Integrated analysis of the tumor microenvironment using a reconfigurable microfluidic cell culture platform

The tumor microenvironment (TME) is a complex network of non-malignant cells and stroma that perform a wide array of vital roles in tumor growth, immune evasion, metastasis, and therapeutic resistance. These highly diverse roles have been shown to be critically important to the progression of cancers and have already shown potential as therapeutic targets. Therefore, there has been a tremendous push to elucidate the pathways that underlie these roles and to develop new TME-directed therapies for cancer treatment. Unfortunately, TME-focused research has been limited by a lack of translational in vitro culture platforms that can model this highly complex niche and can support the integrated analysis of cell biology and function. In the current study, we investigate whether an independently developed reconfigurable microfluidic platform, known as Stacks, can address the critical need for translational multi-cellular tumor models and integrated analytics in TME research. We present data on multi-cellular culture of primary human cells in Stacks as well as the orthogonal analysis of cellular polarization, differentiation, migration, and cytotoxicity in this reconfigurable system. These expanded capabilities of Stacks are highly relevant to the cancer research community with the potential to enhance clinical translation of pre-clinical TME studies and to yield novel biological insight into TME crosstalk, metastasis, and responses to novel drug combinations or immune therapies.

Automated summary

The tumor microenvironment (TME) plays crucial roles in tumor growth, immune evasion, metastasis, and therapeutic resistance. However, the lack of suitable translational in vitro culture platforms has limited TME-focused research. In this study, a reconfigurable microfluidic platform, Stacks, is investigated for its potential in modeling TME and enabling integrated analysis of cell biology and function. The expanded capabilities of Stacks have important implications for enhancing clinical translation of pre-clinical TME studies and gaining novel insights into TME dynamics.