A mass spectrometry-based untargeted metabolomics approach for studying clear cell renal cell carcinoma using an in vitro model

Abstract

Renal Cell Carcinoma (RCC) is among the 10 most common cancers in both men and women worldwide, with clear cell RCC (ccRCC) being the most common (75%) histological subtype. At molecular level, 90% of all ccRCC patients show mutations in Von Hippel Lindau (VHL) gen which is involved in Hypoxia Inducible Factor 1α expression. Current research has shown that several metabolic alterations are associated with RCC, during tumor progression and metastasis. Mammalian cell metabolomics has emerged as a promising tool for studying cellular biochemistry and investigate altered metabolic networks that contribute to cell proliferation, growth and survival in RCC.In this study, we have optimized a protocol for harvesting, extraction, lyophilization and reconstitution of metabolites present in conditioned media derived from human ccRCC cell lines 786-O (VHL-/-) and Caki-1 (VHL+/+), and the non-tumor human cell line HEK-293 (n=22); and we have profiled the exometabolome using a discovery-based metabolomics approach by means of ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS). Metabolic features (Rt, m/z pairs) were analyzed using a cross-validated orthogonal projection to latent structures-discriminant analysis model, using a genetic algorithm variable selection method. A panel of 12 discriminant features with tentative chemical identification allowed differentiating the three cell lines with 100% specificity, sensitivity and accuracy. In addition, 5 of these compounds were present in 10 human serum samples from ccRCC patients and healthy controls. Discriminant metabolic features suggest alterations of the glutathione and phenylalanine metabolism, tryptophan degradation and the pentose phosphate pathway. Current work involves validation of the tentative identification of the compounds with chemical standards.