UNMASC:只有肿瘤的变种呼叫与不匹配的正常对照。

NAR Cancer Pub Date : 2021-10-06 eCollection Date: 2021-12-01 DOI:10.1093/narcan/zcab040
Paul Little, Heejoon Jo, Alan Hoyle, Angela Mazul, Xiaobei Zhao, Ashley H Salazar, Douglas Farquhar, Siddharth Sheth, Maheer Masood, Michele C Hayward, Joel S Parker, Katherine A Hoadley, Jose Zevallos, D Neil Hayes
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引用次数: 4

摘要

尽管取得了多年的进展,但癌症样本中的突变检测仍然需要大量的人工审查作为最后一步。在没有匹配的正常对照DNA进行肿瘤测序的情况下,专家审查尤其具有挑战性。通过去除已知的种系变异,利用不匹配的正常对照,以及构建决策规则来分类测序错误和私人种系变异,已经尝试在没有匹配正常样本的情况下调用体细胞点突变。由于与计算和测序成本相关的预算限制,找到适当数量的控制是识别体细胞变异的关键一步。我们的方法利用了典型体细胞变异和种系变异的公共数据库,并利用了在正常对照中收集的有关附近位置的信息。从我们针对不同肿瘤类型和人口统计学的肿瘤和正常样本的靶向捕获面板测序队列中提取,这些作为我们仅肿瘤变体调用管道的基准,以观察我们正确分类变体与许多不匹配的正常之间的关系。对于我们的基准样本,大约需要10个正常对照才能保持94%的灵敏度,99%的特异性和76%的阳性预测值,远远优于同类方法。我们的方法,称为UNMASC,也可以作为传统肿瘤的补充,具有匹配的正常变异调用工作流程,并且可以潜在地扩展到分析下一代测序数据所引起的其他问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

UNMASC: tumor-only variant calling with unmatched normal controls.

UNMASC: tumor-only variant calling with unmatched normal controls.

UNMASC: tumor-only variant calling with unmatched normal controls.

UNMASC: tumor-only variant calling with unmatched normal controls.

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.

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