Engineered hybrid aging model for disease progression - Project Summary Aging is a risk factor for many diseases such as cancer, cardiovascular diseases and neurodegenerative diseases, with the incidence of such diseases peaking between ages 60 and 80. Although both the cellular and the extracellular components of a tissue change with age, current preclinical models have focused on the aging-related changes in cells and overlooked the alterations in the microenvironment, specifically the extracellular matrix (ECM), which is one of the main reasons for the low success rate of pre-clinical to clinical translation. Thus, it is imperative to create disease models that mimic the aging microenvironment to better study disease initiation and progression, as well as reliably test for drug efficacy. Here, as a proof-of-concept aging-associated disease, for the first time in literature, we propose to engineer decellularized aged human ECM (dECM)-based 3D tumor models and implant them into immunodeficient mice to create hybrid mouse models to study the effect of matrix age on tumor progression and drug response. We will follow a bottom-up approach to establish the hybrid mouse model; first we will engineer the aging stroma using aged human breast dECM and aged human stromal cells both derived from healthy donors, then grow aged patient-derived tumor organoids on the stroma to engineer the 3D in vitro tumor models, and finally implant the 3D tumors into immunodeficient mice to create the hybrid mouse models. Hence, we aim to establish reliable and human representative preclinical models, 3D tumor models and hybrid mouse models, which allow us to distinguish the individual and combined effect of aging components (i.e. ECM, stromal cells, and tumor cells) on tumor initiation and progression. We will then mechanistically test the individual effects of aged ECM characteristics, such as the altered stiffness, fiber structure and biochemical composition on tumor progression. The proposed work aims at solving many problems of the current preclinical models. First, we will produce in vitro and in vivo preclinical models that consider the effect of aging human ECM on cancer progression. Second, by creating hybrid models, we will address the lack of systemic response in the 3D models, and the lack of control and inability to discern the effects of individual components in in vivo models. Finally, we will create tumors in mice that better represent the human response and benchmark our hybrid model with actual patient samples. To achieve these goals, we will combine our expertise in tissue engineering, mouse model systems, transcriptomics, primary cell culture model systems, and breast cancer research. Once fully implemented and functionally validated, we expect our state-of-the-art tissue engineered 3D disease models as well as the hybrid mouse models to serve as the next-generation research platform for both basic and translational cancer research and high-throughput drug discovery.
