Background: In clinical practice, various methods are used to identifyALKgene 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 ofALKfusion 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 inALKcoverage. A total of 50 samples were analyzed, comprising 13 samples with predictedALKfusions and 37 samples without predictedALKfusions. 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:ALKfusions 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 lowALKcoverage 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 ofALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy. Conclusions: RNAseq exon coverage analysis can effectively detectALKrearrangements.
背景：在临床实践中，用于检测肿瘤样本中ALK基因重排的方法多种多样，从“经典”技术（如免疫组织化学、荧光原位杂交和实时定量聚合酶链反应）到更先进的高度多重检测方法（如NanoString技术和二代测序组合检测）。这些方法各有优缺点，但都存在一个共同的局限性：只能检测一组预先选定的（尽管有时范围较广）生物标志物。与此同时，全转录组测序（WTS，RNA测序）原则上可用于检测基因融合，同时分析范围极其广泛的肿瘤特征。然而，由于纯粹的分析限制以及生物信息学分析的高度复杂性（需要相当的专业知识），全转录组测序在实践中并未得到广泛应用。特别是，检测RNA测序数据中基因融合的方法依赖于嵌合读段的识别。然而，RNA测序中真实融合读段的数量通常较少，这限制了其灵敏度。在先前的研究中，我们观察到某些融合转录本3'端伴侣的RNA测序外显子覆盖存在不对称性。本研究通过分析其酪氨酸激酶外显子覆盖的差异，对ALK融合检测的准确性进行了全面评估。方法：利用实验性RNA测序数据对总计906个人类癌症生物样本进行分析，旨在确定ALK覆盖不对称性的程度。共分析了50个样本，包括13个预测存在ALK融合的样本和37个预测无ALK融合的样本。这些样本通过靶向测序进行评估，使用了两个专门设计用于检测融合转录本的二代测序组合检测（TruSight RNA融合组合检测和OncoFu Elite组合检测）。结果：在13个预测病例中，有11例确认存在ALK融合，总体准确率为96%（灵敏度100%，特异性94.9%）。两个不一致的病例显示出较低的ALK覆盖深度，这可以通过算法处理来提高结果的准确性。同时，由于涉及ALK部分区域的反义转录本的存在，考虑读段的链特异性也很重要。在接受ALK靶向治疗的有限患者样本中，该算法成功预测了治疗效果。结论：RNA测序外显子覆盖分析可以有效检测ALK重排。
A New Approach of DetectingALKFusion Oncogenes by RNA Sequencing Exon Coverage Analysis
肿瘤(癌症)患者之家