Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306, Plön, Germany.
Department of Neurosurgery, University Medical Center Schleswig-Holstein UKSH, Campus Kiel, 24105, Kiel, Germany.
BMC Cancer. 2018 Apr 3;18(1):376. doi: 10.1186/s12885-018-4281-1.
Tumors comprise a variety of specialized cell phenotypes adapted to different ecological niches that massively influence the tumor growth and its response to treatment.
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.
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.
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.
肿瘤由多种适应不同生态位的特化细胞表型组成，这些表型极大地影响肿瘤的生长及其对治疗的反应。
在多形性胶质母细胞瘤（一种高度恶性的脑瘤）的背景下，我们考虑了一种快速增殖的表型，这种表型似乎对治疗敏感，而另一种休眠表型则缺乏这种明显的增殖能力，不受标准治疗策略的影响。为了深入了解不同治疗条件下不同肿瘤细胞表型的动态变化比例，我们建立了一个数学模型，并通过实验数据来强调我们的假设。
我们表明，两种细胞表型都有助于肿瘤的不同组成，特别是在周期性低剂量和高剂量治疗中，因此可能以表型特异性的方式影响肿瘤的生长。
我们的模型表明，在不同的治疗环境中，休眠和快速生长的胶质母细胞瘤细胞的动态比例不同，因此应该考虑表型不同的细胞来规划治疗方案的剂量和持续时间。
