Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation
Abstract 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 proli...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-04-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12885-018-4281-1 |
| _version_ | 1811204887054123008 |
|---|---|
| author | Marvin A. Böttcher Janka Held-Feindt Michael Synowitz Ralph Lucius Arne Traulsen Kirsten Hattermann |
| author_facet | Marvin A. Böttcher Janka Held-Feindt Michael Synowitz Ralph Lucius Arne Traulsen Kirsten Hattermann |
| author_sort | Marvin A. Böttcher |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T03:19:50Z |
| format | Article |
| id | doaj.art-ce59cfc064094d9b814f93f9b91dcb3d |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-12T03:19:50Z |
| publishDate | 2018-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-ce59cfc064094d9b814f93f9b91dcb3d2022-12-22T03:49:55ZengBMCBMC Cancer1471-24072018-04-0118111210.1186/s12885-018-4281-1Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulationMarvin A. Böttcher0Janka Held-Feindt1Michael Synowitz2Ralph Lucius3Arne Traulsen4Kirsten Hattermann5Department Evolutionary Theory, Max Planck Institute for Evolutionary BiologyDepartment of Neurosurgery, University Medical Center Schleswig-Holstein UKSHDepartment of Neurosurgery, University Medical Center Schleswig-Holstein UKSHDepartment of Anatomy, University of KielDepartment Evolutionary Theory, Max Planck Institute for Evolutionary BiologyDepartment of Anatomy, University of KielAbstract 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.http://link.springer.com/article/10.1186/s12885-018-4281-1Evolutionary game theoryGliomaDormancy |
| spellingShingle | Marvin A. Böttcher Janka Held-Feindt Michael Synowitz Ralph Lucius Arne Traulsen Kirsten Hattermann Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation BMC Cancer Evolutionary game theory Glioma Dormancy |
| title | Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation |
| title_full | Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation |
| title_fullStr | Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation |
| title_full_unstemmed | Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation |
| title_short | Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation |
| title_sort | modeling treatment dependent glioma growth including a dormant tumor cell subpopulation |
| topic | Evolutionary game theory Glioma Dormancy |
| url | http://link.springer.com/article/10.1186/s12885-018-4281-1 |
| work_keys_str_mv | AT marvinabottcher modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation AT jankaheldfeindt modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation AT michaelsynowitz modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation AT ralphlucius modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation AT arnetraulsen modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation AT kirstenhattermann modelingtreatmentdependentgliomagrowthincludingadormanttumorcellsubpopulation |