Axl and Vascular Endothelial Growth Factor Receptors Exhibit Variations in Membrane Localization and Heterogeneity

Yingye Fang1, Princess I Imoukhuede1

  • 1Department of Bioengineering, University of Washington, Seattle, Washington, USA.

GEN Biotechnology
|March 6, 2023
PubMed

Insights

Ovarian cancer drug screening models differ in receptor tyrosine kinase (RTK) levels. Understanding these differences in 2D monolayers versus 3D spheroids is crucial for accurate drug response prediction.

Area of Science:

  • Oncology
  • Molecular Biology
  • Biochemistry

Background:

  • Receptor tyrosine kinases (RTKs), including VEGFRs and Axl, are key targets in ovarian cancer therapy.
  • Two-dimensional (2D) monolayer and three-dimensional (3D) spheroid cultures are utilized for RTK-targeted drug screening.
  • RTK plasma membrane localization is critical for signaling and drug response but is poorly characterized in these models.

Purpose of the Study:

  • To quantify plasma membrane RTK concentrations in ovarian cancer cell culture models.
  • To compare RTK abundance and heterogeneity between 2D monolayers and 3D spheroids.
  • To inform the selection of appropriate ovarian cancer models for drug screening.

Main Methods:

  • Quantification of plasma membrane concentrations of VEGFR1 and Axl in ovarian cancer cell lines (OVCAR3, OVCAR5, OVCAR8).
  • Comparison of RTK expression in 2D monolayer versus 3D spheroid culture systems.
  • Analysis of RTK heterogeneity within spheroid models.

Main Results:

  • Vascular endothelial growth factor receptor 1 (VEGFR1) plasma membrane concentrations were 10-fold higher in OVCAR8 spheroids than in monolayers.
  • OVCAR8 spheroids exhibited significant Axl heterogeneity, with subpopulations showing bimodal distributions.
  • Plasma membrane Axl concentrations varied by 100-fold between chemosensitive (OVCAR3) and chemoresistant (OVCAR8) cells.

Conclusions:

  • Significant differences exist in RTK plasma membrane abundance and heterogeneity between 2D and 3D ovarian cancer models.
  • These findings highlight the importance of model selection for reliable RTK-targeted drug screening in ovarian cancer.
  • Understanding RTK expression patterns in different models is essential for predicting therapeutic responses.

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