README.md

metadata

language:
  - en
tags:
  - systemic-inflammation
  - NLR
  - lymphocytes
  - CRP
  - ESR
  - breast-cancer
  - sub-saharan-africa
license: cc-by-nc-4.0
pretty_name: SSA Breast Systemic Immune Markers Dataset (Women, Multi-ancestry)
task_categories:
  - other
size_categories:
  - 1K<n<10K

SSA Breast Systemic Immune Markers Dataset (Women, Multi-ancestry, Synthetic)

Dataset summary

This dataset provides a synthetic cohort of invasive breast cancers in women across multiple ancestry groups, with emphasis on sub-Saharan Africa (SSA) and comparable reference populations.

Each tumour is linked to systemic immune and inflammation markers derived from complete blood counts and standard laboratory tests:

• Neutrophil-to-lymphocyte ratio (NLR) – category and continuous value.
• Lymphocyte counts – category and continuous value (x10⁹/L).
• Neutrophil counts – continuous value (x10⁹/L) to maintain internal consistency with NLR.
• C-reactive protein (CRP) – category and continuous value (mg/L).
• Erythrocyte sedimentation rate (ESR) – category and continuous value (mm/hr).

Distributions are qualitatively anchored to global and SSA-focused literature on NLR, lymphopenia, CRP, and ESR in cancer patients, while ensuring that all records are fully synthetic and non-identifiable.

Cohort design

Sample size and populations

• Total N: 10,000 synthetic invasive breast cancers.

• Populations:

  • SSA_West: 2,000
  • SSA_East: 2,000
  • SSA_Central: 1,500
  • SSA_Southern: 1,500
  • AAW (African American women): 1,500
  • EUR (European reference): 1,000
  • EAS (East Asian reference): 500

• Sex:

  • Predominantly Female, with a small fraction of male breast cancers (~1%).

• Age:

  • 18–90 years.
  • Older mean age in EUR/EAS/AAW vs somewhat younger in SSA cohorts, consistent with registry patterns.

Systemic immune markers

Neutrophil-to-lymphocyte ratio (NLR)

Variables:

• nlr_category – Low, Intermediate, High.
• nlr_value – continuous NLR value.
• Derived: high_nlr – True if nlr_category == "High" or nlr_value ≥ 4.0.

Anchoring:

• Meta-analyses in breast cancer identify high NLR (cut-offs ≈3–4) in ~25–40% of patients, associated with worse outcomes.
• In this dataset:
  • SSA and AAW populations have higher fractions of High NLR (~27–31%).
  • EUR/EAS references have lower High NLR (~15%).

NLR is constructed from separately sampled neutrophil and lymphocyte counts but constrained to match the target category distribution.

Lymphocyte counts

Variables:

• lymphocyte_category – Low, Normal, High.
• lymphocyte_count_x10e9_per_L – continuous lymphocyte count.
• Derived: lymphopenia – True if lymphocyte_category == "Low" or lymphocyte_count_x10e9_per_L < 1.0.

Anchoring:

• Lymphopenia (low lymphocyte count) is observed in ~15–25% of oncology patients at diagnosis or during treatment.
• SSA and AAW cohorts have slightly higher Low lymphocyte fractions (21–24%) than EUR/EAS (15–16%).

Counts are sampled from category-specific distributions in the approximate reference ranges of adult lymphocyte counts.

Neutrophil counts

Variable:

• neutrophil_count_x10e9_per_L – continuous neutrophil count.

Anchoring:

• Elevated neutrophils occur in approximately 20–30% of cancer patients with systemic inflammation.
• Neutrophil counts are drawn to produce realistic NLR ranges while remaining within plausible haematology intervals.

C-reactive protein (CRP)

Variables:

• crp_category – Normal, Mildly_elevated, Markedly_elevated.
• crp_mg_per_L – continuous CRP value.

Anchoring:

• Elevated CRP (>10 mg/L) occurs in ~25–40% of breast and other cancer patients.
• SSA and AAW populations have more Markedly_elevated CRP (26–32%) vs EUR/EAS (15%).

Continuous CRP values are drawn from category-specific distributions, e.g. Normal ~0–5 mg/L, Mildly elevated ~3–20 mg/L, Markedly elevated ~10–200 mg/L.

