Evidence map›Paper›PMID 23930139›Full record

ArticleHormone molecular biology and clinical investigation2011

Endoplasmic reticulum stress, the unfolded protein response, and gene network modeling in antiestrogen resistant breast cancer.

Robert Clarke, Ayesha N Shajahan, Yue Wang, John J Tyson, Rebecca B Riggins, Louis M Weiner, William T Bauman, Jianhua Xuan, Bai Zhang, Caroline Facey and 9 more

Open access · greenAbstract read
In one paragraph

Article in Hormone molecular biology and clinical investigation, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

34 citing papers in PubMed, 55 citations in OpenAlex.

  1. Review
  2. Review
  3. Caffeine alleviates acute liver injury by inducing the expression of NEDD4L and deceasing GRP78 level via ubiquitination.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2022
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  18. Lifetime Genistein Intake Increases the Response of Mammary Tumors to Tamoxifen in Rats.Clinical cancer research : an official journal of the American Association for Cancer Research · 2017
    Article
  19. Article
  20. Endocrine resistance in breast cancer--An overview and update.Molecular and cellular endocrinology · 2015
    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

19 authors at 3 institutions in 1 country.

Robert ClarkeDepartment of Oncology, Georgetown University School of Medicine, Washington, DC 20057, U.S.A. ; Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC 20057, U.S.A.
Ayesha N Shajahan
Yue Wang
John J Tyson
Rebecca B Riggins
Louis M Weiner
William T Bauman
Jianhua Xuan
Bai Zhang
Caroline Facey
Harini Aiyer
Katherine Cook
F Edward Hickman
Iman Tavassoly
Anael Verdugo
Chun Chen
Alan Zwart
Anni Wärri
Leena A Hilakivi-Clarke
Virginia Tech · USGeorgetown University · USGeorgetown University Medical Center · US

Funding

Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI STEPHEN W BYERS · 1990 to 2026
$71.5M
TRAINING GRANT IN TUMOR BIOLOGYT32CA009686 · NCI · GEORGETOWN UNIVERSITY · PI ANNA Tate RIEGEL, DAVID J ROBBINS · 1996 to 2026
$10.8M
Novel Targets in Endocrine ResponsivenessU54CA149147 · NCI · GEORGETOWN UNIVERSITY · PI CLARKE, ROBERT R. · 2010 to 2014
$6.9M
Endocrine resistance in breast cancerR01CA131465 · NCI · GEORGETOWN UNIVERSITY · PI CLARKE, ROBERT R. · 2009 to 2013
$1.7M
Uncovering Estrogen Receptor-Signaling Networks to Overcome Endocrine ResistanceR01CA149653 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI XUAN, JASON JIANHUA · 2011 to 2015
$1.5M
Network-based Prediction of Antiestrogen Resistance in Breast CancerR21CA139246 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI XUAN, JASON JIANHUA · 2009 to 2010
$381k
NCI NIH HHS P30 CA051008NCI NIH HHS R01 CA131465NCI NIH HHS R01 CA149653NCI NIH HHS R21 CA139246NCI NIH HHS T32 CA009686NCI NIH HHS U54 CA149147
6 · The paper itself

Abstract

Lack of understanding of endocrine resistance remains one of the major challenges for breast cancer researchers, clinicians, and patients. Current reductionist approaches to understanding the molecular signaling driving resistance have offered mostly incremental progress over the past 10 years. As the field of systems biology has begun to mature, the approaches and network modeling tools being developed and applied therein offer a different way to think about how molecular signaling and the regulation of critical cellular functions are integrated. To gain novel insights, we first describe some of the key challenges facing network modeling of endocrine resistance, many of which arise from the properties of the data spaces being studied. We then use activation of the unfolded protein response (UPR) following induction of endoplasmic reticulum stress in breast cancer cells by antiestrogens, to illustrate our approaches to computational modeling. Activation of UPR is a key determinant of cell fate decision making and regulation of autophagy and apoptosis. These initial studies provide insight into a small subnetwork topology obtained using differential dependency network analysis and focused on the UPR gene XBP1. The XBP1 subnetwork topology incorporates BCAR3, BCL2, BIK, NFκB, and other genes as nodes; the connecting edges represent the dependency structures amongst these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.

Indexed as

Antiestrogenapoptosisautophagybreast cancercell signalingcomputational modelingendoplasmic reticulumestrogensgene networksmathematical modelingsystems biologyunfolded protein response

Identifiers

PMID23930139
PMCPMC3734561
OpenAlexW2084537120

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.