# PAT002-BC-2026: Synthetic Patient Data

## Overview

This directory contains **100% synthetic patient data** created for demonstration and testing purposes. The data represents a realistic but completely fabricated Stage IIA ER+/PR+/HER2- Invasive Ductal Carcinoma (IDC) breast cancer patient case with BRCA2 germline mutation.

**Patient:** Michelle Anne Thompson (synthetic)
**Diagnosis:** Stage IIA (T2N0M0) ER+/PR+/HER2- Breast Cancer, BRCA2+ (c.5946delT)
**Treatment Status:** Post-adjuvant therapy (surgery + chemotherapy + radiation), currently on tamoxifen
**Disease Status:** Disease-free at surveillance (2026-01-15)
**Purpose:** Demonstrate precision medicine MCP server workflows for breast cancer

---

## Data Origin & Creation

### ⚠️ CRITICAL: ALL DATA IS SYNTHETIC

**This is NOT real patient data.** All files were artificially generated using:

1. **Statistical models** - Random number generators with clinically plausible distributions
2. **Expert knowledge** - Curated to reflect realistic breast cancer biology (Luminal A/B subtype)
3. **Published literature** - Based on TCGA-BRCA studies, BRCA2 mutation data, and clinical guidelines
4. **No PHI/PII** - Contains zero protected health information or personally identifiable information

**Source:** Created by the Precision Medicine MCP development team for demonstration purposes (2026-01-31).

---

## Data Modalities (5 types, 39+ files)

### 1. Clinical Data (`clinical/`)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `patient_demographics.json` | 6.5 KB | Patient demographics, BRCA2 status, family history | Synthetic |
| `lab_results.json` | 7.2 KB | CEA/CA 15-3 tumor markers, CBC, liver/lipid panels | Modeled after clinical patterns |
| `fhir_raw/patient-002.json` | 2.1 KB | FHIR R4 Patient resource | Synthetic FHIR-compliant |
| `fhir_raw/condition-breast-cancer.json` | 2.3 KB | FHIR Condition resource (diagnosis) | Synthetic |
| `fhir_raw/observation-cea.json` | 1.8 KB | CEA tumor marker observations | Synthetic |
| `fhir_raw/observation-brca2.json` | 2.0 KB | BRCA2 genetic test result | Synthetic |
| `fhir_raw/observation-er-pr-her2.json` | 2.5 KB | Hormone receptor status (ER 85%, PR 70%, HER2 neg) | Synthetic |
| `fhir_raw/medication-tamoxifen.json` | 1.7 KB | Active tamoxifen prescription | Synthetic |
| `fhir_raw/medication-doxorubicin.json` | 1.6 KB | Completed AC chemotherapy | Synthetic |
| `fhir_raw/medication-paclitaxel.json` | 1.6 KB | Completed taxane chemotherapy | Synthetic |

**Generation method:**
- Demographics: Randomly generated name (Michelle Thompson, 42F), dates, identifiers
- Family history: Maternal breast cancer (age 58), maternal aunt ovarian cancer (age 52), sister BRCA2+ carrier
- CEA/CA 15-3 values: Gaussian noise around normal post-treatment surveillance trajectory
- FHIR resources: Standards-compliant with proper SNOMED CT, ICD-10, LOINC, RxNorm codes

**Clinical scenario:**
- Diagnosis: December 2024 (Stage IIA, T2N0M0, 2.5cm tumor, node-negative)
- Surgery: January 2025 (lumpectomy + sentinel node biopsy)
- Chemotherapy: February-May 2025 (AC-T: doxorubicin/cyclophosphamide → paclitaxel)
- Radiation: June-July 2025 (adjuvant)
- Current: Tamoxifen 20mg daily (started August 2025, planned 5-10 years)
- Surveillance: Q3 months with tumor markers, imaging annually

