title: Integrating predictive biomarkers and classifiers into oncology clinical development programmes.
abstract: The future of drug development in oncology lies in identifying subsets of patients who will benefit from particular therapies, using predictive biomarkers. These technologies offer hope of enhancing the value of cancer medicines and reducing the size, cost and failure rates of clinical trials. However, examples of the failure of predictive biomarkers also exist. In these cases the use of biomarkers increased the costs, complexity and duration of clinical trials, and narrowed the treated population unnecessarily. Here, we present methods to adaptively integrate predictive biomarkers into clinical programmes in a data-driven manner, wherein these biomarkers are emphasized in exact proportion to the evidence supporting their clinical predictive value. The resulting programme demands value from predictive biomarkers and is designed to optimally harvest this value for oncology drug development.
authors.affiliation: Clinical Research Oncology, Daiichi-Sankyo Pharmaceutical Development, 399 Thornall Street, Edison, New Jersey 08837, USA. eniac1@snip.net
authors.forename: Robert A
authors.initials: RA
authors.lastname: Beckman
authors.forename: Jason
authors.initials: J
authors.lastname: Clark
authors.forename: Cong
authors.initials: C
authors.lastname: Chen
mesh_terms: D000818:Animals; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D002986:Clinical Trials as Topic / Q000379:methods* / Q000639:trends; D006801:Humans; D008495:Medical Oncology / Q000379:methods* / Q000639:trends; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D011237:Predictive Value of Tests; D016730:Program Development / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor