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Background. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to a lower dimension as well as controlling the FDR with three popular variable selection methods: LASSO, SCAD, and MCP. Method. The three methods with the proposed screenings were applied to prostate cancer data with presence of metastasis as the outcome. Results. Simulations showed that the three variable selection methods with the proposed screenings controlled the predefined FDR and produced high area under the receiver operating characteristic curve (AUROC) scores. In applying these methods to the prostate cancer example, LASSO and MCP selected 12 and 8 genes and produced AUROC scores of 0.746 and 0.764, respectively. Conclusions. We demonstrated that the variable selection methods with the sequential use of FDR and ISIS not only controlled the predefined FDR in the final models but also had relatively high AUROC scores. [ABSTRACT FROM AUTHOR]

Background: Non-clear cell renal cell carcinomas are histologically and genetically diverse kidney cancers with variable prognoses, and their optimum initial treatment is unknown. We aimed to compare the mTOR inhibitor everolimus and the VEGF receptor inhibitor sunitinib in patients with non-clear cell renal cell carcinoma.Methods: We enrolled patients with metastatic papillary, chromophobe, or unclassified non-clear cell renal cell carcinoma with no history of previous systemic treatment. Patients were randomly assigned (1:1) to receive everolimus (10 mg/day) or sunitinib (50 mg/day; 6-week cycles of 4 weeks with treatment followed by 2 weeks without treatment) administered orally until disease progression or unacceptable toxicity. Randomisation was stratified by Memorial Sloan Kettering Cancer Center risk group and papillary histology. The primary endpoint was progression-free survival in the intention-to-treat population using the RECIST 1.1 criteria. Safety was assessed in all patients who were randomly assigned to treatment. This study is registered with ClinicalTrials.gov, number NCT01108445.Findings: Between Sept 23, 2010, and Oct 28, 2013, 108 patients were randomly assigned to receive either sunitinib (n=51) or everolimus (n=57). As of December, 2014, 87 progression-free survival events had occurred with two remaining active patients, and the trial was closed for the primary analysis. Sunitinib significantly increased progression-free survival compared with everolimus (8·3 months [80% CI 5·8-11·4] vs 5·6 months [5·5-6·0]; hazard ratio 1·41 [80% CI 1·03-1·92]; p=0·16), although heterogeneity of the treatment effect was noted on the basis of histological subtypes and prognostic risk groups. No unexpected toxic effects were reported, and the most common grade 3-4 adverse events were hypertension (12 [24%] of 51 patients in the sunitinib group vs one [2%] of 57 patients in the everolimus group), infection (six [12%] vs four [7%]), diarrhoea (five [10%] vs one [2%]), pneumonitis (none vs five [9%]), stomatitis (none vs five [9%]), and hand-foot syndrome (four [8%] vs none).Interpretation: In patients with metastatic non-clear cell renal cell carcinoma, sunitinib improved progression-free survival compared with everolimus. Future trials of novel agents should account for heterogeneity in disease outcomes based on genetic, histological, and prognostic factors.Funding: Novartis and Pfizer. [ABSTRACT FROM AUTHOR]

Background Several prognostic models for overall survival (OS) have been developed and validated in men with metastatic castration-resistant prostate cancer (mCRPC) who receive first-line chemotherapy. We sought to develop and validate a prognostic model to predict OS in men who had progressed after first-line chemotherapy and were selected to receive second-line chemotherapy. Methods Data from a phase III trial in men with mCRPC who had developed progressive disease after first-line chemotherapy (TROPIC trial) were used. The TROPIC was randomly split into training (n = 507) and testing (n = 248) sets. Another dataset consisting of 488 men previously treated with docetaxel (SPARC trial) was used for external validation. Adaptive least absolute shrinkage and selection operator selected nine prognostic factors of OS. A prognostic score was computed from the regression coefficients. The model was assessed on the testing and validation sets for its predictive accuracy using the time-dependent area under the curve (tAUC). Results The nine prognostic variables in the final model were Eastern Cooperative Oncology Group performance status, time since last docetaxel use, measurable disease, presence of visceral disease, pain, duration of hormonal use, hemoglobin, prostate specific antigen, and alkaline phosphatase,The tAUCs for this model were 0.73 (95% confidence interval [CI] = 0.72 to 0.74) and 0.70 (95% CI = 0.68 to 0.72) for the testing and validation sets, respectively. Conclusions A prognostic model of OS in the postdocetaxel, second-line chemotherapy, mCRPC setting was developed and externally validated. This model incorporates novel prognostic factors and can be used to provide predicted probabilities for individual patients and to select patients to participate in clinical trials on the basis of their prognosis. Prospective validation is needed. [ABSTRACT FROM AUTHOR]