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US-12626784-B2 - Colorectal cancer consensus molecular subtype classifier codesets and methods of use thereof

US12626784B2US 12626784 B2US12626784 B2US 12626784B2US-12626784-B2

Abstract

Provided herein is a consensus molecular subtype (CMS) classifier for colorectal cancer patients. Also provided are methods of using the classifier to identify a clinically beneficial therapeutic regime for each patient as well as methods of treating a patient accordingly Custom Nanostring code sets, which work on formalin-fixed, paraffin-embedded samples, are provide for use in determining the CMS for a colorectal cancer patient.

Inventors

  • Jeffrey Morris
  • Scott Kopetz
  • Dipen MARU
  • David G. MENTER

Assignees

  • BOARD OF REGENTS, THE UNIVERSITY OF TEXAS SYSTEM

Dates

Publication Date
20260512
Application Date
20200402

Claims (17)

  1. 1 . A method of treating a patient having colorectal cancer, the method comprising: (i) classifying a cancer status of a subject having colorectal cancer by: (a) obtaining a tumor sample from the subject; (b) measuring expression levels of a plurality of genes in the tumor sample, wherein the plurality of genes comprises at least 20 genes selected from the group consisting of ACSL5, ADGRG6, AGR2, ANKRD27, ANP32B, APOL1, ARHGAP10, ARHGAP32, ARID3A, ASF1B, ASPH, ASRGL1, ATP10B, ATP9A, BCL2L15, BCL6, BST2, C20orf196, C3orf14, CA8, CAB39L, CACNAID, CCDC109B, CCNO, CDC42EP2, CDC42EP5, CDCA2, CDCA7, CDHR1, CEBPA, CENPE, CEP192, CKAP2, CPNE1, CREB3L1, CRYM, CSGALNACT2, CYTH3, DACT1, DAPK2, DEPDC1, DIDO1, DOCK5, DPM1, EIF6, ENO2, EPB41L4B, ERRFI1, ESRP1, FABP5, FAM105A, FAM122B, FAM3D, FAM84A, FARP1, FBXO34, FITM2, FOXA2, FRZB, FUT8, GALNT5, GALNT8, GFPT1, GGH, GNG4, GPCPD1, GPR143, GPR153, GRM8, GTF2A2, GUCY2C, GYG2, HEPH, HES2, HLA-E, HLX, HOXD11, HSPA4L, HSPA6, HUNK, IFIT3, ILDR1, IMMP2L, JADE3, KCNK1, KCTD1, KIF2C, KLK1, LDLRAD3, LEFTY1, LM04, LNX21, LRRC16A, MAGED1, MAP2K6, MAPRE1, MLLT3, MLPH, MPP1, MRAP2, MT2A, MYRIP, NCAPH, NDFIP2, NEDD9, NOL4L, NR112, NUCB2, OSTM1, PAK6, PALB2, PALLD, PBX1, PCMTD2, PCP4, PDP1, PDZD2, PDZKIIP1, PIGR, PIGU, PITX2, PLCB1, PLCH1, PNP, POLD3, POP1, PPPIR14C, PPP1R14D, PPP1R3D, PPP3CA, PRAP1, PRC1, PRDX5, PRLR, PRR15, RAPIGAP, RARRES3, RBMS1, RETNLB, RNF183, RNF43, SAMD5, SEMASA, SERPINB1, SESN1, SHROOM4, SLC25A37, SLC30A2, SLC4A11, SLC9A2, SLCO1B3, SLCO2B1, SOCS6, SPIRE2, SRPK1, ST6GALNAC1, STAT2, TC2N, TFAP2A, TMEM61, TMEM64, TNFRSF11A, TNNC2, TNS4, TOMM34, TRIM7, TRNP1, TSPAN6, UGT8, UPF3A, USP14, UTP15, WFDC2, XBP1, ZCCHC24, ZDHHC23, ZG16B, ZNF415, ZSCAN18, and ZWINT; (c) generating an expression profile based on a comparison between the expression level of the plurality of genes in the sample from the subject and a corresponding expression level obtained from a reference sample derived from a different subject having a known cancer status; and (d) categorizing the cancer status of the subject based on the expression profile; and (ii) administering an anti-cancer therapy to the subject based on the cancer status.
  2. 2 . The method of claim 1 , wherein step (c) comprises applying a weighted support vector machine to the expression level of the plurality of genes.
  3. 3 . The method of claim 1 , wherein the plurality of genes comprises at least 75 genes selected from Table 1.
  4. 4 . The method of claim 1 , wherein the plurality of genes comprises at least 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, or 195 genes selected from Table 1.
  5. 5 . The method of claim 1 , wherein the plurality of genes comprises all 200 genes selected from Table 1.
  6. 6 . The method of claim 1 , wherein the plurality of genes comprises the 75-gene classifier from Table 1.
  7. 7 . The method of claim 1 , wherein the plurality of genes comprises the 100-gene classifier from Table 1.
  8. 8 . The method of claim 1 , wherein the cancer status is categorized as consensus molecular subtype 1 (CMS1), consensus molecular subtype 2 (CMS2), consensus molecular subtype 3 (CMS3), or consensus molecular subtype 4 (CMS4).
  9. 9 . The method of claim 1 , wherein expression level of the plurality of genes is measured by detecting a level of mRNA transcribed from the plurality of genes.
  10. 10 . The method of claim 1 , wherein the expression level of the plurality of genes is measured by detecting a level of polypeptide encoded by the plurality of genes.
  11. 11 . The method of claim 1 , wherein the sample is a formalin-fixed, paraffin-embedded sample.
  12. 12 . The method of claim 1 , wherein the sample is a fresh frozen sample.
  13. 13 . The method of claim 1 , wherein the anti-cancer therapy is a chemotherapy, a radiation therapy, a hormonal therapy, a targeted therapy, an immunotherapy or a surgical therapy.
  14. 14 . The method of claim 1 , wherein if the subject is determined to have a CMS1 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.
  15. 15 . The method of claim 1 , wherein if the subject is determined to have a CMS2 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor.
  16. 16 . The method of claim 1 , wherein if the subject is determined to have a CMS3 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor.
  17. 17 . The method of claim 1 , wherein if the subject is determined to have a CMS4 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.

