Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Nat Commun. 2024 Aug 27;15(1):7386. doi: 10.1038/s41467-024-51529-w.
Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710-1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.
胚系致病性 TP53 变异使个体易患多种癌症,并成为 Li-Fraumeni 综合征 (LFS) 的标志性特征。我们的研究小组之前曾表明,LFS 患者的血浆无细胞 DNA 片段化较短;独立于癌症状态。为了了解 LFS 中 cfDNA 片段化的功能基础,我们对 199 份来自 82 名 TP53 突变携带者和 30 名健康 TP53 野生型对照者的 cfDNA 样本进行了片段组学分析。我们发现,LFS 个体在片段末端表现出 A/T 核苷酸的增加,p53 结合位点处的核小体定位失调,以及发育相关转录因子结合位点和癌症相关开放染色质区域处的染色质可及性的特异性改变。机器学习分类导致 TP53 突变 cfDNA 样本与野生型 cfDNA 样本之间的稳健区分(AUC-ROC=0.710-1.000),并且 ctDNA 片段化信号的患者内纵向分析可实现早期癌症检测。这些结果表明,cfDNA 片段化可能是 LFS 患者的有用诊断工具,并为癌症早期检测提供了重要的基线。