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

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Neoantigens, derived from tumor-specific mutations, are crucial targets for cancer immunotherapy.
• Identifying immunogenic neoantigens from a vast number of potential candidates is a significant challenge.
• Current neoantigen prediction tools primarily rely on genomic data, limiting their accuracy.

Purpose of the Study:

• To develop and validate a proteogenomics pipeline (ProGeo-neo) for more accurate neoantigen identification.
• To integrate genomic, transcriptomic, and proteomic data for enhanced neoantigen discovery.
• To improve the quality and reliability of neoantigen candidates for therapeutic development.

Main Methods:

• The ProGeo-neo pipeline integrates modules for mining tumor-specific antigens from next-generation sequencing (NGS) data.
• It predicts mutant peptide binding to major histocompatibility complex (MHC) class I molecules using netMHCpan.
• Validation involves mass spectrometry proteomics data analyzed with MaxQuant against a customized protein database and immunogenicity screening.

Main Results:

• The ProGeo-neo pipeline successfully identified neopeptides of significantly higher quality compared to genomics-only approaches.
• This proteogenomics strategy provides a more robust method for neoantigen discovery.
• The pipeline was initially developed using Jurkat leukemia cell line data.

Conclusions:

• The ProGeo-neo pipeline offers a powerful proteogenomics workflow for neoantigen research and development.
• This approach is broadly applicable to various solid cancer types.
• Advancements in sequencing and proteomics technologies will further enhance the utility of this workflow for neoantigen-oriented immunotherapy.