Affiliations of authors: Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland (BKD, VES, RMF, RL, LGF, BSK); CCS Associates, Inc., McLean, VA (CFT); Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC (DR); Information Management Services, Rockville, MD (CC).
Correspondence to: Barbara K. Dunn, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Room 5E534, Bethesda, MD 20892 (e-mail: firstname.lastname@example.org).
Background: Over 25 years, the National Cancer Institute’s Division of Cancer Prevention has entered some 800 agents into a chemopreventive agent testing program. Two critical steps involve: 1) in vitro/in vivo morphologic assays and 2) animal tumor assays (incidence/multiplicity reduction). We sought to determine how accurately the earlier-stage (morphologic) assays predict efficacy in the later-stage (animal tumor) assays.
Methods: Focusing on 210 agents tested in both morphologic and animal tumor assays, we carried out statistical modeling of how well the six most commonly used morphologic assays predicted drug efficacy in animal tumor assays. Using multimodel inference, three statistical models were generated to evaluate the ability of these six morphologic assays to predict tumor outcomes in three different sets of animal tumor assays: 1) all tumor types, 2) mammary cancer only, and 3) colon cancer only. Using this statistical modeling approach, each morphologic assay was assigned a value reflecting how strongly it predicted outcomes in each of the three different sets of animal tumor assays.
Results: We demonstrated differences in the predictive value of specific morphologic assays for positive animal tumor assay results. Some of the morphologic assays were strongly predictive of meaningful positive efficacy outcomes in animal tumor assays representing specific cancer types, particularly the aberrant crypt focus (ACF) assay for colon cancer. Moreover, less strongly predictive assays can be combined and sequenced, resulting in enhanced composite predictive ability.
Conclusions: Predictive models such as these could be used to guide selection of preventive agents as well as morphologic and animal tumor assays, thereby improving the efficiency of our approach to chemopreventive agent development.
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Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.