Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

Abstract

Paclitaxel has been widely used as an anti-mitotic agent in chemotherapy for a variety of cancers and adds substantial efficacy as the first-line chemotherapeutic regimen for ovarian cancers. However, the frequent occurrence of paclitaxel resistance limits its function in long-term management. Despite abundant clinical and cellular demonstration of paclitaxel resistant tumors, the molecular mechanisms leading to paclitaxel resistance are poorly understood. Using genomic approaches, we have previously identified an association between a BTB/POZ gene, Nac1, and paclitaxel resistance in ovarian cancer. The experiments presented here have applied multiple quantitative proteomic methods to identify protein changes associated with paclitaxel resistance and Nac1 function. The SKOV-3 ovarian serous carcinoma cell line, which has inducible expression of dominant negative Nac1, was used to determine the paclitaxel treatment associated changes in the presence and absence of functional Nac1. Quantitative proteomic analyses were performed using iTRAQ labeling and mass spectrometry. Two label-free quantitative proteomic methods: LC-MS and spectral count were used to increase confidence of proteomic quantification. A total of 1371 proteins were quantified by at least one of the quantitative proteomic methods. Candidate proteins related to paclitaxel and NAC1 function were identified in this study. Go analysis of the protein changes identified upon paclitaxel resistance revealed that cell component enrichment related to mitochondria. Moreover, tubulin and mitochondrial proteins were the major cellular components with changes associated with paclitaxel treatment. This suggests that mitochondria may play a role in paclitaxel resistance.

EDRN PI Authors
Medline Author List
  • Chan DW
  • Jinawath N
  • Olson MT
  • Shih IeM
  • Sun X
  • Tan AC
  • Tian Y
  • Xie Z
  • Zhang H
  • Zhang Z
PubMed ID
Appears In
Proteomics Clin Appl, 2009 Nov, volume 3 (issue 11)