Evidence map›Paper›PMID 18412405›Full record

ArticleBiotechnology progress

Role of network branching in eliciting differential short-term signaling responses in the hypersensitive epidermal growth factor receptor mutants implicated in lung cancer.

Jeremy Purvis, Vibitha Ilango, Ravi Radhakrishnan

Open access · greenAbstract read
In one paragraph

Article in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

15 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Structural systems biology and multiscale signaling models.Annals of biomedical engineering · 2012
    Review
  9. Article
  10. Article
  11. Article
  12. Multiscale cancer modeling.Annual review of biomedical engineering · 2011
    Review
  13. Article
  14. Cancer Cell: Linking Oncogenic Signaling to Molecular Structure.Chapman & Hall/CRC mathematical & computational biology series · 2011
    Article
  15. 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

3 authors at 1 institution in 1 country.

Jeremy PurvisCenter for Bioinformatics, University of Pennsylvania, 240 Skirkanich Hall, 210 S. 33rd Street, Philadelphia, Pennsylvania 19104, USA.
Vibitha Ilango
Ravi Radhakrishnan
University of Pennsylvania · US

Funding

YEAST ER MOLECULAR CHAPERONE TRANSLOCATION: GENOME DATABANK HOMOLOGSP41RR006009 · NCRR · MELLON PITTS CORPORATION (MPC CORP) · PI KNOLL, ERIC · 1990 to 2011
$22.0M
NCRR NIH HHS P41 RR006009
6 · The paper itself

Abstract

We study the effects of EGFR inhibition in wild-type and mutant cell lines upon tyrosine kinase inhibitor TKI treatment through a systems level deterministic and spatially homogeneous model to help characterize the hypersensitive response of the cancer cell lines harboring constitutively active mutant kinases to inhibitor treatment. By introducing a molecularly resolved branched network systems model (the molecular resolution is introduced for EGFR reactions and interactions in order to distinguish differences in activation between wild-type and mutants), we are able to quantify differences in (1) short-term signaling in downstream ERK and Akt activation, (2) the changes in the cellular inhibition EC50 associated with receptor phosphorylation (i.e., 50% inhibition of receptor phosphorylation in the cellular context), and (3) EC50 for the inhibition of activated downstream markers ERK-(p) and Akt-(p), where (p) denotes phosphorylated, upon treatment with the inhibitors in cell lines carrying both wild-type and mutant forms of the receptor. Using the branched signaling model, we illustrate a possible mechanism for preferential Akt activation in the cell lines harboring the oncogenic mutants of EGFR implicated in non-small-cell lung cancer and the enhanced efficacy of the inhibitor erlotinib especially in ablating the cellular Akt-(p) response. Using a simple phenomenological model to describe the effect of Akt activation on cellular decisions, we discuss how this preferential Akt activation is conducive to cellular oncogene addiction and how its disruption can lead to dramatic apoptotic response and hence remarkable inhibitor efficacies. We also identify key network nodes of our branched signaling model through sensitivity analysis as those rendering the network hypersensitive to enhanced ERK-(p) and Akt-(p); intriguingly, the identified nodes have a strong correlation with species implicated in oncogenic transformations in human cancers as well as in drug resistance mechanisms identified for the inhibitors in non-small-cell lung cancer therapy.

Indexed as

Models, BiologicalAnimalsAntineoplastic AgentsComputer SimulationDose-Response Relationship, DrugErbB ReceptorsHumansLung NeoplasmsProtein-Tyrosine KinasesSignal TransductionAntineoplastic AgentsErbB ReceptorsProtein-Tyrosine Kinases

Identifiers

PMID18412405
PMCPMC2803016
OpenAlexW2123776480

What OpenQuestion holds

Textmetadata
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.