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Translational research in oncology: key bottlenecks and new paradigms

Published online by Cambridge University Press:  07 October 2010

Richard Simon
Affiliation:
National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA. E-mail: rsimon@nih.gov

Abstract

Translational research is about transforming progress in basic research into products that benefit patients. Here I discuss some of the key obstacles to effective translational research in oncology that have previously received limited attention. Basic research often does not go far enough for straightforward clinical translation, and long-term, high-risk endeavours to fill these key gaps have not been adequately addressed either by industry or by the culture of investigator-initiated research. These key gaps include the identification of causative oncogenic mutations and new approaches to regulating currently undruggable targets such as tumour suppressor genes. Even where an inhibitor of a key target has been identified, new approaches to clinical development are needed. The current approach of treating broad populations of patients based primarily on primary cancer site is not well suited to the development of molecularly targeted drugs. Although developing drugs with predictive diagnostics makes drug development more complex, it can improve the success rate of development, as well as provide benefit to patients and the economics of healthcare. I review here some prospective Phase III designs that have been developed for transition from the era of correlative science to one of reliable predictive and personalised oncology.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2010. This is a work of the US Government and is not subject to copyright protection in the USA.

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References

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Further reading, resources and contacts

The website of the Biometric Research Branch of the National Cancer Institute contains extensive material and web-based computer programs for the planning of genomic clinical trials and the analysis of genomic data:

http://brb.nci.nih.gov

Sawyers, C.L. (2008) The cancer biomarker problem. Nature 452, 548-552CrossRefGoogle ScholarPubMed
Stratton, M.R., Campbell, P.J. and Futreal, A.A. (2009) The cancer genome. Nature 458, 719-724CrossRefGoogle ScholarPubMed
Freidlin, B., Jiang, W. and Simon, R. (2010) The cross-validated adaptive signature design for predictive analysis of clinical trials. Clinical Cancer Research 16, 691-698CrossRefGoogle Scholar
Freidlin, B., McShane, L.M. and Korn, E.L. (2010) Randomized clinical trials with biomarkers: design issues. Journal of the National Cancer Institute 102, 152-160CrossRefGoogle ScholarPubMed
Simon, R. (2008) Using genomics in clinical trial design. Clinical Cancer Research 14, 5984-5993CrossRefGoogle ScholarPubMed
Sawyers, C.L. (2008) The cancer biomarker problem. Nature 452, 548-552CrossRefGoogle ScholarPubMed
Stratton, M.R., Campbell, P.J. and Futreal, A.A. (2009) The cancer genome. Nature 458, 719-724CrossRefGoogle ScholarPubMed
Freidlin, B., Jiang, W. and Simon, R. (2010) The cross-validated adaptive signature design for predictive analysis of clinical trials. Clinical Cancer Research 16, 691-698CrossRefGoogle Scholar
Freidlin, B., McShane, L.M. and Korn, E.L. (2010) Randomized clinical trials with biomarkers: design issues. Journal of the National Cancer Institute 102, 152-160CrossRefGoogle ScholarPubMed
Simon, R. (2008) Using genomics in clinical trial design. Clinical Cancer Research 14, 5984-5993CrossRefGoogle ScholarPubMed