Despite the recent advances in our knowledge of cancer mutations, there has been limited development in novel cancer therapeutics. The emerging field of neoantigens seeks to utilize our body’s innate mechanism of antigen presentation to prime our immune system against cancer. Neoantigens are tumor-specific antigens and can arise from deviations in the protein transcription and translation process. The presentation of said neoantigens to the immune system is heavily reliant on the individual’s HLA class I alleles as different alleles have the tendency to present antigens with varying properties. Moreover, the abundance of neoantigens that are being presented is often overshadowed by peptides generated from endogenous proteins. This poses a challenge when using mass spectrometry-based techniques in immunopeptidomics. In this study, we aim to develop a rational shortlisting approach for neoantigen candidates. Our pipeline seeks to prioritize the best neoantigen candidates with a population-based approach in Singapore, using the most common HLA class I alleles, against established colorectal cancer (CRC) mutations. Population-specific HLA allelic data and prevalent CRC mutations can be used to generate a theoretical list of tumor-specific peptide sequences that are HLA-loadable. This list is shortened by using BLASTp to align these predicted peptides against wild-type proteins, allowing us to exclude matches with endogenously produced peptides that would be unfavorable in clinical applications. Using this approach, we successfully identified 124 neoantigen candidates. These neoantigens may be present in a significant fraction of the local population, and could serve as targets for broad-spectrum cancer vaccination.
Keywords: Neoantigen presentation; Human Leukocyte Antigen; peptide ligands