Key result
Multimodal framework integrating genetics, proteomics, and clinical data drives biomarker discovery across 22 cancer types.
Why the study?
Most existing cancer risk models rely on single modalities and hand-selected features rather than systematic, unbiased integration of genetics, proteomics, and clinical phenotyping.
Does a multimodal biomarker discovery engine improve cancer risk prediction compared to individual modalities in a large population cohort?
Observational (n=502,505)
Yes
Does a multimodal biomarker discovery engine improve cancer risk prediction compared to individual modalities in a large population cohort?
A scalable multimodal machine-learning framework integrating genetics, proteomics, and clinical data has been developed for unbiased cancer risk biomarker discovery.
Should not yet change clinical cancer risk prediction; leaves open added value of multimodal over single modalities pending validation.
Background: Most existing cancer risk models are built on single modalities and hand-selected features. Systematic, unbiased integration of germline genetics, plasma proteomics, and deep clinical phenotyping holds promise for revealing novel risk biomarkers across diverse cancer types. Methods: We developed a multi-modal biomarker discovery engine that can be used for discovering risk, diagnostic, prognostic, predictive and monitoring biomarkers. Currently the framework handles: • Germline genetics and polygenic risk scores • High-dimensional plasma proteomics (Olink) • Longitudinal primary-care records, hospital episodes, laboratory results, lifestyle questionnaires, and cancer registry linkages Key design features include modular cohort handling, automated data preprocessing, and machine-learning models (including: gradient boosting and neural networks). Application: The platform is currently deployed on the UK Biobank (n = 502,505 participants; >46,000 incident cancers across 22 cancer types) with active model training and biomarker discovery in progress. The architecture is cohort-agnostic and ready for direct application to emerging large-scale resources including Our Future Health and the All of Us Research Program. Poster presentation: We will demonstrate the platform’s configurability through examples of cancer-risk modelling in the UK Biobank, showcasing: (i) comparative performance of individual modalities versus multimodal ensembles, (ii) cancer-specific patterns of modality contribution, and (iii) the effect of time-window filtering on separating true predictive signals from prevalent disease effects. Conclusions: By eliminating bias in feature engineering and supporting seamless integration of diverse health data streams, this scalable framework provides a robust foundation for data-driven discovery of multimodal cancer risk biomarkers, paving the way for next-generation precision prevention strategies. Citation Format: Constantin Petrescu, Lisa Schmunk, Jack Monahan, Abbas Salami, Thomas M. Stubbs. A scalable multimodal framework for unbiased risk biomarker discovery across multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1116.
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Petrescu et al. (2026) conducted an observational in Cancer (n=502,505). Multimodal biomarker discovery framework vs. Individual modalities was evaluated on Comparative performance of individual modalities versus multimodal ensembles. A multimodal framework integrating genetics, proteomics, and clinical data was deployed on 502,505 UK Biobank participants with >46,000 incident cancers across 22 types for biomarker discovery.
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