Advertisement

Statistics in Biosciences

, Volume 8, Issue 1, pp 99–128 | Cite as

Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development

  • Xuemin Gu
  • Nan Chen
  • Caimiao Wei
  • Suyu Liu
  • Vassiliki A. Papadimitrakopoulou
  • Roy S. Herbst
  • J. Jack LeeEmail author
Article

Abstract

We propose a Bayesian two-stage biomarker-based adaptive randomization (AR) design for the development of targeted agents. The design has three main goals: (1) to test the treatment efficacy, (2) to identify prognostic and predictive markers for the targeted agents, and (3) to provide better treatment for patients enrolled in the trial. To treat patients better, both stages are guided by the Bayesian AR based on the individual patient’s biomarker profiles. The AR in the first stage is based on a known marker. A Go/No-Go decision can be made in the first stage by testing the overall treatment effects. If a Go decision is made at the end of the first stage, a two-step Bayesian lasso strategy will be implemented to select additional prognostic or predictive biomarkers to refine the AR in the second stage. We use simulations to demonstrate the good operating characteristics of the design, including the control of per-comparison type I and type II errors, high probability in selecting important markers, and treating more patients with more effective treatments. Bayesian adaptive designs allow for continuous learning. The designs are particularly suitable for the development of multiple targeted agents in the quest of personalized medicine. By estimating treatment effects and identifying relevant biomarkers, the information acquired from the interim data can be used to guide the choice of treatment for each individual patient enrolled in the trial in real time to achieve a better outcome. The design is being implemented in the BATTLE-2 trial in lung cancer at the MD Anderson Cancer Center.

Keywords

Adaptive design Outcome-adaptive randomization Bayesian Lasso Predictive and prognostic biomarkers Personalized medicine Targeted therapy Variable selection 

Notes

Acknowledgments

The authors thank Ms. LeeAnn Chastain for editorial assistance. The work was supported in part by grants CA016672 and CA155196 from the National Cancer Institute. The clinical trial was supported in part by Merck Research Laboratories and Bayer HealthCare. We also would like to thank two anonymous reviewers, the associate editor, and the editor for their thorough review and constructive critiques. Our manuscript has been improved by providing answers in addressing these critics.

Conflict of interest

The authors did not have any conflict of interest related to this work to disclose.

Supplementary material

12561_2014_9124_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (docx 48 KB)

