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Simulating Heterogeneous Tumor Cell Populations.

Andrew Sundstrom1,2, Dafna Bar-Sagi3, Bud Mishra2

  • 1Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.

Plos One
|December 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a simulation to model tumor metabolic heterogeneity and hypoxia. The model reveals how cell subpopulations self-sort and form stable hypoxic regions, aiding therapy resistance research.

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Area of Science:

  • Computational Biology
  • Cancer Research
  • Systems Biology

Background:

  • Tumor metabolic heterogeneity and chronic hypoxia are key factors in therapy resistance.
  • Intracellular mechanisms of cancer metabolic reprogramming are known, but macro-scale dynamics are not.
  • Understanding the initial conditions and spatiotemporal dynamics driving these tumor conditions is crucial.

Purpose of the Study:

  • To develop a simulation framework for modeling tissue-scale cell populations.
  • To investigate the self-sorting of metabolically symbiotic cell subpopulations.
  • To analyze the formation and stability of chronic hypoxic regions within tumors.

Main Methods:

  • A minimal, spatially-resolved simulation framework was developed.
  • The model incorporates diffusible particles (consumed/released by cells) influencing cell behavior.
  • Stochastic reproduction and tissue-scale cell population dynamics were simulated.

Main Results:

  • Simulations showed cell populations self-sorting to facilitate metabolic symbiosis, forming alternating striations.
  • Tumor growth patterns influenced by tumor-stroma signaling were observed.
  • Stable local regions of chronic hypoxia near blood vessels were generated.

Conclusions:

  • The simulation framework provides insights into emergent whole-tumor behavior.
  • The stability of hypoxic regions depends on O2 diffusion, vessel release, and cell consumption rates.
  • This framework can guide experimental design and hypothesis generation for cancer research.