Biology subjects

Gencoglu, M. F.

Publications and source records attributed to Gencoglu, M. F..

3 recordsLinked to original sources

bioRxiv2019

An omentum-inspired 3D PEG hydrogel for identifying ECM drivers of drug resistant ovarian cancer

Ovarian cancer (OvCa) is a challenging disease to treat due to poor screening techniques and late diagnosis. There is an urgent need for additional therapy options, as patients recur in 70% of cases. The limited availability of clinical treatment options could be a result of poor predictions in early stage drug screens on standard tissue culture polystyrene (TCPS). TCPS does not capture the mechanical and biochemical cues that cells experience in vivo, which can impact how cells will respond to a drug. Therefore, an in vitro model that captures some of the microenvironment features that the cells experience in vivo could provide better insights into drug response. In this study, we formed 3D multicellular tumor spheroids (MCTS) in microwells, and encapsulated them in 3D omentum-inspired hydrogels. SKOV-3 MCTS were resistant to Paclitaxel in our 3D hydrogels compared to a monolayer on TCPS. Toward clinical application, we tested cells from patients (ovarian carcinoma ascites spheroids (OCAS)), and drug responses predicted by using the 3D omentum-inspired hydrogels correlated with the reported pathology reports of those same patients. Additionally, we observed the presence of collagen production around the encapsulated SKOV-3 MCTS, but not on TCPS. Our results demonstrated that our 3D omentum-inspired hydrogel is an improved in vitro drug testing platform to study OvCa drug response for patient-derived cells, and helped us identify collagen 3 as a potential driver of Paclitaxel resistance.

bioengineering↗

bioRxiv2018

Applicability of Drug Response Metrics for Cancer Studies using Biomaterials

Bioengineers have built increasingly sophisticated models of the tumor microenvironment in which to study cell-cell interactions, mechanisms of cancer growth and metastasis, and to test new potential therapies. These models allow researchers to culture cells in conditions that include features of the in vivo tumor microenvironment (TME) implicated in regulating cancer progression, such as ECM stiffness, integrin binding to the ECM, immune and stromal cells, growth factor and cytokine depots, and a 3D geometry more representative of the TME than tissue culture polystyrene (TCPS). These biomaterials could be particularly useful for drug screening applications to make better predictions of efficacy, offering better translation to preclinical in vivo models and clinical trials. However, it can be challenging to compare drug response reports across different platforms and conditions in the current literature. This is, in part, as a result of inconsistent reporting and use of drug response metrics, and vast differences in cell growth rates across a large variety of biomaterial design. This perspective paper attempts to clarify the definitions of drug response measurements used in the field, and presents examples in which these measurements can and cannot be applied. We suggest as best practice to include appropriate controls, always measure the growth rate of cells in the absence of drug, and follow our provided \"decision tree\" matrix when reporting drug response metrics.

bioengineering↗

bioRxiv2017

Complementary, High-throughput Methods for Creating Multi-dimensional, PEG-based Biomaterials

Tunable biomaterials that mimic selected features of the extracellular matrix (ECM), such as its stiffness, protein composition, and dimensionality, are increasingly popular for studying how cells sense and respond to ECM cues. In the field, there exists a significant trade-off for how complex and how well these biomaterials represent the in vivo microenvironment, versus how easy they are to make and how adaptable they are to automated fabrication techniques. To address this need to integrate more complex biomaterials design with high-throughput screening approaches, we present several methods to fabricate synthetic biomaterials in 96-well plates and demonstrate that they can be adapted to semi-automated liquid handling robotics. These platforms include 1) glass bottom plates with covalently attached ECM proteins, and 2) hydrogels with tunable stiffness and protein composition with either cells seeded on the surface, or 3) laden within the three-dimensional hydrogel matrix. This study includes proof-of-concept results demonstrating control over breast cancer cell line phenotypes via these ECM cues in a semi-automated fashion. We foresee the use of these methods as a mechanism to bridge the gap between high-throughput cell-matrix screening and engineered ECM-mimicking biomaterials.

bioengineering↗