---

### 2. Genomic Data (`genomics/`)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `somatic_variants.vcf` | 2.8 KB | Germline BRCA2 + somatic mutations (original) | Based on TCGA-BRCA + ClinVar |
| `PAT002_somatic.vcf` | 1.6 KB | Patient-prefixed somatic variants (PIK3CA/GATA3/CDH1/MAP3K1/TP53) | Synthetic |
| `copy_number_results.cns` | 0.7 KB | CNVkit segmentation (original) | Synthetic |
| `PAT002_cnv.cns` | 1.3 KB | Patient-prefixed BC CNV profile (25 segments, all autosomes) | Synthetic |

**Generation method:**
- Selected common breast cancer driver mutations from TCGA-BRCA dataset
- VCF format (v4.2) with realistic variant quality scores
- Germline BRCA2 c.5946delT frameshift mutation (heterozygous in tumor and normal)
- Somatic mutations in tumor only
- Patient-prefixed files generated by `generate_PAT002_missing_files.py`

**Key mutations (realistic for BRCA2+ ER+ breast cancer):**

**Germline:**
1. **BRCA2 c.5946delT** (chr13:32339811, frameshift) - GT: 0/1, AF: 0.50 in both tumor/normal
   - COSMIC: COSV57492880 (pathogenic, Class 5)
   - Homologous recombination deficiency (HRD) phenotype
   - PARP inhibitor eligible

**Somatic (tumor-only):**
2. **PIK3CA H1047R** (chr3:178952085, G>A) - AF: 0.42
   - COSMIC: COSM775 (hotspot mutation)
   - Common in ER+ breast cancer (~40% prevalence)
   - PI3K/AKT/mTOR pathway activation

3. **GATA3 frameshift** (chr10:8100656) - AF: 0.38
   - Tumor suppressor in breast epithelium

4. **MAP3K1 missense** (chr5:56111569) - AF: 0.35
   - Loss-of-function, favorable prognosis in ER+ BC

5. **MYC amplification** (chr8:128748315) - Copy number 6
   - Proliferation driver

6. **CCND1 amplification** (chr11:69455873)
   - Common in ER+ breast cancer

7. **TP53 wild-type** - No mutation (typical for Luminal A/B, not TNBC)

8. **ESR1 wild-type** - No mutation (tamoxifen-sensitive, no resistance)

9. **ERBB2 (HER2) wild-type** - No amplification (HER2-negative)

**Patient-prefixed somatic VCF (`PAT002_somatic.vcf`):**
- PIK3CA p.H1047R (chr3:179234297, A→G, VAF 0.42) — most common ER+ BC driver
- GATA3 frameshift (chr10:8095656, VAF 0.31) — common in luminal BC
- CDH1 splice donor (chr16:68771967, G→A, VAF 0.28) — common in lobular BC
- MAP3K1 p.Q761X nonsense (chr5:56798505, C→T, VAF 0.35) — ER+ BC enriched
- TP53 R248W missense (chr17:7675088, G→A, VAF 0.15) — subclonal

**Patient-prefixed CNV profile (`PAT002_cnv.cns`):**
- chr13 BRCA2 loss (log2 −1.1, cn=1) — germline LOH
- chr8 MYC gain (log2 +0.9, cn=3)
- chr11 CCND1 gain (log2 +1.2, cn=3)
- chr3 PIK3CA gain (log2 +0.7, cn=3)
- chr16 CDH1 loss (log2 −0.8, cn=1)
- chr17 ERBB2 neutral (log2 ~0, cn=2) — confirms HER2-negative status
- 25 total segments spanning all autosomes + chrX

---

### 3. Multi-Omics Data (`multiomics/`)

#### Demonstration Data (Pre/Post Treatment Comparison)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `tumor_rna_seq.csv` | 310 KB | RNA-seq for 12 samples (6 pre, 6 post) | Synthetic |
| `tumor_proteomics.csv` | 125 KB | Proteomics for 12 samples | Synthetic |
| `tumor_phosphoproteomics.csv` | 82 KB | Phosphoproteomics for 12 samples | Synthetic |
| `sample_metadata.csv` | 0.5 KB | Sample annotations (pre/post treatment) | Synthetic |

