Speaker: Yury Pritykin, Ph.D., Postdoctoral Researcher, Memorial Sloan Kettering Cancer Center
Please join us for an NCCC and Biomedical Data Science Bioinformatics and Computational Oncology Faculty Candidate Seminar with Yury Pritykin, Ph.D., Postdoctoral Researcher, Memorial Sloan Kettering Cancer Center on Thursday, March 12 at 11:00 a.m., via Zoom.
Talk title: “Systems biology of CD8 T cells in cancer, infection, and self-tolerance”
Hosted by: Brock Christensen, Ph.D.
***In keeping with recent D-H guidance on limiting the size of in-person gatherings, this seminar will be delivered virtually via Zoom. We will not meet in person. Please see instructions below to access Zoom.***
Meeting room ID/attendee ID: 503-779-5102
Phone audio: 1-669-900-6833*
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CD8 T cells play an essential role in defense against viral and bacterial infections, but can fail to mount protective responses in chronic infections and cancer progression. The conspicuous heterogeneity of CD8 T cell states raised a question of their common and unique characteristics in different experimental and clinical settings. We uncover remarkably recurrent chromatin and transcriptional features for CD8 T cell progression towards dysfunctional states in cancer and infection through a comprehensive analysis of 302 bulk ATAC-seq and RNA-seq experiments from 12 studies and new scATAC-seq and scRNA-seq data. We also study CD8 T cell dysfunction in a double-transgenic mouse model of self-tolerance, using DNase-seq and RNA-seq data, and identify a unique epigenetic signature of the self-tolerant state. These results provide a better understanding of basic mechanisms of adaptive immunity and can lead to better strategies for treatments of both solid organ and hematologic malignancies and chronic infections.
Yury Pritykin is a postdoctoral researcher in Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center. He received his M.Sc. and Ph.D. degrees in Mathematics from Lomonosov Moscow State University and a Ph.D. in Computer Science from Princeton University. His main interest and expertise is in using applied statistics, machine learning, and efficient computer algorithms to address fundamental problems in biology and medicine by integrative analysis and interpretation of multi dimensional data. His specific goal is to leverage functional genomics and other high-throughput molecular assays at the bulk and single cell level in order to obtain a systematic molecular understanding of immune cell function in health and disease and address critical problems in cancer immunology and immunotherapy.
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