Mass spectrometry-based strategies for improving diagnosis of clear cell renal cell carcinoma

KNOTT, MARÍA ELENA; GIMÉNEZ MARÍA ISABEL; RIQUELME, GABRIEL; MARTÍN PALAZZO; PATRICIO YANKILEVICH; MANUELA MASTINEFSKI; MALENA MANZI; BEAUSEROY PIERRE; NICOLAS ZABALEGUI; MARÍA E. MONGE

Evento Virtual

Conferencia; 16th Annual Conference of the Metabolomics Society; 2020

The Metabolomics Society

Renal cell carcinoma (RCC) is the main type of kidney cancer with more than 50% of cases incidentally diagnosed. Surgery is the most promising treatment for curation when the disease is detected at early stages, since it is inherently resistant to chemotherapy and radiotherapy. Clear cell RCC (ccRCC) is the most common histological subtype, being a glycolytic and lipogenic tumor. Our research group has developed a metabolic footprinting approach to profile the exometabolome of human renal cell lines using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. The secretome from a ccRCC cell line (786-O, n=22) was differentiated from a non-tumor renal cell line (HEK-293, n=22) through a 21-feature panel. A subset of 9 features, including the identified metabolites isoleucine/leucine, phenylalanine, N-lactoyl-leucine, and N-acetyl-phenylalanine, provided a metabolic footprint capable of differentiating serum samples from stage IV ccRCC patients from controls (n=10). In parallel, serum samples from a cohort including patients with different ccRCC stages (I, II, III, IV; n=112) and controls (n=52) were interrogated with a lipidomics-machine learning approach. A 16-lipid panel, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation, and 77.1% accuracy in a test set. A second model trained to discriminate early from advanced ccRCC stages yielded a panel of 26 lipids that classified stage I patients in a test set with 82.1% accuracy. Results are auspicious for early ccRCC diagnosis after validating the panels in larger and different cohorts.