Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

Abstract Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutat...

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Autores principales:	Hui Qu, Mu Zhou, Zhennan Yan, He Wang, Vinod K. Rustgi, Shaoting Zhang, Olivier Gevaert, Dimitris N. Metaxas
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/08b773bbbe0c42f8a2e65a6cbf18368a
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spelling	oai:doaj.org-article:08b773bbbe0c42f8a2e65a6cbf18368a2021-12-02T15:14:47ZGenetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning10.1038/s41698-021-00225-92397-768Xhttps://doaj.org/article/08b773bbbe0c42f8a2e65a6cbf18368a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00225-9https://doaj.org/toc/2397-768XAbstract Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68–0.85) and copy number alteration of another six genes (AUC 0.69–0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65–0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.Hui QuMu ZhouZhennan YanHe WangVinod K. RustgiShaoting ZhangOlivier GevaertDimitris N. MetaxasNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-11 (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 Hui Qu Mu Zhou Zhennan Yan He Wang Vinod K. Rustgi Shaoting Zhang Olivier Gevaert Dimitris N. Metaxas Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
description	Abstract Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68–0.85) and copy number alteration of another six genes (AUC 0.69–0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65–0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.
format	article
author	Hui Qu Mu Zhou Zhennan Yan He Wang Vinod K. Rustgi Shaoting Zhang Olivier Gevaert Dimitris N. Metaxas
author_facet	Hui Qu Mu Zhou Zhennan Yan He Wang Vinod K. Rustgi Shaoting Zhang Olivier Gevaert Dimitris N. Metaxas
author_sort	Hui Qu
title	Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
title_short	Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
title_full	Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
title_fullStr	Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
title_full_unstemmed	Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
title_sort	genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/08b773bbbe0c42f8a2e65a6cbf18368a
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Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

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