Erythrocyte sedimentation rate (ESR)

Variables:

• esr_category – Normal, Mildly_elevated, Markedly_elevated.
• esr_mm_per_hr – continuous ESR value.

Anchoring:

• High ESR (>40–50 mm/hr) is reported in ~25–40% of oncology patients.
• SSA and AAW cohorts are modeled with higher Markedly_elevated ESR fractions (≈26–31%) vs EUR/EAS (~15%).

ESR values are generated using category-specific distributions spanning normal (0–25 mm/hr), mildly elevated (15–60 mm/hr), and markedly elevated (~30–120 mm/hr) ranges.

Composite inflammatory burden

Variable:

• high_inflammatory_burden – True if any of the following holds:
  • high_nlr is True.
  • crp_category == "Markedly_elevated".
  • esr_category == "Markedly_elevated".

This flag approximates a high systemic inflammatory state for risk stratification and modeling.

File and schema

systemic_immune_markers_data.parquet / systemic_immune_markers_data.csv

Each row represents a synthetic breast cancer case with demographics and systemic markers:

• Demographics

  • sample_id
  • population
  • region
  • is_SSA
  • is_reference_panel
  • sex
  • age

• NLR and counts

  • nlr_category, nlr_value, high_nlr
  • lymphocyte_category, lymphocyte_count_x10e9_per_L, lymphopenia
  • neutrophil_count_x10e9_per_L

• CRP and ESR

  • crp_category, crp_mg_per_L
  • esr_category, esr_mm_per_hr

• Composite marker

  • high_inflammatory_burden

Generation

The dataset is generated using:

• systemic_immune_markers/scripts/generate_systemic_immune_markers.py

with configuration in:

• systemic_immune_markers/configs/systemic_immune_markers_config.yaml

and literature inventory in:

• systemic_immune_markers/docs/LITERATURE_INVENTORY.csv

Key steps:

1. Cohort sampling – multi-ancestry invasive breast cancer cohort with age/sex by population.
2. Lymphocyte assignment – sample lymphocyte_category by population and draw continuous counts.
3. NLR assignment – sample nlr_category by population; draw neutrophil counts and compute nlr_value consistent with the category.
4. CRP and ESR assignment – sample categories by population and generate continuous values within plausible clinical ranges.
5. Derived flags – compute high_nlr, lymphopenia, and high_inflammatory_burden.

Validation

Validation is performed with:

• systemic_immune_markers/scripts/validate_systemic_immune_markers.py

and summarized in:

• systemic_immune_markers/output/validation_report.md

Checks include:

• C01–C02 – Sample size and population counts vs config.
• C03 – NLR category distributions by population.
• C04 – Lymphocyte category distributions by population.
• C05 – CRP category distributions by population.
• C06 – ESR category distributions by population.
• C07 – Missingness across demographics, NLR/lymphocytes, CRP/ESR, and composite flags.

The released version is configured to stay within a 10% absolute deviation tolerance for categorical distributions, with an overall validation status of PASS.

Intended use

This dataset is intended for:

• Risk stratification and prognostic modeling using systemic inflammatory markers.
• Integration with other Electric Sheep Africa synthetic modules (pathology, IHC, immune profiles, comorbidities, environmental exposures) to build multi-modal models.
• Educational use for teaching relationships between NLR, lymphocyte counts, CRP/ESR, and cancer outcomes across ancestries.

It is not intended for:

• Estimating true prevalence of high NLR or elevated CRP/ESR in any specific population.
• Direct clinical decision-making or triage.

Ethical considerations

• No real patient data are used; all cohorts and markers are simulated.
• Differences in systemic inflammation between populations reflect literature-informed trends and must not be used to stigmatize or essentialize groups.
• Users should interpret modeled patterns alongside high-quality epidemiological data and local clinical context.

License

• License: CC BY-NC 4.0.
• Free for non-commercial research, method development, and education with attribution.

Citation

If you use this dataset, please cite:

Electric Sheep Africa. "SSA Breast Systemic Immune Markers Dataset (Women, Multi-ancestry, Synthetic)." Hugging Face Datasets.

and, as appropriate, key literature on NLR, lymphocyte counts, CRP, and ESR in breast cancer and other solid tumours, including studies from Sub-Saharan Africa.