The Role of Network Science in Glioblastoma

Abstract

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

Brain tumour genetic network signatures of survival

“…Overall, these findings overwhelmingly support the value of applied network science in neuro-oncology. 18 …”

“…Organized by a domain of interest—tumour genetics, for example—the inferred communities reveal heterogeneous interrelations that may underpin oncogenesis and suggest avenues for treatment innnovation. 5 , 18 , 23 , 24 Organized by individual patients, the inferred communities identify patient subpopulations with similar oncological signatures that may signal decisive differences in disease mechanisms, evolution or treatment response. The novelty of the approach resides in the ability to model complex interactions between features that may illuminate mechanistic and prognostic relations opaque to models of the same features taken alone or only in linear combination.…”

Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network

“…The application of network science to cancer genomics has opened new avenues for the discovery and molecular characterization of cancer subtypes ( 29 ). Developing network-based methods is promising for disclosing the nature of GBM heterogeneity.…”

Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis

“…Network topology has been shown to identify proteins involved in disease modules that had not been identified during GWAS studies [ 6 ]. Network topology-based methods have been applied extensively in GBM exploration for tasks such as biomarker discovery and patient stratification [ 7 ]. For example, network-based integration of multi-omics data based on non-negative matrix factorization was applied to lower grade glioma (LGG) and GBM, to identify clusters in the data [ 8 ].…”