A genomic approach to identify mechanisms associated with chemotherapy resistance

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2534 Background: Gene expression profiling has shown an ability to predict chemotherapeutic response (Potti et al. Nature Medicine 2006). Building on this work, we used a genomic strategy to explore the biology associated with the development of chemotherapy resistance and sought to determine if disease context impacted results. Methods: Gene set enrichment analysis (GSEA) was performed on expression data for NCI60 cell lines sensitive and resistant to specific chemotherapeutic agents (adriamycin, cyclophosphamide, docetaxel, etoposide, 5-fluoruracil, paclitaxel, topotecan). GSEA was additionally performed on a series of lung cancer cell lines with a defined sensitivity to cisplatin and docetaxel. Sensitive and resistant cell lines with individual mean and confidence intervals greater than 1SD from the mean across all samples were included. Adjusting for multiple hypothesis testing, gene sets with a false discovery rate (FDR) <0.25 were deemed statistically significant. In the discovery mode, gene sets with a nominal p-value <0.05 were also of interest. Finally, overlapping gene sets between agents were assessed. Results: Statistically-significant gene sets, representing biologic pathways associated with resistance, were identified for the various chemotherapeutic agents (i.e., cell death, erbb3, and bad pathways associated with docetaxel resistance). No gene sets with FDR <0.25 or nominal p-value <0.05 were common to all drugs. In assessing disease specific resistance, 22 lung cancer cell lines for cisplatin (15 sensitive, 7 resistant) and 14 lung cancer cell lines for docetaxel (10 sensitive, 4 resistant) were analyzed. GSEA identified the bcl-2 pathway (p<0.002) to be associated with cisplatin resistance. In contrast, the proteosome (p=0.01) and akt (p=0.02) pathways were associated with docetaxel resistance. Pathways involved in the production of V-H+-ATPase were enriched in cisplatin and docetaxel resistant lung cancer cell lines suggesting a global mechanism of resistance. Conclusions: These results support the use of a genomic approach to identify unique drug-specific and global therapeutic targets associated with the development of chemotherapy resistance. Interestingly, disease context appears important in identifying novel targets. No significant financial relationships to disclose.