Evidence map›Paper›PMID 22848369›Full record

ArticlePloS one2012

Concordance of gene expression and functional correlation patterns across the NCI-60 cell lines and the Cancer Genome Atlas glioblastoma samples.

Barry R Zeeberg, Kurt W Kohn, Ari Kahn, Vladimir Larionov, John N Weinstein, William Reinhold, Yves Pommier

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Review
  6. Microarray Analysis in Glioblastomas.Methods in molecular biology (Clifton, N.J.) · 2016
    Article
  7. Review
  8. Article
  9. Cell line modeling for systems medicine in cancers (review).International journal of oncology · 2014
    Review
  10. Article
  11. A self-updating road map of The Cancer Genome Atlas.Bioinformatics (Oxford, England) · 2013
    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

7 authors.

Barry R ZeebergLaboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America. zeebergb@mail.nih.gov
Kurt W Kohn
Ari Kahn
Vladimir Larionov
John N Weinstein
William Reinhold
Yves Pommier

Funding

DNA Repair, Cell Cycle Checkpoints and Apoptosis as Targets for Anticancer DrugsZIABC006150 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI POMMIER, YVES · 2009 to 2025
$23.0M
Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)U24CA143883 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AKBANI, REHAN, MILLS, GORDON B. · 2009 to 2016
$10.6M
Investigating Regulatory Networks that Control Cell Proliferation and ApoptosisZIABC006192 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KOHN, KURT · 2009 to 2014
$3.8M
Intramural NIH HHSNCI NIH HHS U24 CA143883NCI NIH HHS U24CA143883
6 · The paper itself

Abstract

backgroundThe NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. We recently clustered genes based on correlation of expression profiles across the NCI-60. Many of the resulting clusters were characterized by cancer-associated biological functions. The set of curated glioblastoma (GBM) gene expression data from the Cancer Genome Atlas (TCGA) initiative has recently become available. Thus, we are now able to determine which of the processes are robustly shared by both the immortalized cell lines and clinical cancers.

resultsOur central observation is that some sets of highly correlated genes in the NCI-60 expression data are also highly correlated in the GBM expression data. Furthermore, a "double fishing" strategy identified many sets of genes that show Pearson correlation ≥0.60 in both the NCI-60 and the GBM data sets relative to a given "bait" gene. The number of such gene sets far exceeds the number expected by chance.

conclusionMany of the gene-gene correlations found in the NCI-60 do not reflect just the conditions of cell lines in culture; rather, they reflect processes and gene networks that also function in vivo. A number of gene network correlations co-occur in the NCI-60 and GBM data sets, but there are others that occur only in NCI-60 or only in GBM. In sum, this analysis provides an additional perspective on both the utility and the limitations of the NCI-60 in furthering our understanding of cancers in vivo.

Indexed as

Databases, GeneticGene Expression Regulation, NeoplasticGenes, NeoplasmGenome, HumanCell Line, TumorGene Expression ProfilingGlioblastomaHumansNational Cancer Institute (U.S.)United States

Identifiers

PMID22848369
PMCPMC3406063

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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.