bio-clinical-databases-tumor-mutational-burden

What this skill does

## Version Compatibility

Reference examples tested with: cyvcf2 0.30+, VEP 111+ (or snpEff 5.2+), pandas 2.2+, numpy 1.26+, LOHHLA 1.0+ (Marty 2017), DASH 1.0+ (Montesion 2021). v4.1 (May 2024) gnomAD is current for germline subtraction. Friends of Cancer Research TMB harmonization framework (Vega 2021 *Ann Oncol*) and ESMO 2024 (Mosele *Ann Oncol*) define the operational thresholds.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version`

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. TMB calculation requires VCF with VEP / snpEff / Funcotator consequence annotations; the panel size used as denominator MUST match the assay's actual scored region, NOT the panel's total content.

# Tumor Mutational Burden; Calculation, Harmonization, ICI Eligibility

**'Calculate TMB from this somatic VCF and apply ICI eligibility cutoff'** -> Count nonsynonymous coding variants passing VAF/depth/germline filters; divide by assay scored region in Mb; apply assay-calibrated TMB-H cutoff; integrate with MSI / HLA-LOH / neoantigen quality.

- Python: `cyvcf2.VCF()` + VEP/snpEff consequence parsing + panel-size normalization
- CLI: `bcftools view` filtering + custom counting
- HLA-LOH: LOHHLA (Marty 2017 *Cell*) or DASH (Montesion 2021 *Cancer Discov*)
- Neoantigen quality: pVAC-tools, NetMHCpan-4.1, Luksza 2017 fitness model

## Regulatory and Trial Landscape

| Event | Year | Threshold | Notes |
|-------|------|-----------|-------|
| **KEYNOTE-158 + FDA pembrolizumab pan-tumor approval** | 2020 | TMB-H >= 10 mut/Mb | FoundationOne CDx companion diagnostic; 10 cohorts |
| **Friends of Cancer Research TMB harmonization Phase I (Merino 2020)** | 2020 | -- | 11 panels vs WES truth; 3-fold panel-specific differences |
| **Friends of Cancer Research Phase II (Vega 2021)** | 2021 | Calibration equations | 19 platforms; per-assay calibration to WES-aligned TMB-Mb |
| **ESMO 2024 (Mosele *Ann Oncol*)** | 2024 | TMB-H >= 10/Mb retained | NOT endorsed for breast, prostate, glioma |
| **KEYNOTE-189 (NSCLC + pembrolizumab + chemo)** | 2018 | -- | TMB-H did NOT enrich for benefit with chemo backbone |
| **POSEIDON / KEYNOTE-021 / KEYNOTE-407** | 2019-2022 | -- | TMB inconsistent with chemo backbones |
| **B-F1RST + BFAST Cohort C (bTMB)** | 2022 | bTMB >= 16/Mb | BFAST Cohort C FAILED primary endpoint |

## Friends of Cancer Research Harmonization: Cross-Panel Calibration

Merino 2020 *J Immunother Cancer*: in silico panel sampling from TCGA WES truth showed panel-specific TMB can differ 3-fold for identical samples. Vega 2021 *Ann Oncol* derived per-panel calibration equations to translate panel TMB to WES-aligned TMB-Mb.

**Per-panel calibration to FoundationOne 10/Mb sensitivity:**

| Panel | Scored region (Mb) | Equivalent threshold for FDA 10/Mb pan-tumor | Fails when |
|-------|---------------------|----------------------------------------------|-----------|
| **FoundationOne CDx** | 0.8 Mb scored (NOT 1.1 Mb total) | 10 mut/Mb (FDA reference; F1CDx companion) | Using 1.1 Mb panel total inflates TMB ~37%; pipeline excludes synonymous (F1CDx includes them) |
| **MSK-IMPACT v3** | 0.98 Mb | ~10 (full Vega 2021 calibration recommended) | Tumor purity < 30%; non-paired-normal mode |
| **MSK-IMPACT v4** | 1.22 Mb | ~10 | -- |
| **TruSight Oncology 500** | ~1.3 Mb scored (from 1.94 Mb total) | **7.8 mut/Mb** | Pipeline uses 10/Mb instead of Vega 2021 calibrated 7.8 |
| **Oncomine Tumor Mutation Load** | 1.2 Mb | **8.4 mut/Mb** | Pipeline uses 10/Mb instead of Vega 2021 calibrated 8.4 |
| **Caris MI Tumor Seek** | ~1.2 Mb |; (verify Caris docs) | -- |
| **Tempus xT v3** | 0.6 Mb | -- | Below 0.8 Mb minimum reliability threshold |
| **Predicine ATLAS** | ~0.6 Mb | -- | Below 0.8 Mb minimum; high sampling variance |

**TMB =/= TMB across vendors.** Manuscripts that compare TMB across panels without per-assay calibration are unreviewable. Use the Vega 2021 calibration equations or WES re-projection.

