Abstract
Simple SummaryKnowledge 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.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.
Highlights
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisbon, Portugal; IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Among the various findings of the study, the analysis identified a mitosis/cell cycle module in GBM that is downstream of the mutant epidermal growth factor receptor, EGFRvIII, as shown by studies done in an isogenic model system
Molecular network models will be pivotal in single-cell analyses [171], depicting the multiple genetic patterns of individual cells that constitute a single tumor, potentially aiding in the selection and development of anti-GBM targeted therapies that may improve clinical responses, in a paradigm of precision medicine. This narrative review summarizes the network-based strategies for network discovery and disease outcome prediction and the main findings when applied to GBM studies, along with the software developed and open challenges and future research opportunities
Summary
The discovery and molecular characterization of cancer subtypes harboring distinct molecular features and heterogeneous treatment responses comprise a crucial step in cancer treatment and management, as different cancer subtypes may respond differently to available treatment options [1]. The rise of network science in cancer genomics has opened new avenues for the identification of the networks involved in cancer processes, by the identification of the molecular network structures involved in cancer development and progression Such network-based information can be further used to drive predictive models for future outcomes to more biologically meaningful solutions, towards a more personalized clinical and therapy decision, perfectly in line with the precision medicine framework. In this narrative review, we cover many aspects of network science and examples of applications in the study of glioblastoma (GBM), a type of cancer characterized by a marked spatial and temporal heterogeneity at both the cellular and molecular levels [5]. A subsequent manual selection of the relevant studies for the scope of the review was performed
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