Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Opinion
  • Published:

Considerations for the successful co-development of targeted cancer therapies and companion diagnostics

Nature Reviews Drug Discovery volume 12, pages 743–755 (2013)Cite this article

Subjects

Abstract

As diagnostic tests become increasingly important for optimizing the use of drugs to treat cancers, the co-development of a targeted therapy and its companion diagnostic test is becoming more prevalent and necessary. In July 2011, the US Food and Drug Administration released a draft guidance that gave the agency's formal definition of companion diagnostics and introduced a drug–diagnostic co-development process for gaining regulatory approval. Here, we identify areas of drug–diagnostic co-development that were either not covered by the guidance or that would benefit from increased granularity, including how to determine when clinical studies should be limited to biomarker-positive patients, defining the diagnostically selected patient population in which to use a companion diagnostic, and defining and clinically validating a biomarker signature for assays that use more than one biomarker. We propose potential approaches that sponsors could use to deal with these challenges and provide strategies to help guide the future co-development of drugs and diagnostics.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Decision tree for determining the evaluation of biomarker-negative patients.
Figure 2: The relationship of biomarker prevalence versus expected treatment.
Figure 3: Identification of a biomarker in tumours with an associated pathway alteration.

Similar content being viewed by others

References

  1. Naylor, S. & Cole, T. Overview of companion diagnostics in the pharmaceutical industry. Drug Discovery World [online], (2010).

  2. US National Cancer Institute. Targeted cancer therapies (Fact Sheet). US National Cancer Institute [online], (2012).

  3. Savage, D. G. & Antman, K. H. Imatinib mesylate — a new oral targeted therapy. N. Engl. J. Med. 346, 683–693 (2002).

    Article  CAS  Google Scholar 

  4. Demetri, G. D. et al. Efficacy and safety of imatinib mesylate in advanced gastrointestinal stromal tumors. N. Engl. J. Med. 347, 472–480 (2002).

    Article  CAS  Google Scholar 

  5. Garnock-Jones, K. P., Keating, G. M. & Scott, L. J. Trastuzumab: a review of its use as adjuvant treatment in human epidermal growth factor receptor 2 (HER2)-positive early breast cancer. Drugs 70, 215–239 (2010).

    Article  CAS  Google Scholar 

  6. Hudziak, R. M. et al. p185HER2 monoclonal antibody has antiproliferative effects in vitro and sensitizes human breast tumor cells to tumor necrosis factor. Mol. Cell. Biol. 9, 1165–1172 (1989).

    Article  CAS  Google Scholar 

  7. Kulke, M. H. Systemic therapy for advanced pancreatic neuroendocrine tumors. Semin. Oncol. 40, 75–83 (2013).

    Article  CAS  Google Scholar 

  8. Motzer, R. J. et al. Sunitinib in patients with metastatic renal cell carcinoma. JAMA 295, 2516–2524 (2006).

    Article  CAS  Google Scholar 

  9. US Food & Drug Administration. Guidance for industry and Food and Drug Administration staff — in vitro companion diagnostic devices (draft guidance). US Food & Drug Administration [online], (2011).

  10. Teutsch, S. M. et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet. Med. 11, 3–14 (2009).

    Article  Google Scholar 

  11. US Food & Drug Administration. Guidance for industry: enrichment strategies for clinical trials to support approval of human drugs and biological products (draft duidance). US Food & Drug Administration [online], (2012).

  12. European Medicines Agency. Reflection paper on co-development of pharmacogenomic biomarkers and assays in the context of drug development. European Medicines Agency [online], (2010).

  13. US Food & Drug Administration. Drug-diagnostic co-development concept paper (draft — not for implementation). US Food & Drug Administration [online], (2005).

  14. Moore, M. W., Babu, D. & Cotter, P. D. Challenges in the codevelopment of companion diagnostics. Per. Med. 9, 485–496 (2012).

    Article  CAS  Google Scholar 

  15. Beckman, R. A., Clark, J. & Chen, C. Integrating predictive biomarkers and classifiers into oncology clinical development programmes. Nature Rev. Drug Discov. 10, 735–748 (2011).

    Article  CAS  Google Scholar 

  16. Ravnan, M. C. & Matalka, M. S. Vemurafenib in patients with BRAF V600E mutation-positive advanced melanoma. Clin. Ther. 34, 1474–1486 (2012).

    Article  CAS  Google Scholar 

  17. Tanizaki, J. et al. Activation of HER family signaling as a mechanism of acquired resistance to ALK inhibitors in EML4-ALK-positive non-small cell lung cancer. Clin. Cancer Res. 18, 6219–6226 (2012).

    Article  CAS  Google Scholar 

  18. Brand, T. M., Iida, M. & Wheeler, D. L. Molecular mechanisms of resistance to the EGFR monoclonal antibody cetuximab. Cancer Biol. Ther. 11, 777–792 (2011).

    Article  CAS  Google Scholar 

  19. Chung, K. Y. et al. Cetuximab shows activity in colorectal cancer patients with tumors that do not express the epidermal growth factor receptor by immunohistochemistry. J. Clin. Oncol. 23, 1803–1810 (2005).

