Myeloma Mrd Agent

name: '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

1. NGF Analysis: AI-enhanced next-generation flow cytometry for MRD detection at 10^-5 to 10^-6 sensitivity.

2. NGS Clonotype Tracking: Analyze IGH/IGK/IGL rearrangements for molecular MRD.

3. Mass Spectrometry: MALDI-TOF or LC-MS/MS for M-protein detection.

4. Multi-Modal Integration: Combine modalities for comprehensive MRD assessment.

5. Kinetic Modeling: Track MRD dynamics and predict outcomes.

6. Response Classification: Apply IMWG MRD criteria.

MRD Detection Methods

IMWG MRD Response Criteria

Workflow

1. Input: Flow cytometry FCS files, NGS clonotype data, M-protein MS data, clinical parameters.

2. NGF Analysis: AI-assisted gating and aberrant plasma cell identification.

3. NGS Analysis: Clonotype frequency calculation and threshold application.

4. MS Analysis: M-protein peak detection and quantification.

5. Integration: Combine multi-modal MRD data.

6. Kinetics: Model MRD trajectory and predict outcomes.

7. 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:

1. Identify dominant clone at diagnosis (IGH/IGK/IGL)
2. Design clone-specific assay or use multiplex (clonoSEQ)
3. Track clonal frequency in follow-up samples
4. 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

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

1. Circulating tumor cells: Blood-based PC detection
2. Cell-free DNA: Myeloma-specific mutations
3. Imaging: PET/MRI for extramedullary disease
4. Serum-based NGS: M-protein sequencing

Author

AI Group - Biomedical AI Platform