Diseases, Volume 12, Issue 7 (July 2024) – 34 articles
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This study introduces a first-of-its-kind novel method for designing personalized peptide vaccines for cancer, using RNA sequencing to detect prevalent mutations in patient tumors. We focused on highly mutated genes, processed RNAseq data for read alignment and variant calling using our Python tool, SCGeneID. This tool also aligns RNAseq data against the UCSC hg38 reference, identifying target genes using chromosomes and loci. We designed a personalized vaccine for pancreatic ductal adenocarcinoma (PDAC), targeting highly prevalent gene mutations in PDAC patients. Our approach involved predicting peptide–MHC binding affinity, and selecting the top epitopes based on our established vaccine design principles. Validation was achieved by matching mutated genes found in a reference study, confirming the efficacy of our vaccine design method. View this paper
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