**Demonstration specifications:**
- 12 samples: 6 pre-treatment biopsies + 6 post-treatment biopsies
- All samples show treatment response (patient is disease-free)
- Pre-processed counts matrices (not raw files)
- Total size: ~518 KB

**Generation method:**
- Random sampling from log-normal distributions
- Pre-treatment: High proliferation (MKI67, PCNA, TOP2A), high ER/PR
- Post-treatment: Low proliferation, slightly reduced ER/PR (tamoxifen), increased immune markers
- Differential expression: 120 genes (12%), fold-changes 1.5-3.0×
- BRCA2 expression reduced 50% (heterozygous germline mutation)
- PI3K/AKT pathway activation (PIK3CA H1047R mutation effect)

**Simulated biology:**
- **RNA-seq:** 1,000 genes (breast cancer markers + background)
  - High: ESR1, PGR, GATA3, FOXA1 (Luminal subtype)
  - Post-treatment ↓: MKI67, PCNA, proliferation markers
  - Post-treatment ↑: CD8A, CD3D, immune infiltration (TILs)
- **Proteomics:** 400 proteins (ER, PR, HER2, Ki67, p53, AKT, mTOR)
- **Phosphoproteomics:** 250 phosphosites (ESR1_S118, AKT1_S473, MTOR_S2448)
  - Post-treatment ↓: PI3K/AKT signaling

---

### 4. Spatial Transcriptomics (`spatial/`)

#### Demonstration Data (Simplified Visium)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `visium_gene_expression.csv` | 190 KB | Gene expression (35 genes × 900 spots) | Synthetic |
| `visium_spatial_coordinates.csv` | 35 KB | Spatial coordinates for 900 spots | Synthetic |
| `visium_region_annotations.csv` | 19 KB | Tissue region annotations (7 regions) | Synthetic |
| `filtered/visium_gene_expression_filtered.csv` | 178 KB | QC-filtered expression matrix | Synthetic |
| `PAT002_expression.csv` | 187 KB | Patient-prefixed expression (PAT002_ barcodes, luminal A profile) | Synthetic |
| `PAT002_coordinates.csv` | 45 KB | Patient-prefixed spatial coordinates with patient_id column | Synthetic |
| `PAT002_regions.csv` | 38 KB | Patient-prefixed regions (5 BC tissue types) | Synthetic |

**Demonstration specifications:**
- 900 spots (30×30 grid, hexagonal layout)
- 35 curated breast cancer genes
- Total size: ~422 KB

**Generation method:**
- Hexagonal Visium array layout (900 spots, 95% in-tissue)
- 35 curated breast cancer-relevant genes
- Spatial patterns using regional assignments:
  - **Tumor core:** High ER/PR, low proliferation (Luminal phenotype)
  - **Proliferative zones:** Ki67-high hotspots
  - **Invasive front:** Tumor-stroma boundary, EMT markers
  - **Stroma:** CAFs, collagen (COL1A1, ACTA2, FAP)
  - **Immune zones:** TILs (CD8A, CD3D, CD4)
  - **Adipose:** Breast adipose tissue (ADIPOQ, FABP4, LEP)
  - **Normal ductal:** Adjacent normal epithelium

**Tissue regions simulated (7 breast-specific):**
1. `tumor_core_luminal` - ER+/PR+ tumor cells, low proliferation (7.4%)
2. `tumor_proliferative` - Ki67-high tumor zone (4.7%)
3. `tumor_invasive_front` - Invasion boundary (21.8%)
4. `stroma_fibrous` - Dense collagen, CAFs (20.7%)
5. `stroma_immune` - Lymphocyte infiltration (TILs) (13.1%)
6. `adipose` - Normal breast adipose tissue (31.9%)
7. `ductal_normal` - Adjacent normal ductal epithelium (0.4%)

