The use of genomics in clinical trial design

Clinical Cancer Research

Volumn 14, Issue 19, 2008, Pages 5984-5993

  Simon, Richard ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

CANCER GENETICS; CANCER THERAPY; CLINICAL RESEARCH; DRUG RESEARCH; GENOMICS; HUMAN; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROGNOSIS; REVIEW; BIOLOGY; BIOMETRY; BIOTECHNOLOGY; CLINICAL TRIAL; DRUG DESIGN; DRUG INDUSTRY; GENETICS; HUMAN GENOME; METHODOLOGY; NEOPLASM; ONCOLOGY; PHARMACEUTICS; TREATMENT OUTCOME;

BIOMETRY; BIOTECHNOLOGY; CLINICAL TRIALS AS TOPIC; COMPUTATIONAL BIOLOGY; DRUG DESIGN; DRUG INDUSTRY; GENOME, HUMAN; GENOMICS; HUMANS; MEDICAL ONCOLOGY; NEOPLASMS; PROGNOSIS; RESEARCH DESIGN; TECHNOLOGY, PHARMACEUTICAL; TREATMENT OUTCOME;

EID: 58149151278     PISSN: 10780432     EISSN: None     Source Type: Journal    
DOI: 10.1158/1078-0432.CCR-07-4531     Document Type: Review

References (58)

1. Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med 1989;8:431-40.
2. Buyse M, Molensberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics 2000;1:49-67.
3. Torri V, Simon R, Russek-Cohen E, Midthune D, Freidman M. Relationship of response and survival in advanced ovarian cancer patients treated with chemotherapy. J Natl Cancer Inst 1992;84:407.
4. Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytical methods for biomarkers employed in drug development. Clin Cancer Res. Vol. 18. In press 2008.
5. FDA. Guidance for industry: Pharmacogenomics data submissions. Rockville (MD): Food and Drug Administration, U.S. Department of Health and Human Services; 2005.
6. Pusztai L, Ayers M, Stec J, Hortobagyi GN. Clinical application of cDNA microarrays in oncology. Oncologist 2003;8:252-8.
7. Harris L, Fritsche H, Mennel R, et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007;25:5287-310.
8. Simon R, Altman DG. Statistical aspects of prognostic factor studies in oncology. Br J Cancer 1994;69:979-85.
9. Henry NL, Hayes DF. Uses and abuses of tumor markers in the diagnosis, monitoring and treatment of primary and metastatic breast cancer. Oncologist 2006;11:541-52.
10. Hayes DF, Bast RC, Desch CE, et al. Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst 1996;88:1456-66.
11. Hayes DF, Trock B, Harris AL. Assessing the clinical impact of prognostic factors: when is "statistically significant" clinically useful? Breast Cancer Res Treat 1998;52:305-19.
12. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005;97:1180-4.
13. Hayes DF. Prognostic and predictive factors revisited. Breast 2005;14:493-9.
14. Gennari A, Sormani MP, Pronzato P, et al. HER2 status and efficacy of adjuvant anthracyclines in early breast cancer: a pooled analysis of randomized clinical trials. JNatl Cancer Inst 2008;100:14-20.
15. Hayes DF, Thor AD, Dressler LG, et al. HER2 and response to paclitaxel in node-positive breast cancer. N Engl J Med 2007;357:1496-506.
16. Sharma SV, Bell DW, Settleman J, Haber DA. Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer 2007;7:169-81.
17. Toschi L, Cappuzzo F. Understanding the new genetics of responsiveness to epidermal growth factor receptor tyrosine kinase inhibitors. Oncologist 2007;12:211-20.
18. Amado RG, Wolf M, Peeters M, et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol 2008;26:1626-34.
19. Wolff AC, Hammond EH, Schwartz JN, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2007;25:118-45.
20. van't-Veer LJ, Paik S, Hayes DF. Gene expression profiling of breast cancer: a new tumor marker. J Clin Oncol 2005;23:1631-5.
21. Paik S. Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with Tamoxifen. Oncologist 2007;12:631-5.
22. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817-26.
23. Dobbin K, Simon R. Sample size planning for developing classifiers using high dimensional DNA expression data. Biostatistics 2007;8:101-17.
24. Dobbin KK, Zhao Y, Simon RM. How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res 2008;14:108-14.
25. Pusztai L, Anderson K, Hess KR. Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer. Clin Cancer Res 2007;13:6080-6.
26. West M, Blanchette C, Dressman H. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A 2001;98:11462-67.
27. Radmacher MD, McShane LM, Simon R. A paradigm for class prediction using gene expression profiles. J Comput Biol 2002;9:505-12.
28. Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002;97:77-87.
29. Simon R, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y. Design and analysis of DNA microarray investigations. New York: Springer Verlag; 2003.
30. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 2002;99:6567-72.
31. Speed TP, editor. Statistical analysis of gene expression microarray data. Chapman and Hall; 2003.
32. Segal MR. Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited. Biostatistics 2006;7:268-85.
33. Simon R. Diagnostic and prognostic prediction using gene expression profiles in high dimensional microarray data. Br J Cancer 2003;89:1599-604.
34. George SL. Statistical issues in translational cancer research. Clin Cancer Res. Vol.18. In press 2008.
35. Simon R, Lam A, Li MC, Ngan M, Menenzes S, Zhao Y. Analysis of gene expression data using BRB-Array Tools. Cancer Informatics 2007;2:11-7.
36. Taylor JMG, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res. Vol. 18. In press 2008.
37. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: A comparison of resampling methods. Bioinformatics 2005;21:3301-7.
38. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 2003;95:14-8.
39. Dupuy A, Simon R. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007;99:147-57.
40. Owzar K, Barry WT, Jung S-H, Sohn I, George SL. Statistical issues arising in the application of genomics and biomarkers in clinical trials. Clin Cancer Res. Vol 18. In press 2008.
41. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001;344:783-92.
42. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 2005;10:6759-63.
43. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials: supplement and correction. Clin Cancer Res 2006;12:3229.
44. Maitournam A, Simon R. On the efficiency of targeted clinical trials. Stat Med 2005;24:329-39.
45. Pusztai L, Hess KR. Clinical trial design formicroarray predictive marker discovery and assessment. Ann Oncol 2004;15:1731-7.
46. Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol 2005;23:2020-7.
47. Simon R, Wang SJ. Use of genomic signatures in therapeutics development. Pharmacogenomics J 2006;6:1667-173.
48. Giaccone G, Herbst RS, Manegold C, et al. Gefitinib in combination with gemcitabine and cisplatin in advanced non-small cell lung cancer: a phase III trial-INTACT2. J Clin Oncol 2004;22:777-84.
49. Herbst RS, Giaccone G, Schiller JH, et al. Gefitinib in combination with paclitaxel and carboplatin in advanced non-small-cell lung cancer: a phase III trial-INTACT2. J Clin Oncol 2004;22:785-94.
50. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Branigan BW. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004;350:2129-39.
51. Paez JG, Janne PA, Lee JC, et al. EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science 2004;304:1497-500.
52. Shepherd FA, Pereira J, Ciuleanu TE, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med 2005;353:123-32.
53. Tsao MS, Sakurada A, Cutz JC, et al. Erlotinib in lung cancer - molecular and clinical predictors of outcome. N Engl J Med 2005;353:133-44.
54. Song Y, Chi GYH. A method for testing a prespecified subgroup in clinical trials. Stat Med 2007;26:3535-49.
55. Wang SJ, O'Neill RT, Hung HMJ. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 2007;6:227-44.
56. Jiang W, Freidlin B, Simon R. Biomarker adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst 2007;99:1036-43.
57. Johnson BE, Janne PA. Selecting patients for epidermal growth factor receptor inhibitor treatment: A FISH story or a tale of mutations? JClin Oncol 2005;28:6813-5.
58. Freidlin B, Simon R. Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res 2005;11:7872-8.