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

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.

Protein–Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations

“…Preferential localization to the rim regions, which consists of polar and charged solvent-exposed residues, is only observed in the COSMIC dataset for the globular interactions. However, this observation is consistent with the previously reported tendency for cancer mutations to disrupt PPIs through substituting charged residues and perturbing the electrostatic component of binding affinities [11,79]. The most exciting finding of the analysis with the entire COSMIC SNV data is that the cores of interacting IDRs have the highest, statistically significant ORs.…”

“…The localization patterns of COSMIC mutations in globular and IDR-partner tumor suppressors suggests that disruption of PPIs is also a deactivating mechanism. In contrast, the generally activating cancer-associated mutations in oncoproteins tend to be less destabilizing and more site-specific [79], which is reflected in the higher ORs in the protein surface and interface rim regions of the globular interaction set. Interestingly, the interface regions of IDR-partner oncoproteins have no statistically significant enrichment in cancer-associated mutations, which contrast the finding for globular oncoproteins.…”

Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications

“…Nevertheless, spatial and temporal heterogeneity is a critical challenge that the neuro-oncology field must address before precision oncology can be considered a viable option for brain tumor patients (62,69,179,180). Spatial heterogeneity in GBM resected tumors is recognized in transcriptional atlases, where genomic alterations and gene expression patterns vary between the leading edge, infiltrating tumor, cellular tumor, pseudopalisading cells around necrosis, and microvascular proliferation regions (69).…”

ASCARIS: Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations

“…Nonsynonymous single nucleotide variations have been associated with diseases (Hindorff et al, 2009; Manolio et al, 2008) due to their effects, such as perturbing biological processes and impairing molecular functions of proteins by changing their stability or interactions (Fariselli, et al, 2015; Datta et al, 2015; Farh et al, 2015; Halushka et al, 1999; Hindorff et al, 2009; Khurana et al, 2016; Presnyak et al, 2015; Sauna and Kimchi-Sarfaty 2011; Supek et al, 2014; Zwart et al, 2018; Dincer et al, 2019). Interpreting the effect of variations is important for understanding diseases of genetic origin, proposing effective treatment strategies, and developing novel biotechnological products (Dincer et al, 2019). High-throughput technologies have been producing vast amounts of variation data that awaits interpretation.…”