Molecular Characterization of Oncogenic Gene Fusions in a Large Real-World Cohort of Solid Tumors

Gene fusions are a class of important oncogenic drivers, with many matched FDA-approved targeted therapies across multiple solid tumors. However, the prevalence of fusions varies considerably by cancer type and assay. Fusion detection is technically challenging, and studies have shown that RNA-based next-generation sequencing (NGS) can improve fusion detection rates when used in conjunction with DNA-based NGS. In this study, we performed a retrospective pan-cancer analysis of 67,278 patients receiving both RNA- and DNA-NGS in 43 distinct solid-tumor cancer types, including non-small cell lung cancer (18.6%), colorectal cancer (18.2%), and breast cancer (13.1%). In this cohort, 1,497 patients (2.2%) had at least one of nine fusions detected-each having an FDA-approved matched therapy in at least one indication. A total of 316 patients (21.1%) had a fusion detected (RET or NTRK1/2/3) with matched targeted therapy approved in all cancer indications. Concurrent RNA- and DNA-NGS increased the detection of driver gene fusions by 21% compared with DNA-NGS alone. Gene fusions were observed in a range of cancers beyond their approved cancer indications: of 1,501 fusions detected, 29% (n = 437) were detected outside of an FDA-approved indication. Finally, emerging fusion drivers with targets in drug development were found in an additional 218 patients, with combined RNA- and DNA-NGS increasing detection of these variants by 127%. Our findings support combined RNA-NGS and DNA-NGS to maximize detection of clinically actionable fusions with FDA-approved matched therapies and potentially actionable fusions in non-FDA-approved indications or those matched to therapies in clinical development.

This large, real-world pan-cancer study demonstrates that concurrent RNA- and DNA-based NGS significantly improves the detection of clinically actionable gene fusions compared with DNA-NGS alone. Our findings highlight the critical value of integrating RNA-NGS into routine molecular profiling to optimize the detection of driver gene fusions. Doing so may expand the population of patients eligible for matched targeted therapies or clinical trials, particularly in cancers with limited treatment options.