Related Experiment Video
Updated: Aug 7, 2025
09:28
Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles
Published on: November 17, 2018
11.7K
PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions
Xiaoxiu Tan1,2, Linfeng Xu2, Xingxing Jian2
1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Cells
|March 11, 2023
Summary
PGNneo is a new pipeline for identifying cancer neoantigens from noncoding DNA. This tool enhances personalized cancer vaccines by discovering novel immune targets, particularly in low-mutation tumors.
Area of Science:
- Genomics and Bioinformatics
- Cancer Immunotherapy
- Proteogenomics
Background:
- Personalized cancer vaccines hold promise for immunotherapy.
- Identifying neoantigens, especially from noncoding DNA, is crucial but challenging.
- Existing tools often overlook neoantigens derived from noncoding genomic regions.
Purpose of the Study:
- To develop and validate a reliable pipeline for discovering neoantigens from noncoding genomic regions.
- To enhance the identification of potential neoantigens for personalized cancer vaccine development.
- To provide a tool applicable to various cancer types, including those with low tumor mutational burden.
Main Methods:
- Development of PGNneo, a proteogenomics-based pipeline with four modules: noncoding variant calling/HLA typing, peptide extraction/database construction, variant peptide identification, and neoantigen prediction/selection.
- Application and validation of PGNneo in hepatocellular carcinoma (HCC) cohorts.
- Testing PGNneo's applicability to colorectal cancer (CRC) cohorts.
Main Results:
- PGNneo successfully identified 107 neoantigens from noncoding regions in two HCC cohorts, associated with frequently mutating genes (TP53, WWP1, ATM, KMT2C, NFE2L2).
- The methodology was validated in a CRC cohort, demonstrating its versatility across different tumor types.
- PGNneo effectively detects neoantigens from noncoding regions, expanding the repertoire of potential immune targets.
Conclusions:
- PGNneo reliably identifies neoantigens from noncoding genomic regions, offering new targets for cancer immunotherapy.
- The tool is particularly valuable for cancers with low tumor mutational burden in coding regions.
- PGNneo, combined with previous tools, contributes to a comprehensive understanding of the tumor immune target landscape, with source code and a GUI available.
