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DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning
Yi Shi1,2,3, Zehua Guo2,4, Xianbin Su1
1Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
Motivation:
The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity.
Results:
In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy.
Availability And Implementation:
Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
Cancer mutations can create neoantigens for T-cell recognition. We developed a deep learning model incorporating 3D genome data to improve neoantigen prioritization for cancer immunotherapy.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Cancer mutations can generate neoantigens, which are targets for T-cell immunotherapy.
- Accurately prioritizing neoantigens is computationally challenging, especially with abundant mutations.
- Existing methods lack advanced machine learning models and do not consider 3D genome structure for neoantigen immunogenicity prediction.
Purpose of the Study:
- To develop an advanced machine learning model for accurate neoantigen prioritization.
- To investigate the role of 3D genome structure in neoantigen immunogenicity.
- To provide a novel computational tool for personalized cancer immunotherapy.
Main Methods:
- Discovered distinct spatial distribution patterns of immunopositive and immunonegative MHC-I neoantigens in the 3D genome.
- Developed a deep sparse neural network model with group feature selection (DNN-GFS).
- Integrated 3D genome information and an ensemble pMHC-I coding strategy into the DNN-GFS model.
Main Results:
- The DNN-GFS model demonstrated superior neoantigen prioritization power compared to existing sequence-based methods.
- Distinct 3D genome spatial distribution patterns were identified for neoantigens.
- A webserver, deepAntigen, was developed to implement the DNN-GFS model and other machine learning methods.
Conclusions:
- The study introduces a novel perspective on neoantigen prediction by incorporating 3D genome information.
- The DNN-GFS model offers improved accuracy in prioritizing neoantigens.
- The deepAntigen webserver provides a valuable tool for advancing personalized cancer immunotherapy research.
