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Published on: April 11, 2016
pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
Chi Zhou1,2, Zhiting Wei1,2, Zhanbing Zhang1,2
1Department of Endocrinology & Metabolism, Shanghai Tenth People's Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Background:
Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist.
Results:
We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers.
Conclusions:
In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos , with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos .
Insights
pTuneos accurately prioritizes cancer neoantigens from sequencing data, improving cancer immunotherapy and vaccine development. This computational tool enhances neoantigen identification for personalized cancer treatments.
Area of Science:
- Computational biology
- Immunology
- Genomics
Background:
- Cancer neoantigens are crucial for effective cancer immunotherapy.
- Accurate identification of neoantigens is vital for T cell recognition.
- Existing in silico neoantigen prediction tools have limitations.
Purpose of the Study:
- To develop pTuneos, a computational pipeline for prioritizing tumor neoantigens.
- To evaluate pTuneos's performance against existing tools using various cancer datasets.
- To assess the utility of pTuneos's neoantigen immunogenicity score for survival prediction.
Main Methods:
- Developed pTuneos, a computational pipeline for neoantigen prioritization.
- Utilized next-generation sequencing data for neoantigen prediction.
- Benchmarked pTuneos against existing tools on melanoma and TIL-recognized neopeptide data.
Main Results:
- pTuneos accurately predicts MHC presentation and T cell recognition of neoantigens.
- The pipeline surpasses existing tools in neoantigen prioritization performance and speed.
- pTuneos's neoantigen immunogenicity score serves as a pan-cancer survival biomarker.
Conclusions:
- pTuneos offers a user-friendly, state-of-the-art solution for neoantigen prioritization.
- The tool facilitates research in next-generation cancer immunotherapies and personalized vaccines.
- pTuneos is publicly available for research and clinical applications.
