Abstract
Precision oncology involves the use of predictive biomarkers to personalize treatment. However, for most cancer therapeutics or combination regimens, effective biomarkers have been elusive. This challenge has fuelled efforts to interrogate increasingly diverse and complex clinical and molecular determinants of treatment response. Some molecular predictors have been identified (for example, based on analysis of transcriptomic or imaging data), although the limited reproducibility and robustness of many of these candidate biomarkers make them difficult to apply in clinical practice. Moreover, different types of predictor must often be combined to optimize treatment selection (for example, gene signatures plus patient characteristics). Computational methods, including machine learning and artificial intelligence approaches, provide opportunities to identify predictive patterns in both clinical data and preclinical datasets and to predict treatment response for individual patients. Such approaches also offer opportunities to predict the efficacy or synergy of drug combinations, for example, via extrapolation from correlations of monotherapy responses or by linking the cellular responses observed in preclinical drug screens with molecular and clinical data from patients. In this Review, we describe the application of computational methods to predictive biomarker discovery, including current progress, key challenges facing this field, and future opportunities.
Key points
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The discovery and validation of predictive biomarkers is an essential prerequisite to advances in personalized cancer therapies.
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Predictive biomarkers can be derived from analyses of molecular, radiological and/or histopathological data as well as clinical data, and multiple modalities can be combined to capture more complex biological associations.
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Both preclinical models and patient data are useful sources for the discovery of predictive biomarkers, and several publicly available collections exist that can also support biomarker investigations.
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Computational methods and machine learning algorithms can be used to predict responses to treatment both with monotherapies and combination regimens.
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Several remaining challenges relating to the discovery of predictive biomarkers must be addressed in order to improve the generalizability of predictive models, to better capture tumour heterogeneity and to overcome the various limitations that can hinder clinical translation.
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Acknowledgements
We sincerely thank L. Siu of the Princess Margaret Cancer Centre for her insightful comments on this Review. The authors’ research is supported by funding from the ARPA-H ADAPT Program (OT 140D042590013 to T.I. and B.H.-K.).
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X.W. and J.N. initiated and coordinated the Review. X.W., J.N., K.N., M.M., P.W., A.S., T.A. and B.H.-K. researched data for the manuscript and made a substantial contribution to discussions of content. X.W. and P.L.B. provided clinical input. T.I., T.A. and B.H.-K. supervised the Review. All authors edited and/or reviewed the manuscript prior to submission.
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P.L.B. reports research funding (to the institution) from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bicara Therapeutics, Bristol Myers Squibb, Daiichi Sankyo, Genentech/Roche, GlaxoSmithKline, LegoChem, Lilly, Merck, Medicenna, Novartis, Seagen/Pfizer, Takeda and Zymeworks. He also reports uncompensated honoraria/consultancy with Amgen, Daiichi Sankyo, Gilead, Janssen, Lilly, Merck, Repare, Seattle Genetics and Zymeworks, and a compensated advisory role for Boehringer Ingelheim. T.I. is a co-founder, member of the advisory board, and has an equity interest in Data4Cure and Serinus Biosciences, is a consultant of and has an equity interest in Ideaya Biosciences, and is a member of the advisory editorial board of The EMBO Journal. The terms of all consultancy and equity arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. B.H.-K. is part of the scientific advisory boards of Break Through Cancer, Commonwealth Cancer Consortium (USA), Consortium de Recherche Biopharmaceutique (CQDM; Quebec, Canada), the Canadian Institute of Health Research — Institute of Genetics, Cancer Grand Challenges (UK) and Chriners Children (Florida, USA), is a co-founder of the MAQC (Massive Analysis and Quality Control) Society and is part of the board of directors of AACR International (Canada) and The American Association for Cancer Research (USA). X.W., J.N., K.N., M.M., P.W., A.S. and T.A. declare no competing interests.
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Wang, X., Nguyen, J., Nader, K. et al. Discovery of predictive biomarkers for cancer therapy through computational approaches. Nat Rev Clin Oncol (2026). https://doi.org/10.1038/s41571-025-01109-8
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DOI: https://doi.org/10.1038/s41571-025-01109-8
