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name: 'mpn-progression-monitor-agent' description: 'AI-powered myeloproliferative neoplasm monitoring for disease progression prediction, treatment response tracking, and transformation risk assessment in PV, ET, and myelofibrosis.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

MPN Progression Monitor Agent

The MPN Progression Monitor Agent provides comprehensive monitoring of myeloproliferative neoplasms (PV, ET, MF) for disease progression, treatment response, and transformation risk. It integrates molecular profiling, clinical parameters, and AI-based risk models to guide management of chronic phase disease and predict blast transformation.

When to Use This Skill

  • When monitoring JAK2/CALR/MPL mutation burden over time.
  • For predicting fibrosis progression in PV/ET.
  • To assess risk of blast transformation.
  • When tracking treatment response to JAK inhibitors.
  • For calculating dynamic risk scores (DIPSS, MIPSS70).

Core Capabilities

  1. Mutation Monitoring: Track driver and high-risk mutation VAF.

  2. Progression Prediction: Model fibrosis and transformation risk.

  3. Risk Scoring: Calculate DIPSS, MIPSS70+, MTSS dynamically.

  4. Treatment Response: Assess molecular and clinical response.

  5. Clone Evolution: Track clonal dynamics and new mutations.

  6. Transplant Timing: Optimize allo-HSCT timing decisions.

MPN Classification

MPN Type Driver Mutations Progression Risk
PV JAK2 V617F (95%), JAK2 exon 12 Fibrosis 10-15%, AML 2-5%
ET JAK2 (55%), CALR (25%), MPL (5%) Fibrosis 5-10%, AML 1-2%
Pre-PMF Same as PMF Variable
PMF JAK2 (60%), CALR (25%), MPL (5%) AML 10-20%

High-Risk Mutations

Mutation Impact MF Association
ASXL1 Adverse Strong
SRSF2 Adverse Strong (PMF)
EZH2 Adverse Moderate
IDH1/2 Adverse Transformation
RUNX1 Very Adverse Transformation
TP53 Very Adverse Transformation
U2AF1 Adverse Moderate

Risk Scores

Score Components Application
IPSS Age, Hb, WBC, blasts, symptoms PMF at diagnosis
DIPSS Same, dynamic PMF follow-up
DIPSS+ + karyotype, transfusion, platelets PMF refined
MIPSS70 Molecular markers Transplant-age PMF
MIPSS70+ v2.0 + U2AF1, karyotype Most comprehensive
MTSS Transplant-specific Allo-HSCT outcomes

Workflow

  1. Input: Serial molecular testing, CBC, clinical parameters.

  2. Baseline Assessment: Calculate initial risk score.

  3. Mutation Tracking: Monitor VAF trends over time.

  4. Risk Recalculation: Update scores at each timepoint.

  5. Progression Detection: Identify molecular/clinical progression.

  6. Treatment Assessment: Evaluate response to therapy.

  7. Output: Dynamic risk assessment, progression alerts, recommendations.

Example Usage

User: "Monitor this myelofibrosis patient's disease trajectory and update risk scores with new molecular data."

Agent Action:

python3 Skills/Hematology/MPN_Progression_Monitor_Agent/mpn_monitor.py \
    --patient_id MF_001 \
    --molecular_data serial_mutations.csv \
    --cbc_data serial_cbc.csv \
    --clinical_data symptoms.json \
    --mpn_type pmf \
    --baseline_date 2024-01-15 \
    --calculate_scores dipss,mipss70 \
    --output mpn_monitoring/

Input Requirements

Data Type Parameters Frequency
Molecular JAK2/CALR/MPL VAF, NGS panel q3-6 months
CBC Hb, WBC, platelets, blasts Monthly
Clinical Symptoms, spleen size q3 months
Bone Marrow Fibrosis grade, cytogenetics q6-12 months

Output Components

Output Description Format
Risk Scores DIPSS, MIPSS70 over time .csv
VAF Trends Mutation burden plots .png
Progression Alert Warning if criteria met .json
Response Assessment IWG-MRT criteria .json
Transplant Timing Recommendation if indicated .json
Clone Evolution New mutations, clonal shifts .csv

Progression Criteria

Progression Type Criteria Action
Clinical New symptoms, splenomegaly Intensify therapy
Hematologic Cytopenias, increased blasts BMB, cytogenetics
Molecular New high-risk mutations Risk restaging
Fibrotic Increased fibrosis grade Consider transplant
Blast Phase ≥20% blasts Urgent intervention

Response Criteria (IWG-MRT)

Response Definition Implications
Complete Remission No disease manifestations Excellent outcome
Partial Remission >50% improvement Good response
Clinical Improvement Symptom/spleen improvement Benefit
Stable Disease No change Observe
Progressive Disease Progression criteria Change therapy

AI/ML Components

Progression Prediction:

  • Survival analysis with molecular features
  • Random survival forests
  • Deep learning time-to-event

Clone Tracking:

  • VAF trajectory modeling
  • New clone detection
  • Evolutionary tree inference

Transplant Decision:

  • Survival benefit modeling
  • NRM prediction
  • Optimal timing algorithms

Treatment Response Monitoring

Therapy Response Markers Timeline
Ruxolitinib Spleen, symptoms, JAK2 VAF 12-24 weeks
Fedratinib Similar to ruxolitinib 24 weeks
Momelotinib + anemia improvement 24 weeks
Interferon Molecular response, JAK2 VAF 12+ months

Prerequisites

  • Python 3.10+
  • lifelines, scikit-survival
  • Variant annotation tools
  • Risk score calculators
  • Visualization libraries

Related Skills

  • CHIP_Clonal_Hematopoiesis_Agent - Pre-MPN states
  • MDS_Classification_Agent - Overlap syndromes
  • Bone_Marrow_AI_Agent - Morphology analysis
  • Coagulation_Thrombosis_Agent - Thrombosis risk

Thrombosis Risk in MPN

Factor Risk Increase Management
Age >60 2-3x Cytoreduction
Prior thrombosis 3-5x Anticoagulation
JAK2 V617F 2x Higher for homozygous
High WBC 1.5-2x Control counts
CV risk factors Additive Aggressive management

Special Considerations

  1. Triple-Negative MPN: Different prognosis, consider other diagnoses
  2. Cytogenetic Evolution: High-risk signal, BMB follow-up
  3. New Mutations: May indicate disease evolution
  4. Treatment Resistance: Consider second-line or transplant
  5. Quality of Life: Balance treatment intensity

Transplant Indications

Indication Criteria Timing
High-Risk PMF MIPSS70+ high/very high Consider early
Blast Phase ≥20% blasts Urgent if fit
Refractory Disease Failed JAKi Evaluate
Transfusion Dependence RBC/platelet dependent Factor in decision

Author

AI Group - Biomedical AI Platform