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

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.
Author summary Precision medicine aims to find the best treatment strategy based on the information about the patient’s tumor. Molecular heterogeneity is the main obstacle in developing treatment strategies. Therefore, transforming patient specific molecular data into clinically interpretable knowledge is fundamental in precision medicine. In this work, we tackle the mutation profiles of patients with Glioblastoma Multiform (GBM) which is the most aggressive type of brain tumors with a poor survival. Our main motivation is that different mutations, that are spatially in close proximity in the same protein, or function in the same pathway, may result in phenotypically similar tumors. 3D spatial clustering of the mutations, that we call “mutation patch”, significantly decreases the heterogeneity. We additionally identify the affected patient-specific subnetworks and pathways that are inferred from mutations. Indeed, grouping the patients based on the presence of mutations in close proximity together with network-guided grouping is significantly associated with their survival. These results also enable us to suggest several therapeutic hypotheses for each group based on available drug treatment data. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective to the analysis of mutation effects towards the network-guided precision medicine.