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
  • 1. Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore. Electronic address: [email protected].
  • 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: [email protected].
  • 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: [email protected].
  • 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: [email protected].
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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