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APR 23, 2020 10:30 AM PDT

Keynote Presentation: Systematic identification of therapeutic strategies that leverage tumor evolution

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Assistant Professor, Department of Pharmacology and Cancer Biology, Duke University
    Biography
      Kris C. Wood, Ph.D., is an Associate Professor in the Department of Pharmacology and Cancer Biology at Duke University. He received his B.S. degree in Chemical Engineering from the University of Kentucky, where he was recognized with the outstanding sophomore, junior, and senior awards in Chemical Engineering, the Tau Beta Pi outstanding senior award in the College of Engineering, and a Barry M. Goldwater scholarship in science and mathematics. He received his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology, where he developed self-assembling polymeric systems for controlled gene and drug delivery under the supervision of Professors Paula Hammond, Ph.D. and Robert Langer, Sc.D. As an NIH and Misrock Fund postdoctoral fellow in the laboratory of David Sabatini, M.D., Ph.D. at the Whitehead Institute for Biomedical Research and the Broad Institute of Harvard and MIT, his work focused on the development of functional genomic tools to study the determinants of anticancer drug sensitivity.

      Kris' lab at Duke, founded in 2012, focuses on two related themes: (1) identifying, mechanistically characterizing, and translating new, molecularly targeted therapeutic strategies for biomarker-defined cancer subtypes and (2) defining rational strategies to control long term tumor evolution. To power these studies, Kris' team develops and adapts a range of new functional genomic technologies. The lab's work has been recognized by early career awards from the Ovarian Cancer Research Fund Alliance, the V Foundation, the Stewart Trust, the Forbeck Foundation, the Whitehead Foundation, and the NIH BIRCWH Program. It has also inspired the design of multiple ongoing clinical trials and the creation of three biotechnology companies: Celldom (Silicon Valley, CA), Tavros Therapeutics (Durham, NC), and Element Genomics, now a wholly owned subsidiary of UCB Pharma (Brussels).

    Abstract

    Our laboratory uses tools from pharmacology, genomics, and cell signaling to identify new precision anticancer therapeutic strategies. Under this broad heading, our work involves three key areas of emphasis. First, we use custom functional and structural genomic tools to define the landscape of signaling pathways capable of driving resistance to therapy (for example, Science Signaling 2012, 5, rs4; Science Signaling 2014, 7, ra121; and Cell Reports 2017, 20, 999). Second, we utilize knowledge of resistance landscapes to identify new therapeutic strategies that have the potential to circumvent resistance evolution (for example, Science Signaling 2014, 7, ra122; Nature Communications 2017, 8, 15617; Cell Reports 2017, 21, 2796; Nature Communications 2018, 9, 4274; Science Advances 2019, 5, eaaw9162; and Nature Genetics 2020, 52, 408). Finally, we use unbiased approaches to define mechanism-based, targetable vulnerabilities in human cancers for application to malignancies characterized by “undruggable” genetic drivers or immunologically “cold” microenvironments, where in many cases these vulnerabilities center on the dysregulated structure and function of tumor mitochondria (for example, Science Translational Medicine 2016, 8, ra175; Nature Communications 2018, 9, 1677; Nature Communications 2018, 9, 3513; and Cell Metabolism 2019, 29, 1217). Collectively, these studies are leading to both fundamental new insights into the core survival circuitry operating in defined human tumor subsets as well as novel translational therapeutics. In this talk, I will provide an overview of our work, with a particular focus on studies that have identified mechanisms of convergent tumor evolution and associated strategies to leverage this phenomenon to build combination therapies that select against resistance.

    Learning Objectives:
    1. Understand strategies for designing mechanism-based combination therapies to overcome resistance

    2. Learn examples of convergent resistance evolution

    3. Use functional genomics and pharmacological screening to identify therapeutic targets


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