Cancer-on-a-chip for precision cancer medicine

Lunan Liu1, Huishu Wang1, Ruiqi Chen2

  • 1Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA. wchen@nyu.edu.

Lab on a Chip
|May 16, 2025
PubMed

Insights

Cancer-on-a-chip (CoC) models offer a more accurate preclinical testing platform by mimicking the tumor microenvironment (TME). This review highlights CoC advancements for precision cancer medicine and drug discovery.

Area of Science:

  • Biomedical Engineering
  • Oncology
  • Microfluidics

Background:

  • Current cancer preclinical models fail to accurately represent human tumor complexity and patient specificity.
  • This limitation leads to high failure rates of cancer therapies in clinical trials.
  • The tumor microenvironment (TME) plays a critical role in cancer progression and therapy response.

Purpose of the Study:

  • To review the state-of-the-art in Cancer-on-a-chip (CoC) technology for modeling the TME.
  • To discuss the application of CoC systems in cancer therapy screening and drug discovery.
  • To explore future directions for developing next-generation CoC models for precision cancer medicine.

Main Methods:

  • Summarization of recent advancements in CoC development for modeling tumor vasculature, stromal, and immune niches.
  • Review of CoC applications in therapeutic screening.
  • Exploration of future CoC technologies including patient-derived chips, organoids-on-a-chip, and multi-organ systems.

Main Results:

  • CoC systems closely mimic in vivo tumor anatomy and microenvironment interactions, enabling more accurate disease modeling.
  • CoC technology facilitates high-throughput screening for anticancer drug discovery.
  • Integration of sensors and AI can enhance CoC data analysis and disease investigation.

Conclusions:

  • CoC technology is crucial for advancing precision cancer medicine by providing patient-specific and high-fidelity preclinical models.
  • Future CoC development should focus on standardization, translation to clinical settings, and integration with AI.
  • Addressing practical challenges and ethical concerns is essential for the widespread adoption of CoC technology.