SCAPeSCLC: A Harmonized Multi-Level Transcriptomic Resource for Extensive-Stage Small Cell Lung Cancer Integrating Clinical Data, Survival Outcomes, and Bayesian Pathway Activity

The SCAPeSCLC dataset is a harmonized, multi-level transcriptomic and clinical resource derived from the publicly available Gene Expression Omnibus (GEO) datasets GSE261345 and GSE261348. It integrates region-of-interest (ROI)- and patient-level molecular profiles with standardized clinical annotations, survival outcomes, Bayesian pathway activity estimates, and comprehensive survival analysis outputs into a unified analytical framework for extensive-stage small cell lung cancer (ES-SCLC). The source datasets and associated clinical study are referenced in the metadata accompanying this record. The present dataset was constructed by harmonizing and restructuring the source data into a unified analytical framework. ROI- and patient-level gene expression matrices were generated alongside standardized clinical annotations, survival outcomes, and derived analytical variables. Gene expression values are provided in both log₂-normalized and Z-score-scaled formats. Survival data include time-to-event intervals and event indicators for progression-free (PFS), disease-specific (DSS), and overall survival (OS), enabling downstream prognostic modeling. In addition to gene-level data, curated Cancer Transcriptome Atlas (CTA) biological pathway annotations were incorporated to derive ROI-level enrichment scores and Bayesian patient-level posterior pathway activity estimates. These data support both gene- and pathway-level survival analyses, including unadjusted and confounder-adjusted Cox proportional hazards models. Comprehensive diagnostic atlases are provided for all gene-level Cox proportional hazards analyses. Each atlas page summarizes model effect estimates and statistical significance together with Martingale, Schoenfeld, Deviance, and DFBETA residual diagnostics, enabling visual assessment of functional form, proportional hazards, model fit, and influential observations for every analyzed gene and survival endpoint. 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 1.3.0 Added comprehensive gene-level Cox proportional hazards diagnostic atlases for all study endpoints (OS, DSS, and PFS). Diagnostic atlases are provided for both unadjusted and confounder-adjusted Cox regression models and include Martingale, Schoenfeld, Deviance, and DFBETA residual diagnostics together with model summary statistics for every analyzed gene. This release includes the following new files: SCAPeSCLC Diagnostic Atlas - OS.pdf SCAPeSCLC Diagnostic Atlas (Adjusted) - OS.pdf SCAPeSCLC Diagnostic Atlas - DSS.pdf SCAPeSCLC Diagnostic Atlas (Adjusted) - DSS.pdf SCAPeSCLC Diagnostic Atlas - PFS.pdf SCAPeSCLC Diagnostic Atlas (Adjusted) - PFS.pdf Version 1.1.0 Expanded Cox proportional hazards analysis outputs to include proportional hazards assumption test results (Schoenfeld residuals test p-values) in the corresponding gene (D7-8) and biological pathway (D14-15) datasheets. Please refer to the associated Data paper for more information. Update Notice Please refer to Version 1.3.5 for the most current release of the SCAPeSCLC dataset. Version 1.3.5 adds comprehensive CTA gene and pathway-level diagnostic atlases for both unadjusted and confounder-adjusted Cox proportional hazards models. Citation If you use this dataset in your work, please also cite the accompanying data paper: Shirvaliloo M. SCAPeSCLC: An Integrated Spatial Transcriptomic and Bayesian Pathway Enrichment Dataset for Survival Modeling in Extensive-Stage Small Cell Lung Cancer. Data. 2026; 11(7): 152. https://doi.org/10.3390/data11070152.