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Glioblastoma (GBM), characterized by high infiltrative capacity, is the most common and deadly type of primary brain tumor in adults. GBM cells, including therapy‐resistant glioblastoma stem‐like cells (GSCs), invade the healthy brain parenchyma to form secondary tumors even after patients undergo surgical resection and chemoradiotherapy. New techniques are therefore urgently needed to eradicate these residual tumor cells. A thiol‐Michael addition injectable hydrogel for compatibility with GBM therapy is previously characterized and optimized. This study aims to develop the hydrogel further to capture GBM/GSCs through CXCL12‐mediated chemotaxis. The release kinetics of hydrogel payloads are investigated, migration and invasion assays in response to chemoattractants are performed, and the GBM‐hydrogel interactions in vitro are studied. With a novel dual‐layer hydrogel platform, it is demonstrated that CXCL12 released from the synthetic hydrogel can induce the migration of U251 GBM cells and GSCs from the extracellular matrix microenvironment and promote invasion into the synthetic hydrogel via amoeboid migration. The survival of GBM cells entrapped deep into the synthetic hydrogel is limited, while live cells near the surface reinforce the hydrogel through fibronectin deposition. This synthetic hydrogel, therefore, demonstrates a promising method to attract and capture migratory GBM cells and GSCs responsive to CXCL12 chemotaxis.

 

Bioengineers have built models of the tumour microenvironment (TME) in which to study cell–cell interactions, mechanisms of cancer growth and metastasis, and to test new therapies. These models allow researchers to culture cells in conditions that include features of the in vivo TME implicated in regulating cancer progression, such as extracellular matrix (ECM) stiffness, integrin binding to the ECM, immune and stromal cells, growth factor and cytokine depots, and a three-dimensional geometry more representative of the in vivo 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 models and clinical trials. However, it can be challenging to compare drug response reports across different biomaterial platforms in the current literature. This is, in part, a result of inconsistent reporting and improper use of drug response metrics, and vast differences in cell growth rates across a large variety of biomaterial designs. This study 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 measure the growth rate of cells in the absence of drug, and follow our ‘decision tree’ when reporting drug response metrics. This article is part of a discussion meeting issue ‘Forces in cancer: interdisciplinary approaches in tumour mechanobiology’. 

Cell motility is a critical aspect of several processes, such as wound healing and immunity; however, it is dysregulated in cancer. Current limitations of imaging tools make it difficult to study cell migrationin vivo. To overcome this, and to identify drivers from the microenvironment that regulate cell migration, bioengineers have developed 2D (two‐dimensional) and 3D (three‐dimensional) tissue model systems in which to study cell motilityin vitro, with the aim of mimicking elements of the environments in which cells movein vivo. However, there has been no systematic study to explicitly relate and compare cell motility measurements between these geometries or systems. Here, we provide such analysis on our own data, as well as across data in existing literature to understand whether, and which, metrics are conserved across systems. To our surprise, only one metric of cell movement on 2D surfaces significantly and positively correlates with cell migration in 3D environments (percent migrating cells), and cell invasion in 3D has a weak, negative correlation with glioblastoma invasionin vivo. Finally, to compare across complex model systems,in vivodata, and data from different labs, we suggest that groups report an effect size, a statistical tool that is most translatable across experiments and labs, when conducting experiments that affect cellular motility.