RESUMO
Tumor organoids and tumors-on-chips can be built by placing patient-derived cells within an extracellular matrix (ECM) for personalized medicine. The ECM influences the tumor response, and understanding the ECM-tumor relationship is important before translating tumor-on-chips into clinics. In this work, we tuned the physical and structural characteristics of ECM in a bioprinted soft-tissue sarcoma microtissue. We formed 3D spheroids at a controlled size and encapsulated them into our gelatin methacryloyl (GelMA)-based bioink to make perfusable hydrogel-based microfluidic chips. We then demonstrated the scalability and customization flexibility of our hydrogel-based chip via engineering tools. A multiscale physical and structural data analysis suggested a relationship between cell invasion response and bioink characteristics. Tumor cell invasive behavior and focal adhesion properties were observed in response to varying polymer network densities of the GelMA-based bioink. Immunostaining assays and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) helped assess the bioactivity of the microtissue and measure the cell invasion. The RT-qPCR results showed higher expressions of HIF-1α, CD44, and MMP2 genes in a lower polymer density, highlighting the correlation between bioink structural porosity, ECM stiffness, and tumor spheroid response. In conclusion, this work is the first step in modeling STS tumor invasiveness in hydrogel-based microfluidic chips, and our tunable bioink may help reduce the variability of current tumor-on-chips. STATEMENT OF SIGNIFICANCE: We optimized an engineering protocol for making tumor spheroids at a controlled size, embedding spheroids into a gelatin-based matrix, and constructing a perfusable microfluidic device. A higher tumor invasion was observed in a low-stiffness matrix than a high-stiffness matrix. The physical characterizations revealed how the stiffness is controlled by the density of polymer chain networks and porosity. The biological assays revealed how the structural properties of the gelatin matrix and hypoxia in tumor progression impact cell invasion. The cell spheroids' responses underscore the importance of replicating physical and structural properties to mimic tumor response. This work can contribute to personalized medicine by making more effective, tailored cancer models.
RESUMO
Despite decades of research presenting insulin-like growth factor-1 receptor (IGF1R) as an attractive target for cancer therapy, IGF1R inhibitors ultimately failed in clinical trials. This was surprising due to the known cancer-promoting functions of IGF1R, including stimulation of cell invasion, proliferation, and survival. Discourse in the literature has acknowledged that a lack of patient stratification may have impacted the success of IGF1R-inhibitor trials. This argument alludes to the possibility that IGF1R function may be contingent on tumor type and cellular composition. Looking into the known roles of IGF1R, it becomes clear that this receptor interacts with a multitude of different proteins and even has tumor-suppressing functions. IGF1R is implicated in both cell-cell and cell-surface adhesion dynamics, and the effects of either IGF1R downregulation or pharmacological inhibition on cellular adhesion remain poorly understood. In turn, adhesion receptors modulate IGF1R signaling. In addition, our understanding of IGF1R function in tumor-associated immune and stromal cells is lacking, which could contribute to the overwhelming failure of IGF1R inhibitors in the clinic. In this review, we re-investigate clinical trial data to make connections between the failure of these drugs in human cancer patients and the understudied facets of IGF1R function. We describe lesser-known and potentially tumor-suppressive functions of IGF1R that include promoting cell-cell adhesion through E-cadherin, augmenting a pro-inflammatory macrophage phenotype, and stimulating B cells to produce immunoglobulins. We also highlight the important role of adhesion receptors in regulating IGF1R function, and we use this information to infer stratification criteria for selecting patients that might benefit from IGF1R inhibitors.