Cancer-on-a-chip for precision cancer medicine.

Journal: Lab on a chip
Published Date:

Abstract

Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity and patient specificity in human cancer. Cancer-on-a-chip (CoC) microphysiological systems can closely mimic the complex anatomical features and microenvironment interactions in an actual tumor, enabling more accurate disease modeling and therapy testing. This review article concisely summarizes and highlights the state-of-the-art progresses in CoC development for modeling critical TME compartments including the tumor vasculature, stromal and immune niche, as well as its applications in therapying screening. Current dilemma in cancer therapy development demonstrates that future preclinical models should reflect patient specific pathophysiology and heterogeneity with high accuracy and enable high-throughput screening for anticancer drug discovery and development. Therefore, CoC should be evolved as well. We explore future directions and discuss the pathway to develop the next generation of CoC models for precision cancer medicine, such as patient-derived chip, organoids-on-a-chip, and multi-organs-on-a-chip with high fidelity. We also discuss how the integration of sensors and microenvironmental control modules can provide a more comprehensive investigation of disease mechanisms and therapies. Next, we outline the roadmap of future standardization and translation of CoC technology toward real-world applications in pharmaceutical development and clinical settings for precision cancer medicine and the practical challenges and ethical concerns. Finally, we overview how applying advanced artificial intelligence tools and computational models could exploit CoC-derived data and augment the analytical ability of CoC.

Authors

  • Lunan Liu
    Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA. wchen@nyu.edu.
  • Huishu Wang
    Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA. wchen@nyu.edu.
  • Ruiqi Chen
    College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China.
  • Yujing Song
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • William Wei
    Department of Chemical and Biomolecular Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
  • David Baek
    Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
  • Mahan Gillin
    Department of Chemical and Biomolecular Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
  • Katsuo Kurabayashi
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA. Electronic address: katsuo@umich.edu.
  • Weiqiang Chen
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China; Putian Lanhai Nuclear Medicine Research Center, Putian 351152, China.