{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108185"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108185","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deciphering the heterogeneity and spatial architecture of tumors","abstract":"Cancer is caused by the accumulation of somatic mutations that form distinct populations of cells, called clones. The resulting intra-tumor heterogeneity evolves temporally, as well as spatially, and is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets that effectively capture mutational signals in cancer have become available, allowing researchers to closely examine the underlying mechanisms that shape the tumor landscape. In this thesis, we explore the multi-faceted elements of intra-tumor heterogeneity via visualization, quantification, and detection. We begin by introducing ClonArch, a tool which interactively visualizes the evolutionary relationships and spatial distribution of clones in a single tumor mass. ClonArch fills the gap for visualizations that address spatial aspects of clonal architecture. We then adapt a cancer genomics pipeline to quantify intra-tumor heterogeneity in a porcine model, showing its potential impact on translational clinical studies. Finally, we attempt to detect negative selection in the cancer exome by performing a depletion analysis on neoantigens.","abstract_html":"Cancer is caused by the accumulation of somatic mutations that form distinct populations of cells, called clones. The resulting intra-tumor heterogeneity evolves temporally, as well as spatially, and is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets that effectively capture mutational signals in cancer have become available, allowing researchers to closely examine the underlying mechanisms that shape the tumor landscape. In this thesis, we explore the multi-faceted elements of intra-tumor heterogeneity via visualization, quantification, and detection. We begin by introducing ClonArch, a tool which interactively visualizes the evolutionary relationships and spatial distribution of clones in a single tumor mass. ClonArch fills the gap for visualizations that address spatial aspects of clonal architecture. We then adapt a cancer genomics pipeline to quantify intra-tumor heterogeneity in a porcine model, showing its potential impact on translational clinical studies. Finally, we attempt to detect negative selection in the cancer exome by performing a depletion analysis on neoantigens.","abstract_has_math":false,"creators":["Wu, Jiaqi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["El-Kebir, Mohammed"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:46Z","date_published":"2020-08-26T23:58:46Z","updated_at":"2026-07-22T22:24:47Z","subjects":["intra-tumor heterogeneity, cancer genomics, bioinformatics, genome analysis, spatial analytics, visualization"],"languages":["en"],"rights":["Copyright 2020 Jiaqi Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108185","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["El-Kebir, Mohammed"]},{"key":"dc:creator","label":"Author","values":["Wu, Jiaqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:46Z","2022-08-26T23:58:55Z","2020-05-12","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["intra-tumor heterogeneity, cancer genomics, bioinformatics, genome analysis, spatial analytics, visualization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Jiaqi Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108185"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Cancer is caused by the accumulation of somatic mutations that form distinct populations of cells, called clones. The resulting intra-tumor heterogeneity evolves temporally, as well as spatially, and is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets that effectively capture mutational signals in cancer have become available, allowing researchers to closely examine the underlying mechanisms that shape the tumor landscape. In this thesis, we explore the multi-faceted elements of intra-tumor heterogeneity via visualization, quantification, and detection. We begin by introducing ClonArch, a tool which interactively visualizes the evolutionary relationships and spatial distribution of clones in a single tumor mass. ClonArch fills the gap for visualizations that address spatial aspects of clonal architecture. We then adapt a cancer genomics pipeline to quantify intra-tumor heterogeneity in a porcine model, showing its potential impact on translational clinical studies. Finally, we attempt to detect negative selection in the cancer exome by performing a depletion analysis on neoantigens.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Jiaqi Wu, accepted the attached license on 2020-05-11 at 17:52.","The student, Jiaqi Wu, submitted this Thesis for approval on 2020-05-11 at 18:47.","This Thesis was approved for publication on 2020-05-12 at 14:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15338 on 2020-08-25 at 17:31:00","Made available in DSpace on 2020-08-26T23:58:46Z (GMT). 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The resulting intra-tumor heterogeneity evolves temporally, as well as spatially, and is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets that effectively capture mutational signals in cancer have become available, allowing researchers to closely examine the underlying mechanisms that shape the tumor landscape. In this thesis, we explore the multi-faceted elements of intra-tumor heterogeneity via visualization, quantification, and detection. We begin by introducing ClonArch, a tool which interactively visualizes the evolutionary relationships and spatial distribution of clones in a single tumor mass. ClonArch fills the gap for visualizations that address spatial aspects of clonal architecture. We then adapt a cancer genomics pipeline to quantify intra-tumor heterogeneity in a porcine model, showing its potential impact on translational clinical studies. Finally, we attempt to detect negative selection in the cancer exome by performing a depletion analysis on neoantigens.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Jiaqi Wu, accepted the attached license on 2020-05-11 at 17:52.","The student, Jiaqi Wu, submitted this Thesis for approval on 2020-05-11 at 18:47.","This Thesis was approved for publication on 2020-05-12 at 14:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15338 on 2020-08-25 at 17:31:00","Made available in DSpace on 2020-08-26T23:58:46Z (GMT). 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