Feasibility of detecting clear cell Renal Cell Carcinoma by ultraperformance liquid chromatography-mass spectrometry human serum lipidomics

MARÍA ELENA KNOTT; LYDIA INÉS PURICELLI; MARÍA EUGENIA MONGE

Rosario

Simposio; 2nd Latin American Metabolic Profiling Symposium; 2016

Renal Cell Carcinoma (RCC) is among the 10 most common cancers in both men and women and accounts for nearly 300,000 cases worldwide. The lifetime risk for developing kidney cancer is about 1 in 63. RCC consists of several histological subtypes with distinct molecular alterations and clinical outcomes. Clear cell renal cell carcinoma (ccRCC), characterized by high lipid and glycogen reserve, is the most common (75%) lethal subtype of kidney cancer.1 Current research has shown that several metabolic alterations are associated with RCC, such as phospholipid catabolism, sphingolipid, cholesterol, phenylalanine, tryptophan, and arachidonic acid metabolism, and fatty acid beta-oxidation.2 As well, tumor progression and metastasis have been associated with metabolite increases in glutathione and cysteine/methionine metabolism.3 However, metabolic alterations induced by ccRCC are still not well understood, and more studies are needed to find robust biomarkers for early diagnosis. In this pilot study, untargeted lipidomic profiling of age-matched serum samples from 5 patients with advanced ccRCC (stage IV) and 5 healthy individuals was performed using ultra performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. High resolution mass spectra were acquired in both positive and negative ionization modes across the range of m/z 50-1200. Metabolites were extracted from the serum samples using isopropanol. Metabolic features (retention time, m/z pairs) were obtained via Progenesis QI software, and analyzed using a cross-validated orthogonal projection to latent structures-discriminant analysis (OPLS-DA) model. This supervised classification model classified samples as advanced ccRCC or healthy individuals with high accuracy (R2Y=0.9985 and Q2=0.9956). Discriminant metabolic features of this pilot study suggest alterations of glycerophospholipids and cholesterol metabolism in agreement with previous studies. Our current work involves the retrospective analysis of a larger sample cohort (n=240) that includes samples from ccRCC patients with stages I, II, III and IV and healthy individuals, to shed light into the altered metabolic pathways involved in tumor progression and to discover biomarkers useful for early diagnosis, prognosis, and follow-up care.