EvoSet-NeoVax: Label-Blind Evidence Weighting, Constrained Set Selection and Junction-Optimised mRNA Design for Personalised Neoantigen Vaccines

Background/Objectives: Personalised neoantigen vaccines can now be manufactured within weeks, so what a patient receives is decided by which mutations are selected. Selection is organised around predicted peptide–MHC binding, yet presentation also requires that the mutation is transcribed, carried by enough of the tumour, and restricted by an HLA allele the malignant cells still express. A vaccine is also a set rather than a ranked list, and its epitopes must finally be joined into one construct. EvoSet-NeoVax carries these three problems through one label-blind workflow. Methods: Public neoantigen and non-small-cell lung cancer data were harmonised under a typed schema preserving missing values, partitioned under audited leakage control, and used to freeze six comparators. Results: A missingness-aware logistic model reached a precision–recall AUC of 0.869 on the internal test partition of 30 candidates, the best of the six on all five metrics, although no pairwise difference survives Holm correction across the fifteen comparisons. On the external TESLA set all six fell to chance, with receiver-operating-characteristic areas between 0.401 and 0.484. Combining predicted binding with within-patient expression, variant allele frequency and immunopeptidome evidence, twenty peptide records were selected for patient A144 across five retained HLA alleles and both HLA classes, resolving to nineteen mutations in nineteen genes, of which eighteen passed native-flank verification and entered the final modules; none matches the reviewed human proteome exactly. An independent-ranking top-K baseline cannot meet the composition constraints on this pool, and a retrospective comparison against the cohort’s 68 ELISpot outcomes separated neither ranking. Junction-specific linker optimisation reduced predicted linker-overlapping windows from 18 to 11 at high priority and from 88 to 42 at weak priority, and the optimised modules were encoded as three open reading frames of 744, 633 and 531 nucleotides passing exact translation back-check, folding to −268.20, −204.10 and −178.80 kcal/mol, and cloning in silico into an Addgene-verified T7 backbone with exact insert recovery. Conclusions: EvoSet-NeoVax provides a reproducible framework for translating tumour genomic evidence into sequence-verified, synthesis-ready neoantigen vaccine constructs.