Literature DB >> 18549973

The use of genomic tools for the molecular understanding of breast cancer and to guide personalized medicine.

John A Foekens1, Yixin Wang, John W M Martens, Els M J J Berns, Jan G M Klijn.   

Abstract

The use of gene-expression microarray analysis to assess the expression levels of all the genes in the genome has tremendous potential. Important information has been obtained about many disease processes, particularly in classifying tumors in different subtypes and risk groups. Combining gene-expression data with other genomic information and the use of sophisticated bioinformatic tools enables the discovery of potential new targets for treatment, and is helpful for high-throughput drug screening and for designing new classes of drugs for targeted therapy. Here, we provide a short overview of the recent, promising developments in the field with emphasis on breast cancer.

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Year:  2008        PMID: 18549973     DOI: 10.1016/j.drudis.2008.03.003

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

Review 1.  Biobanking residual tissues.

Authors:  Peter H J Riegman; Evert-Ben van Veen
Journal:  Hum Genet       Date:  2011-08-04       Impact factor: 4.132

2.  Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines.

Authors:  Lawson Eng; Irada Ibrahim-zada; Hamdi Jarjanazi; Sevtap Savas; Mehran Meschian; Kathleen I Pritchard; Hilmi Ozcelik
Journal:  BMC Med Genomics       Date:  2011-02-11       Impact factor: 3.063

3.  A pharmacogenomic method for individualized prediction of drug sensitivity.

Authors:  Adam L Cohen; Raffaella Soldi; Haiyu Zhang; Adam M Gustafson; Ryan Wilcox; Bryan E Welm; Jeffrey T Chang; Evan Johnson; Avrum Spira; Stefanie S Jeffrey; Andrea H Bild
Journal:  Mol Syst Biol       Date:  2011-07-19       Impact factor: 11.429

4.  Report of the EORTC-PAMM Meeting, Brussels, 16-18 March 2009: new strategies for a targeted and personalised therapy of cancer.

Authors:  S Camporesi
Journal:  Ecancermedicalscience       Date:  2009-04-02

5.  Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile.

Authors:  Chen Zhao; Leming Shi; Weida Tong; John D Shaughnessy; André Oberthuer; Lajos Pusztai; Youping Deng; W Fraser Symmans; Tieliu Shi
Journal:  BMC Genomics       Date:  2011-12-23       Impact factor: 3.969

Review 6.  Ductal carcinoma in situ of the breast: morphological and molecular features implicated in progression.

Authors:  Dirce M Carraro; Eliana V Elias; Victor P Andrade
Journal:  Biosci Rep       Date:  2014-02-01       Impact factor: 3.840

7.  Performance of survivin mRNA as a biomarker for breast cancer among Vietnamese women.

Authors:  Hien Minh Nguyen; Minh Quang Dao; Huyen Thi La
Journal:  Heliyon       Date:  2019-03-20
  7 in total

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