Evidence map›Paper›PMID 30865299›Full record

ArticleCancer2019

Ancestral characterization of 1018 cancer cell lines highlights disparities and reveals gene expression and mutational differences.

Michael D Kessler, Nicholas W Bateman, Thomas P Conrads, George L Maxwell, Julie C Dunning Hotopp, Timothy D O'Connor

Abstract read
In one paragraph

Article in Cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Microbiology and molecular biology reviews : MMBR · 2026
    Review
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  9. Tackling the lack of diversity in cancer research.Disease models & mechanisms · 2023
    Article
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  15. Article
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  17. Article
  18. MicroRNAs and Cancer Racial Disparities.Wiley interdisciplinary reviews. RNA
    Review
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

6 authors.

Michael D KesslerInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland.ORCID 0000-0003-1258-5221
Nicholas W BatemanGynecologic Cancer Center of Excellence, Department of Obstetrics and Gynecology and the John P. Murtha Cancer Center, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland.ORCID 0000-0002-4425-9511
Thomas P ConradsGynecologic Cancer Center of Excellence, Department of Obstetrics and Gynecology and the John P. Murtha Cancer Center, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland.
George L MaxwellGynecologic Cancer Center of Excellence, Department of Obstetrics and Gynecology and the John P. Murtha Cancer Center, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland.
Julie C Dunning HotoppInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland.
Timothy D O'ConnorInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland.

Funding

University of Maryland NIH Data Commons Facilitation CenterOT3OD025459 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI WHITE, OWEN R · 2017 to 2023
$23.5M
Training Grant in Cancer BiologyT32CA154274 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Toni M Antalis, CURT I CIVIN · 2011 to 2026
$6.8M
Extent and Significance of Bacterial DNA Integrations in the Human Cancer GenomeR01CA206188 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI DUNNING HOTOPP, JULIE · 2015 to 2020
$3.6M
High-performance mixed model toolset for integrative omics analysis of big dataU01HL137181 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI O'CONNELL, JEFFREY R · 2017 to 2019
$1.8M
NCI NIH HHS R01 CA206188NCI NIH HHS T32 CA154274NHLBI NIH HHS U01 HL137181NIH HHS OT3 OD025459
6 · The paper itself

Abstract

backgroundAlthough cell lines are an essential resource for studying cancer biology, many are of unknown ancestral origin, and their use may not be optimal for evaluating the biology of all patient populations.

methodsAn admixture analysis was performed using genome-wide chip data from the Catalogue of Somatic Mutations in Cancer (COSMIC) Cell Lines Project to calculate genetic ancestry estimates for 1018 cancer cell lines. After stratifying the analyses by tissue and histology types, linear models were used to evaluate the influence of ancestry on gene expression and somatic mutation frequency.

resultsFor the 701 cell lines with unreported ancestry, 215 were of East Asian origin, 30 were of African or African American origin, and 453 were of European origin. Notable imbalances were observed in ancestral representation across tissue type, with the majority of analyzed tissue types having few cell lines of African American ancestral origin, and with Hispanic and South Asian ancestry being almost entirely absent across all cell lines. In evaluating gene expression across these cell lines, expression levels of the genes neurobeachin line 1 (NBEAL1), solute carrier family 6 member 19 (SLC6A19), HEAT repeat containing 6 (HEATR6), and epithelial cell transforming 2 like (ECT2L) were associated with ancestry. Significant differences were also observed in the proportions of somatic mutation types across cell lines with varying ancestral proportions.

conclusionsBy estimating genetic ancestry for 1018 cancer cell lines, the authors have produced a resource that cancer researchers can use to ensure that their cell lines are ancestrally representative of the populations they intend to affect. Furthermore, the novel ancestry-specific signal identified underscores the importance of ancestral awareness when studying cancer.

Indexed as

Genetics, PopulationHealth Status DisparitiesMutationAsian PeopleBiomarkers, TumorBlack or African AmericanCell Line, TumorEthnicityFemaleGene Expression ProfilingGenetic Predisposition to DiseaseHumansMaleMiddle AgedNeoplasmsPolymorphism, Single NucleotideBiomarkers, TumoradmixtureAfrican ancestryAsian ancestrycancer genomicsclinical geneticsgenomic ancestrypopulation geneticsprecision medicine

Identifiers

PMID30865299
PMCPMC6541501

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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 OpenQuestion graph.