Searching for combination therapy by clustering methods: Stimulation of PKC in Golgi apparatus combined with

Lenka Lenkavska1, Silvia Tomkova1, Denis Horvath2

  • 1Department of Biophysics, Faculty of Science, P. J. Safarik University in Kosice, 041 54, Kosice, Slovakia.

Insights

This study reveals how protein kinase C (PKC) regulators impact cancer cell organelles, particularly the Golgi apparatus. This finding led to a combination therapy that enhances photodynamic therapy efficacy and cancer cell apoptosis.

Area of Science:

  • Cellular Biology
  • Cancer Research
  • Biochemistry

Background:

  • Cancer cell metabolism is a key target for anticancer therapies.
  • Protein kinase C (PKC) signaling plays a role in cancer progression.
  • Understanding organelle interactions with PKC is crucial for developing new treatments.

Purpose of the Study:

  • To investigate the correlation between the Golgi apparatus (GA), lysosomes, and mitochondria in U87 MG glioma cells.
  • To evaluate the impact of PKC regulators on these organelles.
  • To design a combination therapy based on identified cellular interactions.

Main Methods:

  • Western blot, fluorescence, and time-resolved fluorescence microscopy were used to assess organelle correlations.
  • PKC activators (PMA) and inhibitors (Gö6976, hypericin, rottlerin) were applied to cancer cells.
  • Hierarchical cluster analysis was employed to identify similarities between organelles and PKC regulators.

Main Results:

  • The Golgi apparatus (GA) was identified as a critical organelle significantly influenced by PKC regulators and oxidative stress.
  • Cluster analysis revealed specific correlations between organelles and PKC pathway modulators.
  • A combination therapy strategy was developed based on these findings.

Conclusions:

  • PKC signaling profoundly affects the Golgi apparatus and cellular homeostasis in glioma cells.
  • The developed combination therapy, incorporating hypericin-mediated photodynamic therapy, significantly enhanced apoptosis.
  • This study demonstrates the novel application of cluster analysis for identifying therapeutic targets in cancer research.