Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling

Phillip C Spinosa1, Patrick C Kinnunen1, Brock A Humphries2

  • 1Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA.

Abstract

Insights

Cellular pre-existing states dictate signaling responses through CXCR4 and EGFR, offering insights into cancer signaling heterogeneity and potential therapeutic strategies. This conserved signaling motif aids in optimizing cancer treatments.

Area of Science:

  • Oncology
  • Cell Signaling
  • Computational Biology

Background:

  • CXCR4 and EGFR are key receptors in cancer, activating ERK and Akt kinases with heterogeneous single-cell responses.
  • Signaling heterogeneity may stem from variations in pre-existing intracellular cell states.
  • Understanding these differences is crucial for developing targeted cancer therapies.

Purpose of the Study:

  • To computationally model and experimentally validate the origins of signaling heterogeneity in CXCR4 and EGFR pathways.
  • To investigate how pre-existing cell states influence Akt and ERK responses to receptor stimulation.
  • To explore the potential of a computational model for optimizing cancer treatment strategies.

Main Methods:

  • Development of a single-cell computational model for Akt and ERK signaling.
  • Quantitative live-cell time-lapse imaging to test model predictions.
  • Analysis of signaling responses to CXCR4 and EGFR stimulation across different ligand doses and cell lines.

Main Results:

  • Pre-existing cell states were found to predict single-cell signaling outcomes for both CXCR4 and EGFR.
  • A conserved set of cell states explained signaling heterogeneity for both receptors across multiple doses and in two breast cancer cell lines.
  • The model predicted that PI3K-targeted therapies could potentiate ERK signaling, which could be ablated by combined MEK and PI3K inhibition.

Conclusions:

  • A conserved signaling motif exists for both EGFR and CXCR4 pathways.
  • The developed computational model shows potential clinical utility for optimizing cancer therapy.
  • Findings provide a framework for understanding and potentially manipulating signaling heterogeneity in cancer treatment.

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