTCTACAAGCATATGGCCTTGGGACATTAAGATGGC


 


2386
mir-025-prec
0.4
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2835
mir-208-prec
0.31
ACCTGATGCTCACGTATAAGACGAGCAAAAAGCTTGTTGG


 


2692
mir-18bNo2
0.31
AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA


 


2390
mir-093-prec-7.1 = 093-1
0.39
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.48
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.37
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.28
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2393
mir-018-prec
0.44
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.48
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 110







Depsipeptide (FR901228) microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2320
Hcd415 right
0.27
GATGTTTGGGAAACAATGGGAGTGAGAGAATGGGAGAGCT


 


2308
mir-147-prec
0.27
GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT


 


2845
mir-033b-prec
0.34
GTGCATTGCTGTTGCATTGCACGTGTGTGAGGCGGGTGCA


 


2846
Hcd778 right
0.34
CAGAGGGGAGGCCCAGAGGAGAGGGAAGCTTGGGCAAAGG


 


2847
mir-127-prec
0.25
TCGGATCCGTCTGAGCTTGGCTGGTCGGAAGTCTCATCAT


 


2848
mir-324No1
0.28
TGGAGACCCACTGCCCCAGGTGCTGCTGGGGGTTGTAGTC


 


2287
Hcd794 right
0.35
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2850
Hcd634 left
0.27
CTGCTCCGCTCAGAGCCTTTTCCTCTCCACTTCCTGTTCA
















TABLE 111







Bortezomib microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2304
MPR121 left
0.31
CACCTGGCTCTGAGAACTGAATTCCATAGGCTGTGAGCTC


 


2852
Hcd115 left
0.27
CTCTGTGGCCATTTCGGTTTTTCCAGTCCGATGCCCCTGA


 


2626
Hcd693 right
0.28
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


2760
Hcd704 left
0.25
TCTGTATTTAATTTGGCTCAGCCGGGAAGATTTTTGGCTC


 


2855
HPR100 right
0.28
GGTGTTTGTGCTCCACTCAGCTCCCTTGGTTACATAACAG


 


2763
Hcd760 left
0.26
TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC


 


2308
mir-147-prec
0.3
GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT


 


2845
mir-033b-prec
0.29
GTGCATTGCTGTTGCATTGCACGTGTGTGAGGCGGGTGCA


 


2312
mir-146-prec
0.33
TGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAGACCTC


 


2860
Hcd142 right
0.3
TAAATGTGTAATTTCTCCCTTGACGGCCCCCGGCCGCTGG


 


2861
mir-501No2
0.33
ATGCAATGCACCCGGGCAAGGATTCTGAGAGGGTGAGCCC


 


2330
Hcd716 right
0.26
CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG


 


2331
MPR207 right
0.27
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA


 


2864
Hcd777 left
0.26
CAGGTGGGTGCTGAGGCCGCGTTGTTGCTTGAAGCTAGCC


 


2865
mir-204-precNo2
0.27
AGGCTGGGAAGGCAAAGGGACGTTCAATTGTCATCACTGG


 


2314
mir-146bNo1
0.26
CACCTGGCACTGAGAACTGAATTCCATAGGCTGTGAGCTC


 


2616
Hcd511 right
0.29
TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC


 


2316
Hcd397 left
0.28
GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT


 


2869
MPR130 right
0.33
CAATCACAGATAGCACCCCTCACCTTGAGCCCATTTTCAC


 


2337
Hcd782 left
0.28
GGAGCCCTGTCTGCAAAGAGTGGTGCGTGTGCGTGTGTGA


 


2871
mir-324No2
0.28
CTGACTATGCCTCCCCGCATCCCCTAGGGCATTGGTGTAA


 


2287
Hcd794 right
0.34
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2288
Hcd739 right
0.29
TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT
















TABLE 112







Leukeran microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092prec-X = 092-2
0.39
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2412
mir-096-prec-7No1
0.26
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2430
mir-123-precNo2
0.32
TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG


 


2877
MPR249 left
0.26
TCGGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC


 


2486
HPR232 right
0.28
TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA


 


2413
mir-101-prec-9
0.4
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2391
mir-106aNo1
0.31
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2414
mir-20bNo1
0.38
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2882
Hcd861 right
0.25
AAGGTCTGGATTGATCGTACTGCTTTCTGAAAGGTAAAAA


 


2572
mir-017-precNo2
0.26
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.33
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.3
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2506
Hcd102 left
0.26
ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC


 


2382
MFR216 left
0.32
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2888
Hcd975 left
0.25
GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT


 


2383
mir-019b-1-prec
0.3
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.38
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2524
Hcd581 right
0.26
AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA


 


2525
Hcd536_HPR104 right
0.25
GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA


 


2893
mir-128b-precNo2
0.25
GGGGGCCGATACACTGTACGAGAGTGAGTAGCAGGTCTCA


 


2332
HSTRNL
0.37
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2386
mir-025-prec
0.47
ACGCTGCCCTGGGCATTGCACTTCTCTCGGTCTGACAGTG


 


2692
mir-18bNo2
0.27
AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA


 


2897
HPR262 left
0.26
TCAGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC


 


2298
Hcd923 right
0.33
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2899
Hcd434 right
0.3
CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC


 


2900
Hcd658 right
0.28
GACTGCAGAGCAAAAGACACGATGGGTGTCTATTGTTTTC


 


2901
HPR129 left
0.29
TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG


 


2389
mir-380-5p
0.32
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCCCTAT


 


2390
mir-093-prec-7.1 = 093-1
0.45
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.5
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2544
Hcd627 left
0.31
GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT


 


2300
mir-142-prec
0.33
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2393
mir-018-prec
0.46
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.5
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 113







Fludarabine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2713
Hcd773 left
0.26
CTTCCTCCCTGGGCATCTCTAGCACAGGGGATCCCCAAAC


 


2910
Hcd248 right
0.33
CATTATGCAAATGGTATGAGAGGAAAATTAGGCAATAAGG


 


2571
mir-181dNo1
0.34
GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA


 


2699
MPR74 left
0.3
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


2306
mir-213-precNo1
0.37
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2293
mir-155-prec
0.32
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2291
MPR197 right
0.29
TATTTATTACAAGGTCCTTCTTCCCCGTAAAACTTTGTCC


 


2398
mir-181b-precNo1
0.26
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2827
mir-29b-2 = 102prec7.1 = 7.2
0.32
AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG


 


2918
mir-029c-prec
0.33
TTTTGTCTAGCACCATTTGAAATCGGTTATGATGTAGGGG


 


2919
Hcd318 right
0.32
CAAGTGGTTAATTGAGCCCACAAGTGACCTACTCAATCAG


 


2451
mir-128b-precNo1
0.25
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2921
mir-130a-precNo2
0.27
TGTCTGCACCTGTCACTAGCAGTGCAATGTTAAAAGGGCA


 


2311
mir-140No2
0.26
TTCTACCACAGGGTAGAACCACGGACAGGATACCGGGGCA


 


2588
mir-16-2No1
0.31
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2924
mir-526a-2No1
0.26
GATCTCGTGCTGTGACCCTCTAGAGGGAAGCACTTTCTGT


 


2588
mir-016b-chr3
0.3
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2475
mir-195-prec
0.34
TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG


 


2735
mir-216-precNo1
0.25
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2299
mir-342No1
0.26
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2827
mir-29b-1No1
0.34
AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG


 


2544
Hcd627 left
0.33
GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT


 


2931
mir-102-prec-1
0.33
TCTTTGTATCTAGCACCATTTGAAATCAGTGTTTTAGGAG


 


2300
mir-142-prec
0.32
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.34
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2934
let-7f-2-prec2
0.26
TGAGGTAGTAGATTGTATAGTTTTAGGGTCATACCCCATC


 


2598
mir-016a-chr13
0.36
CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT
















TABLE 114







Vinblastine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2287
Hcd794 right
0.33
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2302
Hcd754 left
0.25
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC
















TABLE 115







Busulfan microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2378
mir-096-prec-7No2
0.27
TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT


 


2711
mir-124a-3-prec
0.25
TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG


 


2413
mir-101-prec-9
0.25
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2752
Hcd712 right
0.27
GAAGATCGGTTGTCATCTGGTCTGGTCAGCCCGGCCCCGA


 


2626
Hcd693 right
0.26
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


2778
mir-219-2No1
0.25
CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG


 


2324
Hcd145 left
0.29
AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG


 


2293
mir-155-prec
0.29
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2765
HPR213 right
0.3
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA


 


2785
mir-212-precNo2
0.34
CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG


 


2326
Hcd913 right
0.33
CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG


 


2330
Hcd716 right
0.51
CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG


 


2331
MFR207 right
0.26
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA


 


2951
Hcd559 right
0.33
TTCTTTGTCTATACATTTCCTAGATTTCTATGCAGTTGGG


 


2285
Hcd654 left
0.28
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2288
Hcd739 right
0.27
TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT


 


2300
mir-142-prec
0.4
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG
















TABLE 116







Dacarbazine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.25
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2430
mir-123-precNo2
0.28
TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG


 


2413
mir-101-prec-9
0.29
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2958
Hcd517 right
0.3
GAGGGATTACAGATTAACTCCCACTTCTCCAGACTCAGAA


 


2715
Hcd796 left
0.37
GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC


 


2960
Hcd749 right
0.28
CGAGGAGGAGGTGACTGCTGTGGATGGTTATGAGACAGAC


 


2961
Hcd674 left
0.25
CTCCAGTGTGGTGTGCCTGCCCCCTTCCGTCATTGCTGTG


 


2381
mir-019b-2-prec
0.27
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.29
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir092-prec-13 = 092-1No2
0.33
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


2658
mir-124a-2-prec
0.29
TTAAGGCACGCGGTGAATGCCAAGAGCGGAGCCTACGGCT


 


2966
mir-143-prec
0.36
CTGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAG


 


2967
mir-516-43p
0.28
AAAGAAAAGAAAGTGCTTCCTTTCAGAGGGTTACTCTTTG


 


2735
mir-216-precNo1
0.31
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2317
Hcd731 left
0.26
AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC


 


2391
mir-106-prec-X
0.26
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.48
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.48
CAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.32
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2393
mir-018-prec
0.27
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC
















TABLE 117







Oxaliplatin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.36
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2772
mir-148-prec
0.27
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2414
mir-20bNo1
0.27
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2380
mir-007-2-precNo2
0.28
GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG


 


2572
mir-017-precNo2
0.28
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.32
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2763
Hcd760 left
0.27
TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC


 


2447
Hcd783 left
0.36
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2382
MPR216 left
0.26
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


278
mir-375
0.33
TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC


 


2383
mir-019b-1-prec
0.36
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.32
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2448
mir-150-prec
0.25
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG


 


2451
mir-128b-precNo1
0.33
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2989
mir-499No2
0.26
GTGAACATCACAGCAAGTCTGTGCTGCTTCCCGTCCCTAC


 


2386
mir-025-prec
0.38
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2283
mir-007-1-prec
0.32
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2388
mir-019a-prec
0.33
TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC


 


2390
mir-093-prec-7.1 = 093-1
0.46
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.45
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.41
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.34
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2393
mir-018-prec
0.4
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.44
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG


 


2453
mir-484
0.33
GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA
















TABLE 118







Hydroxyurea microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2771
Hcd257 left
0.34
CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG


 


2321
Hcd768 right
0.26
GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT


 


2715
Hcd796 left
0.25
GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC


 


2290
HUMTRF
0.48
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2305
HUMTRS
0.3
TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA


 


2699
MPR74 left
0.28
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


2306
mir-213-precNo1
0.29
AACATTCATTGCTGTCGGTGGGTTGAACTGTCTGGACAAG


 


2293
mir-155-prec
0.35
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


3008
Hcd763 right
0.25
GGTGCACTCTAAATTCCTGTCCCTGCGGAAGGCTGACTAA


 


2398
mir-181b-precNo1
0.28
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2433
ath-MIR180aNo2
0.26
TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT


 


2735
mir-216-precNo1
0.37
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2299
mir-342No1
0.31
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.49
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.31
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2342
HUMTRV1A
0.26
ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA


 


2301
mir-223-prec
0.59
CAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.46
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.26
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 119







Tegafur microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2431
Hcd257 right
0.26
CTTGGTTTTTGCAATAATGCTAGCAGAGTACACACAAGAA


 


3020
Hcd946 left
0.26
CACAGGATTTCAGGGGAGAAACGGTGGATTTTCACAAGAG


 


3021
Hcd503 left
0.3
GAGATGAGGTAGCTGCCAGGTGCCATGGGGGTATAGGTGA


 


2625
mir-429No1
0.25
CTAATACTGTCTGGTAAAACCGTCCATCCGCTGCCTGATC


 


2626
Hcd693 right
0.32
AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC


 


3024
miR-373*No1
0.33
GGGATACTCAAAATGGGGGCGCTTTCCTTTTTGTCTGTAC


 


3025
Hcd738 left
0.28
GAAAAACTTAAGATTCCCTCTCGGCCCTCATTTTTAGCTG


 


2779
mir-328No1
0.33
GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC


 


2447
Hcd783 left
0.36
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


3028
Hcd181 right
0.34
GCTCACTGGGCAGGAGCCCTAATCGGATTCGACAGCTGAG


 


3029
Hcd631 left
0.38
CAGATATTTTCTCAGGCAATCCTCAGCCACAGCCTTCTTG


 


3030
Hcd279 left
0.25
CGGACTAACACTCCGCGGGTGTTTCCATGGAGACCGAGGC


 


2631
mir-194-2No1
0.3
TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG


 


2369
mir-197-prec
0.38
TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT


 


2327
HPR163 left
0.39
GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA


 


2448
mir-150-prec
0.32
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG


 


3035
Hcd323 left
0.26
GTTGTAGCATGTGGTTGTATTAATGAACGTTACAGGAGAG


 


2585
mir-103-2-prec
0.28
GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA


 


3037
Hcd243 right
0.27
TATTATACATCATTTCCCATCAATCGACGAACTAAAGCCT


 


2408
Hcd938 right
0.27
ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG


 


2386
mir-025-prec
0.29
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2283
mir-007-1-prec
0.36
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2284
MPR243 left
0.26
GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG


 


2616
Hcd511 right
0.27
TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC


 


2285
Hcd654 left
0.26
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2558
mir-199a-2-prec
0.3
TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT


 


3045
mir-214-prec
0.27
TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG


 


2390
mir-093-prec-7.1 = 093-1
0.33
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.27
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2287
Hcd794 right
0.41
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


3049
Hcd530 right
0.26
AAGGAAAATCAAACCCACAATGCTGAACACAACAATGACC


 


2341
HSHELA01
0.34
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2302
Hcd754 left
0.29
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.29
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 120







Daunorubicin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2321
Hcd768 right
0.25
GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT


 


2290
HUMTRF
0.34
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2324
Hcd145 left
0.28
AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG


 


2298
Hcd923 right
0.27
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2735
mir-216-precNo1
0.27
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2390
mir-093-prec-7.1 = 093-1
0.25
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2299
mir-342No1
0.33
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2287
Hcd794 right
0.28
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2300
mir-142-prec
0.48
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.3
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2301
mir-223-prec
0.33
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.32
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC
















TABLE 121







Bleomycin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





3065
mir-125b-2-precNo2
0.29
ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGAGG


 


2653
mir-022-prec
0.26
TGTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGTTG


 


3067
mir-125b-1
0.29
TCCCTGAGACCCTAACTTGTGATGTTTACCGTTTAAATCC


 


2293
mir-155-prec
0.38
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2309
mir-100No1
0.25
CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTATTA


 


2355
mir-409-3p
0.27
GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT


 


2358
mir-495No1
0.31
GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA


 


2558
mir-199a-2-prec
0.29
TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT


 


2362
mir-382
0.28
GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC


 


2319
mir-100-1/2-prec
0.26
TGAGGCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGG
















TABLE 122







Estramustine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2764
Hcd338 left
0.32
CTTCTCCTCCTGTTCGCCGCAGGCGCCCGTCCCAGTAGTC


 


3076
mir-099b-prec-19No1
0.25
GCCTTCGCCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC


