bio-clinical-databases-tumor-mutational-burden

name	bio-clinical-databases-tumor-mutational-burden
description	Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
origin	openai4s
category	bioskills/clinical-databases
metadata	{"tool_type":"python","primary_tool":"cyvcf2","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: cyvcf2 0.30+, VEP 111+ (or snpEff 5.2+), pandas 2.2+, numpy 1.26+, LOHHLA 1.0+ (McGranahan 2017), DASH 1.0+ (Pyke 2022). 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 (McGranahan 2017 Cell) or DASH (Pyke 2022 Nat Commun)
• 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 (tumour-agnostic, ESCAT IB)	Tumour-type limits per McGrail 2021
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 the TMB2-calibrated 7.8 (Ramos-Paradas 2021)
Oncomine Tumor Mutation Load	1.2 Mb	8.4 mut/Mb	Pipeline uses 10/Mb instead of the TMB2-calibrated 8.4 (Ramos-Paradas 2021)
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 (~83% of MSI-H are TMB-H) but only ~16% of TMB-H solid tumors are MSI-H (Chalmers 2017 Genome Med 9:34).

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 Cancer Discov: 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 (tumour-agnostic, ESCAT IB). The tumour-type limits (poor performance in breast, prostate, glioma) come from McGrail 2021, not ESMO.

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): prospective phase 2 test of bTMB >= 16 as a first-line atezolizumab predictor in NSCLC; did NOT meet its pre-specified primary endpoint (bTMB-H improved ORR 28.6% vs 4.4%, only a non-significant PFS/OS trend).

BFAST Cohort C (Peters 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 (McGranahan 2017 Cell, LOHHLA; Pyke 2022 Nat Commun, DASH; Montesion 2021 Cancer Discov for the ~17% pan-cancer estimate): HLA-LOH occurs in ~40% of NSCLC and abolishes neoantigen presentation for the lost allele. ~17% pan-cancer; >30% in HNSCC / NSCLC / cervical. Co-occurs with high subclonal burden + APOBEC + immune escape.

Decision Tree by Scenario

Scenario	Recommended path	Why
Pan-tumor ICI eligibility (FDA pembrolizumab)	TMB-H >= 10/Mb on FoundationOne CDx	FDA companion diagnostic
Non-FoundationOne panel	Apply per-assay calibration to the FoundationOne 10/Mb equivalent	TSO500 = 7.8; Oncomine = 8.4 (Ramos-Paradas 2021)
WES TMB	Compute directly; threshold per ESMO 2024 = 10/Mb	WES is reference standard
Tissue-naive bTMB	Caution: BFAST Cohort C failed	Research-grade; check ctDNA shed fraction
Breast / prostate / glioma	TMB-H does not enrich ICI response per McGrail 2021	Tumor-type-specific cutoffs
MSI-H + TMB-H concurrence	MSI-H supersedes for ICI biomarker decision	Sha 2020
Hypermutator characterization (>=100/Mb)	Confirm MMR-D or POLE-exo via signatures + IHC	Co-occurrence is common
Neoantigen quality (research)	Luksza fitness + HLA-LOH (LOHHLA / DASH) + clonality (McGranahan)	Beyond raw TMB
Cross-panel comparison	Vega 2021 calibration equations OR WES re-projection	Direct comparison invalid

Standard Workflow

Goal: Compute TMB from a VEP-annotated somatic VCF with full filtering.

