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DeepCC: a novel deep learning-based framework for cancer molecular subtype classification

Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespre...

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Autores principales: Gao, Feng, Wang, Wei, Tan, Miaomiao, Zhu, Lina, Zhang, Yuchen, Fessler, Evelyn, Vermeulen, Louis, Wang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697729/
https://www.ncbi.nlm.nih.gov/pubmed/31420533
http://dx.doi.org/10.1038/s41389-019-0157-8
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author Gao, Feng
Wang, Wei
Tan, Miaomiao
Zhu, Lina
Zhang, Yuchen
Fessler, Evelyn
Vermeulen, Louis
Wang, Xin
author_facet Gao, Feng
Wang, Wei
Tan, Miaomiao
Zhu, Lina
Zhang, Yuchen
Fessler, Evelyn
Vermeulen, Louis
Wang, Xin
author_sort Gao, Feng
collection PubMed
description Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.
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spelling pubmed-66977292019-08-19 DeepCC: a novel deep learning-based framework for cancer molecular subtype classification Gao, Feng Wang, Wei Tan, Miaomiao Zhu, Lina Zhang, Yuchen Fessler, Evelyn Vermeulen, Louis Wang, Xin Oncogenesis Article Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping. Nature Publishing Group UK 2019-08-16 /pmc/articles/PMC6697729/ /pubmed/31420533 http://dx.doi.org/10.1038/s41389-019-0157-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Gao, Feng
Wang, Wei
Tan, Miaomiao
Zhu, Lina
Zhang, Yuchen
Fessler, Evelyn
Vermeulen, Louis
Wang, Xin
DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title_full DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title_fullStr DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title_full_unstemmed DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title_short DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
title_sort deepcc: a novel deep learning-based framework for cancer molecular subtype classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697729/
https://www.ncbi.nlm.nih.gov/pubmed/31420533
http://dx.doi.org/10.1038/s41389-019-0157-8
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