Myeloma Mrd Agent

---name: myeloma-mrd-agent

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---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. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

system: Python 3.10+ allowed-tools:
run_shell_command
read_file
write_file

keywords:

myeloma-mrd-agent
automation
biomedical measurable_outcome: execute task with >95% success rate. ---"

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.
Mass Spectrometry: MALDI-TOF or LC-MS/MS for M-protein detection.
Multi-Modal Integration: Combine modalities for comprehensive MRD assessment.
Kinetic Modeling: Track MRD dynamics and predict outcomes.
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

Input: Flow cytometry FCS files, NGS clonotype data, M-protein MS data, clinical parameters.
NGF Analysis: AI-assisted gating and aberrant plasma cell identification.
NGS Analysis: Clonotype frequency calculation and threshold application.
MS Analysis: M-protein peak detection and quantification.
Integration: Combine multi-modal MRD data.
Kinetics: Model MRD trajectory and predict outcomes.
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

---name: myeloma-mrd-agentdescription: ---name: 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. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility: - system: Python 3.10+ allowed-tools: - run_shell_command - read_file - write_file keywords: - myeloma-mrd-agent - automation - biomedical measurable_outcome: execute task with >95% success rate. ---" # 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 | 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 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**: ```bash 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 | 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 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

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