TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification
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RESEARCH ARTICLE
Open Access
TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification Yunxia Tang1,2,3, Yu Wang1,2, Jiaqian Wang2,4, Miao Li1, Linmin Peng2, Guochao Wei2, Yixing Zhang2, Jin Li5 and Zhibo Gao1,2,4* *Correspondence: [email protected] 1 YuceBio, 2002#, ShenYan Road, Dabaihui Center, Yantian distict, Shenzhen 518020, China Full list of author information is available at the end of the article
Abstract Background: Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice. Results: We develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%). Conclusions: TruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies. Keywords: Neoantigen, Multiple factors, Recall rate, Positive rate, Top-ranked
Background Neoantigens are tumor-specific antigens formed by somatic mutations and are ideal targets for immunotherapy. They are highly immunogenic because they are not expressed in normal tissues and hence bypass central thymic tolerance. In humans, effective antitumor immunity has been associated with the presence of T cells directed at neoantigens © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence,
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