Citations

Purpose Recent evidence has suggested a relationship between the baseline quality of life (QOL) self-reported by patients with cancer and genetic disposition. We report an analysis exploring relationships among baseline QOL assessments and candidate genetic variations in a large cohort of patients with lung cancer. Patients and Methods QOL data were provided by 1,299 patients with non–small-cell lung cancer observed at the Mayo Clinic between 1997 and 2007. Overall QOL and subdomains were assessed by either Lung Cancer Symptom Scale or Linear Analog Self Assessment measures; scores were transformed to a scale of 0 to 10, with higher scores representing better status. Baseline QOL scores assessed within 1 year of diagnosis were dichotomized as clinically deficient (CD) or not. A total of 470 single nucleotide polymorphisms (SNPs) in 56 genes of three biologic pathways were assessed for association with QOL measures. Logistic regression with training/validation samples was used to test the association of SNPs with CD QOL. Results Six SNPs on four genes were replicated using our split schemes. Three SNPs in the MGMT gene (adjusted analysis, rs3858300; unadjusted analysis, rs10741191 and rs3852507) from DNA repair pathway were associated with overall QOL. Two SNPs (rs2287396 [GSTZ1] and rs9524885 [ABCC4]) from glutathione metabolic pathway were associated with fatigue in unadjusted analysis. In adjusted analysis, two SNPs (rs2756109 [ABCC2] and rs9524885 [ABCC4]) from glutathione metabolic pathway were associated with pain. Conclusion We identified three SNPs in three glutathione metabolic pathway genes and three SNPs in two DNA repair pathway genes associated with QOL measures in patients with non–small-cell lung cancer.

Purpose Evidence has suggested a clinically meaningful relationship between self-reported quality of life (QOL) of a patient with cancer at the time of receiving a cancer diagnosis and overall survival (OS). This study evaluated the prognostic value of QOL assessments with regard to OS in a large cohort of patients with lung cancer. Patients and Methods A total of 2,442 patients with non–small-cell lung cancer were observed between 1997 and 2007 and completed a single-item measure of overall QOL within the first 6 months of receiving a lung cancer diagnosis; these were dichotomized using an a priori definition of a clinically deficient score (CDS; ≤ 50 v > 50). Kaplan-Meier estimates and Cox models were used to evaluate the prognostic importance of QOL on OS alone and in the presence of covariates. Logistic regression modeling was used to identify which clinical and patient characteristics were related to a clinically meaningful deficit in QOL. Results QOL deficits at time of lung cancer diagnosis were significantly associated with OS (hazard ratio [HR], 1.55; P < .001), as were performance status, older age, smoking history, male sex, treatment factors, and stage of disease. The median survival for patients with CDS QOL was 1.6 years versus 5.6 years for patients with non-CDS QOL. After controlling for all these covariates, the indication of a clinically deficient baseline QOL still contributed significantly to the prediction of patient survival (HR, 0.67; P < .001). Conclusion Overall QOL measured by a simple single item at the time of lung cancer diagnosis is a significant and independent prognostic factor for survival in patients with lung cancer.

We present a case of an intramedullary melanotic schwannoma (IMS) of the thoracic spinal cord. To our knowledge, this is the seventh reported case of an IMS of the central nervous system. Schwannomas are benign nerve sheath tumors of neural crest origin composed entirely of well differentiated Schwann cells that typically occur in peripheral nerves. Both the intramedullary location and the melanotic component of the reported lesion make it exceedingly rare. We will present our case, theories as to the origin of these tumors, clues in radiographic identification, and current clinical follow-up recommendations.