 


3077
mir-149-prec
0.34
CGAGCTCTGGCTCCGTGTCTTCACTCCCGTGCTTGTCCGA
















TABLE 123







Chlorambucil microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2712
mir-181a-precNo1
0.26
TCAGAGGACTCCAAGGAACATTCAACGCTGTCGGTGAGTT


 


2466
mir-181c-precNo1
0.25
TGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGTT


 


2290
HUMTRF
0.35
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2571
mir-181dNo1
0.26
GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA


 


2699
MPR74 left
0.28
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


2502
Hcd817 left
0.28
TAATGAGAATTATGTTTGCACATTGAGGCAGGATAAATCC


 


2306
mir-213-precNo1
0.42
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2293
mir-155-prec
0.33
TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC


 


2407
Hcd148_HPR225left
0.29
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2294
mir-515-15p
0.27
GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT


 


2398
mir-181b-precNo1
0.41
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2728
HUMTRN
0.27
CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT


 


2451
mir-128b-precNo1
0.37
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2297
mir-450-2No1
0.29
GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG


 


2735
mir-216-precNo1
0.29
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2299
mir-342No1
0.35
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.45
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.39
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.37
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.28
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 124







Mechlorethamine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2711
mir-124a-3-prec
0.33
TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG


 


3020
Hcd946 left
0.3
CACAGGATTTCAGGGGAGAAACGGTGGATTTTCACAAGAG


 


2714
Hcd683 left
0.29
CTATGACAGAAGGTACTCTGTGGGAGGGAGGAGATAATAG


 


3101
HPR264 right
0.25
CAAATGGCGCATCAATGACTATCGCTCTTACAAAGCTCTT


 


3102
MPR185 right
0.3
CAGAACATGCAATGCAACTACAATGCACCACAGCTGCCCG


 


2290
HUMTRF
0.37
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


3104
Hcd294 left
0.25
TTATCATAAAATAATCACAGCCCTCAGGTGCTGTGAGGCA


 


3021
Hcd503 left
0.27
GAGATGAGGTAGCTGCCAGGTGCCATGGGGGTATAGGTGA


 


2414
mir-20bNo1
0.27
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2699
MPR74 left
0.25
CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT


 


3108
MPR234 right
0.28
GCTGACGTCACGGGCAGAATTGTCCCATTTAGGGATCCCG


 


3109
Hcd447 right
0.26
CTCAGGCCATTAACCTCAGTTGGTCACTAATCCCTAGGAA


 


3110
Hcd817 right
0.3
GAATCTTGCCCTTGGATGCATACTGTAATTTCCATTAAAG


 


2407
Hcd148_HPR225left
0.32
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2294
mir-515-15p
0.29
GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT


 


3113
Hcd383 right
0.25
CTGATAGTACACGGGGCCAAAATAGATGTATGCTTCTAAG


 


2295
mir-181b-precNo2
0.31
ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC


 


2447
Hcd783 left
0.33
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


3116
MPR224 left
0.34
TGAGGCCCTCTAGGCCGTGAATTAATGTGTCATAACTCAC


 


2512
HPR172 right
0.28
GTTTAAACAGCCAGTGCAAACATTTAGATCTGAGTCAAAA


 


2382
MPR216 left
0.32
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2728
HUMTRN
0.28
CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT


 


3120
mir-321No1
0.3
TTGGCCTCCTAAGCCAGGGATTGTGGGTTCGAGTCCCACC


 


2519
HFR159 left
0.25
TCCGTCACTTGAACTGGCTGCCAGCGTTCACAGACAGCTG


 


3122
MPR228 left
0.29
TTTTTGCTCCCAGTCAGTAGGAAGATTGTTTCAAATCTGT


 


2433
ath-MIR180aNo2
0.31
TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT


 


2369
mir-197-prec
0.28
TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT


 


2296
mir-124a-1-prec1
0.26
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2451
mir-128b-precNo1
0.31
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


3127
Hcd28_HPR39left
0.28
CTGACTTTCAGTTCCTATTTAAAATGTCTGAATTGGGAGC


 


2530
Hcd889 right
0.25
ATGCCTTGTGCTCTGTGCTAATTCAGAAGAATAAGCCTGT


 


3129
Hcd350 right
0.26
TAGCACTTAGCAGGTTGTATTATCATTGTCCGTGTCTATG


 


2386
mir-025-prec
0.31
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2835
mir-208-prec
0.27
ACCTGATGCTCACGTATAAGACGAGCAAAAAGCTTGTTGG


 


2297
mir-450-2No1
0.25
GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG


 


2298
Hcd923 right
0.29
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2899
Hcd434 right
0.28
CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC


 


2901
HPR129 left
0.27
TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG


 


3136
HPR220 left
0.27
GGAGACACTGTAACAACATTTTACTCCTGACTGATTACAT


 


2389
mir-380-5p
0.3
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2390
mir-093-prec-7.1 = 093-1
0.29
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.3
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2299
mir-342No1
0.28
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.45
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.29
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2301
mir-223-prec
0.32
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.32
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2394
mir-020-prec
0.37
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG


 


2410
mir-4323p
0.26
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 125







Streptozocin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2367
mir-483No1
0.2
ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC


 


3148
Hcd631 right
0.21
AAAACCAAATGGCTGGCTACTCATGTACTGTTGAATGTCT


 


2370
mir-212-precNo1
0.24
CCTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC


 


2408
Hcd938 right
0.21
ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG


 


3151
MPR133 right
0.2
CTGTAGATACTTTCTCCCTGAGCCCCTCCTGCCCCCCTGC


 


2287
Hcd794 right
0.21
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


3153
Hcd438 left
0.24
GTTTATTTGAATGTGTGATGGGGAGGTCATCAAAATGAAC


 


3154
Hcd886 right
0.23
CTCCAGTTGGGGGTGGGGAGTTGGGAACAGTGTGAATGGG
















TABLE 126







Carmustine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.33
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2958
Hcd517 right
0.33
GAGGGATTACAGATTAACTCCCACTTCTCCAGACTCAGAA


 


2715
Hcd796 left
0.28
GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC


 


2290
HUMTRF
0.33
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2414
mir-20bNo1
0.29
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2381
mir-019b-2-prec
0.25
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.27
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir-092-prec-13 = 092-1No2
0.33
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


2407
Hcd148_HPR225left
0.27
AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA


 


2325
HUMTRAB
0.3
ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT


 


2888
Hcd975 left
0.26
GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT


 


2384
mir-135-2-prec
0.28
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2451
mir-128b-precNo1
0.27
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


2966
mir-143-prec
0.32
CTGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAG


 


2386
mir-025-prec
0.33
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2735
mir-216-precNo1
0.34
CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG


 


2390
mir-093-prec-7.1 = 093-1
0.3
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.33
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.61
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.26
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2342
HUMTRV1A
0.26
ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA


 


2301
mir-223-prec
0.52
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.46
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2393
mir-018-prec
0.34
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.35
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 127







Lornustine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2413
mir-101-prec-9
0.27
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2715
Hcd796 left
0.26
GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC


 


2414
mir-20bNo1
0.28
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2325
HUMTRAB
0.35
ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT


 


2384
mir-135-2-prec
0.27
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2472
mir-153-1-prec1
0.32
CAGTTGCATAGTCACAAAAGTGATCATTGGCAGGTGTGGC


 


2386
mir-025-prec
0.29
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2390
mir-093-prec-7.1 = 093-1
0.26
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.31
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.41
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2342
HUMTRV1A
0.28
ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA


 


2302
Hcd754 left
0.35
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2393
mir-018-prec
0.27
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.28
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 128







Mercaptopurine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.39
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2412
mir-096-prec-7No1
0.26
CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA


 


2430
mir-123-precNo2
0.32
TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG


 


2877
MPR249 left
0.26
TCGGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC


 


2486
HPR232 right
0.28
TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA


 


2413
mir-101-prec-9
0.4
GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA


 


2391
mir-106aNo1
0.31
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2414
mir-20bNo1
0.38
AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC


 


2882
Hcd861 right
0.25
AAGGTCTGGATTGATCGTACTGCTTTCTGAAAGGTAAAAA


 


2572
mir-017-precNo2
0.26
GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT


 


2381
mir-019b-2-prec
0.33
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2607
mir-033-prec
0.3
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2506
Hcd102 left
0.26
ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC


 


2386
MPR216 left
0.32
GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT


 


2888
Hcd975 left
0.25
GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT


 


2383
mir-019b-1-prec
0.3
TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG


 


2384
mir-135-2-prec
0.38
CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT


 


2524
Hcd581 right
0.26
AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA


 


2525
Hcd536_HPR104 right
0.25
GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA


 


2893
mir-128b-precNo2
0.25
GGGGGCCGATACACTGTACGAGAGTGAGTAGCAGGTCTCA


 


2332
HSTRNL
0.37
TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA


 


2386
mir-025-prec
0.47
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2692
mir-18bNo2
0.27
AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA


 


2897
HPR262 left
0.26
TCAGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC


 


2298
Hcd923 right
0.33
CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT


 


2899
Hcd434 right
0.3
CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC


 


2900
Hcd658 right
0.28
GACTGCAGAGCAAAAGACACGATGGGTGTCTATTGTTTTC


 


2901
HPR129 left
0.29
TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG


 


2389
mir-380-5p
0.32
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2390
mir-093-prec-7.1 = 093-1
0.45
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.5
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2544
Hcd627 left
0.31
GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT


 


2300
mir-142-prec
0.33
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2393
mir-018-prec
0.46
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.5
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG
















TABLE 129







Teniposide microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2711
mir-124a-3-prec
0.25
TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG


 


2321
Hcd768 right
0.28
GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT


 


2290
HUMTRF
0.28
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2306
mir-213-precNo1
0.25
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2295
mir-181b-precNo2
0.28
ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC


 


2447
Hcd783 left
0.28
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2785
mir-212-precNo2
0.32
CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG


 


2296
mir-124a-1-prec1
0.25
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2299
mir-342No1
0.29
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.49
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2341
HSHELA01
0.3
GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA


 


2301
mir-223-prec
0.27
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2302
Hcd754 left
0.29
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC
















TABLE 130







Dactinomycin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2386
mir-025-prec
0.27
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


2283
mir-007-1-prec
0.28
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2390
mir-093-prec-7.1 = 093-1
0.3
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2287
Hcd794 right
0.33
GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG


 


2300
mir-142-prec
0.34
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG
















TABLE 131







Tretinoin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2771
Hcd257 left
0.42
CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG


 


2772
mir-148-prec
0.45
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2773
Hcd512 left
0.28
CTGCGCTCTCGGAAATGACTCGCTCCAATCCCGCTTCGCG


 


2774
HPR227 right
0.25
CAGTGCAATGATATTGTCAAAGCATCTGGGACCAGCCTTG


 


2775
Hcd421 right
0.37
AGTAAACAATGTCGGCTTTCCGCCTCCTCCCCTGCCATCC


 


2333
MPR203 left
0.39
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2777
mir-017-precNo1
0.26
GCATCTACTGCAGTGAAGGCACTTGTAGCATTATGGTGAC


 


2778
mir-219-2No1
0.26
CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG


 


2779
mir-328No1
0.3
GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC


 


2447
Hcd783 left
0.31
CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA


 


2781
Hcd181 left
0.32
TTGGCGTCCTTGTCTCTCTCTCCCCTGCCCAGTGGCCTCC


 


2765
HPR213 right
0.3
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA


 


2783
mir-191-prec
0.31
CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC


 


278
mir-375
0.31
TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC


 


2785
mir-212-precNo2
0.26
CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG


 


2326
Hcd913 right
0.34
CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG


 


2330
Hcd716 right
0.48
CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG


 


2331
MPR207 right
0.3
AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA


 


2333
HPR206 left
0.26
CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG


 


2588
mir-016b-chr3
0.29
GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT


 


2285
Hcd654 left
0.34
AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG


 


2475
mir-195-prec
0.3
TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG


 


2793
Hcd425 left
0.25
GGTTCTACTCTCTTACCCCTCCCCCACGTGGTTGTTGCTG


 


2772
mir-148aNo1
0.35
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2300
mir-142-prec
0.36
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2598
mir-016a-chr13
0.25
CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT
















TABLE 132







Ifosfamide microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2377
mir-092-prec-X = 092-2
0.28
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


2718
mir-181b-2No1
0.28
CTGATGGCTGCACTCAACATTCATTGCTGTCGGTGGGTTT


 


3275
Hcd417 right
0.28
GGATTTAATGAGAAATATTGAGCCCTTTGGTTCAGGAACT


 


3276
Hcd440_HPR257 right
0.28
GCTCTGTTGTGATAAATTGGCTGTGTGCTTCATTTGGACT


 


2381
mir-019b-2-prec
0.25
GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT


 


2306
mir-213-precNo1
0.39
AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG


 


2607
mir-033-prec
0.29
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir-092-prec-13 = 092-1No2
0.3
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


2398
mir-181b-precNo1
0.36
TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT


 


2451
mir-128b-precNo1
0.46
TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC


 


3283
mir-526a-2No2
0.29
GAAAAGAACATGCATCCTTTCAGAGGGTTACTCTTTGAGA


 


2589
MPR95 left
0.25
TTGTTGGACACTCTTTCCCTGTTGCACTACTGTGGGCCTC


 


3285
HPR220 right
0.27
GAGCATCAGTATGTAGTGCAATCAGTCAGGAGAAAATGAG


 


3286
mir-133a-1
0.35
CCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGTAGCT


 


2772
mir-148aNo1
0.3
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2300
mir-142-prec
0.4
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2427
HPR169 right
0.26
GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG


 


2301
mir-223-prec
0.38
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2393
mir-018-prec
0.27
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC


 


2394
mir-020-prec
0.25
TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG


 


2453
mir-484
0.27
GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA
















TABLE 133







Tamoxifen microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2377
mir-092-prec-X = 092-2
0.31
GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG


 


3295
Hcd547 left
0.27
AAAATCAGCTTTAATTAATTTGAGTGCCAGCTCTGTGTAT


 


2771
Hcd257 left
0.27
CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG


 


2772
mir-148-prec
0.27
TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC


 


2305
HUMTRS
0.25
TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA


 


2607
mir-033-prec
0.27
GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC


 


2608
mir-092-prec-13 = 092-1No2
0.25
TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG


 


278
mir-375
0.46
TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC


 


2441
mir-095-prec-4
0.28
CGTTACATTCAACGGGTATTTATTGAGCACCCACTCTGTG


 


2386
mir-025-prec
0.35
ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG


 


3304
mir-202-prec
0.34
GATCTGGCCTAAAGAGGTATAGGGCATGGGAAGATGGAGC


 


2283
mir-007-1-prec
0.26
TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG


 


2390
mir-093-prec-7.1 = 093-1
0.44
CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT


 


2391
mir-106-prec-X
0.31
CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT


 


2300
mir-142-prec
0.25
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2301
mir-223-prec
0.25
GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT


 


2393
mir-018-prec
0.26
TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC
















TABLE 134







Floxuridine microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













2290
HUMTRF
0.27
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2728
HUMTRN
0.27
CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT


 


2296
mir-124a-1-prec1
0.31
ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG


 


2448
mir-150-prec
0.33
CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG


 


2703
Hcd923 left
0.26
TGGGAACCTTGTTAAAATGCAGATTCTGATTCTCAGGTCT


 


3316
HPR181 left
0.28
GAAGAAACATCTCAAATCATGCTGACAGCATTTTCACTAT


 


3317
Hcd569 right
0.26
TTATTGCTTGAATGAGTTTCAGGGTATTGGCCTTCATAAA


 


2558
mir-199a-2-prec
0.25
TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT


 


2308
Hcd754 left
0.28
TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC


 


2410
mir-4323p
0.3
CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG
















TABLE 135







Irinotecan microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence





2290
HUMTRF
0.27
GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA


 


2389
mir-380-5p
0.27
AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT


 


2299
mir-342No1
0.25
GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA


 


2300
mir-142-prec
0.35
CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG


 


2405
Hcd200 right
0.25
CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT
















TABLE 136







Satraplatin microRNA biomarkers.










