肿瘤异质性在神经肿瘤领域日益被认识为治疗成功的主要障碍。胶质瘤以不同组合的遗传和表观遗传改变为特征，导致多个分子途径之间的复杂相互作用。预测疾病演变和制定个体最佳治疗方案需要复杂到足以捕捉肿瘤发生底层[表观]遗传结构的统计模型。在这里，我们将这一任务形式化为对遗传网络的层次潜在表示中不同连接模式的推断。通过评估来自12个国家、14年时间内的4023例胶质瘤患者的多机构临床、遗传和结果数据，我们应用贝叶斯生成随机块建模来揭示一个跨分子确认的胶质母细胞瘤、IDH野生型；少突胶质瘤、IDH突变和1p/19q代码缺失；以及星形细胞瘤、IDH突变的肿瘤遗传学的层次网络结构。我们的研究结果揭示了脑肿瘤遗传格局中特征之间的复杂依赖关系，并显示出生成网络模型比当前的黄金标准诊断分类具有更好的预后准确性。© 2023作者。由Brain的担保人代表牛津大学出版社发表。

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.