Quantitative Size-Based Analysis of Tumor Spheroids and Responses to Therapeutics

Pradip Shahi Thakuri1, Megha Gupta2, Madison Plaster1

  • 11 Department of Biomedical Engineering and The University of Akron, Akron, Ohio.

Insights

Researchers developed a simple size-based method to analyze 3D cancer cell cultures (spheroids) and predict drug resistance. This technique accurately measures drug responses and identifies effective combination therapies, aiding in overcoming treatment challenges.

Area of Science:

  • Oncology
  • Biotechnology
  • Drug Discovery

Background:

  • Drug resistance is a significant clinical challenge in cancer therapy.
  • Three-dimensional (3D) cancer cell cultures (tumor spheroids) better mimic in vivo tumors but are difficult to analyze.
  • Existing biochemical assays for 2D cultures require optimization for 3D systems.

Purpose of the Study:

  • To develop a simple, quantitative, size-based analysis method for 3D tumor spheroids.
  • To evaluate drug responses and predict resistance in 3D cancer cell cultures.
  • To identify effective drug combinations to overcome treatment resistance.

Main Methods:

  • Utilized an aqueous two-phase system to form consistently sized colorectal cancer spheroids.
  • Employed quantitative image analysis to measure spheroid size over time.
  • Cyclically treated spheroids with a MEK inhibitor (trametinib) and a PI3K/AKT inhibitor (dactolisib), assessing responses via size measurements.

Main Results:

  • Spheroid size measurements strongly correlated with traditional biochemical assays for growth and drug response.
  • Size analysis accurately predicted trametinib resistance development, linked to PI3K/AKT pathway activation.
  • Combination therapy with trametinib and dactolisib effectively prevented drug resistance, as confirmed by size-based analysis.

Conclusions:

  • Spheroid size measurement offers a straightforward, quantitative method for assessing drug responses in 3D cultures.
  • This approach facilitates the identification of drug combinations that effectively block cancer drug resistance.
  • The developed method aids in optimizing targeted therapies and overcoming resistance mechanisms in cancer treatment.

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