Optimized Detection of Unknown Exon 14 Skipping Mutations in Routine Testing for Patients With Non-Small-Cell Lung Cancer.

Purpose: exon 14 (ex14) skipping is an actionable biomarker in non-small-cell lung cancer. However, variants are highly complex and diverse, and not all variants lead to exon 14 skipping. Assessing the skipping effect of unknown variants is still a key issue in molecular diagnosis.

Materials And Methods: We retrospectively collected variants around exon 14 from 4,233 patients with non-small-cell lung cancer who underwent next-generation sequencing testing using DNA, as well as two published data sets.

Results: Among the 4,233 patients, 44 unique variants including 29 novel variants (65.9%) were discovered from 53 patients. Notably, 31 samples (58.5%) failed RNA verification. Using RNA verification, nine novel skipping variants and five nonskipping variants were confirmed. We further used SpliceAI with the delta score cutoff of 0.315 to aid the classification of novel variants (sensitivity = 98.88% and specificity = 100%). When applied to the reported variants, we also found three wrongly classified nonskipping variants. Finally, an optimized knowledge-based interpretation procedure for clinical routine was built according to the mutation type and location, and five more skipping mutations from the 13 unknown variants were determined, which improved the population determination rate to 0.92%.

Conclusion: This study discovered more ex14 skipping variants and optimized an innovative approach that could be adapted for the interpretation of infrequent or novel ex14 variants timely without experimental validation.