de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons200275

Böttcher,  Marvin A.
Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons56973

Traulsen,  Arne
Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society;

Volltexte (frei zugänglich)

s12885-018-4281-1.pdf
(Verlagsversion), 2MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Böttcher, M. A., Held-Feindt, J., Synowitz, M., Lucius, R., Traulsen, A., & Hattermann, K. (2018). Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation. BMC Cancer, 18(376). doi:10.1186/s12885-018-4281-1.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-6C3D-3
Zusammenfassung
Background: Tumors comprise a variety of specialized cell phenotypes adapted to different ecological niches that massively influence the tumor growth and its response to treatment. Methods: In the background of glioblastoma multiforme, a highly malignant brain tumor, we consider a rapid proliferating phenotype that appears susceptible to treatment, and a dormant phenotype which lacks this pronounced proliferative ability and is not affected by standard therapeutic strategies. To gain insight in the dynamically changing proportions of different tumor cell phenotypes under different treatment conditions, we develop a mathematical model and underline our assumptions with experimental data. Results: We show that both cell phenotypes contribute to the distinct composition of the tumor, especially in cycling low and high dose treatment, and therefore may influence the tumor growth in a phenotype specific way. Conclusion: Our model of the dynamic proportions of dormant and rapidly growing glioblastoma cells in different therapy settings suggests that phenotypically different cells should be considered to plan dose and duration of treatment schedules. © 2018 The Author(s).