Cancer is a disease in which abnormal cells divide uncontrollably and destroy body tissue, and currently plagues today’s world. Carcinomas are cancers derived from epithelial cells and include breast and prostate cancer. Breast cancer is a type of carcinoma that forms in breast tissue cells. The tumor cells can be further categorized after testing the cells for the presence of certain molecules. Hormone receptor positive breast cancer includes the tumor cells with receptors that respond to the steroid hormones, estrogen and progesterone, or the peptide hormone, HER2. These forms of cancer respond well to chemotherapy and endocrine therapy. On the other hand, triple negative breast cancer (TNBC) is characterized by the lack of hormone receptor expression and tends to have a worse prognosis in women. Prostate cancer forms in the cells of the prostate gland and has been attributed to mutations in androgen receptor ligand specificity. In a subset of triple negative breast cancer, genetic expression profiling has found a luminal androgen receptor that is dependent on androgen signaling. TNBC has also been found to respond well to enzalutamide, a an androgen receptor inhibitor. As the gene of the androgen receptor, AR, is located on the X chromosome and expressed in a variety of tissues, the responsiveness of TNBC to androgen receptor inhibition could be due to the differential usage of isoforms - different gene mRNA transcripts that produce different proteins. Thus, this study analyzed differential gene expression and differential isoform usage between TNBC cancers – that do and do not express the androgen receptor – and prostate cancer in order to better understand the underlying mechanism behind the effectiveness of androgen receptor inhibition in TNBC. Through the analysis of differential gene expression between the TNBC AR+ and AR- conditions, it was found that seven genes are significantly differentially expressed between the two types of tissues. Genes of significance are AR and EN1, which was found to be a potential prognostic marker in a subtype of TNBC. While some genes are differentially expressed between the TNBC AR+ and AR- tissues, the differences in isoform expression between the two tissues do not reflect the difference in gene expression. We discovered 11 genes that exhibited significant isoform switching between AR+ and AR- TNBC and have been found to contribute to cancer characteristics. The genes CLIC1 and RGS5 have been found to help the rapid, uncontrolled growth of cancer cells. HSD11B2, IRAK1, and COL1Al have been found to contribute to general cancer characteristics and metastasis in breast cancer. PSMA7 has been found to play a role in androgen receptor activation. Finally, SIDT1 and GLYATL1 are both associated with breast and prostate cancers. Overall, through the analysis of differential isoform usage between AR+ and AR- samples, we uncovered differences that were not detected by a gene level differential expression analysis. Thus, future work will focus on analyzing differential gene and isoform expression across all types of breast cancer and prostate cancer to better understand the responsiveness of TNBC to androgen receptor inhibition.

Isolation-by-distance is a specific type of spatial genetic structure that arises when parent-offspring dispersal is limited. Many natural populations exhibit localized dispersal, and as a result, individuals that are geographically near each other will tend to have greater genetic similarity than individuals that are further apart. It is important to identify isolation-by-distance because it can impact the statistical analysis of population samples and it can help us better understand evolutionary dynamics. For this dissertation I investigated several aspects of isolation-by-distance. First, I looked at how the shape of the dispersal distribution affects the observed pattern of isolation-by-distance. If, as theory predicts, the shape of the distribution has little effect, then it would be more practical to model isolation-by-distance using a simple dispersal distribution rather than replicating the complexities of more realistic distributions. Therefore, I developed an efficient algorithm to simulate dispersal based on a simple triangular distribution, and using a simulation, I confirmed that the pattern of isolation-by-distance was similar to other more realistic distributions. Second, I developed a Bayesian method to quantify isolation-by-distance using genetic data by estimating Wright’s neighborhood size parameter. I analyzed the performance of this method using simulated data and a microsatellite data set from two populations of Maritime pine, and I found that the neighborhood size estimates had good coverage and low error. Finally, one of the major consequences of isolation-by-distance is an increase in inbreeding. Plants are often particularly susceptible to inbreeding, and as a result, they have evolved many inbreeding avoidance mechanisms. Using a simulation, I determined which mechanisms are more successful at preventing inbreeding associated with isolation-by-distance.