Spatial transcriptomics reveals molecular heterogeneity and subtype-specific therapeutic targets in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive malignancy with strong associations to smoking, characterized by initial platinum sensitivity followed by rapid recurrence and poor long-term survival. The evolutionary processes driving this high plasticity and intratumoral heterogeneity remain inadequately understood, hampering the development of effective therapies. In this study, we established a comprehensive spatial transcriptomic (ST) landscape of SCLC. Our approach integrated two key methodological innovations: the Edgeindex metric for the quantitative assessment of tumor spatial architecture, and a specialized artificial neural network (ANN) model for precise tumor annotation. Utilizing this analytical framework, we systematically resolved SCLC heterogeneity across clinical, spatial, functional, and temporal dimensions. Furthermore, pathway enrichment analysis was performed to explore the underlying molecular mechanisms. This work provides a multi-dimensional resource for deciphering the complexity of SCLC.