Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.

ABSTRACT: We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liv

SUBMITTER: Yi X 

PROVIDER: S-EPMC11705763 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature