Mathematical and experimental approaches to identify and predict the effects of chemotherapy on neuroglial precursors

Ollivier Hyrien1, Jörg Dietrich, Mark Noble

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA. Ollivier_Hyrien@urmc.rochester.edu

Cancer Research
|November 9, 2010
PubMed
Abstract

Insights

Sublethal chemotherapy doses alter brain precursor cell function, affecting cell cycle and differentiation. These changes compromise tissue repair and may have long-term clinical implications, even without cell death.

Area of Science:

  • Neuroscience
  • Cancer Biology
  • Computational Biology

Background:

  • Chemotherapy's adverse effects on normal cells pose clinical challenges.
  • Limited understanding exists regarding chemotherapy's impact on normal precursor cell function beyond cell death.
  • Precursor cells are crucial for tissue homeostasis and repair.

Purpose of the Study:

  • To investigate the effects of sublethal chemotherapy doses on central nervous system glial precursor cells.
  • To identify novel drug effects on precursor cell function using mathematical and experimental analyses.

Main Methods:

  • Developed a multitype age-dependent branching process model for precursor and differentiated cells.
  • Exposed oligodendrocyte progenitor cells to sublethal doses of carmustine (BCNU).
  • Fitted the model to in vitro clonal experimental data using pseudo-likelihood estimation.

Main Results:

  • Transient carmustine (BCNU) exposure increased progenitor cell cycle length and decreased differentiation time.
  • BCNU decreased the probability of self-renewing divisions in precursor cells.
  • Mathematical modeling indicated spontaneous recovery from timing modifications but not from cell fate alterations.

Conclusions:

  • Sublethal chemotherapy can compromise precursor cell function, impacting the progenitor cell pool long-term, independent of apoptosis.
  • Altered cell cycle, differentiation timing, and self-renewal probability are key drug effects.
  • The developed computational tools can broadly identify effects of chemotherapeutic agents and other stressors.

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