In silico identification of neoantigens using integrated DNA-Seq, RNA-Seq and HLA typing for personalized cancer vaccine design

Objective: This study aimed to develop an integrated computational pipeline for the identification and prioritization of tumor-specific neoantigens using combined whole-exome sequencing (WES), RNA sequencing (RNA-seq), and HLA typing data for personalized cancer vaccine design.

Methodology: Publicly available sequencing data from the triple-negative breast cancer cell line HCC1395 and matched normal HCC1395BL were analyzed. Data preprocessing included quality control and trimming, followed by alignment to the GRCh38 reference genome. Somatic variants were identified and annotated, and protein-altering mutations were used to generate mutant peptides. HLA class I alleles were predicted, and peptide–HLA binding affinity was assessed. Gene expression and mutation clonality were evaluated, and candidate neoantigens were filtered based on binding affinity, expression level, clonality, and immunogenicity. Selected neoantigens were further used for multi-epitope vaccine design and population coverage analysis.

Results: A total of 561 somatic variants were identified, including 386 protein-altering mutations generating 13,452 mutant peptides. Binding predictions yielded 63,100 peptide–HLA combinations, which were refined to 55 high-confidence neoantigens. Seven top candidates demonstrated strong binding affinity, expression, and immunogenic potential. The predicted population coverage was 36.7% globally and 33.73% in the Indian population. The designed multi-epitope vaccine construct showed an antigenicity score of 0.4802 and was predicted to be non-allergenic.

Conclusion: The proposed pipeline effectively identifies and prioritizes biologically relevant neoantigens and supports the development of personalized cancer vaccines using integrated multi-omics and immunoinformatics approaches.