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---
name: 'ctdna-dynamics-mrd-agent'
description: 'AI-powered circulating tumor DNA dynamics analysis for molecular residual disease detection, treatment response monitoring, and early relapse prediction using liquid biopsy.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# ctDNA Dynamics MRD Agent
The **ctDNA Dynamics MRD Agent** provides comprehensive analysis of circulating tumor DNA dynamics for molecular residual disease (MRD) detection, treatment response monitoring, and early relapse prediction. It integrates tumor-informed and tumor-naive approaches with temporal modeling for longitudinal ctDNA analysis.
## When to Use This Skill
* When monitoring minimal/molecular residual disease post-treatment.
* For tracking treatment response through ctDNA kinetics.
* To predict relapse before clinical/radiological detection.
* When assessing tumor burden dynamics during therapy.
* For early detection of acquired resistance mutations.
## Core Capabilities
1. **MRD Detection**: Ultra-sensitive detection of residual disease (LOD 0.001% VAF).
2. **Kinetic Modeling**: Model ctDNA clearance and doubling time.
3. **Response Prediction**: Predict treatment response from early ctDNA dynamics.
4. **Relapse Prediction**: Identify molecular relapse months before imaging.
5. **Resistance Monitoring**: Track emergence of resistance mutations.
6. **Multi-Timepoint Integration**: Analyze longitudinal ctDNA trajectories.
## Detection Approaches
| Approach | Method | LOD | Best Use Case |
|----------|--------|-----|---------------|
| Tumor-Informed | Track known mutations | 0.001% | Post-surgical MRD |
| Tumor-Naive | Panel-based detection | 0.1% | Screening, unknown primary |
| WGS-Based | Fragmentomics + mutations | 0.01% | Comprehensive profiling |
| Methylation | cfDNA methylation | 0.1% | Tissue of origin, early detection |
## Kinetic Parameters
| Parameter | Definition | Clinical Meaning |
|-----------|------------|------------------|
| ctDNA Half-Life | Time to 50% reduction | Treatment sensitivity |
| Doubling Time | Time to 2x increase | Tumor growth rate |
| Nadir | Lowest ctDNA level | Depth of response |
| Time to Nadir | Days to reach nadir | Response kinetics |
| Clearance Rate | Exponential decay constant | Treatment efficacy |
| Lead Time | MRD+ to clinical relapse | Early detection window |
## Workflow
1. **Input**: Serial ctDNA measurements (VAF or copies/mL), timepoints, treatment dates.
2. **QC**: Assess sequencing quality, coverage, tumor fraction.
3. **Mutation Tracking**: Quantify tracked variants across timepoints.
4. **Kinetic Modeling**: Fit exponential/sigmoidal models to dynamics.
5. **MRD Calling**: Determine MRD status with confidence intervals.
6. **Resistance Detection**: Identify emerging resistant clones.
7. **Output**: MRD status, kinetic parameters, predictions, visualizations.
## Example Usage
**User**: "Analyze this patient's serial ctDNA data to assess MRD status and predict relapse risk."
**Agent Action**:
```bash
python3 Skills/Oncology/ctDNA_Dynamics_MRD_Agent/ctdna_mrd_analysis.py \
--ctdna_data serial_ctdna.tsv \
--tracked_mutations tumor_mutations.vcf \
--sample_times 0,14,42,90,180 \
--treatment_start 0 \
--surgery_date 7 \
--cancer_type colorectal \
--output mrd_analysis/
```
## Input Data Format
```tsv
Sample_ID Timepoint_Days Mutation VAF Copies_per_mL Coverage
PT001_T0 0 TP53_R248Q 5.2 1500 15000
PT001_T1 14 TP53_R248Q 2.1 620 18000
PT001_T2 42 TP53_R248Q 0.05 15 20000
PT001_T3 90 TP53_R248Q 0.002 0.6 22000
```
## Output Components
| Output | Description | Format |
|--------|-------------|--------|
| MRD Status | Positive/Negative at each timepoint | .csv |
| Kinetic Parameters | Half-life, doubling time, nadir | .json |
| Response Classification | Major/Minor/No response | .csv |
| Relapse Risk | Probability and predicted time | .json |
| Dynamics Plot | ctDNA trajectory visualization | .png, .pdf |
| Resistance Variants | Emerging mutations | .vcf |
| Clonal Evolution | Clone frequency over time | .csv |
## Response Definitions
| Response Category | ctDNA Change | Clinical Correlation |
|-------------------|--------------|---------------------|
| Major Molecular Response | >2 log reduction | Excellent prognosis |
| Molecular Response | 1-2 log reduction | Good prognosis |
| Stable Molecular Disease | <1 log change | Intermediate |
| Molecular Progression | >0.5 log increase | Poor prognosis |
## Cancer-Specific Parameters
| Cancer Type | Typical Half-Life | MRD Lead Time | ctDNA Shedding |
|-------------|-------------------|---------------|----------------|
| Colorectal | 1-2 days | 6-12 months | High |
| Lung (NSCLC) | 1-3 days | 3-6 months | High |
| Breast | 2-5 days | 6-18 months | Moderate |
| Pancreatic | 1-2 days | 3-6 months | High |
| Melanoma | 2-4 days | 3-9 months | Variable |
## AI/ML Components
**Kinetic Modeling**:
- Non-linear mixed effects models
- Bayesian hierarchical models
- Gaussian process regression
**MRD Detection**:
- Error-suppressed variant calling
- Machine learning noise filtering
- Duplex UMI deduplication
**Relapse Prediction**:
- Time-series forecasting (LSTM, Transformers)
- Survival analysis (Cox, Random Survival Forests)
- Multi-mutation integration
## Clinical Trial Support
| Application | Endpoint | ctDNA Metric |
|-------------|----------|--------------|
| Neoadjuvant | pathCR surrogate | Pre-surgery clearance |
| Adjuvant | DFS surrogate | Post-surgery MRD |
| Metastatic | PFS/OS surrogate | ctDNA dynamics |
| Maintenance | Duration decision | MRD negativity |
## Prerequisites
* Python 3.10+
* Variant callers (Mutect2, Strelka)
* UMI-aware pipelines
* scipy, lifelines, survival analysis tools
* PyTorch for deep learning models
## Related Skills
* MRD_EDGE_Detection_Agent - Ultra-sensitive MRD detection
* Liquid_Biopsy_Analytics_Agent - Comprehensive liquid biopsy
* Tumor_Heterogeneity_Agent - Clonal evolution tracking
* HRD_Analysis_Agent - Genomic biomarkers
## Special Considerations
1. **Tumor Fraction**: Low tumor fraction limits sensitivity
2. **Pre-Analytical**: Plasma processing affects cfDNA quality
3. **Clonal Hematopoiesis**: CHIP variants can confound results
4. **Panel Design**: Ensure sufficient mutation coverage
5. **Timing**: Sample timing relative to treatment critical
## FDA-Cleared ctDNA Tests
| Test | Cancer Types | Application |
|------|--------------|-------------|
| Guardant360 CDx | Pan-cancer | Treatment selection |
| FoundationOne Liquid CDx | Pan-cancer | Treatment selection |
| Signatera | Solid tumors | MRD monitoring |
| Guardant Reveal | CRC | MRD detection |
## Author
AI Group - Biomedical AI Platform
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
