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The cancer stem cell (CSC) theory proposes that a minor population in tumor cells with specific features, such as self-renewal and reproducible tumor phenotype could contribute to tumor relapse and chemotherapy resistance. Several studies have convincingly documented the existence of ovarian CSC, but questions related to the biologic behavior and specific biomarkers of ovarian CSC remain to be clarified. In the present study, we firstly established a tumor cell line with capability of regenerating tumors through serial transplantation of ovarian tumor tissue in non-obese/severe combined immunodeficient (SCID) mice. After separation of CD133+ cells with magnetic beads, we compared the phenotype and biologic behavior of CD133+ versus CD133-cells. It was found that the CD133+ cells were much more potent to produce colonies in semi-solid agar culture than CD133-cells. The proportion of the cells in G0/1 cell cycle is much higher in CD133+ cells than in CD133-cells. Furthermore, in vivo experiments demonstrated that the CD133+ cells were capable of repeatedly regenerate tumors in NOD/SCID mice, while the CD133-cells were not. Compared with CD133-cells, the CD133+ cells expressed much higher levels of the stem cell markers Oct4, Sox2, Nanog and Mcl-1. Clinically, among a total of 290 ovarian epithelial cancers, increased level of CD133 expression was positively correlated with a high cancer stage and had a worse 5-year survival rate. Taken together, the results suggest that the CD133+ cells from human ovarian cancer have the characteristics of CSC, which may contribute to ovarian cancer relapse and anti-apoptotic activity. The method of ovarian CSC enrichment we established provides a feasible and practical way of ovarian cancer research in a molecular level. In addition, CD133 may be used as a prognostic marker for ovarian epithelial cancer, which may have a role for future therapeutic effect.

HPV-associated cancer and carcinoma of the prostate are responsible for significant worldwide morbidity and mortality. The viral transforming proteins E6 and E7 make human papilloma virus positive (HPV+) malignancies an attractive target for cancer immunotherapy however, therapeutic vaccination exerts limited efficacy in the setting of advanced disease. In prostatic carcinoma therapeutic vaccination shows some therapeutic activity but is infrequently curative. A strategy to induce substantial specific immune responses against multiple epitopes of E6 and E7 proteins based on an attenuated transgene from HPV serotypes 16 and 18, that is incorporated into MG1-Maraba virotherapy (MG1-E6E7), was designed. MG1-E6E7 is able to boost specific immunity following priming with either an adenoviral vector (Ad-E6E7) or customised synthetic peptide vaccines resulting in multifunctional CD8+ T cell responses of an enormous magnitude. MG1-E6E7 vaccination in the HPV+ murine model TC1 is curative against large tumours in a CD8+ dependent manner and results in durable immunologic memory. Using the same adenoviral prime and MG1 boosting strategy targeting the prostatic antigen, STEAP, immunity against multiple CD8+ STEAP epitopes was induced. In a murine prostate cancer model, STEAP specific oncolytic virotherapy significantly improved the survival of mice bearing advanced TRAMP-C2 tumours. One significant obstacle to therapeutic cancer vaccination is an immunosuppressive tumour microenvironment. MG1 Maraba is able to lethally infect HPV-associated and prostate cancer cells, increase the immunologic activity within the tumour microenvironment in vivo and exploit molecular hallmarks of HPV-positive cancer and prostatic carcinoma enabling infection of bulky tumours. Pre-clinical data generated within this thesis has been instrumental in securing funding for future clinical trials assessing the safety and activity of MG1 Maraba virotherapy for HPV-associated cancer and prostatic carcinoma. This promising approach has the potential to be directly translatable to human clinical oncology to tackle these two highly prevalent and frequently lethal groups of epithelial neoplasia. / Thesis / Doctor of Philosophy (PhD) / Carcinoma (epithelial cancer) is the most common form of human cancer and two frequently encountered types, namely HPV-associated and prostatic carcinoma are responsible for a substantial worldwide cancer burden. Current therapeutic options show limited clinical benefit and/ or significant long-term side effects for advanced carcinomas, therefore new treatments are urgently required. Oncolytic viruses represent an exciting new form of anti-tumour immunotherapy capable of infecting and killing cancerous cells; here we present a virus called MG1 Maraba that is able to exploit molecular characteristics of these cancers. When MG1 Maraba is engineered to target proteins from HPV-associated cancer and prostatic carcinoma, specific immune attack against these tumours occur in mouse cancer models. MG1 Maraba offers a novel, selective, safe and highly promising therapeutic approach against advanced carcinomas. Based on the information within this thesis human clinical trials assessing MG1 Maraba are due to take place for both HPV-associated and prostate cancer.

This dissertation focuses on the development of algorithms for analyzing and modeling of the signals from optical spectroscopy. This dissertation is motivated by the detection of oral cancer, but some of the methods developed can be generalized to epithelial cancers of other sites. Two main topics are covered in this dissertation: Analysis and Modeling. For analysis, the focus is on developing algorithms to make diagnostic predictions. The analysis methods are empirically tested using an oral cancer dataset. Statistical analyses show that polarized reflectance spectroscopy has the potential to aid screening and diagnosis of oral cancer. Also, a novel adaptive windowing technique is developed to extract spectral features with fewer windows that retain the diagnostic information. For modeling, a Monte Carlo model simulating light-tissue interactions is presented to aid in the design of diagnostic instrumentation. / text