Department of Pathology, Peter MacCallum Cancer Centre, 305 Grattan Street, Parkville, VIC, 3000, Australia.
Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
BMC Med Genomics. 2022 Mar 26;15(1):70. doi: 10.1186/s12920-022-01214-y.
Next generation sequencing for oncology patient management is now routine in clinical pathology laboratories. Although wet lab, sequencing and pipeline tasks are largely automated, the analysis of variants for clinical reporting remains largely a manual task. The increasing volume of sequencing data and the limited availability of genetic experts to analyse and report on variants in the data is a key scalability limit for molecular diagnostics.
To determine the impact and size of the issue, we examined the longitudinally compiled genetic variants from 48,036 cancer patients over a six year period in a large cancer hospital from ten targeted cancer panel tests in germline, solid tumour and haematology contexts using hybridization capture and amplicon assays. This testing generated 24,168,398 sequenced variants of which 23,255 (8214 unique) were clinically reported.
Of the reported variants, 17,240 (74.1%) were identified in more than one assay which allowed curated variant data to be reused in later reports. The remainder, 6015 (25.9%) were not subsequently seen in later assays and did not provide any reuse benefit. The number of new variants requiring curation has significantly increased over time from 1.72 to 3.73 variants per sample (292 curated variants per month). Analysis of the 23,255 variants reported, showed 28.6% (n = 2356) were not present in common public variant resources and therefore required de novo curation. These in-house only variants were enriched for indels, tumour suppressor genes and from solid tumour assays.
This analysis highlights the significant percentage of variants not present within common public variant resources and the level of non-recurrent variants that consequently require greater curation effort. Many of these variants are unique to a single patient and unlikely to appear in other patients reflecting the personalised nature of cancer genomics. This study depicts the real-world situation for pathology laboratories faced with curating increasing numbers of low-recurrence variants while needing to expedite the process of manual variant curation. In the absence of suitably accurate automated methods, new approaches are needed to scale oncology diagnostics for future genetic testing volumes.
下一代测序技术现已广泛应用于肿瘤患者的临床管理,成为临床病理实验室的常规检测手段。尽管湿实验、测序和流程任务在很大程度上实现了自动化,但临床报告中的变异分析在很大程度上仍然是一项手动任务。测序数据量的不断增加,以及能够分析和报告数据中变异的遗传专家的有限可用性,是分子诊断的关键可扩展性限制。
为了确定问题的影响和规模,我们对一家大型癌症医院在六年期间,从十个靶向癌症panel 测试中,对 48036 例癌症患者的纵向基因变异进行了研究。这些测试采用杂交捕获和扩增子方法,共产生了 24168398 个测序变异,其中 23255 个(23255 个独特)被临床报告。
在所报告的变异中,有 17240 个(74.1%)在多个检测中被发现,这使得经过验证的变异数据可以在后续报告中重复使用。其余的 6015 个(25.9%)未在后续检测中再次出现,因此没有提供任何重复使用的好处。需要进行验证的新变异数量随着时间的推移而显著增加,从每个样本 1.72 个增加到 3.73 个(每月 292 个经过验证的变异)。对报告的 23255 个变异进行分析,结果显示 28.6%(2356 个)不存在于常见的公共变异资源中,因此需要进行新的验证。这些仅在内部使用的变异富含插入缺失、肿瘤抑制基因,且来自实体瘤检测。
本分析强调了存在于常见公共变异资源中的变异比例较大,以及需要更多验证工作的非重复变异数量。这些变异中的许多都是患者独有的,不太可能出现在其他患者中,反映了癌症基因组学的个体化特征。本研究描绘了病理实验室在面对不断增加的低频率变异的验证工作的同时,需要加快手动变异验证过程的现实情况。在缺乏适当准确的自动化方法的情况下,需要新的方法来为未来的基因测试量扩大肿瘤学诊断。