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

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

  • Cell Rep Med. 2025 Apr 15;6(4):102053. doi: 10.1016/j.xcrm.2025.102053.
Shumei Chia 1,  Justine Jia Wen Seow 2,  Rafael Peres da Silva 2,  Chayaporn Suphavilai 2,  Niranjan Shirgaonkar 2,  Maki Murata-Hori 2,  Xiaoqian Zhang 2,  Elena Yaqing Yong 2,  Jiajia Pan 3,  Matan Thangavelu Thangavelu 4,  Giridharan Periyasamy 4,  Aixin Yap 2,  Padmaja Anand 2,  Daniel Muliaditan 2,  Yun Shen Chan 5,  Wang Siyu 2,  Chua Wei Yong 2,  Nguyen Hong 2,  Gao Ran 2,  Ngak Leng Sim 2,  Yu Amanda Guo 2,  Andrea Xin Yi Teh 3,  Clarinda Chua Wei Ling 3,  Emile Kwong Wei Tan 6,  Fu Wan Pei Cherylin 6,  Meihuan Chang 6,  Shuting Han 3,  Isaac Seow-En 6,  Lionel Raphael Chen Hui 6,  Anna Hwee Hsia Gan 2,  Choon Kong Yap 2,  Huck Hui Ng 7,  Anders Jacobsen Skanderup 2,  Vitoon Chinswangwatanakul 8,  Woramin Riansuwan 9,  Atthaphorn Trakarnsanga 9,  Manop Pithukpakorn 10,  Pariyada Tanjak 8,  Amphun Chaiboonchoe 11,  Daye Park 12,  Dong Keon Kim 12,  Narayanan Gopalakrishna Iyer 3,  Petros Tsantoulis 13,  Sabine Tejpar 14,  Jung Eun Kim 15,  Tae Il Kim 16,  Somponnat Sampattavanich 11,  Iain Beehuat Tan 17,  Niranjan Nagarajan 18,  Ramanuj DasGupta 19
Affiliations

Affiliations

  • 1 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore. Electronic address: chiasm1@gis.a-star.edu.sg.
  • 2 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore.
  • 3 National Cancer Centre, Singapore, Singapore.
  • 4 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Experimental Drug Development Centre (EDDC), A∗STAR, Singapore, Singapore.
  • 5 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong, China.
  • 6 Department of Colorectal Surgery, Singapore General Hospital, Singapore, Singapore.
  • 7 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • 8 Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Siriraj Cancer Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • 9 Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • 10 Siriraj Genomics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol, Bangkok, Thailand.
  • 11 Siriraj Center of Research Excellence for Precision Medicine and Systems Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • 12 Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
  • 13 Hôpitaux Universitaires de Genève, University of Geneva, Geneva, Switzerland.
  • 14 Department of Oncology, Katholieke Universiteit Leuven, Leuven, Belgium.
  • 15 R&D center PODO Therapeutics Co. 338 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13493, Republic of Korea.
  • 16 R&D center PODO Therapeutics Co. 338 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13493, Republic of Korea; Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
  • 17 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; National Cancer Centre, Singapore, Singapore; Duke-National University of Singapore Medical School, Singapore, Singapore. Electronic address: iaintan@duke-nus.edu.sg.
  • 18 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. Electronic address: nagarajann@gis.a-star.edu.sg.
  • 19 Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; CRUK Scotland Institute, School of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK. Electronic address: ramanuj.dasgupta@glasgow.ac.uk.
PMID: 40187357 DOI: 10.1016/j.xcrm.2025.102053
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.

Keywords

5-FU resistance; PDC; biomarker; chromosome 7 amplification; colorectal cancer; drug screen; head and neck cancer; machine learning; patient-derived cancer models; pharmacogenomics; precision oncology.

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