Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.
Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.
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DOI：
10.1158/0008-5472.can-21-0730
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发表时间：
2021-11-01
期刊：
影响因子：
11.2
通讯作者：
Kuijjer ML
中科院分区：
文献类型：
--
作者：
Lopes-Ramos CM;Belova T;Brunner TH;Ben Guebila M;Osorio D;Quackenbush J;Kuijjer ML
个体胶质母细胞瘤的全基因组网络模型确定了预后不良患者的 PD1 信号传导失调，表明这种方法可用于了解基因调控如何影响癌症进展。胶质母细胞瘤是一种侵袭性大脑和脊柱癌症。虽然胶质母细胞瘤组学数据的分析在一定程度上提高了我们对该疾病的了解，但并没有直接改善患者的生存率。癌症生存通常以基因表达差异为特征，但驱动这些差异的机制通常未知。因此，我们着手模拟与胶质母细胞瘤存活相关的调节机制。我们使用来自癌症基因组图谱的两个不同表达平台的数据推断了个体患者的基因调控网络。我们对长期和短期生存的患者进行了比较网络分析。七个途径被确定与生存相关，所有这些途径都涉及免疫信号传导； PD1 信号传导的差异调节经过验证，与德国神经胶质瘤网络的独立数据集中的结果相对应。在这条途径中，短期幸存者中失去了可用治疗方案的基因转录抑制；这与突变负荷无关，并且与 T 细胞浸润只有微弱的相关性。总的来说，这些结果提供了一种对胶质母细胞瘤患者进行分层的新方法，该方法使用网络特征作为生物标志物来预测生存。他们还确定了新的潜在治疗干预措施，强调了分析个体癌症患者基因调控网络的价值。个体胶质母细胞瘤的全基因组网络模型确定了预后不良患者的 PD1 信号传导失调，表明这种方法可用于了解基因调控如何影响癌症进展。
Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression. Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.