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通过转移性乳腺癌的循环肿瘤 DNA 对狭窄时间范围内的克隆结构进行建模。

Modeling clonal structure over narrow time frames via circulating tumor DNA in metastatic breast cancer.

机构信息

The Ohio State University Comprehensive Cancer Center, The Ohio State University, 460 W. 12th Avenue, Columbus, OH, 43210, USA.

Division of Medical Oncology, Department of Medicine, College of Medicine, The Ohio State University, 320 W. 10th Avenue, Columbus, OH, 43210, USA.

出版信息

Genome Med. 2021 May 20;13(1):89. doi: 10.1186/s13073-021-00895-x.

Abstract

BACKGROUND

Circulating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.

METHODS

In this exploratory analysis, we characterize high-frequency ctDNA sample series collected over narrow time frames from seven patients with metastatic triple-negative breast cancer, each undergoing treatment with Cabozantinib, a multi-tyrosine kinase inhibitor (NCT01738438, https://clinicaltrials.gov/ct2/show/NCT01738438 ). Applying orthogonal whole exome sequencing, ultra-low pass whole genome sequencing, and 396-gene targeted panel sequencing, we analyzed 42 plasma-derived ctDNA libraries, representing 4-8 samples per patient with 6-42 days between samples. Integrating tumor fraction, copy number, and somatic variant information, we model tumor clonal dynamics, predict neoantigens, and evaluate consistency of genomic information from orthogonal assays.

RESULTS

We measured considerable variation in ctDNA tumor faction in each patient, often conflicting with RECIST imaging response metrics. In orthogonal sequencing, we found high concordance between targeted panel and whole exome sequencing in both variant detection and variant allele frequency estimation (specificity = 95.5%, VAF correlation, r = 0.949), Copy number remained generally stable, despite resolution limitations posed by low tumor fraction. Through modeling, we inferred and tracked distinct clonal populations specific to each patient and built phylogenetic trees revealing alterations in hallmark breast cancer drivers, including TP53, PIK3CA, CDK4, and PTEN. Our modeling revealed varied responses to therapy, with some individuals displaying stable clonal profiles, while others showed signs of substantial expansion or reduction in prevalence, with characteristic alterations of varied literature annotation in relation to the study drug. Finally, we predicted and tracked neoantigen-producing alterations across time, exposing translationally relevant detection patterns.

CONCLUSIONS

Despite technical challenges arising from low tumor content, metastatic ctDNA monitoring can aid our understanding of response and progression, while minimizing patient risk and discomfort. In this study, we demonstrate the potential for high-frequency monitoring of evolving genomic features, providing an important step toward scalable, translational genomics for clinical decision making.

摘要

背景

循环肿瘤 DNA(ctDNA)提供了一种微创手段来反复检测肿瘤基因组,从而有机会监测转移和治疗选择压力诱导的克隆动力学。在转移性癌症中,ctDNA 分析可同时分析局部和远处复发部位。尽管 ctDNA 采样具有很大的应用前景,但在实时遗传监测方面的应用仍在很大程度上未得到探索。

方法

在这项探索性分析中,我们对 7 名接受卡博替尼(Cabozantinib)治疗的转移性三阴性乳腺癌患者的窄时间框架内采集的高频 ctDNA 样本系列进行了描述,卡博替尼是一种多酪氨酸激酶抑制剂(NCT01738438,https://clinicaltrials.gov/ct2/show/NCT01738438)。我们应用正交全外显子组测序、超低覆盖全基因组测序和 396 基因靶向面板测序,分析了 42 个来自血浆的 ctDNA 文库,每个患者代表 4-8 个样本,样本之间的时间间隔为 6-42 天。整合肿瘤分数、拷贝数和体细胞变异信息,我们构建肿瘤克隆动力学模型、预测新抗原,并评估正交检测的基因组信息的一致性。

结果

我们在每个患者中测量了 ctDNA 肿瘤分数的相当大的变化,这通常与 RECIST 成像反应指标相冲突。在正交测序中,我们在变异检测和变异等位基因频率估计方面发现靶向面板和全外显子组测序具有很高的一致性(特异性=95.5%,VAF 相关性,r=0.949),拷贝数保持相对稳定,尽管存在由低肿瘤分数引起的分辨率限制。通过建模,我们推断并跟踪了每个患者特有的不同克隆群体,并构建了系统发育树,揭示了标志性乳腺癌驱动因素的改变,包括 TP53、PIK3CA、CDK4 和 PTEN。我们的模型揭示了对治疗的不同反应,一些个体显示出稳定的克隆特征,而另一些个体则显示出明显的扩增或减少,其特征与研究药物相关的文献注释发生变化。最后,我们预测并跟踪了随时间推移产生的新抗原改变,揭示了具有翻译相关性的检测模式。

结论

尽管存在低肿瘤含量引起的技术挑战,但转移性 ctDNA 监测可以帮助我们了解反应和进展,同时最小化患者的风险和不适。在这项研究中,我们展示了对不断进化的基因组特征进行高频监测的潜力,为可扩展的转化基因组学提供了重要步骤,以用于临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fff/8136130/4e53c4713a5f/13073_2021_895_Fig1_HTML.jpg

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