Evidence map›Paper›PMID 23559098›Full record

ArticleIntegrative biology : quantitative biosciences from nano to macro2013

Hormonally responsive breast cancer cells in a microfluidic co-culture model as a sensor of microenvironmental activity.

Jessica D Lang, Scott M Berry, Ginny L Powers, David J Beebe, Elaine T Alarid

Abstract read
In one paragraph

Article in Integrative biology : quantitative biosciences from nano to macro, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

19 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Review
  3. Breast tumor-on-chip models: From disease modeling to personalized drug screening.Journal of controlled release : official journal of the Controlled Release Society · 2021
    Review
  4. Review
  5. Article
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  13. Progress towards understanding heterotypic interactions in multi-culture models of breast cancer.Integrative biology : quantitative biosciences from nano to macro · 2016
    Review
  14. Review
  15. Article
  16. Article
  17. Microfluidic organs-on-chips.Nature biotechnology · 2014
    Article
  18. Article
  19. Streamlining gene expression analysis: integration of co-culture and mRNA purification.Integrative biology : quantitative biosciences from nano to macro · 2014
    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.

Jessica D LangUniversity of Wisconsin-Madison Carbone Comprehensive Cancer Center, Madison, WI 53705, USA.
Scott M Berry
Ginny L Powers
David J Beebe
Elaine T Alarid
University of Wisconsin–Madison · USUniversity of Wisconsin Carbone Cancer Center · US

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
Training in Cancer Biology Training GrantT32CA009135 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI SUGDEN, WILLIAM M. · 1985 to 2024
$10.4M
Estrogen Receptor Alpha Proteostasis in Breast CancerR01CA159578 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI ALARID, ELAINE T · 2011 to 2015
$1.5M
Microchannel cell-based assays to enable cancer researchR33CA137673 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BEEBE, DAVID J · 2009 to 2011
$1.4M
Integrated Micro Scale transcriptional profiling of cell communication networksR33CA160344 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI ALARID, ELAINE T, BEEBE, DAVID J · 2011 to 2013
$818k
NCI NIH HHS CA137673NCI NIH HHS CA159578NCI NIH HHS P30 CA014520NCI NIH HHS R01 CA159578NCI NIH HHS R33 CA137673NCI NIH HHS R33 CA160344NCI NIH HHS T32 CA009135
6 · The paper itself

Abstract

Breast cancer cell growth and therapeutic response are manipulated extrinsically by microenvironment signals. Despite recognition of the importance of the microenvironment in a variety of tumor processes, predictive measures that incorporate the activity of the surrounding cellular environment are lacking. In contrast, tumor cell biomarkers are well established in the clinic. Expression of Estrogen Receptor-alpha (ERα) is the primary defining feature of hormonally responsive tumors and is the molecular target of therapy in the most commonly diagnosed molecular subtype of breast cancer. While a number of soluble factors have been implicated in ERα activation, the complexity of signaling between the cellular microenvironment and the cancer cell implies multivariate control. The cumulative impact of the microenvironment signaling, which we define as microenvironmental activity, is more difficult to predict than the sum of its parts. Here we tested the impact of an array of microenvironments on ERα signaling utilizing a microfluidic co-culture model. Quantitative immunofluorescence was employed to assess changes in ERα protein levels, combined with gene expression and phosphorylation status, as measures of activation. Analysis of microenvironment-induced growth under the same conditions revealed a previously undescribed correlation between growth and ERα protein down-regulation. These data suggest an expanded utility for the tumor biomarker ERα, in which the combination of dynamic regulation of ERα protein and growth in a breast cancer biosensor cell become a read-out of the microenvironmental activity.

Indexed as

Biomarkers, TumorBreast NeoplasmsCell Line, TumorCoculture TechniquesEstrogen Receptor alphaFemaleGene Expression Regulation, NeoplasticHumansMicrofluidicsMicroscopy, FluorescenceNeoplasms, Hormone-DependentSignal TransductionBiomarkers, TumorESR1 protein, humanEstrogen Receptor alpha

Identifiers

PMID23559098
PMCPMC3648339
OpenAlexW2077583083

What OpenQuestion holds

Textmetadata
LicenceTDM
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.