# Brain tumour genetic network signatures of survival

arXiv: https://arxiv.org/abs/2301.06111 · HTML: https://mlsanity.com/paper/2301.06111/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2023.

## Built on by (most cited first, 6 of 6)

- [Computational limits to the legibility of the imaged human brain](https://mlsanity.com/paper/2309.07096/) (2023)
- [VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI](https://mlsanity.com/paper/2404.15318/) (2024)
- [The minimal computational substrate of fluid intelligence](https://mlsanity.com/paper/2308.07039/) (2023)
- [Compressed representation of brain genetic transcription](https://mlsanity.com/paper/2310.16113/) (2023)
- [Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance](https://mlsanity.com/paper/2512.19707/) (2025)
- [Fairboard: a quantitative framework for equity assessment of healthcare models](https://mlsanity.com/paper/2604.09656/) (2026)

## Abstract (arXiv)

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.