A metabolic footprinting study for kidney cancer detection

KNOTT ME; PURICELLI LI; MONGE ME; MANZI M; ZABALEGUI N; SALAZAR MO

Río de Janeiro

Simposio; 3rd Latin American Metabolic Profiling Symposium; 2018

Renal Cell Carcinoma (RCC) is considered a metabolic disease and is among the 10 most common cancers in both men and women worldwide. Clear cell RCC (ccRCC) is the most common (75-80%) lethal subtype. RCC patients are often incidentally diagnosed by imaging procedures, and the disease is inherently resistant to chemotherapy and radiotherapy. Therefore, the discovery of potential biomarkers may provide more opportunities for early intervention and improved outcome of patients. In this work, we developed a protocol for harvesting and extracting extracellular metabolites from an in vitro model of human renal cell lines to profile the exometabolome by means of a discovery-based metabolomics approach using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. Metabolic footprints provided by conditioned media (CM) samples (n=66) of two ccRCC cell lines with different genetic backgrounds and a non-tumor renal cell line, were compared with the human serum metabolic profile of a pilot cohort (n=10) comprised of stage IV ccRCC patients and healthy individuals. Using a cross-validated orthogonal projection to latent structures-discriminant analysis model, a panel of 21 discriminant features selected by a genetic algorithm, allowed differentiating control from tumor cell lines with 100% specificity, sensitivity, and accuracy. Isoleucine/leucine, phenylalanine, N-lactoyl-leucine, and N-acetyl-phenylalanine, and cysteinegluthatione disulfide (CYSSG) were identified by chemical standards, and hydroxyprolyl-valine was identified with MS and MS/MS experiments. A subset of 9 discriminant features, including the identified metabolites except for CYSSG, produced a fingerprint of classification value that enabled discerning ccRCC patients from healthy individuals. Results from this study provide a proof of concept that CM can be used as a serum proxy to obtain disease-related metabolic signatures, providing a window of opportunity for ccRCC detection. Our current work involves the analysis of a larger sample cohort (n=219) that includes patients with different ccRCC stages (I, II, III, IV) collected before and after undergoing surgery. Serum samples were provided by the Public Oncologic Serum Biobank from Instituto de Oncología A. H. Roffo.