Multiplexed transcriptome analysis to detect ALK, ROS1 and RET rearrangements in lung cancer
AuthorRogers, T-M; Arnau, GM; Ryland, GL; Huang, S; Lira, ME; Emmanuel, Y; Perez, OD; Irwin, D; Fellowes, AP; Wong, SQ; ...
Source TitleScientific Reports
PublisherNATURE PUBLISHING GROUP
AffiliationSir Peter MacCallum Department of Oncology
Document TypeJournal Article
CitationsRogers, T. -M., Arnau, G. M., Ryland, G. L., Huang, S., Lira, M. E., Emmanuel, Y., Perez, O. D., Irwin, D., Fellowes, A. P., Wong, S. Q. & Fox, S. B. (2017). Multiplexed transcriptome analysis to detect ALK, ROS1 and RET rearrangements in lung cancer. SCIENTIFIC REPORTS, 7 (1), https://doi.org/10.1038/srep42259.
Access StatusOpen Access
ALK, ROS1 and RET gene fusions are important predictive biomarkers for tyrosine kinase inhibitors in lung cancer. Currently, the gold standard method for gene fusion detection is Fluorescence In Situ Hybridization (FISH) and while highly sensitive and specific, it is also labour intensive, subjective in analysis, and unable to screen a large numbers of gene fusions. Recent developments in high-throughput transcriptome-based methods may provide a suitable alternative to FISH as they are compatible with multiplexing and diagnostic workflows. However, the concordance between these different methods compared with FISH has not been evaluated. In this study we compared the results from three transcriptome-based platforms (Nanostring Elements, Agena LungFusion panel and ThermoFisher NGS fusion panel) to those obtained from ALK, ROS1 and RET FISH on 51 clinical specimens. Overall agreement of results ranged from 86-96% depending on the platform used. While all platforms were highly sensitive, both the Agena panel and Thermo Fisher NGS fusion panel reported minor fusions that were not detectable by FISH. Our proof-of-principle study illustrates that transcriptome-based analyses are sensitive and robust methods for detecting actionable gene fusions in lung cancer and could provide a robust alternative to FISH testing in the diagnostic setting.
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