Prabhakar Chalise

Effects of Immunonutrition for Cystectomy on Immune Response and Infection Rates: A Pilot Randomized Controlled Clinical Trial

UNLABELLED After radical cystectomy (RC), patients are at risk for complications including infections. The expansion of myeloid-derived suppressor cells (MDSCs) after surgery may contribute to the lower resistance to infection. Immune response and postoperative complications were compared in men consuming either specialized immunonutrition (SIM; n=14) or an oral nutrition supplement (ONS; n=15) before and after RC. MDSC count (Lin- CD11b+ CD33+) was significantly different between the groups over time (p=0.005) and significantly lower in SIM 2 d after RC (p<0.001). MDSC count expansion from surgery to 2 d after RC showed a weak association with an increase in infection rate 90 d after surgery (p=0.061). Neutrophil:lymphocyte ratio was significantly lower in SIM compared with ONS 3h after the first incision (p=0.039). Participants receiving SIM had a 33% reduction in postoperative complication rate (95% confidence interval [CI], 1-64; p=0.060) and a 39% reduction in infection rate (95% CI, 8-70; p=0.027) during late-phase recovery. The small sample size limits the study findings. PATIENT SUMMARY Results show that the immune response to surgery and late infection rates differ between radical cystectomy patients receiving specialized immunonutrition versus oral nutrition supplement in the perioperative period. TRIAL REGISTRATION ClinicalTrials.gov NCT01868087.

Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer

BackgroundBoth genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and the extent to which they interact to impact disease risk. In the present study, we aimed to gain insight into this relationship by identifying DNA methylation marks that are candidate mediators of ovarian cancer genetic risk.MethodsWe used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). The Causal Inference Test (CIT) was implemented to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype.ResultsControlling for the estimated distribution of immune cells and other key covariates, our initial epigenome-wide association analysis revealed 1,993 significantly differentially methylated CpGs that between cases and controls (FDR, q < 0.05). The relationship between methylation and case-control status for these 1,993 CpGs was found to be highly consistent with the results of previously published, independent study that consisted of peripheral blood DNA methylation signatures in 131 pretreatment cases and 274 controls. Implementation of the CIT test revealed 17 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. Of these 13 CpGs, several are associated with immune related genes and genes that have been previously shown to exhibit altered expression in the context of cancer.ConclusionsThese findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility.

Comparison of penalty functions for sparse canonical correlation analysis

Canonical correlation analysis (CCA) is a widely used multivariate method for assessing the association between two sets of variables. However, when the number of variables far exceeds the number of subjects, such in the case of large-scale genomic studies, the traditional CCA method is not appropriate. In addition, when the variables are highly correlated the sample covariance matrices become unstable or undefined. To overcome these two issues, sparse canonical correlation analysis (SCCA) for multiple data sets has been proposed using a Lasso type of penalty. However, these methods do not have direct control over sparsity of solution. An additional step that uses Bayesian Information Criterion (BIC) has also been suggested to further filter out unimportant features. In this paper, a comparison of four penalty functions (Lasso, Elastic-net, SCAD and Hard-threshold) for SCCA with and without the BIC filtering step have been carried out using both real and simulated genotypic and mRNA expression data. This study indicates that the SCAD penalty with BIC filter would be a preferable penalty function for application of SCCA to genomic data.

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.

In vivo nicotine exposure in the zebra finch: A promising innovative animal model to use in neurodegenerative disorders related research

Nicotine improves cognitive enhancement and there are indications that neurodegenerative (age-related) cognitive disorders could be treated with nicotine-based drugs. The zebra finch is a well-recognized model to study cognitive functioning; hence this model could be used to study the effects of nicotine in neurodegenerative cognitive disorders. However, nicotines in vivo physiological and behavioral effects have never been studied in the zebra finch. Here we present the first in vivo nicotine study in zebra finches. We evaluated the dose-response effects of nicotine on locomotor activity, song production, food intake and body weight. A liquid chromatography tandem mass spectrometry method was developed and validated for quantification of nicotine and cotinine in feces. The subcutaneous nicotine drug regiment (0.054-0.54mg/kg) induced physiologically significant values of nicotine and cotinine. The mid (0.18mg/kg) and high (0.54mg/kg) dose of nicotine promoted the development and expression of a sensitized response of song production and locomotor activity. Food intake and body weight were not affected following nicotine exposure. In conclusion, the zebra finch can be used as an innovative animal model not only in nicotine-related research studying cognitive functioning, but also in studies examining nicotine dependence and addictive mechanisms.

Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis.

Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results from these analyses found little overlap in terms of genes detected, possibly detecting different biological mechanisms. In addition, we found the proposed weighted SCCA to outperform its unweighted counterpart in detecting associations between the genomic features and phenotype. Further research is needed to develop and assess new integrative methods for pharmacogenomic studies, as these types of analyses may uncover novel insights into the relationship between genomic variation and drug response.

Performance and Prediction for Varying Survival Time Scales

The Cox proportional hazards model is widely used for analyzing associations between risk factors and occurrences of events. One of the essential requirements of defining Cox proportional hazards model is the choice of a unique and well-defined time scale. Two time scales are generally used in epidemiological studies: time-on-study and chronological age. The former is the most frequently used time scale, both in clinical studies and longitudinal observation studies. However, there is no general consensus on which time scale is the most appropriate for a given question or study. In this article, we address the question of robustness of the results using one time scale when the other is actually the correct one. We use three criteria to measure the performances of these models through simulations: magnitude of the bias of the regression coefficients, mean square errors, and the measure of overall predictive discrimination of the models. We conclude that the time-on-study models are more robust to misspecification of the underlying time scale.

Baseline age effect on parameter estimates in Cox models

The Cox proportional hazards model is widely used in time-to-event analysis. Two time scales are used in practice: time-on-study and chronological age. The former is the most frequently used time scale in clinical studies and longitudinal observation studies. However, there is no general consensus about which time scale is the best. It has been asserted that if the cumulative baseline hazard is exponential or if the age-at-entry is independent of the covariate, then the two models are equivalent. We show that neither of these conditions leads to equivalency. Variability in the age-at-entry of individuals in the study causes the models to differ significantly. This is shown both analytically and through a simulation study. Additionally, we show that the time-on-study model is more robust to changes in age-at-entry than the chronological age model.

Germline Copy Number Variation and Ovarian Cancer Survival

Copy number variants (CNVs) have been implicated in many complex diseases. We examined whether inherited CNVs were associated with overall survival among women with invasive epithelial ovarian cancer. Germline DNA from 1,056 cases (494 deceased, average of 3.7 years follow-up) was interrogated with the Illumina 610 quad genome-wide array containing, after quality control exclusions, 581,903 single nucleotide polymorphisms (SNPs) and 17,917 CNV probes. Comprehensive analysis capitalized upon the strengths of three complementary approaches to CNV classification. First, to identify small CNVs, single markers were evaluated and, where associated with survival, consecutive markers were combined. Two chromosomal regions were associated with survival using this approach (14q31.3 rs2274736 p = 1.59 × 10−6, p = 0.001; 22q13.31 rs2285164 p = 4.01 × 10−5, p = 0.009), but were not significant after multiple testing correction. Second, to identify large CNVs, genome-wide segmentation was conducted to characterize chromosomal gains and losses, and association with survival was evaluated by segment. Four regions were associated with survival (1q21.3 loss p = 0.005, 5p14.1 loss p = 0.004, 9p23 loss p = 0.002, and 15q22.31 gain p = 0.002); however, again, after correcting for multiple testing, no regions were statistically significant, and none were in common with the single marker approach. Finally, to evaluate associations with general amounts of copy number changes across the genome, we estimated CNV burden based on genome-wide numbers of gains and losses; no associations with survival were observed (p > 0.40). Although CNVs that were not well-covered by the Illumina 610 quad array merit investigation, these data suggest no association between inherited CNVs and survival after ovarian cancer.

Time Scales in Epidemiological Analysis: An Empirical Comparison

The Cox proportional hazards model is routinely used to analyze time-to-event data. To use this model requires the definition of a unique well-defined time scale. Most often, observation time is used as the time scale for both clinical and observational studies. Recently after a suggestion that it may be a more appropriate scale, chronological age has begun to appear as the time scale used in some reports. There appears to be no general consensus about which time scale is appropriate for any given analysis. It has been suggested that if the baseline hazard is exponential or if the age-at-entry is independent of covariates used in the model, then the two time scales provide similar results. In this report we provide an empirical examination of the results using the two different time scales using a large collection of data sets to examine the relationship between systolic blood pressure and coronary heart disease death (CHD death). We demonstrate, in this empirical example that the two time-scales can lead to differing results even when these two conditions appear to hold.