Abstract A026: AI-Enabled Digital Pathology and Multi-Omics Integration for Cell-Type–Resolved Biomarker Discovery in Environment-Associated Cancers

Abstract

Rare cancers present persistent challenges in biomarker discovery and clinical translation due to limited sample availability, histopathologic heterogeneity, and fragmented molecular data. To overcome these barriers, we developed an artificial intelligence (AI)-driven integrative framework that combines digital pathology with multi-omics profiling to enable cell-type–resolved characterization of tumor biology in rare and environmentally associated cancers. Our approach leverages whole-slide imaging and computational pathology algorithms to perform high-resolution cell typing and spatial characterization of the tumor microenvironment. These spatially informed features are integrated with matched transcriptomic and proteomic data using machine learning models to identify robust, biologically interpretable biomarkers. By linking cellular architecture with molecular signatures, our framework captures tumor heterogeneity on both structural and functional levels. As a proof-of-concept, we applied this platform to arsenic-associated bladder cancer, an exposure-driven malignancy with regionally rare incidence but significant global health impact. We identified distinct cell-type–specific gene and protein expression patterns associated with disease risk and progression. Integrative modeling revealed key pathways linking environmental exposure, tumor organization, and immune microenvironment dynamics. Biomarker candidates demonstrated reproducibility across independent cohorts and tissue-based validation datasets. Importantly, this framework is designed for translational scalability, incorporating predictive modeling for patient stratification and deployment through cloud-based analytical pipelines. By enabling the integration of histopathologic features with multi-omics data in low-sample settings, our approach addresses a critical gap in rare cancer research and supports the development of clinically actionable biomarkers. This study highlights the power of AI-driven digital pathology combined with transcriptomic and proteomic integration to uncover cell-type–specific mechanisms underlying rare cancers. Our platform provides a generalizable strategy to advance precision oncology, improve diagnostic accuracy, and facilitate equitable access to data-driven care for patients with rare and understudied malignancies. Sandeep K. Singhal. AI-Enabled Digital Pathology and Multi-Omics Integration for Cell-Type–Resolved Biomarker Discovery in Environment-Associated Cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr A026.