Evidence map›Paper›PMID 19582160›Full record

ArticlePloS one2009

Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery.

Jessica Kao, Keyan Salari, Melanie Bocanegra, Yoon-La Choi, Luc Girard, Jeet Gandhi, Kevin A Kwei, Tina Hernandez-Boussard, Pei Wang, Adi F Gazdar and 2 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 438 papers.

0numbers the graph read from it
0cells of the map it votes in
438citing papers in PubMed
17.9field-weighted citation impact, top 1% 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

438 citing papers in PubMed, 794 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

378 more citing papers are in PubMed but not listed here.

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

12 authors at 5 institutions in 2 countries.

Jessica KaoDepartment of Pathology, Stanford University, Stanford, CA, USA.
Keyan Salari
Melanie Bocanegra
Yoon-La Choi
Luc Girard
Jeet Gandhi
Kevin A Kwei
Tina Hernandez-Boussard
Pei Wang
Adi F Gazdar
John D Minna
Jonathan R Pollack
Stanford University · USSouthwestern Medical Center · USFred Hutch Cancer Center · USSungkyunkwan University · KRThe University of Texas Southwestern Medical Center · US

Funding

Leveraging innovative technologies in basic and clinical cancer researchT32CA009302 · NCI · STANFORD UNIVERSITY · PI LAURA D ATTARDI, Aaron M Newman · 1985 to 2026
$19.1M
TUMOR BIOLOGYT32CA009151 · NCI · STANFORD UNIVERSITY · PI CLEARY, MICHAEL L · 1985 to 2014
$5.5M
Role of Gene Amplification in Breast CancerR01CA097139 · NCI · STANFORD UNIVERSITY · PI POLLACK, JONATHAN R · 2003 to 2007
$1.6M
NCI NIH HHS CA97139NCI NIH HHS R01 CA097139NCI NIH HHS T32 CA009151NCI NIH HHS T32 CA009302
6 · The paper itself

Abstract

backgroundBreast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes-luminal A, luminal B, ERBB2-associated, basal-like and normal-like-with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes.

methodsWhole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression.

findingsTranscriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem/progenitor-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 multi-copy deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes.

conclusionsOverall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, cancer stem cell biology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes.

Indexed as

Gene Expression ProfilingBase SequenceBlotting, WesternBreast NeoplasmsCell Line, TumorDNA PrimersFemaleGenes, BRCA1HumansImmunohistochemistryOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionRNA, MessengerTranscription, GeneticDNA PrimersRNA, Messenger

Identifiers

PMID19582160
PMCPMC2702084
OpenAlexW2066122607

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.