## Variant-Counting Subtleties

These choices alter TMB by 5-20%:

| Variable | Convention | Notes |
|----------|-----------|-------|
| **Synonymous variants** | **FoundationOne CDx INCLUDES synonymous** (rationale: reduces sampling noise); MSK-IMPACT and most academic pipelines exclude | The FDA companion diagnostic counts synonymous; frequent misconception |
| **Indels** | FoundationOne includes; some assays exclude frameshift only | 5-15% TMB impact |
| **Germline subtraction** | Paired-normal (gold standard); else gnomAD AF <=0.5% (sometimes 1%) for tumor-only | Population-stratified gnomAD AF for ancestry-diverse cohorts |
| **VAF threshold** | FoundationOne >=5%; >=10% for tumor-only no UMI; down to 2% with paired-normal | Lower VAF risks contamination/artifacts |
| **Hotspots** | COSMIC-confirmed driver hotspots typically EXCLUDED (not random) | Inflates TMB if included |
| **Tumor purity** | FoundationOne >=20%; MSK-IMPACT >=30% | Below floor erodes VAF-based filtering |
| **VEP version** | Pin to assay's annotation version | gnomAD v4 uses VEP 105 |

## Hypermutator Tiering

| Class | Threshold | Common etiology |
|-------|-----------|----------------|
| **TMB-H (FDA pan-cancer)** | >= 10 mut/Mb | Variable; ICI eligible |
| **Hypermutator (research)** | >= 100 mut/Mb | MMR-D, POLE-exo |
| **Ultra-hypermutator** | >= 500 mut/Mb | POLE+MMR concurrent |

MSI-H typically 30-50 mut/Mb; pure POLE-exo P286R 100-300 mut/Mb; POLE-exo + MMR-D exceeds 500. MSI-H and TMB-H overlap substantially in CRC and endometrial (~80% of MSI-H are TMB-H) but only ~16% of TMB-H solid tumors are MSI-H (Salem ME et al 2018 *Mol Cancer Res* 16:805-812).

## The Tumor-Type-Specific Cutoff Debate

**McGrail 2021** *Ann Oncol* is the most damning paper for the universal 10/Mb cutoff. TMB-H predicts ICI response in melanoma, NSCLC, bladder; but FAILS in breast, prostate, glioma. ORR in TMB-H melanoma/NSCLC/bladder was 39.8%; TMB-H breast/prostate/glioma was 15.3%. Mechanistic explanation: TMB only predicts when baseline CD8 T-cell infiltrate is present.

**Sha 2020** *Cell Rep Med*: TMB-H predicts ICI benefit in MSS subset but adds nothing on top of MSI-H (because MSI-H is uniformly hypermutator and uniformly responsive).

**Samstein 2019** *Nat Genet* (MSK-IMPACT 1,662 ICI-treated): cancer-specific TMB cutoffs (top 20% within each tumor type) outperform universal 10/Mb.

**ESMO 2024** retained TMB-H >= 10/Mb pan-tumor but explicitly noted exceptions: **NOT endorsed for breast, prostate, glioma** based on negative real-world data.

## Blood TMB (bTMB): The Negative-Trial Story

**Gandara 2018** *Nat Med*: bTMB on Foundation Medicine FoundationACT panel; POPLAR + OAK retrospective. bTMB >= 16 mut/Mb showed PFS benefit with atezolizumab in NSCLC.

**B-F1RST (Kim 2022)**: bTMB >= 16 prospectively predictive for atezolizumab first-line NSCLC.

**BFAST Cohort C (Dziadziuszko 2022)**: FAILED primary endpoint; atezolizumab vs chemo in bTMB-H NSCLC did not improve investigator-assessed PFS. Dominant confounder: low ctDNA shed fraction produces false-negative bTMB.

**Operational state:** bTMB is research-grade in tissue-naive settings; tissue TMB remains the regulatory standard.

## Neoantigen Quality: Beyond Raw TMB

**Luksza 2017** *Nature*: neoantigen fitness model. Combines "non-selfness" (TCR recognition probability via IEDB similarity) + "selfness" (MHC binding affinity differential vs WT peptide). Pancreatic-cancer validation (Balachandran 2017 *Nature*): long-term survivors had higher-quality neoantigens. Luksza 2022 *Nature*: immunoediting over 10 years.

**McGranahan 2016** *Science*: **clonal neoantigen burden** (mutations present in all tumor cells) predicts ICI response better than total. Subclonal-rich tumors evade despite high TMB.

**HLA-LOH** (Marty 2017 *Cell*,