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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

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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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