Identification of gene sets that predict Acute Myeloid Leukemia prognosis using integrative gene network analysis

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dc.contributor.advisorMetsis, Vangelis
dc.contributor.authorSamimi, Hanie
dc.contributor.committeeMemberLewis, Lyle Kevin
dc.contributor.committeeMemberZare, Habil
dc.date.accessioned2022-01-14T19:08:27Z
dc.date.available2022-01-14T19:08:27Z
dc.date.issued2018-08
dc.description.abstractOrthogonal data types can potentially provide new opportunities to pinpoint the underlying molecular mechanisms of diseases. However, currently-available techniques to capitalize on information from different data types suffer from a substantial loss of statistical power. Therefore, there is urgent need to develop algorithms to integrate data types. In this thesis, I have developed a data integration approach based on multi-view clustering. I demonstrate the usefulness of my approach in prognostication of Acute Myeloid Leukemia (AML), a particular type of blood cancer. AML accounts for 1.2% of cancer deaths per year in the USA. AML patients are categorized into low, medium and high-risk groups. The variable survival rate for medium-risk patients leads to difficulties in deciding on the appropriate treatment for these patients. Current methods of prognostication of AML use only gene expression, mutations and molecular cytogenetic abnormalities. However, the DNA methylation data, which have valuable information that would be useful for prognostication, have not yet been effectively used in the existing clinical tests. In this project, I have used The Cancer Genome Atlas (TCGA) dataset and developed a method that analyzes both gene expression and DNA methylation data in a single model using network analysis. The model based on this methodology correctly classified 13 out of 90 patients as high-risk, whereas they were previously labeled as medium-risk using current clinical methods. All 13 of these cases died within two years after diagnosis. To validate these results, I tested the method using an independent dataset. The model labeled 11 out of 228 patients as high-risk, whereas they were previously labeled as medium-risk based on the European Leukemia Net (ELN) 2010 criteria. All 11 patients died within two years of diagnosis, and their risk group is not predictable with other currently used methods.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent56 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationSamimi, H. (2018). <i>Identification of gene sets that predict Acute Myeloid Leukemia prognosis using integrative gene network analysis</i> (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/15159
dc.language.isoen
dc.subjectNetwork analysis
dc.subjectGene expression
dc.subjectDNA methylation
dc.titleIdentification of gene sets that predict Acute Myeloid Leukemia prognosis using integrative gene network analysis
dc.typeThesis
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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