**Genes profiled (35 breast cancer markers):**
- **Hormone receptors:** ESR1, PGR, GATA3, FOXA1
- **Proliferation:** MKI67, PCNA, TOP2A, CCND1, MYC
- **Tumor suppressors:** BRCA2, GATA3, MAP3K1
- **Oncogenes:** PIK3CA, AKT1, MTOR
- **Luminal markers:** KRT8, KRT18, KRT19, EPCAM
- **Basal markers:** KRT5, KRT14, EGFR (low expression)
- **Stromal:** COL1A1, COL3A1, ACTA2, FAP, VIM
- **Immune:** CD3D, CD8A, CD4, FOXP3, CD68, CD163
- **Hypoxia:** HIF1A, VEGFA, CA9

---

### 5. Imaging Data (`imaging/`)

| Directory | Files | Size | Description | Source |
|-----------|-------|------|-------------|--------|
| Main | 7 TIFFs | ~12 MB | 1024×1024 immunofluorescence + H&E | Synthetic microscopy |
| `he/` | 3 TIFFs | ~25 MB | H&E high/low resolution (2048×2048, 512×512) | Synthetic histology |
| `if/` | 2 TIFFs | ~16 MB | ER/Ki67 high-resolution (2048×2048) | Synthetic IF |

**Main directory (1024×1024, 16-bit grayscale):**
- `PAT002_tumor_HE_20x.tiff` - H&E histology (RGB 8-bit, pink/purple staining)
- `PAT002_tumor_IF_ER.tiff` - Estrogen receptor (85% positive, nuclear)
- `PAT002_tumor_IF_PR.tiff` - Progesterone receptor (70% positive, nuclear)
- `PAT002_tumor_IF_HER2.tiff` - HER2 (negative, IHC 0, weak membrane)
- `PAT002_tumor_IF_KI67.tiff` - Ki67 proliferation (20% positive, nuclear)
- `PAT002_tumor_IF_CD8.tiff` - CD8+ T cells (8% positive, membrane)
- `PAT002_tumor_IF_DAPI.tiff` - Nuclear staining (all cells)

**High-resolution subdirectories:**
- `he/PAT002_tumor_HE_20x_high.tif` - 2048×2048 H&E
- `he/sample_002_high.tif`, `he/sample_002_low.tif` - Additional H&E images
- `if/PAT002_tumor_IF_ER_high.tif` - High-resolution ER staining
- `if/PAT002_tumor_IF_KI67_high.tif` - High-resolution Ki67

**Generation method:**
- Synthetic nuclei: 150-450 elliptical cells per image (size-dependent)
- Nuclear markers (ER/PR/Ki67): Circular gradients in nucleus
- Membrane markers (HER2/CD8): Ring patterns around cells
- H&E simulation: Pink eosin (cytoplasm/stroma) + purple hematoxylin (nuclei)
- Gaussian noise (σ=200-250), PSF blur (σ=1.2-1.5)
- 16-bit grayscale (0-65535 range), RGB for H&E

**Marker specifications:**
- **ER:** 85% cells positive, strong nuclear signal (9,000-15,000 intensity)
- **PR:** 70% cells positive, moderate nuclear signal
- **HER2:** Negative (all cells weak membrane, 800-2,000 intensity)
- **Ki67:** 20% cells positive (post-treatment proliferation)
- **CD8:** 8% cells positive (tumor-infiltrating lymphocytes)
- **H&E:** Realistic histology with nuclear detail and stromal texture

---

### 6. h5ad Files (AnnData Format)

#### Spatial Single-Cell Data (`quantum/`)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `PAT002_tumor_spatial.h5ad` | 7.9 MB | 500 cells × 2,000 genes, spatial coordinates | Synthetic AnnData |

