Abstract LB-351: Identification of new drugs for treatment of renal cell cancer using genomics and the Connectivity Map Database

Abstract

Abstract Clear cell renal cell cancer (RCC) is the most lethal of urological malignancies. Despite numerous recent advances in diagnostic imaging, surgical therapy, and basic molecular understanding, many patients still present or develop metastatic disease. More recently, anti-angiogenic therapies such as the VEGF receptor antagonist sunitinib have shown significant benefit in delaying Progression Free Survival (PFS); however, resistance to sunitinib invariably develops. There are few if any complete or durable responses to these agents and median survival for patients remains less than 2 years with less than 10% of patients surviving 5 years. There is therefore a great unmet need for more efficacious therapies for RCC. The Connectivity Map (C-Map) database is a compendium of gene expression profiles of cancer cell lines treated with a spectrum of bioactive small molecules. One potential use of the C-Map is to find drugs that induce a desired gene expression pattern. As proof-of-concept for identifying drugs for RCC, we used an early release of the C-Map database (453 gene expression profiles of 164 drugs) to search for drugs that induced gene expression changes correlated with normal kidney and anti-correlated with a malignant RCC gene expression pattern. We selected the highest scoring FDA approved drugs for further analysis and demonstrated that 6 of them induced apoptosis in vitro in three different RCC cell lines. We used a xenograft model of RCC (786-0 cells) to evaluate 3 of these agents, and found that one of the drugs, a commonly used FDA-approved antiparasitic drug with very little previous evidence for anti-cancer activity, had significant anti-tumor efficacy in vivo. To identify drugs that may delay or prevent resistance to sunitinib we identified a gene signature for 786-O human RCC xenografts resistant to sunitinib in comparison to the same xenografts during the time of response to sunitinib and applied this sunitinib response gene signature to C-Map analysis (7000 gene expression profiles of 1309 drugs) to search for drugs that induced gene expression changes anti-correlated with the sunitinib resistance gene signature. We selected 9 high scoring FDA approved drugs for further analysis and demonstrated that 6 of them induced apoptosis in vitro in RCC cells. We demonstrated that 3 of these 6 drugs reversed RCC as well as sunitinib resistance gene signatures. We are currently testing the efficacy of these 3 drugs alone and in combination with sunitinib in RCC xenograft models, and positive results could be rapidly tested in patients with RCC. The results establish proof-of-concept for using the C-Map database to identify novel treatments for RCC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-351. doi:1538-7445.AM2012-LB-351