Tumor-Associated Antigen Prediction Using a Single-Sample Gene Expression State Inference Algorithm

Authors

Xinpei Yi
Hongwei Zhao
Shunjie Hu
Liangqing Dong
Yongchao Dou
Jing Li
Qiang Gao
Bing Zhang

Publication Date

11-18-2024

Journal

Cell Reports Methods

DOI

10.1016/j.crmeth.2024.100906

PMID

39561714

PMCID

PMC11705763

PubMedCentral® Posted Date

11-18-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Antigens, Neoplasm, Algorithms, Bayes Theorem, Neoplasms, Liver Neoplasms, Peptides, Gene Expression Regulation, Neoplastic, HLA-A2 Antigen, tumor-associated antigen, Bayesian algorithm, gene expression state inference, liver cancer, immunotherapy, therapeutic target, pan-cancer, CAR-T, TCGA, CPTAC

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 liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.