Literature DB >> 29890424

The additive to background assumption in cancer risk assessment: A reappraisal.

Edward J Calabrese1.   

Abstract

The assumption that chemical and radiation induced cancers act in a manner that is additive to background was proposed in the mid-1970s. It was adopted by the U.S. Environmental Protection Agency (EPA) in 1986 and then subsequently by other regulatory agencies worldwide for cancer risk assessment. It ensured that cancer risks at low doses act in a linear fashion. The additive to background process assumes that the mechanism(s) resulting in induced (i.e., treatment related) and spontaneous (i.e., control group) cancers are identical. This assumption could not be properly evaluated due to inadequate mechanistic data when it was proposed in the 1970s. Using the findings of modern molecular toxicology, including oncogene activation/mutation, gene regulation, and molecular pathway analyses, the additive to background assumption was evaluated in the present paper. Based on published studies with 45 carcinogens over 13 diverse mammalian models and for a broad range of tumor types compelling evidence indicates that carcinogen-induced tumors are mediated in general via mechanisms that are not identical to those affecting the occurrence of the same type of spontaneous tumors in appropriate control groups. These findings, which challenge a fundamental assumption of the additive to background concept, have significant implications for cancer risk assessment policy, regulatory agency practices, as well as fundamental concepts of cancer biology.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Keywords:  Cancer risk assessment; Carcinogen mechanism; Linear non-threshold dose-response; Oncogenes; Threshold dose-response

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Year:  2018        PMID: 29890424     DOI: 10.1016/j.envres.2018.05.015

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   6.498


  1 in total

Review 1.  Implications of nonlinearity, confounding, and interactions for estimating exposure concentration-response functions in quantitative risk analysis.

Authors:  Louis Anthony Cox
Journal:  Environ Res       Date:  2020-05-19       Impact factor: 6.498

  1 in total

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