4. Personalized Vaccine Design Protocol We employed a vaccine design protocol that has been previously outlined in our published studies [ 21]. This protocol integrates cutting-edge bioinformatics tools to predict and select epitopes from mutations identified in genomic data. Diseases 2024 ,12, 149 5 of 14 4.1. Epitope Prediction and Selection Initially, we used the IEDB NetMHC 4.1 tool to predict epitopes. NetMHC 4.1 is specifically designed to return potential epitopes along with their predicted binding affinity for the top 27 expressed HLA alleles in the human population. The binding affinity indicated by the IC 50value measured in nanomolar (nM) determines the strength of the interaction between the epitope and the HLA molecules, which is a critical factor in the immune response efficacy. 4.2. Clinical Checkpoint Parameters Subsequently, we computed several epitope-specific clinical checkpoint parameters. The immunogenicity of each epitope was determined using the IEDB Class I Immunogenic- ity 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 affinity, 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, fo- cusing on identifying epitopes that are strong binders, highly immunogenic, and antigenic. Table 1. Restriction criteria to quantitatively filter and qualitatively assess each epitope. Parameter Restriction Binding affinity ( b)Strong binder 0 nM ≤b≤50 nM Normal binder 50 nM < b≤500 nM Weak binder 500 nM < b≤5000 nM Immunogenicity ( i) i≥0 Antigenicity ( a) a≥0.4 Toxicity Non-toxic Allergenicity Non-allergenic 4.4. Physicochemical Property Assessment In addition to these functional assessments, we analyzed various physicochemical properties of the epitopes using ProtParam https://web.expasy.org/protparam/ (Accessed on 23 September 2023). This analysis included determining parameters such as half-life, instability index, isoelectric point, aliphatic index, and GRAVY score. Although these parameters were informative for understanding the physical and chemical characteristics of the epitopes, they were not used in the epitope selection process. Further, we assessed toxicity using ToxinPred and screened for allergenic potential using AllerTOP v2.0, ensuring that only non-toxic and non-allergenic epitopes were considered for further analysis. 4.5. Epitope Selection and Workflow Integration After applying the filtration restrictions (Table 1), we selected the top 50 and 1 00 epito pes that met all the specified criteria, warranting a robust selection of candidates for potential vaccine design. We employed binary filters on toxicity and allergenicity to ensure the selection of epitopes that were both non-toxic and non-allergenic. 4.6. Methodological Workflow Figure 3 shows the comprehensive workflow of our methodology, starting from the collection of Illumina sequencing data, performing RNAseq analysis, and the selection of top epitopes for the development of peptide vaccines. This streamlined workflow integrates Diseases 2024 ,12, 149 6 of 14 multiple stages of data processing and epitope evaluation, indicating the robustness of our approach in vaccine design. Diseases 2024 , 12, x FOR PEER REVIEW 7 of 15 Figure 3. Peptide Based Personalized Cancer Vaccine Design methodological overall work ﬂow. 5. Results We obtained Illumina sequencing data from two patients out of the 19 available in the GEO accession project [23]. The sequencing data represent the genetic landscape of the patient’s solid tumor sample. We performe d RNAseq analysis to determine prevalent Figure 3. Peptide Based Personalized Cancer Vaccine Design methodological overall workflow. Diseases 2024 ,12, 149 7 of 14 5. Results We obtained Illumina sequencing data from two patients out of the 19 available in the GEO accession project [ 23]. The sequencing data represent the genetic landscape of the patient’s solid tumor sample. We performed RNAseq analysis to determine prevalent mutations. Using these mutations, we determined strong and normal binding MHC class I epitopes that are immunogenic, antigenic, non-toxic, and non-allergenic. We selected the top 50 and top 100 epitopes from these data for a peptide vaccine. 5.1. Determination of Genetic Variants with RNAseq Analysis We performed RNAseq analysis on Illumina sequencing data to obtain a list of genetic variants identified in a solid PDAC tumor. For Patient 1, the RNAseq analysis performed using Partek Flow resulted in 100,819 mutations. These mutations included single-nucleotide polymorphisms, multi-nucleotide polymorphisms, deletions, and insertions. Isolating the single-nucleotide polymorphisms, we identified 189 unique variants, which we could use to develop the peptide vaccine. For Patient 2, the RNAseq analysis resulted in 87,128 mutations., of which we identified 125 unique variants we could use to develop the peptide vaccine. 5.2. Confirmation of Genetic Variants and Sequencing Alignment Using SCGeneID We confirmed the alignment of the sequencing data to the hg38 human reference genome using our SCGeneID program version 1. Using SCGeneID, we qualitatively identified the corresponding genes to all mutations obtained through RNAseq analy- sis loci against the hg38 human reference genome. We found 100% similarity between the genes identified through Partek Flow and genes identified using SCGeneID for both Patients 1 and 2 . Therefore, we were confident that the variant genes identified using Partek Flow were correctly aligned to the reference genome. 