Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach

Abstract

The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. Our approach provides a non-invasive method to measure and predict hypoxia using known blood vasculature. Formulating a reaction-diffusion model for oxygen distribution, we apply it to derive the corresponding hypoxia profile. The modeling and simulations successfully replicate the observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor tissues (breast, ovarian, and pancreatic) in our dataset. Employing a data-driven approach, we propose a method to deduce partial differential equation (PDE) models with spatially dependent parameters, enabling us to comprehend the variability of hypoxia profiles within a tissue. The versatility of our framework lies not only in capturing diverse and dynamic behaviors of tumor oxygenation but also in categorizing states of vascularization. These categories are distinguished based on the dynamics of oxygen molecules, as identified by the model parameters.