2026-09-18T22:11:54Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/551302026-04-15T10:00:33Zcom_1773_4888col_1773_4891

Bozic, Ivana Sholokhova, Alanna Pauline 2026-02-05T19:30:48Z 2026-02-05 2025 Sholokhova_washington_0250E_29109.pdf https://hdl.handle.net/1773/55130 Thesis (Ph.D.)--University of Washington, 2025 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. application/pdf en_US none cancer clonal evolution dynamical systems immunotherapy mathematical oncology stochastic processes Applied mathematics Biology Medicine Applied mathematics From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers Thesis Delay release for 1 year -- then make Open Access 2027-02-05T19:30:48Z embargo