Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

中文翻译：

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无肿瘤和无循环 DNA 甲基化可识别临床相关的小细胞肺癌亚型

小细胞肺癌（SCLC）是一种由不同转录亚型组成的侵袭性恶性肿瘤，但在临床上实施亚型分型仍然具有挑战性，特别是由于组织可用性有限。鉴于已知的关键 SCLC 转录程序的表观遗传调控，我们假设可以在 SCLC 患者的肿瘤或血液中检测到 DNA 甲基化的亚型特异性模式。我们在总共 179 名 SCLC 患者的两个队列中使用全基因组简化代表性亚硫酸氢盐测序 (RRBS)，并使用机器学习方法，报告了一种高度准确的基于 DNA 甲基化的分类器 (SCLC-DMC)，它可以区分 SCLC 亚型。我们进一步将血浆中循环游离 DNA (cfDNA) 的分类器调整为 SCLC 亚型。使用 cfDNA 分类器 (cfDMC)，我们证明 SCLC 表型可以在疾病进展过程中进化，强调在临床治疗过程中纵向追踪 SCLC 的必要性。这些数据表明，肿瘤和 cfDNA 甲基化可用于识别 SCLC 亚型，并可能指导精准 SCLC 治疗。