3052 Background: Next-generation sequencing of DNA, which can provide valid information for clinical therapeutic decision-making, has been widely used in the management of lung cancer especially adenocarcinoma. However, due to its technical limitations for detecting certain alterations such as gene rearrangement, the DNA-based sequencing (DNA-seq) may miss the actionable alteration in some cases, who would have benefited from targeted therapy. The study aimed to evaluate the capability of RNA sequencing (RNA-seq) in identifying DNA-seq undetectable gene alterations in lung adenocarcinomas. Methods: A total of 219 lung adenocarcinomas, which had no driver alteration detected by DNA-seq (OncoScreen Plus, Burning Rock Biotech) and had a max AF ≥5%, underwent capture-based RNA-seq using a custom panel (OncoRNA, Burning Rock Biotech) spanning full transcripts of 115 genes commonly involved in cancer genomic rearrangements. Furthermore, an independent cohort of 100 DNA-seq driver–negative lung adenocarcinomas were also subjected to RNA-seq with the same panel. Results: In the discovery cohort, 166/219 samples (75.8%) generated qualified RNA-seq data for subsequent analyses. RNA-seq identified 44 previously undetected alterations (26.5%), including 40 gene fusions (24.1%), 1 MET exon14 skipping variant ( METex14, 0.6%) and 3 other alternative splicing variants (1.8%). Among them, 14 (8.4%) were potential actionable alterations, consisting of METex14 and in-frame fusions containing functional domain of the driver gene (4 ROS1 fusions, 3 BRAF fusions, 2 NRG1 fusions, 2 EGFR fusions, 1 ALK fusion and 1 MET fusion). In the validation cohort, 69/100 samples (69.0%) generated qualified data. RNA-seq identified 22 DNA-seq undetected alterations (31.9%), with 7 of them being potential actionable fusions (10.1%). ROS1 fusion remained as the most common actionable alteration (n = 3), followed by ALK fusion (n = 2), EGFR fusion (n = 1) and MET fusion (n = 1). Further analyses of the two datasets revealed that lacking sufficient coverage spanning the rearrangement breakpoint in the DNA-seq panel mainly accounted for the failure of DNA-seq on detecting these fusions. This can be improved by increasing the corresponding probe coverage in the DNA-seq panel. In addition, complex genomic rearrangement at DNA level and the presence of repetitive sequence in the intronic region spanning or adjacent to the breakpoint might lead to missed calling of canonical fusions by DNA-seq. Conclusions: Targeted RNA-seq can effectively identify genomic rearrangements that are undetectable by DNA-seq and provide lung adenocarcinoma patients with more opportunities for targeted therapy. Therefore, it should be recommended for all patients, in whom DNA-seq fails to detect driver alteration.