Fantastic Sources Of Tumor Heterogeneity And How To Characterize Them

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dc.contributor.advisorRuppin, Eytanen_US
dc.contributor.authorPatkar, Sushant Aen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-07-07T05:42:46Z
dc.date.available2021-07-07T05:42:46Z
dc.date.issued2021en_US
dc.description.abstractCancer constantly evolves to evade the host immune system and resist different treatments. As a consequence, we see a wide range of inter and intra-tumor heterogeneity. In this PhD thesis, we present a collection of computational methods that characterize this heterogeneity from diverse perspectives. First, we developed computational frameworks for predicting functional re-wiring events in cancer and imputing the functional effects of protein-protein interactions given genome-wide transcriptomics and genetic perturbation data. Second, we developed a computational framework to characterize intra-tumor genetic heterogeneity in melanoma from bulk sequencing data and study its effects on the host immune response and patient survival independently of the overall mutation burden. Third, we analyzed publicly available genome-wide copy number, expression and methylation data of distinct cancer types and their normal tissues of origin to systematically uncover factors driving the acquisition of cancer type-specific chromosomal aneuploidies. Lastly, we developed a new computational tool: CODEFACS (COnfident Deconvolution For All Cell Subsets) to dissect the cellular heterogeneity of each patient’s tumor microenvironment (TME) from bulk RNA sequencing data, and LIRICS (LIgand Receptor Interactions between Cell Subsets): a supporting statistical framework to discover clinically relevant cellular immune crosstalk. Taken together, the methods presented in this thesis offer a way to study tumor heterogeneity in large patient cohorts using widely available bulk sequencing data and obtain new insights on tumor progression.en_US
dc.identifierhttps://doi.org/10.13016/czrw-vgfe
dc.identifier.urihttp://hdl.handle.net/1903/27279
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledBioinformaticsen_US
dc.subject.pquncontrolledCancer Immunotherapyen_US
dc.subject.pquncontrolledComputational Biologyen_US
dc.subject.pquncontrolledImmunologyen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledOptimizationen_US
dc.titleFantastic Sources Of Tumor Heterogeneity And How To Characterize Themen_US
dc.typeDissertationen_US

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