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Decoding epigenetic programs in differentiation and disease
Friday, November 16, 2018, 11:00 am-12:00 pm Calendar
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Dysregulated epigenetic programs are a feature of many cancers, and the diverse differentiation states of immune cells as well as their dysfunctional states in tumors are in part epigenetically encoded.  We will present recent analysis work and computational methodologies from our lab to decode epigenetic programs from genome-wide data sets.
We will first describe recent collaborative work to decipher chromatin states governing T cell dysfunction in cancer.  Through analysis of the dynamics of chromatin accessibility using ATAC-seq and gene expression using RNA-seq in a mouse cancer model, we show that tumor-specific T cells differentiate to dysfunction through two discrete chromatin states: an initial plastic state that can be functionally rescued (i.e. through immunotherapy) and a later fixed state that is resistant to therapeutic reprogramming.  We will also show how transcription factor (TF) motif analysis identifies potential drivers of global changes accessibility changes, and how in vivo pharmacological modulation of identified TFs decreases or delays progression to fixed dysfunction.  Next, we will introduce new computational methodology to decipher transcriptional programs governing chromatin accessibility and gene expression in normal and dysfunctional T cell responses through a large-scale analysis of published data from mouse tumor and chronic viral infection models.  This modeling shows that in all these systems — and contrary to current understanding of chronic infection — T cells commit to becoming dysfunctional early after an immune challenge, rather than first mounting and then losing an effector response.  Finally, we will describe a novel machine learning approach called BindSpace to leverage massive in vitro TF binding data from SELEX-seq experiments through a joint embedding of DNA k-mers and TF labels, leading to improved prediction of TF binding.

Christina Leslie did her undergraduate degree in Pure and Applied Mathematics at the University of Waterloo in Canada.  She was awarded an NSERC 1967 Science and Engineering Fellowship for graduate study and did a PhD in Mathematics at the University of California, Berkeley, where her thesis work dealt with differential geometry and representation theory.  She won an NSERC Postdoctoral Fellowship and did her postdoctoral training in the Mathematics Department at Columbia University in 1999-2000.  She then joined the faculty of the Computer Science Department and later the Center for Computational Learning Systems at Columbia University and began to work in computational biology and machine learning.  In 2007, she moved to Memorial Sloan Kettering Cancer Center, where she is currently Member of the Computational and Systems Biology Program.  Dr. Leslie's research group uses computational methods to study the regulation of gene expression in mammalian cells and the dysregulation of expression programs in cancer.   She is well known for developing machine learning approaches for analysis of epigenomic and transcriptomic data.  Focus areas in the lab include dissecting transcriptional and epigenetic programs in differentiation, microRNA-mediated gene regulation, alternative cleavage and polyadenylation, and integrative analysis of tumor data sets.

This talk is organized by Brandi Adams