pTuneos: prioritizing tumor neoantigens from next-generation sequencing data

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.

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

“…Both RNA‐seq and WES data were available in Van Allen's melanoma cohort [23]. The processing results (including HLA, TMB, CYT) of the two melanoma cohorts were retrieved from our previous studies [25–27]. Chowell's melanoma cohort [9] contains 164 patients under anti‐CTLA4 treatment.…”

NeoScore integrates characteristics of the neoantigen:MHC class I interaction and expression to accurately prioritize immunogenic neoantigens

“…The hydrophobicity of the neoantigen has been proposed to be associated with greater neoantigen immunogenicity because of the hydrophobicity of key anchor residues in the MHC class I binding cleft, as well as residues in the TCR, having the greatest interaction with the neoantigen (Chowell et al 2015). Mixed results have been reported for the association of hydrophobicity and immunogenicity to date (Chowell et al 2015;Zhou et al 2019;Łuksza et al 2017;Wells et al 2020). One reason is the use of different methods for hydrophobicity, three of which are considered here.…”

“…In the Carreno dataset, the NeoScore slightly outperformed Łuksza and the pTuneos hydrophobicity model (0.704 AUC for NeoScore, 0.696 AUC for pTuneos hydrophobicity, and 0.657 AUC for Łuksza) (Table 1; Figure 6D). Published results of the immunogenicity scores from the pTuneos model were used (Zhou et al 2019), as the model was not able to be successfully run with the other datasets. Both the hydrophobicity-only model and the full model provided by pTuneos are included, as the hydrophobicity model outperformed the full model.…”

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer

“…We only kept 'A to G' substitutions in the plus strand encoding genes or 'T to C' substitutions in the minus strand encoding genes. The nucleotide change in non-synonymous RE was translated into the corresponding amino acid change, which was then applied to the proteome reference sequence, leading to a 21-mer peptide containing variant site, and the long peptide was then chopped up into 9-11-mer short peptides (17). HLA allele information was determined from RNA-seq data by OptiType (18).…”

“…For paired peptide (mutant peptide and normal peptide) and MHC allele, R m representing %rank of affinity of the mutant peptide, was obtained by NetMHCpan 4.0 (19); R n representing %rank of affinity of the normal peptide, was obtained by NetMHCpan 4.0; F represents expression level of the mutant gene in Transcript Per Million (TPM); E represents editing level of the mutant gene, and editing level was defined as the proportion of edited reads among the total mapped reads at a given position in BAM file; S represents sequence dissimilarity between mutant peptide and normal peptide, calculated by 1 minus sequence similarity; H represents T cell recognition probability of MHC-peptide determined by peptide hydrophobicity information, which was calculated by a machine learning model proposed in our previous study (17). RE neoantigen immunogenicity score was calculated based on the product of a term representing neo-peptide abundance, a term representing dissimilarity between the mutant peptide and the normal peptide, and a term representing T cell recognition probability.…”