--- 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 --- Source attribution: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Dave AI resource metadata Creator / publisher: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Healthcare & Life Sciences Roles: Clinical Research, Health Informatics, Medical Writer Specialties: API Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c68a259d10b17d8b89e084b634e208fa20760fa3c46a2ca45861ab0d8379ad60 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.