3D microgels to quantify tumor cell properties and therapy response dynamics

Nila C Wu1, Jose L Cadavid2, Xinzhu Tan3

  • 1Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.

Biomaterials
|March 1, 2022
PubMed

Insights

A new 96-well plate platform, 96-GLAnCE, uses 3D organoids and imaging to better assess anti-cancer drug effectiveness by capturing tumor cell diversity and regrowth dynamics.

Area of Science:

  • Oncology
  • Biotechnology
  • Drug Discovery

Background:

  • Tumors exhibit cellular heterogeneity, challenging traditional chemotherapy efficacy.
  • Current drug screening methods using 2D cell cultures and endpoint assays fail to capture complex tumor biology.
  • Novel therapeutic strategies are needed to target diverse tumor cell properties and improve pre-clinical drug discovery.

Purpose of the Study:

  • To develop and validate a 3D in vitro tumor model for enhanced pre-clinical drug screening.
  • To assess tumor cell growth and drug-induced regrowth dynamics in a heterogeneous cell environment.
  • To identify novel therapeutic targets by analyzing longitudinal, image-based readouts of tumor phenotypes.

Main Methods:

  • Adaptation of the Gels for Live Analysis of Compartmentalized Environments (GLAnCE) platform into a 96-well format (96-GLAnCE).
  • Integration of patient-derived organoids (PDOs) within a 3D extracellular matrix (ECM) microgel environment.
  • Longitudinal automated imaging for quantitative assessment of tumor cell growth and in situ regrowth post-treatment.

Main Results:

  • The 96-GLAnCE platform successfully quantified tumor aggressiveness, including cell growth and post-drug regrowth, in both cell lines and PDOs.
  • Longitudinal imaging revealed distinct tumor cell phenotypes and subpopulation dynamics not detectable by standard bulk assays.
  • The platform demonstrated robustness in combining 3D ECM models, PDOs, and real-time imaging for drug response assessment.

Conclusions:

  • The 96-GLAnCE platform offers a versatile tool for pre-clinical anti-cancer drug discovery.
  • This model overcomes limitations of traditional methods by incorporating tumor heterogeneity and dynamic responses.
  • It facilitates the identification of novel therapeutic targets with potential clinical significance.

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