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循环瘤DNA的标记物选择策略以遗传学推断为指导

Xuecong Fu¹, Zhicheng Luo¹, Yueqian Deng²

¹Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15217, United States.

Bioinformatics (Oxford, England)

|March 31, 2025

概括

新的计算方法增强了液体活检分析,用于跟踪瘤演变. 这可以改善早期癌症检测和治疗监测,通过从循环瘤DNA (ctDNA) 提炼克隆动态.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
癌症研究癌症研究

背景情况:

使用循环瘤DNA (ctDNA) 的液体活检显示出对非侵入性癌症诊断和监测的希望.
目前的计算方法需要改进,以便对瘤克隆进化的强有力的定量分析.
准确地描述克隆动态对于早期诊断和指导治疗决策至关重要.

研究的目的:

利用纵向ctDNA数据,开发新的计算方法来改进瘤类型模型.
从ctDNA量化克隆频率的变化,表明治疗反应或进展.
建立一个概率框架,用于最佳的标记物识别和克隆进化的特征.

主要方法:

在克隆树模型上估计密度,使用来自预处理组织数据的引导样本.
通过连续的纵向ctDNA样本改进克隆树模型.
开发标记物选择的优化问题,以减少遗传学不确定性和量化克隆频率.

主要成果:

提出的方法有效地提炼了树密度,并在合成数据上推断了克隆频率.
对真实瘤数据的应用表明,改进了谱系模型的精细化和克隆频率评估.
计算框架增强了标记物选择,血统重建和克隆动态分析.

结论:

计算进步是使液体活检成为瘤克隆进化的强有力的定量分析的关键.
开发的方法提高了体质演变和瘤进展分析的精度和量化.
这项工作为更准确的非侵入性癌症诊断和个性化治疗策略铺平了道路.

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