git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/myeloma-mrd-agent" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-myeloma-mrd-agent && rm -rf "$T"
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/myeloma-mrd-agent" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-myeloma-mrd-agent && rm -rf "$T"
skills/myeloma-mrd-agent/SKILL.mdname: 'myeloma-mrd-agent' description: 'AI-powered minimal residual disease (MRD) analysis for multiple myeloma using next-generation flow cytometry, NGS, and mass spectrometry approaches.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Myeloma MRD Agent
The Myeloma MRD Agent provides comprehensive AI-driven minimal residual disease assessment for multiple myeloma. It integrates next-generation flow cytometry (NGF), NGS-based clonotype tracking, and mass spectrometry M-protein detection for ultra-sensitive MRD monitoring.
When to Use This Skill
- When assessing MRD status in multiple myeloma patients post-treatment.
- To select optimal MRD testing modality (NGF vs NGS vs MS).
- For predicting progression-free survival based on MRD kinetics.
- When integrating MRD with other response criteria (IMWG).
- To guide treatment intensification or de-escalation decisions.
Core Capabilities
-
NGF Analysis: AI-enhanced next-generation flow cytometry for MRD detection at 10^-5 to 10^-6 sensitivity.
-
NGS Clonotype Tracking: Analyze IGH/IGK/IGL rearrangements for molecular MRD.
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Mass Spectrometry: MALDI-TOF or LC-MS/MS for M-protein detection.
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Multi-Modal Integration: Combine modalities for comprehensive MRD assessment.
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Kinetic Modeling: Track MRD dynamics and predict outcomes.
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Response Classification: Apply IMWG MRD criteria.
MRD Detection Methods
| Method | Sensitivity | Sample | Advantages |
|---|---|---|---|
| NGF (EuroFlow) | 10^-5 to 10^-6 | BM | Standardized, fast |
| NGS (clonoSEQ) | 10^-6 | BM | Ultra-sensitive |
| ASO-qPCR | 10^-5 | BM | Quantitative |
| PET-CT | N/A | Whole body | Extramedullary |
| MS (MALDI/LC-MS) | 10^-5 | Serum | Non-invasive |
IMWG MRD Response Criteria
| Category | Definition |
|---|---|
| MRD-negative (10^-5) | No clonal plasma cells by NGF or NGS at 10^-5 |
| MRD-negative (10^-6) | No clonal plasma cells at 10^-6 sensitivity |
| Sustained MRD-neg | MRD-neg confirmed ≥1 year apart |
| Flow MRD-neg | NGF negative, sensitivity ≥10^-5 |
| Sequencing MRD-neg | NGS negative, sensitivity ≥10^-5 |
Workflow
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Input: Flow cytometry FCS files, NGS clonotype data, M-protein MS data, clinical parameters.
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NGF Analysis: AI-assisted gating and aberrant plasma cell identification.
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NGS Analysis: Clonotype frequency calculation and threshold application.
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MS Analysis: M-protein peak detection and quantification.
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Integration: Combine multi-modal MRD data.
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Kinetics: Model MRD trajectory and predict outcomes.
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Output: MRD status, response category, prognostic estimate.
Example Usage
User: "Analyze MRD status for this myeloma patient using flow and NGS data."
Agent Action:
python3 Skills/Hematology/Myeloma_MRD_Agent/myeloma_mrd.py \ --flow_fcs bone_marrow_ngf.fcs \ --ngs_clonotype clonoseq_results.json \ --ms_mprotein maldi_spectrum.csv \ --baseline_clone diagnosis_clone.json \ --treatment_phase post_consolidation \ --output mrd_report.json
NGF Panel (EuroFlow-Based)
Tube 1: CD138/CD38/CD45/CD19/CD56/CD27/CD81/CD117
Aberrant Plasma Cell Phenotype:
- CD138+, CD38++
- CD19- or dim (normal PC: CD19+)
- CD56+ (normal PC: CD56-)
- CD45- or dim (normal PC: CD45+)
- CD27- or dim
- CD117+ (often aberrant)
AI-Assisted Flow Cytometry
Automated Gating:
- CNN-based plasma cell identification
- Aberrant vs normal PC discrimination
- Consistent quantification across samples
Quality Control:
- Sample adequacy assessment
- Hemodilution detection
- Event count validation
NGS Clonotype Analysis
Process:
- Identify dominant clone at diagnosis (IGH/IGK/IGL)
- Design clone-specific assay or use multiplex (clonoSEQ)
- Track clonal frequency in follow-up samples
- Apply MRD threshold (typically 10^-5 or 10^-6)
Considerations:
- Clonal evolution may affect tracking
- Biclonal disease requires tracking both
- IGK/IGL backup if IGH fails
Prognostic Significance
| MRD Status | PFS HR | OS HR |
|---|---|---|
| MRD-neg (10^-5) | 0.35-0.45 | 0.40-0.50 |
| MRD-neg (10^-6) | 0.25-0.35 | 0.30-0.40 |
| Sustained MRD-neg | 0.20-0.30 | 0.25-0.35 |
Clinical Decision Support
MRD-Guided Treatment:
- De-escalation in sustained MRD-neg
- Intensification if MRD conversion
- Maintenance duration decisions
Monitoring Frequency:
- Post-induction
- Post-consolidation
- Post-transplant (Day +100)
- Every 6-12 months on maintenance
Prerequisites
- Python 3.10+
- FlowJo or equivalent for FCS files
- NGS analysis pipelines
- Mass spectrometry processing tools
Related Skills
- Flow_Cytometry_AI - For general flow analysis
- Multiple_Myeloma_AI - For disease-specific analysis
- Liquid_Biopsy_Analytics_Agent - For ctDNA approaches
Emerging Methods
- Circulating tumor cells: Blood-based PC detection
- Cell-free DNA: Myeloma-specific mutations
- Imaging: PET/MRI for extramedullary disease
- Serum-based NGS: M-protein sequencing
Author
AI Group - Biomedical AI Platform
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