The Role of Network Science in Glioblastoma

SIMPLE SUMMARY: Knowledge extraction from cancer genomic studies is continuously challenged by the fast-growing technological advances generating high-dimensional data. Network science is a promising discipline to cope with the resulting complex and heterogeneous datasets, enabling the disclosure of the molecular networks involved in cancer development and progression. We present a narrative review of the network-based strategies that have been applied to glioblastoma (GBM), a complex and heterogeneous disease, along with a discussion on the relevant findings and open challenges and future research opportunities. 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.