Approach: Parse cyvcf2; apply VAF + depth + germline (gnomAD) filters; count nonsynonymous coding consequences; divide by scored Mb.

from cyvcf2 import VCF
import re

NONSYNONYMOUS_CONSEQUENCES = {
    'missense_variant', 'stop_gained', 'stop_lost', 'start_lost', 'start_retained',
    'frameshift_variant', 'inframe_insertion', 'inframe_deletion',
    'splice_donor_variant', 'splice_acceptor_variant',
    'protein_altering_variant', 'initiator_codon_variant'
}

# Vega 2021-calibrated scored regions (Mb)
PANEL_SCORED_REGION = {
    'FoundationOne_CDx': 0.8,           # Scored region; NOT 1.1 panel total
    'MSK_IMPACT_v3': 0.98,
    'MSK_IMPACT_v4': 1.22,
    'TSO500': 1.3,                       # Scored from 1.94 total
    'Oncomine_TML': 1.2,
    'Caris_MI': 1.2,
    'Tempus_xT_v3': 0.6,                 # Borderline reliability
    'WES': 30.0,
    'WGS': 3000.0
}

# TMB2 (Ramos-Paradas 2021) equivalent thresholds for FDA 10/Mb FoundationOne sensitivity
ASSAY_TMB_H_CUTOFF = {
    'FoundationOne_CDx': 10.0,
    'TSO500': 7.8,
    'Oncomine_TML': 8.4,
    'MSK_IMPACT_v3': 10.0,  # Approximate; full Vega 2021 calibration recommended
    : ,
    : 
}


 ():
    
      csq_field:
         []
    cons_idx = csq_header.index()
    out = []
     transcript  csq_field.split():
        fields = transcript.split()
         (fields) > cons_idx:
            out.append(fields[cons_idx])
     out


 ():
    
    target = (NONSYNONYMOUS_CONSEQUENCES)
     include_synonymous:
        target.add()
     cons_str  consequences:
         cons  cons_str.split():
             cons  target:
                 
     


 ():
    
    vcf = VCF(vcf_path)
    cons_idx = csq_header.index()    csq_header  
    nonsyn_count = 
    total_pass = 

     v  vcf:
         v.FILTER   :  
            
        depth = v.INFO.get(, )
         depth < min_depth:
            
        vaf = _get_vaf(v)
         vaf    vaf < min_vaf:
            
        gnomad_af = v.INFO.get(, )  v.INFO.get(, )  
         gnomad_af > max_gnomad_af:
            
        total_pass += 

        csq = v.INFO.get(, )
        consequences = parse_consequences_from_vep(csq, csq_header)
         is_nonsynonymous(consequences, include_synonymous=include_synonymous):
            nonsyn_count += 

    tmb = nonsyn_count / scored_region_mb
     {
        : (tmb, ),
        : nonsyn_count,
        : total_pass,
        : scored_region_mb
    }


 ():
    
    :
        ad = variant.()
         ad     (ad) > :
            ad0 = ad[]
            total = (ad0)
             ad0[] / total  total >   
     Exception:
        
    :
        af = variant.()
         af     (af) > :
             (af[])
     Exception:
        
     


 ():
    
    cutoff = ASSAY_TMB_H_CUTOFF.get(assay, )
     tmb_value >= :
        category = 
     tmb_value >= :
        category = 
     tmb_value >= cutoff:
        category = 
    :
        category = 
     category

TMB-MSI Concordance and Reconciliation

Goal: When MSI-H is present, TMB-H adds no information (Sha 2020).

def tmb_msi_reconcile(tmb_value, msi_status, tumor_type=None):
    '''Reconcile TMB + MSI for ICI decision.'''
    tmb_high = tmb_value >= 10
    msi_high = msi_status == 'MSI-H'

    if msi_high:
        return ('ICI eligible by MSI-H (FDA 2017 pembrolizumab); TMB-H adds no information '
                '(Sha 2020 Cancer Discov).')
    if tmb_high and tumor_type in ('breast', 'prostate', 'glioma'):
        return ('TMB-H present but does not enrich ICI response in this tumor type (McGrail 2021). '
                'Tumor-specific cutoffs recommended.')
    if tmb_high:
        return ('TMB-H pan-tumor; ICI eligible (FDA pembrolizumab 2020). '
                'Confirm baseline CD8 infiltrate; check HLA-LOH (McGranahan 2017 LOHHLA).')
    return 'TMB-low; MSS. Standard-of-care chemo.'

