JUN 20, 2019 7:30 AM PDT

Keynote Presentation: The Impact of Patient-to-Patient Variability on Combination Cancer Therapy and Precision Oncology

C.E. Credits: P.A.C.E. CE Florida CE
Speakers
  • Systems Biologist, Laboratory of Systems Pharmacology, Harvard Medical School
    Biography
      Adam Palmer is a systems biologist working on the analysis and design of combination cancer therapies. Adam applies experiments, theory, and analysis of data from clinical trials of cancer therapies, with a particular interest in the origins and therapeutic consequences of cell-to-cell and patient-to-patient heterogeneity in cancers. Adam is presently a postdoctoral fellow with Peter Sorger in the Laboratory of Systems Pharmacology at Harvard Medical School. Previously, Adam completed his Ph.D in Systems Biology at Harvard University with Roy Kishony, researching the relationships between mechanisms of drug action and the evolution of antibiotic resistance.

    Abstract:

    Most cancer therapies have highly variable activity from one patient to another, with only a fraction of patients’ cancers responding to a given treatment. In many types of cancer, combination therapies can improve the probability of response. Since 1961 it was known that combinations of individually active therapies can be expected to improve response rate to some degree simply by increasing each patient’s chance of receiving at least one personally effective therapy. Here I describe recent analyses of contemporary combination therapies showing that this phenomenon of patient-to-patient variability in single-drug activity accounts for the clinical activity of many approved combination cancer therapies. These results highlight widespread opportunities to make more precise use of cancer therapies both individually and in combinations.

    Learning Objectives: 

    1. How patient-to-patient variability in response to individual cancer therapies affects responses to combinations of cancer therapies, and the implications for precision medicine in oncology.
    2. How to calculate the 'independent effect' Progression Free Survival distribution for a combination of cancer therapies, which can be used to analyze clinical trial results, and to predict the activity of combinations of independently acting drugs.


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