October 13, 2023
Journal Article

Deep learning integrates histopathology and proteogenomics at a pan-cancer level


We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.

Published: October 13, 2023


Wang J., R. Hong, E.G. Demicco, J. Tan, R. Lazcano, A.L. Moreira, and Y. Li, et al. 2023. Deep learning integrates histopathology and proteogenomics at a pan-cancer level. Cell Reports Medicine 4, no. 9:Art. No. 101173. PNNL-SA-191052. doi:10.1016/j.xcrm.2023.101173

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