CAI-Vax: Computational Design of Personalized mRNA Cancer Vaccine Constructs After Antigen Selection
CAI-Vax (Cancer Antigen Individualized Vaccine Design) arranges selected antigens and generates synonymous coding sequences for personalized mRNA vaccine constructs. We evaluated 357 vaccine-candidate peptides from 31 patients across four published cohorts. At the primary cumulative MHCflurry percentile-rank threshold of [≤]2%, HLA-aware search reduced predicted junction-spanning peptide-allele binder occurrences by 3.49 per junction relative to amino-acid-pattern optimization (paired Hodges-Lehmann estimate; nominal 95% interval 2.75-4.48; Holm-adjusted P = 7.45 x 10-). All 31 primary paired differences and the estimated effects in all 16 threshold-by-normalization settings favored HLA-aware search within the MHCflurry model used for optimization. Among 25 patients with an exact solution for the additive search objective, single-start greedy search found the minimum in 5 patients and the best individual heuristic in 16. Selecting each patients best result across all tested heuristics increased this to 19. MHCflurry and NetMHCpan EL identified identical tied best-plan sets in 6 of 31 patients after rescoring six frozen plans per patient. In a retrospective internal analysis, repeat-aware synonymous encoding reduced maximum 10-mer multiplicity in every patient, with mean codon-use-preference losses of 0.0041-0.0055 across cohorts; however, it performed worse than matched random encoding for 6-mers. These findings demonstrate that HLA-guided construct optimization can reduce predicted junctional binding within the optimization model, although the preferred antigen arrangement depends on the prediction model used. Experimental studies are needed to determine the biological and manufacturing relevance of these computational differences.