Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients’ Human Leukocyte Antigen (HLA) types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity.
To tackle this problem, we have developed nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict neoepitopes and patient-specific features associated with tumor immunogenicity and response to immunotherapy. nextNEOpi infers class-I and -II (SNV/indel and gene fusion derived) neoantigens from WES/WGS (DNA-seq) along with RNA-seq data and exploits tumor purity information to derive the cancer cell fraction (CCF) and clonality of mutations and resulting neoantigens. Our pipeline is implemented in the Nextflow workflow language to assure portability, scalability, as well as reproducibility and uses multi-method consensus approaches to guarantee robust results in the case of suboptimal data.Availability – nextNEOpi source code and documentation are available athttps://github.com/icbi-lab/nextNEOpi
Rieder D, Fotakis G, Ausserhofer M, René G, Paster W, Trajanoski Z, Finotello F, nextNEOpi: a comprehensive pipeline for computational neoantigen prediction, Bioinformatics, 2021;, btab759, https://doi.org/10.1093/bioinformatics/btab759