title: The effect of different tumor groupings on findings of anticarcinogenic responses in long-term rodent bioassays.
abstract: Many investigators have found that there is a decrease in tumor rates at some sites when rodents are exposed to some chemicals. The generality of this finding of anticarcinogenicity has been questioned. In this study, we evaluate the effect of several alternative ways of grouping the 3000 tumor types in the Cancer Bioassay Data System (CBDS) database of carcinogenesis test results into a limited number of classes on findings of anticarcinogenicity. We also study the influence of random variation of tumor rate on the apparent anticarcinogenic effects of specific chemicals. We compare the numbers of chemicals classified as anticarcinogenic in (1) our "standard" classification system, (2) a modification of that system to correct some deficiencies in the CBDS database pointed out by Dr. J. Haseman, (3) an alternative classification system developed by Dr. K. S. Crump and colleagues, (4) the number of animals displaying at least one tumor, or (5) the total number of tumors appearing in all animals in control and dosed groups. Although there is a difference in the number of chemicals classified as anticarcinogens by these alternative classification schemes, all of them show a statistically significant increase in the number of anticarcinogenic responses above the random rate predicted by a Monte Carlo simulation of the rodent bioassay. The number of anticarcinogenic responses is similar in our standard classification, our modified classification, and a classification scheme developed by Crump, though specific site/organs may differ. This arises because these schemes use approximately the same number of tumor groups (about 100). If the number of tumor groups decreases (for example, in the total tumors or tumor-bearing animal schemes), the number of anticarcinogenic responses decreases because of the decrease in overall sensitivity of the test. A scheme which combines tumor types can be useful by integrating carcinogenic and anticarcinogenic responses to exposure. At the same time, combination may hide important biological information. We urge consideration of both increases and decreases when evaluating the likely human effects of exposure.
authors.affiliation: Harvard Center for Risk Analysis, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
authors.forename: George M
authors.initials: GM
authors.lastname: Gray
authors.forename: Henry
authors.initials: H
authors.lastname: Huang
authors.forename: Igor
authors.initials: I
authors.lastname: Linkov
authors.forename: Michael
authors.initials: M
authors.lastname: Polkanov
authors.forename: Richard
authors.initials: R
authors.lastname: Wilson
mesh_terms: D000818:Animals; D016588:Anticarcinogenic Agents / Q000494:pharmacology*; D001681:Biological Assay / Q000706:statistics & numerical data; D015197:Carcinogenicity Tests / Q000379:methods* / Q000706:statistics & numerical data; D002273:Carcinogens / Q000633:toxicity; D003198:Computer Simulation; D016208:Databases, Factual; D004305:Dose-Response Relationship, Drug; D005260:Female; D008297:Male; D051379:Mice; D009010:Monte Carlo Method; D009369:Neoplasms / Q000145:classification / Q000473:pathology / Q000517:prevention & control; D051381:Rats
chemical_list: D016588:Anticarcinogenic Agents; D002273:Carcinogens