Date

2018

Author

Kaya, Tuğba

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Cancer is one of the most common cause of death worldwide. It occurs as a result of a collection of somatic deviations from normal state. Therefore, many efforts have been invested to profile mutations in different types of tumors; such as, the Cancer Genome Atlas (TCGA) which deposits multiple omic data for more than 11,000 tumor samples. In this thesis, we present a pipeline which retrieves patient-specific mutation data in Glioblastoma from TCGA, maps these mutations on the protein structures in Protein Databank (PDB) and finds the location and functional effect of the mutations and reconstruct functional networks by integrating mutation data with interactome. As a result of this thesis study, we found that some mutations are specific to alternative isoform sequence of the protein instead of the canonical sequence. We also showed that functional impact of mutations in interface region is more damaging compared to the surface region and more similar to the core region of the protein. We showed that most common change in the protein core is that hydrophobic residues are mutated to another hydrophobic residue. However, in the surface or interface region a charged residue is changed either to another charged residue or a polar residue when we analyzed the chemical classes of mutations. From these mutation profiles of the patients, we reconstructed 290 GBM-specific networks with Omics Integrator which solves the prize-collecting Steiner forest (PCSF) problem and optimally connects the given set of proteins in a network context. We merged the most common nodes and edges across these patients and clustered the merged network into functional communities. The ontology and pathway enrichment analyses gave us that Wnt signaling, ERBB signaling and NfKb/Ikb signaling pathways are the most commonly enriched pathways. From mutation to protein structures and functional networks, we believe that the result of this thesis will have significant contribution in cancer research.

Subject Keywords

Cancer., Tumors., Cancer, Neural networks (Computer science).

URI

http://etd.lib.metu.edu.tr/upload/12622643/index.pdf
https://hdl.handle.net/11511/27635

Collections

Graduate School of Informatics, Thesis