Detection of Multiple Types of Cancer Driver Mutations Using Targeted RNA Sequencing in Non-small Cell Lung Cancer

Background: DNA-based next-generation sequencing has been widely used in the selection of target therapies for patients with nonsmall cell lung cancer (NSCLC). RNA-based next-generation sequencing has been proven to be valuable in detecting fusion and exon-skipping mutations and is recommended by National Comprehensive Cancer Network guidelines for these mutation types.

Methods: The authors developed an RNA-based hybridization panel targeting actionable driver oncogenes in solid tumors. Experimental and bioinformatics pipelines were optimized for the detection of fusions, single-nucleotide variants (SNVs), and insertion/deletion (indels). In total, 1253 formalin-fixed, paraffin-embedded samples from patients with NSCLC were analyzed by DNA and RNA panel sequencing in parallel to assess the performance of the RNA panel in detecting multiple types of mutations.

Results: In analytical validation, the RNA panel achieved a limit of detection of 1.45-3.15 copies per nanogram for SNVs and 0.21-6.48 copies per nanogram for fusions. In 1253 formalin-fixed, paraffin-embedded NSCLC samples, the RNA panel identified a total of 124 fusion events and 26 MET exon 14-skipping events, in which 14 fusions and six MET exon 14-skipping mutations were missed by DNA panel sequencing. By using the DNA panel as the reference, the positive percent agreement and the positive predictive value of the RNA panel were 98.08% and 98.62%, respectively, for detecting targetable SNVs and 98.15% and 99.38%, respectively, for detecting targetable indels.

Conclusions: Parallel DNA and RNA sequencing analyses demonstrated the accuracy and robustness of the RNA sequencing panel in detecting multiple types of clinically actionable mutations. The simplified experimental workflow and low sample consumption will make RNA panel sequencing a potentially effective method in clinical testing.