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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: Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC). The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings. Results: The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73. Conclusions: This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations. [ABSTRACT FROM AUTHOR]

A prognostic model that predicts overall survival (OS) for metastatic urothelial cancer (MetUC) patients treated with cisplatin-based chemotherapy was developed, validated, and compared with a commonly used Memorial Sloan-Kettering Cancer Center (MSKCC) risk-score model. Data from 7 protocols that enrolled 308 patients with MetUC were pooled. An external multi-institutional dataset was used to validate the model. The primary measurement of predictive discrimination was Harrell’s c-index, computed with 95% confidence interval (CI). The final model included four pretreatment variables to predict OS: visceral metastases, albumin, performance status, and hemoglobin. The Harrell’s c-index was 0.67 for the four-variable model and 0.64 for the MSKCC risk-score model, with a prediction improvement for OS (the U statistic and its standard deviation were used to calculate the two-sided P = .002). In the validation cohort, the c-indices for the four-variable and the MSKCC risk-score models were 0.63 (95% CI = 0.56 to 0.69) and 0.58 (95% CI = 0.52 to 0.65), respectively, with superiority of the four-variable model compared with the MSKCC risk-score model for OS (the U statistic and its standard deviation were used to calculate the two-sided P = .02). [ABSTRACT FROM AUTHOR]