Scott G. Bruce: Cluny and the Muslims of la Garde-Freinet

Tischler, Matthias M.

doi:10.1515/jtms-2017-0010

Cited by 1 publication

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“…Reads were aligned against the GRCh37d5 reference genome using the Sanger cgpmap workflow [50]. This workflow uses bwa-mem (v0.7.15-1140) [51] and biobambam2 (v2.0.69) [52] to generate genome coordinate-sorted alignments with duplicates marked. Realignment around indels and base recalibration were performed using Genome Analysis Toolkit (v3.6) [53].…”

Section: Analysis Of Neoepitopes In Melanoma Patientsmentioning

confidence: 99%

Population-level distribution and putative immunogenicity of cancer neoepitopes

Wood

Paralkar

et al. 2017

Preprint

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Background: Tumor neoantigens are a driver of cancer immunotherapy response; however, current neoantigen prediction tools produce many candidates that require further prioritization for research/clinical applications. Additional filtration criteria and population-level understanding may help to produce refined lists of putative neoantigens. Herein, we show neoepitope immunogenicity is likely related to measures of peptide novelty and report population-level behavior of these and other metrics. Methods: We propose four peptide novelty metrics to refine predicted neoantigenicity: binding difference, paired similarity, homolog similarity, and microorganism similarity. We apply these metrics to tumor neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, as well as to a small validation cohort of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq [1]. Results: We show neoepitope burden varies across TCGA disease sites and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 19.9% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or even to bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Within our validation cohort, a linear model incorporating neoepitope binding affinity, protein similarity between neoepitopes and their closest viral peptides, and paired binding difference was able to predict actual immunogenicity with an AUROC of 0.66. Conclusions: Our proposed neoepitope prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but with respect to the entire human proteome, as well as bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of both cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

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Section: Analysis Of Neoepitopes In Melanoma Patientsmentioning

confidence: 99%