SEQ





ID





NO
Medianprobe
Corr
Sequence













3326
Hcd289 left
0.31
TTCCTCTCAGAGCATGTTGTCATAGAAGTAAATGAAAAGG


 


3327
Hcd939 right
0.25
CTCTCCTGCACATAATGAGGTCTGATTTACTGTGATCATT


 


3328
Hcd330 right
0.28
ATTAATGGTAATTATGTGCGTAAATCCCCATGCTCTCAAT


 


3329
HPR76 right
0.25
GAGCCGTTTAAATTTAGCGCTTTGGGCTGCCTGGAGCGAG


 


3330
Hcd111 left
0.29
GCAGGGGATTTGAGGGGTGGTTGTGTGATTTGTACAGCTG


 


3331
Hcd976 right
0.36
CTTCTCAGAGTTGGAGATGAAAGAAAGAGAAGGTGGCCAC


 


3332
mir-15aNo1
0.29
CCTTGGAGTAAAGTAGCAGCACATAATGGTTTGTGGATTT


 


3333
mir-001b-1-prec1
0.26
AATGCTATGGAATGTAAAGAAGTATGTATTTTTGGTAGGC


 


2292
mir-450-1
0.36
AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC


 


3335
mir-200bNo2
0.3
CCAGCTCGGGCAGCCGTGGCCATCTTACTGGGCAGCATTG


 


3336
Hcd578 right
0.3
AATGATTGTAGAGGGGCGGGGCATGAAGAGTGCCGTTCTG


 


2578
mir-200a-prec
0.28
GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG

## Claims

1. A method of determining sensitivity of a cancer patient to a treatment for cancer comprising contacting a sample comprising one or more nucleic acid molecules from said patient to a device comprising single-stranded oligonucleotides, wherein at least one of said oligonucleotides comprises a sequence that is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of a first microRNA selected from mir-142-prec or a product of a first gene selected from ZNFN1A1, and measuring hybridization between said nucleic acid molecules from said patient and said single-stranded oligonucleotides of said device to determine a level of expression of at least said first microRNA or said first gene in a cell of said patient, wherein an increase or decrease in the level of expression of said first microRNA or said first gene in said cell of said patient, relative to the level of expression of said first microRNA or said first gene in a control cell sensitive to said treatment, indicates said cell is sensitive to said treatment.

2. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPS4X, S100A4, NDUFS6, C14orf139, SLC25A5, RPL10, RPL12, EIF5A, RPL36A, BLMH, CTBP1, TBCA, MDH2, and DX59879E, and, optionally, a third gene selected from at least one of UBB, B2M, MAN1A1, and SUI1, or
ii) a second additional microRNA selected from at least one of Hcd892, Hcd678, hsa-mir-007-1-prec, MPR243, Hcd654, hsa-mir-487, Hcd794, Hcd739, and Hcd562,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Vincristine.

3. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of C1QR1, SLA, PTPN7, ZNFNIA1, CENTB1, IFI16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRF1, ARHGAP15, TM6SF1, and TCF4, and, optionally, a third gene selected from at least one of HCLS1, CD53, PTPRCAP, and PTPRC, or
ii) a second microRNA selected from at least one of HUMTRF, HPR187, hsa-mir-450-1, hsa-mir-155-prec, hsa-mir-515-15p, hsa-mir-181b-prec, hsa-mir-124a-1-prec1, hsa-mir-450-2, Hcd923, hsa-mir-342, hsa-mir-142-prec, hsa-mir-223-prec, Hcd754, and Hcd213_HPR182,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Cisplatin.

4. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of SRM, SCARB1, SIAT1, CUGBP2, ICAM1, WASPIP, ITM2A, PALM2-AKAP2, PTPNS1, MPP1, LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCP1, IFI16, MCAM, MEF2C, SLC1A4, FYN, C1orf38, CHS1, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-1, FLJ10539, FLJ35036, DOCK10, TRPV2, IFRG28, LEF1, and ADAMTS1, and, optionally, a third gene selected from at least one of MSN, SPARC, VIM, GAS7, ANPEP, EMP3, BTN3A2, FN1, and CAPN3, or
ii) a second microRNA selected from at least one of MPR121, HUMTRS, hsa-mir-213-prec, hsa-mir-155-prec, hsa-mir-147-prec, hsa-mir-100, hsa-mir-138-1-prec, hsa-mir-140, hsa-mir-146-prec, hsa-mir-509, hsa-mir-146b, Hcd514, Hcd397, Hcd731, hsa-mir-034-prec, and hsa-mir-100-1/2-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Azaguanine.

5. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, INSIG1, PRG1, MUF1, SLA, SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFN1A1, CENTB1, NAP1L1, HLA-DRA, IFI16, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2DIA, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRF1, ARHGAP15, NOTCH1, and UBASH3A, and, optionally, a third gene selected from at least one of LAPTM5, HCLS1, CD53, GMFG, PTPRCAP, PTPRC, CORO1A, and ITK, or
ii) a second microRNA selected from at least one of Hcd415, Hcd768, HUMTRF, Hcd866, Hcd145, HUMTRAB, Hcd913, HPR163, Hcd697, Hcd755, Hcd716, MPR207, HSTRNL, HPR206, MPR243, Hcd654, MPR130, Hcd782, Hcd794, Hcd739, hsa-mir-142-prec, HSHELA01, HUMTRVIA, and Hcd754,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Etoposide.

6. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, ALDOC, SLA, SSBP2, IL2RG, CXorf9, RHOH, ZNFN1A1, CENTB1, CD1C, MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CD1A, LAIR1, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, A1F1, ARHGAP15, NARF, and PACAP, and, optionally, a third gene selected from at least one the group consisting of LAPTM5, HCLS1, CD53, GMFG, PTPRCAP, TCF7, CD1B, PTPRC, CORO1A, HEM1, and ITK, or
ii) a second microRNA selected from at least one of Hcd768, hsa-mir-483, Hcd145, hsa-mir-197-prec, hsa-mir-212-prec, HPR163, Hcd654, hsa-mir-342, Hcd794, hsa-mir-142-prec, and Hcd754,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Adriamycin.

7. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPL12, RPLP2, MYB, ZNFN1A1, SCAP1, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ12270, and HNRPD, and, optionally, a third gene selected from at least one of RPL32, FBL, and PTPRC, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-096-prec-7, Hcd605, hsa-mir-007-2-prec, hsa-mir-019b-2-prec, MPR216, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, HSTRNL, hsa-mir-025-prec, hsa-mir-007-1-prec, hsa-mir-019a-prec, hsa-mir-380-5p, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Aclarubicin.

8. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PGAM1, DPYSL3, INSIG1, GJA1, BNIP3, PRG1, G6PD, PLOD2, LOXL2, SSBP2, C1orf29, TOX, STC1, TNFRSF1A, NCOR2, NAP1L1, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AF1Q, MAP1B, TRIM22, CD3D, BCAT1, IFI44, CUTC, NAP1L2, NME7, FLJ21159, and COL5A2, and, optionally, a third gene selected from at least one of BASP1, COL6A2, PTPRC, PRKCA, CCL2, and RAB31, or
ii) a second microRNA selected from at least one of Hcd768, HUMTRF, hsa-mir-213-prec, hsa-mir-181b-prec, MPR244, hsa-mir-409-3p, HSTRNL, hsa-mir-382, hsa-mir-342, hsa-mir-142-prec, and Hcd200,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Mitoxantrone.

9. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of STC1, GPR65, DOCK10, COL5A2, FAM46A, and LOC54103, or
ii) a second microRNA selected from at least one of HUMTRF, Hcd148_HPR225 left, Hcd938, MPR174, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Mitomycin.

10. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPL10, RPS4X, NUDC, DKC1, DKFZP564C186, PRP19, RAB9P40, HSA9761, GMDS, CEP1, IL13RA2, MAGEB2, HMGN2, ALMS1, GPR65, FLJ10774, NOL8, DAZAP1, SLC25A15, PAF53, DXS9879E, PITPNC1, SPANXC, and KIAA1393, and, optionally, RALY, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-096-prec-7, hsa-mir-101-prec-9, hsa-mir-20b, hsa-mir-019b-2-prec, hsa-mir-032-prec, MPR156, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, hsa-mir-025-prec, hsa-mir-007-1-prec, hsa-mir-361, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-098-prec-X, hsa-mir-142-prec, HPR169, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Paclitaxel.

11. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PFN1, PGAM1, K-ALPHA-1, CSDA, UCHL1, PWP1, PALM2-AKAP2, TNFRSF1A, ATP5G2, AF1Q, NME4, and FHOD1, or
ii) a second microRNA selected from at least one of hsa-mir-123-prec, Hcd257, hsa-mir-155-prec, ath-MIR180a, Hcd448, HSTRNL, MPR174, Hcd200, hsa-mir-4323p, and HPR244,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Gemcitabine.

12. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ANP32B, GTF3A, RRM2, TRIM14, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFC1, EPB41L4B, and CALML4, or
ii) a second microRNA selected from at least one of hsa-mir-096-prec-7, hsa-mir-095-prec-4, HSTRNL, and hsa-mir-007-1-prec,
wherein an increase or decrease in said level of expression of said second gene or said second additional microRNA indicates that said cell is sensitive to Taxotere.

13. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASP1, PTPN7, CXorf9, RHOH, GIT2, ZNFN1A1, CEP1, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIR1, SH2D1A, SEPT6, HA-1, ERCC2, CD3D, LST1, AIF1, ADA, DATF1, ARHGAP15, PLACE, CECR1, LOC81558, and EHD2, and, optionally, a third gene selected from at least one of LAPTM5, ITGB2, ANPEP, CD53, CD37, ADORA2A, GNA15, PTPRC, CORO1A, HEM1, FLII, and CREB3L1, or
ii) a second microRNA selected from at least one of MPR141, hsa-mir-424, Hcd690, Hcd783, hsa-mir-150-prec, Hcd266, hsa-mir-503, hsa-mir-128b-prec, Hcd397, and hsa-mir-484,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Dexamethasone.

14. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
I) a second gene selected from at least one of ITM2A, RHOH, PRIM1, CENTB1, NAP1L1, ATP5G2, GATA3, PRKCQ, SH2DIA, SEPT6, NME4, CD3D, CD1E, ADA, and FHOD1, and, optionally, a third gene selected from at least one of GNA15, PTPRC, and RPL13, or
ii) a second microRNA selected from at least one of HUMTRF, hsa-mir-155-prec, hsa-mir-515-15p, Hcd938, Hcd642, Hcd120, hsa-mir-380-5p, hsa-mir-342, hsa-mir-142-prec, hsa-mir-223-prec, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Ara-C.

15. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, ARHGDIB, VWF, ITM2A, LGALS9, INPP5D, SATB1, TFDP2, SLA, IL2RG, MFNG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFN1A1, CENTB1, LCP2, SPI1, GZMA, CEP1, CD8A, SCAP1, CD2, CD1C, TNFRSF7, VAV1, MAP4K1, CCR7, C6orf32, ALOX15B, BRDT, CD3G, LTB, ATP2A3, NVL, RASGRP2, LCP1, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2DIA, CHRNA3, CD1A, LST1, LAIR1, CACNA1G, TRB@, SEPT6, HA-1, DOCK2, CD3D, TRD@, T3JAM, FNBP1, CD6, AIF1, FOLH1, CD1E, LY9, ADA, CDKL5, TRIM, EVL, DATF1, RGC32, PRKCH, ARHGAP15, NOTCH1, BIN2, SEMA4G, DPEP2, CECR1, BCL11B, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEF1, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, and, optionally, a third gene selected from at least one of SRRM1, LAPTM5, ITGB2, CD53, CD37, GMFG, PTPRCAP, GNA15, BLM, PTPRC, CORO1A, PRKCB1, HEM1, and UGT2B17, or
ii) a second microRNA selected from at least one of Hcd544, hsa-mir-181c-prec, Hcd517, MPR151, hsa-mir-213-prec, hsa-mir-181b-prec, hsa-mir-150-prec, hsa-mir-153-1-prec1, hsa-mir-128b-prec, Hcd812, hsa-mir-195-prec, hsa-mir-342, hsa-mir-370, hsa-mir-142-prec, hsa-mir-223-prec, and hsa-mir-484,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Methylprednisolone.

16. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PRPF8, RPL18, GOT2, RPL13A, RPS15, RPLP2, CSDA, KHDRBS1, SNRPA, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIM1, PFDN5, OXA1L, H3F3A, ATIC, CIAPIN1, RPS2, PCCB, SHMT2, RPLP0, HNRPA1, STOML2, SKB1, GLTSCR2, CCNB1IP1, MRPS2, FLJ20859, and FLJ12270, and, optionally, a third gene selected from at least one of RNPS1, RPL32, EEF1G, PTMA, RPL13, FBL, RBMX, and RPS9, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-096-prec-7, hsa-mir-123-prec, Hcd250, hsa-mir-518e, HPR232, Hcd263, hsa-mir-516-33p, Hcd605, Hcd373, MPR254, MPR215, HUMTRF, hsa-mir-106a, hsa-mir-20b, Hcd361, Hcd412, Hcd781, hsa-mir-019b-2-prec, HPR214, Hcd807, Hcd817, Hcd788, Hcd970, Hcd148_HPR225 left, Hcd102, Hcd246, HPR199, HPR233, Hcd383, MPR224, HPR172, MPR216, hsa-mir-321, Hcd586, Hcd587, Hcd249, Hcd279, HPR159, Hcd689, Hcd691, hsa-mir-019b-1-prec, Hcd413, Hcd581, Hcd536_HPR104, Hcd230, HPR154, Hcd270, Hcd649, Hcd889, Hcd938, HPR266, hsa-mir-025-prec, Hcd355_HPR190, MPR162, Hcd923, MPR237, MPR174, hsa-mir-019a-prec, hsa_mir—490_Hcd20, hsa-mir-380-5p, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, Hcd627, hsa-mir-142-prec, HPR169, hsa-mir-001b-2-prec, hsa-mir-018-prec, hsa-mir-020-prec, Hcd404, hsa-mir-384, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Methotrexate.

17. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PFN1, HK1, MCL1, ZYX, RAP1B, GNB2, EPAS1, PGAM1, CKAP4, DUSP1, MYL9, K-ALPHA-1, LGALS1, CSDA, IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMB1, FHL1, INSIG1, TIMP1, GJA1, PSME2, PRG1, EXT1, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAP1, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STC1, PTGER4, IL6, SMAD3, WNT5A, BDNF, TNFRSFIA, FLNC, OKFZP564K0822, FLOT1, PTRF, HLA-B, MGC4083, TNFRSF108, PLAGL1, PNMA2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCN1, ERP70, AF1Q, HIC, COL6A1, IFITM3, MAPIB, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MT1E, CDC10, DOCK2, TRIM22, RIS1, BCAT1, PRF1, DBN1, MT1K, TMSB10, FLJ10350, C1orf24, NME7, TMEM22, TPK1, COL5A2, ELK3, CYLD, ADAMTS1, EHD2, and ACTB, and, optionally, a third gene selected from at least one of MSN, ACTR2, AKR1B1, VIM, ITGA3, OPTN, M6PRBP1, COL1A1, BASP1, ANPEP, TGFB1, NFIL3, NK4, CSPG2, PLAU, COL6A2, UBC, FGFR1, BAX, COL4A2, and RAB31, or
ii) a second microRNA selected from at least one of hsa-mir-376a, hsa-mir-155-prec, hsa-mir-409-3p, hsa-mir-495, Hcd498, hsa-mir-199a-2-prec, hsa-mir-382, HPR271, hsa-mir-145-prec, and hsa-mir-199a-1-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Bleomycin.

18. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of SSRP1, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINA1, SSSCA1, EZH2, MYB, PRIM1, H2AFX, HMGA1, HMMR, TK2, WHSC1, DIAPH1, LAMB3, DPAGT1, UCK2, SERPINB1, MDN1, BRRN1, GOS2, RAC2, MGC21654, GTSE1, TACC3, PLEK2, PLACE, HNRPD, and PNAS-4, and, optionally, PTMA, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-101-prec-9, hsa-mir-144-prec, hsa-mir-519a-1, hsa-mir-519b, hsa-mir-015b-prec, hsa-mir-106a, hsa-mir-16-1, hsa-mir-181d, hsa-mir-017-prec, hsa-mir-019b-2-prec, hsa-mir-192, hsa-mir-213-prec, hsa-mir-215-prec, hsa-mir-107, hsa-mir-200b, hsa-mir-103-prec-5=103-1, hsa-mir-519a-1/526c, MPR216, hsa-mir-019b-1-prec, hsa-mir-107-prec-10, hsa-mir-135-2-prec, hsa-mir-103-2-prec, hsa-mir-519a-2, hsa-mir-025-prec, hsa-mir-16-2, MPR95, hsa-mir-016b-chr3, Hcd948, hsa-mir-195-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, hsa-mir-519c/526c, hsa-mir-200a-prec, hsa-mir-016a-chr13, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Methyl-GAG.

19. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, DOCK10, LEPRE1, COL5A2, and ADAMTS1, and, optionally, a third gene selected from at least one of MSN, VIM, CSPG2, and FGFR1, or
ii) a second microRNA selected from at least one of Hcd829, HUMTRF, HPR187, Hcd210_, HPR205, hsa-mir-379, hsa-mir-213-prec, hsa-mir-4325p, hsa-mir-450-1, hsa-mir-155-prec, Hcd28_HPR39 right, MPR244, hsa-mir-409-3p, hsa-mir-124a-1-prec1, hsa-mir-154-prec1, hsa-mir-495, hsa-mir-515-23p, Hcd438 right, Hcd770, hsa-mir-382, hsa-mir-223-prec, Hcd754, and Hcd213_HPR182,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Carboplatin.

20. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPL18, RPL10A, ANAPC5, EEFIB2, RPL13A, RPS15, AKAP1, NDUFAB1, APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPL17, RPS2, PCCB, TOMM20, SHMT2, RPLP0, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, and, optionally, a third gene selected from at least one of RNPS1, RPL13, RPS6, and RPL3, or
ii) a second microRNA selected from at least one of hsa-mir-096-prec-7, hsa-mir-429, Hcd693, HPR214, Hcd586, Hcd249, Hcd689, hsa-mir-194-2, Hcd581, Hcd270, hsa-mir-025-prec, Hcd340, hsa-mir-007-1-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, Hcd794, hsa-mir-020-prec, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to 5-FU (5-Fluorouracil).

21. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of KIFC1, VLDLR, RUNX1, PAFAH1B3, H1FX, RNF144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6VIB2, CD1D, ADA, CD99, CD2, CNP, ERG, CD3E, CD1A, PSMC3, RPS4Y1, AKT1, TAL1, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, AKR1C1, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, H1F0, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, SOX4, MAL, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, PLEKHC1, CD47, POLR2F, RHOH, and ADD1, and, optionally, a third gene selected from at least one of ITK, RALY, PSMC5, MYL6, CD1B, STMN1, GNA15, MDK, CAPG, ACTN1, CTNNA1, FARSLA, E2F4, CPSF1, SEPW1, TFRC, ABL1, TCF7, FGFR1, NUCB2, SMA3, FAT, VIM, and ATP2A3,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene indicates that said cell is sensitive to Rituximab.

22. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of TRA1, ACTN4, CALM1, CD63, FKBP1A, CALU, IQGAP1, MGC8721, STAT1, TACC1, TM4SF8, CD59, CKAP4, DUSP1, RCN1, MGC8902, LGALS1, BHLHB2, RRBP1, PRNP, IER3, MARCKS, LUM, FERIL3, SLC20A1, HEXB, EXT1, TJP1, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAMS, DNAJC7, PLSCR1, PRSS23, PLOD2, NPC1, TOB1, GFPT1, IL8, PYGL, LOXL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCL1, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDH11, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSF10B, KIAA1128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A11, MPZL1, SSA2, NQO1, ASPH, ASAH1, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAP1B, MAPK1, MYO1B, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCB1, KIAA0802, RAB3GAP, SERPINB1, TIMMI7A, SOD2, HLA-A, NOMO2, L0055831, PHLDAI, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCK10, LRP12, TXNDC5, CDCl4B, HRMT1L1, CORO1C, DNAJC10, TNP01, LONP, AMIGO2, DNAPTP6, and ADAMTS1, and, optionally, a third gene selected from at least one of WARS, CD81, CTSB, PKM2, PPP2CB, CNN3, ANXA2, JAK1, EIF4G3, COL1A1, DYRK2, NFIL3, ACTN1, CAPN2, BTN3A2, IGFBP3, FN1, COL4A2, and KPNB1, or
ii) a second microRNA selected from at least one of hsa-mir-136-prec, Hcd570, Hcd873, Hcd282PO, Hcd799, Hcd829, Hcd210_HPR205, hsa-mir-219-prec, hsa-mir-202, hsa-mir-429, Hcd693, hsa-mir-022-prec, MPR88, hsa-mir-198-prec, hsa-mir-199b-prec, Hcd145, hsa-mir-124a-2-prec, hsa-mir-138-2-prec, Hcd960, Hcd869, Hcd384, hsa-mir-027b-prec, Hcd444, hsa-mir-194-2, hsa-mir-197-prec, Hcd913, HPR163, hsa-mir-138-1-prec, hsa-mir-010a-prec, hsa-mir-023b-prec, hsa-mir-193b, Hcd654, Hcd542, hsa-mir-199a-2-prec, hsa-mir-214-prec, Hcd608, Hcd684, hsa-mir-145-prec, hsa-mir-023a-prec, hsa-mir-024-2-prec, and hsa-mir-199a-1-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to radiation therapy.

23. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of FAU, NOL5A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVER1, LRMP, ICAM2, RIMS3, FMNL1, MYB, PTPN7, LCK, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, DBT, CEP1, IL6R, VAV1, MAP4K1, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCP1, CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CDlA, GATA2, P2RX5, LAIR1, C1orf38, SH2DIA, TRB@, SEPT6, HA-1, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPA1, A1F1, CYFIP2, GLTSCR2, C11orf2, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP, and MGC2744, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-123-prec, hsa-mir-106a, hsa-mir-20b, hsa-mir-017-prec, hsa-mir-019b-2-prec, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, hsa-mir-122a-prec, Hcd783, MPR216, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, hsa-mir-128b-prec, hsa-mir-025-prec, Hcd511, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, HPR169, hsa-mir-223-prec, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to PXD101 (belinostat).

24. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSE1, MYB, PTPN7, CXorf9, RHOH, ZNFNIA1, CENTB1, LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCP1, SELPLG, CD3Z, PRKCQ, GZMB, SCN3A, LAIR1, SH2D1A, SEPT6, CG018, CD3D, C18orf10, PRF1, AIF1, MCM5, LPXN, C22orf18, ARHGAP15, and LEF1, or
ii) a second microRNA selected from at least one of hsa-mir-096-prec-7, Hcd605, hsa-mir-20b, hsa-miR-373*, HUMTRAB, hsa-mir-019b-1-prec, HPR163, hsa-mir-371, hsa-mir-025-prec, hsa-mir-18b, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to 5-Aza-2′-deoxycytidine (Decitabine).

25. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of SLC9A3R1, RPS19, ITM2A, SSBP2, CXorf9, RHOH, ZNFN1A1, FXYD2, CCR9, NAP1L1, CXCR4, SH2D1A, CD1A, TRB@, SEPT6, RPS2, DOCK2, CD3D, CD6, ZAP70, A1F1, CD1E, CYFIP2, ADA, TRIM, GLTSCR2, FLJ10858, BCL11B, GIMAP6, STAG3, UBASH3A, and, optionally, a third gene selected from at least one of MRPS24, TRIM22, TRIM41, LAT, CD1C, MRPS22, ADAM11, RPL13, RPS27, RPL13, RPS25, RPL18A, CORO1A, PTPRCAP, GMFG, ITK, CD1B, GMFG, PTPRCAP, CORO1A, ITGB2, HCLS1, and ATP2A3, or
ii) a second microRNA selected from at least one of HUMTRF, hsa-mir-483, MPR74, hsa-mir-122a-prec, ath-MIR180a, hsa-mir-128b-prec, Hcd923, hsa-mir-106-prec-X, hsa-mir-342, hsa-mir-142-prec, HPR169, hsa-mir-223-prec, Hcd754, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Idarubicin.

26. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, HLA-DPB1, ARHGDIB, IFITM1, UBE2L6, ITM2A, SERPINA1, STAT5A, INPP5D, DGKA, SATB1, SEMA4D, TFDP2, SLA, IL2RG, CD48, MFNG, ALOX5AP, GPSM3, PSMB9, KIAA0711, SELL, ADA, EDG1, RIMS3, FMNL1, MYB, PTPN7, LCK, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, FXYD2, CD1D, BATF, STAT4, VAV1, MAP4K1, CCR7, PDE4C, CD3G, CCR9, SP110, LCP1, IFI16, CXCR4, ARHGEF6, GATA3, SELPLG, SEC31L2, CD3Z, PRKCQ, SH2D1A, GZMB, CD1A, SCN3A, LAIR1, FYB, TRB@, SEPT6, HA-1, DOCK2, CG018, CD3D, T3JAM, FNBP1, CD6, ZAP70, LST1, GPR65, PRF1, A1F1, FLJ20331, RAG2, WDR45, CD1E, CYFIP2, TARP, TRIM, RPL10L, GLTSCR2, GIMAP5, ARHGAP15, NOTCH1, BIN2, C13 orf18, CECR1, BCL11B, GIMAP6, STAG3, TM6SF1, HSD17B7, UBASH3A, MGC5566, FLJ22457, TPK1, PHF11, and DKFZP434B0335, and, optionally, a third gene selected from at least one of FLJ10534, PTPRC, TRIM22, C18orf1, EVL, TRIM41, PSME2, LAT, CD1C, MYBBP1A, ICAM3, ADAM11, CD53, FARSLA, RPL13, RAC2, RPL13, GNA15, PGF, LAPTM5, RPL18A, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GNA15, ITK, CD1B, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, GNA15, TCF7, ITGB2, PTPRC, HCLS1, ATP2A3, MYBL1, and FARSLA, or
ii) a second microRNA selected from at least one of hsa-mir-124a-3-prec, hsa-mir-181a-prec, Hcd773, Hcd683, Hcd796, HUMTRF, HUMTRS, hsa-mir-181b-2, Hcd294, hsa-mir-20b, hsa-mir-181d, hsa-mir-213-prec, Hcd148_HPR225 left, hsa-mir-515-15p, hsa-mir-181b-prec, Hcd783, HUMTRAB, HUMTRN, hsa-mir-181b-1, hsa-mir-124a-1-prec1, hsa-mir-367, hsa-mir-128b-prec, Hcd438 right, hsa-mir-025-prec, hsa-mir-216-prec, Hcd731, hsa-mir-093-prec-7A=093-1, hsa-mir-106-prec-X, hsa-mir-342, hsa-mir-142-prec, HSHELA01, HUMTRV1A, hsa-mir-223-prec, Hcd754, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second cone and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Melphalan.

27. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of MCL1, DDX23, JUNB, ZFP36, IFITM1, CKSIB, SERPINA1, IL4R, CLDN3, ARL4A, HMMR, FLJ12671, ANKHDI, KIF2C, RPA3, MCCC2, CDH17, LSM5, PRF1, ROD1, FLJ12666, SUV420H1, MUC13, C13orf18, and CDCA8, and, optionally, a third gene selected from at least one of ETS2, ARID1A, ID1, DDC, NID2, CCT3, ID2, NFIL3, and AREG, or
ii) a second microRNA selected from at least one of Hcd829, hsa-mir-197-prec, HPR163, and hsa-mir-150-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to IL4-PE38 fusion protein.

28. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of MCL1, DDX23, JUNB, ZFP36, IFITM1, CKS1B, SERPINA1, IL13R, CLDN3, ARL4A, HMMR, FLJ12671, ANKHD1, KIF2C, RPA3, MCCC2, CDH17, LSM5, PRF1, ROD1, FLJ12666, SUV420H1, MUC13, C13orf18, and CDCA8, and, optionally, a third gene selected from at least one of ETS2, ARID1A, 1D1, DDC, NID2, CCT3, ID2, NFIL3, and AREG, or
ii) a second microRNA selected from at least one of Hcd829, hsa-mir-197-prec, HPR163, and hsa-mir-150-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to IL13-PE38QQR fusion protein (cintredekin besudotox).

29. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of STOM, TNFAIP3, ASNS, GARS, CXCR4, EGLN3, LBH, and GDF15, and, optionally, at least one a third gene selected from at least one of STOML1 and KIAA0746, or
ii) a second microRNA selected from at least one of hsa-mir-034prec, Hcd255, Hcd712, Hcd965, Hcd891, Hcd210_HPR205, hsa-mir-429, Hcd753, Hcd693, MPR203, Hcd704, Hcd863PO, hsa-mir-122a-prec, Hcd760, Hcd338, HPR213, Hcd852, Hcd366, MPR103, Hcd669, and hsa-mir-188-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Valproic acid (VPA).

30. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PPIB, ZFP36L2, 1F130, USP7, SRM, SH3BP5, ALDOC, FADS2, GUSB, PSCD1, IQGAP2, STS, MFNG, FLI1, PIM2, INPP4A, LRMP, ICAM2, EVI2A, MAL, BTN3A3, PTPN7, IL10RA, SPI1, TRAF1, ITGB7, ARHGAP6, MAP4K1, CD28, PTP4A3, LTB, C1orf38, WBSCR22, CD8B1, LCP1, FLJ13052, MEF2C, PSCDBP, IL16, SELPLG, MAGEA9, LAIR1, TNFRSF25, EVI2B, IGJ, PDCD4, RASA4, HA-1, PLCL2, RNASE6, WBSCR20C, NUP210, RPL10L, C11orf2, CABC1, ARHGEF3, TAPBPL, CHST12, FKBP11, FLJ35036, MYLIP, TXNDC5, PACAP, TOSO, PNAS-4, IL21R, and TCF4, and, optionally, a third gene selected from at least one of CLTB, BTN3A2, BCL2, SETBP1, ICAM3, BCL2, BCL2, BCL2, CD53, CCND2, CLTB, CLTB, BCL2L11, BTN3A2, CD37, MYCL2, CTSS, LAPTM5, CD53, CORO1A, HEM1, CD53, CORO1A, HEM1, HCLS1, BCL2L11, MYCL1, MYC, and MAN1A1, or
ii) a second microRNA selected from at least one of Hcd257, hsa-mir-148-prec, Hcd512, HPR227, Hcd421, MPR203, hsa-mir-017-prec, hsa-mir-219-2, hsa-mir-328, Hcd783, Hcd181, HPR213, hsa-mir-191-prec, hsa-mir-375, hsa-mir-212-prec, Hcd913, Hcd716, MPR207, HPR206, hsa-mir-016b-chr3, Hcd654, hsa-mir-195-prec, Hcd425, hsa-mir-148a, hsa-mir-142-prec, and hsa-mir-016a-chr13,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to All-trans retinoic acid (ATRA).

31. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of C6orf29, TRIM31, CD69, LRRN3, GPR35, and CDW52, or
ii) a second microRNA selected from at least one of Hcd99, hsa-mir-520c/526a, hsa-mir-191-prec, hsa-mir-205-prec, hsa-mir-375, hsa-mir-423, hsa-mir-449, and hsa-mir-196-2-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Cytoxan.

32. The method of claim 1, wherein said measuring further comprises comprising determining a level of expression of:
i) at least one a second gene selected from at least one the group consisting of K-ALPHA-1, CSDA, UCHL1, NAP1L1, ATP5G2, HDGFRP3, and IFI44, or
ii) a second microRNA selected from at least one of HUMTRF, MPR74, hsa-mir-213-prec, hsa-mir-155-prec, hsa-mir-181b-prec, hsa-mir-342, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene or said second additional microRNA indicates that said cell is sensitive to Topotecan (Hycamtin).

33. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of NOL5A, STOM, SIAT1, CUGBP2, GUSB, ITM2A, JARID2, RUNX3, ICAM2, PTPN7, VAV1, PTP4A3, MCAM, MEF2C, IDH3B, RFP, SEPT6, SLC43A3, WBSCR20C, SHMT2, GLTSCR2, CABC1, FLJ20859, FLJ20010, MGC10993, and FKBP11, and, optionally, a third gene selected from at least one of STOML1, E1F4A1, PDE3B, BCL11A, INPP4B, HLA-DMA, TRFP, EIF4A1, GAS7, MYCL2, HCLS1, MYCL1, and MYC, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-123-prec, hsa-mir-514-1, hsa-mir-101-prec-9, hsa-mir-148-prec, hsa-mir-106a, hsa-mir-20b, Hcd781, hsa-mir-017-prec, hsa-mir-019b-2-prec, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, hsa-mir-107, hsa-mir-103-prec-5=103-1, MPR216, hsa-mir-29b-2=102prec7.1=7.2, hsa-mir-019b-1-prec, hsa-mir-107-prec-10, hsa-mir-135-2-prec, Hcd581, hsa-mir-103-2-prec, Hcd230, hsa-mir-025-prec, hsa-mir-208-prec, hsa-mir-18b, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, HPR169, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza).

34. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ZFP36L2, TRIB2, LCP2, C6orf32, IL16, CACNA1G, SPDEF, HAB1, TOSO, and ARHGAP25, and, optionally, a third gene selected from at least one of SGCD and CAPN3, or
ii) a second microRNA selected from at least one of Hcd415, hsa-mir-147-prec, hsa-mir-033b-prec, Hcd778, hsa-mir-127-prec, hsa-mir-324, Hcd794, and Hcd634,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Depsipeptide (FR901228).

35. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PLEKHB2, ARPC1B, MX1, CUGBP2, IFI16, TNFRSF14, SP110, ELF1, LPXN, IFRG28, LEF1, and PYCARD, and, optionally, HMX1, or
ii) a second microRNA selected from at least one of MPR121, Hcd115, Hcd693, Hcd704, HPR100, Hcd760, hsa-mir-147-prec, hsa-mir-033b-prec, hsa-mir-146-prec, Hcd142, hsa-mir-501, Hcd716, MPR207, Hcd777, hsa-mir-204-prec, hsa-mir-146b, Hcd511, Hcd397, MPR130, Hcd782, hsa-mir-324, Hcd794, and Hcd739,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Bortezomib.

36. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of SSRP1, ALDOC, C1QR1, TTF1, PRIM1, USP34, TK2, GOLGIN-67, NPD014, KIAA0220, SLC43A3, WBSCR20C, ICAM2, TEX10, CHD7, SAMSN1, and TPRT, and, optionally, a third gene selected from at least one of PTPRC, CD53, RNPS1, H3F3A, NUDC, SMARCA4, RPL32, PTMA, CD53, PTPRCAP, PTPRC, RPL32, PTPRCAP, PTPRC, CD53, PTPRC, HCLS1, and SLC19A1, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-096-prec-7, hsa-mir-123-prec, MPR249, HPR232, hsa-mir-101-prec-9, hsa-mir-106a, hsa-mir-20b, Hcd861, hsa-mir-017-prec, hsa-mir-019b-2-prec, hsa-mir-033-prec, Hcd102, MPR216, Hcd975, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, Hcd581, Hcd536_HPR104, hsa-mir-128b-prec, HSTRNL, hsa-mir-025-prec, hsa-mir-18b, HPR262, Hcd923, Hcd434, Hcd658, HPR129, hsa-mir-380-5p, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, Hcd627, hsa-mir-142-prec, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Leukeran.

37. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of HLA-E, BAT3, ENO2, UBE2L6, CUGBP2, ITM2A, PALM2-AKAP2, JARID2, DGKA, SLC7A6, TFDP2, ADA, EDG1, ICAM2, PTPN7, CXorf9, RHOH, MX2, ZNFN1A1, COCH, LCP2, CLGN, BNC1, FLNC, HLA-DRB3, UCP2, HLA-DRB1, GATA3, PRKCQ, SH2DIA, NFATC3, TRB@, FNBP1, SEPT6, NME4, DKFZP434C171, ZC3HAV1, SLC43A3, CD3D, AIF1, SPTAN1, CD1E, TRIM, DATF1, FHOD1, ARHGAP15, STAG3, SAP130, and CYLD, and, optionally, a third gene selected from at least one of PTPRC, MX2004PA11424, TRIM22, TRIM41, CD1C, CHD8, ADAM11, ANPEP, RBMX2, RAC2, GNA15, LAPTM5, PTPRCAP, PTPRC, GNA15, CD1B, PTPRCAP, PTPRC, GNA15, PTPRC, and ATP2A3, or
ii) a second microRNA selected from at least one of Hcd773, Hcd248, hsa-mir-181d, MPR74, hsa-mir-213-prec, hsa-mir-155-prec, MPR197, hsa-mir-181b-prec, hsa-mir-29b-2=102prec7.1=7.2, hsa-mir-029c-prec, Hcd318, hsa-mir-128b-prec, hsa-mir-130a-prec, hsa-mir-140, hsa-mir-16-2, hsa-mir-526a-2, hsa-mir-016b-chr3, hsa-mir-195-prec, hsa-mir-216-prec, hsa-mir-342, hsa-mir-29b-1, Hcd627, hsa-mir-102-prec-1, hsa-mir-142-prec, hsa-mir-223-prec, hsa-let-7f-2-prec2, and hsa-mir-016a-chr13,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Fludarabine.

38. The method of claim 1, wherein said measuring further comprises determining a level of expression of CD99 or at least a second microRNA selected from at least one of Hcd794 and Hcd754,
wherein an increase or decrease in said level of expression of said CD99 or said second microRNA indicates that said cell is sensitive to Vinblastine.

39. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPLP2, BTG1, CSDA, ARHGDIB, INSIG1, ALDOC, WASPIP, C1QR1, EDEM1, SLA, MFNG, GPSM3, ADA, LRMP, EVI2A, FMNL1, PTPN7, RHOH, ZNFN1A1, CENTB1, MAP4K1, CD28, SP110, NAP1L1, IFI16, ARHGEF6, SELPLG, CD3Z, SH2DIA, LAIR1, RAFTLIN, HA-1, DOCK2, CD3D, T3JAM, ZAP70, GPR65, CYFIP2, LPXN, RPL10L, GLTSCR2, ARHGAP15, BCL11B, TM6SF1, PACAP, and TCF4, and, optionally, a third gene selected from at least one of PTPRC, BCL2, LAT, ICAM3, BCL2, BCL2, BCL2, ADAM11, CD53, FARSLA, BCL2L11, RPL13, RAC2, RPL13, MYCL2, LAPTM5, RPL18A, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, PTPRC, HCLS1, BCL2L11, MYCL1, FARSLA, and MYC, or
ii) a second microRNA selected from at least one of hsa-mir-096-prec-7, hsa-mir-124a-3-prec, hsa-mir-101-prec-9, Hcd712, Hcd693, hsa-mir-219-2, Hcd145, hsa-mir-155-prec, HPR213, hsa-mir-212-prec, Hcd913, Hcd716, MPR207, Hcd559, Hcd654, Hcd739, and hsa-mir-142-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Busulfan.

40. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one ARHGDIB, ITM2A, SSBP2, PIM2, SELL, ICAM2, EVI2A, MAL, PTPN7, ZNFN1A1, LCP2, ARHGAP6, CD28, CD8B1, LCP1, NPD014, CD69, NFATC3, TRB@, IGJ, SLC43A3, DOCK2, FHOD1, and PACAP, and, optionally, a third gene selected from at least one of ICAM3, CD53, SMARCA4, CD37, LAPTM5, CD53, CORO1A, HEM1, GMFG, GMFG, CD53, CORO1A, HEM1, and HCLS1, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-123-prec, hsa-mir-101-prec-9, Hcd517, Hcd796, Hcd749, Hcd674, hsa-mir-019b-2-prec, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, hsa-mir-124a-2-prec, hsa-mir-143-prec, hsa-mir-516-43p, hsa-mir-216-prec, Hcd731, hsa-mir-106-prec-X, hsa-mir-142-prec, hsa-mir-223-prec, Hcd754, and hsa-mir-018-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Dacarbazine.

41. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPL18, RPL10A, RPS3A, EEF1B2, GOT2, RPL13A, RPS15, NOL5A, RPLP2, SLC9A3R1, EIF3S3, MTHFD2, IMPDH2, ALDOC, FABP5, ITM2A, PCK2, MFNG, GCH1, PIM2, ADA, ICAM2, TTF1, MYB, PTPN7, RHOH, ZNFN1A1, PRIM1, FH1T, ASS, SYK, OXA1L, LCP1, DDX18, NOLA2, KIAA0922, PRKCQ, NFATC3, ANAPC5, TRB@, CXCR4, FNBP4, SEPT6, RPS2, MDN1, PCCB, RASA4, WBSCR20C, SFRS7, WBSCR20A, NUP210, SHMT2, RPLP0, MAP4K1, HNRPA1, CYFIP2, RPL10L, GLTSCR2, MRPL16, MRPS2, FLJ12270, CDK5RAP3, ARHGAP15, CUTC, FKBP11, ADPGK, FLJ22457, PUS3, PACAP, and CALML4, and, optionally, a third gene selected from at least one of MRPS24, DUSP2, EIF4A1, BRD2, BCL11A, RASSF2, MRPL37, MRPL30, RASSF1, MYBBPIA, LASS2, MRPS22, ADAM11, CD53, RPS6 KB1, RNPS1, BRD2, EIF4A1, FBL, BRD2, RPL36A, RPL13, RPL38, H3F3A, KIAA0182, RPS27, RPS6, EEF1G, RPL13, MYCL2, FBLN1, RPS25, RPL32, PTMA, RPL18A, RPL3, CD53, CORO1A, HEM1, GMFG, RPL32, GMFG, CD53, CORO1A, HEM1, HCLS1, ATP2A3, RASSF7, MYCL1, MYBL1, MYC, RPS15A, RASSF2, and LASS6, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-148-prec, hsa-mir-20b, hsa-mir-007-2-prec, hsa-mir-017-prec, hsa-mir-019b-2-prec, Hcd760, Hcd783, MPR216, hsa-mir-375, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, hsa-mir-150-prec, hsa-mir-128b-prec, hsa-mir-499, hsa-mir-025-prec, hsa-mir-007-1-prec, hsa-mir-019a-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, HPR169, hsa-mir-018-prec, hsa-mir-020-prec, and hsa-mir-484,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Oxaliplatin.

42. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CSDA, INSIG1, UBE2L6, PRG1, ITM2A, DGKA, SLA, PCBP2, IL2RG, ALOX5AP, PSMB9, LRMP, ICAM2, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTBI, LCP2, STAT4, CCR7, CD3G, SP110, TNFAIP8, IFI16, CXCR4, ARHGEF6, SELPLG, CD3Z, PRKCQ, SH2DIA, CDIA, NFATC3, LAIR1, TRB@, SEPT6, RAFTLIN, DOCK2, CD3D, CD6, AIF1, CD1E, CYFIP2, TARP, ADA, ARHGAP15, GIMAP6, STAG3, FLJ22457, PACAP, and TCF4, and, optionally, a third gene selected from at least one of PTPRC, TRIM22, PSME2, LAT, CD1C, ICAM3, ADAM11, CD53, FARSLA, RPL13, RAC2, RPL13, NK4, LAPTM5, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, ITGB2, PTPRC, HCLS1, ATP2A3, and FARSLA, or
ii) a second microRNA selected from at least one of Hcd257, Hcd768, Hcd796, HUMTRF, HUMTRS, MPR74, hsa-mir-213-prec, hsa-mir-155-prec, Hcd763, hsa-mir-181b-prec, ath-MIR180a, hsa-mir-216-prec, hsa-mir-342, hsa-mir-142-prec, HSHELA01, HUMTRV1A, hsa-mir-223-prec, Hcd7.54, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Hydroxyurea.

43. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPL11, RPL17, ANAPC5, RPL13A, STOM, TUFM, SCARB1, FABP5, KIAA0711, IL6R, WBSCR22, UCK2, GZMB, C1 orf38, PCBP2, GPR65, GLTSCR2, and FKBP11, and, optionally, a third gene selected from at least one of STOML1, MRPL37, MRPL30, RPL36A, RPL38, HSPD1, MIF, RPL32, RPL3, and RPL32, or
ii) a second microRNA selected from at least one of Hcd257, Hcd946, Hcd503, hsa-mir-429, Hcd693, hsa-miR-373*, Hcd738, hsa-mir-328, Hcd783, Hcd181, Hcd631, Hcd279, hsa-mir-194-2, hsa-mir-197-prec, HPR163, hsa-mir-150-prec, Hcd323, hsa-mir-103-2-prec, Hcd243, Hcd938, hsa-mir-025-prec, hsa-mir-007-1-prec, MPR243, Hcd511, Hcd654, hsa-mir-199a-2-prec, hsa-mir-214-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, Hcd794, Hcd530, HSHELA01, Hcd754, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Tegafur.

44. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ALDOC, ITM2A, SLA, SSBP2, IL2RG, MFNG, SELL, STC1, LRMP, MYB, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTB1, MAP4K1, CCR7, CD3G, CCR9, CBFA2T3, CXCR4, ARHGEF6, SELPLG, SEC31L2, CD3Z, SH2D1A, CDIA, SCN3A, LAIR1, TRB@, DOCK2, WBSCR20C, CD3D, T3JAM, CD6, ZAP70, GPR65, A1F1, WDR45, CD1E, CYFIP2, TARP, TRIM, ARHGAP15, NOTCH1, STAG3, UBASH3A, MGC5566, and PACAP, and, optionally, a third gene selected from at least one of PTPRC, TRIM22, TRIM41, LAT, CD1C, MYBBP1A, CD53, FARSLA, PPP2CA, LAPTM5, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, ITK, CDIB, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, TCF7, PTPRC, HCLS1, ATP2A3, MYBL1, and FARSLA, or
ii) a second microRNA selected from at least one of Hcd768, HUMTRF, Hcd145, Hcd923, hsa-mir-216-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-342, Hcd794, hsa-mir-142-prec, HSHELA01, hsa-mir-223-prec, and Hcd754,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Daunorubicin.

45. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PFN1, CALU, ZYX, PSMD2, RAPIB, EPAS1, PGAM1, STAT1, CKAP4, DUSP1, RCN1, UCHL1, ITGA5, NFKBIA, LAMB1, TGFBI, FHL1, GJA1, PRG1, EXT1, MVP, NNMT, TAP1, CRIM1, PLOD2, RPS19, AXL, PALM2-AKAP2, IL8, LOXL2, PAPSS2, CAV1, F2R, PSMB9, LOX, C1orf29, STC1, LIF, KCNJ8, SMAD3, HPCAL1, WNT5A, BDNF, TNFRSF1A, NCOR2, FLNC, HMGA2, HLA-B, FLOT1, PTRF, IFI16, MGC4083, TNFRSF10B, PNMA2, TFPI, CLECSF2, SP110, PLAUR, ASPH, FSCN1, HIC, HLA-C, COL6A1, IL6ST, IFITM3, MAP1B, FLJ46603, RAFTLIN, FTL, KIAA0877, MT1E, CDC10, ZNF258, BCAT1, IFI44, SOD2, TMSB10, FLJ10350, C1orf24, EFHD2, RPS27L, TNFRSF12A, FAD104, RAB7L1, NME7, TMEM22, TPK1, ELKS, CYLD, AMIGO2, ADAMTS1, and ACTB, and, optionally, a third gene selected from at least one of ACLY, MPZL1, STC2, BAX, RAB31, RAB31, (UBC12, LOXL1, EMP3, FGFR1OP, IL6, TRIM22, OPTN, CYR61, METAP1, SHC1, FN1, EMP3, RAB31, LOXL1, BAX, BAX, RAB31, FN1, CD44, ANXA1, COL5A2, LGALS1, FGFR1, PLAU, TFPI2, TFPI2, VCAM1, SHC1, CSF2RA, EMP3, COL1A1, TGFB1, COL6A2, FGFR1, ITGA3, AKR1B1, MSN, EMP3, VIM, EMP3, COL6A2, MSN, PSMC5, UBC, FGFR1, BASP1, ANXA11, CSPG2, M6PRBP1, PRKCA, OPTN, OPTN, SPARC, CCL2, and ITGA3, or
ii) a second microRNA selected from at least one of hsa-mir-125b-2-prec, hsa-mir-022-prec, hsa-mir-125b-1, hsa-mir-155-prec, hsa-mir-100, hsa-mir-409-3p, hsa-mir-495, hsa-mir-199a-2-prec, hsa-mir-382, and hsa-mir-100-1/2-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Bleomycin.

46. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of HSPCB, LDHA, and TM4SF7, and, optionally, LY6E, or
ii) a second microRNA selected from at least one of Hcd338, hsa-mir-099b-prec-19, and hsa-mir-149-prec,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates that said cell is sensitive to Estramustine.

47. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CSDA, INSIG1, UBE2L6, PRG1, ITM2A, DGKA, TFDP2, SLA, IL2RG, ALOX5AP, GPSM3, PSMB9, SELL, ADA, EDG1, FMNL1, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, CD1D, STAT4, VAV1, MAP4K1, CCR7, PDE4C, CD3G, CCR9, SP110, TNFAIP8, LCP1, IFI16, CXCR4, ARHGEF6, SELPLG, SEC31L2, CD3Z, PRKCQ, SH2D1A, GZMB, CD1A, LAIR1, AFIQ, TRB@, SEPT6, DOCK2, RPS19, CD3D, T3JAM, FNBP1, CD6, ZAP70, LST1, BCAT1, PRF1, A1F1, RAG2, CDIE, CYFIP2, TARP, TRIM, GLTSCR2, GIMAP5, ARHGAP15, NOTCH1, BCL11B, GIMAP6, STAG3, TM6SF1, UBASH3A, MGC5566, FLJ22457, and TPK1, and, optionally, a third gene selected from at least one of PTPRC, TRIM22, EVL, TRIM41, PSME2, LAT, CD1C, ADAM11, CD53, FARSLA, RPL13, RAC2, RPL13, GNA15, LAPTM5, RPL18A, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GNA15, ITK, CD1B, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, GNA15, ITGB2, PTPRC, HCLS1, ATP2A3, and FARSLA, or
ii) a second microRNA selected from at least one of hsa-mir-181a-prec, hsa-mir-181c-prec, HUMTRF, hsa-mir-181d, MPR74, Hcd817, hsa-mir-213-prec, hsa-mir-155-prec, Hcd148_HPR225 left, hsa-mir-515-15p, hsa-mir-181b-prec, HUMTRN, hsa-mir-128b-prec, hsa-mir-450-2, hsa-mir-216-prec, hsa-mir-342, hsa-mir-142-prec, hsa-mir-223-prec, Hcd754, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Chlorambucil.

48. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PRG1, SLC2A3, RPS19, PSMB10, ITM2A, DGKA, SEMA4D, SLA, IL2RG, MFNG, ALOX5AP, GPSM3, PSMB9, SELL, ADA, FMNL1, MYB, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTB1, FXYD2, CD1D, STAT4, MAP4K1, CCR7, PDE4C, CD3G, CCR9, SP110, TK2, TNFAIP8, NAP1L1, SELPLG, SEC31L2, CD3Z, PRKCQ, SH2DIA, GZMB, CD1A, LAIR1, TRB@, SEPT6, DOCK2, CG018, WBSCR20C, CD3D, CD6, LST1, GPR65, PRF1, ALMS1, A1F1, CDIE, CYFIP2, TARP, GLTSCR2, FLJ12270, ARHGAP15, NAP1L2, CECR1, GIMAP6, STAG3, TM6SF1, C15orf25, MGC5566, FLJ22457, ET, TPK1, and PHF11, and, optionally, a third gene selected from at least one of ETS2, PTPRC, PETER, SETBP1, LAT, MYBBP1A, ETV5, METAP1, ETS1, ADAM11, CD53, FARSLA, RPL13, ARMET, TETRAN, BET1, RPL13, MET, LAPTM5, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, CD1B, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, ETV4, ITGB2, PTPRC, HCLS1, MYBL1, FARSLA, and METAP2, or
ii) a second microRNA selected from at least one of hsa-mir-124a-3-prec, Hcd946, Hcd683, HPR264, MPR185, HUMTRF, Hcd294, Hcd503, hsa-mir-20b, MPR74, MPR234, Hcd447, Hcd817, Hcd148_HPR225 left, hsa-mir-515-15p, Hcd383, hsa-mir-181b-prec, Hcd783, MPR224, HPR172, MPR216, HUMTRN, hsa-mir-321, HPR159, MPR228, ath-MIR180a, hsa-mir-197-prec, hsa-mir-124a-1-prec1, hsa-mir-128b-prec, Hcd28_HPR39 left, Hcd889, Hcd350, hsa-mir-025-prec, hsa-mir-208-prec, hsa-mir-450-2, Hcd923, Hcd434, HPR129, HPR220, hsa-mir-380-5p, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-342, hsa-mir-142-prec, HSHELA01, hsa-mir-223-prec, Hcd754, hsa-mir-020-prec, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Mechlorethamine.

49. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PGK1, SCD, INSIG1, IGBP1, TNFAIP3, TNFSF10, ABCA1, AGA, ABCA8, DBC1, PTGER2, UGTIA3, C10 orf10, TM4SF13, CGI-90, LXN, DNAJC12, HIPK2, and C9orf95, and, optionally, a third gene selected from at least one of FGFR10P, PLXNA1, PSCD2L, TUBB, FGFR1, TUBB2, PAGA, TUBB2, UBB, TUBB2, FGFR1, FGFR1, and TUBB-PARALOG, or
ii) a second microRNA selected from at least one of hsa-mir-483, Hcd631, hsa-mir-212-prec, Hcd938, MPR133, Hcd794, Hcd438, and Hcd886,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Streptozocin.

50. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPLP2, CD99, IFITM1, INSIG1, ALDOC, ITM2A, SERPINA1, C1QR1, STAT5A, INPP5D, SATB1, VPS16, SLA, IL2RG, MFNG, SELL, LRMP, ICAM2, MYB, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, ZNFN1A1, CENTB1, ADD2, LCP2, SPI1, DBT, GZMA, CD2, BATF, HIST1H4C, ARHGAP6, VAV1, MAP4K1, CCR7, PDE4C, CD3G, CCR9, SP140, TK2, LCP1, IFI16, CXCR4, ARHGEF6, PSCDBP, SELPLG, SEC31L2, CD3Z, PRKCQ, SH2D1A, GZMB, CD1A, GATA2, LY9, LAIR1, TRB@, SEPT6, HA-1, SLC43A3, DOCK2, CG018, MLC1, CD3D, T3JAM, CD6, ZAP70, DOK2, LST1, GPR65, PRF1, ALMS1, AIF1, PRDX2, FLJ12151, FBXW12, CD1E, CYFIP2, TARP, TRIM, RPL10L, GLTSCR2, CKIP-1, NRN1, ARHGAP15, NOTCH1, PSCD4, C13orf18, BCL11B, GIMAP6, STAG3, NARF, TM6SF1, C15orf25, FLJ11795, SAMSN1, UBASH3A, PACAP, LEF1, IL21R, TCF4, and DKFZP434B0335, and, optionally, a third gene selected from at least one of FLJ10534, PTPRC, CD27BP, TRIM22, TRIM41, PSCD2L, CD1C, MYBBP1A, ICAM3, CD53, FARSLA, GAS7, ABCD2, CD24, CD29, RAC2, CD37, GNA15, PGF, LAPTM5, RPL18A, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GNA15, ITK, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, GNA15, TCF7, ITGB2, PTPRC, HCLS1, PRKCB1, ATP2A3, PRKCBI, MYBL1, and FARSLA, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, Hcd517, Hcd796, HUMTRF, hsa-mir-20b, hsa-mir-019b-2-prec, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, Hcd148_HPR225 left, HUMTRAB, Hcd975, hsa-mir-135-2-prec, hsa-mir-128b-prec, hsa-mir-143-prec, hsa-mir-025-prec, hsa-mir-216-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, HSHELA01, HUMTRV1A, hsa-mir-223-prec, Hcd754, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Carmustine.

51. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of RPS15, INSIG1, ALDOC, ITM2A, C1QR1, STAT5A, INPP5D, VPS16, SLA, USP20, IL2RG, MFNG, LRMP, EVI2A, PTPN7, ARHGAP25, RHOH, ZNFN1A1, CENTBI, LCP2, SPIT, ARHGAP6, MAP4K1, CCR7, LY96, C6orf32, MAGEA1, SP140, LCP1, IFI16, ARHGEF6, PSCDBP, SELPLG, CD3Z, PRKCQ, GZMB, LAIR1, SH2DIA, TRB@, RFP, SEPT6, HA-1, SLC43A3, CD3D, T3JAM, GPR65, PRF1, AIF1, LPXN, RPL10L, SITPEC, ARHGAP15, C13orf18, NARF, TM6SF1, PACAP, and TCF4, and, optionally, a third gene selected from at least one of PTPRC, ICAM3, TRFP, CD53, FARSLA, RAC2, MAGEA11, LAPTM5, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, PTPRC, HCLS1, SLC19A1, FARSLA, and RPS15A, or
ii) a second microRNA selected from at least one of hsa-mir-101-prec-9, Hcd796, hsa-mir-20b, HUMTRAB, hsa-mir-135-2-prec, hsa-mir-153-1-prec1, hsa-mir-025-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, HUMTRV1A, Hcd754, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Lomustine.

52. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of SSRP1, ALDOC, C1QR1, TTF1, PRIM1, USP34, TK2, GOLGIN-67, NPD014, KIAA0220, SLC43A3, WBSCR20C, ICAM2, TEX10, CHD7, SAMSN1, and TPRT, and, optionally, a third gene selected from at least one of PTPRC, CD53, RNPS1, H3F3A, NUDC, SMARCA4, RPL32, PTMA, CD53, PTPRCAP, PTPRC, RPL32, PTPRCAP, PTPRC, CD53, PTPRC, HCLS1, and SLC19A1, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-096-prec-7, hsa-mir-123-prec, MPR249, HPR232, hsa-mir-101-prec-9, hsa-mir-106a, hsa-mir-20b, Hcd861, hsa-mir-017-prec, hsa-mir-019b-2-prec, hsa-mir-033-prec, Hcd102, MPR216, Hcd975, hsa-mir-019b-1-prec, hsa-mir-135-2-prec, Hcd581, Hcd536_HPR104, hsa-mir-128b-prec, HSTRNL, hsa-mir-025-prec, hsa-mir-18b, HPR262, Hcd923, Hcd434, Hcd658, HPR129, hsa-mir-380-5p, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, Hcd627, hsa-mir-142-prec, hsa-mir-018-prec, and hsa-mir-020-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Mercaptopurine.

53. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CD99, INSIG1, PRG1, ALDOC, ITM2A, SLA, SSBP2, IL2RG, MFNG, ALOX5AP, C1 orf29, SELL, STC1, LRMP, MYB, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTBI, ADD2, CD1D, BATF, MAP4K1, CCR7, PDE4C, CD3G, CCR9, SP110, TNFAIP8, NAP1L1, CXCR4, ARHGEF6, GATA3, SELPLG, SEC31L2, CD3Z, SH2D1A, GZMB, CDIA, SCN3A, LAIR1, AF1Q, TRB@, DOCK2, MLC1, CD3D, T3JAM, CD6, ZAP70, IFI44, GPR65, PRF1, A1F1, WDR45, CD1E, CYFIP2, TARP, TRIM, ARHGAP15, NOTCH1, STAG3, NARF, TM6SF1, UBASH3A, and MGC5566, and, optionally, a third gene selected from at least one of FLJ10534, PTPRC, TRIM22, C18orf1, TRIM41, LAT, CDIC, MYBBP1A, CD53, FARSLA, PPP2CA, COL5A2, LAPTM5, CD53, CORO1A, PTPRCAP, PTPRC, HEM1, GMFG, ITK, CDIB, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, HEM1, TCF7, PTPRC, HCLS1, ATP2A3, MYBL1, and FARSLA, or
ii) a second microRNA selected from at least one of hsa-mir-124a-3-prec, Hcd768, HUMTRF, hsa-mir-213-prec, hsa-mir-181b-prec, Hcd783, hsa-mir-212-prec, hsa-mir-124a-1-prec1, hsa-mir-342, hsa-mir-142-prec, HSHELA01, hsa-mir-223-prec, and Hcd754,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Teniposide.

54. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ALDOC, C1QR1, SLA, WBSCR20A, MFNG, SELL, MYB, RHOH, ZNFNIA1, LCP2, MAP4K1, CBFA2T3, LCP1, SELPLG, CD3Z, LAIR1, WBSCR20C, CD3D, GPR65, ARHGAP15, FLJ10178, NARF, and PUS3, and, optionally, a third gene selected from at least one of PTPRC, MYBBP1A, ICAM3, CD53, FARSLA, CD53, PTPRCAP, PTPRC, HEM1, GMFG, GMFG, PTPRCAP, PTPRC, CD53, HEM1, PTPRC, HCLS1, PRKCB1, PRKCB1, MYBL1, and FARSLA, or
ii) a second microRNA selected from at least one of hsa-mir-025-prec, hsa-mir-007-1-prec, hsa-mir-093-prec-7.1=093-1, Hcd794, and hsa-mir-142-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Dactinomycin.

55. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of PPIB, ZFP36L2, IFI30, USP7, SRM, SH3BP5, ALDOC, FADS2, GUSB, PSCD1, IQGAP2, STS, MFNG, FLI1, PIM2, INPP4A, LRMP, ICAM2, EVI2A, MAL, BTN3A3, PTPN7, IL10RA, SPI1, TRAF1, ITGB7, ARHGAP6, MAP4K1, CD28, PTP4A3, LTB, C1orf38, WBSCR22, CD8B1, LCP1, FLJ3052, MEF2C, PSCDBP, IL16, SELPLG, MAGEA9, LAIR1, TNFRSF25, EVI2B, IGJ, PDCD4, RASA4, HA-1, PLCL2, RNASE6, WBSCR20C, NUP210, RPL10L, C11orf2, CABC1, ARHGEF3, TAPBPL, CHST12, FKBP11, FLJ35036, MYL1P, TXNDC5, PACAP, TOSO, PNAS-4, IL21R, and TCF4, and, optionally, a third gene selected from at least one of CLTB, BTN3A2, BCL2, SETBP1, ICAM3, BCL2, BCL2, BCL2, CD53, CCND2, CLTB, CLTB, BCL2L11, BTN3A2, CD37, MYCL2, CTSS, LAPTM5, CD53, CORO1A, HEM1, CD53, CORO1A, HEM1, HCLS1, BCL2L11, MYCL1, MYC, and MAN1A1, or
ii) a second microRNA selected from at least one of Hcd257, hsa-mir-148-prec, Hcd512, HPR227, Hcd421, MPR203, hsa-mir-017-prec, hsa-mir-219-2, hsa-mir-328, Hcd783, Hcd181, HPR213, hsa-mir-191-prec, hsa-mir-375, hsa-mir-212-prec, Hcd913, Hcd716, MPR207, HPR206, hsa-mir-016b-chr3, Hcd654, hsa-mir-195-prec, Hcd425, hsa-mir-148a, hsa-mir-142-prec, and hsa-mir-016a-chr13,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Tretinoin.

56. The method of claim 1, wherein said measuring further comprises determining a level of expression of a second gene selected from at least one of PDGFRB, KDR, KIT, and FLT3, and, optionally, a third gene selected from at least one of FLT1, FLT4, PDGFRA, and CSF1R,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene indicates that said cell is sensitive to sunitinib.

57. The method of claim 1, wherein said measuring further comprises determining a level of expression of BCL2,
wherein an increase or decrease in said level of expression of said BCL2 indicates that said cell is sensitive to SPC2996.

58. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of ARHGDIB, ZFP36L2, ITM2A, LGALS9, INPP5D, SATB1, TFDP2, IL2RG, CD48, SELL, ADA, LRMP, RIMS3, LCK, CXorf9, RHOH, ZNFN1A1, LCP2, CD1D, CD2, ZNF91, MAP4K1, CCR7, IGLL1, CD3G, ZNF430, CCR9, CXCR4, KIAA0922, TARP, FYN, SH2D1A, CDIA, LST1, LA1R1, TRB@, SEPT6, CD3D, CD6, AIF1, CD1E, TRIM, GLTSCR2, ARHGAP15, BIN2, SH3TC1, CECR1, BCL11B, GIMAP6, STAG3, GALNT6, MGC5566, PACAP, and LEF1, and, optionally, a third gene selected from at least one of CD27BP, TRIM22, TRA@, C18 orf1, EVL, PRKCH, TRIM41, PSCD2L, CD1C, ADAM11, ABCD2, CD24, CD29, CD37, GNA15, LAPTM5, CORO1A, HEM1, GMFG, GNA15, CD1B, GMFG, CORO1A, HEM1, GNA15, ITGB2, PRKCB1, ATP2A3, and PRKCB1, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, hsa-mir-181b-2, Hcd417, Hcd440_HPR257, hsa-mir-019b-2-prec, hsa-mir-213-prec, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, hsa-mir-181b-prec, hsa-mir-128b-prec, hsa-mir-526a-2, MPR95, HPR220, hsa-mir-133a-1, hsa-mir-148a, hsa-mir-142-prec, HPR169, hsa-mir-223-prec, hsa-mir-018-prec, hsa-mir-020-prec, and hsa-mir-484,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Ifosfamide.

59. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of MLP, GLUL, SLC9A3R1, ZFP36L2, INSIG1, TBL1X, NDUFAB1, ESP, TRIM14, SRPK2, PMM2, CLDN3, GCH1, IDI1, TTF1, MYB, RASGRP1, HIST1H3H, CBFA2T3, SRRM2, ANAPC5, MBD4, GATA3, HIST1H2BG, RAB14, PIK3R1, MGC50853, ELF1, ZRF1, ZNF394, S100A14, SLC6A14, GALNT6, SPDEF, TPRT, and CALML4, and, optionally, a third gene selected from at least one of EIF4A1, TFF1, TFF1, MYBBP1A, AKAP1, DGKZ, EIF4A1, KIAA0182, SLC19A1, ATP2A3, MYBL1, EIF4EBP2, GIP2, and MANIA1, or
ii) a second microRNA selected from at least one of hsa-mir-092-prec-X=092-2, Hcd547, Hcd257, hsa-mir-148-prec, HUMTRS, hsa-mir-033-prec, hsa-mir-092-prec-13=092-1, hsa-mir-375, hsa-mir-095-prec-4, hsa-mir-025-prec, hsa-mir-202-prec, hsa-mir-007-1-prec, hsa-mir-093-prec-7.1=093-1, hsa-mir-106-prec-X, hsa-mir-142-prec, hsa-mir-223-prec, and hsa-mir-018-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Tamoxifen.

60. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CSDA, F8A1, KYNU, PHF14, SERPINB2, OPHN1, HRMT1L2, TNFRSF1A, PPP4C, CES1, TP53AP1, TM4SF4, RPL5, BC008967, TLK2, COL4A6, PAK3, RECK, LOC51321, MST4, DERP6, SCD4, and FLJ22800, and, optionally, a third gene selected from at least one of STC2, BAX, CDKN1A, DDB2, RGS2, BAX, BAX, RPL13, RPL13, CDKN1A, and GABPB2, or
ii) a second microRNA selected from at least one of HUMTRF, HUMTRN, hsa-mir-124a-1-prec1, hsa-mir-150-prec, Hcd923, HPR181, Hcd569, hsa-mir-199a-2-prec, Hcd754, and hsa-mir-4323p,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Floxuridine.

61. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of CSDA, UBE2L6, TAP1, RPS19, SERPINA1, C1QR1, SLA, GPSM3, PSMB9, EDG1, FMNL1, PTPN7, ZNFN1A1, CENTB1, BATE, MAP4K1, PDE4C, SP110, HLA-DRA, IFI16, HLA-DRB1, ARHGEF6, SELPLG, SEC31L2, CD3Z, PRKCQ, SH2DIA, GZMB, TRB@, HLA-DPA1, AIM1, DOCK2, CD3D, IFITM1, ZAP70, PRF1, C1orf24, ARHGAP15, C13orf18, and TM6SF1, and, optionally, a third gene selected from at least one of PTPRC, TRIM22, PSME2, LAT, METAP1, CD53, FARSLA, RPL13, RAC2, RPL13, PTMA, CD53, CORO1A, PTPRCAP, PTPRC, GMFG, ITK, GMFG, PTPRCAP, PTPRC, CD53, CORO1A, ITGB2, PTPRC, HCLS1, and FARSLA, or
ii) a second microRNA selected from at least one of HUMTRF, hsa-mir-380-5p, hsa-mir-342, hsa-mir-142-prec, and Hcd200,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to lrinotecan.

62. The method of claim 1, wherein said measuring further comprises determining a level of expression of:
i) a second gene selected from at least one of STAT1, HSBP1, IF130, RIOK3, TNFSF10, ALOX5AP, ADFP, IRS2, EFEMP2, RIPK2, DKFZp56411922, MT1K, RNASET2, EFHD2, TRIB3, ACSL5, IFIH1, and DNAPTP6, and, optionally, a third gene selected from at least one of IFI27, OPTN, C20orf18, FN1, LOC0051123, FN1, OPTN, and OPTN, or
ii) a second microRNA selected from at least one of Hcd289, Hcd939, Hcd330, HPR76, Hcd111, Hcd976, hsa-mir-15a, hsa-mir-001b-1-prec1, hsa-mir-450-1, hsa-mir-200b, Hcd578, and hsa-mir-200a-prec,
wherein an increase or decrease in said level of expression of said second gene and, optionally, said third gene or said second microRNA indicates that said cell is sensitive to Satraplatin.

63. The method of claim 1, wherein said level of expression of said gene is determined by detecting the level of mRNA transcribed from said gene.

64. The method of claim 1, wherein said level of expression of said gene is determined by detecting the level of a protein product of said gene.

65. The method of claim 1, wherein said level of expression of said gene is determined by detecting the level of the biological activity of a protein product of said gene.

66. The method of claim 1, wherein an increase in the level of expression of said gene or microRNA indicates increased sensitivity of said cell to said treatment.

67. The method of claim 1, wherein said cell is a cancer cell.

68. The method of claim 1, wherein a decrease in the level of expression of said gene or microRNA indicates increased sensitivity of said cell to said treatment.

69. The method of claim 1, wherein said level of expression of said gene or microRNA is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

70. The method of claim 1, wherein said measuring further comprises further determining a level of expression of:
i) a second gene selected from at least one of ACTB, ACTN4, ADA, ADAM9, ADAMTS1, ADD1, AF1Q, A1F1, AKAP1, AKAP13, AKR1C1, AKT1, ALDH2, ALDOC, ALG5, ALMS1, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXA1, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCAT1, BCHE, BCL11B, BDNF, BHLHB2, BIN2, BLMH, BMI1, BNIP3, BRDT, BRRN1, BTN3A3, C11orf2, C14orf139, C15orf25, C18orf10, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C6orf32, CACNA1G, CACNB3, CALM1, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNB11P1, CCND3, CCR7, CCR9, CD1A, CD1C, CD1D, CD1E, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDC10, CDCl4B, CDH11, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-1, CNP, COL4A1, COL5A2, COL6A1, CORO1C, CRABP1, CRK, CRY1, CSDA, CTBP1, CTSC, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDX18, DDX5, DGKA, DIAPH1, DKC1, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAJC7, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DXS9879E, EEF1B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHL1, FHOD1, FKBP1A, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOT1, FMNL1, FNBP1, FOLH1, FOXF2, FSCN1, FTL, FYB, FYN, GOS2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPT1, GIMAP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GZMA, GZMB, H1F0, H1FX, H2AFX, H3F3A, HA-1, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HRMT1L1, HSA9761, HSPA5, HSU79274, HTATSF1, ICAM1, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIG1, IQGAP1, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K-ALPHA-1, KHDRBS1, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1128, KIAA1393, KIFC1, LAIR1, LAMB1, LAMB3, LAT, LBR, LCK, LCP1, LCP2, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAP1B, MAP1 LC3B, MAP4K1, MAPK1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCM7, MDH2, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPP1, MPZL1, MRP63, MRPS2, MT1E, MT1K, MUF1, MVP, MYB, MYL9, MYO1B, NAP1L1, NAP1L2, NARF, NASP, NCOR2, NON, NDUFAB1, NDUFS6, NFKB1A, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCH1, NPC1, NQO1, NRID2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OXA1L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFN1, PFN2, PGAM1, PHEMX, PHLDA1, PIM2, PITPNC1, PLAC8, PLAGL1, PLAUR, PLCB1, PLEK2, PLEKHC1, PLOD2, PLSCR1, PNAS-4, PNMA2, POLR2F, PPAP2B, PRF1, PRG1, PRIM1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOV1, PTP4A3, PTPN7, PTPNS1, PTRF, PURA, PWP1, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAP1B, RASGRP2, RBPMS, RCN1, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOK3, RIPK2, RIS1, RNASE6, RNF144, RPL10, RPL10A, RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLP0, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A4, SART3, SATB1, SCAP1, SCARB1, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT10, SEPT6, SERPINA1, SERPINB1, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHD1, SHMT2, SIAT1, SKBI, SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPI1, SRF, SRM, SSA2, SSBP2, SSRP1, SSSCA1, STAGS, STAT1, STAT4, STAT5A, STC1, STC2, STOML2, T3JAM, TACC1, TACC3, TAF5, TAL1, TAP1, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB10, TMSNB, TNFAIP3, TNFAIP8, TNFRSF10B, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAV1, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFN1A1, and ZYX; or
ii) a second microRNA selected from at least one of ath-MIR180aNo2, Hcd102 left, Hcd111 left, Hcd115 left, Hcd120 left, Hcd142 right, Hcd145 left, Hcd148_HPR225 left, Hcd181 left, Hcd181 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, Hcd28_HPR39 left, Hcd28_HPR39 right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd350 right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd438 right, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd511 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left, Hcd760 left, Hcd763 right, Hcd768 left, Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPR100 right, HPR129 left, HPR154 left, HPR159 left, HPR163 left, HPR169 right, HPR172 right, HPR181 left, HPR187 left, HPR199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left, HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir—490_Hcd20 right, HSHELA01, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRVIA, let-7f-2-prec2, mir-001b-1-prec1, mir-001b-2-prec, mir-007-1-prec, mir-007-2-precNo2, mir-010a-precNo1, mir-015b-precNo2, mir-016a-chr13, mir-016b-chr3, mir-017-precNo1, mir-017-precNo2, mir-018-prec, mir-019a-prec, mir-019b-1-prec, mir-019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2-prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNo1, mir-034-precNo2, mir-092-prec-13=092-1No2, mir-092-prec-X=092-2, mir-093-prec-7.1=093-1, mir-095-prec-4, mir-096-prec-7No1, mir-096-prec-7No2, mir-098-prec-X, mir-099b-prec-19No1, mir-100-1/2-prec, mir-100No1, mir-101-prec-9, mir-102-prec-1, mir-103-2-prec, mir-103-prec-5=103-1, mir-106aNo1, mir-106-prec-X, mir-107No1, mir-107-prec-10, mir-122a-prec, mir-123-precNo1, mir-123-precNo2, mir-124a-1-prec1, mir-124a-2-prec, mir-124a-3-prec, mir-125b-1, mir-125b-2-precNo2, mir-127-prec, mir-128b-precNo1, mir-128b-precNo2, mir-133a-1, mir-135-2-prec, mir-136-precNo2, mir-138-1-prec, mir-140No2, mir-143-prec, mir-144-precNo2, mir-145-prec, mir-146bNo1, mir-146-prec, mir-147-prec, mir-148aNo1, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-1-prec1, mir-154-prec1No1, mir-155-prec, mir-15aNo1, mir-16-1No1, mir-16-2No1, mir-181a-precNo1, mir-181b-1No1, mir-181b-2No1, mir-181b-precNo1, mir-181b-precNo2, mir-181c-precNo1, mir-181dNo1, mir-188-prec, mir-18bNo2, mir-191-prec, mir-192No2, mir-193bNo2, mir-194-2No1, mir-195-prec, mir-196-2-precNo2, mir-197-prec, mir-198-prec, mir-199a-1-prec, mir-199a-2-prec, mir-199b-precNo1, mir-200a-prec, mir-200bNo1, mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir-208-prec, mir-20bNo1, mir-212-precNo1, mir-212-precNo2, mir-213-precNo1, mir-214-prec, mir-215-precNo2, mir-216-precNo1, mir-219-2No1, mir-219-prec, mir-223-prec, mir-29b-1No1, mir-29b-2=102prec7.1=7.2, mir-321No1, mir-321No2, mir-324No1, mir-324No2, mir-328No1, mir-342No1, mir-361No1, mir-367No1, mir-370No1, mir-371No1, miR-373*No1, mir-375, mir-376aNo1, mir-379No1, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423No1, mir-424No2, mir-429No1, mir-429No2, mir-4323p, mir-4325p, mir-449No1, mir-450-1, mir-450-2No1, mir-483No1, mir-484, mir-487No1, mir-495No1, mir-499No2, mir-501No2, mir-503No1, mir-509No1, mir-514-1No2, mir-515-15p, mir-515-23p, mir-516-33P, mir-516-43p, mir-518e/526c, mir-519a-1/52, mir-519a-2No2, mir-519b, mir-519c152, mir-520c/52, mir-526a-2No1, mir-526a-2No2, MPR103 right, MPR121 left, MPR121 left, MPR130 left, MPR130 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPR174 right, MPR185 right, MPR197 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, and MPR95 left,
wherein an increase or decrease in said level of expression of said second gene or said second microRNA indicates said cell is sensitive to said treatment.

71. The method of claim 1, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 20 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFN1A1.

72. The method of claim 71, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 25 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFN1A1.

73. The method of claim 1, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 2300.

74. The method of claim 1, wherein said device further comprises at least one single-stranded oligonucleotide that is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 1, 2, 4, 6, 7, 10, 11, 16, or 24.

