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

Abstract

Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.

Structural analysis of mammalian protein phosphorylation at a proteome level

“…We previously demonstrated that stratification of mutations observed in glioblastoma multiforme tumors as 3D patches reduces interpatient heterogeneity and provides distinct druggable 3D mutation signatures (Dincer et al, 2019). Grouping phosphorylation sites based on their 3D position would provide distinct functional signatures.…”

“…The more negative score represents the more damaging mutation. Dincer et al (Dincer et al, 2019) have previously shown that mutations in the interface and core regions have more damaging effects than on the surface. In line with that, the same applies to phospho-sites as well.…”

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

“…Curated driver genes and mutations have been deposited in multiple databases [8][9][10]and used by multiple research groups to develop computational approaches to predict driver genes and driver mutations [11][12][13][14][15][16]. These methods, including the frequency-based methods, subnetwork identification methods, and 3D mutation search methods, have been comprehensively compared [17][18][19]. One of the concerns with frequency-based approaches is that prohibitively large sample sizes are needed to identify infrequently mutated driver genes.…”