References

  1. 1.
    DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22(2):151–185CrossRefGoogle Scholar
  2. 2.
    Herper M (2012) The truly staggering cost of inventing new drugs. Forbes Website. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/
  3. 3.
    CSDD Outlook (2009) Backgrounder: a methodology for counting cost for pharmaceutical R&D. Tufts Center for the study of Drug Development, BostonGoogle Scholar
  4. 4.
    Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–716CrossRefGoogle Scholar
  5. 5.
    Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7:201–204CrossRefGoogle Scholar
  6. 6.
    Simon R (2010) Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Pers Med 7(1):33–47CrossRefGoogle Scholar
  7. 7.
    Mandrekar SJ, Sargent DJ (2009) Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J Clin Oncol 27(24):4027–4034CrossRefGoogle Scholar
  8. 8.
    Mandrekar SJ, Sargent DJ (2001) Design of clinical trials for biomarker research in oncology. Clin Invest 1(12):1629–1636Google Scholar
  9. 9.
    Wang SJ (2007) Biomarker as a classifier in pharmacogenomics clinical trials: a tribute to 30th anniversary of PSI. Pharm Stat 6(4):283–296CrossRefGoogle Scholar
  10. 10.
    Scher HI, Nasso SF, Rubin EH, Simon R (2011) Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clin Cancer Res 17(21):6634–6640CrossRefGoogle Scholar
  11. 11.
    Goozner M (2012) Drug approvals 2011: focus on companion diagnostics. J Natl Cancer Inst 104(2):84–86CrossRefGoogle Scholar
  12. 12.
    Poste G, Carbone DP, Parkinson DR, Verweij J, Hewitt SM, Jessup JM (2012) Leveling the playing field: bringing development of biomarkers and molecular diagnostics up to the standards for drug development. Clin Cancer Res 18(6):1515–1523CrossRefGoogle Scholar
  13. 13.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674CrossRefGoogle Scholar
  14. 14.
    Reck M, Gatzemeier U (2004) Chemotherapy in stage-IV NSCLC. Lung Cancer 45:s217–222CrossRefGoogle Scholar
  15. 15.
    Schiller JH, Harrington D, Belani CP, Langer C, Sandler A, Krook J, Zhu J, Johnson DH for the Eastern Cooperative Oncology Group (2002) Comparison of four chemotherapy regimens for advanced non-small-cell lung cancer. N Engl J Med 346:92–98CrossRefGoogle Scholar
  16. 16.
    Silvestri G, Rivera M (2005) Targeted therapy for the treatment of advanced non-small cell lung cancer: a review of the epidermal growth factor receptor antagonists. Chest 183:29–42Google Scholar
  17. 17.
    McClellan M, Benner J, Schilsky R et al (2011) An accelerated pathway for targeted cancer therapies. Nat Rev Drug Discov 10:79–80CrossRefGoogle Scholar
  18. 18.
    Chabner BA (2011) Early accelerated approval for highly targeted cancer drugs. N Engl J Med 364(12):1087–1089CrossRefGoogle Scholar
  19. 19.
    Bates SE, Amiri-Kordestani L, Giaccone G (2012) Drug development: portals of discovery. Clin Cancer Res 18(1):23–32CrossRefGoogle Scholar
  20. 20.
    Sharma MR, Schilsky RL (2012) Role of randomized phase III trials in an era of effective targeted therapies. Nat Rev Clin Oncol 9(4):208–214CrossRefGoogle Scholar
  21. 21.
    Rubin EH, Gilliland DG (2012) Drug development and clinical trials–the path to an approved cancer drug. Nat Rev Clin Oncol 9(4):215–222CrossRefGoogle Scholar
  22. 22.
    Zhou X, Liu SY, Kim ES, Herbst RS, Lee JJ (2008) Bayesian adaptive design for targeted therapy development in lung cancer–a step toward personalized medicine. Clin Trials 5:181–193CrossRefGoogle Scholar
  23. 23.
    Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR, Tsao A, Stewart DJ, Hicks ME, Erasmus J, Gupta S, Alden CM, Liu S, Tang X, Khuri FR, Tran HT, Johnson BE, Heymach JV, Mao L, Fossella F, Kies MS, Papadimitrakopoulou V, Davis SE, Lippman SM, Hong WK (2011) The BATTLE trial: Personalizing therapy for lung cancer. Cancer Discov 1(1):44–53CrossRefGoogle Scholar
  24. 24.
    Berry DA (2005) Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin Trials 2:295–300; discussion 301–304, 364–378Google Scholar
  25. 25.
    Berry DA (2006) A guide to drug discovery: Bayesian clinical trials. Nat Rev Drug Discov 5:27–36CrossRefGoogle Scholar
  26. 26.
    Berry DA (2011) Adaptive clinical trials in oncology. Nat Rev Clin Oncol 9(4):199–207CrossRefGoogle Scholar
