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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

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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:

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

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

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