Citations

Abstract Background: While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC). Methods: The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped). Results: No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E−6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E−6; Pcorrected = 0.01). Conclusions: Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed. Impact: This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446–54. ©2016 AACR.

In smaller single‐center studies, patients with cirrhosis are at a high readmission risk, but a multicenter perspective study is lacking. We evaluated the determinants of 3‐month readmissions among inpatients with cirrhosis using the prospective 14‐center North American Consortium for the Study of End‐Stage Liver Disease cohort. Patients with cirrhosis hospitalized for nonelective indications provided consent and were followed for 3 months postdischarge. The number of 3‐month readmissions and their determinants on index admission and discharge were calculated. We used multivariable logistic regression for all readmissions and for hepatic encephalopathy (HE), renal/metabolic, and infection‐related readmissions. A score was developed using admission/discharge variables for the total sample, which was validated on a random half of the total population. Of the 1353 patients enrolled, 1177 were eligible on discharge and 1013 had 3‐month outcomes. Readmissions occurred in 53% (n = 535; 316 with one, 219 with two or more), with consistent rates across sites. The leading causes were liver‐related (n = 333; HE, renal/metabolic, and infections). Patients with cirrhosis and with worse Model for End‐Stage Liver Disease score or diabetes, those taking prophylactic antibiotics, and those with prior HE were more likely to be readmitted. The admission model included Model for End‐Stage Liver Disease and diabetes (c‐statistic = 0.64, after split‐validation 0.65). The discharge model included Model for End‐Stage Liver Disease, proton pump inhibitor use, and lower length of stay (c‐statistic = 0.65, after split‐validation 0.70). Thirty percent of readmissions could not be predicted. Patients with liver‐related readmissions consistently had index‐stay nosocomial infections as a predictor for HE, renal/metabolic, and infection‐associated readmissions (odds ratio = 1.9‐3.0). Conclusions: Three‐month readmissions occurred in about half of discharged patients with cirrhosis, which were associated with cirrhosis severity, diabetes, and nosocomial infections; close monitoring of patients with advanced cirrhosis and prevention of nosocomial infections could reduce this burden. (Hepatology 2016;64:200–208)

Neuroendocrine systems control many of the most fundamental physiological processes, e.g., reproduction, growth, adaptations to stress, and metabolism. Each such system involves the hypothalamus, the pituitary, and a specific target gland or organ. In the quantification of the interactions among these components, biostatistical modeling has played an important role. In the present article, five key challenges to an understanding of the interactions of these systems are illustrated and discussed critically.