75. A method for determining the development of resistance of cells in a patient to a treatment to which said cells have previously been sensitive, said method comprising contacting a sample comprising one or more nucleic acid molecules from said patient to a device comprising single-stranded oligonucleotides, wherein at least one of said oligonucleotides comprises a sequence that is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of a first microRNA selected from mir-142-prec or a product of a first gene selected from ZNFN1A1, and measuring hybridization between said nucleic acid molecules from said patient and said single-stranded oligonucleotides of said device to determine a level of expression of said first microRNA or said first gene in at least one of said cells, wherein a decrease in said level of expression of said first microRNA or said first gene in at least one of said cells, relative to the level of expression of said first microRNA or said first gene in a control cell sensitive to said treatment, indicates resistance or a propensity to develop resistance to the treatment by said patient and, optionally, wherein said method further comprises measuring a level of expression of at least one second gene in at least one of said cells selected from:
ACTB, ACTN4, ADA, ADAM9, ADAMTS1, ADD1, AF1Q, AIF1, AKAP1, AKAP13, AKR1C1, AKT1, ALDH2, ALDOC, ALG5, ALMS1, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXA1, APIG2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGD1B, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCAT1, BCHE, BCL11B, BDNF, BHLHB2, BIN2, BLMH, BMI1, BNIP3, BRDT, BRRN1, BTN3A3, C11orf2, C14orf139, C15orf25, C18orf10, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C6orf32, CACNA1G, CACNB3, CALM1, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNB1IP1, CCND3, CCR7, CCR9, CDIA, CD1C, CD1D, CD1E, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDC10, CDCl4B, CDH11, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-1, CNP, COL4A1, COL5A2, COL6A1, CORO1C, CRABP1, CRK, CRY1, CSDA, CTBP1, CTSC, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDX18, DDX5, DGKA, DIAPH1, DKC1, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAJC7, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DXS9879E, EEFIB2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHL1, FHOD1, FKBPIA, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOT1, FMNL1, FNBP1, FOLH1, FOXF2, FSCN1, FTL, FYB, FYN, GOS2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPT1, GIMAP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GZMA, GZMB, H1F0, H1 FX, H2AFX, H3F3A, HA-1, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HRMTIL1, HSA9761, HSPA5, HSU79274, HTATSF1, ICAM1, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIG1, IQGAP1, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K-ALPHA-1, KHDRBS1, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1128, KIAA1393, KIFC1, LA1R1, LAMB1, LAMB3, LAT, LBR, LCK, LCP1, LCP2, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAP1B, MAP1LC3B, MAP4K1, MAPK1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCM7, MDH2, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPP1, MPZL1, MRP63, MRPS2, MT1E, MT1K, MUF1, MVP, MYB, MYL9, MYO1B, NAPIL1, NAP1L2, NARF, NASP, NCOR2, NDN, NDUFAB1, NDUFS6, NFKB1A, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCH1, NPC1, NQO1, NRID2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OXA1L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFN1, PFN2, PGAM1, PHEMX, PHLDA1, PIM2, PITPNC1, PLACE, PLAGL1, PLAUR, PLCB1, PLEK2, PLEKHC1, PLOD2, PLSCR1, PNAS-4, PNMA2, POLR2F, PPAP2B, PRF1, PRG1, PRIM1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOV1, PTP4A3, PTPN7, PTPNS1, PTRF, PURA, PWP1, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAP1B, RASGRP2, RBPMS, RCN1, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOK3, RIPK2, RIS1, RNASE6, RNF144, RPL10, RPL10A, RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLP0, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A4, SART3, SATB1, SCAP1, SCARB1, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT10, SEPT6, SERPINA1, SERPINB1, SERPINB6, SFRS5, SFRS6, SFRS7, SH2DIA, SH3GL3, SH3TC1, SHD1, SHMT2, SIAT1, SKB1, SKP2, SLA, SLCIA4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPI1, SRF, SRM, SSA2, SSBP2, SSRP1, SSSCA1, STAG3, STAT1, STAT4, STAT5A, STC1, STC2, STOML2, T3JAM, TACC1, TACC3, TAF5, TAL1, TAP1, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB10, TMSNB, TNFAIP3, TNFAIP8, TNFRSF10B, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAV1, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFN1A1, and ZYX; or at least one second microRNA in at least one of said cells selected from:
ath-MIR180aNo2, Hcd102 left, Hcd111 left, Hcd115 left, Hcd120 left, Hcdl 42 right, Hcd145 left, Hcd148_HPR225 left, Hcd181 left, Hcd181 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, Hcd28_HPR39 left, Hcd28_HPR39 right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd350 right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd438 right, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd511 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left, Hcd760 left, Hcd763 right, Hcd768 left, Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPR100 right, HPR129 left, HPR154 left, HPR159 left, HPR163 left, HPR169 right, HPR172 right, HPR181 left, HPR187 left, HPR199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left, HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir—490_Hcd20 right, HSHELA01, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRVIA, let-7f-2-prec2, mir-001b-1-prec1, mir-001b-2-prec, mir-007-1-prec, mir-007-2-precNo2, mir-010a-precNo1, mir-015b-precNo2, mir-016a-chr13, mir-016b-chr3, mir-017-precNo1, mir-017-precNo2, mir-018-prec, mir-019a-prec, mir-019b-1-prec, mir-019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2-prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNo1, mir-034-precNo2, mir-092-prec-13=092-1No2, mir-092-prec-X=092-2, mir-093-prec-7.1=093-1, mir-095-prec-4, mir-096-prec-7No1, mir-096-prec-7No2, mir-098-prec-X, mir-099b-prec-19No1, mir-100-1/2-prec, mir-100No1, mir-101-prec-9, mir-102-prec-1, mir-103-2-prec, mir-103-prec-5=103-1, mir-106aNo1, mir-106-prec-X, mir-107No1, mir-107-prec-10, mir-122a-prec, mir-123-precNo1, mir-123-precNo2, mir-124a-1-prec1, mir-124a-2-prec, mir-124a-3-prec, mir-125b-1, mir-125b-2-precNo2, mir-127-prec, mir-128b-precNo1, mir-128b-precNo2, mir-133a-1, mir-135-2-prec, mir-136-precNo2, mir-138-1-prec, mir-140No2, mir-143-prec, mir-144-precNo2, mir-145-prec, mir-146bNo1, mir-146-prec, mir-147-prec, mir-148aNo1, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-1-prec1, mir-154-prec1No1, mir-155-prec, mir-15aNo1, mir-16-1No1, mir-16-2No1, mir-181a-precNo1, mir-181b-1No1, mir-181b-2No1, mir-181b-precNo1, mir-181b-precNo2, mir-181c-precNo1, mir-181dNo1, mir-188-prec, mir-18bNo2, mir-191-prec, mir-192No2, mir-193bNo2, mir-194-2No1, mir-195-prec, mir-196-2-precNo2, mir-197-prec, mir-198-prec, mir-199a-1-prec, mir-199a-2-prec, mir-199b-precNo1, mir-200a-prec, mir-200bNo1, mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir-208-prec, mir-20bNo1, mir-212-precNo1, mir-212-precNo2, mir-213-precNo1, mir-214-prec, mir-215-precNo2, mir-216-precNo1, mir-219-2No1, mir-219-prec, mir-223-prec, mir-29b-1No1, mir-29b-2=102prec7.1=7.2, mir-321No1, mir-321No2, mir-324No1, mir-324No2, mir-328No1, mir-342No1, mir-361No1, mir-367No1, mir-370No1, mir-371No1, miR-373*No1, mir-375, mir-376aNo1, mir-379No1, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423No1, mir-424No2, mir-429No1, mir-429No2, mir-4323p, mir-4325p, mir-449No1, mir-450-1, mir-450-2No1, mir-483No1, mir-484, mir-487No1, mir-495No1, mir-499No2, mir-501No2, mir-503No1, mir-509No1, mir-514-1No2, mir-515-15p, mir-515-23p, mir-516-33p, mir-516-43p, mir-518e/526c, mir-519a-1/52, mir-519a-2No2, mir-519b, mir-519c/52, mir-520c/52, mir-526a-2No1, mir-526a-2No2, MPR103 right, MPR121 left, MPR121 left, MPR130 left, MPR130 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPR174 right, MPR185 right, MPR197 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, and MPR95 left,
wherein a decrease in the level of expression of said second gene or said second microRNA in at least one of said cells, relative to the level of expression of said second gene or said second microRNA in a control cell sensitive to said treatment, indicates resistance or a propensity to develop resistance to the treatment by said patient.

76. A method for determining the development of resistance of cells in a patient to a treatment to which said cells have previously been sensitive, said method comprising contacting a sample comprising one or more nucleic acid molecules from said patient to a device comprising single-stranded oligonucleotides, wherein at least one of said oligonucleotides comprises a sequence that is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of a first microRNA selected from mir-142-prec or a product of a first gene selected from ZNFN1A1, and measuring hybridization between said nucleic acid molecules from said patient and said single-stranded oligonucleotides of said device to determine a level of expression of said first microRNA or said first gene in at least one of said cells, wherein an increase in said level of expression of said first microRNA or said first gene in at least one of said cells, relative to the level of expression of said first microRNA or said first gene in a control cell sensitive to said treatment, indicates resistance or a propensity to develop resistance to the treatment by said patient and, optionally, wherein said method further comprises measuring a level of expression of at least one second gene in at least one of said cell selected from:
ACTB, ACTN4, ADA, ADAM9, ADAMTS1, ADD1, AF1Q, A1F1, AKAP1, AKAP13, AKR1C1, AKT1, ALDH2, ALDOC, ALG5, ALMS1, ALOX15B, AMIGO2, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXA1, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCAT1, BCHE, BCL11B, BDNF, BHLHB2, BIN2, BLMH, BMI1, BNIP3, BRDT, BRRN1, BTN3A3, C11orf2, C14orf139, C15 orf25, C18orf10, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C6orf32, CACNA1G, CACNB3, CALM1, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNB1IP1, CCND3, CCR7, CCR9, CD1A, CD1C, CD1D, CD1E, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDC10, CDCl4B, CDH11, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-1, CNP, COL4A1, COL5A2, COL6A1, CORO1C, CRABP1, CRK, CRY1, CSDA, CTBP1, CTSC, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDX18, DDX5, DGKA, DIAPH1, DKC1, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAJC7, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DXS9879E, EEFIB2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD104, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHL1, FHOD1, FKBP1A, FKBP9, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOT1, FMNL1, FNBP1, FOLH1, FOXF2, FSCN1, FTL, FYB, FYN, GOS2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPT1, GIMAP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, GOT2, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GZMA, GZMB, H1F0, H1FX, H2AFX, H3F3A, HA-1, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HRMT1L1, HSA9761, HSPA5, HSU79274, HTATSF1, ICAM1, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, LOST, IL8, IMPDH2, INPP5D, INSIG1, IQGAP1, IQGAP2, IRS2, ITGA5; ITM2A, JAR102, JUNB, K-ALPHA-1, KHDRBS1, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1128, KIAA1393, KIFC1, LA1R1, LAMB1, LAMBS, LAT, LBR, LCK, LCP1, LCP2, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAP1B, MAP1LC3B, MAP4K1, MAPK1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCM7, MDH2, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPP1, MPZL1, MRP63, MRPS2, MT1E, MT1K, MUF1, MVP, MYB, MYL9, MYO1B, NAPIL1, NAP1L2, NARF, NASP, NCOR2, NDN, NDUFAB1, NDUFS6, NFKB1A, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCH1, NPC1, NQO1, NRID2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OXA1 L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFN1, PFN2, PGAM1, PHEMX, PHLDA1, PIM2, PITPNC1, PLAC8, PLAGL1, PLAUR, PLCB1, PLEK2, PLEKHC1, PLOD2, PLSCR1, PNAS-4, PNMA2, POLR2F, PPAP2B, PRF1, PRG1, PRIM1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOV1, PTP4A3, PTPN7, PTPNS1, PTRF, PURA, PWP1, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAP1B, RASGRP2, RBPMS, RCN1, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RIOK3, RIPK2, RIS1, RNASE6, RNF144, RPL10, RPL10A, RPL12, RPL13A, RPL17, RPL18, RPL36A, RPLP0, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A4, SART3, SATB1, SCAP1, SCARB1, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT10, SEPT6, SERPINA1, SERPINB1, SERPINB6, SFRS5, SFRS6, SFRS7, SH2DIA, SH3GL3, SH3TC1, SHD1, SHMT2, SIAT1, SKB1, SKP2, SLA, SLCIA4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPI1, SRF, SRM, SSA2, SSBP2, SSRP1, SSSCA1, STAG3, STAT1, STAT4, STAT5A, STC1, STC2, STOML2, T3JAM, TACC1, TACC3, TAF5, TAL1, TAP1, TARP, TBCA, TCF12, TCF4, TFDP2, TFPI, TIMM17A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB10, TMSNB, TNFAIP3, TNFAIP8, TNFRSF10B, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAV1, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFN1A1, and ZYX; or at least one second microRNA in at least one of said cells selected from:
ath-MIR180aNo2, Hcd102 left, Hcd111 left, Hcd115 left, Hcd120 left, Hcd142 right, Hcd145 left, Hcd148_HPR225 left, Hcd181 left, Hcd181 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, Hcd28_HPR39 left, Hcd28_HPR39 right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd350 right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd438 right, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd511 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left, Hcd760 left, Hcd763 right, Hcd768 left, Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPR100 right, HPR129 left, HPR154 left, HPR159 left, HPR163 left, HPR169 right, HPR172 right, HPR181 left, HPR187 left, HPR199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left, HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir—490_Hcd20 right, HSHELA01, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRV1A, let-7f-2-prec2, mir-001b-1-prec1, mir-001b-2-prec, mir-007-1-prec, mir-007-2-precNo2, mir-010a-precNo1, mir-015b-precNo2, mir-016a-chr13, mir-016b-chr3, mir-017-precNo1, mir-017-precNo2, mir-018-prec, mir-019a-prec, mir-019b-1-prec, mir-019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2-prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNo1, mir-034-precNo2, mir-092-prec-13=092-1No2, mir-092-prec-X=092-2, mir-093-prec-7.1=093-1, mir-095-prec-4, mir-096-prec-7No1, mir-096-prec-7No2, mir-098-prec-X, mir-099b-prec-19No1, mir-100-1/2-prec, mir-100No1, mir-101-prec-9, mir-102-prec-1, mir-103-2-prec, mir-103-prec-5=103-1, mir-106aNo1, mir-106-prec-X, mir-107No1, mir-107-prec-10, mir-122a-prec, mir-123-precNo1, mir-123-precNo2, mir-124a-1-prec1, mir-124a-2-prec, mir-124a-3-prec, mir-125b-1, mir-125b-2-precNo2, mir-127-prec, mir-128b-precNo1, mir-128b-precNo2, mir-133a-1, mir-135-2-prec, mir-136-precNo2, mir-138-1-prec, mir-140No2, mir-143-prec, mir-144-precNo2, mir-145-prec, mir-146bNo1, mir-146-prec, mir-147-prec, mir-148aNo1, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-1-prec1, mir-154-prec1 No1, mir-155-prec, mir-15aNo1, mir-16-1No1, mir-16-2No1, mir-181a-precNo1, mir-181b-1No1, mir-181b-2No1, mir-181b-precNo1, mir-181b-precNo2, mir-181c-precNo1, mir-181dNo1, mir-188-prec, mir-18bNo2, mir-191-prec, mir-192No2, mir-193bNo2, mir-194-2No1, mir-195-prec, mir-196-2-precNo2, mir-197-prec, mir-198-prec, mir-199a-1-prec, mir-199a-2-prec, mir-199b-precNo1, mir-200a-prec, mir-200bNo1, mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir-208-prec, mir-20bNo1, mir-212-precNo1, mir-212-precNo2, mir-213-precNo1, mir-214-prec, mir-215-precNo2, mir-216-precNo1, mir-219-2No1, mir-219-prec, mir-223-prec, mir-29b-1No1, mir-29b-2=102prec7.1=7.2, mir-321No1, mir-321No2, mir-324No1, mir-324No2, mir-328No1, mir-342No1, mir-361No1, mir-367No1, mir-370No1, mir-371No1, miR-373*No1, mir-375, mir-376aNo1, mir-379No1, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423No1, mir-424No2, mir-429No1, mir-429No2, mir-4323p, mir-4325p, mir-449No1, mir-450-1, mir-450-2No1, mir-483No1, mir-484, mir-487No1, mir-495No1, mir-499No2, mir-501No2, mir-503No1, mir-509No1, mir-514-1No2, mir-515-15p, mir-515-23p, mir-516-33p, mir-516-43p, mir-518e/526c, mir-519a-1/52, mir-519a-2No2, mir-519b, mir-519c/52, mir-520c/52, mir-526a-2No1, mir-526a-2No2, MPR103 right, MPR121 left, MPR121 left, MPR130 left, MPR130 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPR174 right, MPR185 right, MPR197 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, and MPR95 left,
wherein an increase in the level of expression of said second gene or said second microRNA in at least one of said cells, relative to the level of expression of said second gene or said second microRNA in a control cell sensitive to said treatment, indicates resistance or a propensity to develop resistance to the treatment by said patient.

77. The method of claim 75, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 20 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFN1A1.

78. The method of claim 77, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 25 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFN1A1.

79. The method of claim 75, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 2300.

80. The method of claim 75, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 1, 2, 4, 6, 7, 10, 11, 16, or 24.

81. The method of claim 76, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 20 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFN1A1.

82. The method of claim 81, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 25 consecutive nucleotides of said first microRNA selected from mir-142-prec or said product of said first gene selected from ZNFNIA1.

83. The method of claim 76, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 2300.

84. The method of claim 76, wherein said at least one single-stranded oligonucleotide is substantially complementary to or substantially identical to at least 15 consecutive nucleotides of SEQ ID NO: 1, 2, 4, 6, 7, 10, 11, 16, or 24.