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

Abstract

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

Immunopeptidogenomics: Harnessing RNA-Seq to Illuminate the Dark Immunopeptidome

“…, Refs. ( 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 )). The immunopeptidogenomics workflow presented here needs only sample-specific RNA-Seq data to produce a single customized DB incorporating conventional peptides, neopeptides from SNVs/polymorphisms and noncomplex indels, and cryptic peptides derived from noncanonical translation, such as alternative reading frames and noncoding RNA regions/transcripts.…”

Main Strategies for the Identification of Neoantigens

“…In summary, by combining genomics-based predictions with high-throughput HLA-ligandome mass-spectrometry data, the performance of neoantigen discovery procedures could be significantly enhanced. For instance, the currently available ProGeo-neo pipeline [ 150 ] utilizes LC-MS/MS data to verify NGS-based derived neoantigen candidates.…”

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics

“…A substantial part of this supplement issue is composed by biomarker predictions, including seven works with utilizing [1] deep learning model to handle non-linear dependency, [2] selection and [3] prediction of transcriptional regulatory features, augmenting feature space by [4] pseudo genes and [5] network features for predicting cancer patients' overall and disease progression free survival, and one work focused on identifying differential alternative splicing events in HIV infected T cells. Their novel modeling considerations achieved significantly increased prediction performance comparing to classic models and identified sets of new biomarkers.…”

“…Li et al developed a novel computational workflow for customized neoantigen prediction and selection [3]. The workflow takes RNA-seq, genomic sequencing and customized proteomics data as inputs and is composed by data processing, NetMHCpan based neoantigen prediction, mutant peptides filtering and selection, proteogenomics and mutant peptidome data based neoantigen filtering, and further selection of the most likely immunogenic neoantigen by their similarity with cross reactive microbial peptides.…”