Evaluating lipid fingerprints for clear cell renal cell carcinoma prognosis

NICOLÁS ZABALEGUI; MAXIMILIAN A. REY; MALENA MANZI; GABRIEL RIQUELME; MARÍA EUGENIA MONGE

Conferencia; 18th Annual Conference of the Metabolomics Society; 2022

Kidney cancer is a disease of dysregulated cellular metabolism. 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, and it is characterized for being lipid and glycogen-laden and exhibiting abnormal cholesterol metabolism and fat storage. Our research group has recently analyzed serum samples from a cohort comprised of ccRCC patients with different stages (I, II, III, IV; n=112) and healthy individuals (n=52) with a coupled untargeted UHPLC-HRMS-based lipidomics machine learning approach.[1] A 16-lipid panel, including cholic acid, undecylenic acid, lauric acid, linoleic acid, LPC(16:0/0:0), and PC(18:2/18:2), allowed discriminating ccRCC patients from healthy individuals with 77.1% accuracy in an independent test set.[1] This panel was further evaluated as a signature for metabolic response after nephrectomy in paired-serum samples (n=41). Changes in the lipid signature of samples collected before and after surgery were investigated, and were compared with those fingerprints obtained from healthy individuals. Results suggest that ccRCC-associated lipid phenotypes may be used for evaluating the metabolic phenoreversion to a healthy metabolic state, and contribute to patient prognosis stratification. Currently, we are evaluating the feasibility of detecting this lipid panel and its discrimination capability for ccRCC diagnosis and prognosis using high-throughput analytical methods based on Direct Analysis in Real Time (DART)-QTOF-MS. Serum samples were provided by the Argentine Public Oncologic Serum Biobank “Biobanco Público de Muestras Séricas Oncológicas” from “Instituto de Oncología A. H. Roffo”. References1Manzi, M.; Palazzo, M.; Knott, M. E.; Beauseroy, P.; Yankilevich, P.; Giménez, M. I.; Monge, M. E., Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma. J Proteome Res 2021, 20, (1), 841-857.