新型胶质母细胞瘤治疗方法：关注肿瘤异质性、治疗耐药性和计算工具。
Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.
机构信息
Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch (UTMB), Galveston, Texas.
Department of Biomedicine and Clinic Neuroscience, University of Palermo, Palermo, Italy.
出版信息
Cancer Rep (Hoboken). 2019 Dec;2(6):e1220. doi: 10.1002/cnr2.1220. Epub 2019 Nov 11.
BACKGROUND
Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the ability of tumor cells to deeply infiltrate the surrounding tissue makes complete resection quite impossible, and, in consequence, the probability of tumor recurrence is high, and the prognosis is not positive. GBM is highly heterogeneous and adapts to treatment in most individuals. Nevertheless, these mechanisms of adaption are unknown.
RECENT FINDINGS
In this review, we will discuss the recent discoveries in molecular and cellular heterogeneity, mechanisms of therapeutic resistance, and new technological approaches to identify new treatments for GBM. The combination of biology and computer resources allow the use of algorithms to apply artificial intelligence and machine learning approaches to identify potential therapeutic pathways and to identify new drug candidates.
CONCLUSION
These new approaches will generate a better understanding of GBM pathogenesis and will result in novel treatments to reduce or block the devastating consequences of brain cancers.
背景
胶质母细胞瘤（GBM）是一种高度侵袭性的原发性脑肿瘤。目前，建议的治疗方案是手术切除，然后进行放疗，并辅助使用替莫唑胺，这是一种 DNA 烷化剂。然而，肿瘤细胞向周围组织浸润的能力使得完全切除几乎不可能，因此肿瘤复发的概率很高，预后也不容乐观。GBM 具有高度异质性，在大多数患者中会对治疗产生适应性。然而，这些适应机制尚不清楚。
最近的发现
在这篇综述中，我们将讨论分子和细胞异质性、治疗耐药机制的最新发现，以及识别 GBM 新治疗方法的新技术方法。生物学和计算机资源的结合可以使用算法应用人工智能和机器学习方法来识别潜在的治疗途径，并鉴定新的药物候选物。
结论
这些新方法将更好地了解 GBM 的发病机制，并为减少或阻断脑癌的毁灭性后果提供新的治疗方法。
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