    Article  CAS  Google Scholar 

  20. Fridlyand, J. et al. An industry statistician's perspective on PHC drug development. Contemp. Clin. Trials http://dx.doi.org/10.1016/j.cct.2013.04.006 (2013).

  21. Freidlin, B., McShane, L. M., Polley, M. Y. & Korn, E. L. Randomized phase II trial designs with biomarkers. J. Clin. Oncol. 30, 3304–3309 (2012).

    Article  Google Scholar 

  22. Kaiser, L., Becker, C., Kukreti, S. & Fine, B. Decision making for a companion diagnostic in an oncology clinical development program. Drug Inform. J. 46, 294–302 (2012).

    Article  Google Scholar 

  23. Mandrekar, S. J. & Sargent, D. J. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J. Clin. Oncol. 27, 4027–4034 (2009).

    Article  Google Scholar 

  24. Mandrekar, S. J. & Sargent, D. J. All-comers versus enrichment design strategy in phase II trials. J. Thorac. Oncol. 6, 658–660 (2011).

    Article  Google Scholar 

  25. Simon, R. Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per. Med. 7, 33–47 (2010).

    Article  Google Scholar 

  26. Zhan, S. et al. Defective neuropeptide processing and ischemic brain injury: a study on proprotein convertase 2 and its substrate neuropeptide in ischemic brains. J. Cereb. Blood Flow Metab. 29, 698–706 (2009).

    Article  CAS  Google Scholar 

  27. Spiegl-Kreinecker, S. et al. O6-methylguanine DNA methyltransferase protein expression in tumor cells predicts outcome of temozolomide therapy in glioblastoma patients. Neuro Oncol. 12, 28–36 (2010).

    Article  CAS  Google Scholar 

  28. Brannath, W. et al. Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat. Med. 28, 1445–1463 (2009).

    Article  Google Scholar 

  29. Chen, C. & Beckman, R. A. Hypothesis testing in a confirmatory phase III trial with a possible subset effect. Stat. Biopharm. Res. 1, 431–440 (2009).

    Article  Google Scholar 

  30. Hochberg, Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800–802 (1988).

    Article  Google Scholar 

  31. Wang, S. J., O'Neill, R. T. & Hung, H. M. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm. Stat. 6, 227–244 (2007).

    Article  Google Scholar 

  32. 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. 99, 1036–1043 (2007).

    Article  Google Scholar 

  33. Christine, M. M., Sharly, J. N. & Gilbert, S. O. (eds) Evolution of Translational Omics: Lessons Learned and the Path Forward (The National Academies Press, 2012).

    Google Scholar 

  34. Breiman, L. Bagging predictors. Machine Learn. 24, 123–140 (1996).

    Google Scholar 

  35. Freidlin, B., Jiang, W. & Simon, R. The cross-validated adaptive signature design. Clin. Cancer Res. 16, 691–698 (2010).

    Article  Google Scholar 

  36. 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. 11, 7872–7878 (2005).

    Article  CAS  Google Scholar 

  37. Kaiser, L. D. Stratification of randomization is not required for a pre-specified subgroup analysis. Pharm. Stat. 12, 43–47 (2013).

    Article  Google Scholar 

  38. Scher, H. I., Nasso, S. F., Rubin, E. H. & Simon, R. Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clin. Cancer Res. 17, 6634–6640 (2011).

    Article  Google Scholar 

  39. Simon, R. M., Paik, S. & Hayes, D. F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl Cancer Inst. 101, 1446–1452 (2009).

    Article  Google Scholar 

  40. European Medicines Agency. Reflection paper on methodological issues associated with pharmacogenomic biomarkers in relation to clinical development and patient selection. European Medicines Agency [online], (2011).

  41. Goldberg, P. KRAS finding changes oncology practice but poses profound regulatory dilemma. The Cancer Letter 35, 1–8 (2009).

    Google Scholar 

  42. Patterson, S. D. et al. Prospective-retrospective biomarker analysis for regulatory consideration: white paper from the industry pharmacogenomics working group. Pharmacogenomics 12, 939–951 (2011).

    Article  Google Scholar 

  43. Simon, R. & Roychowdhury, S. Implementing personalized cancer genomics in clinical trials. Nature Rev. Drug Discov. 12, 358–369 (2013).

    Article  CAS  Google Scholar 

  44. Park, R. Companion diagnostics: a blossoming field. IVD Technology [online], (2011).

  45. Gaffney, A. Device group launches offensive against pending EU legislation. Regulatory Focus [online], (2013).

  46. European Commission. In vitro diagnostic medical devices: Directive 98/79/EC. European Commission [online], (1998).

  47. Stynen, D. Revision of Europe's IVD Directive 98/79/EC. IVD Technology [online], (2011).

  48. European Commission. Revision of the medical device directives. European Commission [online], (2012).

  49. Study Group 1 of the Global Harmonization Task Force. Principles of In Vitro Diagnostic (IVD) Medical Devices Classification [online], (Global Harmonization Task Force, 2008).