Per-Operation Failure Modes

1. Using panel total size as denominator (NOT scored region)

• Trigger: Compute TMB = nonsynonymous count / 1.1 Mb for FoundationOne.
• Mechanism: FoundationOne CDx total panel is 1.1 Mb; SCORED region (counted for TMB denominator) is 0.8 Mb.
• Symptom: TMB underestimated by ~37%.
• Fix: Use 0.8 Mb for FoundationOne CDx scored region per Vega 2021.

2. Cross-panel comparison without calibration

• Trigger: TSO500 reports TMB = 9.5; compared to FoundationOne 10/Mb cutoff.
• Mechanism: the TMB2 project (Ramos-Paradas 2021) showed the equivalent threshold is 7.8/Mb on TSO500 (not 10/Mb).
• Symptom: TSO500 TMB-H called positive at incorrect threshold.
• Fix: Apply assay-specific calibration; the TSO500 equivalent cutoff = 7.8/Mb (Ramos-Paradas 2021).

3. FoundationOne synonymous mis-handling

• Trigger: Compare academic pipeline (no synonymous) to FoundationOne reference (synonymous included).
• Mechanism: FoundationOne CDx counts synonymous; MSK-IMPACT and most academic pipelines exclude.
• Symptom: Academic pipeline TMB systematically lower than FoundationOne by ~10-20%.
• Fix: Match the counting convention to the comparison reference; document explicitly.

4. Tumor-only TMB inflated

• Trigger: Tumor-only WES with naive germline filter (gnomAD AF > 1%).
• Mechanism: Population-specific common variants leak through if gnomAD AF threshold not stratified by ancestry.
• Symptom: AFR/EAS patient TMB inflated 1.5-3x; misclassified as TMB-H.
• Fix: Stratify gnomAD AF by patient ancestry; use grpmax FAF95; threshold <= 0.5%.

5. bTMB applied without ctDNA shed check

• Trigger: Report bTMB low in a metastatic patient.
• Mechanism: Low ctDNA shed fraction produces false-negative bTMB (BFAST Cohort C failure mechanism).
• Symptom: Patient with high tissue TMB labeled bTMB-low; ICI not offered.
• Fix: Check tumor fraction (e.g., ichorCNA, MAF of known driver) before trusting bTMB-low; consider tissue TMB.

6. TMB-H applied to breast / prostate / glioma

• Trigger: ICI prescribed for TMB-H breast cancer based on pan-tumor approval.
• Mechanism: McGrail 2021 demonstrated TMB fails to enrich for ICI response in breast, prostate, glioma.
• Symptom: ICI offered with low expectation of benefit; patient bears unnecessary toxicity.
• Fix: Apply tumor-type-specific cutoffs (Samstein 2019); document the McGrail 2021 tumor-type caveat in report.

7. Hotspots inflating TMB

• Trigger: Include BRAF V600E and KRAS G12C in TMB count.
• Mechanism: Driver hotspots are non-random; including biases TMB upward in driver-mutated samples.
• Symptom: TMB inflated in samples with strong drivers.
• Fix: Exclude COSMIC-confirmed hotspots (provide hotspot BED).

8. MSI-H -> add TMB-H -> additive ICI confidence

• Trigger: Report TMB-H as additional support for ICI in MSI-H patient.
• Mechanism: MSI-H is uniformly hypermutator + uniformly ICI-responsive; adding TMB-H is statistical tautology (Sha 2020).
• Symptom: Reviewer flag.
• Fix: Report MSI-H + TMB-H concurrence but explicitly note TMB-H is NOT additive given MSI-H.

9. Ignoring HLA-LOH

• Trigger: TMB-H + neoantigen prediction without LOH check.
• Mechanism: HLA-LOH abolishes neoantigen presentation for lost allele in ~17% pan-cancer (>30% HNSCC / NSCLC / cervical).
• Symptom: Apparent neoantigen burden inflated.
• Fix: Run LOHHLA (McGranahan 2017) or DASH (Pyke 2022); flag HLA-LOH-positive tumors.

