(91b) Elucidating Gender Differences in the Immune Response in the Zebrafish Melanoma Model (Industry Candidate)

Approximately 600,000 cancer deaths occurred in the United States each year. Even when a patients undergoes an apparently successful treatment, the disease sometimes returns more than ten years later, a phenomenon called tumor dormancy and recurrence. In order to capture the complex dynamics of tumor growth, we propose a population balance model that uses power law size-dependent parameters representing tumor growth, reduction and metastasis to predict how the distribution of tumor sizes in a cohort of patients changes with time.

Previously, we used the zebrafish melanoma animal model to obtain data under different growth conditions. We examined gender differences in immune-competent and immune-deficient fish orthotopically-injected with a non-syngeneic, fluorescence-labelled zebrafish melanoma cell line. We have developed a fitting procedure to obtain unambiguous values for the three rate parameters by determining each from separate data. While gender differences in the determined tumor growth and metastasis parameters were not significant, we detected a strong difference between male and female immunity parameters, particularly in its power law exponent that expresses its size dependence. The upshot was that it predicted that female fish would have better outcomes than males for larger tumors. Examples of gender disparities in cancer outcomes – generally males do far worse than females – exist in other animals and humans. We followed up these initial experiments with longer term, larger inoculation-size experiments in immune-competent fish of both genders.

In this presentation, we will compare theoretical predictions with new experimental data and present a detailed analysis of male and female disparities found in the new data. We use our model-based simulations of cancer progression in fish and speculate as to the potential clinical consequences of this this type of gender difference in human melanoma. These include mortality rate, disease duration and the probability of recurrence after treatment.