Abstract B53: Predicting efficacy of chemopreventive agents in animal tumor assays by statistical modeling

• Published in: Cancer prevention research (Philadelphia, Pa.) Pa.), 2015-10, Vol.8 (10_Supplement), p.B53-B53
• Main Authors: Dunn, Barbara K., Steele, Vernon E., Fagerstrom, Richard M., Topp, Carol F., Ransohoff, David, Ford, Leslie G., Kramer, Barnett S.
• Format: Article
• Language: English
• ISSN: 1940-6207; 1940-6215
• DOI: 10.1158/1940-6215.PREV-14-B53

NCI's Division of Cancer Prevention has over 25 years entered some 800 agents into a program designed to test cancer preventive efficacy. Early in the testing pathway are 2 sequential critical steps: (1) in vitro/in vivo morphologic assays and, (2) for agents successful in step 1, cancer-preventive testing (measured in terms of tumor incidence and multiplicity reduction) in animal tumor assays. The ultimate intention is to follow successful testing in animals with entry into clinical trial testing in humans. To facilitate the optimal selection of appropriate morphologic tests for specific disease-site animal tumor models, we undertook the current project. We evaluated the success of our strategy by determining how accurately the earlier stage (morphologic) assays predict efficacy in the later-stage (animal tumor) assays. Focusing on 210 agents that were tested in both morphologic and animal tumor assays, we carried out statistical modeling of how well the 6 most commonly used morphologic assays predicted drug efficacy in animal tumor models. Using multimodel inference, three statistical models were generated to evaluate the ability of these 6 morphologic assays to predict tumor outcomes in 3 different sets of animal tumor assays: (1) all tumor types, (2) breast 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 3 different sets of animal tumor assays. The goal is to use predictive models such as these to guide our future decision-making with respect to selection of preventive agents as well as morphologic and animal tumor assays. In this manner, we hope to improve the efficiency of our overall approach to chemoprevention agent development. Citation Format: Barbara K. Dunn, Vernon E. Steele, Richard M. Fagerstrom, Carol F. Topp, David Ransohoff, Leslie G. Ford, Barnett S. Kramer. Predicting efficacy of chemopreventive agents in animal tumor assays by statistical modeling. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr B53.