Neoantigen prediction in human breast cancer using RNA sequencing data

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Neoantigens are key biomarkers and therapeutic targets in cancer. Accurate neoantigen prediction remains a significant challenge, balancing accuracy and cost.
• RNA sequencing (RNA-seq) is a potentially cost-effective tool for variant detection, but its utility for neoantigen prediction requires further investigation.

Purpose of the Study:

• To evaluate the feasibility of using RNA-seq data instead of whole-exome sequencing (WES) for neoantigen candidate detection.
• To compare different variant calling protocols for neoantigen prediction accuracy and cost-effectiveness.

Main Methods:

• Somatic variants were called using three protocols: DNA method (tumor/normal WES), RNA method (tumor/normal RNA-seq), and Combination method (tumor RNA-seq/normal WES).
• Data from six breast cancer patients with matched tumor and normal samples were analyzed.
• Neoantigen candidates were identified and compared across the different methods.

Main Results:

• The RNA method exhibited high false-positive and false-negative rates due to reliance on normal transcript expression.
• The Combination method identified approximately 70% of highly expressed neoantigen candidates with sufficient tumor RNA coverage.
• Only 14% of neoantigen candidates from the DNA method were found in the Combination method, often due to low RNA coverage.

Conclusions:

• The Combination method (tumor RNA-seq and normal WES) is an efficient and cost-effective strategy for predicting highly expressed neoantigens.
• RNA-seq data alone is insufficient for accurate neoantigen prediction.
• Integrating tumor RNA-seq with normal WES data improves neoantigen discovery in cancer.