"Calibrating tumor growth and invasion parameters with spectral-spatial Analysis of cancer biopsy tissues"
Predictive modeling in oncology is a growing field. The calibration of mathematical model parameters based on limited clinical data is critical to reliable predictions per-patient basis. One omnipresent mathematical model is the reaction-diffusion equation, which has been shown to simulate and predict clinical parameters in different cancer types. Here, we focus on analyzing cell-level data routinely obtained from tissue biopsies at diagnosis for most cancers. We analyze the spatial architecture in biopsy tissues stained with multiplex immunofluorescence. We derive the two-point correlation function and the corresponding spatial power spectral distribution. We show that the data-deduced spatial power spectral distribution can fit the spatial power spectrum of the solution of the reaction-diffusion equation, thereby identifying patient-specific tumor growth and invasion rates from a single, routinely collected clinical tissue. This novel approach is essential for model-parameter-inference for tumor infiltration, which may ultimately be used to inform clinical treatments.
Additional authors: Michael Montejo, Moffitt Cancer Center and Research Institute; Mohammad U. Zahid, Moffitt Cancer Center and Research Institute; Marilin Rosa, Moffitt Cancer Center and Research Institute; Robert Gatenby, Moffitt Cancer Center and Research Institute; Roberto Diaz, Moffitt Cancer Center and Research Institute; Heiko Enderling, Moffitt Cancer Center and Research Institute