SCAPeSCLC: A Harmonized Multi-Level Transcriptomic and Survival Dataset Derived from GSE261345 and GSE261348 Integrating ROI- and Patient-Level Gene Expression, Clinical Annotations, and Bayesian Modeling of Cancer Transcriptome Atlas Biological Pathway Activity in Extensive-Stage Small Cell Lung Cancer

The SCAPeSCLC dataset is a harmonized, multi-level transcriptomic and clinical resource derived from publicly available datasets GSE261345 and GSE261348, originally generated from the CANTABRICO and IMfirst cohorts of patients with extensive-stage small cell lung cancer (ES-SCLC). The clinical outcomes and primary analyses of these cohorts were reported by Peressini et al. in Clinical Cancer Research (2024) [1], and the corresponding raw and processed molecular data were deposited by the original investigators in the Gene Expression Omnibus (GEO). The present dataset was constructed by integrating and restructuring these source datasets into a unified analytical framework. Data were processed to generate region-of-interest (ROI)–level and patient-level gene expression matrices, along with standardized clinical annotations, survival outcomes, and derived analytical features. Gene expression values are provided in both log2-normalized and Z-score–scaled formats. Survival information includes time-to-event intervals and censoring status, enabling downstream survival modeling. In addition to gene-level data, curated Cancer Transcriptome Atlas (CTA) biological pathway annotations were incorporated, and pathway-level enrichment scores were computed at both ROI and patient levels. Quality control metrics and Bayesian posterior estimates for pathway activity were generated, followed by Cox proportional hazards modeling using both unadjusted and confounder-adjusted frameworks. All data are provided as one comprehensive Excel workbook (SCAPeSCLC.xlsx) and as individually labeled CSV files to facilitate compatibility with user-friendly statistical software environments. The dataset is intended to support reproducible analyses, secondary investigations, and methodological development in translational cancer research. ----------------------------- Release Notes Version 1.1.0 Updated Cox proportional hazards analysis outputs to include proportional hazards assumption testing results (Schoenfeld residual test p-values).