3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

PLoS Comput Biol. 2019 Sep 17;15(9):e1006789. doi: 10.1371/journal.pcbi.1006789. eCollection 2019 Sep.

Abstract

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Neoplasms / epidemiology
  • Brain Neoplasms / genetics*
  • Chromosome Mapping
  • Cluster Analysis*
  • Computational Biology / methods*
  • Glioblastoma / epidemiology
  • Glioblastoma / genetics*
  • Humans
  • Models, Molecular
  • Mutation / genetics*
  • Neoplasm Proteins / chemistry
  • Neoplasm Proteins / genetics
  • Precision Medicine / methods

Substances

  • Neoplasm Proteins

Grant support

NT has received support from the Career Development Program of TUBITAK under the project number 117E192, UNESCO-L’Oreal National for Women in Science Fellowship and UNESCO-L’Oreal International Rising Talent Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.