BRAIN TUMOUR GENETIC NETWORK SIGNATURES SUPERIORLY FORECAST SURVIVAL

Abstract

Abstract AIMS Gliomas typically exhibit a spectrum of genetic mutations, reflecting multiple, potentially complex interactions across distinct molecular pathways. Predicting disease evolution and prescribing individually optimal treatment plausibly requires statistical models complex enough to capture the intricate genetic structure underpinning oncogenesis. METHOD Here we formalize this task as the discovery of distinct patterns of connectivity within network representations of the interactions between genetic loci. Evaluating multi-institutional clinical, genetic, and outcome data from 4023 consecutive patients over a 14-year period, we employ Bayesian stochastic block modelling to reveal a hierarchical network structure of tumour genetic interactions spanning glioblastoma, oligodendroglioma, and astrocytoma. RESULTS We show that this structure not only identifies diagnosis but predicts patient survival with greater fidelity than the latest WHO tumour classification or linear models of genetic features. For example, a diagnosis of IDH- wildtype glioblastoma yielded median survivals varying from 289 days in patients with the signature of marked EGFR amplification, MGMT methylation, and histone mutations, to 425 days in those with the signature of TERT mutations, histone, and EGFR wild-types, and variable MGMT methylation. CONCLUSIONS Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show network analysis reveals distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories, paving the way for personalised prognostication.