PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions

Abstract

The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.

Neoantigen cancer vaccines: a new star on the horizon

“…The non-canonical variants that are present as valuable targets due to superior immunogenicity compared to SNVs and INDEL-derived neoantigens have been extensively investigated in ongoing clinical trials 46 , 51 . Accumulating evidence suggests that other sources of cancer neoantigens, including coding and non-coding sequences, such as gene fusions and alternative splice variants at the transcriptome level, and post-translational modifications (PTMs), proteasome processing, and transporter associated with antigen processing (TAP) at the proteome level, represent promising novel targets for immunotherapy 43 , 50 , 52 .…”

Combination of multiple omics techniques for a personalized therapy or treatment selection

“…Peptides encompassing such mutations are then subjected to "in silico" analysis for the possibility to give rise to epitopes presented via the HLA alleles expressed by the patient (19). In recent years, next to "standard" HLA class I-restricted peptides, these strategies have identified many neoAg-restricted to HLA class II molecules (20, 21) or derived from non-coding sequences (22) leading to the development of new algorithms for their improved identification from sequencing data (23)(24)(25). Continuous progresses in artificial intelligence approaches are further improving the capabilities to identify neoAg for clinical application (26).…”