  27. 27.
    Hu F, Rosenberger WF (2003) Optimality, variability, power: evaluating response-adaptive randomization procedures for treatment comparisons. J Am Stat Assoc 98:671–678MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Hu F, Rosenberger WF (2006) The theory of response-adaptive randomization in clinical trials. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  29. 29.
    Thall PF (2002) Ethical issues in oncology biostatistics. Stat Methods Med Res 11:429–448CrossRefzbMATHGoogle Scholar
  30. 30.
    Berry DA (2004) Bayesian statistics and the efficiency and ethics of clinical trials. Stat Sci 19:175–187Google Scholar
  31. 31.
    Korn EL, Freidlin B (2010) Outcome-adaptive randomization: is it useful? J Clin Oncol 21:100–120Google Scholar
  32. 32.
    Berry DA (2010) Adaptive clinical trials: the promise and the caution. J Clin Oncol 21:606–609Google Scholar
  33. 33.
    Lee JJ, Chen N, Yin G (2012) Worth adapting? Revisiting the usefulness of outcome-adaptive randomization. Clin Cancer Res 18(17):4498–4507CrossRefGoogle Scholar
  34. 34.
    Barker AD et al (2009) I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharm Ther 86:97–100CrossRefGoogle Scholar
  35. 35.
    Berry SM, Carlin BP, Lee JJ, Müller P (2010) Bayesian adaptive methods for clinical trials. Chapman & Hall, Boca RatonCrossRefzbMATHGoogle Scholar
  36. 36.
    Lee JJ, Gu X, Liu S (2010) Bayesian adaptive randomization designs for targeted agent development. Clin Trials 7(5):584–596CrossRefGoogle Scholar
  37. 37.
    Eickhoff JC, Kim K, Beach J, Kolesar JM, Gee JR (2010) A Bayesian adaptive design with biomarkers for targeted therapies. Clin Trials 7:546–556CrossRefGoogle Scholar
  38. 38.
    Lara PN, Natale R, Crowley J et al (2009) Phase III trial of irinotecan/cisplatin Compared with etoposide/cisplatin in extensive-stage small-cell lung cancer: clinical and pharmacogenomic results from SWOG S0124. J Clin Oncol 27:2530–2535CrossRefGoogle Scholar
  39. 39.
    Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103(482):681–686MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Zou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101(476):1418–1429MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Kyung M, Gill J, Ghosh M (2010) Penalized regression, standard errors, and Bayesian lassos. Bayesian Anal 5:369–412MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Meier L, van de Geer S, Buhlmann P (2008) The group lasso for logistic regression. J R Stat Soc B 70:53–71MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Chipman H (1996) Bayesian variable selection with related predictors. Can J Stat 24:17–36MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    McCullagh P, Nelder J (1989) Generalized linear models. Chapman and Hall, Boca RatonCrossRefzbMATHGoogle Scholar
  45. 45.
    Barbieri MM, Berger J (2004) Optimal predictive model selection. Ann Stat 32(3):870–897MathSciNetCrossRefzbMATHGoogle Scholar
  46. 46.
    Brannath W, Zuber E, Branson M, Bretz F, Gallo P, Posch M, Racine-Poon A (2009) Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med 28(10):1445–1463MathSciNetCrossRefGoogle Scholar
  47. 47.
    Biswas S, Liu DD, Lee JJ, Berry DA (2009) Bayesian clinical trials at the University of Texas MD Anderson Cancer Center. Clin Trials 6(3):205–216CrossRefGoogle Scholar
  48. 48.
    Lee JJ, Chu CT (2012) Bayesian clinical trials in action. Stat Med 31(25):2955–2972MathSciNetCrossRefGoogle Scholar
  49. 49.
    Wang S-J, Li M-C (2013) Impacts of predictive genomic classifier performance on subpopulation-specific treatment effects assessment. Stat Biosci. doi: 10.1007/s12561-013-9092-y

Copyright information

© International Chinese Statistical Association 2014

Authors and Affiliations

  • Xuemin Gu
    • 1
    • 2
  • Nan Chen
    • 1
  • Caimiao Wei
    • 1
  • Suyu Liu
    • 1
  • Vassiliki A. Papadimitrakopoulou
    • 3
  • Roy S. Herbst
    • 4
  • J. Jack Lee
    • 1
    Email author
  1. 1.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.PrincetonUSA
  3. 3.Department of Thoracic, Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  4. 4.Department of Internal MedicineYale School of MedicineNew HavenUSA

Personalised recommendations