BACKGROUND Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. RESULTS Here we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed > 70% concordance with fluorescence in situ hybridization (FISH) results. Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches. CONCLUSIONS We demonstrated that CNV Radar is more sensitive than other CNV detection methods, accurately detects clinically important cytogenetic events, and allows for further interrogation of novel disease biology. Overall, CNV Radar exhibited high concordance with standard methods such as FISH, and its success in the POLLUX and CASTOR clinical trials demonstrated its potential utility for informing clinical and therapeutic decisions.

中文翻译：

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CNV雷达：一种用于肿瘤学中体细胞拷贝数改变表征的改进方法。

背景技术与癌症相关的拷贝数变异（CNV）事件提供了用于识别患者亚组和建议治疗策略的重要信息。然而，技术和后勤问题使得在临床研究中以具有成本效益的方式准确检测异常拷贝数事件具有挑战性。结果在这里，我们介绍CNV Radar，这是一种软件工具，利用下一代测序读取的深度信息和变异的等位基因频率模式，从整个外显子组测序数据中推断出基因和基因组区域的真实拷贝数状态。在公共多发性骨髓瘤数据集中对CNV Radar的评估表明，CNV Radar能够检测与进展风险相关的多种CNV，并且我们观察到与荧光原位杂交（FISH）结果的一致性> 70％。与其他CNV呼叫者相比，CNV雷达显示出高灵敏度和特异性。当将CNV Radar呼叫与来自急性髓细胞性白血病和TCGA上的前列腺癌数据集的单核苷酸多态性阵列结果进行比较时，观察到相似的结果。更重要的是，CNV Radar证明了其在临床试验环境中的实用性：在POLLUX和CASTOR中，两项针对复发或难治性多发性骨髓瘤患者的3期三期研究中，我们观察到FISH与del17p（定义CNV事件的风险）的一致性高（88）在POLLUX中占90％，在CASTOR中占90％），因此可以对临床相关疾病亚组进行疗效评估。我们的案例研究还表明，CNV雷达能够检测其他方法所无法实现的异常，例如杂合性的复制中性丢失​​。结论我们证明了CNV雷达比其他CNV检测方法更灵敏，可以准确检测临床上重要的细胞遗传学事件，并可以进一步审视新型疾病生物学。总体而言，CNV雷达与FISH等标准方法显示出高度一致性，其在POLLUX和CASTOR临床试验中的成功证明了其在告知临床和治疗决策方面的潜在效用。