**Contents:**
- **Cells:** 500 single cells with spatial coordinates (x, y)
- **Genes:** 2,000 genes (79 breast cancer markers + 1,921 background)
- **Cell types (8):** tumor_cells_luminal (36%), CAFs (20%), CD8_T_cells (14%), CD4_T_cells (10%), macrophages (8%), B_cells (6%), endothelial (4%), adipocytes (2%)
- **Spatial organization:**
  - Tumor core at center (luminal subtype, high ER/PR)
  - CAFs surrounding tumor
  - TILs at margins (CD8+, CD4+)
  - Adipocytes at periphery (breast-specific)
- **Metadata:** patient_id, diagnosis, receptor_status, BRCA2_status, treatment_status

#### Perturbation Data (`perturbation/`)

| File | Size | Description | Source |
|------|------|-------------|--------|
| `pat002_tcells.h5ad` | 463 KB | 500 cells × 100 genes, CRISPR screen | Synthetic perturbation |
| `gears_pat002_results.json` | 2.1 KB | GEARS GNN perturbation predictions (5 perturbations) | Synthetic |

**GEARS Predictions (`gears_pat002_results.json`):**
- **ESR1 knockout** — loss of luminal identity, models tamoxifen resistance (confidence 0.91)
- **PIK3CA inhibition** — reduced proliferation, alpelisib sensitivity prediction (confidence 0.87)
- **CDK4 inhibition** — G1 arrest, palbociclib response prediction (confidence 0.93)
- **BRCA2 knockout** — HR deficiency, PARP inhibitor sensitivity (confidence 0.89)
- **FOXP3 overexpression** — immune suppression, Treg expansion limiting immunotherapy (confidence 0.82)
- **Summary:** most actionable target CDK4, HRD score 35, immune evasion 0.41, luminal stability 0.78

**Contents:**
- **Experiment:** PD-1 (PDCD1) knockout CRISPR screen in CD8+ T cells
- **Conditions:** Control (250 cells) vs PDCD1_KO (250 cells)
- **Genes:** 100 T cell markers (effector, exhaustion, proliferation, metabolism)
- **Expected phenotype:**
  - Control: High PD-1 (PDCD1=339), high exhaustion (TOX=161)
  - PDCD1 KO: No PD-1 (PDCD1=0.8), restored effector function (GZMB=203, IFNG=206, 4.4× FC)
  - PDCD1 KO: Increased proliferation (MKI67=91, 5.6× FC)
- **Purpose:** Demonstrate immune checkpoint blockade effects for BRCA2+ breast cancer immunotherapy

---

## Data Quality & Realism

### Realistic Features ✅

1. **Clinically plausible genetics** - BRCA2 c.5946delT pathogenic variant + PIK3CA H1047R hotspot
2. **Biologically coherent** - ER+/PR+ Luminal phenotype with HRD signature
3. **Spatial organization** - Breast tumor microenvironment with adipocytes, CAFs, TILs
4. **Temporal dynamics** - Pre/post treatment comparison showing therapy response
5. **Treatment trajectory** - Realistic adjuvant therapy sequence (AC-T chemo + radiation + tamoxifen)
6. **Surveillance pattern** - Normal CEA/CA 15-3 trajectory, disease-free status

### Synthetic Limitations ❌

1. **No batch effects** - Real data would have technical variation across timepoints/platforms
2. **Simplified biology** - Real tumors have >20,000 genes, not 35-1,000
3. **Idealized spatial patterns** - Real tissue is more heterogeneous, with necrosis and artifacts
4. **No sequencing errors** - Real VCF would have mapping quality issues, strand bias
5. **Perfect correlations** - Real multi-omics has more noise and missing data
6. **Simplified family history** - Real BRCA2 families have more complex pedigrees

---

## Intended Use Cases

### ✅ Appropriate Uses

- **Workflow development** - Test MCP server integration for breast cancer precision medicine
- **CI/CD testing** - Automated pipeline validation (genomics, imaging, spatial)
- **Demonstration** - Show capabilities to stakeholders (oncologists, bioinformaticians)
- **Education** - Teach breast cancer biology, BRCA2 testing, HRD, immunotherapy
- **Software testing** - Verify analysis algorithms (ER/PR/HER2 scoring, spatial deconvolution)
- **FHIR implementation** - Test HL7 FHIR R4 interoperability

