La Creis R. Kidd

Association of IL-10 polymorphisms with prostate cancer risk and grade of disease.

Animal and inxa0vitro models of prostate cancer demonstrate high IL-10 levels result in smaller tumors, fewer metastases, and reduced angiogenesis compared to controls. We sought to examine the hypothesis that genotypes correlated with low IL-10 production may be associated with increased prostate cancer risk among Finnish male participants from the Alpha-tocopherol Beta-carotene Cancer Prevention Study. DNA from 584 prostate cancer cases and 584 controls was genotyped for four IL-10 alleles, −1082, −819, −592, and 210. DNA from more of the controls than cases failed to amplify, resulting in 509 cases and 382 controls with genotype data for −1082; 507 and 384 for −819; 511 and 386 for −592; and 491 and 362 for 210. Odds ratios for the association between the IL-10 genotypes and risk of prostate cancer or, among cases only, high-grade disease were calculated using logistic regression. In this population, the −819 TT and −592 AA low expression genotypes were highly correlated. These two genotypes also were associated with increased prostate cancer susceptibility (ORxa0=xa01.92, 95% CI 1.07–3.43 for −819) and, among cases, with high-grade tumors (ORxa0=xa02.56, 95% CI 1.26–5.20 for −819). These data demonstrate genotypes correlated with low IL-10 production are associated with increased risk of prostate cancer and with high-grade disease in this population.

Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among Men of African Descent: A case-control study

BackgroundPolymorphisms in glutathione S-transferase (GST) genes may influence response to oxidative stress and modify prostate cancer (PCA) susceptibility. These enzymes generally detoxify endogenous and exogenous agents, but also participate in the activation and inactivation of oxidative metabolites that may contribute to PCA development. Genetic variations within selected GST genes may influence PCA risk following exposure to carcinogen compounds found in cigarette smoke and decreased the ability to detoxify them. Thus, we evaluated the effects of polymorphic GSTs (M1, T1, and P1) alone and combined with cigarette smoking on PCA susceptibility.MethodsIn order to evaluate the effects of GST polymorphisms in relation to PCA risk, we used TaqMan allelic discrimination assays along with a multi-faceted statistical strategy involving conventional and advanced statistical methodologies (e.g., Multifactor Dimensionality Reduction and Interaction Graphs). Genetic profiles collected from 873 men of African-descent (208 cases and 665 controls) were utilized to systematically evaluate the single and joint modifying effects of GSTM1 and GSTT1 gene deletions, GSTP1 105 Val and cigarette smoking on PCA risk.ResultsWe observed a moderately significant association between risk among men possessing at least one variant GSTP1 105 Val allele (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049), which was confirmed by MDR permutation testing (p = 0.001). We did not observe any significant single gene effects among GSTM1 (OR = 1.08; 95%CI = 0.65-1.82; p = 0.718) and GSTT1 (OR = 1.15; 95%CI = 0.66-2.02; p = 0.622) on PCA risk among all subjects. Although the GSTM1-GSTP1 pairwise combination was selected as the best two factor LR and MDR models (p = 0.01), assessment of the hierarchical entropy graph suggested that the observed synergistic effect was primarily driven by the GSTP1 Val marker. Notably, the GSTM1-GSTP1 axis did not provide additional information gain when compared to either loci alone based on a hierarchical entropy algorithm and graph. Smoking status did not significantly modify the relationship between the GST SNPs and PCA.ConclusionA moderately significant association was observed between PCA risk and men possessing at least one variant GSTP1 105 Val allele (p = 0.049) among men of African descent. We also observed a 2.1-fold increase in PCA risk associated with men possessing the GSTP1 (Val/Val) and GSTM1 (*1/*1 + *1/*0) alleles. MDR analysis validated these findings; detecting GSTP1 105 Val (p = 0.001) as the best single factor for predicting PCA risk. Our findings emphasize the importance of utilizing a combination of traditional and advanced statistical tools to identify and validate single gene and multi-locus interactions in relation to cancer susceptibility.

Interaction Among Variant Vascular Endothelial Growth Factor (VEGF) and Its Receptor in Relation to Prostate Cancer Risk

Prostate cancer (PCa) incidence and mortality are disproportionately high among African‐American (AA) men. Its detection and perhaps its disparities could be improved through the identification of genetic susceptibility biomarkers within essential biological pathways. Interactions among highly variant genes, central to angiogenesis, may modulate susceptibility for prostate cancer, as previous demonstrated. This study evaluates the interplay among three highly variant genes (i.e., IL‐10, TGFβR‐1, VEGF), their receptors and their influence on PCa within a case‐control study consisting of an under‐served population.

