A New Approach of Detecting ALK Fusion Oncogenes by RNA Sequencing Exon Coverage Analysis

In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from "classic" techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3' partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons.

A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel).

ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy.

RNAseq exon coverage analysis can effectively detect ALK rearrangements.