Using microarrays to predict resistance to chemotherapy in cancer patients

Author:

Lee Chung-Hae1,Macgregor Pascale F2

Affiliation:

1. Microarray Centre, Clinical Genomics Centre, University Health Network,200 Elizabeth Street, MBRC 5R414, Toronto, Ontario, M5G 2C4, Canada

2. Microarray Centre, Clinical Genomics Centre, University Health Network, Canadian Breast Cancer Research Alliance, 790 Bay Street, Ste. 1000, Toronto, ON, M5G 1NB, Canada. pfmacgregor@cbcf.org

Abstract

Chemotherapy resistance remains a major obstacle to successful treatment and better outcome in cancer patients. The advent of whole genome experimental strategies, such as DNA microarrays, has transformed the way researchers approach cancer research. There is considerable hope that microarray technology will lead to the identification of new targets for therapeutic intervention, a better understanding of the disease process, and, ultimately, to higher survival rates and more personalized medicine. The question at hand is what is the best approach to apply these new technologies to the study of anticancer drug resistance, and how can the results obtained in the laboratory be quickly moved to a clinical setting? This review offers an overview of the microarray technology, including its recently associated strategies, such as array comparative genomic hybridization and promoter arrays. It also highlights some recent examples of microarray studies, which represent a first step toward a better understanding of drug resistance in cancer and, ultimately, personalized medicine.

Publisher

Future Medicine Ltd

Subject

Pharmacology,Genetics,Molecular Medicine

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2. Effect of ketoprofen and indomethacin on methotrexate pharmacokinetics in mice plasma and tumor tissues;European Journal of Drug Metabolism and Pharmacokinetics;2012-11-17

3. Taxane resistance in breast cancer: Mechanisms, predictive biomarkers and circumvention strategies;Cancer Treatment Reviews;2012-11

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5. Prediction of multiple drug resistance phenotype in cancer cell lines using gene expression profiles and phylogenetic trees;Chinese Science Bulletin;2010-11

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