Abstract | PURPOSE:
Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers. EXPERIMENTAL DESIGN: We developed a pan- cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy. RESULTS: CONCLUSIONS: Our study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.
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Authors | Feng Xie, Jianjun Zhang, Jiayin Wang, Alexandre Reuben, Wei Xu, Xin Yi, Frederick S Varn, Yongsheng Ye, Junwen Cheng, Miao Yu, Yue Wang, Yufeng Liu, Mingchao Xie, Peng Du, Ke Ma, Xin Ma, Penghui Zhou, Shengli Yang, Yaobing Chen, Guoping Wang, Xuefeng Xia, Zhongxing Liao, John V Heymach, Ignacio I Wistuba, P Andrew Futreal, Kai Ye, Chao Cheng, Tian Xia |
Journal | Clinical cancer research : an official journal of the American Association for Cancer Research
(Clin Cancer Res)
Vol. 26
Issue 12
Pg. 2908-2920
(06 15 2020)
ISSN: 1557-3265 [Electronic] United States |
PMID | 31911545
(Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
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Copyright | ©2020 American Association for Cancer Research. |
Chemical References |
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Topics |
- Biomarkers, Tumor
(genetics)
- DNA Copy Number Variations
- Deep Learning
- Follow-Up Studies
- Gene Expression Regulation, Neoplastic
- Genomics
(methods)
- Humans
- Immunotherapy
(mortality)
- Microsatellite Instability
- Neoplasms
(drug therapy, genetics, immunology, pathology)
- Prognosis
- Survival Rate
- Tumor Microenvironment
(immunology)
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