Marker selection strategies for circulating tumor DNA guided by phylogenetic inference.
Marker selection strategies for circulating tumor DNA guided by phylogenetic inference.
复制标题
系统发育推断指导下的循环肿瘤 DNA 标记选择策略。
DOI：
10.1101/2024.03.21.585352
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Schwartz,Russell
中科院分区：
文献类型：
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作者：
Fu,Xuecong;Luo,Zhicheng;Deng,Yueqian;LaFramboise,William;Bartlett,David;Schwartz,Russell
基于血液的肿瘤DNA分析（“液体活检”）为非侵入性早期癌症诊断、治疗监测和临床指导提供了巨大的前景，但需要进一步发展计算方法，以成为肿瘤克隆进化的可靠定量分析。我们提出了新的方法，以更好地表征肿瘤克隆动力学从循环肿瘤DNA（ctDNA），通过应用到两个具体的问题：1）如何应用纵向ctDNA数据来完善克隆进化的遗传模型，以及2）如何量化的克隆频率的变化，可能是治疗反应或肿瘤进展的指示。我们提出这些问题，通过概率框架，最佳识别最大似然标记，并将它们应用到克隆evolution.ResultsWe的特点，我们首先估计一个分布在合理的克隆谱系模型，使用引导样本在预处理组织为基础的序列数据。然后，我们完善这些谱系模型和克隆频率，他们意味着在连续的纵向样本。我们使用所得到的框架建模和细化树分布提出了一组优化问题，选择ctDNA标记，以最大限度地提高效用捕获能力的措施，以解决两个问题，减少不确定性的遗传模型或量化克隆频率给定的模型。我们在合成数据上测试了我们的方法，并表明它们在细化树模型和克隆频率的分布方面是有效的，以便最大限度地减少相对于地面真实的树距离的测量。将树细化方法应用于真实的肿瘤数据进一步证明了它们在细化克隆谱系模型和评估其克隆频率方面的有效性。这项工作显示了计算方法的力量，可以改善标记物选择，克隆谱系重建和克隆动力学分析，以进行更精确和定量的肿瘤分析。progression.Availabilityhttps://github.com/CMUSchwartzLab/Mase-phi.git.Contactrussells@andrew.cmu.edu
MotivationBlood-based profiling of tumor DNA (“liquid biopsy”) has offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but require further advances in computational methods to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific questions: 1) How to apply longitudinal ctDNA data to refine phylogeny models of clonal evolution, and 2) how to quantify changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions through a probabilistic framework for optimally identifying maximum likelihood markers and applying them to characterizing clonal evolution.ResultsWe first estimate a distribution over plausible clonal lineage models, using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these lineage models and the clonal frequencies they imply over successive longitudinal samples. We use the resulting framework for modeling and refining tree distributions to pose a set of optimization problems to select ctDNA markers to maximize measures of utility capturing ability to solve the two questions of reducing uncertain in phylogeny models or quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining distributions of tree models and clonal frequencies so as to minimize measures of tree distance relative to the ground truth. Application of the tree refinement methods to real tumor data further demonstrated their effectiveness in refining a clonal lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of tumor progression.Availabilityhttps://github.com/CMUSchwartzLab/Mase-phi.git.Contactrussells@andrew.cmu.edu