A census of predicted mutational epitopes suitable for immunologic cancer control

Area of Science:

• Immunology
• Oncology
• Bioinformatics

Background:

• The adaptive immune system plays a role in tumor surveillance.
• Cancer vaccines are effective against infectious agents but not spontaneous mutations due to high costs and lack of validated tumor epitopes.
• Identifying tumor-specific antigens is crucial for developing preventative cancer vaccines.

Purpose of the Study:

• To computationally evaluate the antigenic potential of all known somatic mutations in human tumors.
• To identify validated tumor epitopes for potential use in preventative cancer vaccines.
• To assess the feasibility of prophylactic vaccination against common recurrent cancer mutations.

Main Methods:

• Screening of human leukocyte antigen (HLA) class I presented peptides from recurrent somatic cancer mutations (>5% frequency).
• Utilizing three independent epitope prediction algorithms: SYFPEITHI, BIMAS, and IEDB.
• Applying stringent filters to identify candidate epitopes presented by common HLA class I alleles.

Main Results:

• Identification of 20 genes, 35 mutations, and 159 candidate epitopes with antigenic potential.
• KRAS mutations were top-ranked due to high frequency, prevalence in cancers, and presentation by common HLA alleles.
• Candidate epitopes were predicted to be presented by up to four distinct HLA class I alleles.

Conclusions:

• Prophylactic vaccination strategies targeting common recurrent mutations are feasible.
• Computational screening of somatic mutations can identify neoantigens for cancer vaccine development.
• This approach could significantly reduce cancer incidence by priming adaptive immune responses.