Grant: Travel Awards - Institute Community Support

Ovarian cancer is still a deadly disease and much of the drug discoveries in the field rely on preclinical models, the most common being in vitro models. 2D systems are often used due to cost and time efficiency, but may not reflect true patient response and attrition rates for cancer drugs entering early clinical trials has reached disturbing heights. This suggests that the current preclinical development has not been successful in consistently identifying agents that can modify the outcome of human cancers. Based on preliminary data from our group demonstrating differences in drug response between 2D and 3D models, we hypothesize that 3D models better reflect physiological response. To this end, we are characterizing the chemosensitivity in a panel of our ovarian cancer cell lines using 2D, 3D and mouse models to answer this question. Thus far in our experiments, very resistant cancer cells become more sensitive in our mouse model and conversely some sensitive cancer cells become more resistant. Our 3D model will determine whether it is a more representative model. These results will help define the most reliable preclinical model with the highest translational potential to predict treatment response. With potential discrepancies in therapeutic response with our current preclinical models, this will encourage investment in the development of systems such as 3D ex-vivo models that may better reflect patient response.