Characterisation and modulation of drug resistance in lung cancer cell lines

Chemotherapy drug resistance is a major obstacle in the treatment of cancer. It can result from an increase in levels of cellular drug efflux pumps such as P-glycoprotein (P-gp). Using cellular models, this thesis aimed to investigate resistance in lung cancer cells while developing siRNA and membrane proteomic techniques and to increase our knowledge of the effect of lapatinib, a newly developed targeted therapy, in these resistant cells. Lapatinib, a growth factor receptor tyrosine kinase inhibitor synergised with P-gp substrate cytotoxics in P-gp over-expressing resistant cells. However, lapatinib treatment, at clinically relevant concentrations, also increased levels of the P-gp drug transporter in a dose-responsive manner. Conversely, exposure to the epidermal growth factor (EGF), an endogenous growth factor receptor ligand, resulted in a decrease in P-gp expression. Using drug accumulation, efflux and toxicity assays we determined that alteration in P-gp levels by either lapatinib or EGF had little functional significance. P-gp is not the only resistance mechanism so siRNA-mediated gene silencing was exploited to investigate the role of additional proteins with potential roles in resistance. Firstly, P-gp knockdown by siRNA was coupled with toxicity and accumulation assays to determine the impact of silencing this protein in the chosen resistant lung cells. Additional putative targets were chosen from microarray data identifying genes associated with the development of paclitaxel resistance. Of the three genes investigated, ID3, CRYZ and CRIP1, ID3 emerged as having a potential role in contributing to resistance in one of the resistant lung carcinoma cell lines investigated. Many of the proteins important in resistance are membrane expressed but due to their size and hydrophobic nature, can be difficult to characterise. A 2D-LC-MS method was designed and employed to examine membrane proteins from the resistant lung cell models. Suitable parameters important in optimal identification of the proteins were determined. Large numbers of proteins were identified and comparisons made, highlighting those that were differentially expressed