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

Dincer, Cansu
Kaya, Tugba
Keskin, Ozlem
Gursoy, Attila
Tunçbağ, Nurcan
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, 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 each group 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 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.


3D spatial organization and network-guided comparison of mutation profiles in glioblastoma
Dinçer, Cansu; Tunçbağ, Nurcan; Department of Bioinformatics (2019)
Glioblastoma multiforme (GBM) is the most aggressive and heterogeneous type of brain tumor. The heterogeneity of GBM is the main obstacle to develop effective treatment strategies. In this study, we aimed to decrease the heterogeneity among GBM patients from The Cancer Genome Atlas (TCGA), classify the patients and propose therapeutic hypothesis for patient groups by using patient mutation profiles. We therefore implemented a systems level approach to mutations using their biophysical characteristics and or...
Network Modeling Identifies Patient-specific Pathways in Glioblastoma.
Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular profiling experiments have revealed that these tumors are extremely heterogeneous. This heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesize that despite the diverse molecular profiles, it might still be possible to identify common signaling changes that could be targeted in some or all tumors. Using a network modeling approach, we reconstruct the altered signaling pathways from t...
Differential gene expression analysis in drug resistant multiple myeloma cell lines
Mutlu, Pelin; Gündüz, Ufuk; Department of Biology (2009)
The emergence of drug-resistance of tumor cells is a major complication for succesful chemotherapy. In this study, the molecular mechanisms of resistance to prednisone, vincristine and melphalan in multiple myeloma cell lines, RPMI-8226 and U-266 were investigated. Drug resistance was induced by application of the drugs by stepwise dose increments and confirmed by XTT cytotoxicity assay. Gene expression analysis demostrated that MDR1 gene is one of the most important factor causing the multidrug resistance ...
Synthesis of poly (dl-lactic-co-glycolic acid) coated magnetic nanoparticles for anti-cancer drug delivery
Tansık, Gülistan; Gündüz, Ufuk; Department of Biology (2012)
One of the main problems of current cancer chemotherapy is the lack of selectivity of anti-cancer drugs to tumor cells which leads to systemic toxicity and adverse side effects. In order to overcome these limitations, researches on controlled drug delivery systems have gained much attention. Nanoscale based drug delivery systems provide tumor targeting. Among many types of nanocarriers, superparamagnetic nanoparticles with their biocompatible polymer coatings can be targeted to an intented site by an extern...
An investigation of microRNAs mapping to breast cancer related genomic gain and loss regions
Selcuklu, S. D.; Yakicier, M. C.; Erson Bensan, Ayşe Elif (Elsevier BV, 2009-02-01)
Various regions of amplification or loss are observed in breast tumors as a manifestation of genomic instability. To date, numerous oncogenes or tumor suppressors on some of these regions have been characterized. An increasing body of evidence suggests that such regions also harbor microRNA genes with crucial regulatory roles in cellular processes and disease mechanisms, including cancer. Here, we investigated 35 microRNAs localized to common genomic gain and/or loss regions in breast cancers. To examine am...
Citation Formats
C. Dincer, T. Kaya, O. Keskin, A. Gursoy, and N. Tunçbağ, “3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients,” PLOS COMPUTATIONAL BIOLOGY, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31469.