Citations

Abstract Genome-Wide Association Studies (GWAS) have begun to investigate associations between inherited genetic variations and breast cancer prognosis. Here, we report our findings from a GWAS conducted in 536 patients with early-onset breast cancer aged 40 or less at diagnosis and with a mean follow-up period of 4.1 years (SD = 1.96). Patients were selected from the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer. A Bonferroni correction for multiple testing determined that a P value of 1.0 × 10−7 was a statistically significant association signal. Following quality control, we identified 487,496 single nucleotide polymorphisms (SNP) for association tests in stage 1. In stage 2, 35 SNPs with the most significant associations were genotyped in 1,516 independent cases from the same early-onset cohort. In stage 2, 11 SNPs remained associated in the same direction (P ≤ 0.05). Fixed effects meta-analysis models identified one SNP associated at close to genome wide level of significance 556 kb upstream of the ARRDC3 locus [HR = 1.61; 95% confidence interval (CI), 1.33–1.96; P = 9.5 × 10−7]. Four further associations at or close to the PBX1, RORα, NTN1, and SYT6 loci also came close to genome-wide significance levels (P = 10−6). In the first ever GWAS for the identification of SNPs associated with prognosis in patients with early-onset breast cancer, we report a SNP upstream of the ARRDC3 locus as potentially associated with prognosis (median follow-up time for genotypes: CC = 4 years, CT = 3 years, and TT = 2.7 years; Wilcoxon rank-sum test CC vs. CT, P = 4 × 10−4 and CT vs. TT, P = 0.76). Four further loci may also be associated with prognosis. Cancer Res; 73(6); 1883–91. ©2012 AACR.

AbstractIntroductionSepsis biomarker research requires an infrastructure to identify septic patients efficiently and to collect and store specimens properly. We developed a novel infrastructure to study biomarkers of sepsis in children.MethodsPatients in pediatric and neonatal intensive care units were enrolled prospectively; enrollment information was stored in a secure, remotely accessible database. Researchers were notified of electronic medical record (EMR) orders for blood cultures (a surrogate for a diagnostic evaluation of suspected sepsis) by a page triggered by the order. Staff confirmed patient enrollment and remotely submitted an EMR order for collection of study specimens simultaneous with the blood culture. Specimens were processed and stored by a mobile clinical research unit.ResultsOver 2 years, 2029 patients were admitted; 138 were enrolled. Staff received pages for 95% of blood cultures collected from enrolled patients. The median time between the blood culture order and collection was 34 minutes (range 9–241). Study specimens were collected simultaneously with 41 blood cultures. The median times between specimen collection and storage for flow cytometry and cytokine analysis were 33 minutes (range 0–82) and 52 minutes (range 28–98), respectively.ConclusionThis novel infrastructure facilitated prompt, proper collection and storage of specimens for sepsis biomarker analysis. Clin Trans Sci 2012; Volume #: 1–5

AbstractObjectivesRadiocontrast nephropathy (RCN) is a known complication of procedures in which intravascular iodinated contrast material is used. The authors sought to determine the risk factors for RCN after emergency department (ED) contrast‐enhanced computerized tomography (CECT).MethodsThis was a retrospective case–control study of patients presenting to a tertiary care ED between January 1, 2004, and December 31, 2006. Inclusion criteria were CECT performed in the ED, serum creatinine measured prior to CECT, and serum creatinine measured 48 to 96 hours after CECT. Exclusion criterion was dialysis‐dependent renal failure prior to CECT. The outcome of RCN was defined as an absolute creatinine increase of greater than or equal to 0.5 mg/dL, or a 25% increase above baseline. The charts of all RCN patients and a random sample of non‐RCN patients were reviewed to document the presence or absence of potential risk factors. Univariate analysis was performed using chi‐square and multiple logistic regression applying a weighted technique to account for sampling of non‐RCN patients.ResultsAmong the 5,006 patients meeting inclusion criteria, 349 (7%) developed RCN. Multiple regression analysis demonstrated that serum creatinine > 2 mg/dL, liver disease, heart failure, hematocrit < 30%, hypertension, and diabetes were risk factors for RCN, whereas age > 75 years, vascular disease, and serum creatinine > 1.5 mg/dL were not. The area under the curve (AUC) for the model was 0.65. Although the risk of RCN increased with the number of risk factors present, we could not develop a model with sufficient diagnostic accuracy to guide clinical decision‐making.ConclusionsThe authors report risk factors for RCN in a large case–control study, but could not develop an accurate decision tool to identify patients at increased risk for RCN after ED CECT.