A METABOLIC FOOTPRINTING STUDY FOR HUMAN KIDNEY CANCER DETECTION

MARÍA EUGENIA MONGE; MALENA MANZI; MARÍA ELENA KNOTT; NICOLÁS ZABALEGUI; MARIO O. SALAZAR; LYDIA I. PURICELLI

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 menand women worldwide.1,2 Clear cell RCC (ccRCC) is the most common (75-80%) lethal subtype.2 RCC patients are oftenincidentally 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 outcomeof patients. In this work,3 we developed a protocol for harvesting and extracting extracellular metabolites from an in vitro modelof human renal cell lines to profile the exometabolome by means of a discovery-based metabolomics approach usingultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. Metabolic footprints providedby conditioned media (CM) samples (n=66) of two ccRCC cell lines with different genetic backgrounds and a non-tumor renalcell line, were compared with the human serum metabolic profile of a pilot cohort (n=10) comprised of stage IV ccRCC patientsand healthy individuals. Using a cross-validated orthogonal projection to latent structures-discriminant analysis model, a panelof 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, andcysteinegluthatione disulfide (CYSSG) were identified by chemical standards, and hydroxyprolyl-valine was identified with MSand MS/MS experiments. A subset of 9 discriminant features, including the identified metabolites except for CYSSG, produceda fingerprint of classification value that enabled discerning ccRCC patients from healthy individuals. Results from this studyprovide a proof of concept that CM can be used as a serum proxy to obtain disease-related metabolic signatures, providing awindow of opportunity for ccRCC detection. Our current work involves the analysis of a larger sample cohort (n=219) thatincludes patients with different ccRCC stages (I, II, III, IV) collected before and after undergoing surgery. Serum samples wereprovided by the Public Oncologic Serum Biobank from Instituto de Oncología A. H. Roffo.