Samprit Banerjee

Perioperative Comparative Effectiveness of Anesthetic Technique in Orthopedic Patients

Background:The impact of anesthetic technique on perioperative outcomes remains controversial. We studied a large national sample of primary joint arthroplasty recipients and hypothesized that neuraxial anesthesia favorably influences perioperative outcomes. Methods:Data from approximately 400 hospitals between 2006 and 2010 were accessed. Patients who underwent primary hip or knee arthroplasty were identified and subgrouped by anesthesia technique: general, neuraxial, and combined neuraxial–general. Demographics, postoperative complications, 30-day mortality, length of stay, and patient cost were analyzed and compared. Multivariable analyses were conducted to identify the independent impact of choice of anesthetic on outcomes. Results:Of 528,495 entries of patients undergoing primary hip or knee arthroplasty, information on anesthesia type was available for 382,236 (71.4%) records. Eleven percent were performed under neuraxial, 14.2% under combined neuraxial–general, and 74.8% under general anesthesia. Average age and comorbidity burden differed modestly between groups. When neuraxial anesthesia was used, 30-day mortality was significantly lower (0.10, 0.10, and 0.18%; P < 0.001), as was the incidence of prolonged (>75th percentile) length of stay, increased cost, and in-hospital complications. In the multivariable regression, neuraxial anesthesia was associated with the most favorable complication risk profile. Thirty-day mortality remained significantly higher in the general compared with the neuraxial or neuraxial–general group for total knee arthroplasty (adjusted odds ratio [OR] of 1.83, 95% CI 1.08–3.1, P = 0.02; OR of 1.70, 95% CI 1.06–2.74, P = 0.02, respectively). Conclusions:The utilization of neuraxial versus general anesthesia for primary joint arthroplasty is associated with superior perioperative outcomes. More research is needed to study potential mechanisms for these findings.

Epigenetic Repression of miR-31 Disrupts Androgen Receptor Homeostasis and Contributes to Prostate Cancer Progression

Androgen receptor signaling plays a critical role in prostate cancer pathogenesis. Yet, the regulation of androgen receptor signaling remains elusive. Even with stringent androgen deprivation therapy, androgen receptor signaling persists. Here, our data suggest that there is a complex interaction between the expression of the tumor suppressor miRNA, miR-31, and androgen receptor signaling. We examined primary and metastatic prostate cancer and found that miR-31 expression was reduced as a result of promoter hypermethylation, and importantly, the levels of miR-31 expression were inversely correlated with the aggressiveness of the disease. As the expression of androgen receptor and miR-31 was inversely correlated in the cell lines, our study further suggested that miR-31 and androgen receptor could mutually repress each other. Upregulation of miR-31 effectively suppressed androgen receptor expression through multiple mechanisms and inhibited prostate cancer growth in vivo. Notably, we found that miR-31 targeted androgen receptor directly at a site located in the coding region, which was commonly mutated in prostate cancer. In addition, miR-31 suppressed cell-cycle regulators including E2F1, E2F2, EXO1, FOXM1, and MCM2. Together, our findings suggest a novel androgen receptor regulatory mechanism mediated through miR-31 expression. The downregulation of miR-31 may disrupt cellular homeostasis and contribute to the evolution and progression of prostate cancer. We provide implications for epigenetic treatment and support clinical development of detecting miR-31 promoter methylation as a novel biomarker.

Bayesian Quantitative Trait Loci Mapping for Multiple Traits

Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genomewide mapping strategy for correlated traits in the literature. We develop Bayesian multiple-QTL mapping methods for correlated continuous traits using two multivariate models: one that assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model. Our methods have been implemented in the freely available package R/qtlbim (http://www.qtlbim.org), which greatly facilitates the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits.

Mechanism-Based Epigenetic Chemosensitization Therapy of Diffuse Large B-Cell Lymphoma

UNLABELLED Although aberrant DNA methylation patterning is a hallmark of cancer, the relevance of targeting DNA methyltransferases (DNMT) remains unclear for most tumors. In diffuse large B-cell lymphoma (DLBCL) we observed that chemoresistance is associated with aberrant DNA methylation programming. Prolonged exposure to low-dose DNMT inhibitors (DNMTI) reprogrammed chemoresistant cells to become doxorubicin sensitive without major toxicity in vivo. Nine genes were recurrently hypermethylated in chemoresistant DLBCL. Of these, SMAD1 was a critical contributor, and reactivation was required for chemosensitization. A phase I clinical study was conducted evaluating azacitidine priming followed by standard chemoimmunotherapy in high-risk patients newly diagnosed with DLBCL. The combination was well tolerated and yielded a high rate of complete remission. Pre- and post-azacitidine treatment biopsies confirmed SMAD1 demethylation and chemosensitization, delineating a personalized strategy for the clinical use of DNMTIs. SIGNIFICANCE The problem of chemoresistant DLBCL remains the most urgent challenge in the clinical management of patients with this disease. We describe a mechanism-based approach toward the rational translation of DNMTIs for the treatment of high-risk DLBCL.

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses

UNLABELLED R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). AVAILABILITY The package is freely available from cran.r-project.org.

Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

We develop hierarchical generalized linear models and computationally efficient algorithms for genomewide analysis of quantitative trait loci (QTL) for various types of phenotypes in experimental crosses. The proposed models can fit a large number of effects, including covariates, main effects of numerous loci, and gene–gene (epistasis) and gene–environment (G × E) interactions. The key to the approach is the use of continuous prior distribution on coefficients that favors sparseness in the fitted model and facilitates computation. We develop a fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the package R. We propose a model search strategy to build a parsimonious model. Our method takes advantage of the special correlation structure in QTL data. Simulation studies demonstrate reasonable power to detect true effects, while controlling the rate of false positives. We illustrate with three real data sets and compare our method to existing methods for multiple-QTL mapping. Our method has been implemented in our freely available package R/qtlbim (www.qtlbim.org), providing a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach.

An Efficient Bayesian Model Selection Approach for Interacting Quantitative Trait Loci Models With Many Effects

We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses to include environmental effects and gene–environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more probable models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene–sex interactions for obesity-related traits in two real data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.

Genetic variation of genes involved in dihydrotestosterone metabolism and the risk of prostate cancer.

Purpose: Dihydrotestosterone (DHT) is an important factor in prostate cancer (PCA) genesis and disease progression. Given PCAs strong genetic component, we evaluated the possibility that variation in genes involved in DHT metabolism influence PCA risk. Experimental Design: We investigated copy number variants (CNV) and single nucleotide polymorphisms (SNP). We explored associations between CNV of uridine diphospho-glucuronosyltransferase (UGT) genes from the 2B subclass, given their prostate specificity and/or involvement in steroid metabolism and PCA risk. We also investigated associations between SNPs in genes (HSD3B1, SRD5A1/2, and AKR1C2) involved in the conversion of testosterone to DHT, and in DHT metabolism and PCA risk. The population consisted of 426 men (205 controls and 221 cases) who underwent prostate-specific antigen screening as part of a PCA early detection program in Tyrol, Austria. Results: No association between CNV in UGT2B17 and UGT2B28 and PCA risk was identified. Men carrying the AA genotype at SNP rs6428830 (HSD3B1) had an odds ratio (OR) of 2.0 [95% confidence intervals (95% CI), 1.1-4.1] compared with men with GG, and men with AG or GG versus AA in rs1691053 (SRD5A1) had an OR of 1.8 (95% CI, 1.04-3.13). Individuals carrying both risk alleles had an OR of 3.1 (95% CI, 1.4-6.7) when compared with men carrying neither (P = 0.005). Controls with the AA genotype on rs7594951 (SRD5A2) tended toward higher serum DHT levels (P = 0.03). Conclusions: This is the first study to implicate the 5α-reductase isoform 1 (SRD5A1) and PCA risk, supporting the rationale of blocking enzymatic activity of both isoforms of 5α-reductase for PCA chemoprevention. Cancer Epidemiol Biomarkers Prev; 19(1); 229–39

Bayesian mapping of genomewide interacting quantitative trait loci for ordinal traits.

Development of statistical methods and software for mapping interacting QTL has been the focus of much recent research. We previously developed a Bayesian model selection framework, based on the composite model space approach, for mapping multiple epistatic QTL affecting continuous traits. In this study we extend the composite model space approach to complex ordinal traits in experimental crosses. We jointly model main and epistatic effects of QTL and environmental factors on the basis of the ordinal probit model (also called threshold model) that assumes a latent continuous trait underlies the generation of the ordinal phenotypes through a set of unknown thresholds. A data augmentation approach is developed to jointly generate the latent data and the thresholds. The proposed ordinal probit model, combined with the composite model space framework for continuous traits, offers a convenient way for genomewide interacting QTL analysis of ordinal traits. We illustrate the proposed method by detecting new QTL and epistatic effects for an ordinal trait, dead fetuses, in a F2 intercross of mice. Utility and flexibility of the method are also demonstrated using a simulated data set. Our method has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of the Bayesian methodology for genomewide interacting QTL analysis for continuous, binary, and ordinal traits in experimental crosses.

Identification of functionally active, low frequency copy number variants at 15q21.3 and 12q21.31 associated with prostate cancer risk

Copy number variants (CNVs) are a recently recognized class of human germ line polymorphisms and are associated with a variety of human diseases, including cancer. Because of the strong genetic influence on prostate cancer, we sought to identify functionally active CNVs associated with susceptibility of this cancer type. We queried low-frequency biallelic CNVs from 1,903 men of Caucasian origin enrolled in the Tyrol Prostate Specific Antigen Screening Cohort and discovered two CNVs strongly associated with prostate cancer risk. The first risk locus (P = 7.7 × 10−4, odds ratio = 2.78) maps to 15q21.3 and overlaps a noncoding enhancer element that contains multiple activator protein 1 (AP-1) transcription factor binding sites. Chromosome conformation capture (Hi-C) data suggested direct cis-interactions with distant genes. The second risk locus (P = 2.6 × 10−3, odds ratio = 4.8) maps to the α-1,3-mannosyl-glycoprotein 4-β-N-acetylglucosaminyltransferase C (MGAT4C) gene on 12q21.31. In vitro cell-line assays found this gene to significantly modulate cell proliferation and migration in both benign and cancer prostate cells. Furthermore, MGAT4C was significantly overexpressed in metastatic versus localized prostate cancer. These two risk associations were replicated in an independent PSA-screened cohort of 800 men (15q21.3, combined P = 0.006; 12q21.31, combined P = 0.026). These findings establish noncoding and coding germ line CNVs as significant risk factors for prostate cancer susceptibility and implicate their role in disease development and progression.