Exploring the Tumor and Premetastatic Microenvironment of the Ovary

Abstract

Ovarian cancers are the most lethal gynecological malignancies, responsible for more than 150,000 deaths around the globe annually. Among ovarian cancers, high-grade serous ovarian cancer has a 5-year survival rate of only 40%. This poor survival is due to a widespread lack of understanding of this disease, from suboptimal prevention and screening methods to failures in treatment. Moving towards novel prevention and treatment methods requires better models of ovarian cancer that phenotypically and genetically recapitulate the features of ovarian cancers that are seen clinically. This thesis highlights the characterization of a novel syngeneic model of high-grade serous ovarian cancer that exhibits the growth, expression profile, histology, and a tumor-initiating cell population that closely resembles human disease. We expand on our initial characterization of the STOSE model in a proof-of-principle study using deep learning of second-harmonic generation and two-photon-excited-fluorescence images to classify normal compared to cancerous tissues. The use of deep learning for image classification based on extracellular matrix and cellular structure could have robust application to complementing common histological examination of tissues and in treatment planning. Building on the changes in structure found in normal compared to cancerous ovarian tissue and recent research that showed age-associated fibrosis develops in murine ovaries, we assessed the non-hereditary ovarian cancer risk factors of age and ovulation number for their effects in altering ovarian tissue structure. This thesis concludes with the first evidence of ovarian fibrosis in non-pathological post-menopausal human ovaries. We show that ovarian fibrosis correlates with the development of a pre-metastatic (tumor-permissive) niche, revealing a novel avenue of research into ovarian cancer risk. Interestingly, age-associated fibrosis could be prevented or reversed by metformin use, revealing a possible mechanism for the previously identified ovarian cancer risk reduction seen with metformin use and further supporting the use of metformin for ovarian cancer prevention.