Abstract A19: A qPCR assay, OncoScore Colon, predicts resistance to cetuximab in formalin-fixed, paraffin-embedded colorectal cancer tissue independent of KRAS status

Abstract

Abstract Gene expression modules derived from an unsupervised analysis of 20 independent microarray datasets comprising more than 2,000 colorectal cancer patients were identified. Each module represents a set of highly co-expressed genes related to an important aspect of underlying cancer variability. Modules containing genes related to epithelial and mesenchymal biology associated with sensitivity and resistance to EGFR family targeted inhibitors (gefitinib and lapatinib), respectively. In retrospective analysis of clinical samples, the epithelial-mesenchymal axis associated with cetuximab response in two independent patient cohorts. The first study was a Phase II clinical trial (Khambata-Ford et al., J Clin Oncol, 2007) with accompanying microarray data from pre-treatment metastatic colorectal tumor biopsies. Expression of the modules was determined by normalizing and averaging co-expressed module genes. Patients with a more epithelial and less mesenchymal module expression profile were enriched for cetuximab response. An independent cohort of patients was analyzed using module scores that were generated from a qPCR gene expression module test, OncoScore™ Colon, which quantifies modules by averaging three representative module genes relative to housekeeping genes using formalin-fixed-paraffin-embedded primary tumor samples. In these patients, presence of the mesenchymal module was significantly associated with a decrease in progression free survival. Notably, the status of the mesenchymal module was independent of KRAS mutation status—as KRAS mutations occurred in both mesenchymal module-positive and -negative patients. Further clinical studies are ongoing to continue to support the development of the OncoScore™ Colon assay and to further test the predictive capacity of the module with regards to cetuximab resistance and other MAPK pathway inhibitors. This study demonstrates the value of a gene expression module-based qPCR panel for stratifying colorectal cancer patients for treatment response, and suggests that our approach may have immediate utility for cetuximab treatment response prediction.