PO.CL01.10 · 临床研究

通过液体活检非侵入性追踪克隆演化和治疗反应

Non-invasive tracking of clonal evolution and treatment response through liquid biopsies

编号 5322 展板 17 时间 4/21 09:00–12:00 区域 Section 45 主讲 Farhia Kabeer, MBBS;MS;PhD

分会场 Liquid Biopsies: Circulating Nucleic Acids 4

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作者与单位 Authors & Affiliations

Farhia Kabeer¹, Matteo Lepur², Branden Lynch³, Emilia Hurtado³, Janine Senz³, Ding Ma³, Vinci Au³, Caroline Baril³, Gavin Ha⁴, Samuel Aparicio³, Jessica N. McAlpine⁵, Alexandre Bouchard-Côté², David G. Huntsman¹, Yvette Drew⁶, Andrew Roth⁷

¹Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,²Department of Statistics, University of British Columbia, Vancouver, BC, Canada,³Molecular Oncology, BC Cancer Research Institute, Vancouver, BC, Canada,⁴Fred Hutchinson Cancer Center, Seattle, WA,⁵Department of Obstetrics & Gynaecology, University of British Columbia, Vancouver, BC, Canada,⁶Department of Gynaecology and Obstetrics, Division of Medical Oncology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,⁷Department of Pathology and Laboratory Medicine, Department of Computer Science, University of British Columbia, Vancouver, BC, Canada

摘要 Abstract

中文摘要

背景：高级别浆液性卵巢癌表现出高度的基因组不稳定性和异质性的肿瘤亚群，这驱动了可变的治疗反应和治疗失败。在治疗过程中追踪肿瘤细胞群体的变化对于理解癌症的反应、耐药和转移至关重要。克隆动态反映了选择性清除、增强适应度的基因组改变以及空间受限的亚群生长。为了捕捉这些动态，我们开发了一个肿瘤指导的框架，从单细胞全基因组测序（scWGS）中解析单倍型特异性拷贝数（HSCN）状态。这些高分辨率的克隆图谱被整合到一种贝叶斯循环肿瘤DNA（ctDNA）反卷积方法cfClone中，从而能够从液体活检中灵敏且具有生物学可解释性地定量克隆变化。方法：从20例高级别浆液性卵巢癌（HGSOC）患者中采集血浆ctDNA，同时采集来自多个部位的肿瘤样本。进行单细胞全基因组测序（scWGS），并使用HapClone进行分析，HapClone是一种重建肿瘤克隆遗传结构和动态的贝叶斯模型。HapClone生成了反映等位基因特异性扩增、缺失和结构变化的单倍型特异性拷贝数（HSCN）图谱。随后这些图谱被用于通过cfClone对血浆中的克隆贡献进行反卷积，残差分析揭示了隐藏的肿瘤亚群。结果：在HGSOC病例中，将scWGS衍生的克隆状态与cfDNA整合，实现了肿瘤动态的重建、跨转移部位克隆生长的定量以及驱动复发的优势克隆的鉴定，表明转移是由复发克隆而非新克隆驱动的。在一个病例中，cfClone的纵向追踪揭示了多个时间点上肿瘤分数的变化，凸显了治疗期间出现的耐药亚克隆。纳入肿瘤含量使得能够检测克隆并对反应动态进行定量评估，例如在另一个病例中的快速克隆反应。鉴定了难治性克隆和敏感克隆之间的差异，展示了纵向数据如何揭示耐药机制。HSCN框架检测到的克隆多样性比最先进的基于结构变异的方法更大。结论：scWGS指导的HSCN分析结合ctDNA反卷积提供了一种灵敏的、具有生物学基础的方法来追踪肿瘤演化、鉴定耐药克隆和测量ctDNA。该框架能够实时监测肿瘤变化，通过以非侵入性方式将克隆动态与治疗决策联系起来，从而能够非侵入性地监测响应治疗决策的克隆动态，具有指导精准癌症治疗的强大潜力。

查看英文原文 English abstract

Background: High-grade serous ovarian cancer shows high genomic instability and heterogeneous tumorsubpopulations that drive variable therapy responses and treatment failure. Tracking tumor cellpopulation changes during treatment is crucial for understanding cancer response, resistance, andmetastasis. Clonal dynamics reflects selective sweeps, fitness-enhancing genomic alterations,and spatially restricted subpopulation outgrowth. To capture these dynamics, we developed atumor-informed framework that resolves haplotype-specific copy number (HSCN) states fromsingle cell whole genome sequencing (scWGS). These high-resolution clonal profiles areintegrated into a Bayesian circulating tumor DNA (ctDNA) deconvolution method, cfClone,enabling sensitive, biologically interpretable quantification of clonal shifts from liquid biopsies. Methods: Plasma ctDNA was collected from 20 high-grade serous ovarian cancer (HGSOC) patients,along with tumor samples from multiple sites. Single-cell whole-genome sequencing (scWGS)was performed and analyzed with HapClone, a Bayesian model reconstructing the geneticstructure and dynamics of tumor clones. HapClone generated haplotype-specific copy-number(HSCN) profiles reflecting allele-specific amplifications, deletions, and structural changes. Theseprofiles were then used to deconvolute clonal contributions in plasma with cfClone, and residualanalyses revealed hidden tumor subpopulations. Results: In HGSOC cases, integrating scWGS-derived clonal states with cfDNA enabled reconstructionof tumor dynamics, quantification of clonal growth across metastatic sites, and identification ofthe dominant clone driving recurrence, indicating that metastasis is driven by the recurrent clonerather than new clones. cfClone longitudinal tracking in one case revealed tumor-fractionchanges over multiple time points, highlighting resistant subclones emerging during therapy.Including tumor content allowed detection of clones and quantitative assessment of responsedynamics, such as rapid clonal responses in another case. Differences between refractory andsensitive clones were identified, showing how longitudinal data reveal resistance mechanisms.The HSCN framework detected greater clonal diversity than state of the art structural-variant-based methods. Conclusions: scWGS-informed HSCN analysis combined with ctDNA deconvolution provides a sensitive,biologically grounded approach to track tumor evolution, identify resistant clones, and measurectDNA. This framework enables real-time monitoring of tumor changes and has strong potentialto guide precision cancer treatment by enabling non-invasive monitoring of clonal dynamics inresponse to therapeutic decisions by linking clonal dynamics to therapy decisions in a non-invasive way.

利益披露 Disclosure

F. Kabeer, None.. M. Lepur, None.. B. Lynch, None.. E. Hurtado, None.. J. Senz, None.. D. Ma, None.. V. Au, None.. C. Baril, None.. S. Aparicio, None.. J. N. McAlpine, None.. A. Bouchard-Côté, None.. D. G. Huntsman, None.. Y. Drew, None.. A. Roth, None.

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