Targeted Multistage Delivery of Nanoparticles to the Bone Marrow

Bone marrow is a target organ site involved in multiple diseases including myeloproliferative disorders and hematologic malignancies and metastases from breast and prostate. Most of these diseases are characterized with poor quality of life, and the treatment options are only palliative due to lack of delivery mechanisms for systemically injected drugs which results in dose limitation to protect the healthy hematopoietic cells. Therefore, there is a critical need to develop effective therapeutic strategies that allow for selective delivery of therapeutic payload to the bone marrow. Nanotechnology-based drug delivery systems provide the opportunity to deliver drugs to the target tissue while decreasing exposure to normal tissues. E-selectin is constitutively expressed on the bone marrow vasculature, but almost absent in normal vessels, and therefore, E-selectin targeted drug delivery presents an ideal strategy for the delivery of therapeutic nanoparticles to the bone marrow. The objective of this study was to develop a novel bone marrow targeted multistage vector (MSV) via E-selectin for delivery of therapeutics and imaging agents. To achieve this goal, Firstly, an E-selectin thioaptamer (ESTA) ligand was identified through a two-step screening from a combinatorial thioaptamer library. Next, ESTA-conjugated MSV (ESTA-MSV) were developed and evaluated for their stability and binding to E-selectin expressing endothelial cells. Different types of nanoparticles including liposomes, quantum dots, and iron oxide nanoparticles were loaded into the porous structure of ESTA-MSV. In vivo targeting experiments demonstrated 8-fold higher accumulation of ESTA-MSV in the mouse bone marrow as compared to non-targeted MSV Furthermore, intravenous injection of liposomes loaded ESTA-MSV resulted in a significantly higher accumulation of liposome in the bone marrow space as compared to injection of non-targeted MSV or liposomes alone. Overall this study provides first evidence that E-selectin targeted multistage vector preferentially targets to bone marrow vasculature and delivers larger amounts of nanoparticles. This delivery strategy holds potential for the selective delivery of large amounts of therapeutic payload to the vascular niches in the bone marrow for the treatment of bone marrow associated diseases.

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.

Investigating the Effects of Silencing EphA2 in Metastatic Breast Cancer Cells

EphA2, also known as ECK (epithelial cell kinase), is a transmembrane receptor tyrosine kinase that is commonly over-expressed in cancers such as those of the prostate, colon, lung, and breast. For breast cancers, EphA2 overexpression is most prominent in the ER-negative subtype, and is associated with a higher rate of lung metastasis. Studies conducted to demonstrate the role of EphA2 in a non-cancerous environment have shown that it is very important in developmental processes, but not in normal adult tissues. These results make EphA2 a prospective therapeutic target since new therapies are needed for the more aggressive ER-negative breast cancers. A panel of breast cancer cell lines was screened for expression of EphA2 by immunoblotting. Several of the overexpressing cell lines, including BT549, MDA-MB-231, and HCC 1954 were selected for experiments utilizing siRNA for transient knockdown and shRNA for stable knockdown. Targeted knockdown of EphA2 was measured using RT-PCR and immunoblotting techniques. Here, the functions of EphA2 in the process of metastasis have been elucidated using in vitro assays that indicate cancer cell metastatic potential and in vivo studies that reveal the effect of EphA2 on mammary fat pad tumor growth, vessel formation, and the effect of using EphA2-targeting siRNA on pre-established mammary fat pad tumors. A decrease in EphA2 expression both in vitro and in vivo correlated with reduced migration and experimental metastasis of breast cancer cells. Current work is being done to investigate the mechanism behind EphA2’s participation in some of these processes. These studies are important because they have contributed to understanding the role that EphA2 plays in the progression of breast cancers to a metastatic state.