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...

Full description

Saved in:
Bibliographic Details
Published in:Oncogenesis (New York, NY) Vol. 8; no. 9; pp. 44 - 12
Main Authors: Gao, Feng, Wang, Wei, Tan, Miaomiao, Zhu, Lina, Zhang, Yuchen, Fessler, Evelyn, Vermeulen, Louis, Wang, Xin
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 16.08.2019
Nature Publishing Group
Subjects:
38/39
38/61
38/91
631/208/69
631/67/2329
Apoptosis
Breast cancer
Cancer
Cell Biology
Classification
Deep learning
Gene expression
Human Genetics
Internal Medicine
Medicine
Medicine & Public Health
Oncology
ISSN:2157-9024, 2157-9024
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2157-9024
2157-9024
DOI:10.1038/s41389-019-0157-8