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WO 2013/095793 Al 27 June 2013 (27.06.2013) W P O P C T (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2013/095793 Al 27 June 2013 (27.06.2013) W P O P C T (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every C12Q 1/68 (2006.01) kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, (21) International Application Number: BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, PCT/US2012/063579 DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, (22) International Filing Date: HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, 5 November 20 12 (05 .11.20 12) KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, (25) Filing Language: English NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, (26) Publication Language: English RW, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, (30) Priority Data: ZM, ZW. 61/579,530 22 December 201 1 (22. 12.201 1) US (84) Designated States (unless otherwise indicated, for every (71) Applicant: AVEO PHARMACEUTICALS, INC. kind of regional protection available): ARIPO (BW, GH, [US/US]; 75 Sidney Street, Fourth Floor, Cambridge, MA GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, SZ, TZ, 02139 (US). UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, (72) Inventors: ROBINSON, Murray; 1200 Washington EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, Street, Boston, MA 021 18 (US). FENG, Bin; 32 Mt. Ver MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, non Street, North Reading, MA 01864 (US). TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, NICOLETTI, Richard; 159 Woodland Road, Southbor- ML, MR, NE, SN, TD, TG). ough, MA 01772 (US). FREDERICK, Joshua, P.; 4 1 Garden Street, Apt. 5, Boston, MA 021 14 (US). PILI- Published: POVIC, Lejla; 474 Broadway #48, Somerville, MA 02145 — with international search report (Art. 21(3)) (US). — before the expiration of the time limit for amending the (74) Agents: GUSTAFSON, Megan, A. et al; Goodwin claims and to be republished in the event of receipt of Procter LLP, Exchange Place, Boston, MA 02109 (US). amendments (Rule 48.2(h)) * © (54) Title: IDENTIFICATION OF MULTIGENE BIOMARKERS 2 (57) Abstract: Methods for identifying multigene biomarkers for predicting sensitivity or resistance to an anti-cancer drug of in- terest, or multigene cancer prognostic biomarkers are disclosed. The disclosed methods are based on the classification of the ma malian genome into 51 transcription clusters, i.e., non-overlapping, functionally relevant groups of genes whose intra- group tran- script levels are highly correlated. Also disclosed are specific multigene biomarkers for predicting sensitivity or resistance to tivoz - anib, or rapamycin, and a specific multigene biomarker for determining breast cancer prognosis, all of which were identified using the methods disclosed herein. IDENTIFICATION OF MULTIGENE BIOMARKERS CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of and priority to U.S. provisional application serial number 61/579,530, filed December 22, 201 1; the entire contents are incorporated herein by reference. FIELD OF THE INVENTION [0002] The field of the invention is molecular biology, genetics, oncology, bioinformatics and diagnostic testing. BACKGROUND [0003] Most cancer drugs are effective in some patients, but not others. This results from genetic variation among tumors, and can be observed even among tumors within the same patient. Variable patient response is particularly pronounced with respect to targeted therapeutics. Therefore, the full potential of targeted therapies cannot be realized without suitable tests for determining which patients will benefit from which drugs. According to the National Institutes of Health (NIH), the term "biomarker" is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacological response to a therapeutic intervention." [0004] The development of improved diagnostics based on the discovery of biomarkers has the potential to accelerate new drug development by identifying, in advance, those patients most likely to show a clinical response to a given drug. This would significantly reduce the size, length and cost of clinical trials. Technologies such as genomics, proteomics and molecular imaging currently enable rapid, sensitive and reliable detection of specific gene mutations, expression levels of particular genes, and other molecular biomarkers. In spite of the availability of various technologies for molecular characterization of tumors, the clinical utilization of cancer biomarkers remains largely unrealized because few cancer biomarkers have been discovered. For example, a recent review article states: There is a critical need for expedited development of biomarkers and their use to improve diagnosis and treatment of cancer. (Cho, 2007, Molecular Cancer 6:25) [0005] Another recent review article on cancer biomarkers contains the following comments: The challenge is discovering cancer biomarkers. Although there have been clinical successes in targeting molecularly defined subsets of several tumor types - such as chronic myeloid leukemia, gastrointestinal stromal tumor, lung cancer and glioblastoma multiforme - using molecularly targeted agents, the ability to apply such successes in a broader context is severely limited by the lack of an efficient strategy to evaluate targeted agents in patients. The problem mainly lies in the inability to select patients with molecularly defined cancers for clinical trials to evaluate these exciting new drugs. The solution requires biomarkers that reliably identify those patients who are most likely to benefit from a particular agent. (Sawyers, 2008, Nature 452:548-552, at 548) Comments such as the foregoing illustrate the recognition of a need for the discovery of clinically useful predictive biomarkers, particularly in the field of oncology. [0006] There is a well-recognized need for methods of identifying multigene biomarkers for identifying which patients are suitable candidates for treatment with a given drug or therapy. This is particularly true with regard to targeted cancer therapeutics. SUMMARY [0007] Using gene expression profiling technologies, proprietary bioinformatics tools, and applied statistics, we have discovered that the mammalian genome can be usefully represented by 51 non-overlapping, functionally relevant groups of genes whose intra-group transcript level is coordinately regulated, i.e., strongly correlated, or "coherent," across various microarray datasets. We have designated these groups of genes Transcription Clusters 1-51 (TC1-TC51). Based on this discovery, we have discovered a broadly applicable method for rapidly identifying: (a) a multigene predictive biomarker for sensitivity or resistance to an anti-cancer drug of interest; or (b) a multigene cancer prognostic biomarker. We call such a multigene biomarker a Predictive Gene Set, or PGS. [0008] A PGS can be based on one transcription cluster or a multiplicity of transcription clusters. In some embodiments, a PGS is based on one or more transcription clusters in their entirety. In other embodiments, the PGS is based on a subset of genes in a single transcription cluster or subsets of a multiplicity of transcription clusters. A subset of genes from any given transcription cluster is representative of the entire transcription cluster from which it is taken, because expression of the genes within that transcription cluster is coherent. Thus, when a subset of genes in a transcription cluster is used, the subset is a representative subset of genes from the transcription cluster. [0009] Provided herein is a method for identifying a predictive gene set ("PGS") for classifying a cancerous tissue as sensitive or resistant to a particular anticancer drug or class of drug. The method comprises the steps of (a) measuring expression levels of a representative number of genes (such as 10, 15, 20 or more genes) from a transcription cluster in Table 1, in (i) a set of tissue samples from a population of cancerous tissues identified as sensitive to the anticancer drug, and (ii) a set of a tissue samples from a population of cancerous tissues identified as resistant to the anticancer drug; and (b) determining whether there is a statistically significant difference between the expression levels of the representative number of genes in the set of tissue samples from the sensitive population, and the set of tissue samples from the resistant population. A representative number of genes whose gene expression levels in the sensitive population are significantly different from its gene expression levels in the resistant population is a PGS for classifying a sample as sensitive or resistant to the anticancer drug. A Student's t test or Gene Set Enrichment Analysis (GSEA) can be used for determining whether there is a statistically significant difference between the expression levels of the representative number of genes in the set of tissue samples from the sensitive population and the set of tissue samples from the resistant population. In some embodiments, steps (a) and (b) are performed for each of the 51transcription clusters disclosed herein. The tissue sample
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