Evidence map›Paper›PMID 30792906›Full record

ArticleESMO open2019

Implementing TMB measurement in clinical practice: considerations on assay requirements.

Reinhard Büttner, John W Longshore, Fernando López-Ríos, Sabine Merkelbach-Bruse, Nicola Normanno, Etienne Rouleau, Frédérique Penault-Llorca

Open access · goldAbstract read
In one paragraph

Article in ESMO open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
206citing papers in PubMed, 4 pooled it
38.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

206 citing papers in PubMed, 4 syntheses or guidelines pooled it, 349 citations in OpenAlex.

  1. Pooled it
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  4. Updated guidelines for predictive biomarker testing in advanced non-small-cell lung cancer: a National Consensus of the Spanish Society of Pathology and the Spanish Society of Medical Oncology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2020
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  13. Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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  14. Review
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146 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 4 countries.

Reinhard BüttnerInstitute of Pathology, University Hospital Cologne, Cologne, Germany.
John W LongshoreAtrium Health, Carolinas Pathology Group, Charlotte, North Carolina, USA.
Fernando López-RíosLaboratorio de Dianas Terapéuticas, Hospital Universitario HM Sanchinarro, Madrid, Spain.
Sabine Merkelbach-BruseInstitute of Pathology, University Hospital Cologne, Cologne, Germany.
Nicola NormannoCell Biology and Biotherapy Unit, Istituto Nazionale Tumori 'Fondazione Giovanni Pascale' IRCCS, Naples, Italy.
Etienne RouleauDepartment of Pathology and Medical Biology, Institut Gustave Roussy, Villejuif, France.
Frédérique Penault-LlorcaDepartment of Biopathology, Centre Jean Perrin, Clermont-Ferrand, France.
University Hospital Cologne · DECentre Jean Perrin · FRHospital Universitario HM Sanchinarro · ESInstitut Gustave Roussy · FRIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale" · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical evidence demonstrates that treatment with immune checkpoint inhibitor immunotherapy agents can have considerable benefit across multiple tumours. However, there is a need for the development of predictive biomarkers that identify patients who are most likely to respond to immunotherapy. Comprehensive characterisation of tumours using genomic, transcriptomic, and proteomic approaches continues to lead the way in advancing precision medicine. Genetic correlates of response to therapy have been known for some time, but recent clinical evidence has strengthened the significance of high tumour mutational burden (TMB) as a biomarker of response and hence a rational target for immunotherapy. Concordantly, immune checkpoint inhibitors have changed clinical practice for lung cancer and melanoma, which are tumour types with some of the highest mutational burdens. TMB is an implementable approach for molecular biology and/or pathology laboratories that provides a quantitative measure of the total number of mutations in tumour tissue of patients and can be assessed by whole genome, whole exome, or large targeted gene panel sequencing of biopsied material. Currently, TMB assessment is not standardised across research and clinical studies. As a biomarker that affects treatment decisions, it is essential to unify TMB assessment approaches to allow for reliable, comparable results across studies. When implementing TMB measurement assays, it is important to consider factors that may impact the method workflow, the results of the assay, and the interpretation of the data. Such factors include biopsy sample type, sample quality and quantity, genome coverage, sequencing platform, bioinformatic pipeline, and the definitions of the final threshold that determines high TMB. This review outlines the factors for adoption of TMB measurement into clinical practice, providing an understanding of TMB assay considerations throughout the sample journey, and suggests principles to effectively implement TMB assays in a clinical setting to aid and optimise treatment decisions.

Indexed as

assay implementationimmune checkpoint inhibitorimmunotherapynext-generation sequencingTumor mutational burden

Identifiers

PMID30792906
PMCPMC6350758
OpenAlexW2913484031

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.