Details

Autor(en) / Beteiligte

• Wang, Joshua M
• Hong, Runyu
• Demicco, Elizabeth G
• Tan, Jimin
• Lazcano, Rossana
• Moreira, Andre L
• Li, Yize
• Calinawan, Anna
• Razavian, Narges
• Schraink, Tobias
• Gillette, Michael A
• Omenn, Gilbert S
• An, Eunkyung
• Rodriguez, Henry
• Tsirigos, Aristotelis
• Ruggles, Kelly V
• Ding, Li
• Robles, Ana I
• Mani, D R
• Rodland, Karin D
• Lazar, Alexander J
• Liu, Wenke
• Fenyö, David

Titel

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

Ist Teil von

• Cell reports. Medicine, 2023-09, Vol.4 (9), p.101173-101173, Article 101173

Ort / Verlag

United States: Cell Press

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.