Microfluidic devices: The application in TME modeling and the potential in immunotherapy optimization

Abstract

With continued advances in cancer research, the crucial role of the tumor microenvironment (TME) in regulating tumor progression and influencing immunotherapy outcomes has been realized over the years. A series of studies devoted to enhancing the response to immunotherapies through exploring efficient predictive biomarkers and new combination approaches. The microfluidic technology not only promoted the development of multi-omics analyses but also enabled the recapitulation of TME in vitro microfluidic system, which made these devices attractive across studies for optimization of immunotherapy. Here, we reviewed the application of microfluidic systems in modeling TME and the potential of these devices in predicting and monitoring immunotherapy effects.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid‐Based 3D In Vitro Models

“…[111][112][113][114][115][116][117][118] Microfluidic devices designed for modeling the TME are reviewed in more detail elsewhere. [3,47,[119][120][121][122][123][124] In the context of PDO integration with microfluidic devices, one notable design was developed by Schuster et al, who created a two-layer microfluidic culture chamber with 200 wells to enable testing of up to 20 independent conditions across 10 PDO lines simultaneously. In this design, the lower layer was a 200-well array with dimensions optimized for organoid culture and the upper layer provided fluidic channels.…”

Unlocking the Potential of Biomarkers for Immune Checkpoint Inhibitors in Cancer Therapy

“…For instance, the classic two-dimensional (2D) coculture models often entail cultivating immune effector cells (T, B, and NK cells) on monolayers of cancer or stromal cells, encompassing various components such as MSCs, fibroblasts, and macrophages [ 37 ]. This model is frequently complemented by animal models or three-dimensional (3D) culture systems and organs-on-chips, offering the advantage of emulating the intricate cellular dynamics and mechanical complexity observed in patients [ 38 , 39 , 40 ]. By integrating these preclinical models with advanced high-resolution imaging and real-time monitoring, a complete comprehension of the dynamic interactions can be achieved, not only deepening our understanding of the mechanisms underlying resistance to ICIs, but also highlighting potential drug targets that could enhance the efficacy of ICIs [ 40 , 41 ].…”