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Translational research in oncology: key bottlenecks and new paradigms

  • Richard Simon (a1)

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.

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1 A. Jemal , E. Ward and M. Thun (2010) Declining death rates reflect progress against cancer. PLoS ONE 5, e9584

3 D.G. Nathan and H.E. Varmus (2000) The National Institutes of Health and clinical research: a progress report. Nature Medicine 6, 1201-1204

4 N.S. Sung (2003) Central challenges facing the national clinical research enterprise. Journal of the American Medical Association 289, 1278-1287

() . , -5 E.T. Hawk 2008 The translational research working group developmental pathways: introduction and overview Clinical Cancer Research 14 56645671

7 E.A. Zerhouni (2005) US biomedical research: basic, translational, and clinical sciences. Journal of the American Medical Association 294, 1352-1358

8 B.J. Druker (2009) Perspectives on the development of imatinib and the future of cancer research. Nature Medicine 15, 1149-1152

9 N. Lydon (2009) Attacking cancer at its foundation. Nature Medicine 15, 1153-1157

10 M.R. Stratton , P.J. Campbell and A.A. Futreal (2009) The cancer genome. Nature 458, 719-724

11 P. Workman (2003) The opportunities and challenges of personalized genome-based molecular therapies for cancer: targets, technologies, and molecular chaperones. Cancer Chemotherapy Pharmacology 52, S45-S56

12 I.B. Weinstein and A.K. Joe (2006) Mechanisms of disease: oncogene addiction – a rationale for molecular targeting in cancer therapy. Nature Clinical Practice: Oncology 3, 448-457

13 C.I. Sawyers (2009) Shifting paradigms: the seeds of oncogenic addiction. Nature Medicine 15, 1158-1161

14 A. Knudson (1971) Mutation and cancer: statistical study of retinoblastoma. Proceedings of the National Academy of Science 68, 820-823

15 S.H. Moolgavkar (1986) Carcinogenesis modeling: from molecular biology to epidemiology. Annual Review of Public Health 7, 151-169

16 X. Zhang and R. Simon (2005) Estimating the number of rate-limiting genomic changes for human breast cancer. Breast Cancer Research and Treatment 91, 121-124

17 R. Simon and X. Zhang (2008) On the dynamics of breast tumor development in women carrying germline BRCA1 or BRCA2 mutations. International Journal of Cancer 122, 1916-1917

18 P. Armitage and R. Doll (1957) A two-stage theory of carcinogenesis in relation to the age distribution of human cancer. British Journal of Cancer 11, 161-169

21 D. Hanahan and R.A. Weinberg (2000) The hallmarks of cancer. Cell 100, 57-70

22 N. Beerenwinkel (2007) Genetic progression and the waiting time to cancer. PLoS Computational Biology 3, 2239-2246

23 B. Elenbaas (2001) Human breast cancer cells generated by oncogenic transformation of primary mammary epithelial cells. Genes and Development 15, 50-65

24 C.L. Sawyers (2008) The cancer biomarker problem. Nature 452, 548-552

25 L. Pusztai (2004) Perspectives and challenges of clinical pharmacogenomics in cancer. Pharmacogenomics 5, 451-454

26 L. Pusztai , K. Anderson and K.R. Hess (2007) Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer. Clinical Cancer Research 13, 6080-6086

27 K.R. Hess (2006) Pharmacogenomic predictor of sensitivity to preoperative paclitaxel and 5-fluorouracil, doxorubicin, cyclophosphamide chemotherapy in breast cancer. Journal of Clinical Oncology 24, 4236-4244

28 R. Simon and A. Maitournam (2005) Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10, 6759-6763

30 A. Maitournam and R. Simon (2005) On the efficiency of targeted clinical trials. Statistics in Medicine 24, 329-339

31 A. Hoering , M. LeBlanc and J. Crowley (2008) Randomized phase III clinical trial designs for targeted agents. Clinical Cancer Research 14, 4358-4367

32 S. Mandrekar (2005) Clinical trial designs for prospective validation of biomarkers. American Journal of Pharmacogenomics 5, 317-325

33 S. Mandrekar and D. Sargent (2009) Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. Journal of Clinical Oncology 27, 4027-4034

34 R. Simon (2008) Using genomics in clinical trial design. Clinical Cancer Research 14, 5984-5993

35 R. Simon (2008) Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics. Expert Review of Molecular Diagnostics 2, 721-729

36 D.J. Sargent (2005) Clinical trial designs for predictive marker validation in cancer treatment trials. Journal of Clinical Oncology 23, 2020-2027

37 S. Mandrekar and D. Sargent (2009) Clinical trial designs for predictive biomarker validation: one size does not fit all. Journal of Biopharmaceutical Statistics 19, 530-542

39 L. Pusztai and K.R. Hess (2004) Clinical trial design for microarray predictive marker discovery and assessment. Annals of Oncology 15, 1731-1737

40 X. Zhou (2008) Bayesian adaptive design for targeted therapy development in lung cancer – a step toward personalized medicine. Clinical Trials 5, 181-193

41 L.M. McShane , S. Hunsberger and A.A. Adjei (2009) Effective incorporation of biomarkers into phase II trials. Clinical Cancer Research 15, 1898-1905

42 W. Jiang , B. Freidlin and R. Simon (2007) Biomarker adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect. Journal of the National Cancer Institute 99, 1036-1043

43 B. Freidlin and R. Simon (2005) Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Research 11, 7872-7878

45 R. Simon (2010) Clinical trials for predictive medicine: new challenges and paradigms. Clinical Trials Mar 25; [Epub ahead of print]

46 R.M. Simon , S. Paik and D.F. Hayes (2009) Use of archived specimens in evaluation of prognostic and predictive biomarkers. Journal of the National Cancer Institute 101, 1-7

47 S. Jones (2008) Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801-1806

B. Freidlin , W. Jiang and R. Simon (2010) The cross-validated adaptive signature design for predictive analysis of clinical trials. Clinical Cancer Research 16, 691-698

B. Freidlin , L.M. McShane and E.L. Korn (2010) Randomized clinical trials with biomarkers: design issues. Journal of the National Cancer Institute 102, 152-160

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Expert Reviews in Molecular Medicine
  • ISSN: -
  • EISSN: 1462-3994
  • URL: /core/journals/expert-reviews-in-molecular-medicine
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