Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wang JM

PROVIDER: S-EPMC10518635 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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

Wang Joshua M JM Hong Runyu R Demicco Elizabeth G EG Tan Jimin J Lazcano Rossana R Moreira Andre L AL Li Yize Y Calinawan Anna A Razavian Narges N Schraink Tobias T Gillette Michael A MA Omenn Gilbert S GS An Eunkyung E Rodriguez Henry H Tsirigos Aristotelis A Ruggles Kelly V KV Ding Li L Robles Ana I AI Mani D R DR Rodland Karin D KD Lazar Alexander J AJ Liu Wenke W Fenyö David D

Cell reports. Medicine 20230814 9

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 pow ...[more]

PMID: 37582371

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Dataset Information

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

Publications

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

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