Multi-institutional prostate cancer study of genetic susceptibility in populations of African descent

Prostate cancer disparities have been reported in men of African descent who show the highest incidence, mortality, compared with other ethnic groups. Few studies have explored the genetic and environmental factors for prostate cancer in men of African ancestry. The glutathione-S-transferases family conjugates carcinogens before their excretion and is expressed in prostate tissue. This study addressed the role of GSTM1 and GSTT1 deletions on prostate cancer risk in populations of African descent. This multi-institutional case-control study gathered data from the Genetic Susceptibility to Environmental Carcinogens (GSEC) database, the African-Caribbean Cancer Consortium (AC3) and Men of African Descent and Carcinoma of the Prostate Consortium (MADCaP). The analysis included 10 studies (1715 cases and 2363 controls), five in African-Americans, three in African-Caribbean and two in African men. Both the GSTM1 and the GSTT1 deletions showed significant inverse associations with prostate cancer [odds ratio (OR): 0.90, 95% confidence interval (CI) 0.83-0.97 and OR 0.88, 95% CI: 0.82-0.96, respectively]. The association was restricted to Caribbean and African populations. A significant positive association was observed between GSTM1 deletion and prostate cancer in smokers in African-American studies (OR: 1.28, 95% CI: 1.01-1.56), whereas a reduced risk was observed in never-smokers (OR: 0.66, 95% CI: 0.46-0.95). The risk of prostate cancer increased across quartiles of pack-years among subjects carrying the deletion of GSTM1 but not among subjects carrying a functional GSTM1. Gene-environment interaction between smoking and GSTM1 may be involved in the etiology of prostate cancer in populations of African descent.

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

BackgroundMolecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.MethodsTo address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.ResultsFollowing LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011).ConclusionsIn summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators—such as synthetic feature constructors—that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

No Association between Variant DNA Repair Genes and Prostate Cancer Risk among Men of African descent

Recent reports hypothesize that multiple variant DNA repair gene interactions influence cancer susceptibility. However, studies identifying high‐risk cancer‐related genes use single gene approaches that lack the statistical rigor to model higher order interactions.

No Association Between Variant N-acetyltransferase Genes, Cigarette Smoking and Prostate Cancer Susceptibility Among Men of African Descent

Objective We evaluated the individual and combination effects of NAT1, NAT2 and tobacco smoking in a case-control study of 219 incident prostate cancer (PCa) cases and 555 disease-free men. Methods Allelic discriminations for 15 NAT1 and NAT2 loci were detected in germ-line DNA samples using Taqman polymerase chain reaction (PCR) assays. Single gene, gene-gene and gene-smoking interactions were analyzed using logistic regression models and multi-factor dimensionality reduction (MDR) adjusted for age and subpopulation stratification. MDR involves a rigorous algorithm that has ample statistical power to assess and visualize gene-gene and gene-environment interactions using relatively small samples sizes (i.e., 200 cases and 200 controls). Results Despite the relatively high prevalence of NAT1*10/*10 (40.1%), NAT2 slow (30.6%), and NAT2 very slow acetylator genotypes (10.1%) among our study participants, these putative risk factors did not individually or jointly increase PCa risk among all subjects or a subset analysis restricted to tobacco smokers. Conclusion Our data do not support the use of N-acetyltransferase genetic susceptibilities as PCa risk factors among men of African descent; however, subsequent studies in larger sample populations are needed to confirm this finding.

Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing

Given infinite time, humans would progress through modeling complex data in a manner that is dependent on prior expert knowledge. The goal of the present study is make extensions and enhancements to a computational evolution system (CES) that has the ultimate objective of tinkering with data as a human would. This is accomplished by providing flexibility in the model-building process and a meta-layer that learns how to generate better models. The key to the CES system is the ability to identify and exploit expert knowledge from biological databases or prior analytical results. Our prior results have demonstrated that CES is capable of efficiently navigating these large and rugged fitness landscapes toward the discovery of biologically meaningful genetic models of disease. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical or biological expert knowledge. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of Pareto-optimization to help address overfitting in the learning system. We further introduce a post-processing step that uses hierarchical cluster analysis to generate expert knowledge from the landscape of best models and their predictions across patients. We find that the combination of Pareto-optimization and post-processing of results greatly improves the genetic analysis of prostate cancer.

Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer

The paradigm of identifying genetic risk factors for common human diseases by analyzing one DNA sequence variation at a time is quickly being replaced by research strategies that embrace the multivariate complexity of the genotype to phenotype mapping relationship that is likely due, in part, to nonlinear interactions among many genetic and environmental factors. Embracing the complexity of common diseases such as cancer requires powerful computational methods that are able to model nonlinear interactions in high-dimensional genetic data. Previously, we have addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods, such as genetic programming. Our results have demonstrated that CES is capable of efficiently navigating these large and rugged fitness landscapes toward the discovery of biologically meaningful genetic models of disease predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge, derived from a family of machine learning techniques known as Relief, or biological expert knowledge, derived from sources such as protein-protein interaction databases. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of 3D visualization methods to identify interesting patterns in CES results. Information extracted from the visualization through human-computer interaction are then provide as expert knowledge to newCES runs in a cascading framework. We present aCES-derived multivariate classifier and provide a statistical and biological interpretation in the context of prostate cancer prediction. The incorporation of human-computer interaction into CES provides a first step towards an interactive discovery system where the experts can be embedded in the computational discovery process. Our working hypothesis is that this type of human-computer interaction will provide more useful results for complex problem solving than the traditional black box machine learning approach.