Model of Epstein-Barr virus infection of human thymocytes: expression of viral genome and impact on cellular receptor expression in the T- lymphoblastic cell line, HPB-ALL

Infection of B lymphocytes and epithelial tissue by Epstein-Barr virus (EBV) is associated with malignancy and autoimmunity. The cellular receptor for EBV has been identified as CD21 (CR2). A molecule, which is biochemically and immunologically similar to B-cell CD21, has been identified on a subpopulation of immature thymocytes, suggesting a role for this molecule in the regulation of T-cell development and further suggesting that immature T cells might be susceptible to EBV infection. A growing body of literature now documents the presence of EBV in tumors of T-cell origin. We have evaluated the susceptibility of the human immature T cell line, HPB-ALL, to infection by EBV. Electron microscopy studies showed a rapid internalization of virus by HPB cells. Southern blotting showed the intracellular presence of linear EBV genomes, and components of the virus replicative cycle were identified. Expression of the BamHI Z region of the genome, encoding the nuclear protein, ZEBRA, which is strictly associated with productive infection in B cells, was detected in HPB-ALL cells. A spliced variant of Z, RAZ, was also identified. Cell surface expression of EBV late antigens was observed to occur transiently. Infection of HPB cells was also accompanied by altered expression of T-cell surface molecules involved in antigen recognition, a process critical to normal development of the T-cell repertoire. Delineation of the outcome of T- cell infection by EBV may lead to a better understanding of the role of this virus in autoimmune processes and malignancy.     

A semiparametric joint model for terminal trend of quality of life and survival in palliative care research

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients’ diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub‐models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.     

PreCimp: Pre‐collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables

Imputation is widely used for obtaining information about rare variants. However, one issue concerning imputation is the low accuracy of imputed rare variants as the inaccurate imputed rare variants may distort the results of region‐based association tests. Therefore, we developed a pre‐collapsing imputation method (PreCimp) to improve the accuracy of imputation by using collapsed variables. Briefly, collapsed variables are generated using rare variants in the reference panel, and a new reference panel is constructed by inserting pre‐collapsed variables into the original reference panel. Following imputation analysis provides the imputed genotypes of the collapsed variables. We demonstrated the performance of PreCimp on 5,349 genotyped samples using a Korean population specific reference panel including 848 samples of exome sequencing, Affymetrix 5.0, and exome chip. PreCimp outperformed a traditional post‐collapsing method that collapses imputed variants after single rare variant imputation analysis. Compared with the results of post‐collapsing method, PreCimp approach was shown to relatively increase imputation accuracy about 3.4–6.3% when dosage r² is between 0.6 and 0.8, 10.9–16.1% when dosage r² is between 0.4 and 0.6, and 21.4 ～ 129.4% when dosage r² is below 0.4.     

Joint modeling quality of life and survival using a terminal decline model in palliative care studies

Palliative medicine is a relatively new specialty that focuses on preventing and relieving the suffering of patients facing life‐threatening illness. For cancer patients, clinical trials have been carried out to compare concurrent palliative care with usual cancer care in terms of longitudinal measurements of quality of life (QOL) until death, and overall survival is usually treated as a secondary endpoint. It is known that QOL of patients with advanced cancer decreases as death approaches; however, in previous clinical trials, this association has generally not been taken into account when inferences about the effect of an intervention on QOL or survival have been made. We developed a new joint modeling approach, a terminal decline model, to study the trajectory of repeated measurements and survival in a recently completed palliative care study. This approach takes the association of survival and QOL into account by modeling QOL retrospectively from death. For those patients whose death times are censored, marginal likelihood is used to incorporate them into the analysis. Our approach has two submodels: a piecewise linear random intercept model with serial correlation and measurement error for the retrospective trajectory of QOL and a piecewise exponential model for the survival distribution. Maximum likelihood estimators of the parameters are obtained by maximizing the closed‐form expression of log‐likelihood function. An explicit expression of quality‐adjusted life years can also be derived from our approach. We present a detailed data analysis of our previously reported palliative care randomized clinical trial. Copyright © 2012 John Wiley & Sons, Ltd.     

