Pattools实现的甲基化载体分析揭示肺癌组织和血浆cfDNA中异常的亚型特异性甲基化
Pattools‑implemented methylation vector analysis reveals aberrant subtype‑specific methylation in lung cancer across tissue and plasma cfDNA.
作者
作者单位
- Department of Cardiology of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
- Department of Radiology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
- State Key Laboratory of Transvascular Implantation Devices, Zhejiang University, Hangzhou, China.
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Clinical Laboratory, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
- Department of Thoracic and Cardiovascular Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
- Department of Cardiovascular Surgery of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
- Department of Research and Development, Zhejiang Gaomei Genomics, Hangzhou, China.
摘要
中文
肺癌以高死亡率为特征，包含多个治疗结果显著不同的亚型。虽然单个亚型已被研究，但跨亚型的全基因组和精细尺度DNA甲基化分析仍未探索。本研究旨在通过开发一种新方法，消除细胞来源和免疫混杂因素，超越传统方法，识别真正的亚型特异性异常甲基化。我们组建了一个包含五组（CTL、LUAD、LUSC、LCC、SCLC）115份组织样本的内部队列，并附有详细的临床病理注释。收集匹配的血浆cfDNA样本（n=24；9例LUAD、7例LUSC、8例健康）用于转化评估。我们开发了一种在开源工具包pattools中实现的MV分析方法，以区分批量BS-seq数据中的细胞特异性片段，并识别亚型特异性甲基化载体区域（SMVR）。全基因组甲基化谱显示，亚型特异性信号来源于不同的细胞类型——LUAD中的肺泡上皮、LUSC中的头颈部上皮、LCC中的成纤维细胞和SCLC中的内分泌细胞——以及其他免疫浸润影响。基于pattools的MV分析，我们为每个亚型识别了500个关键SMVR。在匹配的血浆cfDNA中，这些组织来源的SMVR在二分类中对LUAD（AUC=0.98，95%CI：0.95至1.00）和LUSC（AUC=0.88，95%CI：0.79至0.97）表现出有前景的判别能力，在多分类中对LUAD、LUSC和健康样本的AUC分别为0.95（95%CI：0.90至1.00）、0.75（95%CI：0.62至0.88）和0.63（95%CI：0.48至0.78）。pattools中的MV分析有效地揭示了亚型特异性异常甲基化信号，为组织和液体活检中的精准诊断和亚型分型提供了潜力。在临床转化前，需要在更大队列中进行独立验证。
English
Lung cancer is characterised by high mortality and encompasses various subtypes with markedly different treatment outcomes. While individual subtypes have been studied, comprehensive genome-wide and fine-scale DNA methylation analyses across subtypes remain unexplored. This study aims to identify true subtype-specific aberrant methylation by developing a novel method that eliminates cell-origin and immune confounding, moving beyond traditional approaches. We assembled an in-house cohort of 115 tissue samples across five groups (CTL, LUAD, LUSC, LCC, SCLC) with detailed clinicopathological annotations. Matched plasma cfDNA samples (n = 24; 9 LUAD, 7 LUSC, 8 healthy) were collected for translational assessment. We developed an MV analysis method implemented in the open-source toolkit pattools to differentiate cell-specific fragments in bulk BS-seq data and identify subtype-specific methylation vector regions (SMVRs). Genome-wide methylation profiling revealed that subtype-specific signals originate from distinct cell types-alveolar epithelium in LUAD, head and neck epithelium in LUSC, fibroblasts in LCC, and endocrine cells in SCLC-with additional immune infiltration influences. Using pattools-based MV analysis, we identified 500 key SMVRs per subtype. In matched plasma cfDNA, these tissue-derived SMVRs demonstrated promising discriminatory power for LUAD (AUC = 0.98, 95% CI: 0.95 to 1.00) and LUSC (AUC = 0.88, 95% CI: 0.79 to 0.97) in binary classification, and AUCs of 0.95 (95% CI: 0.90 to 1.00), 0.75 (95% CI: 0.62 to 0.88), and 0.63 (95% CI: 0.48 to 0.78) for LUAD, LUSC and healthy samples, respectively, in multi-class classification. The MV analysis in pattools effectively uncovers subtype-specific aberrant methylation signals, offering potential for precise diagnosis and subtyping in tissue and liquid biopsy. Independent validation in larger cohorts is required before clinical translation.
分类与指标
- 研究类型
- 基础研究
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 7.9
- 新锐分区
- 2区