Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well‐defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype‐specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype‐specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

中文翻译：

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用于推断胶质母细胞瘤亚型特异性敏感性的综合调控模型

多形性胶质母细胞瘤（GBM）是一种高度恶性的癌症，缺乏有效的治疗方案或明确的个性化癌症治疗策略。该疾病已分为不同的分子亚型；然而，引起这种异质性的潜在调节回路及其对治疗的影响仍不清楚。我们开发了一个模块化计算流程，即用于癌症易感性系统推断的转录调控相互作用的综合建模（inTRINSiC），以剖析亚型特异性调控程序并预测个体患者肿瘤的遗传依赖性。使用由 518 个转录因子 (TF)、10,733 个靶基因和 3,132 个蛋白质的信号传导层组成的多层网络，我们能够准确识别形成亚型特异性表达景观的 TF 的差异调节活性。我们的模型还允许推断不同 GBM 亚型中 TF 行为改变的机制。最重要的是，我们能够使用多层模型进行 计算机 微扰分析，以推断 GBM 亚型之间的差异遗传脆弱性，并确定 MYB 家族成员 MYBL2 作为 Proneural 亚型的特异性药物靶点。