Abstract

8577 Background: Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, defined by the predominant expression of one of the three transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N) and POU2F3 (SCLC-P) as well as an inflamed subtype (SCLC-I; see Gay et al. Cancer Cell. 2021), each with potential therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Methods: We included 179 patients with SCLC and performed RNA sequencing and genomic-wide reduced-representation bisulfite sequencing (RRBS). We further analyzed DNA methylation in 68 plasma samples including longitudinal samples to track SCLC subtype evolution over time. Results: Using machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we could demonstrate that SCLC subtypes evolve frequently during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models like CDK and AURK inhibitors, and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC (p = 0.95). Conclusions: These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.

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