---
language:
- en
tags:
- tumour-markers
- ctDNA
- treatment-response
- trajectories
- breast-cancer
- sub-saharan-africa
license: cc-by-nc-4.0
pretty_name: SSA Breast Biomarker Trajectories (CA15-3 / CA27-29 / ctDNA)
task_categories:
- other
size_categories:
- 1K<n<10K
---

# SSA Breast Biomarker Trajectories (Synthetic)

## Dataset summary

This module provides a **synthetic biomarker trajectory dataset** for breast cancer treatment monitoring, focused on:

- **Serum tumour markers**: CA 15-3 and CA 27-29.
- **Circulating tumour DNA (ctDNA)** levels.
- **Treatment response monitoring** over 0–24 months.

It is designed to emulate:

- Higher baseline marker levels in advanced-stage disease.
- Early declines in CA 15-3 and ctDNA in responders.
- Rising or non-clearing ctDNA in early and late progression.

All records are **fully synthetic**, grounded in clinical literature on serial CA15-3/CA27-29 measurement and ctDNA dynamics in metastatic and early breast cancer.

## Cohort design

### Sample size and populations

- **Baseline patients**: 6,000.
- **Populations**:
  - `SSA_West`: 1,500
  - `SSA_East`: 1,500
  - `SSA_Central`: 1,000
  - `SSA_Southern`: 1,000
  - `AAW` (African American women): 1,000
  - `EUR`: 600
  - `EAS`: 400

### Baseline variables

- `sex`: predominantly `Female` (~99%).
- `age_years`: 18–90, with slightly younger distribution in SSA vs EUR/EAS.
- `stage_at_diagnosis`: I–IV, with later stages more frequent in SSA.
- `tumor_subtype`:
  - `Luminal_A`, `Luminal_B`, `HER2_enriched`, `Basal_like`, `Normal_like`, with higher basal-like in SSA and AAW.
- `therapy_class`:
  - `Endocrine_only`
  - `Chemo_only`
  - `Chemo_plus_endocrine`
  - `HER2_targeted`
  - `CDK4_6_plus_endocrine`
- `response_class` (treatment course):
  - `Deep_response`
  - `Partial_response`
  - `Stable_disease`
  - `Early_progression`
  - `Late_progression`

These response classes drive **biomarker trajectories**.

## Time structure

Patients are followed at:

- `time_months`: [0, 3, 6, 9, 12, 18, 24].

At each time point, biomarker levels are simulated conditional on:

- `stage_at_diagnosis` (baseline tumour burden).
- `response_class` (kinetic pattern).

## Biomarkers

### CA 15-3

- Units: `U/mL`.
- Baseline means increase with stage (approximate):
  - Stage I: ~15 U/mL
  - Stage II: ~30 U/mL
  - Stage III: ~60 U/mL
  - Stage IV: ~120 U/mL
- SD fraction ~0.6 (log-normal variability).

Response-class–specific trajectories (multipliers on baseline):

- `Deep_response`: rapid drop (e.g., ~50% at 3 months, ~75% reduction by 12 months).
- `Partial_response`: moderate drop (~30% at 3 months, ~50% reduction by 12 months).
- `Stable_disease`: flat or slightly drifting around baseline.
- `Early_progression`: early rise (>10% increase by 3 months, continuing upward).
- `Late_progression`: initial drop, followed by rise after ~12 months.

These patterns reflect published CA15-3 kinetics linking rapid declines to radiologic response and surges to progression.

### CA 27-29

- Units: `U/mL`.
- Modelled as a **correlated companion marker** to CA 15-3:
  - `ca2729_u_per_ml` is sampled as CA 27-29 ≈ CA 15-3 × log-normal noise.
- Captures that CA 27-29 is clinically used in a similar role to CA 15-3 but with independent assay noise.

### ctDNA

- Units: `copies_per_mL`.
- Baseline log10 means increase with stage:
  - Stage I: ~10^1.3
  - Stage II: ~10^1.7
  - Stage III: ~10^2.1
  - Stage IV: ~10^2.5
- Detection limit: ~5 copies/mL.

