一个可扩展的多模态框架用于跨多种癌症类型的无偏倚风险生物标志物发现

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.