Integrative multiomics-histopathology analysis for breast cancer classification

Abstract Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hemat...

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Autores principales:	Yasha Ektefaie, William Yuan, Deborah A. Dillon, Nancy U. Lin, Jeffrey A. Golden, Isaac S. Kohane, Kun-Hsing Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/0054a716bf894cfeb760d0ff078ec507
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spelling	oai:doaj.org-article:0054a716bf894cfeb760d0ff078ec5072021-12-05T12:20:17ZIntegrative multiomics-histopathology analysis for breast cancer classification10.1038/s41523-021-00357-y2374-4677https://doaj.org/article/0054a716bf894cfeb760d0ff078ec5072021-11-01T00:00:00Zhttps://doi.org/10.1038/s41523-021-00357-yhttps://doaj.org/toc/2374-4677Abstract Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.Yasha EktefaieWilliam YuanDeborah A. DillonNancy U. LinJeffrey A. GoldenIsaac S. KohaneKun-Hsing YuNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Breast Cancer, Vol 7, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yasha Ektefaie William Yuan Deborah A. Dillon Nancy U. Lin Jeffrey A. Golden Isaac S. Kohane Kun-Hsing Yu Integrative multiomics-histopathology analysis for breast cancer classification
description	Abstract Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.
format	article
author	Yasha Ektefaie William Yuan Deborah A. Dillon Nancy U. Lin Jeffrey A. Golden Isaac S. Kohane Kun-Hsing Yu
author_facet	Yasha Ektefaie William Yuan Deborah A. Dillon Nancy U. Lin Jeffrey A. Golden Isaac S. Kohane Kun-Hsing Yu
author_sort	Yasha Ektefaie
title	Integrative multiomics-histopathology analysis for breast cancer classification
title_short	Integrative multiomics-histopathology analysis for breast cancer classification
title_full	Integrative multiomics-histopathology analysis for breast cancer classification
title_fullStr	Integrative multiomics-histopathology analysis for breast cancer classification
title_full_unstemmed	Integrative multiomics-histopathology analysis for breast cancer classification
title_sort	integrative multiomics-histopathology analysis for breast cancer classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0054a716bf894cfeb760d0ff078ec507
work_keys_str_mv	AT yashaektefaie integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT williamyuan integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT deborahadillon integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT nancyulin integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT jeffreyagolden integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT isaacskohane integrativemultiomicshistopathologyanalysisforbreastcancerclassification AT kunhsingyu integrativemultiomicshistopathologyanalysisforbreastcancerclassification
_version_	1718371996716236800

Integrative multiomics-histopathology analysis for breast cancer classification

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