虽然进化医学的方法显示出潜力，但由于实验限制和身体系统的动态性，衡量进化本身是困难的。在癌症进化中，连续观察克隆结构是不可能的，而从多个时间点获取纵向样本也很少见。单细胞水平上越来越多的可用DNA测序数据集能够利用突变历史重建过去的进化，从而更好地了解可检测疾病之前的动力学。对于从这些数据集中量化克隆生长动力学的准确、快速和易于使用的方法有一个未满足的需求。我们基于共同演化理论推导了一种估计克隆净增长率的方法，可以使用重建的系统发育树或共享突变数量。我们应用和验证了我们估计克隆净增长率的分析方法，消除了以前方法中使用复杂模拟的需要。当应用于造血数据时，我们表明我们的估计可能具有广泛的应用，以改善机制理解和预后能力。与具有单个或未知驱动突变的克隆相比，具有多个驱动因子的克隆具有显著增长率的增加（中位数为0.94与0.25每年，p = 1.6×10-6）。此外，根据最适克隆的生长速率对患有骨髓增殖性肿瘤（MPN）的患者进行分层显示，较高的生长速率与更短的MPN诊断时间相关（中位数为13.9与26.4个月; p = 0.0026）。我们开发了一个公开可用的R软件包cloneRate来实现我们的方法（软件包网站：https://bdj34.github.io/cloneRate/）。源代码：https://github.com/bdj34/cloneRate/。补充材料可在Bioinformatics在线获得。©2023年作者。由牛津大学出版社出版。

While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single-cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. There is an unmet need for an accurate, fast, and easy-to-use method to quantify clone growth dynamics from these datasets.We derived methods based on coalescent theory for estimating the net growth rate of clones using either reconstructed phylogenies or the number of shared mutations. We applied and validated our analytical methods for estimating the net growth rate of clones, eliminating the need for complex simulations used in previous methods. When applied to hematopoietic data, we show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6×10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026).We developed a publicly available R package, cloneRate, to implement our methods (Package website: https://bdj34.github.io/cloneRate/). Source code: https://github.com/bdj34/cloneRate/.Supplementary material is available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.