OCT 06, 2016 10:30 AM PDT

Precision Genomics and Precision Medicine for Immuno-Oncology

Speakers
  • Manager, Field Applications Scientist, Personalis, Inc.
    Biography
      Erin Newburn, MS, PhD, joined Personalis as a Field Application Scientist in 2013 with over 12 years of research experience in the areas of molecular biology, genetics, and biotechnology. As the Field Applications Manager, Erin's team has the responsibility for providing both pre and post-sale technical support for the Personalis ACE Cancer Portfolio. She completed her postdoctoral training at the National Institute of Mental Health (NIMH) investigating candidate susceptibility genes for major psychiatric illnesses. Erin received her Ph.D. from the Ohio State University in Integrated Biomedical Science as a Presidential Fellow.

    Abstract:

    Tumors often evade immune destruction by adapting and circumventing immune checkpoints to create resistance.  This adaptive immunity can be reversed to stimulate the endogenous immune response.  Extensive efforts using Immunotherapeutic approaches are currently being investigated for various cancer types to activate our immune system, making it able to recognize these cancer cells and destroy them.  Due to the rapid expansion of genomic technologies, Precision Medicine in cancer is leading to exciting therapeutic capabilities.  By a deeper understanding of the tumor genetics, patients can be prescribed more effective therapies or even avoid those that may be potentially adverse.  This webinar will highlight the current renaissance in immunotherapy and discuss the expanding role of both genomics and transcriptomics in providing more personalized cancer care.

    Learning Objectives:

    • Review of the opportunities for precision medicine in immuno-oncology from checkpoint modulators to adoptive cell therapy
    • Understanding the key accuracy challenges in utilizing technologies like NGS for the development of particular immunotherapies such as personalized cancer vaccines
    • Evaluation of the current methods to predict the likelihood of positive response to immunotherapy


     


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