Augmenting precision medicine via targeted RNA-Seq detection of expressed mutations

Area of Science:

• Genomics
• Molecular Biology
• Precision Medicine

Background:

• DNA assays are crucial but insufficient for predicting cancer drug efficacy.
• Protein analysis is challenging and not cost-effective for high-throughput cancer specimen profiling.
• RNA may bridge the DNA-to-protein gap, enhancing therapeutic predictability in oncology.

Purpose of the Study:

• To evaluate targeted RNA sequencing (RNA-seq) for expressed variant detection.
• To explore RNA-seq's potential to complement or independently detect variants compared to DNA sequencing.
• To assess the integration of expressed mutation data analytics with DNA panels for precision oncology.

Main Methods:

• Targeted RNA sequencing (RNA-seq) was performed on a reference sample set.
• Expressed variant detection was conducted to assess RNA-seq's capabilities.
• Bioinformatics analysis was used to compare variants detected by RNA-seq and DNA-seq.

Main Results:

• RNA-seq uniquely identified pathologically relevant variants missed by DNA sequencing, with a controlled false positive rate.
• Some variants were detected by both methods, while others were missed by one or both, due to variant nature or bioinformatics limitations.
• Variants missed by RNA-seq were often unexpressed or lowly expressed, suggesting potentially lower clinical relevance.

Conclusions:

• Targeted RNA-seq demonstrates potential for uncovering clinically actionable mutations, complementing DNA sequencing in precision oncology.
• Incorporating RNA-seq into clinical biomarker panels can improve the strength and reliability of somatic mutation findings.
• This advancement can enhance clinical diagnosis, prognosis, and prediction of therapeutic efficacy, ultimately improving patient outcomes.