Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation

Slide 1 of 7: Cancer Pharmacogenomics Workshop Panel Discussion

David A. Flockhart, M.D., Ph.D.

July 21, 2009

Slide 2 of 7: Introduction

Identify infrastructures, resources, and pressing clinical issues, as well as current limitations that need to be addressed to advance cancer pharmacoepidemiology and pharmacogenomics research.

Slide 3 of 7: Infrastructures

  • Support for training a new generation of scientists trained in epidemiology, pharmacogenomics and clinical therapeutics
    • Not T32 but integrated into multidisciplinary oncology pharmacogenomics research centers.
  • Support for collection, transport and extraction of DNA from all trials independent of an individual specific hypothesis.
  • Support for highly collaborative multidisciplinary groups to carry out the pharmacoepidemiologic pharmacogenomic research needed outside RPCTs. Access to quality medication and phenotypic outcome data key.
    • Database access with cancer outcomes
    • Toxicity outcomes
    • Highly Granular Medication Data, e.g. Marshfield, Regenstrief, Mayo, Vanderbilt, Medco

Slide 4 of 7: Resources

  • Access to cores able to carry out Next Gen Sequencing
  • Support for the final stages of moving tests to clinic

Slide 5 of 7: Pressing Clinical Issues

  • Economic Consequences of Pharmacogenomic Testing in Different Resource Environments:
    • Need to demonstrate substantial cost savings may be most evident with biologic pharmacogenomics.
  • Performance of pharmacogenomic and genetic tests outside the RPCT environment in the real world.

Slide 6 of 7: Where We Need to Go Next

An integrated approach that addresses:

  • Clinical Practice

Requires Integration of clinical and biomarker data into clinically meaningful, easily communicable “indices” validated in large retrospective datasets or randomized trials

  • E.g. Oncotype Dx™
  • Mammoprint™
  • CYP2D6, UGT2B7, ER
  • TPMT +, UGT1A1

Requires high sensitivity and specificity:

  • Next Gen Sequencing for relatively rare variants
  • Improved specificity using pattern recognition informatics

Slide 7 of 7: Where We Need to Go Next

  • Clinical Pharmacology in Large Datasets
    • Careful characterization of drug effect and toxicity
    • Careful medication data collection
    • Adherence research and the effects of genomics on adherence
  • Integration with Basic pharmacology:
    • Pathway analysis to identify variants relevant to drug development and most likely worth testing in RPCTs and large datasets.

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