Literature DB >> 18676365

Chemical and pathway proteomics: powerful tools for oncology drug discovery and personalized health care.

Ulrich Kruse1, Marcus Bantscheff, Gerard Drewes, Carsten Hopf.   

Abstract

In recent years mass spectrometry-based proteomics has moved beyond a mere quantitative description of protein expression levels and their possible correlation with disease or drug action. Impressive progress in LC-MS instrumentation together with the availability of new enabling tools and methods for quantitative proteome analysis and for identification of posttranslational modifications has triggered a surge of chemical and functional proteomics studies dissecting mechanisms of action of cancer drugs and molecular mechanisms that modulate signal transduction pathways. Despite the tremendous progress that has been made in the field, major challenges, relating to sensitivity, dynamic range, and throughput of the described methods, remain. In this review we summarize recent advances in LC-MS-based approaches and their application to cancer drug discovery and to studies of cancer-related pathways in cell culture models with particular emphasis on mechanistic studies of drug action in these systems. Moreover we highlight the emerging utility of pathway and chemical proteomics techniques for translational research in patient tissue.

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Year:  2008        PMID: 18676365     DOI: 10.1074/mcp.R800006-MCP200

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  12 in total

1.  Cardioproteomics: advancing the discovery of signaling mechanisms involved in cardiovascular diseases.

Authors:  Ziyou Cui; Shannamar Dewey; Aldrin V Gomes
Journal:  Am J Cardiovasc Dis       Date:  2011-09-10

2.  Affinity-based profiling of dehydrogenase subproteomes.

Authors:  Xia Ge; Daniel S Sem
Journal:  Methods Mol Biol       Date:  2012

Review 3.  Target profiling of small molecules by chemical proteomics.

Authors:  Uwe Rix; Giulio Superti-Furga
Journal:  Nat Chem Biol       Date:  2009-09       Impact factor: 15.040

4.  Proteomics strategy for quantitative protein interaction profiling in cell extracts.

Authors:  Kirti Sharma; Christoph Weber; Michaela Bairlein; Zoltán Greff; György Kéri; Jürgen Cox; Jesper V Olsen; Henrik Daub
Journal:  Nat Methods       Date:  2009-09-13       Impact factor: 28.547

5.  Personalized medicine: identifying the appropriate patient through biomarkers in oncology.

Authors: 
Journal:  P T       Date:  2011-07

6.  Biomarker Validation for Aging: Lessons from mtDNA Heteroplasmy Analyses in Early Cancer Detection.

Authors:  Peter E Barker; Mahadev Murthy
Journal:  Biomark Insights       Date:  2009-11-27

7.  Quantitative analysis of cell surface membrane proteins using membrane-impermeable chemical probe coupled with 18O labeling.

Authors:  Haizhen Zhang; Roslyn N Brown; Wei-Jun Qian; Matthew E Monroe; Samuel O Purvine; Ronald J Moore; Marina A Gritsenko; Liang Shi; Margaret F Romine; James K Fredrickson; Ljiljana Pasa-Tolić; Richard D Smith; Mary S Lipton
Journal:  J Proteome Res       Date:  2010-05-07       Impact factor: 4.466

8.  A miniaturized chemical proteomic approach for target profiling of clinical kinase inhibitors in tumor biopsies.

Authors:  Ivo Chamrád; Uwe Rix; Alexey Stukalov; Manuela Gridling; Katja Parapatics; André C Müller; Soner Altiok; Jacques Colinge; Giulio Superti-Furga; Eric B Haura; Keiryn L Bennett
Journal:  J Proteome Res       Date:  2013-08-20       Impact factor: 4.466

9.  A single lysis solution for the analysis of tissue samples by different proteomic technologies.

Authors:  Pavel Gromov; Julio E Celis; Irina Gromova; Fritz Rank; Vera Timmermans-Wielenga; José M A Moreira
Journal:  Mol Oncol       Date:  2008-10-02       Impact factor: 6.603

Review 10.  Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture.

Authors:  Debasish Paul; Avinash Kumar; Akshada Gajbhiye; Manas K Santra; Rapole Srikanth
Journal:  Biomed Res Int       Date:  2013-03-17       Impact factor: 3.411

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