ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection

• DOI: 10.1186/s12920-020-0683-4
• Publication Date: 2020-04-03T00:02:56Z
• Authors: Yuyu Li; Guangzhi Wang; Xiaoxiu Tan; Jian Ouyang; Menghuan Zhang; Xiaofeng Song; Qi Liu; Qibin Leng; Lanming Chen; Lu Xie
• Keywords: Proteogenomics

Abstract Background Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and unique to the tumor. The discovery of neoantigens is first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation potentially give rise multiple neopeptides, resulting in a vast total number, but only small percentage peptides may achieve immune-dominant status with given major histocompatibility complex (MHC) class I allele. Specific identification immunogenic candidate consequently challenge. Currently almost all neoantigen prediction tools genomics data. Results Here we report construction proteogenomics (ProGeo-neo) pipeline, which incorporates following modules: mining specific antigens next-generation sequencing mRNA expression data, predicting binding MHC molecules latest netMHCpan (v.4.0), verifying MHC-peptides MaxQuant mass spectrometry proteomics data searched against customized protein database, checking potential immunogenicity T-cell-recognization additional screening methods. ProGeo-neo pipeline achieves strategy neopeptides identified were much higher quality compared those using only. Conclusions was constructed Jurkat leukemia line generally applicable other solid cancer research. With massively parallel profiling increasing, this workflow should useful oriented research