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DeepHLApan: A Deep Learning Approach for High-Confidence Neoantigen Prediction

41 Pages Posted: 7 Apr 2019

See all articles by Jingcheng Wu

Jingcheng Wu

Institute of Drug Metabolism and Pharmaceutical Analysis

Wenzhe Wang

Zhejiang University

Jiucheng Zhang

Zhejiang University

Binbin Zhou

Zhejiang University

Wenyi Zhao

Zhejiang University - College of Pharmaceutical Sciences

Zhixi Su

Fudan University, School of Life Sciences, MOE Key Laboratory of Contemporary Anthropology

Xun Gu

Iowa State University

Jian Wu

Zhejiang University

Zhan Zhou

Institute of Drug Metabolism and Pharmaceutical Analysis; Zhejiang University - Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research

Shuqing Chen

Institute of Drug Metabolism and Pharmaceutical Analysis; Zhejiang University - Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research

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Abstract

Background:  Neoantigens are the most widely recognized elements to distinguish cancer and normal cells and consequently play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction.  

Methods:  We apply deep learning techniques to predict neoantigens considering both the possibility of mutant peptide presentation (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA) present on the cell surface.  

Findings:  The binding model achieves performance comparable to or even better than that of other well-acknowledged tools with the latest Immune Epitope Database (IEDB) benchmark datasets. Using the immunogenicity model, we demonstrate that limited immunogenicity data could significantly improve the identification of high-confidence neoantigens. We further apply our method to mutations with pre-existing T-cell responses and ranked most of them (69%) in the top 20 under an expression threshold of transcripts per million (TPM)>2.  

Interpretation:  The process of neoantigens inducing T cell response is complex and the immunogenicity of pHLA should be considered for high-confidence neoantigen prediction.  

Funding Statement: This work has been supported by the National Key R&D Program of China (Grant No. 437 2017YFC0908600), the Zhejiang Provincial Natural Science Foundation of China 438 (Grant No. LY19H300003), and the Fundamental Research Funds for the Central 439 Universities of China.

Declaration of Interests: The authors declare that they have no competing interests.

Ethics Approval Statement: Not needed.

Keywords: Deep learning; Neoantigen; Recurrent Neural Network; Human leukocyte antigen; Cancer immunology

Suggested Citation

Wu, Jingcheng and Wang, Wenzhe and Zhang, Jiucheng and Zhou, Binbin and Zhao, Wenyi and Su, Zhixi and Gu, Xun and Wu, Jian and Zhou, Zhan and Chen, Shuqing, DeepHLApan: A Deep Learning Approach for High-Confidence Neoantigen Prediction (April 3, 2019). Available at SSRN: https://ssrn.com/abstract=3365058

Jingcheng Wu

Institute of Drug Metabolism and Pharmaceutical Analysis

Hangzhou
China

Wenzhe Wang

Zhejiang University

38 Zheda Road
Hangzhou, Zhejiang 310058
China

Jiucheng Zhang

Zhejiang University

38 Zheda Road
Hangzhou, Zhejiang 310058
China

Binbin Zhou

Zhejiang University

38 Zheda Road
Hangzhou, Zhejiang 310058
China

Wenyi Zhao

Zhejiang University - College of Pharmaceutical Sciences

China

Zhixi Su

Fudan University, School of Life Sciences, MOE Key Laboratory of Contemporary Anthropology

China

Xun Gu

Iowa State University

613 Wallace Road
Ames, IA 50011-2063
United States

Jian Wu

Zhejiang University

38 Zheda Road
Hangzhou, Zhejiang 310058
China

Zhan Zhou (Contact Author)

Institute of Drug Metabolism and Pharmaceutical Analysis ( email )

Hangzhou
China

Zhejiang University - Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research ( email )

Hangzhou
China

Shuqing Chen

Institute of Drug Metabolism and Pharmaceutical Analysis ( email )

Hangzhou
China

Zhejiang University - Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research ( email )

Hangzhou
China

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