Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Somatic single nucleotide variants (SNVs) are crucial in cancer, but their detection via whole genome/exome sequencing (WGS/WES) in matched tumor-normal samples is costly.
• Tumor-only sequencing is common, yet identifying SNVs reliably without normal samples remains challenging.
• RNA sequencing (RNA-Seq) is widely used for gene expression and shows potential for cost-effective SNV detection.

Purpose of the Study:

• To develop and evaluate a method for identifying SNVs in tumor-only RNA-Seq data.
• To focus on detecting a small panel of clinically relevant SNVs.
• To assess RNA-Seq as a cost-effective alternative to WES for SNV detection.

Main Methods:

• Developed a novel method for SNV identification using tumor-only RNA-Seq data.
• Analyzed matched tumor-normal WES and tumor-only RNA-Seq data from 14 cancer patients.
• Compared SNVs detected by the RNA-Seq method against WES tumor-only and matched tumor-normal WES using Mutect2.

Main Results:

• Reliably identified a subset of clinically relevant SNVs in RNA-Seq data.
• Demonstrated that RNA-Seq can detect mutations with pathological annotations.
• RNA-Seq analysis showed comparable performance for a focused panel of SNVs.

Conclusions:

• RNA sequencing is a viable and cost-effective tool for detecting both gene expression and specific clinically relevant SNVs in cancer research.
• This method facilitates parallel analysis, reducing costs and biological sample requirements.
• RNA-Seq offers a valuable alternative for SNV detection in tumor-only settings.