Simon RClinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per Med 7: 33-47

National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA, Tel.: +1 301 496 0975.
Personalized Medicine (Impact Factor: 1.34). 01/2010; 7(1):33-47. DOI: 10.2217/pme.09.49
Source: PubMed


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.

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Available from: Richard Simon, Sep 17, 2014
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    • "We also point the reader to statistical software when available. The various approaches enable investigators to ascertain the extent to which one should expect a new untreated patient to respond to each candidate therapy and thereby select the treatment that maximizes the expected therapeutic response for the specific patient [3] [19]. Section 2 discusses the limitations of conventional approaches based on post hoc stratified analysis. "
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    ABSTRACT: The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available.
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    • "For reliable clinical validation of a predictive biomarker for a clinical endpoint, a randomized clinical trial would be required to estimate treatment effects (of a new treatment relative to a control treatment) unbiasedly and to assess whether the treatment effects vary depending on the status of the biomarker, that is, a treatment-by-biomarker interaction. Lastly, clinical utility requires that the biomarker is actionable in clinical practice and the use of the biomarker results in improved outcome of patients and leads to patient benefit [7]. Therefore, one critical element in establishing clinical utility is to evaluate the improved patient outcomes associated with the use of the developed prognostic biomarker, through comparing with those based on a standard of care without the biomarker. "
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    ABSTRACT: The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.
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    • "Imaging can also improve the efficiency of clinical trials through patient selection. Imaging biomarkers may be used to screen patients for study enrollment in an enrichment design [4] [5], also called a " targeted " or " marker positive " design. Fig. 1B shows a study schema that uses FDG PET uptake as a study entry criterion to identify thyroid cancer patients with aggressive disease for a phase 2 study of systemic therapy [6]. "
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    ABSTRACT: Quantitative imaging using computed tomography, magnetic resonance imaging and positron emission tomography modalities will play an increasingly important role in the design of oncology trials addressing molecularly targeted, personalized therapies. The advent of molecularly targeted therapies, exemplified by antiangiogenic drugs, creates new complexities in the assessment of response. The Quantitative Imaging Network addresses the need for imaging modalities which can accurately and reproducibly measure not just change in tumor size but changes in relevant metabolic parameters, modulation of relevant signaling pathways, drug delivery to tumor and differentiation of apoptotic cell death from other changes in tumor volume. This article provides an overview of the applications of quantitative imaging to phase 0 through phase 3 oncology trials. We describe the use of a range of quantitative imaging modalities in specific tumor types including malignant gliomas, lung cancer, head and neck cancer, lymphoma, breast cancer, prostate cancer and sarcoma. In the concluding section, we discuss potential constraints on clinical trials using quantitative imaging, including complexity of trial conduct, impact on subject recruitment, incremental costs and institutional barriers. Strategies for overcoming these constraints are presented.
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