Comparison of DNA+RNA co-detection and DNA-only NGS for driver gene identification and co-mutation profiling in Chinese patients with non–small cell lung cancer.

e20750 Background: Comprehensive identification of oncogenic drivers is essential for precision treatment of NSCLC. Although DNA-based NGS is widely used, it has limited sensitivity for gene fusions and splice variants. DNA+RNA co-detection enables simultaneous detection of DNA- and RNA-level alterations and may improve identification of actionable drivers. However, real-world comparisons of these strategies in Chinese NSCLC populations are limited. This study compared driver detection rates and co-mutation landscapes between DR co-detection and DNA-NGS. Methods: Two independent cohorts of Chinese patients with NSCLC were analyzed. The DR co-detection cohort included 1,309 patients tested using a 57-gene DNA+RNA co-detection panel, whereas the DNA-NGS cohort included 2,362 patients tested using a 733-gene DNA-only NGS panel. Driver gene detection rates for major actionable genes (EGFR, ALK, RET, ROS1, MET, ERBB2, BRAF, KRAS) were compared. Co-mutation patterns involving multiple driver genes were further analyzed to assess differences in molecular complexity captured by each testing strategy. Results: DR co-detection demonstrated superior performance in identifying fusion and structural-variant–driven oncogenes. Compared with DNA-NGS, DR co-detection yielded higher detection rates for ALK (6.95% vs 4.70%), RET (4.81% vs 1.44%), MET (5.96% vs 4.11%), ERBB2 (5.35% vs 4.40%), BRAF (4.35% vs 2.41%), and ROS1 (2.14% vs 1.48%), highlighting the added value of RNA-based interrogation for fusion and splicing events. In contrast, DNA-NGS showed slightly higher detection rates for classic point-mutation–driven genes, including EGFR (48.05% vs 47.39%) and KRAS (12.40% vs 11.92%), consistent with its technical strengths in SNV/Indel detection. Both methods identified patients harboring multiple driver alterations (79 in the DR co-detection cohort and 83 in the DNA-NGS cohort); however, the composition of co-mutation profiles differed substantially. DR co-detection preferentially identified fusion-involved co-mutation patterns, such as ALK/EGFR, RET/EGFR, ROS1/KRAS, and MET/EGFR, whereas DNA-NGS primarily detected SNV-dominant co-mutations, including EGFR/KRAS, EGFR/MET, and EGFR/BRAF. Overall, DR co-detection captured greater driver gene diversity and structural-variant complexity. Conclusions: In Chinese patients with NSCLC, DNA+RNA co-detection significantly improves the detection of fusion and structural-variant driver alterations and expands the identification of complex driver co-mutation landscapes, whereas DNA-NGS remains advantageous for classic point mutations. These complementary strengths support the clinical value of integrated DNA+RNA testing strategies for comprehensive molecular profiling and precision treatment decision-making in NSCLC.