TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou Z
PROVIDER: S-EPMC8385119 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.
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Project description:Tumour antigens have attracted much attention because of their importance to cancer diagnosis, prognosis and targeted therapy. With the development of cancer genomics, the identification of tumour-specific neoantigens became possible, which is a crucial step for cancer immunotherapy. In this study, we developed software called the tumour-specific neoantigen detector for detecting cancer somatic mutations following the best practices of the genome analysis toolkit and predicting potential tumour-specific neoantigens, which could be either extracellular mutations of membrane proteins or mutated peptides presented by class I major histocompatibility complex molecules. This pipeline was beneficial to the biologist with little programmatic background. We also applied the software to the somatic mutations from the International Cancer Genome Consortium database to predict numerous potential tumour-specific neoantigens. This software is freely available from https://github.com/jiujiezz/tsnad.
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