PO.BCS01.04 · 生物信息与计算
利用 RRBS 和 EM-seq 从小细胞肺癌循环肿瘤 DNA 甲基化特征推断转录组程序
Inferring transcriptomic programs from circulating tumor DNA methylation signatures in small cell lung cancer using RRBS and EM-seq
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：小细胞肺癌（SCLC）表现出深刻的表观遗传重塑和转录可塑性，但组织稀缺限制了多组学分析。通过简化代表性亚硫酸氢盐测序（RRBS）或酶法甲基化测序（EM-seq）对循环肿瘤 DNA（ctDNA）进行甲基化分析，提供了一个观察肿瘤生物学的微创窗口。然而，ctDNA 甲基化特征与上皮-间质转化（EMT）或神经内分泌（NE）分化等转录组状态之间的关系仍不明确，但其可能可作为监测患者治疗反应的手段。
方法：我们分析了来自 N = 43 例 SCLC 患者在基线时采集的配对 ctDNA 甲基化 RRBS 和 bulk RNA-seq 数据。对 CpG 甲基化水平进行定量，并映射到基因体、启动子和远端调控区域。为捕捉功能性甲基化特征，CpG 位点按其与相应基因 RNA 表达的相关性、以及预测表达水平或计算所得转录组评分（例如 EMT、MYC、ASCL1/NEUROD1 亚型指数）的特征重要性进行排序。将高排名的 CpG 位点聚合为基因水平和通路水平的特征标签，随后进行 GO 和 KEGG 富集分析以识别关键调控网络。这些特征进一步在接受一线化疗纵向治疗的 SCLC 患者中进行了评估。
结果：全基因组 ctDNA 甲基化图谱以高度一致性预测了 bulk RNA-seq 衍生的基因表达以及预定义的评分指标（如 EMT 评分、NE 评分）（Wilcoxon R² = 0.63 ± 0.07，p < 0.001）。排序分析显示，预测性 CpG 位点富集于与 EMT 调控因子和 NE 谱系基因相关的增强子和启动子区域。在功能上，GSEA 进一步将高 NE 评分与 Hedgehog 信号的激活相关联，将 EMT 评分与 G2/M 检查点通路的富集相关联，提示 SCLC 表型背后存在不同的调控程序。
结论：ctDNA 甲基化与转录组数据的整合分析表明，经排序、功能注释的 CpG 特征能够准确推断 SCLC 中的基因表达程序和生物学状态。这些方法提供了一个解读 ctDNA 甲基化特征的机制框架，并使 SCLC 患者肿瘤亚型和治疗耐药性的无创表征成为可能。
查看英文原文 English abstract
Background: Small cell lung cancer (SCLC) exhibits profound epigenetic remodeling and transcriptional plasticity, yet tissue scarcity limits multi-omic profiling. Circulating tumor DNA (ctDNA) methylation analysis via reduced representation bisulfite sequencing (RRBS) or enzymatic methyl-seq (EM-seq) provides a minimally invasive window into tumor biology. However, the relationship between ctDNA methylation features and transcriptomic states such as epithelial-mesenchymal transition (EMT) or neuroendocrine (NE) differentiation remains poorly defined but may serve as means to monitor patient therapeutic response.
Methods: We analyzed matched ctDNA methylation RRBS and bulk RNA-seq data from N = 43 SCLC patients collected at baseline. CpG methylation levels were quantified and mapped to gene bodies, promoters, and distal regulatory regions. To capture functional methylation signatures, CpG sites were ranked by their correlation with RNA expression of the corresponding genes and by feature importance predicting expression levels or calculated transcriptomic scores (e.g., EMT, MYC, ASCL1/NEUROD1 subtype indices). High-ranking CpG sites were aggregated into gene-level and pathway-level signatures, followed by GO and KEGG enrichment analyses to identify key regulatory networks. These were further evaluated in SCLC patients treated longitudinally with frontline chemotherapy.
Results: Genome-wide ctDNA methylation profiles predicted bulk RNA-seq-derived gene expression as well as predefined score metrics (such as EMT score, NE score) with high concordance (Wilcoxon R² = 0.63 ± 0.07, p < 0.001). Ranking analysis revealed that predictive CpG sites were enriched in enhancer and promoter regions associated with EMT regulators and NE lineage genes. Functionally, GSEA further linked high NE scores to activation of Hedgehog signaling, and EMT scores to enrichment of G2/M checkpoint pathways, suggesting distinct regulatory programs underlying SCLC phenotypes.
Conclusions: Integrative analysis of ctDNA methylation and transcriptomic data reveals that ranked, functionally annotated CpG features can accurately infer gene expression programs and biological states in SCLC. These approaches provide a mechanistic framework to interpret ctDNA methylation signatures and enable noninvasive characterization of tumor subtypes and therapeutic resistance in SCLC patients.
利益披露 Disclosure
Y. Xi, None..
A. Stewart, None..
L. Diao, None..
Q. Wang, None..
L. Shen, None..
R. Wang, None..
A. Duarte, None..
A. Halliday, None..
K. Ramkumar, None..
R. Cardnell, None..
B. Zhang, None..
C. M. Gay, None..
L. A. Byers, None..
J. Wang, None.