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, 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 (C<sub>max</sub>), 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.

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Publication Details

Journal
Cell Reports Medicine
Published
2025-04-01
DOI
https://doi.org/10.1016/j.xcrm.2025.102053
Citations
6
Primary Topic
Colorectal Cancer Treatments and Studies
Type
article
Field-Weighted Citation Impact
6.27
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article

CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer

Chayaporn Suphavilai, Manop Pithukpakorn, Yun Shen Chan, Emile Tan et al.
6 citations
Cell Reports Medicine
Colorectal Cancer Treatments and Studies
6.27
article

CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer

Chayaporn Suphavilai, Manop Pithukpakorn, Yun Shen Chan, Emile Tan, Woramin Riansuwan, Somponnat Sampattavanich, Yu Amanda Guo, Ramanuj DasGupta, N. Gopalakrishna Iyer, Iain Beehuat Tan, Huck‐Hui Ng, Aixin Yap, Shuting Han, Atthaphorn Trakarnsanga, Ngak Leng Sim, Meihuan Chang, Sabine Tejpar, Isaac Seow‐En, A. Teh, Shumei Chia, Pariyada Tanjak, Anders J. Skanderup, Padmaja Anand, Amphun Chaiboonchoe, Rafael Peres da Silva, Matan Thangavelu Thangavelu, Niranjan Nagarajan, Justine Jia Wen Seow, Maki Murata‐Hori, Giridharan Periyasamy, Clarinda Chua Wei Ling, Daniel Muliaditan, Petros Tsantoulis, Nguyễn Thị Hồng, Niranjan Shirgaonkar, Jung Eun Kim, Tae Il Kim, Siyu Wang, Xiaoqian Zhang, Ran Gao, Dong-Keon Kim, David Park, Elena Yaqing Yong, A. Gan, Carmen S. Yong, Vitoon Chinswangwatanakul, Choon Kong Yap, Jiajia Pan, Linping Hui, Fu Wan Pei Cherylin
article en
6 citations

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 (C<sub>max</sub>), 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.

Cell Reports MedicineVol. 6(4)
University of Geneva (CH), Agency for Science, Technology and Research (SG), Siriraj Hospital (TH), Yonsei University (KR), Singapore General Hospital (SG), Mahidol University (TH), Severance Hospital (KR), Experimental Drug Development Centre (SG), CHA University Bundang Medical Center (KR), National Cancer Centre Singapore (SG), Gangnam Severance Hospital (KR), Genome Institute of Singapore (SG), KU Leuven (BE)
Good health and well-being
Openalex Percentile: Top 3%
Colorectal Cancer Treatments and Studies
6.27
DOIOpen Access PDFOpenAlex
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