UNASSIGNED: e15039 Background: @entity2240 is a standard therapy for metastatic @entity1358 , but markers that identify @entity1 who will not benefit from the drug are needed. In this study, we applied high-throughput MicroRNA (miRNA) expression profiling in peripheral blood samples to identify predictive biomarkers. METHODS: This multicentre prospective study included @entity1 with @entity703 and no previous therapy. A blood sample was collected before therapy initiation. @entity1 received @entity2240 50 mg/day in cycles 4-w-on/2w-off and then grouped according to their response (progression in <= 6 months vs. remaining @entity1 ). MiRNAs were isolated from peripheral blood samples using LeucoLOCK (Ambion) and miRNesay (Quiagen) kits. MiRNA profiling was performed using @entity1 miRNA Microarray Release 14.0 (Agilent). Boosting for classification with classical Akaike Information criterion was applied to filter miRNA expression data according to progression results. Twelve predictive models for 1, 2, 3 or 4 variables were built using a Binary Logistic Regression with the @entity16043 's scoring as optimization technique. Predictive accuracy for calibration was assessed by boostrap (R2) and by the Hosmer-Lemeshow test. Discriminant power was assessed by the AUC for data training and leave-one-out cross-validation for ROC curves. RESULTS: At the time of the analysis, 44 @entity1 have been enrolled and 37 are available for response. Median age was 62, 78% of @entity1 had prior nephrectomy and median follow-up is 9 months. There were 4 complete and 13 partial responses, whereas disease remained stable in 9 (>6 months in 6 of them) and progressed in 11. Peripheral blood samples from 37 @entity1 were analyzed for novel predictive biomarkers. MicroRNA profiling data show that 29 miRNAs were differentially expressed between @entity1 with early progression versus the other @entity1 . Several predictive models comprising 1, 2, and 3 different miRNAs were generated and evaluated. CONCLUSIONS: MicroRNA profiling in peripheral blood may be a reliable way to identify lack of response to @entity2240 in @entity1 with metastatic @entity1358 .

The role of microRNA profiling in peripheral blood in predicting early progression to XXXX in @entity1358 .

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Human'] @entity2240 :: ('MESH:C473478', 'Chemical') :: ['Sunitinib', 'sunitinib'] @entity1358 :: ('MESH:D002292', 'Disease') :: ['renal-cell carcinoma'] @entity16043 :: ('76720', 'Species') :: ['Fisher'] @entity703 :: ('MESH:D007680', 'Disease') :: ['clear-cell metastatic kidney cancer']
@entity2240 :: (MESH:C473478,Chemical) :: ['sunitinib']
