GENE EXPRESSION PROFILES REVEAL ALTERNATIVE TARGETS OF THERAPEUTIC INTERVENTION FOR THE TREATMENT OF DRUG-RESISTANT NON-SMALL CELL LUNG CANCERS

More than 80% of lung cancer patients die from drug-resistant, metastatic disease. Our focus is to identify new drug targets and alternative therapeutic strategies to improve outcomes for this majority of lung cancer patients. We aimed to satisfy the need for new treatment approaches by leveraging the information gained from the development of two multigene biomarker predictors of Epidermal Growth Factor Receptor Inhibitors (EGFRI) response in Non-Small Cell Lung Cancer (NSCLC). From these data, we first identified TGFβ signaling as a possible modulator of EGFRI resistance and I hypothesized that TGFβ signaling participates in the development and maintenance of erlotinib-resistance and -sensitivity and regulates the gene expression of the miRNA comprising the signature of response. To identify novel putative treatment strategies for overcoming EGFRI resistance, we leveraged the raw data used to build the EGFRI-response predictors of NSCLC cells with divergent EGFRI responses using mathematical and protein-protein interaction modeling to identify a network of deregulated proteins in EGFRI-resistant cells. From this analysis, we identified a drug combination that is kills EGFRI-resistant NSCLC cells and further study will confirm if this novel strategy translates into a clinically utilizable option for the treatment of EGFRI-resistant NSCLC