Evidence mapPaperPMID 31250328Full record

ArticleMolecular diagnosis & therapy2019

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer.

Han Chang, Ariella Sasson, Sujaya Srinivasan, Ryan Golhar, Danielle M Greenawalt, William J Geese, George Green, Kim Zerba, Stefan Kirov, Joseph Szustakowski

Abstract read
In one paragraph

Article in Molecular diagnosis & therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
field-weighted citation impact
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

36 citing papers in PubMed.

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  7. Neoadjuvant Nivolumab for Patients With Resectable Merkel Cell Carcinoma in the CheckMate 358 Trial.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2020
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  8. Randomized Phase II Trial and Tumor Mutational Spectrum Analysis from Cabozantinib versus Chemotherapy in Metastatic Uveal Melanoma (Alliance A091201).Clinical cancer research : an official journal of the American Association for Cancer Research · 2020
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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

10 authors.

Han ChangTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Ariella SassonTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Sujaya SrinivasanTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Ryan GolharTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Danielle M GreenawaltTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
William J GeeseTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
George GreenTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Kim ZerbaGlobal Biometric Sciences, Bristol-Myers Squibb, Princeton, NJ, USA.
Stefan KirovTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA.
Joseph SzustakowskiTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA. Joseph.Szustakowski@bms.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment.

objectiveHere we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial.

methodsIn the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne

resultsTMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman's r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman's r = 0.90).

conclusionsParameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

Indexed as

Biomarkers, TumorComputational BiologyMutationCarcinoma, Non-Small-Cell LungExome SequencingGenetic Association StudiesGenetic Predisposition to DiseaseHumansLung NeoplasmsPrognosisReproducibility of ResultsWorkflowBiomarkers, Tumor

Identifiers

PMID31250328
PMCPMC6675777

What Socratic holds

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Registered trials

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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 Socratic graph.