CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer
Issued Date
2025-01-01
Resource Type
eISSN
26663791
Scopus ID
2-s2.0-105001955427
Journal Title
Cell Reports Medicine
Rights Holder(s)
SCOPUS
Bibliographic Citation
Cell Reports Medicine (2025)
Suggested Citation
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. CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer. Cell Reports Medicine (2025). doi:10.1016/j.xcrm.2025.102053 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109504
Title
CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer
Author(s)
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.
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.
Author's Affiliation
Bio Island Laboratory
Siriraj Hospital
Severance Hospital
Duke-NUS Medical School
National Cancer Centre, Singapore
A-Star, Genome Institute of Singapore
NUS Yong Loo Lin School of Medicine
Agency for Science, Technology and Research, Singapore
KU Leuven
Singapore General Hospital
Hôpitaux Universitaires de Genève
University of Glasgow
R&D center PODO Therapeutics Co. 338 Pangyo-ro
Siriraj Hospital
Severance Hospital
Duke-NUS Medical School
National Cancer Centre, Singapore
A-Star, Genome Institute of Singapore
NUS Yong Loo Lin School of Medicine
Agency for Science, Technology and Research, Singapore
KU Leuven
Singapore General Hospital
Hôpitaux Universitaires de Genève
University of Glasgow
R&D center PODO Therapeutics Co. 338 Pangyo-ro
Corresponding Author(s)
Other Contributor(s)
Abstract
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, 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.