Cancer is driven both by genetic mutations and microenvironmental factors (e.g. cell-cell interactions). It is now clear that in order to develop effective therapies and predict patient response we need to better understand these interactions. Biomimetic tissue-engineered technologies and models hold the promise of incorporating more components into in vitro/ex vivo models and, thus, make them more accurately reflect human disease. Here we focus on kidney cancer and specifically, on the interactions between cancer cells and the vasculature (i.e. endothelial cells) - an interaction that is understudied, yet critical to better understanding, applying anti-angiogenic drugs and developing new such therapies. Our proposal describes an integrated approach that increases relevance by enabling physiologically relevant models that replicate structure/function relationships while providing patient specificity. Our approach is transdisciplinary including expertise in oncology (Christos Kyriakopoulos), surgery (Jason Abel), pathology (Wei Huang), clinical imaging (Steve Cho), biostatistics/clinical analysis (Kyungmann Kim) and microtechnology/organotypic models (David Beebe). Specifically, we will develop a robust microvessel culture approach (uVESSEL) to create an in vitro/ex vivo model of kidney cancer-vascular interactions using the patient's own cells to measure therapy response of patient specific uVESSEL model (Aim 1), measure patient response via multiple endpoints (incl. PSMA-based PET/CT imaging) (Aim 2), and evaluate predictive value of patient specific uVESSEL model (Aim 3). Our approach is bench to bedside and back again taking advantage of our teams' location with the University of Wisconsin Carbone Cancer Center and access to patient samples. Our goal is to push the boundaries between in vitro models and ex vivo experiments by putting the patient's cells into biomimetic models.
In order to develop effective cancer therapies and predict patient response, better models of cancer are needed. Focusing on kidney cancer, we will build bioengineered patient specific models of kidney cancer and use them to predict response to anti-angiogenic therapies.
| Ayuso, Jose M; Gillette, Amani; Lugo-Cintrón, Karina et al. (2018) Organotypic microfluidic breast cancer model reveals starvation-induced spatial-temporal metabolic adaptations. EBioMedicine 37:144-157 |
