A vascularized breast cancer spheroid platform for the ranked evaluation of tumor microenvironment-targeted drugs by

David Ascheid1, Magdalena Baumann1, Jürgen Pinnecker2

  • 1Institute of Anatomy and Cell Biology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.

Nature Communications
|April 27, 2024
PubMed

Insights

A new screening platform using vascularized tumor spheroids (VTSs) enables precise evaluation of cancer drug candidates targeting the tumor microenvironment (TME). This system reliably ranks drug efficacy for improved cancer therapy development.

Area of Science:

  • Oncology
  • Biotechnology
  • Drug Discovery

Background:

  • Targeting the tumor microenvironment (TME) is crucial in cancer drug development.
  • Assessing TME-targeted drug candidates faces significant challenges.
  • Existing methods lack the complexity to model TME interactions effectively.

Purpose of the Study:

  • To develop a comprehensive screening platform for evaluating drug effects on the TME.
  • To enable monitoring, quantification, and ranking of drug candidates.
  • To facilitate the study of complex TME composition and cell-cell interactions.

Main Methods:

  • Development of a screening platform using self-organizing, vascularized tumor spheroids (VTSs).
  • Recreation of TME complexity using four human-derived cell populations.
  • Utilizing light sheet fluorescence microscopy and advanced image analysis for data acquisition and processing.

Main Results:

  • The platform demonstrated robustness and strong interassay reliability.
  • Successfully enabled side-by-side evaluation of antifibrotic and antiangiogenic drugs.
  • Facilitated differential evaluation of closely related drug candidates based on therapeutic needs.

Conclusions:

  • The developed VTS screening platform offers a powerful tool for TME-targeted drug discovery.
  • The system provides reliable, multi-parametric data for assessing drug efficacy.
  • This platform can accelerate the development of novel cancer therapies by enabling precise drug candidate selection.

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