Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions

Abstract

We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.

Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome

“…This performance is consistent with existing studies of lung cancer subtyping using cfDNA fragmentation patterns ( 34 ) and cfDNA methylation at a limited number of genes ( 35 ). Apart from them, another epigenetic molecular marker, transcription factor binding inferred from cfDNA fragmentation patterns, has been used in classifying lung cancer subtypes with promising accuracy ( 36 ). cfMethyl-Seq retains the genome-wide methylation profiles of cancer abnormalities in a cost-effective manner, allowing the classification model to learn and exploit newly identified significant features as the training cohorts expand ( 9 ).…”