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PhD Seminar: Shirlyn Wang
February 23 @ 4:30 pm - 5:00 pm
Rackham Building, Earl Lewis Room, 3rd Floor East
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Shirlyn Wang, PhD Candidate, Applied & Interdisciplinary Mathematics and Scientific Computing
Shirlyn Wang is a Ph.D candidate in Applied and Interdisciplinary Mathematics. Her research interest is in mathematical oncology, the study of cancer initiation, progression, and therapy through data-driven mathematical models and simulations.
Modeling CTL-mediated Tumor Cell Death Mechanisms and the Activity of Immune Checkpoints in Immunotherapy
Immunotherapy has dramatically transformed the cancer treatment landscape. Of the variety of types of immunotherapies available, immune checkpoint inhibitors (ICIs), which block inhibitory signals from tumor cells and reinvigorate killing activities of immune cells, have gained the spotlight. Although ICIs have shown promising results for some patients, the low response rates in many cancers highlight the challenges of using immune checkpoint blockade as an effective treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms. The first process is fast-acting and perforin/granzyme-mediated, and the second is a slower, Fas ligand (FasL)-driven killing mechanism. There is also evidence suggesting that the preferred killing mechanism by CTLs depends on the antigenicity of tumor cells. To determine the key factors affecting responses to checkpoint blockade therapy, we constructed an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PDL1 immune checkpoint. Specifically, we analyzed which aspects of the tumor-immune landscape affect the response to ICIs with endpoints of tumor size and composition in the short and long term. By generating a virtual cohort with heterogeneous tumor and immune attributes, we also simulated the therapeutic outcomes of immune checkpoint blockade in a largely diverse population. In this way, we identified key tumor and immune characteristics that are associated with tumor elimination, dormancy and escape. This talk will also shed light on which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy.
This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDEfirstname.lastname@example.org