CAN-IMMUNE: a comprehensive platform for cancer-specific immunopeptidomics-based neoantigen mapping and evaluation

Neoantigens are formed due to the somatic mutations that arise during carcinogenesis; they give rise to unique neopeptides bearing cancer-specific mutations that are presented on the surface of the tumour in complex with human leukocyte antigen (HLA) molecules for scrutiny by T cell recognition. A subset of these neopeptides can be recognised by tumoricidal T cells. As such, the neoepitopes derived from these neoantigens can serve as targets for cancer vaccines and antigen-based immunotherapies. Liquid Chromatography-Mass Spectrometry (LC-MS) is the technology of choice for identifying HLA-presented peptides, generally through spectral matching to inferred peptide sequences generated from a reference proteome database. However, the reference proteome database only contains canonical wild-type protein/peptide sequences and lacks cancer-specific mutations, hindering the identification of cancer-specific neoantigens using conventional database searching approaches. To tackle this challenge, we developed a comprehensive platform, CAN-IMMUNE, for MS-based identification of cancer-specific neopeptides. To construct a cancer-specific mutant peptide library, we collected missense mutations identified by whole exome sequencing (WES) from various sources, including the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Cell Line Encyclopedia (CCLE), and published literature. To date, CAN-IMMUNE documents 854,813 mutant peptides across 33 cancer types from 1428 cancer cell lines and 52 tumour tissues. CAN-IMMUNE allows users to browse, search, and download bespoke mutant peptide libraries for their cancer of interest. In addition, CAN-IMMUNE provides customisable source codes for users to generate their mutant peptide libraries using WES data. To assess the utility of CAN-IMMUNE, we searched our raw spectra data of MDA-MB-231 (a triple-negative breast cancer cell line), HCT116 (a colorectal cancer cell line), and various melanoma cell lines using both PEAKS 11 Online and deep learning-rescoring-assisted MSFragger database search engines. The results demonstrate that our constructed databases successfully identified potential cancer-specific neoantigens that cannot be found using the canonical proteome library, providing promising neoantigen candidates for further immunogenicity assessment. We anticipate that CAN-IMMUNE can accelerate the identification of cancer neoantigens via its WES-based mutant peptide libraries, thereby better informing the development of targeted cancer immunotherapies.