DATASET from "A systemic immune signature stratifies early-stage breast cancer patients and reveals soluble IL- 2RA and PD-1 as independent prognostic biomarkers"

Title: Cytokine and Immune Biomarker Dataset in Breast Cancer Patients

General Overview

This dataset contains clinical, pathological, survival, and immunological data collected from a cohort of 279 breast cancer patients. The primary aim of the dataset is to support the investigation of associations between systemic immune mediators, tumor characteristics, and clinical outcomes, with special emphasis on cytokine profiles and immune checkpoint–related biomarkers.

The dataset integrates demographic variables, tumor pathological features, molecular subtype classification, survival outcomes, and quantitative measurements of a broad panel of circulating cytokines, chemokines, growth factors, and immune regulatory molecules.

Dataset Structure

The dataset includes 94 variables, organized into the following main categories:

1. Demographic and Clinical Variables

   • Patient identifier (anonymized ID)

   • Age at diagnosis

   • Categorized age variables

   • Clinical status and follow-up information

2. Tumor Pathology Characteristics

   • Histological type

   • Tumor size and categorized tumor size

   • Histological grade and grouped grade variables

   • Tumor necrosis and invasion parameters

   • Lymph node involvement

   • Tumor-infiltrating lymphocytes (TILs)

   • Molecular phenotype classification (including luminal vs non-luminal categories)

   • Proliferation index (Ki-67 and categorized versions)

3. Clinical Outcomes

   • Recurrence status

   • Disease-free survival (DFS / SLE)

   • Overall survival (OS / SG)

   • Current patient status

4. Immune and Cytokine Profiling
   Quantitative measurements of multiple immune mediators, including:

   • Interleukins (e.g., IL-1, IL-2, IL-4, IL-5, IL-6, IL-10, IL-17 family, IL-23, IL-27, IL-33)

   • Interferons and inflammatory cytokines (e.g., IFN-γ, TNF-α)

   • Chemokines (e.g., MCP-1, IP-10, MIG, GROα, TARC)

   • Growth factors and angiogenic markers (e.g., VEGF, EGF, HGF, PDGF, SCF)

   • Immune checkpoint and regulatory molecules (e.g., CTLA-4, PD-1, PD-L1, PD-L2, TIM-3, LAG-3, Galectin-9)

5. Derived and Categorized Variables

   • Dichotomized biomarker variables generated for survival analyses

   • Categorized clinical predictors

   • Variables derived from ROC-based cutoff optimization for outcome analyses

Data Type and Format

All variables are numerically encoded. Continuous biomarker concentrations and clinical measurements coexist with categorical variables represented as numeric codes. Missing values are present in some biomarkers due to technical or sampling limitations.

Potential Applications

This dataset can be used for:

• Biomarker discovery in oncology

• Immune profiling and tumor microenvironment studies

• Survival and prognostic modeling

• Machine learning approaches for outcome prediction

• Translational cancer immunology research

Ethical Considerations

Patient identifiers have been anonymized to preserve confidentiality. The dataset is intended exclusively for research and educational purposes in accordance with applicable ethical and data protection regulations.