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Integrating predictive biomarkers and classifiers into oncology clinical development programmes

Nature Reviews Drug Discovery volume 10, pages 735–748 (2011)Cite this article

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Abstract

The future of drug development in oncology lies in identifying subsets of patients who will benefit from particular therapies, using predictive biomarkers. These technologies offer hope of enhancing the value of cancer medicines and reducing the size, cost and failure rates of clinical trials. However, examples of the failure of predictive biomarkers also exist. In these cases the use of biomarkers increased the costs, complexity and duration of clinical trials, and narrowed the treated population unnecessarily. Here, we present methods to adaptively integrate predictive biomarkers into clinical programmes in a data-driven manner, wherein these biomarkers are emphasized in exact proportion to the evidence supporting their clinical predictive value. The resulting programme demands value from predictive biomarkers and is designed to optimally harvest this value for oncology drug development.

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Figure 1: Relative efficiency of two small POC trials (E2) compared to one traditionally sized POC trial (E1).
Figure 2: Two-dimensional decision graph for decision analysis-guided Phase II–Phase III predictive biomarker transition.
Figure 3: Traditional confirmatory phase development compared to the Phase II+ method.

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Acknowledgements

We thank R. M. Simon, head of the Biometrics Division at the US National Cancer Institute, for his very helpful discussions and for his critical review of the manuscript. We also thank D. Parkinson, Chair of the Cancer Steering Committee, Biomarkers Consortium of the Foundation for the US National Institutes of Health, for his helpful comments. The inspiration for this article, which represents the opinion of the authors, came from crossfunctional discussions — led by R.A.B. — of predictive biomarker integration into clinical development plans. From Amgen, the following individuals contributed to these discussions: A. Ang, D. Beaupre, L. Chen, D. Freeman, E. Loh, I. McCaffrey, E. Rasmussen and M. Wolf. From Daiichi-Sankyo Pharmaceutical Development, the following individuals contributed to these discussions: M. Aonuma, T. Bocanegra, B. Dornseif, S. Ge, I. Gorbatchevsky, G. Gormley, A. Halim, X. Jin, P. Kumar, K. Liu, K. Nakamaru, M. Rosen, D. Salazar, R. Scheyer, G. Senaldi, S. Smith, A. Tse, R. von Roemeling, J. Walker, Q. Wang and Y. Wang. From Merck Research Laboratories, the following individuals contributed to these discussions: K. Anderson, D. Bergstrom, C. Buser-Doepner, P. Carroll, T. Demuth, P. Ehrlich, C. Gause, D. Geho, B. Gertz, G. Harris, K. Hsu, J. Milloy, R. Phillips, C. Pickett, P. Shaw and L. Yan. From Rosetta BioInpharmatics, T. Fare contributed to these discussions. From U3 Pharma, the following individuals contributed to these discussions: K. Akahane and T. Hettman. The affiliations listed are the affiliations of the individuals at the time the discussions occurred.

Author information

Authors and Affiliations

  1. Robert A. Beckman is at Clinical Research Oncology, Daiichi-Sankyo Pharmaceutical Development, 399 Thornall Street, Edison, New Jersey 08837, USA.,

    Robert A. Beckman

  2. Jason Clark is at Biostatistics, Incyte Pharmaceuticals, Rte 141 & Henry Clay Road, Wilmington, Delaware 19880, USA.,

    Jason Clark

  3. Cong Chen is at Biostatistics and Research Decision Sciences, Merck Research Laboratories, 351 N. Sumneytown Pike, North Wales, Pennsylvania 19454, USA.,

    Cong Chen

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Corresponding author

Correspondence to Robert A. Beckman.

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Competing interests

Robert A. Beckman is an employee of Daiichi Sankyo Pharmaceutical Development and is a stockholder in Daiichi Sankyo Inc. and Johnson & Johnson corporation.

Jason Clark is an employee of Incyte Pharmaceuticals and a stockholder in Merck & Co., Inc.

Cong Chen is an employee of Merck & Co., Inc. and a stockholder in Merck & Co., Inc., Sanofi and Johnson & Johnson corporation.

Glossary

Analytical validation

A process for demonstrating that a biomarker assay (or assays) have suitable calibration properties, sensitivity, specificity, accuracy and reproducibility to be potentially approved for commercial use after further clinical validation.

Clinical benefit identification (ID) hypothesis

The hypothesis that a particular predictive biomarker classifier identifies the subset of patients who will benefit from the therapy.

Clinical validation

The demonstration that a biomarker assay (or assays) identify the subset of patients who will benefit clinically.

Ethics committees

Committees that are composed of clinical development experts, patient advocates and lay people; these committees evaluate proposed clinical research studies for a particular hospital or clinic before the studies are permitted to commence at that institution. These committees focus particularly on protecting the rights of patients. This term is used particularly in Europe.

Go/no-go criteria

Criteria that determine whether or not to proceed to a Phase III pivotal trial. If the observed clinical benefit in a Phase II proof-of-concept study is equal to or better than the go/no-go criterion, the same drug–indication combination is studied in a randomized Phase III study for the approval of health authorities.

In vitro diagnostic

A biomarker assay that has been approved for commercial use in conjunction with a therapy, for the purpose of identifying the subset of patients who will benefit from the therapy.

Institutional review boards

Committees that are composed of clinical development experts, patient advocates and lay people; these committees evaluate proposed clinical research studies for a particular hospital or clinic before the studies are permitted to commence at that institution. These committees focus particularly on protecting the rights of patients. This term is applied particularly in the United States.

Objective utility functions

Mathematical functions that quantify the usefulness (utility) of a particular strategy. They can be used to objectively compare strategies. An example is the efficiency function for proof-of-concept studies.

Power

The probability that the proof-of-concept study or another confirmatory study will correctly declare an effective drug to be effective. It is equal to 100% minus the false-negative rate (the type II error).

Predictive biomarkers

Biomarkers that utilize a baseline characteristic of a patient to predict whether or not the patient will benefit from an associated therapy.

Prognostic biomarker

A biomarker that utilizes a baseline characteristic of a patient to predict the patient's outcome independently of any specific therapy.

Proof of concept

Statistical evidence from a randomized Phase II study that a particular drug is beneficial in a particular clinical setting or indication.

Qualified clinical end points

Clinical end points in Phase II studies that have a reasonable likelihood of predicting true clinical benefit. Examples of qualified clinical end points include RECIST responses (tumour shrinkage or stabilization of tumour size, according to standard criteria) or — when measured in randomized studies — progression-free survival (the time from the start of the therapy to tumour worsening or death). These qualified clinical end points may predict actual clinical benefit measured by overall survival (the time from the start of the therapy to death) or quality of life.

Research grade assay

A biomarker assay that has undergone informal testing in the research laboratory but has not undergone formal analytical validation.

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Beckman, R., Clark, J. & Chen, C. Integrating predictive biomarkers and classifiers into oncology clinical development programmes. Nat Rev Drug Discov 10, 735–748 (2011). https://doi.org/10.1038/nrd3550

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