The abnormal regulation of H+ ions, leading to a reversed pH gradient in cancer cells when compared to normal cells, is considered to be one of the most distinctive features of cancer. However, this characteristic has yet to be fully exploited as a therapeutic target in cancer. This project assessed whether targeting pH regulating proteins, which permit cancer cells to survive in the hostile hypoxic and acidic tumour microenvironment, could produce an effective therapeutic response in breast cancer. The pH regulating proteins carbonic anhydrase IX (CAIX), Na+/H+ exchanger 1 (NHE1) and vacuolar (H+)-ATPase (V-ATPase) were the focus of this thesis. Initial experiments were conducted in 2D tissue culture before progressing into 3D, using models that more faithfully re-create the in vivo tumour microenvironment. Expression analysis conducted with MCF-7, MDA-MB-231 and HBL-100 human breast cancer cell lines cultured in 2D, and in 3D as multicellular tumour spheroids, showed that protein and mRNA levels of CAIX were very responsive to lower O2 concentrations. Both MDA-MB-231 and HBL-100 cells displayed large increases in CAIX expression levels in hypoxia, with the HBL-100 cell line exhibiting the largest change in CAIX mRNA (42-fold increase) and protein (78-fold increase) levels in 0.5% O2 conditions. Hypoxia inducible factor 1-α (HIF-1α) controls the expression of CAIX, but the induction of CAIX in hypoxic MCF-7 cells was lower in comparison to the other cell lines, despite the presence of similar levels of HIF-1α. The differences in CAIX expression observed between the cell lines was consistent with a varying activity of factor inhibiting HIF-1 (FIH-1), an oxygen sensor that controls signalling through HIF-1α, as siRNA targeting FIH-1 led to increased levels of CAIX in hypoxic MCF-7 cells. While NHE1 protein levels did increase in hypoxic conditions in MCF-7 cells in 3D, overall, the expression levels of both NHE1 and V-ATPase were not as responsive to changes in O2 concentrations when compared to CAIX across differing O2 conditions in each of the cell lines. Inhibitors targeting CAIX, NHE1 and V-ATPase were all shown to reduce cancer cell number in 2D culture conditions. Differing O2 conditions changed the sensitivity of these cell lines to CAIX inhibition. Cells cultured in 20% O2 conditions were responsive to CAIX inhibition, while acute hypoxic cells cultured in 0.5% O2 displayed an increased resistance to drug treatment. Chronically hypoxic cells, which had spent over 10 weeks in 0.5% O2 before treatment, exhibited a re-sensitisation to CAIX inhibition. 3D invasion assays demonstrated that CAIX inhibition significantly reduced the invasion of cells from MDA-MB-231 spheroids into collagen type 1 in both 20% O2 and 0.5% O2 conditions, while drugs targeting either NHE1 or V-ATPase had no such inhibitory effects. Preliminary clonogenic assays, used to assess radiation sensitivity and performed with MDA-MB-231 spheroids, indicated that inhibitors targeting CAIX and NHE1 led to a significant decrease in colony formation when combined with irradiation, compared to either drug treatment or irradiation alone. Further invasion assays, carried out with primary tissue derived from human patients, showed that drugs targeting CAIX retained their inhibitory effects when tested on heterogeneous cancer material of varying tumour subtypes. CAIX inhibition also exhibited anti-cancer effects in vivo on mouse MDA-MB-231 xenografts, significantly reducing the proliferation and growth of tumours within mice. Together, this work demonstrates that inhibitors targeting the pH regulation mechanisms of cancer cells have potential in the treatment of breast cancer, highlighted by their capacity to affect cancer cell number, cancer cell invasion, and their ability to combine with irradiation. Of the 3 pH regulatory molecules studied, CAIX appears to be the target with the most therapeutic potential.

Glutamate carboxypeptidase II (GCPII) usually called prostate specific membrane antigen (PSMA) is membrane bound metallopeptidase expressed mainly in prostate carcinoma (PCa). Agents targeting GCPII suitable for both imaging and treatment of PCa are in development and they show promising results in advanced clinical trials. Some studies showed that GCPII may serve also as PCa blood serum marker, but this has not been validated due to the lack of methods suitable for accurate detection of GCPII in human blood. Moreover, GCPII is also expressed in brain, where it cleaves inhibitory N-acetyl-α-L- aspartyl-L-glutamate (NAAG) to release excitatory L-glutamate and GCPII inhibition has been shown to be neuroprotective in animal models of several neuropathies. Tight binding inhibitors of GCPII have been identified by rational design, but all have poor bioavailability and thus cannot be used in clinics. Identifying new scaffolds by 'brute force' screening methods is thus essential; however, no such method for GCPII has been developed so far. Glutamate carboxypeptidase III (GCPIII) is also expressed in brain and cleaves NAAG. It is thus an important protein for understanding of GCPII function as well as GCPII targeting in medicine. Here, we focused on development of novel methods for quantification of both...

