CAN-Scan: a multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer
Application of machine learning (ML) on cancer-specific pharmacogenomic datasets shows immense promise for identifying predictive response-biomarkers to enable personalized treatment. We introduce CAN-Scan, a precision oncology platform, that applies ML on next-generation pharmacogenomic datasets generated from a freeze-viable biobank of patient-derived primary cell lines (PDCs). These PDCs are screened against 84 FDA-approved drugs at clinically relevant doses (Cmax), focusing on colorectal cancer (CRC) as a model system. CAN-Scan uncovers prognostic biomarkers and alternative treatment strategies, particularly for patients unresponsive to first-line chemotherapy. Specifically, it identifies gene expression signatures linked to resistance against 5-Fluorouracil (5FU)-based drugs and a focal copy number gain on chromosome 7q, harbouring critical resistance-associated genes. CAN-Scan-derived response signatures accurately predict clinical outcomes across four independent, ethnically-diverse CRC cohorts. Notably, drug-specific ML models reveal Regorafenib and Vemurafenib as alternative treatments for BRAF-expressing, 5FU-insensitive CRC. Altogether, this approach demonstrates significant potential in improving biomarker-discovery and guiding personalized treatments. Overall design: Patient derived cells (PDC) were generated from resected samples. Baseline gene expression profiles of these samples were generated.