Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, Maharashtra, 411008, India.
Academy of Scientific & Innovative Research (AcSIR), CSIR-NCL Campus, Pune, India.
Sci Rep. 2019 Jul 1;9(1):9488. doi: 10.1038/s41598-019-45892-8.
The phenotypic plasticity and self-renewal of adult neural (aNSCs) and glioblastoma stem cells (GSCs) are both known to be governed by active Notch pathway. During development, GSCs can establish differential hierarchy to produce heterogeneous groups of tumor cells belong to different grades, which makes the tumor ecosystem more complex. However, the molecular events regulating these entire processes are unknown hitherto. In this work, based on the mechanistic regulations of Notch pathway activities, a novel computational framework is introduced to inspect the intra-cellular reactions behind the development of normal and tumorigenic cells from aNSCs and GSCs, respectively. The developmental dynamics of aNSCs/GSCs are successfully simulated and molecular activities regulating the phenotypic plasticity and self-renewal processes in normal and tumor cells are identified. A novel scoring parameter "Activity Ratio" score is introduced to find out driver molecules responsible for the phenotypic plasticity and development of different grades of tumor. A new quantitative method is also developed to predict the future risk of Glioblastoma tumor of an individual with appropriate grade by using the transcriptomics profile of that individual as input. Also, a novel technique is introduced to screen and rank the potential drug-targets for suppressing the growth and differentiation of tumor cells.
已知成人神经(aNSC)和神经胶质瘤干细胞(GSCs)的表型可塑性和自我更新都受活跃的 Notch 途径调控。在发育过程中,GSCs 可以建立差异层次结构,产生属于不同等级的异质肿瘤细胞群体,这使得肿瘤生态系统更加复杂。然而,迄今为止,调节这些整个过程的分子事件尚不清楚。在这项工作中,基于 Notch 途径活性的机制调节,引入了一种新的计算框架,分别检查来自 aNSC 和 GSC 的正常和致瘤细胞发育背后的细胞内反应。成功模拟了 aNSC/GSC 的发育动力学,并确定了调节正常和肿瘤细胞表型可塑性和自我更新过程的分子活性。引入了一个新的评分参数“活性比”评分,以找出负责不同等级肿瘤表型可塑性和发育的驱动分子。还开发了一种新的定量方法,通过使用个体的转录组学特征作为输入,预测具有适当等级的个体患胶质母细胞瘤肿瘤的未来风险。此外,还引入了一种新的技术来筛选和排名潜在的药物靶点,以抑制肿瘤细胞的生长和分化。