1. A New Approach of Detecting ALK Fusion Oncogenes by RNA Sequencing Exon Coverage Analysis.
- Author
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Zakharova, Galina, Suntsova, Maria, Rabushko, Elizaveta, Mohammad, Tharaa, Drobyshev, Alexey, Seryakov, Alexander, Poddubskaya, Elena, Moisseev, Alexey, Smirnova, Anastasia, Sorokin, Maxim, Tkachev, Victor, Simonov, Alexander, Guguchkin, Egor, Karpulevich, Evgeny, and Buzdin, Anton
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RESEARCH funding , *POLYMERASE chain reaction , *TUMOR markers , *COST benefit analysis , *DESCRIPTIVE statistics , *IMMUNOHISTOCHEMISTRY , *GENES , *ONCOGENES , *ANAPLASTIC lymphoma kinase , *PROTEIN-tyrosine kinases , *FLUORESCENCE in situ hybridization , *GENE expression profiling , *SEQUENCE analysis , *SENSITIVITY & specificity (Statistics) - Abstract
Simple Summary: Chimeric transcripts frequently function as oncogenic drivers and represent potential targets for tumor-specific therapies. Routine methods for detecting these transcripts include (i) approaches that can detect a limited number of pre-defined targets, such as immunohistochemistry, fluorescence in situ hybridization, and reverse transcription–quantitative polymerase chain reaction, and (ii) approaches that can simultaneously detect multiple fusions, such as NanoString assays and NGS targeted panels. Whole transcriptome sequencing enables the analysis of a wide range of cancer biomarkers, including dysregulated genes, molecular pathways, and both known and novel cancer driver fusion transcripts. However, this method is not commonly employed for fusion discovery due to inadequate coverage at the fusion breakpoint. To address this limitation, we have developed a novel bioinformatics approach that enables the highly accurate prediction of clinically significant ALK fusions from RNA sequencing data. This extends the functionality of whole transcriptome NGS and improves its cost-effectiveness. Background: 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. Methods: 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). Results: 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. Conclusions: RNAseq exon coverage analysis can effectively detect ALK rearrangements. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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