Putative Surrogate Biomarkers to Predict Patients with Acquired Platinum Resistance in Ovarian Cancer
Mu Wang*, Dawn P G Brown, Jinsam You and Kerry G Bemis
Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- *Corresponding Author:
- Mu Wang
Department of Biochemistry and Molecular Biology
Indiana University School of Medicine
635 Barnhill Drive, MS 4053, Indianapolis, USA
Tel: 317- 278-0296
Fax: 317- 274-4686
E-mail: [email protected]
Received Date: May 07, 2014; Accepted Date: May 30, 2014; Published Date: June 03, 2014
Citation: Wang M, Brown DPG, You J, Bemis KG (2014) Putative Surrogate Biomarkers to Predict Patients with Acquired Platinum Resistance in Ovarian Cancer. J Mol Biomark Diagn 5:184. doi:10.4172/2155-9929.1000184
Copyright: © 2014 Wang M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Over 15,000 women die from ovarian cancer and there are approximately 23,000 new cases diagnosed each year. Platinum-based chemotherapy is still the primary treatment for ovarian cancer. Most patients with the disease are initially responsive to chemotherapeutic treatment. However, a majority of ovarian cancer patients eventually relapse and become refractory to additional treatment. This drug-resistance is a major impediment to the successful treatment of ovarian cancer. To date the mechanisms of drug-resistance remain poorly understood. Previous studies have suggested that many proteins, such as BRCA1, BRCA2, MDR1, MRP1, MDM2, hMLH1, HSP27, and HSP70, are differentially expressed in drug-resistant ovarian tumor cells by mRNA differential display analysis. However, biomarkers that can be used to differentiate chemotherapy responders from non-responders have not yet been developed. With recent developments in proteomic technologies, differential protein expression in complex biological samples can be analyzed. In this cell model based study, we applied a label-free protein quantification technology to discover potential protein biomarker candidates that can differentiate chemo-drug responders from non-responders. This experimental approach could also serve as a model tool for further clinical validation and biomarker development for other diseases.