Background and Purpose— American Indians have high rates of stroke. Improved risk stratification could enhance prevention, but the ability of biochemical and echocardiographic markers of preclinical disease to improve stroke prediction is not well-defined. Methods— We evaluated such markers as predictors of ischemic stroke in a community-based cohort of American Indians without prevalent cardiovascular or renal disease. Laboratory markers included C-reactive protein, fibrinogen, urine albumin-to-creatinine ratio, and glycohemoglobin (HbA1c), whereas echocardiographic parameters comprised left atrial diameter, left ventricular mass, mitral annular calcification, and the ratio of early to late mitral diastolic velocities. Predictive performance was judged by indices of discrimination, reclassification, and calibration. Results— After adjustment for standard risk factors, only HbA1c, albuminuria, and left atrial diameter were significantly associated with first ischemic stroke. Addition of HbA1c, although not urine albumin-to-creatinine ratio, to a basic clinical model significantly improved the C-statistic (0.714 versus 0.695; P =0.044), whereas left atrial diameter modestly enhanced integrated discrimination improvement (0.90%; P =0.004), but not the C-statistic (0.701; P =0.528). When combined with HbA1c, left atrial diameter further increased integrated discrimination improvement (1.81%; P <0.001) but not the C-statistic (0.716). No marker achieved significant net reclassification improvement. Conclusions— In this cohort at high cardiometabolic risk, HbA1c emerged as the foremost predictor of ischemic stroke when added to traditional risk factors, affording substantially improved discrimination, with a more modest contribution for left atrial diameter. These findings bolster the role of HbA1c in cardiovascular risk assessment among persons with glycometabolic disorders and provide impetus for further study of the incremental value of echocardiography in high-risk populations.

AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene‐sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene‐sets, we use an efficient computational strategy that accounts for LD and provides accurate step‐down adjustedP‐values for each gene‐set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene‐set analyses.

AbstractOver the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.Genet. Epidemiol. 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.

Abstract Chronic obstructive pulmonary disease (COPD) is a strong risk factor for lung cancer. Published studies about variations of genes encoding glutathione metabolism, DNA repair, and inflammatory response pathways in susceptibility to COPD were inconclusive. We evaluated 470 single-nucleotide polymorphisms (SNP) from 56 genes of these three pathways in 620 cases and 893 controls to identify susceptibility markers for COPD risk, using existing resources. We assessed SNP- and gene-level effects adjusting for sex, age, and smoking status. Differential genetic effects on disease risk with and without lung cancer were also assessed; cumulative risk models were established. Twenty-one SNPs were found to be significantly associated with risk of COPD (P &lt; 0.01); gene-based analyses confirmed two genes (GCLC and GSS) and identified three additional genes (GSTO2, ERCC1, and RRM1). Carrying 12 high-risk alleles may increase risk by 2.7-fold; eight SNPs altered COPD risk without lung cancer by 3.1-fold and 4 SNPs altered the risk with lung cancer by 2.3-fold. Our findings indicate that multiple genetic variations in the three selected pathways contribute to COPD risk through GCLC, GSS, GSTO2, ERCC1, and RRM1 genes. Functional studies are needed to elucidate the mechanisms of these genes in the development of COPD, lung cancer, or both. Cancer Prev Res; 5(3); 365–73. ©2011 AACR.

Ano1 is a recently discovered Ca2+-activated Cl− channel expressed on interstitial cells of Cajal (ICC) that has been implicated in slow-wave activity in the gut. However, Ano1 is expressed on all classes of ICC, even those that do not contribute to generation of the slow wave, suggesting that Ano1 may have an alternate function in these cells. Ano1 is also highly expressed in gastrointestinal stromal tumors. Mice lacking Ano1 had fewer proliferating ICC in whole mount preparations and in culture, raising the possibility that Ano1 is involved in proliferation. Cl− channel blockers decreased proliferation in cells expressing Ano1, including primary cultures of ICC and in the pancreatic cancer-derived cell line, CFPAC-1. Cl− channel blockers had a reduced effect on Ano1(−/−) cultures, confirming that the blockers are acting on Ano1. Ki67 immunoreactivity, 5-ethynyl-2′-deoxyuridine incorporation, and cell-cycle analysis of cells grown in low-Cl− media showed fewer proliferating cells than in cultures grown in regular medium. We confirmed that mice lacking Ano1 had less phosphorylated retinoblastoma protein compared with controls. These data led us to conclude that Ano1 regulates proliferation at the G1/S transition of the cell cycle and may play a role in tumorigenesis.