Citations

Abstract Rationale Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. Objectives To identify additional genetic susceptibility factors of asthma in European American and African American populations. Methods A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. Measurements and Main Results The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. Conclusions This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.

Abstract Background: The strongest known risk factor for endometrial cancer is obesity. To determine whether SNPs associated with increased body mass index (BMI) or waist–hip ratio (WHR) are associated with endometrial cancer risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and endometrial cancer in 6,609 cases and 37,926 country-matched controls. Methods: Logistic regression analysis and fixed effects meta-analysis were used to test for associations between endometrial cancer risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for endometrial cancer was assessed using Mendelian randomization, with BMIwGRS as instrumental variable. Results: The BMIwGRS was significantly associated with endometrial cancer risk (P = 3.4 × 10−17). Scaling the effect of the BMIwGRS on endometrial cancer risk by its effect on BMI, the endometrial cancer OR per 5 kg/m2 of genetically predicted BMI was 2.06 [95% confidence interval (CI), 1.89–2.21], larger than the observed effect of BMI on endometrial cancer risk (OR = 1.55; 95% CI, 1.44–1.68, per 5 kg/m2). The association attenuated but remained significant after adjusting for BMI (OR = 1.22; 95% CI, 1.10–1.39; P = 5.3 × 10−4). There was evidence of directional pleiotropy (P = 1.5 × 10−4). BMI SNP rs2075650 was associated with endometrial cancer at study-wide significance (P < 4.0 × 10−4), independent of BMI. Endometrial cancer was not significantly associated with individual WHR SNPs or the WHRwGRS. Conclusions: BMI, but not WHR, is causally associated with endometrial cancer risk, with evidence that some BMI-associated SNPs alter endometrial cancer risk via mechanisms other than measurable BMI. Impact: The causal association between BMI SNPs and endometrial cancer has possible implications for endometrial cancer risk modeling. Cancer Epidemiol Biomarkers Prev; 25(11); 1503–10. ©2016 AACR.

Abstract BACKGROUND Electrophoretic separation of serum and urine proteins has played a central role in diagnosing and monitoring plasma cell disorders. Despite limitations in resolution and analytical sensitivity, plus the necessity for adjunct methods, protein gel electrophoresis and immunofixation electrophoresis (IFE) remain front-line tests. METHODS We developed a MALDI mass spectrometry–based assay that was simple to perform, automatable, analytically sensitive, and applicable to analyzing the wide variety of monoclonal proteins (M-proteins) encountered clinically. This assay, called MASS-FIX, used the unique molecular mass signatures of the different Ig isotypes in combination with nanobody immunoenrichment to generate information-rich mass spectra from which M-proteins could be identified, isotyped, and quantified. The performance of MASS-FIX was compared to current gel-based electrophoresis assays. RESULTS MASS-FIX detected all M-proteins that were detectable by urine or serum protein electrophoresis. In serial dilution studies, MASS-FIX was more analytically sensitive than IFE. For patient samples, MASS-FIX provided the same primary isotype information for 98% of serum M-proteins (n = 152) and 95% of urine M-proteins (n = 55). MASS-FIX accurately quantified M-protein to <1 g/dL, with reduced bias as compared to protein electrophoresis. Intraassay and interassay CVs were <20% across all samples having M-protein concentrations >0.045 g/dL, with the ability to detect M-proteins <0.01 g/dL. In addition, MASS-FIX could simultaneously measure κ:λ light chain ratios for IgG, IgA, and IgM. Retrospective serial monitoring of patients with myeloma posttreatment demonstrated that MASS-FIX provided equivalent quantitative information to either protein electrophoresis or the Hevylite™ assay. CONCLUSIONS MASS-FIX can advance how plasma cell disorders are screened, diagnosed, and monitored.

AbstractFor many people the emergency department (ED) is the first point of access to healthcare for acute needs and a recurring location for many with chronic healthcare needs. While the ED is well placed to identify unmet needs it can also be a net that people slip through when faced with uncoordinated and expensive healthcare challenges. Thus the ED has a responsibility to set patients on a safe and meaningful care trajectory, which can only be done in consultation and partnership with the patients themselves. The purpose of this article is to present crucial aspects of patient engagement that are essential for future research to foster an environment of colearning and respect that encourages ongoing involvement by patients, families, and staff.

Key Clinical MessageA de novo GFAP variant, p.R376W, was identified in a child presenting with hypotonia, developmental delay, and abnormal brain MRI. Following the 2015 ACMG variant classification guidelines and the functional studies showing protein aggregate formation in vitro, p.R376W should be classified as a pathogenic variant, causative for Alexander disease.

Abstract BACKGROUND Current recommendations for screening for monoclonal gammopathies include serum protein electrophoresis (PEL), imunofixation electrophoresis (IFE), and free light chain (FLC) ratios to identify or rule out an M-protein. The aim of this study was to examine the feasibility of an assay based on immunoenrichment and MALDI-TOF-MS (MASS-SCREEN) to qualitatively screen for M-proteins. METHODS Serum from 556 patients previously screened for M-proteins by PEL and IFE were immunopurified using a κ/λ-specific nanobody bead mixture. Following purification, light chains (LC) were released from their heavy chains by reduction. MALDI-TOF analysis was performed and the mass-to-charge LC distributions were visually examined for the presence of an M-protein by both unblinded and blinded analysts. RESULTS In unblinded analysis, MASS-SCREEN detected 100% of the PEL-positive samples with an analytical sensitivity and specificity of 96% and 81% using IFE positivity as the standard. In a blinded analysis using 6 different laboratory personnel, consensus was reached in 92% of the samples. Overall analytical sensitivity and specificity were reduced to 92% and 80%, respectively. FLC ratios were found to be abnormal in 28% of MASS-SCREEN–negative samples, suggesting FLC measurements need to be considered in screening. CONCLUSIONS MASS-SCREEN could replace PEL in a panel that would include FLC measurements. Further studies and method development should be performed to validate the clinical sensitivity and specificity and to determine if this panel will suffice as a general screen for monoclonal proteins.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.