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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

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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 In July 2011, the US Food and Drug Administration released a draft guidance that gave the agency's formal definition of companion diagnostics and introduced a drug–diagnostic co-development process for gaining regulatory approval.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.

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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.

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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.

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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

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Correspondence to Jessica C. Walrath.

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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.

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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

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