TSAFinder: exhaustive tumor-specific antigen detection with RNAseq

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Tumor-specific antigens (TSAs) are crucial for predicting immunotherapy response and developing cancer vaccines.
• High RNA expression of TSAs correlates with their presentation on MHC-I molecules, enabling potential therapeutic targeting.
• Accurate TSA identification is essential for advancing personalized cancer therapies.

Purpose of the Study:

• To develop and validate a novel computational pipeline for predicting tumor-specific antigens (TSAs) directly from RNA sequencing data.
• To identify high-confidence TSAs that are highly expressed in tumors and have potential as therapeutic targets.
• To assess the utility of RNA sequencing for predicting neoantigens compared to exome sequencing.

Main Methods:

• Human leukocyte antigen (HLA)-I genotypes were predicted using seq2HLA.
• RNA sequencing (RNAseq) fastq files were processed to generate all possible peptides of lengths 8-11.
• Peptides were analyzed for differential expression between tumor and control samples and their MHC-I binding potential was assessed using netMHCpan-4.0.

Main Results:

• A novel pipeline successfully predicted TSAs from RNA sequencing data in murine and human tumor models.
• Neoantigens predicted by exome sequencing showed poor RNA expression and some expression in normal tissues.
• High-confidence TSAs identified through this method, particularly in lung adenocarcinoma, are highly expressed and represent promising vaccine targets.

Conclusions:

• The developed RNA sequencing-based pipeline offers a robust method for identifying high-confidence tumor-specific antigens.
• These identified TSAs are potential targets for developing effective cancer vaccines and adoptive T-cell therapies.
• The approach facilitates the discovery of broadly applicable cancer vaccine targets from readily available RNA sequencing data.