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

Autor:	Wang JM; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA., Hong R; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA., Demicco EG; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital and Laboratory Medicine and Pathobiology, University of Toronto, Toronto M5G 1X5, ON, Canada., Tan J; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA., Lazcano R; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Moreira AL; Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA., Li Y; Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA., Calinawan A; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA., Razavian N; Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA., Schraink T; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA., Gillette MA; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Massachusetts General Hospital Division of Pulmonary and Critical Care Medicine, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02115, USA., Omenn GS; Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA., An E; Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA., Rodriguez H; Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA., Tsirigos A; Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA., Ruggles KV; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA., Ding L; Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA., Robles AI; Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA., Mani DR; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA., Rodland KD; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA., Lazar AJ; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: alazar@mdanderson.org., Liu W; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA. Electronic address: wenke.liu@nyulangone.org., Fenyö D; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA. Electronic address: david@fenyolab.org.
Jazyk:	angličtina
Zdroj:	Cell reports. Medicine [Cell Rep Med] 2023 Sep 19; Vol. 4 (9), pp. 101173. Date of Electronic Publication: 2023 Aug 14.
DOI:	10.1016/j.xcrm.2023.101173
Abstrakt:	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. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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