tumor-mutational-burden-agent

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.

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