SCAPeSCLC

SCAPeSCLC is a harmonized multi-level transcriptomic and clinical resource derived from the publicly available GEO datasets GSE261345 and GSE261348, originating from the CANTABRICO and IMfirst cohorts of patients with extensive-stage small cell lung cancer (ES-SCLC).

This repository contains the R scripts and supporting datasets used to generate Bayesian pathway posterior estimates, perform gene- and pathway-level survival analyses, assess Cox proportional hazards model assumptions, and generate comprehensive diagnostic atlases for both gene expression and biological pathway activity.

The repository accompanies SCAPeSCLC v1.3.5, the published dataset and associated data paper.

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Current Release (v1.3.5)

Major additions include:

• Gene-level diagnostic atlases (unadjusted and confounder-adjusted)
• Biological pathway diagnostic atlases (unadjusted and confounder-adjusted)
• Proportional hazards assumption testing for all gene and pathway Cox models
• Bayesian estimation of patient-level Cancer Transcriptome Atlas pathway activities

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Associated Resources

• Zenodo dataset: https://doi.org/10.5281/zenodo.19897644
• 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

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Repository Structure

SCAPeSCLC
├── data/
│   ├── D5_scaled_gene_expression.csv
│   ├── D10_ROI_CTA_Zscores.csv
│   └── D13_patient_BP_posteriors.csv
│
├── figures/
│   └── SCAPeSCLC_pipeline.png
│
└── scripts/
    ├── 01_gene_level_cox_models.R
    ├── 01_gene_level_cox_ph_assumptions.R
    ├── 02_bayesian_patient_level_pathways.R
    ├── 03_pathway_posterior_cox_models.R
    ├── 03_pathway_posterior_cox_ph_assumptions.R
    ├── 04_SCAPeSCLC_diagnostic_atlas_generator_for_genes.R
    ├── 04_SCAPeSCLC_diagnostic_atlas_generator_for_genes_confounder_adjusted.R
    ├── 05_SCAPeSCLC_diagnostic_atlas_generator_for_BPs.R
    └── 05_SCAPeSCLC_diagnostic_atlas_generator_for_BPs_confounder_adjusted.R

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Data Files

File	Description
D5_scaled_gene_expression.csv	Patient-level standardized gene expression matrix.
D10_ROI_CTA_Zscores.csv	ROI-level Cancer Transcriptome Atlas pathway enrichment Z-scores.
D13_patient_BP_posteriors.csv	Patient-level Bayesian posterior pathway activity estimates.

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Analysis Workflow

The repository implements the following analytical workflow:

1. Bayesian estimation of patient-level pathway activities from ROI-level Cancer Transcriptome Atlas pathway enrichment scores.
2. Gene-level Cox proportional hazards regression for progression-free, disease-specific, and overall survival.
3. Pathway-level Cox proportional hazards regression using Bayesian posterior pathway activity estimates.
4. Assessment of proportional hazards assumptions using Schoenfeld residuals for both gene- and pathway-level Cox regression models.
5. Generation of comprehensive diagnostic atlases for both genes and biological pathways, including:
   • model summary statistics,
   • hazard ratios and 95% confidence intervals,
   • Wald test statistics,
   • proportional hazards test results,
   • Martingale residuals,
   • Schoenfeld residuals,
   • Deviance residuals,
   • DFBETA influence diagnostics.
6. Generation of both unadjusted and confounder-adjusted diagnostic atlases for all survival endpoints (OS, DSS, and PFS).

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Data Provenance and Analytical Workflow

The diagram below summarizes the provenance of the major data products included in SCAPeSCLC and their relationships to the analytical pipelines implemented in this repository.

flowchart TD

    A["GEO Datasets<br/>GSE261345 & GSE261348"]

    A --> B["Clinical and Transcriptomic Harmonization"]

    B --> C["Patient-Level Gene Expression<br/>(D5_scaled_gene_expression.csv)"]
    B --> D["ROI-Level CTA Pathway Z-scores<br/>(D10_ROI_CTA_Zscores.csv)"]

    D --> E["Bayesian Pathway Activity Estimation"]
    E --> F["Patient-Level Bayesian Pathway Posteriors<br/>(D13_patient_BP_posteriors.csv)"]

    C --> G["Gene-Level Cox Proportional Hazards Models"]
    G --> H["Gene PH Assumption Testing"]
    H --> I["Gene Diagnostic Atlases"]

    F --> J["Pathway-Level Cox Proportional Hazards Models"]
    J --> K["Pathway PH Assumption Testing"]
    K --> L["Biological Pathway Diagnostic Atlases"]

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Requirements

R 4.2 or later is recommended.

Required packages:

install.packages(c(
  "dplyr",
  "survival",
  "broom",
  "purrr",
  "brms",
  "tidyr",
  "stringr",
  "ggplot2",
  "patchwork",
  "cowplot",
  "gtable"
))

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Citation

If you use SCAPeSCLC in your work, please cite both the Zenodo dataset and the accompanying data paper.

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License

This project is distributed under the MIT License.