Evidence map›Paper›PMID 22411897›Full record

ArticleMolecular cancer therapeutics2012

Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition.

Kian Kani, Vitor M Faca, Lindsey D Hughes, Wenxuan Zhang, Qiaojun Fang, Babak Shahbaba, Roland Luethy, Jonathan Erde, Joanna Schmidt, Sharon J Pitteri and 9 more

Open access · greenAbstract read
In one paragraph

Article in Molecular cancer therapeutics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 39% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. A blood biomarker for monitoring response to anti-EGFR therapy.Cancer biomarkers : section A of Disease markers · 2018
    Article
  3. Review
  4. 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

19 authors at 4 institutions in 1 country.

Kian KaniUniversity of Southern California, Los Angeles, CA 90033, USA.
Vitor M Faca
Lindsey D Hughes
Wenxuan Zhang
Qiaojun Fang
Babak Shahbaba
Roland Luethy
Jonathan Erde
Joanna Schmidt
Sharon J Pitteri
Qing Zhang
Jonathan E Katz
Mitchell E Gross
Sylvia K Plevritis
Martin W McIntosh
Anjali Jain
Samir Hanash
David B Agus
Parag Mallick
Cedars-Sinai Medical Center · USStanford University · USFred Hutch Cancer Center · USUniversity of Southern California · US

Funding

Stanford Nanocharacterization LaboratoryU54CA119367 · NCI · STANFORD UNIVERSITY · PI GAMBHIR, SANJIV S · 2006 to 2010
$19.8M
TRANS-NETWORK PROJECTSU54CA143907 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NOLAN, GARRY P · 2009 to 2013
$15.2M
Pilot ProjectsU54CA151459 · NCI · STANFORD UNIVERSITY · PI GAMBHIR, SANJIV S · 2010 to 2014
$12.6M
(PQB6)An Integrative Computational and Bioengineered Tissue Model of MetastasisR01CA180149 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AGUS, DAVID B, ATALA, ANTHONY · 2013 to 2016
$2.3M
NCI NIH HHS 1U54CA151459NCI NIH HHS 5U54CA119367NCI NIH HHS 5U54CA143907NCI NIH HHS R01 CA180149NCI NIH HHS U54 CA119367NCI NIH HHS U54 CA143907NCI NIH HHS U54 CA151459
6 · The paper itself

Abstract

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response.

Indexed as

ProteomeAnimalsAntineoplastic AgentsCell Line, TumorDrug Resistance, NeoplasmErbB ReceptorsFemaleGefitinibHumansMiceMice, Inbred BALB CMice, NudeProtein Kinase InhibitorsProteomicsQuinazolinesReproducibility of ResultsAntineoplastic AgentsErbB ReceptorsGefitinibProtein Kinase InhibitorsProteomeQuinazolines

Identifiers

PMID22411897
PMCPMC3959865
OpenAlexW2004251320

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.