Deep learning integrates histopathology and proteogenomics at a pan-cancer level
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
|---|---|
| Enthalten in: |
Cell reports. Medicine - 4(2023), 9 vom: 19. Sept., Seite 101173 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Joshua M [VerfasserIn] |
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| Links: |
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| Themen: |
CPTAC |
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| Anmerkungen: |
Date Completed 22.09.2023 Date Revised 26.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.xcrm.2023.101173 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360816762 |
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| 100 | 1 | |a Wang, Joshua M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep learning integrates histopathology and proteogenomics at a pan-cancer level |
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| 500 | |a Date Completed 22.09.2023 | ||
| 500 | |a Date Revised 26.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a CPTAC | |
| 650 | 4 | |a cancer imaging | |
| 650 | 4 | |a cancer proteogenomics | |
| 650 | 4 | |a computational pathology | |
| 650 | 4 | |a molecular diagnostics | |
| 700 | 1 | |a Hong, Runyu |e verfasserin |4 aut | |
| 700 | 1 | |a Demicco, Elizabeth G |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Jimin |e verfasserin |4 aut | |
| 700 | 1 | |a Lazcano, Rossana |e verfasserin |4 aut | |
| 700 | 1 | |a Moreira, Andre L |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yize |e verfasserin |4 aut | |
| 700 | 1 | |a Calinawan, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Razavian, Narges |e verfasserin |4 aut | |
| 700 | 1 | |a Schraink, Tobias |e verfasserin |4 aut | |
| 700 | 1 | |a Gillette, Michael A |e verfasserin |4 aut | |
| 700 | 1 | |a Omenn, Gilbert S |e verfasserin |4 aut | |
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| 700 | 1 | |a Rodriguez, Henry |e verfasserin |4 aut | |
| 700 | 1 | |a Tsirigos, Aristotelis |e verfasserin |4 aut | |
| 700 | 1 | |a Ruggles, Kelly V |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Robles, Ana I |e verfasserin |4 aut | |
| 700 | 1 | |a Mani, D R |e verfasserin |4 aut | |
| 700 | 1 | |a Rodland, Karin D |e verfasserin |4 aut | |
| 700 | 1 | |a Lazar, Alexander J |e verfasserin |4 aut | |
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| 700 | 1 | |a Fenyö, David |e verfasserin |4 aut | |
| 700 | 0 | |a Clinical Proteomic Tumor Analysis Consortium |e verfasserin |4 aut | |
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| 700 | 1 | |a Chinnaiyan, Arul M |e investigator |4 oth | |
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