Hot TopicClinical trial designs incorporating predictive biomarkers☆
Introduction
As cancer has become increasingly understood on the molecular level, therapeutic research has largely shifted from a focus on cytotoxic agents to newer drugs that inhibit specific cancer cell growth and survival mechanisms or that enhance immune responses to cancer cells. Increasingly common are trials of targeted therapies intended to show enhanced efficacy in patient subpopulations, such as those with a known biomarker value or genetic tumor mutation. For example, panitumumab and cetuximab have been indicated as treatment options for advanced colorectal cancer patients with KRAS wild-type tumors [1], [2], and therapies targeting epidermal growth factor receptor mutation have improved outcomes in a subset of patients with advanced non-small-cell lung cancer [3], [4].
In the past decade, a number of biomarker-based design solutions have been proposed to study treatments within possibly heterogeneous patient subpopulations. These can be broadly classified on several levels. First, clinical trials for targeted therapies may be generally classified as follows: “phase I” trials, where the marker and treatment are studied together in normal versus tumor tissue, the assay validated, and any relevant marker positivity thresholds tentatively selected; phase II trials, where interest lies in identifying and possibly validating a marker-based subpopulation where efficacy of a targeted therapy is most promising; and phase III trials, which generally entails a usual randomized treatment comparison in the population identified and believed to benefit from earlier phase II studies [5]. Marker-based trial designs may further be classified as retrospective (evaluation of the marker-treatment-outcome relationship after the trial has been completed) or prospective (formal incorporation of predictive markers in the design considerations), where the latter is typically required for clinical validation. A third classification of biomarker designs is a purely statistical one: frequentist or “classical” designs versus Bayesian designs, where differences between the two approaches lie primarily in the methods for hypothesis testing, decision-making, and use of prior (or historical) information.
In this review of biomarker-based trial methodology, we focus on prospective trial designs, both classical and Bayesian, with emphasis given to phase II and III studies where discovery, clinical validation, or subsequent use of a predictive biomarker are the primary objectives (Early literature on biomarker designs and A movement toward adaptive designs sections). Of importance but not covered in this review are the earlier stages of biomarker development, such as construction and assay validation of genomic signature classifiers or creation of diagnostic tests meant to detect patients with potentially enhanced treatment sensitivity. Selected case studies: implementation of biomarker-based designs in oncology section presents several recent or ongoing biomarker-based trials as case studies, and Going forward: future design challenges section concludes with a discussion of areas of future need for biomarker-based designs.
Section snippets
Targeted or enriched design
Among the earliest explorations of biomarker-based clinical trial designs were those of Simon and Maitournam [6], [7], who compared conventional trials randomizing all patients with a particular disease to those in which only patients positive for a particular biomarker were randomized to experimental versus control treatments (i.e., “targeted” or “enriched” designs; Fig. 1A). Relative efficiency in terms of sample size was reported as a function of marker prevalence and differential treatment
A movement toward adaptive designs
After the first few years of biomarker-based trial design literature yielded the fixed designs described above, a movement toward adaptive biomarker-based trial designs emerged. By “adaptive”, we refer to designs utilizing data accumulated from patients early in the trial to prospectively shift accrual, eligibility, or objectives later on in the trial. The estimation and decision rules for adaptive designs may be performed within either classical or Bayesian statistical paradigms.
Selected case studies: implementation of biomarker-based designs in oncology
In this section, we highlight some practical considerations and challenges faced within selected recently completed or ongoing biomarker-based trials. For a comprehensive example detailing the marker design selection process for SWOG trial S0819, we recommend the article by Redman et al. [81]. Some of the following trials were previously reviewed by Mandrekar et al. [82].
Going forward: future design challenges
The past decade has seen tremendous advances, both in molecular understanding of cancer and clinical trial design methodology to address biomarker-based objectives. Ultimately, for truly personalized treatment strategies in cancer to become the standard of care, additional work in both areas is needed. Specifically within clinical trial methodology, the need remains for a flexible design paradigm that incorporates both prospective identification of naturally continuous or combination biomarkers
Conflict of interest statement
The authors have no conflicts of interest to disclose.
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Research support: This publication was supported by CTSA Grant No. KL2 TR000136 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Funding was also provided by R01 CA174779/CA/NCI NIH HHS/United States.