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Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
Kaixuan Diao1,2,3, Jing Chen1,2,3, Tao Wu1
1School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, China.
International Journal of Molecular Sciences
|October 14, 2022
Summary
Seq2Neo predicts neoantigen immunogenicity from raw sequencing data. This tool enhances cancer immunotherapy by identifying effective neoantigens for combination therapies, improving outcomes for non-responders.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neoantigens from somatic DNA alterations are key cancer targets.
- Combination therapy with PD-1/PD-L1 blockers and neoantigen vaccines shows promise.
- Predicting neoantigen immunogenicity remains a challenge.
Purpose of the Study:
- To develop an efficient pipeline for neoepitope feature prediction.
- To improve the prediction of neoantigen immunogenicity.
- To support combination cancer immunotherapy strategies.
Main Methods:
- Developed the Seq2Neo pipeline for neoepitope prediction.
- Utilized raw sequencing data as input.
- Trained a convolutional neural network (CNN)-based model for immunogenicity prediction.
Main Results:
- Seq2Neo supports various DNA alterations including point mutations, indels, and gene fusions.
- The CNN model demonstrated superior performance in immunogenicity prediction compared to existing tools.
- The pipeline offers a comprehensive solution for neoantigen analysis.
Conclusions:
- Seq2Neo is a valuable tool for predicting neoantigen immunogenicity.
- This pipeline can aid in the development of personalized cancer immunotherapies.
- Seq2Neo is open-source and available for academic and research use.
