Evidence map›Paper›PMID 39091515›Full record

ArticleNAR cancer2024

CytoCellDB: a comprehensive resource for exploring extrachromosomal DNA in cancer cell lines.

Jacob Fessler, Stephanie Ting, Hong Yi, Santiago Haase, Jingting Chen, Saygin Gulec, Yue Wang, Nathan Smyers, Kohen Goble, Danielle Cannon and 3 more

Abstract read
In one paragraph

Article in NAR cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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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

13 authors.

Jacob FesslerDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Stephanie TingDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Hong YiRenaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Santiago HaaseIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina, Chapel Hill, USA.
Jingting ChenDepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Saygin GulecCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Yue WangDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Nathan SmyersCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Kohen GobleDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Danielle CannonDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Aarav MehtaDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
Christina FordIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina, Chapel Hill, USA.
Elizabeth BrunkDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.ORCID https://orcid.org/0000-0001-8578-8658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, the cancer community has gained a heightened awareness of the roles of extrachromosomal DNA (ecDNA) in cancer proliferation, drug resistance and epigenetic remodeling. However, a hindrance to studying ecDNA is the lack of available cancer model systems that express ecDNA. Increasing our awareness of which model systems express ecDNA will advance our understanding of fundamental ecDNA biology and unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations for cell lines within the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). We identify 139 cell lines that express ecDNA, a 200% increase from what is currently known. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase, covering 31% of cell lines in CCLE/DepMap. We experimentally validate several cell lines that we predict express ecDNA or homogeneous staining regions (HSRs). We demonstrate that CytoCellDB can be used to characterize aneuploidy alongside other molecular phenotypes, (gene essentialities, drug sensitivities, gene expression). We anticipate that CytoCellDB will advance cytogenomics research as well as provide insights into strategies for developing therapeutics that overcome ecDNA-driven drug resistance.

Identifiers

PMID39091515
PMCPMC11292414

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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.