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Updated: Jun 7, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

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

Xinpei Yi¹, Hongwei Zhao², Shunjie Hu²

¹Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

Cell Reports Methods

|November 19, 2024

Summary

This summary is machine-generated.

Researchers identified 212 tumor-associated antigens (TAAs) using a novel algorithm and RNA sequencing data. Eighteen TAAs were validated, with 10 showing promise for liver cancer immunotherapy, including specific peptides with strong HLA binding.

Keywords:

Bayesian algorithm CAR-T CP: Cancer biology CP: Systems biology CPTAC TCGA gene expression state inference immunotherapy liver cancer pan-cancer therapeutic target tumor-associated antigen

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Area of Science:

Genomics
Immunology
Bioinformatics

Background:

Tumor-associated antigens (TAAs) are crucial targets for cancer immunotherapy.
Identifying novel TAAs requires robust computational and experimental validation methods.

Purpose of the Study:

To develop and apply a Bayesian-based algorithm for identifying TAAs across diverse cancer types.
To validate candidate TAAs using independent datasets and experimental methods.
To discover potential therapeutic targets for liver cancer immunotherapy.

Main Methods:

Bayesian-based algorithm for gene expression state inference.
Analysis of RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects.
Validation using independent RNA-seq datasets, proteomics data, and experimental assays for peptide binding and immunogenicity.

Main Results:

Identified 212 candidate TAAs across 33 cancer types.
Validated 78 TAAs in independent datasets and 18 TAAs with proteomics data.
Discovered 10 TAAs linked to liver cancer, with 21 predicted peptides showing strong HLA-A02 binding and immunogenicity.
Found that ~64% of liver tumors express TAAs associated with these validated peptides.

Conclusions:

The integrated computational and experimental approach successfully identified and validated promising TAAs for cancer immunotherapy.
The discovered TAAs and peptides represent potential therapeutic candidates for liver cancer, including peptide vaccines and T cell receptor (TCR)-T cell therapies.
This study underscores the value of multi-omics data integration for advancing precision oncology.