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Optimisation of robust singleplex and multiplex droplet digital PCR assays for high confidence mutation detection in circulating tumour DNA

机译:优化用于循环肿瘤DNA中高置信度突变检测的可靠的单重和多重液滴数字PCR检测方法

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摘要

Liquid biopsies offer the potential to monitor cancer response and resistance to therapeutics in near real-time. However, the plasma cell free DNA (cfDNA) level can be low and the fraction of circulating tumour DNA (ctDNA) bearing a mutation – lower still. Detection of tumour-derived mutations in ctDNA is thus challenging and requires highly sensitive and specific assays. Droplet digital PCR (ddPCR) is a technique that enables exquisitely sensitive detection and quantification of DNA/RNA markers from very limiting clinical samples, including plasma. The Bio-Rad QX200 ddPCR system provides absolute quantitation of target DNA molecules using fluorescent dual-labelled probes. Critical to accurate sample analysis are validated assays that are highly specific, reproducible, and with known performance characteristics, especially with respect to false positives. We present a systematic approach to the development and optimisation of singleplex and multiplex ddPCR assays for the detection of point mutations with a focus on ensuring extremely low false positives whilst retaining high sensitivity. We also present a refined method to determine cfDNA extraction efficiency allowing for more accurate extrapolation of mutational levels in source samples. We have applied these approaches to successfully analyse many ctDNA samples from multiple clinical studies and generated exploratory data of high quality.
机译:液体活检提供了近乎实时监测癌症反应和对治疗药物耐药性的潜力。但是,浆细胞游离DNA(cfDNA)的水平可能较低,而带有突变的循环肿瘤DNA(ctDNA)的比例则更低。因此,ctDNA中肿瘤来源的突变的检测具有挑战性,并且需要高度敏感和特异性的检测方法。液滴数字PCR(ddPCR)是一项技术,可以对非常有限的临床样本(包括血浆)中的DNA / RNA标记进行灵敏的检测和定量。 Bio-Rad QX200 ddPCR系统使用荧光双标记探针对靶DNA分子进行绝对定量。准确的样品分析至关重要的是经过验证的测定方法,该方法必须高度特异性,可重复性,并且具有已知的性能特征,尤其是对于误报而言。我们提出了一种系统的方法来开发和优化用于检测点突变的单重和多重ddPCR分析,重点在于确保极低的假阳性率,同时保持高灵敏度。我们还提出了一种确定cfDNA提取效率的改进方法,可以更准确地推断来源样品中的突变水平。我们已经应用这些方法成功地分析了来自多个临床研究的许多ctDNA样品,并生成了高质量的探索性数据。

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