4. Personalized Vaccine Design Protocol We employed a vaccine design protocol that has been previously outlined in our pub- lished studies [21]. This protocol integrates cu tting-edge bioinformatics tools to predict and select epitopes from mutations identi ﬁed in genomic data. 4.1. Epitope Prediction and Selection Initially, we used the IEDB NetMHC 4.1 tool to predict epitopes. NetMHC 4.1 is spe- ciﬁcally designed to return potential epitop es along with their predicted binding a ﬃnity for the top 27 expressed HLA alleles in the human population. The binding a ﬃnity indi- cated by the IC 50 value measured in nanomolar (nM) determines the strength of the inter- action between the epitope and the HLA molecules, which is a critical factor in the im-mune response e ﬃcacy. 4.2. Clinical Checkpoint Parameters Subsequently, we computed several epitope-speci ﬁc clinical checkpoint parameters. The immunogenicity of each epitope was determined using the IEDB Class I Immunogen- icity Tool, which assesses the potential of an epitope to trigger an immune response. The antigenicity, which evaluates the capability of the epitope to be recognized by antibodies, was determined using VaxiJen v2.0. 4.3. Data Filtering and Selection Criteria With the binding a ﬃnity, immunogenicity, and antigenicity data computed for each epitope and its associated HLA allele, we employed stringent ﬁlters to select the most promising epitopes. These ﬁlters were applied based on the criteria outlined in Table 1, Figure 2. SCGeneID Python program workflow. Created using BioRender.com.