Chia S.Wen Seow J.J.Peres da Silva R.Suphavilai C.Shirgaonkar N.Murata-Hori M.Zhang X.Yong E.Y.Pan J.Thangavelu M.T.Periyasamy G.Yap A.Anand P.Muliaditan D.Chan Y.S.Siyu W.Yong C.W.Hong N.Ran G.Sim N.L.Guo Y.A.Yi Teh A.X.Wei Ling C.C.Wei Tan E.K.Pei Cherylin F.W.Chang M.Han S.Seow-En I.Chen Hui L.R.Hsia Gan A.H.Yap C.K.Ng H.H.Skanderup A.J.Chinswangwatanakul V.Riansuwan W.Trakarnsanga A.Pithukpakorn M.Tanjak P.Chaiboonchoe A.Park D.Kim D.K.Iyer N.G.Tsantoulis P.Tejpar S.Kim J.E.Kim T.I.Sampattavanich S.Tan I.B.Nagarajan N.DasGupta R.Mahidol University2025-04-122025-04-122025-01-01Cell Reports Medicine (2025)https://repository.li.mahidol.ac.th/handle/123456789/109504Application 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, which 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 Food and Drug Administration (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 (5-FU)-based drugs and a focal copy-number gain on chromosome 7q, harboring 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, 5-FU-insensitive CRC. Altogether, this approach demonstrates significant potential in improving biomarker discovery and guiding personalized treatments.Biochemistry, Genetics and Molecular BiologyArticleSCOPUS10.1016/j.xcrm.2025.1020532-s2.0-10500195542726663791