title: Clinical trial designs incorporating predictive biomarkers.
abstract: Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA. Electronic address: renfro.lindsay@mayo.edu.
authors.forename: Lindsay A
authors.initials: LA
authors.lastname: Renfro
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
authors.forename: Himel
authors.initials: H
authors.lastname: Mallick
authors.affiliation: Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA.
authors.forename: Ming-Wen
authors.initials: MW
authors.lastname: An
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Sargent
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
authors.forename: Sumithra J
authors.initials: SJ
authors.lastname: Mandrekar
mesh_terms: D000970:Antineoplastic Agents / Q000378:metabolism / Q000494:pharmacology; D015415:Biomarkers / Q000032:analysis / Q000378:metabolism; D002986:Clinical Trials as Topic / Q000379:methods; D006801:Humans; D058990:Molecular Targeted Therapy / Q000379:methods* / Q000639:trends; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism; D011237:Predictive Value of Tests; D012107:Research Design / Q000639:trends*
chemical_list: D000970:Antineoplastic Agents; D015415:Biomarkers
keywords: Adaptive trial design; Bayesian adaptive design; Biomarker-based design; Clinical trial design; Enrichment designs; Targeted therapies
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