Kinetic patterns by `response_class`:

- `Deep_response`:
  - Rapid and deep ctDNA decline; many become undetectable by 3–6 months.
- `Partial_response`:
  - Substantial decline but less complete clearance.
- `Stable_disease`:
  - Small changes, often hovering near baseline.
- `Early_progression`:
  - Transient modest fall, followed by strong rise (often >2–3× by 12–24 months).
- `Late_progression`:
  - Early decline then late rise (mirroring late progression in CA 15-3).

Summary flags:

- `ctdna_baseline_detectable`
- `ctdna_cleared_by_6m` (detectable at baseline and undetectable at all timepoints ≤6 months).

## Files and schema

### Baseline table

Files:

- `biomarker_trajectories_baseline.parquet`
- `biomarker_trajectories_baseline.csv`

Columns (per patient):

- Identifiers & demographics:
  - `sample_id`, `population`, `region`, `is_SSA`, `is_reference_panel`, `sex`, `age_years`.
- Tumour & therapy:
  - `stage_at_diagnosis`, `tumor_subtype`, `therapy_class`, `response_class`.
- CA 15-3 summary:
  - `ca153_baseline_u_per_ml`
  - `ca153_nadir_u_per_ml`
  - `ca153_best_delta_fraction` (nadir vs baseline, negative values indicate decline).
- CA 27-29 summary:
  - `ca2729_baseline_u_per_ml`
  - `ca2729_nadir_u_per_ml`
  - `ca2729_best_delta_fraction`.
- ctDNA summary:
  - `ctdna_baseline_copies_per_ml`
  - `ctdna_min_copies_per_ml`
  - `ctdna_baseline_detectable`
  - `ctdna_cleared_by_6m`.

### Timeseries table

Files:

- `biomarker_trajectories_timeseries.parquet`
- `biomarker_trajectories_timeseries.csv`

Columns (per patient × timepoint):

- `sample_id`
- `population`
- `stage_at_diagnosis`
- `tumor_subtype`
- `therapy_class`
- `response_class`
- `time_months`
- `ca153_u_per_ml`
- `ca2729_u_per_ml`
- `ctdna_copies_per_ml`
- `ctdna_detectable`

## Generation

The dataset is generated using:

- `biomarker_trajectories/scripts/generate_biomarker_trajectories.py`

with configuration:

- `biomarker_trajectories/configs/biomarker_trajectories_config.yaml`

and literature inventory:

- `biomarker_trajectories/docs/LITERATURE_INVENTORY.csv`

Key steps:

1. **Baseline cohort**: draw populations, sex, age, stage, subtype, therapy, and response class.
2. **Baseline markers**: sample CA 15-3 and ctDNA baseline values by stage using log-normal distributions.
3. **Trajectories**: apply response-class–specific multipliers at each timepoint, with log-normal noise, to generate CA 15-3, CA 27-29, and ctDNA levels.
4. **Summaries**: compute nadirs, best percentage changes, baseline detectability, and ctDNA clearance.

## Validation

Validation is performed with:

- `biomarker_trajectories/scripts/validate_biomarker_trajectories.py`

and summarised in:

- `biomarker_trajectories/output/validation_report.md`

Checks include:

- **C01–C02**: Sample size and population counts vs configuration.
- **C03**: CA 15-3 baseline means increase monotonically with stage.
- **C04**: CA 15-3 kinetics by response class (deep responders show early declines; early progressors show rises).
- **C05**: ctDNA clearance is higher in deep responders and low in progression classes.
- **C06**: Baseline ctDNA detectability is substantially higher in stage IV than in stage I.
- **C07**: Missingness in key baseline and timeseries variables.

## Intended use

This dataset is intended for:

- Developing and benchmarking models for **biomarker-based response assessment**.
- Exploring **surrogate endpoints** based on CA 15-3, CA 27-29, and ctDNA dynamics.
- Teaching about biomarker kinetics in metastatic and advanced breast cancer.

It is **not intended** for:

- Estimating real-world cutoffs or predictive values.
- Guiding individual patient management.

## Ethical considerations

- All data are **synthetically generated** from literature-informed parameters.
- Population and clinical patterns are inspired by published cohorts but do not represent any specific registry or trial.
- The goal is to support method development and education around biomarker trajectories in diverse breast cancer populations, including SSA.

## 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 Biomarker Trajectories (CA15-3 / CA27-29 / ctDNA, Synthetic)." Hugging Face Datasets.