Molecular epidemiological studies on genetic predispositions to squamous cell carcinoma of the head and neck

Two molecular epidemiological studies were conducted to examine associations between genetic variation and risk of squamous cell carcinoma of the head and neck (SCCHN). In the first study, we hypothesized that genetic variation in p53 response elements (REs) may play roles in the etiology of SCCHN. We selected and genotyped five polymorphic p53 REs as well as a most frequently studied p53 codon 72 (Arg72Pro, rs1042522) polymorphism in 1,100 non-Hispanic White SCCHN patients and 1,122 age-and sex-matched cancer-free controls recruited at The University of Texas M. D. Anderson Cancer Center. In multivariate logistic regression analysis with adjustment for age, sex, smoking and drinking status, marital status and education level, we observed that the EOMES rs3806624 CC genotype had a significant effect of protection against SCCHN risk (adjusted odds ratio= 0.79, 95% confidence interval =0.64–0.98), compared with the -838TT+CT genotypes. Moreover, a significantly increased risk associated with the combined genotypes of p53 codon 72CC and EOMES -838TT+CT was observed, especially in the subgroup of non-oropharyneal cancer patients. The values of false-positive report probability were also calculated for significant findings. In the second study, we assessed the association between SCCHN risk and four potential regulatory single nucleotide polymorphisms (SNPs) of DEC1 (deleted in esophageal cancer 1) gene, a candidate tumor suppressor gene for esophageal cancer. After adjustment for age, sex, and smoking and drinking status, the variant -606CC (i.e., -249CC) homozygotes had a significantly reduced SCCHN risk (adjusted odds ratio = 0.71, 95% confidence interval = 0.52–0.99), compared with the -606TT homozygotes. Stratification analyses showed that a reduced risk associated with the -606CC genotype was more pronounced in subgroups of non-smokers, non-drinkers, younger subjects (defined as ≤ 57 years), carriers of TP53 Arg/Arg (rs1042522) genotype, patients with oropharyngeal cancer or late-stage SCCHN. Further in silico analysis revealed that the -249 T-to-C change led to a gain of a transcription factor binding site. Additional functional analysis showed that the -249T-to-C change significantly enhanced transcriptional activity of the DEC1 promoter and the DNA-protein binding activity. We conclude that the DEC1 promoter -249 T>C (rs2012775) polymorphism is functional, modulating susceptibility to SCCHN among non-Hispanic Whites. Additional large-scale, preferably population-based studies are needed to validate our findings.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Transformation studies of a nontumorigenic rat prostatic epithelial cell line with activated {\it myc\/} and {\it neu\/} oncogenes

A cloned nontumorigenic prostatic epithelial cell line, NbE-1.4, isolated from Noble (nbl/crx) rat ventral prostate, was used to examine the potential role of activated myc and neu oncogenes in prostate carcinogenesis. Transfection of SV40 promoter/enhancer driven constructs containing either v-myc, truncated c-myc, or neu-T (activated neu) oncogenes was accomplished using calcium phosphate-mediated DNA transfer. Cells were cotransfected, as necessary, with pSV2neo, allowing for selection of positive clones using the antibiotic geneticin (G418). G418 resistant colonies were pooled in some cases or limiting dilution exclusion cloned in others as described. Transfection of NbE-1.4 cells with activated myc oncogenes resulted only in the partial transformation. These cells display an altered morphology and decreased dependence on serum factors in vitro; however, saturation density, soft agar colony formation and growth assay in male athymic nude mice were all negative. Transfection and overexpression of NbE-1.4 cells with an activated neu oncogene alone resulted in tumorigenic conversion. Cell transformation was evident following an examination of the altered cellular morphology, an increased soft agar colony formation, and an acquisition of a tumorigenic potential when injected s.c. into male athymic nude mice. neu-transformed NbE-1.4 cells displayed elevated activity of the neu receptor tyrosine kinase. Furthermore, qualitative changes in tyrosine phosphorylated proteins were found in neu transformed cell clones. These changes were associated with elevated expression of mRNAs for laminin $\beta$1, $\beta$2, and procollagen type IV. The expression of fibronectin and E-cadherin, which are often lost during tumorigenesis, did not correlate with the tumorigenic phenotype. Therefore, it appears that neu oncogene overexpression has been found to be associated with the transformation of rat prostatic epithelial cells, presumably through alterations in gene expression that regulate extracellular matrix. The possible interrelationship and functional significance between neu oncogene expression and the elevated extracellular matrix gene expression is discussed. ^

Attitudes of health-care workers toward pregnant, HIV -positive women

The management of HIV infection with antiretroviral drugs has succeeded in increasing survival rates, but the subject of pregnancy in HIV-positive women continues to garner debate. Discrimination and stigma have been identified as barriers to health care, suggesting that women with HIV may be disinclined to seek prenatal care if health-care workers exhibit negative attitudes toward the women's pregnancies. To optimize prenatal and medical care for women with HIV infection, it is important to understand the general social conditions and cultural context in which these women have children. Goffman's treatise on stigma, Foucault's discussion of the knowledge/power matrix, and Bandura's Social Cognitive Theory offer theoretical perspectives by which we can evaluate the gender, race, and class issues that are inherent in pregnancy decision-making for women with HIV infection. It is also necessary to evaluate prevailing attitudes on childbearing toward HIV-positive women and to review the historical background of prejudice in which HIV-positive women make decisions regarding childbearing. ^ This qualitative study used a survey instrument and one-on-one interviews with HIV-infected women to elicit their perceptions of how they were treated by care providers when they became pregnant. It also included interviews with health-care workers to determine what their feelings are about pregnancy within the context of HIV infection. Results of the ethnographic inquiry reveal that most of the women had negative experiences at some point during a pregnancy, but that the situation improved when they sought care from a provider who was familiar with HIV infection. The health-care providers interviewed were firm in their belief that HIV-positive women deserved optimal care and treated the women with respect, but these are individuals who are also experts in providing care to HIV-positive patients. The question remains as to what kind of care HIV-positive women are receiving generally and what types of attitudes they are being subjected to if they see less experienced providers. Further research is also needed to determine whether HIV-positive women from a broader ethnic representation and higher socioeconomic status experience similar negative attitudes. ^