### ❌ Inappropriate Uses

- **Clinical decision-making** - NOT real patient data, do not use for treatment decisions
- **Research publications** - Cannot be cited as real clinical data
- **Regulatory submissions** - Not acceptable for FDA/EMA applications
- **IRB protocols** - Not subject to human subjects research oversight
- **Training machine learning models** - Synthetic data may not generalize to real patients

---

## Real Data Alternatives

If you need real breast cancer patient data, consider these public resources:

1. **TCGA-BRCA** (The Cancer Genome Atlas - Breast Cancer)
   - https://portal.gdc.cancer.gov/projects/TCGA-BRCA
   - 1,098 breast cancer cases with genomics, transcriptomics, clinical data
   - Includes ER+/PR+/HER2- Luminal A/B cases

2. **10x Genomics Public Datasets**
   - https://www.10xgenomics.com/datasets/human-breast-cancer-visium-fresh-frozen-whole-transcriptome-1-standard
   - Visium spatial transcriptomics on breast cancer tissue
   - Whole transcriptome, H&E images, spatial coordinates

3. **METABRIC** (Molecular Taxonomy of Breast Cancer)
   - https://www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric
   - 1,980 breast cancer cases with gene expression and clinical outcomes
   - Long-term follow-up data

4. **ClinVar BRCA1/BRCA2 Variants**
   - ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/
   - Clinical significance of BRCA germline variants
   - Pathogenic/benign classification

5. **BRCA Exchange**
   - https://brcaexchange.org/
   - BRCA1/BRCA2 variant aggregation and interpretation
   - ENIGMA expert panel classifications

6. **Single Cell Portal (Broad Institute)**
   - https://singlecell.broadinstitute.org/
   - Search for "breast cancer" - multiple studies available
   - Single-cell RNA-seq, spatial transcriptomics

---

## Technical Details

### File Formats

| Format | Usage | Tools |
|--------|-------|-------|
| JSON | Clinical data, FHIR resources | `jq`, Python `json` |
| VCF | Genomic variants | `bcftools`, `vcftools`, `GATK` |
| CSV | Multi-omics matrices, spatial data | Python `pandas`, R `read.csv` |
| TIFF | Microscopy images (8/16-bit) | ImageJ/Fiji, `scikit-image`, QuPath |
| h5ad | AnnData (single-cell, spatial) | Python `scanpy`, `anndata` |

### Dataset Size

**Demonstration (current):**
- **Clinical:** ~50 KB (FHIR + JSON)
- **Genomics:** ~6 KB (VCFs + CNS)
- **Multi-omics:** ~520 KB (CSV matrices)
- **Spatial:** ~695 KB (Visium CSVs + patient-prefixed CSVs)
- **Imaging:** ~55 MB (TIFFs)
- **h5ad:** ~8.4 MB (AnnData)
- **Perturbation:** ~465 KB (h5ad + GEARS JSON)
- **Total:** ~65 MB (demonstration-scale)

**Production (typical hospital per-patient):**
- **Clinical:** ~500 KB (full EHR extract)
- **Genomics:** ~3 GB (WGS BAM files), ~10 MB (VCF)
- **Multi-omics:** ~5 GB (RNA-seq BAM), ~500 MB (proteomics raw), ~20 MB (matrices)
- **Spatial:** ~500 MB (Visium H5 matrix + histology image)
- **Imaging:** ~1-5 GB (whole slide images, multiplex IF)
- **Total:** ~10-15 GB per patient (raw files), ~100-200 MB (processed matrices)

---

## Data License

This synthetic dataset is released under the **Apache License 2.0**.

```
Copyright 2026 Lynn Langit

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0
```

