The Development of a Tissue Engineered 3D in vitro Model of Pancreatic Cancer for the Evaluation of Novel Anti-cancer Therapeutics
Creators: name: Almouemen, Nour
Publisher: name: Royal College of Surgeons in Ireland
Year: 2022
Subjects: subject: Biological Engineering
subject: 111201 Cancer Cell Biology
subject: FOS: Clinical medicine
subject: 111504 Pharmaceutical Sciences

description: Pancreatic ductal adenocarcinoma (PDAC) represents 85-90% of all pancreatic cancers and is the 11th most common cancer worldwide. Prognosis of PDAC remains strikingly poor, with almost equal rates of incidence and mortality, and a 5-year survival rate of 8-9%. Despite the urgent, unmet clinical requirement for early screening and diagnosis, as well as the need to identify novel and effective therapeutic targets, few 2D in vitro models capture the desmoplastic extracellular matrix and embedded stromal cells that are significant drivers of PDAC. Moreover, they lack the mechanical properties that are key in guiding PDAC progression. The use of 3D in vitro models, alternatively, can be advantageous as they mimic the 3D in vivo characteristics of the tumour microenvironment. However, few 3D models have integrated stromal stiffness, stromal cells, and cancer cells into one preclinical platform. Therefore, the overall aim of this thesis was to develop an in vitro model of PDAC with 3D tissue structure and tuneable matrix properties as a predictive tool for the evaluation of new anticancer therapeutics. Our central hypothesis was that tissue engineering strategies could be utilised to create an in vitro model that combined disease-relevant physical properties with incorporation of major stromal components that mediate tumour disease progression, such as cancer associated fibroblasts (CAFs), to ultimately create a preclinical tool to advance successful translation of novel therapies into the clinic. This thesis initially investigated a gelatin-based hydrogel biomaterial, gelatin methacryloyl (GelMA), for its tuneable mechanical properties to fabricate substrates with stiffnesses that closely mimic in vivo PDAC tissue stiffness. A GelMA hydrogel system with a stiffness range was successfully developed that represented early-to-advanced PDAC. Moreover, we fabricated a system with gradually-increasing substrate stiffness independent of polymer concentration, which has the potential to pave the way towards a better understanding of cell-matrix and cell- cell signalling in advanced PDAC, as it provides a more accurate representation of the native tumour microenvironment. In addition, the selected GelMA formulations displayed the characteristics required for successful 3D cell culture studies following assessment of the degradability and permeability of the hydrogel constructs. Next, the thesis evaluated the suitability of GelMA hydrogels for use as a 3D in vitro model of the stiffening PDAC microenvironment using PANC-1 pancreatic cancer cell line. Results obtained show a direct influence of the increasing hydrogel stiffness on the growth and proliferation of cancer cells. Thereafter, a PANC-1/pancreatic stellate cell co-culture was studied to further ecapitulate in vivo milieu of cancer cells and CAFs. Our hydrogel model not only enhanced cellular proliferation, but also the influenced the secretion of common cytokines such as IL-6, and proteins that are involved in cancer progression, although this was not linked to a specific cell type. Moreover, it exhibited the potential to guide CAF differentiation, which further supports the use of this 3D hydrogel model as a more suitable approach to studying PDAC progression and evaluation of novel anti-cancer therapeutics. Overall, the research presented in this thesis has investigated the ability to formulate different GelMA formulation with varying stiffness for 3D in vitro applications. This thesis has highlighted the suitability of these formulations for co-culture studies incorporating cancer and stromal cells, therefore has the potential to overcome the limitations of the current available PDAC in vitro models.