脑肿瘤生存的遗传网络特征
Brain tumour genetic network signatures of survival
AI总结：
本研究利用4023例跨12国、14年的胶质瘤多机构数据，通过贝叶斯生成式随机块模型推断遗传网络的分层连接模式，发现其生存预后特征优于现有金标准诊断分类。
摘要：
肿瘤异质性日益被认为是神经肿瘤学治疗成功的主要障碍。胶质瘤的特征在于遗传和表观遗传改变的不同组合，导致跨多个分子通路的复杂相互作用。预测疾病演变并制定个体化最佳治疗方案，需要足够复杂的统计模型，以捕捉支撑肿瘤发生的复杂（表观）遗传结构。在此，我们将该任务形式化为推断遗传网络分层潜在表征内不同连接模式的问题。我们评估了来自12个国家、历时14年的4023名胶质瘤患者的多机构临床、遗传和结局数据，采用贝叶斯生成式随机块建模，揭示了跨越经分子确认的IDH野生型胶质母细胞瘤、IDH突变且1p/19q共缺失少突胶质细胞瘤以及IDH突变型星形细胞瘤的肿瘤遗传分层网络结构。我们的发现阐明了脑肿瘤遗传图谱中各特征之间的复杂依赖关系，并表明生成式网络模型能够揭示不同的生存特征，其预后保真度优于当前金标准诊断类别。
英文摘要：
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised 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.
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