Clinical and translational medicine · 2026
Pattools‑implemented methylation vector analysis reveals aberrant subtype‑specific methylation in lung cancer across tissue and plasma cfDNA.
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Dong Zehua, Luo Yifeng, Hu Tingting, Cheng Yihang, Ren Qiaoling, Xu Li, Tan Yuan, Li Wei, Sun Yaoxiang, Chen Mingzhi, Shen Zhonghua, Zhang Bin, Bai Youhuang, Tao Yue, Cao Zhihong, Sun Deqiang
Аннотация
INTRODUCTION: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.