Zador Zsolt, Landry Alexander P, Saha Ashirbani, Cusimano Michael D
Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, ON, Canada.
Front Oncol. 2020 Nov 5;10:541928. doi: 10.3389/fonc.2020.541928. eCollection 2020.
Meningiomas are the most common brain tumor, with prevalence of approximately 3%. Histological grading has a major role in determining treatment choice and predicting outcome. While indolent grade 1 and aggressive grade 3 meningiomas exhibit relatively homogeneous clinical behavior, grade 2 meningiomas are far more heterogeneous, making outcome prediction challenging. We hypothesized two subgroups of grade 2 meningiomas which biologically resemble either World Health Organization (WHO) grade 1 or WHO grade 3. Our aim was to establish gene expression signatures that separate grade 2 meningiomas into two homogeneous subgroups: a more indolent subtype genetically resembling grade 1 and a more aggressive subtype resembling grade 3.
We carried out an observational meta-analysis on 212 meningiomas from six distinct studies retrieved from the open-access platform Microarray data was analyzed with systems-level gene co-expression network analysis. Fuzzy C-means clustering was employed to reclassify 34 of the 46 grade 2 meningiomas (74%) into a benign "grade 1-like" (13/46), and malignant "grade 3-like" (21/46) subgroup based on transcriptomic profiles. We verified shared biology between matching subgroups based on meta-gene expression and recurrence rates. These results were validated further using an independent RNA-seq dataset with 160 meningiomas, with similar results.
Recurrence rates of "grade 1-like" and "grade 3- like" tumors were 0 and 75%, respectively, statistically similar to recurrence rates of grade 1 (17%) and 3 (85%). We also found overlapping biological processes of new subgroups with their adjacent grades 1 and 3.
These results underpin molecular signatures as complements to histological grading systems. They may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials.
脑膜瘤是最常见的脑肿瘤,患病率约为3%。组织学分级在确定治疗选择和预测预后方面起着重要作用。虽然惰性的1级和侵袭性的3级脑膜瘤表现出相对一致的临床行为,但2级脑膜瘤的异质性要大得多,这使得预后预测具有挑战性。我们假设2级脑膜瘤存在两个亚组,其生物学特性分别类似于世界卫生组织(WHO)1级或WHO 3级。我们的目的是建立基因表达特征,将2级脑膜瘤分为两个同质亚组:一个更惰性的亚型,其基因特征类似于1级;一个更具侵袭性的亚型,类似于3级。
我们对从开放获取平台检索到的六项不同研究中的212例脑膜瘤进行了观察性荟萃分析。使用系统水平的基因共表达网络分析对微阵列数据进行分析。基于转录组谱,采用模糊C均值聚类将46例2级脑膜瘤中的34例(74%)重新分类为良性的“1级样”(13/46)和恶性的“3级样”(21/46)亚组。我们基于元基因表达和复发率验证了匹配亚组之间的共同生物学特性。使用包含160例脑膜瘤的独立RNA测序数据集进一步验证了这些结果,结果相似。
“1级样”和“3级样”肿瘤的复发率分别为0和75%,在统计学上与1级(17%)和3级(85%)的复发率相似。我们还发现新亚组与其相邻的1级和3级存在重叠的生物学过程。
这些结果支持将分子特征作为组织学分级系统的补充。它们可能有助于重塑未来和正在进行的临床试验的预测、随访计划、治疗决策和招募方案。