Evidence map›Paper›PMID 38062992›Full record

ArticleExperimental biology and medicine (Maywood, N.J.)2023

Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis.

Dan Li, Dong Wang, Donald J Johann, Huixiao Hong, Joshua Xu

Open access · greenAbstract read
In one paragraph

Article in Experimental biology and medicine (Maywood, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 29% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Integrating artificial intelligence with bioinformatics promotes public health.Experimental biology and medicine (Maywood, N.J.) · 2023
    Article
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

5 authors at 2 institutions in 1 country.

Dan LiDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.ORCID 0000-0001-6394-7953
Dong WangDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.
Donald J JohannWinthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Huixiao HongDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.ORCID 0000-0001-8087-3968
Joshua XuDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.ORCID 0000-0001-5313-5847
National Center for Toxicological Research · USWinthrop Rockefeller Foundation · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor mutational burden (TMB), when at a high level, is an emerging indicative factor of sensitivity to immune checkpoint inhibitors. Previous studies have shown that the more affordable and accurate targeted panels can be used to measure TMB as a substitute for whole exome sequencing (WES). However, additional processes, such as hotspot mutations exclusion and TMB adjustment, are usually required to deal with the effect of the limited panel sizes. A comprehensive investigation of the effective factors is needed for accurate TMB estimation by targeted panels. In this study, we quantitatively evaluated the variances of TMB values calculated by WES and targeted panels using 10,000 simulated targeted panels with panel sizes ranging from 0.2 to 3.1 million bases. With The Cancer Genome Atlas (TCGA) cancer samples and mutation profiles, we fixed regressions on WES-TMBs and panel-TMBs to assess the performance of a given targeted panel. Panel size was found as one of the major effective factors of TMB estimation. Meanwhile, by investigating the well-performing small panels that reported TMB values similar to those of WES, we demonstrated the evidence of the cancer type-specific impacts of genes on TMB estimation and identified high-impact gene sets for different cancer types based on the TCGA data. This study revealed the quantitative correlations between TMB variance and panel size, and the potential impacts of individual genes on TMB estimation. Our results suggested that for cancer patients diagnosed using targeted panels, it would be highly beneficial to have the capability to directly measure TMB from the targeted sequencing data. This would greatly assist in making decisions regarding the use of immunotherapies.

Indexed as

High-Throughput Nucleotide SequencingNeoplasmsBiomarkers, TumorComputer SimulationHumansMutationBiomarkers, Tumorsimulation analysistargeted panel sequencingTMBTumor mutational burden

Identifiers

PMID38062992
PMCPMC10798187
OpenAlexW4389515749

What OpenQuestion holds

Textmetadata
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.