Evidence map›Paper›PMID 19118495›Full record

ArticleBMC systems biology2009

Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance.

Ozgür Sahin, Holger Fröhlich, Christian Löbke, Ulrike Korf, Sara Burmester, Meher Majety, Jens Mattern, Ingo Schupp, Claudine Chaouiya, Denis Thieffry and 4 more

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed
10.3field-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

73 citing papers in PubMed, 347 citations in OpenAlex.

  1. Article
  2. Article
  3. Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
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  6. Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022
    Article
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  15. Review
  16. Article
  17. Article
  18. Review
  19. Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018
    Review
  20. Article

13 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

14 authors at 5 institutions in 2 countries.

Ozgür SahinGerman Cancer Research Center, Heidelberg, Germany. oe.sahin@dkfz-heidelberg.de
Holger Fröhlich
Christian Löbke
Ulrike Korf
Sara Burmester
Meher Majety
Jens Mattern
Ingo Schupp
Claudine Chaouiya
Denis Thieffry
Annemarie Poustka
Stefan Wiemann
Tim Beissbarth
Dorit Arlt
German Cancer Research Center · DEHeidelberg University · DEInserm · FRMundipharma (Germany) · DERoche Pharma AG (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.

resultsFirst, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.

conclusionIn this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.

Indexed as

Models, BiologicalAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedBlotting, WesternBreast NeoplasmsCell Line, TumorComputer SimulationDNA-Binding ProteinsDrug Delivery SystemsErbB ReceptorsFemaleG1 PhaseHumansProtein EngineeringReverse Transcriptase Polymerase Chain ReactionSignal TransductionAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedDNA-Binding ProteinsEGFR protein, humanErbB ReceptorsMYCBP protein, humanTranscription FactorsTrastuzumab

Identifiers

PMID19118495
PMCPMC2652436
OpenAlexW2042459594

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.