Abstract
Resistance to chemotherapy in cancer is common. As gene expression profiling has been shown to anticipate chemotherapeutic resistance, we sought to identify cellular pathways associated with resistance to facilitate effective combination therapy. Gene set enrichment analysis was used to associate pathways with resistance in two data sets: the NCI-60 cancer cell lines deemed sensitive and resistant to specific chemotherapeutic agents (Adriamycin, cyclophosphamide, docetaxel, etoposide, 5-fluorouracil, paclitaxel, and topotecan) and a series of 40 lung cancer cell lines for which sensitivity to cisplatin and docetaxel was determined. Candidate pathways were further screened in silico using the Connectivity Map. The lead candidate pathway was functionally validated in vitro. Gene set enrichment analysis associated the matrix metalloproteinase, p53, methionine metabolism, and free pathways with cytotoxic resistance in the NCI-60 cell lines across multiple agents, but no gene set was common to all drugs. Analysis of the lung cancer cell lines identified the bcl-2 pathway to be associated with cisplatin resistance and the AKT pathway enriched in cisplatin- and docetaxel-resistant cell lines. Results from Connectivity Map supported an association between phosphatidylinositol 3-kinase/AKT and docetaxel resistance but did not support the association with cisplatin. Targeted inhibition of the phosphatidylinositol 3-kinase/AKT pathway with LY294002, in combination with docetaxel, resulted in a synergistic effect in previously docetaxel-resistant cell lines but not with cisplatin. These results support the use of a genomic approach to identify drug-specific targets associated with the development of chemotherapy resistance and underscore the importance of disease context in identifying these pathways.
Highlights
The development of chemotherapy resistance poses a significant problem to patients and providers who rely on conventional cytotoxic agents for the treatment of malignant disease
By applying Gene set enrichment analysis (GSEA) [6], a computational method that identifies shared differential gene expression of predefined, functionally related gene sets representing biological pathways,10 we identified biological pathways associated with resistance for each chemotherapy agent tested
Cytotoxicity Assays for Lung Cancer Cell Lines Given the broad but sparse representation of specific cancer types in the NCI-60 and the potential importance of cellular context in the biological mechanisms resulting in resistance, we sought to determine molecular pathways associated with chemotherapy resistance in a large collection of non-small cell lung cancer cell lines
Summary
The development of chemotherapy resistance poses a significant problem to patients and providers who rely on conventional cytotoxic agents for the treatment of malignant disease. The identification and subsequent targeting of key molecular pathways associated with resistance may allow for increased response rates and improved clinical outcomes for patients. Gene expression profiling has proven to be a powerful tool allowing for the characterization of tumors at a molecular level. Individual genomic tumor profiles have been used to identify histologic classes of tumor and develop prediction tools for the development of metastatic disease, disease relapse, prognosis, and response to therapy in a variety of malignancies [4]. We have shown that global gene expression can be used to identify patterns predictive of chemotherapy response and/or resistance [5]
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