Literature DB >> 22411897

Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition.

Kian Kani1, 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.   

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. ©2012 AACR

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Year:  2012        PMID: 22411897      PMCID: PMC3959865          DOI: 10.1158/1535-7163.MCT-11-0852

Source DB:  PubMed          Journal:  Mol Cancer Ther        ISSN: 1535-7163            Impact factor:   6.261


  44 in total

1.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

2.  DAVID: Database for Annotation, Visualization, and Integrated Discovery.

Authors:  Glynn Dennis; Brad T Sherman; Douglas A Hosack; Jun Yang; Wei Gao; H Clifford Lane; Richard A Lempicki
Journal:  Genome Biol       Date:  2003-04-03       Impact factor: 13.583

3.  A statistical model for identifying proteins by tandem mass spectrometry.

Authors:  Alexey I Nesvizhskii; Andrew Keller; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2003-09-01       Impact factor: 6.986

4.  Glycosylation-induced conformational modification positively regulates receptor-receptor association: a study with an aberrant epidermal growth factor receptor (EGFRvIII/DeltaEGFR) expressed in cancer cells.

Authors:  H Fernandes; S Cohen; S Bishayee
Journal:  J Biol Chem       Date:  2000-11-21       Impact factor: 5.157

5.  Human epidermal growth factor receptor cDNA sequence and aberrant expression of the amplified gene in A431 epidermoid carcinoma cells.

Authors:  A Ullrich; L Coussens; J S Hayflick; T J Dull; A Gray; A W Tam; J Lee; Y Yarden; T A Libermann; J Schlessinger
Journal:  Nature       Date:  1984 May 31-Jun 6       Impact factor: 49.962

6.  Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib.

Authors:  Thomas J Lynch; Daphne W Bell; Raffaella Sordella; Sarada Gurubhagavatula; Ross A Okimoto; Brian W Brannigan; Patricia L Harris; Sara M Haserlat; Jeffrey G Supko; Frank G Haluska; David N Louis; David C Christiani; Jeff Settleman; Daniel A Haber
Journal:  N Engl J Med       Date:  2004-04-29       Impact factor: 91.245

7.  Tyrosine kinase inhibitors: why does the current process of clinical development not apply to them?

Authors:  Carlos L Arteaga; Jose Baselga
Journal:  Cancer Cell       Date:  2004-06       Impact factor: 31.743

8.  FDA drug approval summary: gefitinib (ZD1839) (Iressa) tablets.

Authors:  Martin H Cohen; Grant A Williams; Rajeshwari Sridhara; Gang Chen; Richard Pazdur
Journal:  Oncologist       Date:  2003

9.  Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways.

Authors:  Raffaella Sordella; Daphne W Bell; Daniel A Haber; Jeffrey Settleman
Journal:  Science       Date:  2004-07-29       Impact factor: 47.728

10.  Protein expression signatures for inhibition of epidermal growth factor receptor-mediated signaling.

Authors:  Matthew V Myers; H Charles Manning; Robert J Coffey; Daniel C Liebler
Journal:  Mol Cell Proteomics       Date:  2011-12-05       Impact factor: 5.911

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  2 in total

1.  Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor.

Authors:  Hermann B Frieboes; Louis T Curtis; Min Wu; Kian Kani; Parag Mallick
Journal:  Cancer Inform       Date:  2015-12-20

Review 2.  Harnessing Integrative Omics to Facilitate Molecular Imaging of the Human Epidermal Growth Factor Receptor Family for Precision Medicine.

Authors:  Martin Pool; H Rudolf de Boer; Marjolijn N Lub-de Hooge; Marcel A T M van Vugt; Elisabeth G E de Vries
Journal:  Theranostics       Date:  2017-05-27       Impact factor: 11.556

  2 in total

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