Description

REFERENCE TO RELATED APPLICATIONS The present application is a national phase application under 35 U.S.C. § 371 of International Application No. PCT/US2020/026409, filed Apr. 2, 2020, which claims the priority benefit of U.S. provisional application No. 62/828,098, filed Apr. 2, 2019, the entire contents of each of which are incorporated herein by reference. REFERENCE TO A SEQUENCE LISTING The instant application contains a Sequence Listing, which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Mar. 27, 2020, is named UTFCP1374WO_ST25.txt and is 46.2 kilobytes in size. BACKGROUND 1. Field The present invention relates generally to the fields of medicine and oncology. More particularly, it concerns compositions and methods for classifying colorectal cancer as well as using such classification in treating patients having colorectal cancer. 2. Description of Related Art Colorectal cancer is the third most common cancer and a leading cause of cancer death worldwide. Patients with stage III colon cancer have a 60 to 70% chance of remaining disease-free, with slightly over 50% relative risk reduction with the use of adjuvant 5-FU and oxaliplatin-based regimens. As a result, approximately 1 in 5 patients derive benefit with adjuvant treatment, and for the remaining four patients, the use of adjuvant therapy results in added toxicity with no benefit. Many low-risk stage III patients may opt for single agent 5-FU therapy given the modest absolute risk reduction and added toxicity from oxaliplatin. Conversely, the early identification of high-risk patients allows a more informed discussion about the risks and benefits of adjuvant therapy and a more risk-adapted patient management. Many efforts have been made to identify reliable risk factors to assess individual risk of these patients, including the use of gene expression signatures. Current efforts in the NCI Colon Cancer Task Force of the GI Steering Committee are focused on identification of high-risk stage III colon cancer and efforts to escalate intensity of adjuvant chemotherapy. This was identified as one of the top three priorities for CRC research in 2015-2016 by the Colon Cancer Task Force. This current push recognizes that there are many high-risk stage III patients that are not being adequately treated with the current regimens given their high recurrence rate. Conversely, international efforts are ongoing to identify the proper intensity of adjuvant therapy including discussions about over-utilization of oxaliplatin for low-risk stage III patients (as exemplified by the CALGB 80702 study of 3 months of FOLFOX chemotherapy instead of 6 months) and treatment intensification (in the ECOG EA2153 study to escalate to FOLFOXIRI in high-risk patients) (Kurniali et al., 2014; Yaffee et al., 2015). Several reviews have reiterated the importance of prognostication in stage III colon cancer, given the increased recognition of long-term toxicities and heterogeneity of outcomes in the population (Goel et al., 2014; Kelley et al., 2011). The two most commonly utilized popular and commercially available assays are Oncotype DX (Genomic Health Inc.) and Coloprint (Agendia Inc.). These assays were developed as prognostic biomarkers in stage II and III colon cancers. The Coloprint assay in validation studies demonstrated a five-year relapse-free survival rate for low risk patients of 87.6% for high-risk patients of 67.2% (Kennedy et al., 2011). The hazard ratio in the multivariate model was 2.69 (P=0.003). While this assay provides strong prognostic information, the assay requires fresh frozen tissue for analysis. Although a prospective study is ongoing, the fresh tissue requirement precludes practical application in the community. Efforts to transfer this signature from an Agilent array to formalin fixed paraffin embedded samples using the same platform have not been successful and are no longer being pursued. The Oncotype DX assay in contrast has been designed to utilize FFPE samples and utilizes a 13-gene RT-PCR technique that is more robust to sample degradation. In the validation cohorts for this assay the interquartile range was fairly narrow in the continuous recurrence score and resulted in a hazard ratio of 1.38 (P=0.004) (Goel et al., 2014). A second validation looking at the NSABP C07 study utilized tertiles of risk and identified only as an 8%-9% difference in absolute risk between the groups in stage III colon cancer. Other tools have not been as well studied and have similarly poor performance in multivariate overall survival models in mixed cohorts of stage II and III colon cancer (Mittempergher et al., 2011; Kelley et al., 2011). As such, there is a clear need for better tools to prognosticate stage III patients to help with implementation of value-based patient management decisions. SUMMARY Provided herein is a gene set and customized list of gene probes for use in classify