Brain tumour genetic network signatures of survival

TL;DR

This study uses Bayesian network models to uncover genetic interaction patterns in gliomas, revealing signatures that better predict patient survival than current diagnostic categories.

Contribution

It introduces a hierarchical Bayesian network approach to model complex genetic interactions in gliomas, improving survival prediction accuracy.

Findings

Hierarchical genetic network signatures correlate with survival outcomes.

Bayesian models outperform current diagnostic categories in prognostic accuracy.

Distinct genetic interaction patterns identified across glioma subtypes.

Abstract

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.