Background Droplet digital PCR (ddPCR) is a widely used and sensitive application for circulating tumor DNA (ctDNA) detection. As ctDNA is often found in low abundance, methods to separate low-signal readouts from noise are necessary. We aimed to characterize the ddPCR-generated noise and, informed by this, create a sensitive and specific ctDNA caller. Methods We built 2 novel complimentary ctDNA calling methods: dynamic limit of blank and concentration and assay-specific tumor load estimator (CASTLE). Both methods are informed by empirically established assay-specific noise profiles. Here, we characterized noise for 70 mutation-detecting ddPCR assays by applying each assay to 95 nonmutated samples. Using these profiles, the performance of the 2 new methods was assessed in a total of 9447 negative/positive reference samples and in 1311 real-life plasma samples from colorectal cancer patients. Lastly, performances were compared to 7 literature-established calling methods. Results For many assays, noise increased proportionally with the DNA input amount. Assays targeting transition base changes were more error-prone than transversion-targeting assays. Both our calling methods successfully accounted for the additional noise in transition assays and showed consistently high performance regardless of DNA input amount. Calling methods that were not noise-informed performed less well than noise-informed methods. CASTLE was the only calling method providing a statistical estimate of the noise-corrected mutation level and call certainty. Conclusions Accurate error modeling is necessary for sensitive and specific ctDNA detection by ddPCR. Accounting for DNA input amounts ensures specific detection regardless of the sample-specific DNA concentration. Our results demonstrate CASTLE as a powerful tool for ctDNA calling using ddPCR.

中文翻译：

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误差表征和统计建模改进了微滴式数字 PCR 对循环肿瘤 DNA 的检测

背景 液滴数字 PCR (ddPCR) 是一种广泛使用且敏感的循环肿瘤 DNA (ctDNA) 检测应用。由于 ctDNA 的丰度通常很低，因此需要将低信号读数与噪声分离的方法。我们旨在表征 ddPCR 产生的噪声，并据此创建一个敏感且特定的 ctDNA 调用程序。方法 我们构建了 2 种新的互补 ctDNA 调用方法：空白和浓度的动态限制以及检测特异性肿瘤负荷估计器 (CASTLE)。这两种方法都是通过经验建立的特定于测定的噪声曲线来通知的。在这里，我们通过将每个检测应用于 95 个非突变样本来表征 70 个突变检测 ddPCR 检测的噪声。使用这些配置文件，在总共 9447 个阴性/阳性参考样本和 1311 个来自结肠直肠癌患者的真实血浆样本中评估了 2 种新方法的性能。最后，将性能与 7 种文献建立的调用方法进行比较。结果 对于许多测定，噪声随 DNA 输入量成比例增加。针对转换碱基变化的分析比针对颠换的分析更容易出错。我们的两种调用方法都成功地解决了转换分析中的额外噪音，并且无论 DNA 输入量如何，都显示出始终如一的高性能。调用非噪声通知方法的性能不如噪声通知方法。CASTLE 是唯一提供噪声校正突变水平和调用确定性统计估计的调用方法。结论 准确的误差建模对于通过 ddPCR 进行灵敏和特异的 ctDNA 检测是必要的。考虑 DNA 输入量可确保特定检测，无论样本特异性 DNA 浓度如何。我们的结果表明 CASTLE 是使用 ddPCR 进行 ctDNA 调用的强大工具。