Nanotopography as Artificial Microenvironment for Accurate Visualization of Metastasis Development via Simulation of ECM Dynamics.

Affiliation

Tai CS(1)(2), Lan KC(1)(2), Wang E(1), Chan FE(3), Hsieh MT(3), Huang CW(1)(4), Weng SL(5)(6), Chen PC(3)(7), Chen WL(1)(8).
Author information:
(1)Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
(2)Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, Taiwan.
(3)Department of Materials and Mineral Resources Engineering, National Taipei University of Technology, Taipei, Taiwan.
(4)Division of Thoracic Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
(5)Department of Medicine, MacKay Medical College, New Taipei City, Taiwan.
(6)Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan.
(7)Institute of Material Science and Engineering, National Taipei University of Technology, Taipei, Taiwan.
(8)Center for Intelligent Drug Systems and Smart Bio-devices
(IDS2B), National Chiao Tung University, Hsinchu, Taiwan.

Abstract

Metastatic progression is mediated by complex interactions between deregulated extracellular matrix (ECM) and cancer cells and remains a major challenge in cancer management. To investigate the role of ECM dynamics in promoting metastasis development, we developed an artificial microenvironment (AME) platform comprised of nanodot arrays of increasing diameter. Cells cultured on the platform showed increasing signs of mesenchymal-like cell transition as AME diameter increased, suggesting accurate simulation of ECM-mediated gene regulation. Gene expression was analyzed to determine genes significant to transition, which were then used to select appropriate small molecule drugs for time course treatments. Our results suggest that the platform can identify critical target genes as well as possible drug candidates. Overall, the AME platform allows for the study of intricate ECM-induced gene expression trends across metastasis development that would otherwise be difficult to visualize in vivo and may open new avenues toward successful personalized cancer management.