Physical Sciences Oncology Network (PSON) Project

"Physical Dynamics of Cancer Response to Chemotherapy

in 3D Microenvironments"

Project overview:

Our project aims to develop a new computationally driven platform to examine complex physical and chemical microenvironments utilizing organ-on-chip microfluidic bioreactor technology coupled with a predictive mathematical model of tumor growth and therapeutic response. Malignant breast tumors are highly heterogeneous in terms of their cellular composition, varying levels of oxygenation, acidity, and nutrients, as well as local changes in the extracellular matrix. Furthermore, tumor tissue and tumor microenvironment properties can dynamically evolve not only during tumor growth but also when anticancer treatments are administered. Despite this, nearly all pre-clinical assessments of drug efficacy and optimal dosing are performed using homogeneous 2D cell cultures that do not resemble the cellular, metabolic, and physical features manifest in tumors in vivo. Such approach suffered from overly reductionist ex vivo/in vitro and studies may not fully recapitulate the complexity of cancers, especially their physical and chemical microenvironments. To address these issues, we concentrate on developing an integrated quantitative platform that combines the power of organ-on-chip 3D tissue bioreactor, developed to include non-uniform fully controlled physical and chemical microenvironments, together with a 3DMultiCell math model that allows predictive testing of a broad range of microenvironmental combinations around the experimentally validated baseline.

Project Goals:

• Develop a predictive methodology to assess effects of defined microenvironments on the dynamics of normal and tumorigenic breast organoids and their sensitivity to therapeutics.
• Construct and validate in silico model-guided complex spatial and temporal microenvironmental gradients established within TTb-G reactor, and assess breast tumor organoids response to chemotherapeutics
• Apply our integrated computational/engineering approach to guide therapy and predict therapeutic response ex vivo and in vivo