Download references

Acknowledgements

The authors would like to thank the following for their contributions to, and critical review of, this manuscript: S. Averbush, R. Beckman, L. Burdette, T. Bush, R. Canetta, S. Dahm, J. Dudinak, L. Farrington, S. Ford, G. Hampton, L. Hashimoto, D. Hayes, S. Ho, F. Houn, E. Ibia, J. Jenkins-Showalter, L. Lavange, G. Lieberman, M. Liu, S. Lutzker, P. Mahaffy, L. Mansfield, A.-M. Martin, I. McCaffery, D. Miller, V. Miller, A. Mueller, P. Paoletti, S. Patterson, C. Paulding, R. Pazdur, D. Rasmussen, J. Roche, S. Scherer, J. Shuren, J. Siegel, G. Spaniolo, B. Trepicchio, W. Verbiest, J. Wiezorek, J. Woodcock and L. Zydowski.

Author information

Authors and Affiliations

  1. Genentech, 1 DNA Way, MS 441b, South San Francisco, 94080, California, USA

    Jane Fridlyand

  2. US National Cancer Institute, 9000 Rockville Pike, Bethesda, 20892-7434, Maryland, USA

    Richard M. Simon

  3. Friends of Cancer Research, 1800M Street NW, Suite 1050, 20036, South Washington, DC, USA

    Jessica C. Walrath, Jeff D. Allen & Ellen V. Sigal

  4. Fight Colorectal Cancer, 1414 Prince Street, Suite 204, Alexandria, 22314, Virginia, USA

    Nancy Roach

  5. Pfizer,10555 Science Center Drive, San Diego, 92121, California, USA

    Richard Buller

  6. Agios Pharmaceuticals, 38 Sidney Street, Suite 2, Cambridge, 02139, Massachusetts, USA

    David P. Schenkein

  7. Massachusetts General Hospital, 55 Fruit Street, Yawkey 9E, Boston, 02114, Massachusetts, USA

    Keith T. Flaherty

  8. Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, 10065, New York, USA

    Howard I. Scher

Authors
  1. Jane Fridlyand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard M. Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jessica C. Walrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nancy Roach
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard Buller
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David P. Schenkein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Keith T. Flaherty
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jeff D. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ellen V. Sigal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Howard I. Scher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jessica C. Walrath.

Ethics declarations

Competing interests

J.F. is employed by Genentech.

R.B. is employed by Pfizer.

D.P.S. is employed by Agios Pharmaceuticals.

All other authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (box)

Threshold readjustment using a split-dataset approach (PDF 111 kb)

PowerPoint slides

Glossary

Bootstrap aggregating

A type of model averaging that improves the stability and accuracy of the algorithms that are used in biomarker studies, and is typically conducted by repeatedly re-sampling data points from a given data set.

Bridging studies

Studies in which clinical samples that were tested initially with an assay used in a clinical trial are re-tested with another assay to support the approval or clearance of that assay.

Classifier

An algorithm (or statistical rule) that can be used to predict prognosis or the responsiveness of patients to a given therapy and thereby used to select and/or stratify patients for therapy in clinical trials. The inputs to the algorithm are the values obtained from one or more predefined biomarkers.

Diagnostic platform

A form of molecular diagnostic testing that provides patient-specific information using parallelized platform sequencing technology.

Effect size

An estimate of the treatment effect relative to the control (or any other parameter of interest).

Hierarchical approaches

Sequential approaches to the testing of multiple hypotheses where a given null hypothesis can only be tested if all null hypotheses that are ranked higher are rejected.

Investigational device exemption

A regulatory submission that allows a medical device to be used in a clinical study without full approval from the US Food and Drug Administration in order to collect data on the safety and effectiveness of the device.

Laboratory-developed tests

A class of in vitro diagnostic tests that are currently not regulated by the US Food and Drug Administration.

Next-generation sequencing

A set of technologies that enable the rapid generation of enormous amounts of DNA or RNA sequencing data.

Notified body

An organization that has been accredited by a member country of the European Union to determine whether a product meets certain predetermined standards.

Split-α approaches

Approaches that are undertaken for the testing of multiple hypotheses to maintain the study-wise type I error at the intended 0.05 level by splitting the threshold for declaring significance (that is, α) among the hypotheses to be tested.

Statistical analysis plan

The pre-specified analyses that will be applied to the data generated from a clinical trial.

Summary measures

The mathematical combination of values produced by one or more biomarkers, resulting in a single value that can be used for making decisions about drug treatments.

Training set

A data set that is used for the development of a statistical model and all of its parameters. Another data set known as the test set is then used to test the accuracy of the model.

Type I error

The chance of falsely rejecting the null hypothesis in favour of the alternative (the probability of the false positive); for example, falsely claiming that a relationship exists between treatment effect and biomarker value in the absence of a true relationship.

Unbiased effect estimate

An estimate of the treatment effect in which the expected value (based on hypothetical repetitions of the study) equals the true value of the effect.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fridlyand, J., Simon, R., Walrath, J. et al. Considerations for the successful co-development of targeted cancer therapies and companion diagnostics. Nat Rev Drug Discov 12, 743–755 (2013). https://doi.org/10.1038/nrd4101

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd4101

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research