name: 'tumor-mutational-burden-agent' description: 'Calculates and harmonizes Tumor Mutational Burden (TMB) across platforms to predict immunotherapy response.' keywords:

• tmb
• immunotherapy
• biomarker
• harmonization
• oncology measurable_outcome: 'Harmonizes TMB scores across 5+ assay platforms with <5% variance from WES gold standard.' allowed-tools:
• read_file
• run_shell_command

Tumor Mutational Burden Agent

The Tumor Mutational Burden Agent provides comprehensive TMB analysis for immunotherapy response prediction. It harmonizes TMB calculation across different assays, integrates with other biomarkers (PD-L1, MSI), and provides evidence-based therapy recommendations.

When to Use This Skill

• When calculating TMB from panel sequencing, WES, or WGS data.
• To harmonize TMB values across different assay platforms.
• For predicting immunotherapy response using TMB and integrated biomarkers.
• When determining TMB-High status for pembrolizumab eligibility.
• To analyze TMB in context of tumor type-specific distributions.

Core Capabilities

1. TMB Calculation: Compute TMB from different sequencing platforms with appropriate normalization.

2. Platform Harmonization: Standardize TMB across FoundationOne, MSK-IMPACT, WES, and other assays.

3. TMB-High Classification: Apply FDA-approved and tumor-specific thresholds.

4. Biomarker Integration: Combine TMB with PD-L1, MSI, and gene signatures.

5. Response Prediction: ML models predicting ICI response from TMB-inclusive features.

6. Tumor-Specific Context: Interpret TMB relative to cancer type distributions.

TMB Calculation Methods

TMB Thresholds

Workflow

1. Input: VCF/MAF file with somatic mutations, assay details, tumor type.

2. Filtering: Remove germline, artifacts, known drivers (optional).

3. Calculation: Count mutations and normalize to coverage.

4. Harmonization: Convert to WES-equivalent TMB if needed.

5. Classification: Assign TMB-High/Low based on thresholds.

6. Integration: Combine with PD-L1, MSI for composite score.

7. Output: TMB value, classification, response prediction, recommendations.

Example Usage

User: "Calculate TMB from this panel sequencing data and predict immunotherapy response."

Agent Action:

python3 Skills/Oncology/Tumor_Mutational_Burden_Agent/tmb_analyzer.py \
    --mutations tumor_somatic.maf \
    --panel foundation_one \
    --tumor_type nsclc \
    --pdl1_tps 50 \
    --msi_status stable \
    --harmonize_to wes \
    --output tmb_report.json

Platform Harmonization

Different panels yield different TMB values for the same tumor:

TMB_WES = a * TMB_panel + b

Conversion factors (example):
- FoundationOne CDx: TMB_WES ≈ 1.0 × TMB_F1
- MSK-IMPACT: TMB_WES ≈ 1.1 × TMB_IMPACT
- TSO500: TMB_WES ≈ 0.9 × TMB_TSO

Harmonization Considerations:

• Panel size affects precision
• Gene content affects which mutations counted
• Algorithmic differences in filtering

Integrated Biomarker Analysis

TMB + PD-L1 + MSI Integration:

Cancer Type TMB Distributions

AI/ML Enhancement

Response Prediction Model:

• Features: TMB, PD-L1, MSI, gene expression signatures
• Additional: Clonal vs subclonal TMB, driver mutations
• Performance: AUC 0.70-0.80 across tumor types

TMB Components Analysis:

• Clonal TMB: Mutations in all cells
• Subclonal TMB: Mutations in subpopulations
• Clonal TMB more predictive of response

Prerequisites

• Python 3.10+
• Variant annotation tools
• Panel BED files for coverage
• Reference mutation databases

Related Skills

• Variant_Annotation - For mutation calling
• Liquid_Biopsy_Analytics_Agent - For blood-based TMB
• Immune_Checkpoint_Combination_Agent - For ICI selection

Clinical Decision Support

1. TMB-H Pembrolizumab: FDA-approved pan-tumor indication
2. TMB + PD-L1: Combined scoring for NSCLC
3. TMB Monitoring: Track under immunotherapy
4. TMB Heterogeneity: Consider multiple samples

Author

AI Group - Biomedical AI Platform