Factors Associated with Preoperative Magnetic Resonance Imaging Use among Medicare Beneficiaries with Nonmetastatic Breast Cancer

Preoperative breast magnetic resonance imaging (MRI) use among Medicare beneficiaries with breast cancer has substantially increased from 2005 to 2009. We sought to identify factors associated with preoperative breast MRI use among women diagnosed with ductal carcinoma in situ (DCIS) or stage I–III invasive breast cancer (IBC). Using Surveillance, Epidemiology, and End Results and Medicare data from 2005 to 2009 we identified women ages 66 and older with DCIS or stage I–III IBC who underwent breast‐conserving surgery or mastectomy. We compared preoperative breast MRI use by patient, tumor and hospital characteristics stratified by DCIS and IBC using multivariable logistic regression. From 2005 to 2009, preoperative breast MRI use increased from 5.9% to 22.4% of women diagnosed with DCIS and 7.0% to 24.3% of women diagnosed with IBC. Preoperative breast MRI use was more common among women who were younger, married, lived in higher median income zip codes and had no comorbidities. Among women with IBC, those with lobular disease, smaller tumors (<1 cm) and those with estrogen receptor negative tumors were more likely to receive preoperative breast MRI. Women with DCIS were more likely to receive preoperative MRI if tumors were larger (>2 cm). The likelihood of receiving preoperative breast MRI is similar for women diagnosed with DCIS and IBC. Use of MRI is more common in women with IBC for tumors that are lobular and smaller while for DCIS MRI is used for evaluation of larger lesions.     

Increased soluble interleukin-1 type II receptor concentrations in postoperative patients and in patients with sepsis syndrome

Plasma interleukin-1 (IL-1) activity is modulated in part through the simultaneous appearance of several inhibitors of IL-1 action, including interleukin-1 receptor antagonist (IL-1ra) and the soluble IL-1 type II receptor (IL-1RII). However, little is known concerning the plasma appearance of these inhibitors in patients following operative trauma or those with sepsis syndrome. In the present report, plasma IL-1beta, IL-1ra, and soluble IL-1RI and IL-1RII concentrations were evaluated in 118 patients with sepsis syndrome or after elective operative trauma. Plasma concentrations of IL-1ra increased significantly following elective operative repair of thoraco-abdominal and abdominal aortic aneurysms, and after bowel resection for inflammatory bowel disease, but did not increase after laparoscopic cholecystectomy. Plasma IL-1ra levels were also elevated in patients with sepsis syndrome. In contrast, soluble IL-1RII levels were only increased in patients after operative repair of thoraco-abdominal aortic aneurysms and in sepsis syndrome, whereas concentrations were unaffected by the other more modest surgical procedures. Plasma IL-1RI concentrations decreased in all postoperative patients in the first 24 hours after surgery. We conclude that both plasma IL-1ra and soluble IL-1RII concentrations often increase in sepsis and following some operative trauma. Less severe operative trauma increases the plasma concentration of only IL- 1ra, whereas both IL-1ra and soluble IL-1RII are increased in patients with sepsis syndrome or following thoraco-abdominal aneurysm repair.     

Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility

Study results of single nucleotide polymorphisms (SNPs) and cancer susceptibility are often conflicting, possibly because of the analytic challenges of testing for multiple genetic and environmental risk factors using traditional analytic tools. We investigated the relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility in 355 cases and 559 controls enrolled in a population-based study of bladder cancer in the US. Our multifaceted analytical approach included logistic regression, multifactor dimensionality reduction, and hierarchical interaction graphs for the analysis of gene-gene and gene-environment interactions followed by linkage disequilibrium and haplotype analysis. Overall, we did not find an association between any single DNA repair gene SNP and bladder cancer risk. We did find a marginally significant elevated risk of the XPD codon 751 homozygote variant among never smokers [adjusted odds ratio (OR) 2.5, 95% confidence interval (CI) 1.0-6.2]. In addition, the XRCC1 194 variant allele was associated with a reduced bladder cancer risk among heavy smokers [adjusted OR 0.4, 95% CI 0.2-0.9)]. The best predictors of bladder cancer included the XPD codon 751 and 312 SNPs along with smoking. Interpretation of this multifactor model revealed that the relationship between the XPD SNPs and bladder cancer is mostly non-additive while the effect of smoking is mostly additive. Since the two XPD SNPs are in significant linkage disequilibrium (D' = 0.52, P = 0.0001), we estimated XPD haplotypes. Individuals with variant XPD haplotypes were more susceptible to bladder cancer [e.g. adjusted OR 2.5, 95% CI 1.7-3.6] and the effect was magnified when smoking was considered. These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and emphasize the need for a multifaceted statistical approach to identify gene-gene and gene-environment interactions.