5.3. Collection of 9-Mer and 10-Mer Top Epitopes from Genetic Variants From the pool of identified genetic variants, we curated lists of the top 50 and t op 10 0 epitopes, prioritized based on their binding affinity and immunogenic properties. The top epitopes for Patients 1 and 2 can be found in Supplementary Files S2–S5. All selected epitopes consisted of 9 or 10 amino acids, representing an epitope capable of binding to an MHC class I molecule. All the top 50 epitopes were classified as having strong binding affinity to their associated HLA allele. The top 100 epitopes included both strong and normal binders. We found no epitopes in the top 100, which were classified as weak binders. Table 2 shows the top 50 epitopes for Patient 1, along with their associated genes, mutations, and binding HLA alleles. 5.4. Population Coverage Analysis of Top 100 Epitopes We also performed a population coverage analysis to assess the extent of the global population that could potentially benefit from the personalized vaccine. The analysis for Patient 1 showed that the vaccine could cover 69.64% of the global population. Table 3 provides this coverage along with average hit rates and PC 90data for various world subregions. While the population coverage may appear relatively low at first glance, it is essential to consider the context of this study. The vaccine was uniquely designed based on the gene expression profile of a specific individual, making it personalized and tailored to the specific mutations and characteristics of their tumor. Consequently, the expectation for widespread coverage across diverse populations is not high. As the patient cohort from whom the vaccine was developed predominantly comprised individuals with European ancestry, the vaccine’s performance in these regional subgroups aligned with the genetic background of the patients involved. Diseases 2024 ,12, 149 8 of 14 Table 2. Top 50 epitopes along with their strong-binding associated HLA allele. Gene Mutation Epitope HLA Alleles GNAS R201C AMSNLVPPV HLA-A*02:01 SMAD4 Y353C QSIKETPCW HLA-B*58:01 TP53 R248Q CTYSPALNK HLA-A*03:01 KRAS G12D KSFEDIHHY HLA-B*58:01 SMAD4 Y353C MPIADPQPL HLA-B*39:01 SMAD4 Y353C CLSDHAVFV HLA-A*02:01 SMAD4 Y353C KIYPSAYIK HLA-A*03:01 TP53 R248Q LEDSSGNLL HLA-B*40:01 KRAS G12D LARSYGIPF HLA-B*15:01 TP53 R248Q APAAPTPAA HLA-B*07:02 SMAD4 Y353C LLDEVLHTM HLA-A*02:01 TP53 R248Q KTYQGSYGF HLA-B*58:01 SMAD4 Y353C APAISLSAA HLA-B*07:02 SMAD4 Y353C LQSNAPSSM HLA-B*15:01 TP53 R248Q LLGRNSFEV HLA-A*02:01 KRAS G12D KSALTIQLI HLA-B*58:01 SMAD4 Y353C KETPCWIEI HLA-B*40:01 GNAS R201C NQFRVDYIL HLA-B*39:01 TP53 R248Q LQIRGRERF HLA-B*15:01 SMAD4 Y353C LPHHQNGHL HLA-B*07:02 SMAD4 Y353C LQVAGRKGF HLA-B*15:01 SMAD4 Y353C CILRMSFVK HLA-A*03:01 KRAS G12D CLLDILDTA HLA-A*02:01 SMAD4 Y353C LRRLCILRM HLA-B*27:05 GNAS R201C LIDCAQYFL HLA-A*02:01 5.5. 3D-Structure Modeling of Epitope-MHC and TCR Interaction Complex TCR (T-cell receptor) and pMHC (peptide-major histocompatibility complex) interac- tions play a fundamental role in immunogenicity, which involves the ability of a peptide to initiate an immune response against tumor cells. TCRs on the surface of T cells recog- nize antigens that are presented by MHC molecules on the surface of antigen-presenting cells. This recognition is specific to the peptide being presented by the MHC. The cor- rect configuration and interaction of a TCR with a pMHC complex is essential for the T cell to become activated and initiate an immune response. Thus, to explore the bind- ing of our designed peptide vaccines, we initiated TCR-pMHC peptide interaction mod- eling. We found the PDB files for the HLA alleles HLA-B*58:01 on the RCSB protein data bank https://www.rcsb.org/ (accessed on 23 September 2023). Using MDockPeP https://zougrouptoolkit.missouri.edu/mdockpep/ (accessed on 23 September 2023) and CABS-dock [ 25,26], we attached a top epitope to the binding grooves of the HLA allele. We created two models of the peptide-MHC binding complex (Figure 4). TCR binding models were created using the same method as Kim et al. [ 21]. We used TCRModel https://tcrmodel.ibbr.umd.edu/ (accessed on 23 September 2023) to create 3D models of a TCR complex binding to our peptide-MHC complexes. Subsequently, we used PyMOL version 2.5.5 to edit all of the 3D models. In Figure 4, yellow color represents HLA alleles, and red represents epitopes. The 3D models we obtained were KSFEDIHHY, a mutation of Diseases 2024 ,12, 149 9 of 14 the KRAS gene, binding to the MHC Class I molecule HLA-B*58:01 as well as KTYQGSYGF, a mutation of the TP53 gene, binding to the MHC Class I molecule HLA-B*58:01. All pMHC- TCR 3D molecular model structures generated in this study can be found in Figure 5 and Supplementary Files S6–S9. Table 3. Population coverage of the personalized PDAC vaccine for regional subgroups. Population/Area Coverage Average Hit pc90 Central Africa 39.22 1.84 0.16 Central America 1.4 0.06 0.41 East Africa 41.73 2.16 0.17 East Asia 55.26 2.8 0.22 Europe 81.05 4.98 0.53 North Africa 43.55 2.29 0.18 North America 70.36 4.09 0.34 Northeast Asia 47.97 2.21 0.19 Oceania 38.93 1.59 0.16 South Africa 23.99 0.93 0.13 South America 36.87 1.86 0.16 South Asia 37.28 1.66 0.16 Southeast Asia 55.59 2.34 0.23 Southwest Asia 43.73 2.33 0.18 West Africa 42.65 2.14 0.17 West Indies 63.52 3.47 0.27 Average 45.19 2.3 0.23 Standard deviation 17.8 1.13 0.11 Diseases 2024 , 12, x FOR PEER REVIEW 10 of 15 recognize antigens that are presented by MH C molecules on the surface of antigen-pre- senting cells. This recognition is speci ﬁc to the peptide being presented by the MHC.