Literature DB >> 17059381

Point mutations of protein kinases and individualised cancer therapy.

Michael Davies1, Bryan Hennessy, Gordon B Mills.   

Abstract

The treatment of cancer is rapidly changing, with an increasing focus on converting our improved understanding of the molecular basis of disease into clinical benefit for patients. Protein kinases that are mutated in cancer represent attractive targets, as they may result in cellular dependency on the mutant kinase or its associated pathway for survival, a condition known as 'oncogene addiction'. Early clinical experiences have demonstrated dramatic clinical benefit of targeting oncogenic mutations in diseases that have been largely resistant to traditional cytotoxic chemotherapy. Further, mutational activation of kinases can indicate which patients are likely to respond to targeted therapeutics. However, these experiences have also illuminated a number of critical challenges that will have to be addressed in the development of effective drugs across different cancers, to fully realise the potential of individualised molecular therapy. This review utilises examples of genetic activation of kinases to illustrate many of the lessons learned, as well as those yet to be implemented.

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Year:  2006        PMID: 17059381     DOI: 10.1517/14656566.7.16.2243

Source DB:  PubMed          Journal:  Expert Opin Pharmacother        ISSN: 1465-6566            Impact factor:   3.889


  9 in total

1.  Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics.

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Journal:  J Am Stat Assoc       Date:  2018-08-15       Impact factor: 5.033

Review 2.  Targeted therapy for melanoma: a primer.

Authors:  Michael A Davies; Jeffrey E Gershenwald
Journal:  Surg Oncol Clin N Am       Date:  2011-01       Impact factor: 3.495

Review 3.  Analysis of the genome to personalize therapy for melanoma.

Authors:  M A Davies; Y Samuels
Journal:  Oncogene       Date:  2010-08-09       Impact factor: 9.867

Review 4.  The PIK3CA gene as a mutated target for cancer therapy.

Authors:  John P Gustin; David P Cosgrove; Ben Ho Park
Journal:  Curr Cancer Drug Targets       Date:  2008-12       Impact factor: 3.428

Review 5.  Pancreatic cancer.

Authors:  Anirban Maitra; Ralph H Hruban
Journal:  Annu Rev Pathol       Date:  2008       Impact factor: 23.472

6.  Activity of dasatinib against L576P KIT mutant melanoma: molecular, cellular, and clinical correlates.

Authors:  Scott E Woodman; Jonathan C Trent; Katherine Stemke-Hale; Alexander J Lazar; Sabrina Pricl; Giovanni M Pavan; Maurizio Fermeglia; Y N Vashisht Gopal; Dan Yang; Donald A Podoloff; Doina Ivan; Kevin B Kim; Nicholas Papadopoulos; Patrick Hwu; Gordon B Mills; Michael A Davies
Journal:  Mol Cancer Ther       Date:  2009-08-11       Impact factor: 6.261

7.  BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.

Authors:  Veerabhadran Baladandayuthapani; Rajesh Talluri; Yuan Ji; Kevin R Coombes; Yiling Lu; Bryan T Hennessy; Michael A Davies; Bani K Mallick
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

8.  Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics.

Authors:  Lin Zhang; Jeffrey S Morris; Jiexin Zhang; Robert Z Orlowski; Veerabhadran Baladandayuthapani
Journal:  Cancer Inform       Date:  2014-12-07

9.  PIK3CA-activating mutations and chemotherapy sensitivity in stage II-III breast cancer.

Authors:  Cornelia Liedtke; Luca Cardone; Attila Tordai; Kai Yan; Henry L Gomez; Luis J Barajas Figureoa; Rebekah E Hubbard; Vicente Valero; Eduardo A Souchon; W Fraser Symmans; Gabriel N Hortobagyi; Alberto Bardelli; Lajos Pusztai
Journal:  Breast Cancer Res       Date:  2008-03-27       Impact factor: 6.466

  9 in total

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