BioTender-max 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
TMB Calculation: Compute TMB from different sequencing platforms with appropriate normalization.
Platform Harmonization: Standardize TMB across FoundationOne, MSK-IMPACT, WES, and other assays.
TMB-High Classification: Apply FDA-approved and tumor-specific thresholds.
Biomarker Integration: Combine TMB with PD-L1, MSI, and gene signatures.
Response Prediction: ML models predicting ICI response from TMB-inclusive features.
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
Input: VCF/MAF file with somatic mutations, assay details, tumor type.
Filtering: Remove germline, artifacts, known drivers (optional).
Calculation: Count mutations and normalize to coverage.
Harmonization: Convert to WES-equivalent TMB if needed.
Classification: Assign TMB-High/Low based on thresholds.
Integration: Combine with PD-L1, MSI for composite score.
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
- TMB-H Pembrolizumab: FDA-approved pan-tumor indication
- TMB + PD-L1: Combined scoring for NSCLC
- TMB Monitoring: Track under immunotherapy
- TMB Heterogeneity: Consider multiple samples
Author
AI Group - Biomedical AI Platform