  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.
