Adverse drug effects remain a major barrier to safe and effective cancer therapy, underscoring the need for tools that predict treatment-related toxicities. We analyzed multimodal real-world data from 14,596 cancer patients across 38 cancer entities, encompassing 330 clinical, tumor, and imaging characteristics, along with 89 anticancer agents. Hematological adverse events (HAE), defined by nadirs of hemoglobin, leukocyte, neutrophil, and platelet values within two months of treatment initiation, were highly prevalent (87.7%; 33.1% severe). We developed Toxix, an explainable artificial intelligence (xAI) framework modeling interactions between patient characteristics and drug combinations. Toxix achieved strong predictive performance for severe toxicities (median AUROC 0.85 for anemia; >0.76 for leukopenia, neutropenia, and thrombocytopenia) and was validated in an external cohort of 2,768 patients with non-small cell lung cancer. Model explainability enabled systematic characterization of drug-patient interactions underlying HAEs. Toxix provides a real-world informed framework for personalized and toxicity-aware cancer therapy planning.

B.H. reports consulting fees from Johnson&Johnson, Bayer, ABX, Astellas, Merck, Amgen, MSD/Pfizer, Novartis, BMS, Monrol, Onkowissen, POINT Biopharma, Ipsen, AstraZeneca, Lightpoint medical, Telix, and Accord Healthcare; travel support from AstraZeneca, BMS, Janssen, Bayer, Pfizer and Ipsen; grants or contracts from Janssen, Deutsche Forschungsgesellschaft, Novartis, and BMS; participation on data safety monitoring boards for Johnson&Johnson and ABX. V.G: Speaker fee: Bristol-Myers Squibb, Ipsen, Eisai, MSD, Merck KGa, AstraZeneca, AAA/Novartis, Amgen, Johnson & Johnson, Teilx Pharmaceuticals, Gilead Sciences, Roche. Consulting fee: Bristol-Myers Squibb, Pfizer, Novartis, MSD, Ipsen, Johnson & Johnson, Eisai, Debiopharm, Gilead Sciences, Oncorena, Synthekine, Recordati. Travel support: Pfizer, Johnson & Johnson, Merck KGa, Ipsen, Amgen. J.Kleesiek, J.Keyl, P.Keyl, F.K., G.M., and K.-R.M have filed a patent application related to this work.

J.Keyl is supported by a German Research Foundation (DFG)-funded clinician scientist program (FU 356/12-2). KRM was supported in part by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D, 01IS18037A as well as Berlin Institute for the Foundations of Learning and Data (BIFOLD). Furthermore, KRM was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2019-II190079, Artificial Intelligence Graduate School Program, Korea University) and grant funded by the Korea government (MSIT) (No. RS-2024-00457882, AI Research Hub Project). The West German Cancer Center Essen is funded by an Oncology Center of Excellence grant by the German Cancer Aid (Deutsche Krebshilfe, 70116526).

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The study was approved by the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen (No. 21-10347-BO).

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Data Availability Statement Anonymized data are available upon reasonable request. Data cannot be shared with investigators outside the institution without consent. The data that support the findings of this study have been originated by and are the property of Flatiron Health, Inc. Requests for data sharing by license or by permission for the specific purpose of replicating results in this manuscript can be submitted to PublicationsDataAccessflatiron.com.