Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology

Abstract

Physicians need improved tools for selecting treatments for individual patients. Many diagnostic entities hat were traditionally viewed as individual diseases are heterogeneous in their molecular pathogenesis and treatment responsiveness. This results in the treatment of many patients with ineffective drugs, incursion of substantial medical costs for the treatment of patients who do not benefit and the conducting of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. In oncology, new genomic technologies provide powerful tools for the selection of patients who require systemic treatment and are most (or least) likely to benefit from a molecularly targeted therapeutic. In the large amount of literature on biomarkers, there is considerable uncertainty and confusion regarding the specifics involved in the development and evaluation of prognostic and predictive biomarker diagnostics. There is a lack of appreciation that the development of drugs with companion diagnostics increases the complexity of clinical development. Adapting to the fundamental importance of tumor heterogeneity and achieving the benefits of personalized oncology for patients and healthcare costs will require paradigm changes for clinical and statistical investigators in academia, industry and regulatory agencies. In this review, I attempt to address some of these issues and provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.

Figure 1. Marker strategy design randomizes eligible patients between two treatment assignment strategies

The control arm determines treatment using practice standards based on staging and existing prognostic factors. The new biomarker is not measured for patients that are randomized to the control arm. Patients randomized to the experimental arm have the candidate biomarker measured and this is used in conjunction with staging and other prognostic factors to determine treatment. This design is very flexible, but often very inefficient in the sense that the same objectives can be obtained with fewer patients using other designs.

Figure 2. Modified marker strategy design measures the candidate marker in all eligible patients

Before randomization, the practice standard-determined treatment and the marker-based treatment are identified. Only patients for whom the two treatments differ are randomized. This design is generally much more efficient than the marker strategy design.

Figure 3. Targeted enrichment design is used for evaluating a new treatment in the population of patients who are identified using a predictive biomarker as the best candidates to receive potential benefit from the new treatment

The targeted enrichment design is primarily for settings where there is a compelling basis for not expecting that marker-negative patients can benefit from the new treatment and an analytically accurate test is available. The compelling basis is generally based on biology but could be based on substantial prior evidence for the new treatment. When the proportion of marker-positive patients is less than a half, this design can require substantially fewer randomized patients than the standard design. RX: Treatment.

Figure 4. The stratification design is used for evaluating the effectiveness of a new treatment versus a control in a population that is prospectively characterized by a binary predictive biomarker

A detailed prospective plan should describe the primary comparison of the treatment with the control overall and in the marker-positive and marker-negative subsets. Several analysis plans are described in the text. With a focused analysis plan, claims of treatment effectiveness in marker-positive patients need not be restricted to cases where the treatment is effective overall for all patients. Ideally, a single completely defined, analytically defined binary biomarker will be determined prior to the randomized trial. Adaptive modifications of the stratified design in which the biomarker is refined based on trial data are described in the text. RX: Treatment.