ProGeo-neo

ProGeo-neo integrates genomic and mRNA expression data with mass spectrometry proteomics in a proteogenomics workflow to identify and prioritize tumor-specific neoantigens for tumor-specific antigen (TSA)-based immunotherapy.

Key Features:

• Integration of genomic, transcriptomic and proteomic data: Combines next-generation sequencing genomic and mRNA expression data with mass spectrometry proteomics data.
• Tumor-specific antigen mining: Mines tumor-specific antigens from genomic and mRNA datasets to identify mutant protein-derived sequences unique to tumors.
• MHC class I binding prediction (netMHCpan v4.0): Predicts binding affinity of mutant peptides to class I MHC molecules using netMHCpan v4.0.
• Verification with mass spectrometry (MaxQuant): Verifies predicted MHC–peptide candidates against mass spectrometry proteomics data using MaxQuant and a customized protein database.
• Immunogenicity screening and selection: Applies additional screening methods to assess potential T-cell recognition and select immune-dominant neopeptides.

Scientific Applications:

• Personalized cancer vaccine development: Supports identification and prioritization of high-quality neoantigens for developing personalized cancer vaccines and other TSA-based therapies.
• Neoantigen discovery and prioritization: Enables neoantigen-oriented research by processing massively parallel sequencing and proteomics profiling to discover candidate neoantigens.
• Application to diverse cancer types: Demonstrates applicability to hematologic malignancies and solid cancers, exemplified by analysis of Jurkat leukemia cell line data.

Methodology:

Combines NGS genomic and mRNA expression data with mass spectrometry proteomics; mines tumor-specific antigens from genomic and mRNA datasets; predicts class I MHC binding using netMHCpan v4.0; verifies candidates with MaxQuant against a customized protein database; performs additional immunogenicity screening for T-cell recognition; workflow demonstrated on Jurkat leukemia cell line data.