<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

---

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

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->