Comparative Effectiveness Research in Cancer Genomics and Precision Medicine: Current Landscape and Future Prospects
- Naoko I. Simonds,
- Muin J. Khoury,
- Sheri D. Schully,
- Katrina Armstrong,
- Wendy F. Cohn,
- David A. Fenstermacher,
- Geoffrey S. Ginsburg,
- Katrina A.B. Goddard,
- William A. Knaus,
- Gary H. Lyman,
- Scott D. Ramsey,
- Jianfeng Xu and
- Andrew N. Freedman
+ Author Affiliations
- Correspondence to: Naoko I. Simonds, Clinical and Translational Epidemiology Branch, DCCPS, 9609 Medical Center Dr, Rm 4E228, Bethesda, MD 20892 (e-mail: naoko.simonds@nih.gov).
- Received January 8, 2013.
- Revision received March 20, 2013.
- Accepted March 21, 2013.
Abstract
A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the “real-world” effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.
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