RESUMO
Platinum-based chemotherapy, such as cisplatin, is the primary treatment for ovarian cancer. However, drug resistance has become a major impediment to the successful treatment of ovarian cancer. To date, the molecular mechanisms of resistance to platinum-based chemotherapy remain unclear. In this study, we applied an LC/MS-based protein quantification method to examine the global protein expression of two pairs of ovarian cancer cell lines, A2780/A2780-CP (cisplatin-sensitive/cisplatin-resistant) and 2008/2008-C13*5.25 (cisplatin-sensitive/cisplatin-resistant). We identified and quantified over 2000 proteins from these cell lines and 760 proteins showed significant expression changes with a false discovery rate of less than 5% between paired groups. Based on the results we obtained, we suggest several potential pathways that may be involved in cisplatin resistance in human ovarian cancer. This study provides not only a new proteomic platform for large-scale quantitative protein analysis, but also important information for discovery of potential biomarkers of cisplatin resistance in ovarian cancer. Furthermore, these results may be clinically relevant for diagnostics, prognostics, and therapeutic improvement for ovarian cancer treatment.
Assuntos
Proteínas/química , Proteômica/métodos , Sequência de Aminoácidos , Cromatografia Líquida/métodos , Eletroforese em Gel de Poliacrilamida/métodos , Informática , Espectrometria de Massas/métodos , Modelos Moleculares , Conformação Molecular , Dados de Sequência Molecular , Mapeamento de Peptídeos/métodos , Proteínas/isolamento & purificação , Reprodutibilidade dos TestesRESUMO
Biomarkers of drug efficacy and toxicity are becoming a key need in the drug development process. Mass spectral-based proteomic technologies are ideally suited for the discovery of protein biomarkers in the absence of any prior knowledge of quantitative changes in protein levels. The success of any biomarker discovery effort will depend upon the quality of samples analysed, the ability to generate quantitative information on relative protein levels and the ability to readily interpret the data generated. This review will focus on the strengths and weaknesses of technologies currently utilised to address these issues.