Reconciliation: When Sources Disagree

Pattern	Likely cause	Action
Vendor TMB vs WES TMB differ 2-3x	Panel-specific scored region + counting convention	Apply Vega 2021 calibration
FoundationOne vs MSK-IMPACT same sample differ	Synonymous handling differs	Document both; cite Vega 2021
Tissue TMB vs bTMB differ	ctDNA shed fraction low; tumor heterogeneity	Trust tissue; check ctDNA fraction for bTMB confidence
TMB-H + MSI-H	Expected concurrence	MSI-H is the primary biomarker; TMB-H not additive
TMB-H + clinical PD-L1-negative	Independent biomarkers	Report both; ICI eligibility still per TMB-H pan-tumor
Patient with TMB-H but PR rate low	Tumor-type-specific cutoff; HLA-LOH	Apply Samstein 2019 cancer-specific cutoff; check HLA-LOH
POLE-exo + MMR-D	Ultra-hypermutator	ICI excellent response expected

Quantitative Thresholds and Conventions

Threshold	Convention	Source
FDA pembrolizumab pan-tumor	TMB-H >= 10 mut/Mb on FoundationOne CDx	FDA 2020
TSO500 equivalent cutoff	7.8 mut/Mb	Ramos-Paradas 2021
Oncomine TML equivalent cutoff	8.4 mut/Mb	Ramos-Paradas 2021
Hypermutator	>= 100 mut/Mb	Research convention
Ultra-hypermutator	>= 500 mut/Mb	POLE+MMR; ICI excellent
MSI-H typical TMB	30-50 mut/Mb	Research convention
MSI-H + TMB-H overlap	~83% MSI-H are TMB-H; ~16% TMB-H are MSI-H	Chalmers 2017 Genome Med 9:34
Tumor purity floor	FoundationOne >=20%; MSK-IMPACT >=30%	Vendor documentation
Min VAF	FoundationOne 5%; tumor-only no UMI 10%	Vendor documentation
Tumor-only germline filter	gnomAD AF <=0.5% (sometimes 1%)	Convention
Panel size minimum	>= 0.8 Mb workable; >= 1.0 Mb preferred; < 0.5 Mb unreliable	Vega 2021
HLA-LOH frequency	~17% pan-cancer; >30% HNSCC / NSCLC / cervical	Montesion 2021

Common Errors

Symptom	Cause	Solution
TMB much lower than FoundationOne report	Used panel total (1.1) instead of scored (0.8)	Use 0.8 Mb for FoundationOne
Academic TMB systematically lower	Excluded synonymous; FoundationOne includes	Match counting convention
AFR / EAS tumor-only TMB inflated	gnomAD AF filter EUR-only	Use grpmax FAF95; stratify by patient ancestry
bTMB negative but tissue positive	Low ctDNA shed	Use tissue TMB; check fraction
TMB-H in breast cancer with poor response	McGrail 2021 tumor-type limitation	Use tumor-type-specific cutoff
MSI-H + TMB-H reported as additive	Tautology	MSI-H is primary biomarker
POLE-exo + low TMB	Tumor sequencing artifact OR low tumor purity	Check VAF distribution; re-call if purity low
Variant counting differs across replicates	Random VAF sampling at borderline thresholds	Set explicit VAF floor + replicate-stable filter

