Abstract
BackgroundCancer 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.ResultsWe 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.ConclusionsIn 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.
Highlights
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
Several in silico tools for singlenucleotide variant (SNV)-based candidate neoepitopes prediction have been described, including pVAC-Seq [11], MuPeXI [12], TSNAD [13], and Neopepsee [14]. pVAC-Seq and TSNAD focus on MHC-I binding affinity and implement filter-based strategies to obtain the final neopeptide without prioritization, which prevents its further clinical utilization
General pipeline of pTuneos The pTuneos workflow consists of four steps (Fig. 1): data preprocessing, candidate neoepitope identification, modelbased filtering, and neoepitope prioritization based on the refined immunogenicity score
Summary
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. Neopepsee constructs a machine-learning model based on the immunogenicity features of the peptide to optimize the candidate neoepitope set Among these tools, only Neopepsee provides a learning-based measurement of neoepitopes, but issues remain to be overcame: (1) features used in Neopepsee might be irrelevant and difficult to interpret biologically, (2) the training data used in Neopepsee lack specificity as the peptides come from generic antigens rather than true noeantigens with experimental validation, and (3) the training data used in Neopepsee are highly imbalanced. The actual immunogenicity of neoantigen in patient tumor might be influenced by the MHC presentation and T cell recognition, and by many other endogenous factors including neopeptide cleavage probability, transporter associated with antigen processing (TAP) transport efficiency, peptide expression level, mutation allele fraction, and neoantigen cellular prevalence. None of the existing tools provides a quantitative and comprehensive metric to evaluate these characteristics and the immunogenicity of the naturally processed and presented neoantigen, which is the most challenging issue for clinical application of these tools
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