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Clin Cancer Res. 2020 Jan 7. pii: clincanres.1744.2019. doi: 10.1158/1078-0432.CCR-19-1744. [Epub ahead of print]

Multifactorial deep learning reveals pan-cancer genomic tumor clusters with distinct immunogenomic landscape and response to immunotherapy.

Author information

1
School of Electronic Information and Communications, Huazhong University of Science and Technology.
2
Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center JZhang20@mdanderson.org.
3
Computer Science and Technology, Xi'an Jiaotong University.
4
Thoracic/Head & Neck Medical Oncology, University of Texas MD Anderson Cancer Center.
5
Geneplus-Beijing Institute.
6
The Jackson Laboratory for Genomic Medicine.
7
School of Computer Science and Technology, Xi'an Jiaotong University.
8
State Key Laboratory of Oncology in Southern China, Sun Yat-sen University Cancer Center.
9
Cancer Center, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.
10
Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.
11
Genomic Medicine, GenePlus-Beijing Institute.
12
Radiation Oncology, University of Texas MD Anderson Cancer Center.
13
Department of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center.
14
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center.
15
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center.
16
Faculty of Electronic and Information,First Affiliated Hospital, Xi'an Jiaotong University.
17
Department of Medicine, Baylor College of Medicine.

Abstract

BACKGROUND:

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 immunological features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers.

METHODS:

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, assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy.

RESULTS:

Our model grouped 8,646 tumors of 29 cancer types from the Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden.

CONCLUSION:

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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