Anticipated Reviewer Pushback

Pushback	Standard response
"Why panel-specific cutoffs?"	Vega 2021 demonstrated panel variance; the FoundationOne 10/Mb = TSO500 7.8 = Oncomine 8.4 equivalences are from the TMB2 project (Ramos-Paradas 2021). Universal 10/Mb is wrong across non-F1 platforms.
"TMB-H is supposed to be tumor-agnostic"	FDA pan-tumor approval based on KEYNOTE-158; ESMO 2024 retained it tumour-agnostic. McGrail 2021 + Samstein 2019 demonstrate tumor-type-specific limits.
"Synonymous variants?"	FoundationOne CDx counts synonymous; academic pipelines exclude. We document the counting convention and apply Vega 2021 calibration.
"Why exclude hotspots?"	Driver hotspots are non-random; including biases TMB upward in driver-mutated samples vs cohort comparator.
"Tumor-only TMB unreliable"	Acknowledged; we apply stringent gnomAD grpmax FAF95 filtering stratified by patient ancestry; report paired-normal-validated subset separately.
"Why HLA-LOH integration?"	McGranahan 2017 (LOHHLA) + Montesion 2021 show ~17% pan-cancer (>30% HNSCC / NSCLC / cervical) lose HLA via LOH; apparent neoantigen burden over-estimated without LOH check.
"bTMB?"	BFAST Cohort C failed primary endpoint (Peters 2022); bTMB is research-grade in tissue-naive only; we use tissue TMB as regulatory standard.
"Why ultra-hypermutator distinction?"	POLE+MMR (>=500 mut/Mb) shows superior ICI response per multiple case series; mechanistically distinct from MMR-D alone.

References

• Marabelle A et al. 2020. Association of TMB with efficacy of pembrolizumab in advanced solid tumours from the phase 2 KEYNOTE-158 study. Lancet Oncol 21:1353.
• Merino DM et al. 2020. Establishing guidelines to harmonize tumor mutational burden (TMB). J Immunother Cancer 8:e000147. (FoC Phase I)
• Vega DM et al. 2021. Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project. Ann Oncol 32:1626.
• Mosele MF et al. 2024. Recommendations for the use of next-generation sequencing for patients with advanced cancer in 2024. Ann Oncol 35:588. (ESMO 2024)
• McGrail DJ et al. 2021. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol 32:661.
• Sha D et al. 2020. Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov 10:1808.
• Ramos-Paradas J et al. 2021. Tumor mutational burden assessment in non-small-cell lung cancer samples: results from the TMB2 harmonization project comparing three NGS panels. J Immunother Cancer 9:e001904.
• Samstein RM et al. 2019. TMB and survival after immunotherapy across cancer types. Nat Genet 51:202.
• Chalmers ZR et al. 2017. Analysis of 100,000 human cancer genomes reveals the landscape of TMB. Genome Med 9:34.
• Yarchoan M et al. 2017. Tumor mutational burden and response rate to PD-1 inhibition. NEJM 377:2500.
• Gandara DR et al. 2018. Blood-based TMB as a predictor of response to atezolizumab in NSCLC. Nat Med 24:1441.
• Peters S et al. 2022. Atezolizumab versus chemotherapy in advanced or metastatic NSCLC with high blood-based tumor mutational burden: BFAST Cohort C. Nat Med 28:1831.
• Salem ME et al. 2018. Landscape of tumor mutation load, mismatch repair deficiency, and PD-L1 expression in a large patient cohort of gastrointestinal cancers. Mol Cancer Res 16:805.
• Luksza M et al. 2017. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551:517.
• Luksza M et al. 2022. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature 606:389.
• McGranahan N et al. 2016. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351:1463.
• McGranahan N et al. 2017. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171:1259. (LOHHLA)
• Montesion M et al. 2021. Somatic HLA class I loss is a widespread mechanism of immune evasion which refines the use of TMB as a biomarker. 11:282.

Related Skills

• clinical-databases/somatic-signatures - Mutational signatures including HRD (PARP) and MMR-D (ICI)
• clinical-databases/msi-detection - MSI-H is the related ICI biomarker
• clinical-databases/hla-typing - HLA typing for neoantigen prediction and LOH
• variant-calling/variant-calling - Mutect2 / Strelka2 somatic upstream
• variant-calling/clinical-interpretation - ACMG / AMP cancer framework