A metabolic footprinting study of clear cell Renal Cell Carcinoma cell lines

MARÍA ELENA KNOTT; LYDIA INÉS PURICELLI; MARÍA EUGENIA MONGE

Rosario

Simposio; 2nd Latin American Metabolic Profiling Symposium; 2016

Renewed interest on understanding cancer cell metabolism has emerged over the last few years.1 In Argentina, Renal Cell Carcinoma (RCC) is the 9th cause of cancer-related mortality in men and 13th in women, and accounts for nearly 300,000 cases worldwide. Clear cell renal cell carcinoma (ccRCC) is the most common (75%) histological subtype of kidney cancer.2 ccRCC is associated with genetic and epigenetic changes in the von Hippel-Lindau (VHL) tumor suppressor gene in 90% of cases. This mutation leads to hypoxia-inducible factor (HIF-1α) protein overexpression, triggering hypoxia response activation. Current research has shown that several metabolic alterations are associated with RCC, during tumor progression and metastasis.3,4 Mammalian cell metabolomics has emerged as a promising tool for cellular biochemistry. In-vitro cell models can be used to investigate altered central metabolic networks that contribute to cell proliferation, growth and survival in RCC. In this study, we have optimized a protocol for harvesting, and extraction of extracellular metabolites from human renal cell lines, and we have utilized a discovery-based metabolomics approach to profile the exometabolome from cultured cells by means of ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS). Conditioned media were obtained from a non-tumor human embryonic kidney cell line, HEK-293, which was used as control; and ccRCC cell lines with different genetic background: 786-O, which derives from primary ccRCC tumor and has a deletion of VHL gene (VHL-/-); Caki-1, which was obtained from ccRCC metastasis, and expresses wild type VHL protein (VHL+/+). Isopropanol was proven to be the best solvent for metabolite extraction. Metabolite extracts were lyophilized and reconstituted in methanol:water (20:80 v/v %) prior to reversed-phase UPLC-QTOF-MS analysis. A total of 22 conditioned media were analyzed for each cell type, including biological replicates. We are currently analyzing the metabolic features obtained by means of different multivariate statistical methods to find a panel of discriminant metabolites that would contribute to the understanding of tumor biology in ccRCC.