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Biomarker Enrichment Strategies

Matching study design and analysis strategies for use in biomarker-driven, randomised clinical trials
24 Apr 2014
Personalised medicine;  Translational research

A recent article, published in the Nature Reviews Clinical Oncology, considers which clinical trial designs and analysis strategies are appropriate for use in phase III, biomarker-driven, randomised clinical trials, on the basis of pre-existing evidence that the biomarker can successfully identify patients who will respond to the treatment. In the article, authors describe enrichment strategies based on the use of prognostic biomarkers to separate a population into subgroups with better and worse outcomes, regardless of treatment. They discuss about possibility of using a biomarker during phase II drug development, to select what type of biomarker-driven strategy should be used in the phase III trial.

Clinical development of new therapies for cancer is a complex process 

Many new anticancer agents are molecularly targeted and might only benefit a subgroup of patients. Traditional randomised clinical trials are suboptimal for evaluation of targeted treatments. Efficient development of new anticancer drugs might, therefore, need to use clinical trial designs that are driven by predictive biomarkers. The clinical utility of a biomarker (in its role as the companion diagnostic for a specific new therapy) is defined by its ability to reliably identify a patient subgroup that benefits from the new therapy.

In randomised clinical trial methodology, selection of a subpopulation of patients in which the efficacy of a treatment is most likely to be demonstrated is referred to as 'enrichment'. Successful enrichment improves the efficiency of a trial design by increasing the power of the study and minimising the required sample size or duration. However, a study conducted solely in a subpopulation does not provide any evidence for the risk-to-benefit ratio of using the treatment in any other group.

New phase III randomised clinical trials increasingly use biomarker-driven designs

In their article, Boris Freidlin and Edward Kornof of the Biometric Research Branch, Division of Cancer Treatment and Diagnosis, US National Cancer Institute, focus on the implications of using enrichment approaches in biomarker-driven randomised clinical trial designs used in the clinical development of cancer treatments.

In the paper, they consider how the credentials of a predictive biomarker affect the choice of phase III trial design; review the use of interim monitoring in biomarker-driven randomised clinical trials; discuss how phase II trials can be used to streamline the co-development of a treatment and that of its associated biomarker before beginning a phase III trial; and review phase III designs that evaluate the clinical utility of prognostic biomarkers. 

A careful verification of the analytical validity of a biomarker is an essential prerequisite for commencing assessment of its clinical utility.

The goal of a phase III clinical trial is to provide sufficiently compelling evidence on the risk-to-benefit ratio of a new therapy to direct its use in clinical practice. In the evaluation of targeted anticancer therapies that might only benefit a biomarker-defined subgroup of the overall population, at the conclusion of the phase III trial, one should ideally be able to distinguish between the following three scenarios:

  • the therapy is beneficial in a broad population;
  • the therapy is beneficial in biomarker-positive patients, but is not sufficiently beneficial in the biomarker-negative subgroup to recommend it for that subgroup;
  • the therapy is not sufficiently beneficial for any patients.

Biomarker-driven strategies range from limiting evaluation to the biomarker-positive subgroup, to sequential or parallel procedures that test the treatment in biomarker-positive, biomarker-negative and the overall population in a prespecified order. In the presence of treatment-effect heterogeneity, it might not be appropriate to generalise the treatment benefit observed in the biomarker-positive subgroup to the overall population; conversely, it might not be too appropriate to use the overall treatment effect to make treatment recommendations for the biomarker-negative patients. The choice of an appropriate biomarker-driven phase III trial design should, therefore, be based on the strength of the existing evidence for the predictive value of the biomarker.

If reliable evidence indicates that a treatment is unlikely to benefit the biomarker-negative subgroup, an enrichment design can be used. This evidence is necessary because an enrichment design is not able to show whether the therapy is beneficial in the overall population, or is adequately beneficial only in biomarker-positive patients. If the evidence indicates that the biomarker-positive subgroup is more likely to benefit from the treatment than the biomarker-negative subgroup, a biomarker-stratified design using a sequential, subgroup-specific or Marker Sequential Test strategy would enable all three scenarios to be distinguished. If the evidence for the biomarker is weak and the treatment is expected to work in the overall population, a fall-back design can be used to minimise the possibility of missing an important treatment effect in the biomarker-positive patients with insufficient benefit in the biomarker-negative subgroup, that a standard randomised clinical trial would miss.

The authors concluded that phase III trials are expensive and time consuming. Careful selection of an appropriate phase III strategy for integrating evaluation of a new treatment and its companion diagnostic is key to the successful and timely development of molecularly targeted agents.

The full article you can read through the ESMO Scientific Journal Access program. 


Freidelin B, Korn EL. Biomarker enrichment strategies: matching trial design to biomarker credentials. Nature Reviews Clinical Oncology 2014; 11(2): 81-90.

Last update: 24 Apr 2014

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