MicroRNA expression profiling of peripheral blood samples predicts resistance to first-line sunitinib in advanced renal cell carcinoma patients.

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作者:Gámez-Pozo Angelo, Antón-Aparicio Luis M, Bayona Cristina, Borrega Pablo, Gallegos Sancho María I, García-Domínguez Rocío, de Portugal Teresa, Ramos-Vázquez Manuel, Pérez-Carrión Ramón, Bolós María V, Madero Rosario, Sánchez-Navarro Iker, Fresno Vara Juan A, Espinosa Arranz Enrique
Anti-angiogenic therapy benefits many patients with advanced renal cell carcinoma (RCC), but there is still a need for predictive markers that help in selecting the best therapy for individual patients. MicroRNAs (miRNAs) regulate cancer cell behavior and may be attractive biomarkers for prognosis and prediction of response. Forty-four patients with RCC were recruited into this observational prospective study conducted in nine Spanish institutions. Peripheral blood samples were taken before initiation of therapy and 14 days later in patients receiving first-line therapy with sunitinib for advanced RCC. miRNA expression in peripheral blood was assessed using microarrays and L2 boosting was applied to filtered miRNA expression data. Several models predicting poor and prolonged response to sunitinib were constructed and evaluated by binary logistic regression. Blood samples from 38 patients and 287 miRNAs were evaluated. Twenty-eight miRNAs of the 287 were related to poor response and 23 of the 287 were related to prolonged response to sunitinib treatment. Predictive models identified populations with differences in the established end points. In the poor response group, median time to progression was 3.5 months and the overall survival was 8.5, whereas in the prolonged response group these values were 24 and 29.5 months, respectively. Ontology analyses pointed out to cancer-related pathways, such angiogenesis and apoptosis. miRNA expression signatures, measured in peripheral blood, may stratify patients with advanced RCC according to their response to first-line therapy with sunitinib, improving diagnostic accuracy. After proper validation, these signatures could be used to tailor therapy in this setting.

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