tumor-mutational-burden-agent

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---

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.

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

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

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

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

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

TMB Calculation Methods

| Platform | Coverage | TMB Formula | Normalization |

|----------|----------|-------------|---------------|

| WES | 30-50 Mb | Nonsynonymous/coding Mb | Per exome size |

| FoundationOne | 1.1 Mb | Syn + nonsyn/panel Mb | FDA validated |

| MSK-IMPACT | 1.0-1.2 Mb | Nonsyn + splice/panel Mb | Panel-specific |

| TSO500 | 1.94 Mb | Coding mutations/Mb | Illumina validated |

| WGS | 3 Gb | Various metrics | Genome-wide |

TMB Thresholds

| Context | Threshold | Evidence |

|---------|-----------|----------|

| FDA (pan-tumor) | ≥10 mut/Mb | KEYNOTE-158 |

| Melanoma | ≥10 mut/Mb | Practice standard |

| NSCLC | ≥10 mut/Mb | Multiple trials |

| SCLC | ≥10 mut/Mb | Variable benefit |

| Colorectal (MSS) | Limited utility | MSI more predictive |

| Urothelial | ≥10 mut/Mb | IMvigor trials |

Workflow

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

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

1. Calculation: Count mutations and normalize to coverage.

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

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

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

1. 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:

| TMB | PD-L1 | MSI | ICI Benefit |

|-----|-------|-----|-------------|

| High | High | MSI-H | Very high |

| High | Low | MSS | Moderate-high |

| Low | High | MSS | Moderate |

| Low | Low | MSS | Limited |

| Any | Any | MSI-H | High (pembrolizumab) |

Cancer Type TMB Distributions

| Cancer Type | Median TMB | TMB-High % |

|-------------|------------|------------|

| Melanoma | 13.5 | 45% |

| NSCLC | 7.2 | 25% |

| SCLC | 9.8 | 35% |

| Bladder | 6.5 | 20% |

| Colorectal | 4.0 | 5% (MSS) |

| Breast | 2.5 | 5% |

| Prostate | 2.0 | 3% |

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

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