Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

Oncolytic Adenoviruses for Gynecologic Cancer

Gene therapy is a promising novel approach for treating cancers resistant to or escaping currently available modalities. Treatment approaches are based on taking advantage of molecular differences between normal and tumor cells. Various strategies are currently in clinical development with adenoviruses as the most popular vehicle. Recent developments include improving targeting strategies for gene delivery to tumor cells with tumor specific promoters or infectivity enhancement. A rapidly developing field is as well replication competent agents, which allow improved tumor penetration and local amplification of the anti-tumor effect. Adenoviral cancer gene therapy approaches lack cross-resistance with other treatment options and therefore synergistic effects are possible. This study focused on development of adenoviral vectors suitable for treatment of various gynecologic cancer types, describing the development of the field from non-replicating adenoviral vectors to multiple-modified conditional replicating viruses. Transcriptional targeting of gynecologic cancer cells by the use of the promoter of vascular endothelial growth factor receptor type 1 (flt-1) was evaluated. Flt-1 is not expressed in the liver and thus an ideal promoter for transcriptional targeting of adenoviruses. Our studies implied that the flt-1 promoter is active in teratocarcinomas.and therefore a good candidate for development of oncolytic adenoviruses for treatment of this often problematic disease with then poor outcome. A tropism modified conditionally replicating adenovirus (CRAd), Ad5-Δ24RGD, was studied in gynecologic cancers. Ad5-Δ24RGD is an adenovirus selectively replication competent in cells defective in the p16/Rb pathway, including many or most tumor cells. The fiber of Ad5-Δ24RGD contains an integrin binding arginine-glycine-aspartic acid motif (RGD-4C), allowing coxackie-adenovirus receptor independent infection of cancer cells. This approach is attractive because expression levels of CAR are highly variable and often low on primary gynecological cancer cells. Oncolysis could be shown for a wide variety of ovarian and cervical cancer cell lines as well as primary ovarian cancer cell spheroids, a novel system developed for in vitro analysis of CRAds on primary tumor substrates. Biodistribution was evaluated and preclinical safety data was obtained by demonstrating lack of replication in human peripheral blood mononuclear cells. The efficicacy of Ad5-Δ24RGD was shown in different orthotopic murine models including a highly aggressive intraperitoneal model of disseminated ovarian cancer cells, where Ad5-Δ24RGD resulted in complete eradication of intraperitoneal disease in half of the mice. To further improve the selectivity and specificity of CRAds, triple-targeted oncolytic adenoviruses were cloned, featuring the cyclo-oxygenase-2 (cox-2) promoter, E1A transcomplementation and serotype chimerism. Those viruses were evaluated on ovarian cancer cells for specificity and oncolytic potency with regard to two different cox2 versions and three different variants of E1A (wild type, delta24 and delta2delta24). Ad5/3cox2Ld24 emerged as the best combination due to enhanced selectivity without potency lost in vitro or in an aggressive intraperitoneal orthotopic ovarian tumor model. In summary, the preclinical therapeutic efficacy of the CRAds tested in this study, taken together with promising biodistribution and safety data, suggest that these CRAds are interesting candidates for translation into clinical trials for gynecologic cancer.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.