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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.

  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

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.

  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:

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