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dc.contributor.advisorCantley, Lewis
dc.contributor.authorMurphy, Charles
dc.date.accessioned2019-03-26T19:13:06Z
dc.date.available2019-08-13T06:00:47Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1813/64788
dc.description.abstractProtein kinases are a diverse family of about 500 proteins that all share the common ability to catalyze phosphorylation of the side chains of amino acids in proteins. Kinases play a vital role across diverse biological functions including proliferation, differentiation, cell migration, and cell-cycle control. Moreover, they are frequently altered across most cancers types and have been a focus for development of anti- cancer drugs, which has led to the development of 38 approved kinase inhibitors as of 2018. In this thesis, I developed two orthogonal computational approaches for investigating kinase function and deregulation. Starting with data from a large cohort of mouse triple negative breast cancer (TNBC) tumors, I use a combination of whole exome sequencing (WES) and RNA-seq to identify somatic alterations that drive individual tumors. I discovered that a large number of these alterations involve protein kinases and subsequent therapeutic targeting led to tumor regression. For my second approach, I utilized a large peptide library dataset from about 300 kinases. Which kinase phosphorylate which phosphorylation site is determined by both kinase- intrinsic and contextual factors. Peptide library approaches provide kinase-intrinsic amino acid specificity, which I used to predict novel kinase substrates and map out kinase phosphorylation networks. In summary, I developed methods using next-generation sequencing and peptide library data to generate novel insights into protein kinase function and deregulation.
dc.language.isoen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectkinases
dc.subjectmouse
dc.subjectnext-generation sequencing
dc.subjectpeptide library
dc.subjectprecision medicine
dc.subjecttriple-negative breast cancer
dc.titleComputational Approaches For Assessing Kinome Function And Deregulation
dc.typedissertation or thesis
thesis.degree.disciplineComputational Biology and Medicine
thesis.degree.grantorWeill Cornell Graduate School of Medical Sciences
thesis.degree.levelDoctor of Philosophy


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