The purpose of this Thesis is to present a Bayesian analysis of oncological data sets with particular focus on cervical carcinomas and ovarian cancers. Bayesian methods of data analysis have a very long history, and have been used with great success in many disciplines, from Physics to Econometrics. Nonetheless, they remain very controversial among statisticians who belong to the orthodox - i.e, frequentist school, and are not well known by the medical community. To help in that direction, we reviewed in the introductory chapter the basic philosophical and practical differences between the two schools, and in the second chapter, we briefly reviewed the history of Bayesian methodology, from the early efforts of Thomas Bayes and of Pierre Simon de Laplace to the modern contributions of Harold Jeffreys, Richard Cox, and Edwin Jaynes. In many aspects of medical research, we deal with experimental data from which a certain proposition or hypothesis is validated. Unlike in physics, where we have strong and solid foundations such as Newton's law of motion, Snell's optical laws, Kirchoff's laws, Einstein's relativity theory, and many more, we do not have such privileges in medical research. Hence, many hypotheses are constantly tested as new evidence becomes available. One of the actively-researched medical areas is cancer research about which our understanding is still in its infancy. Numerous experiments (both in vivo and in vitro) and clinical trials have been conducted to further our knowledge; thus, Bayesian methodology finds its place to aid us in obtaining scientific inferences about certain propositions or hypotheses from available data and resources. In this work, we use data given to us by our medical collaborators at the Princess Margaret Hospital (PMH) in Toronto to carry out two main projects: Firstly, to make an inference about the oxygenation status (oxygen partial pressure, pO$_2$) within human cervical carcinomas and secondly, an inference about the effectiveness of various molecularly-targeted agents (MTAs) in phase II clinical trials of relapsed ovarian cancer patients. In the first problem, we address the challenges of tumor hypoxia - a state of oxygen deprivation in tumors. Currently, there are two methods to obtain tumor oxygen status, namely the direct Eppendorf needle electrode and the indirect immunohistochemical assay of a protein marker, Carbonic Anhydrase IX (CAIX). In this project, we introduce Bayesian probability theory to obtain inferences about tumor oxygenation from each technique and the concordance between the two techniques. From this study, we conclude that under certain conditions, two biopsies are sufficient to infer the tumor oxygenation level based on the immunohistochemical assays of CAIX. Additionally, there is a fair concordance between the direct and the indirect measurements of tumor oxygenation. In the latter problem, ovarian cancer is the topic of study. Ovarian cancer has the highest mortality rate among gynecological cancers and one that is known to relapse. CA-125 is still the most inexpensive biomarker for monitoring ovarian cancers. From the phase II clinical trial data, we demonstrate the survival advantage of CA-125 responsive group of patients by means of a non-parametric Kaplan-Meier statistic.

The purpose of this Thesis is to present a Bayesian analysis of oncological data sets with particular focus on cervical carcinomas and ovarian cancers. Bayesian methods of data analysis have a very long history, and have been used with great success in many disciplines, from Physics to Econometrics. Nonetheless, they remain very controversial among statisticians who belong to the orthodox - i.e, frequentist school, and are not well known by the medical community. To help in that direction, we reviewed in the introductory chapter the basic philosophical and practical differences between the two schools, and in the second chapter, we briefly reviewed the history of Bayesian methodology, from the early efforts of Thomas Bayes and of Pierre Simon de Laplace to the modern contributions of Harold Jeffreys, Richard Cox, and Edwin Jaynes. In many aspects of medical research, we deal with experimental data from which a certain proposition or hypothesis is validated. Unlike in physics, where we have strong and solid foundations such as Newton's law of motion, Snell's optical laws, Kirchoff's laws, Einstein's relativity theory, and many more, we do not have such privileges in medical research. Hence, many hypotheses are constantly tested as new evidence becomes available. One of the actively-researched medical areas is cancer research about which our understanding is still in its infancy. Numerous experiments (both in vivo and in vitro) and clinical trials have been conducted to further our knowledge; thus, Bayesian methodology finds its place to aid us in obtaining scientific inferences about certain propositions or hypotheses from available data and resources. In this work, we use data given to us by our medical collaborators at the Princess Margaret Hospital (PMH) in Toronto to carry out two main projects: Firstly, to make an inference about the oxygenation status (oxygen partial pressure, pO$_2$) within human cervical carcinomas and secondly, an inference about the effectiveness of various molecularly-targeted agents (MTAs) in phase II clinical trials of relapsed ovarian cancer patients. In the first problem, we address the challenges of tumor hypoxia - a state of oxygen deprivation in tumors. Currently, there are two methods to obtain tumor oxygen status, namely the direct Eppendorf needle electrode and the indirect immunohistochemical assay of a protein marker, Carbonic Anhydrase IX (CAIX). In this project, we introduce Bayesian probability theory to obtain inferences about tumor oxygenation from each technique and the concordance between the two techniques. From this study, we conclude that under certain conditions, two biopsies are sufficient to infer the tumor oxygenation level based on the immunohistochemical assays of CAIX. Additionally, there is a fair concordance between the direct and the indirect measurements of tumor oxygenation. In the latter problem, ovarian cancer is the topic of study. Ovarian cancer has the highest mortality rate among gynecological cancers and one that is known to relapse. CA-125 is still the most inexpensive biomarker for monitoring ovarian cancers. From the phase II clinical trial data, we demonstrate the survival advantage of CA-125 responsive group of patients by means of a non-parametric Kaplan-Meier statistic.