### Attribution

If using this synthetic dataset in presentations or educational materials, please attribute:

```
Synthetic patient data (PAT002-BC-2026) generated for MCP server demonstration
Source: github.com/lynnlangit/precision-medicine-mcp
License: Apache 2.0
```

---

## Generation Scripts

All data in this directory was generated using Python scripts:

| Script | Purpose | Location |
|--------|---------|----------|
| (manual creation) | Clinical FHIR JSON files | `clinical/fhir_raw/` |
| (manual creation) | Genomics VCF file | `genomics/` |
| `generate_PAT002_multiomics.py` | Multi-omics CSV matrices | `multiomics/` |
| `generate_PAT002_spatial.py` | Spatial transcriptomics CSVs | `spatial/` |
| `generate_PAT002_imaging.py` | Microscopy TIFF images | `imaging/` |
| `create_PAT002_spatial_data.py` | Spatial h5ad file | `quantum/` |
| `generate_PAT002_perturbation.py` | Perturbation h5ad file | `perturbation/` |
| `generate_PAT002_missing_files.py` | 6 patient-prefixed files (CNV, somatic VCF, spatial CSVs, GEARS JSON) | Root |

**Dependencies:**
- Python 3.9+
- NumPy, Pandas, SciPy
- scikit-image, Pillow (imaging)
- anndata, scanpy (h5ad)

**To regenerate data:**
```bash
# Multi-omics
python3 multiomics/generate_PAT002_multiomics.py

# Spatial transcriptomics
python3 spatial/generate_PAT002_spatial.py

# Imaging
python3 imaging/generate_PAT002_imaging.py

# h5ad files
python3 quantum/create_PAT002_spatial_data.py
python3 perturbation/generate_PAT002_perturbation.py

# Patient-prefixed files (CNV, somatic VCF, spatial CSVs, GEARS JSON)
python3 generate_PAT002_missing_files.py
```

---

## Patient Summary

**Michelle Anne Thompson** (synthetic, 42 years old, female)

**Medical History:**
- Diagnosed December 2024 with Stage IIA (T2N0M0) ER+/PR+/HER2- Invasive Ductal Carcinoma, Left Breast
- BRCA2 germline pathogenic variant (c.5946delT, frameshift, Class 5)
- Strong family history: Mother breast cancer (age 58), maternal aunt ovarian cancer (age 52), sister BRCA2+ carrier (no cancer)

**Treatment Timeline:**
- January 2025: Lumpectomy + sentinel lymph node biopsy (node-negative)
- February-April 2025: AC chemotherapy (doxorubicin 60mg/m² + cyclophosphamide q21d × 4 cycles)
- April-May 2025: Paclitaxel 80mg/m² IV weekly × 12 weeks
- June-July 2025: Adjuvant radiation therapy (whole breast + tumor bed boost)
- August 2025: Started tamoxifen 20mg PO daily (planned 5-10 years)

**Current Status (January 2026):**
- Disease-free (No Evidence of Disease, NED)
- CEA 2.1 ng/mL (normal), CA 15-3 20 U/mL (normal)
- Tolerating tamoxifen well (mild hypercholesterolemia, expected side effect)
- Surveillance: Q3 months with tumor markers, annual mammography

**Genomic Profile:**
- BRCA2 c.5946delT heterozygous (germline, pathogenic)
- PIK3CA H1047R (somatic, hotspot)
- MYC amplification, CCND1 amplification
- TP53 wild-type (favorable for Luminal subtype)
- ESR1 wild-type (tamoxifen-sensitive)

**Clinical Significance:**
- PARP inhibitor eligible (BRCA2+, HRD phenotype)
- Good prognosis (Stage IIA, node-negative, ER+/PR+, responsive to therapy)
- Surveillance for second primary breast/ovarian cancer (BRCA2 carrier)
- Family counseling recommended (genetic testing for siblings/children)

---

---

**Last Updated:** 2026-05-06 | **Status:** 100% Synthetic — Research/Demo Only
