{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/55130"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/55130","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers","abstract":"We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we analyze data from a recent mouse model and utilize a Bayesian modeling approach to show that growth patterns present in human disease are recapitulated in murine CLL/RS. Next, we use a stochastic branching process model to simulate the acquisition of tumor-specific neoantigens in MMR-D CRC. By using these in-silico tumors as initial conditions in a dynamical systems model of tumor-immune interactions, parameterized using clinical trial data, we characterize features associated with a durable response to immune checkpoint inhibitor (ICI) immunotherapy in MMR-D CRC.","abstract_html":"We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we analyze data from a recent mouse model and utilize a Bayesian modeling approach to show that growth patterns present in human disease are recapitulated in murine CLL/RS. Next, we use a stochastic branching process model to simulate the acquisition of tumor-specific neoantigens in MMR-D CRC. By using these in-silico tumors as initial conditions in a dynamical systems model of tumor-immune interactions, parameterized using clinical trial data, we characterize features associated with a durable response to immune checkpoint inhibitor (ICI) immunotherapy in MMR-D CRC.","abstract_has_math":false,"creators":["Sholokhova, Alanna Pauline"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Bozic, Ivana"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-02-05","date_published":"2026-02-05","updated_at":"2026-07-24T05:58:03Z","subjects":["cancer","clonal evolution","dynamical systems","immunotherapy","mathematical oncology","stochastic processes","Applied mathematics","Biology","Medicine"],"languages":["en_US"],"rights":["none"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1773/55130","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bozic, Ivana"]},{"key":"dc:creator","label":"Author","values":["Sholokhova, Alanna Pauline"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-05T19:30:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["cancer","clonal evolution","dynamical systems","immunotherapy","mathematical oncology","stochastic processes","Applied mathematics","Biology","Medicine"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["none"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["Sholokhova_washington_0250E_29109.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1773/55130"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Ph.D.)--University of Washington, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we analyze data from a recent mouse model and utilize a Bayesian modeling approach to show that growth patterns present in human disease are recapitulated in murine CLL/RS. Next, we use a stochastic branching process model to simulate the acquisition of tumor-specific neoantigens in MMR-D CRC. By using these in-silico tumors as initial conditions in a dynamical systems model of tumor-immune interactions, parameterized using clinical trial data, we characterize features associated with a durable response to immune checkpoint inhibitor (ICI) immunotherapy in MMR-D CRC."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bozic, Ivana"],"dc:creator":["Sholokhova, Alanna Pauline"],"dc:date.accessioned":["2026-02-05T19:30:48Z"],"dc:date.issued":["2026-02-05"],"dc:description":["Thesis (Ph.D.)--University of Washington, 2025"],"dc:description.abstract":["We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we analyze data from a recent mouse model and utilize a Bayesian modeling approach to show that growth patterns present in human disease are recapitulated in murine CLL/RS. Next, we use a stochastic branching process model to simulate the acquisition of tumor-specific neoantigens in MMR-D CRC. By using these in-silico tumors as initial conditions in a dynamical systems model of tumor-immune interactions, parameterized using clinical trial data, we characterize features associated with a durable response to immune checkpoint inhibitor (ICI) immunotherapy in MMR-D CRC."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Sholokhova_washington_0250E_29109.pdf"],"dc:identifier.uri":["https://hdl.handle.net/1773/55130"],"dc:language.iso":["en_US"],"dc:rights":["none"],"dc:subject":["cancer","clonal evolution","dynamical systems","immunotherapy","mathematical oncology","stochastic processes","Applied mathematics","Biology","Medicine"],"dc:title":["From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:03Z"}