The Tumor Microenvironment: An Introduction to the Development of Microfluidic Devices

B Kundu1,2, D Caballero3,4, C M Abreu3,4

  • 13B's Research Group, I3Bs-Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Parque de Ciência e Tecnologia, Zona Industrial da Gandra, Barco, Guimarães, Portugal. kundu.banani83@gmail.com.

Insights

The tumor microenvironment (TME) is a complex system influencing cancer growth and metastasis. Modeling the TME in labs is crucial for developing effective cancer therapies and improving patient outcomes.

Area of Science:

  • Oncology
  • Biotechnology
  • Cell Biology

Background:

  • The tumor microenvironment (TME) comprises cells, stroma, and signaling molecules crucial for tumorigenesis.
  • TME heterogeneity complicates understanding tumor dynamics, therapeutic responses, and metastasis.
  • Accurate laboratory recapitulation of the TME is vital for advancing cancer research and patient prognosis.

Purpose of the Study:

  • To provide an overview of TME characteristics and modeling approaches.
  • To discuss limitations of current in vitro platforms for TME simulation.
  • To highlight microfluidic platforms for TME simulation and future design considerations.

Main Methods:

  • Review of TME features and established modeling techniques.
  • Analysis of existing in vitro experimental limitations.
  • Exploration of microfluidic platforms for simulating TME dynamics.

Main Results:

  • The TME's complexity, including its heterogeneity, significantly impacts tumor progression and therapeutic resistance.
  • Current in vitro models often fail to capture the dynamic and heterogeneous nature of the TME.
  • Microfluidic platforms offer promising avenues for more realistic TME simulation.

Conclusions:

  • Advanced modeling strategies, particularly microfluidic tumor-on-a-chip systems, are essential for dissecting TME complexity.
  • Overcoming current limitations in TME modeling is critical for developing predictive preclinical tools and effective cancer treatments.
  • Future research should focus on designing more realistic and predictive tumor-on-a-chip platforms to improve cancer patient outcomes.