Machine learning stratification of LUAD-to-SCLC transformation states and drug-response signatures

Histologic transformation from lung adenocarcinoma (LUAD) to small cell lung cancer (SCLC) is a clinically important mechanism of acquired resistance to EGFR tyrosine kinase inhibitors, but the regulatory programs associated with this lineage transition remain incompletely characterized. We reanalyzed mouse and human single-cell and bulk transcriptomic datasets to define a 13-regulon Histologic Transformation Regulon Signature (HTRS) and developed TransRiskML, a machine-learning framework that stratifies LUAD samples into low-, medium-, and high-HTRS molecular states. Across independent cohorts, high-HTRS tumors showed poorer survival, stronger SCLC-like transcriptional features, distinct genomic features, and transcriptome-inferred drug-response patterns. HTRS captured transformation-associated molecular states that were largely independent of general proliferative activity across cohorts, although proliferation contributed substantially to the prognostic signal in advanced-stage NSCLC, where adjustment attenuated the association. A simplified 15-gene surrogate score was developed for downstream analyses, and exploratory pathology suggested a trend toward higher tumor-cell nuclear HDAC2 staining in pre-transformation LUAD and transformed SCLC specimens. These findings provide a systems-level framework for transformation-associated molecular-state stratification and prioritize candidate regulatory features for prospective investigation of LUAD-to-SCLC lineage plasticity. The framework is intended for hypothesis generation; prospective studies with documented transformation outcomes are required to determine its clinical utility.