Historical perspective on the role of cell proliferation in carcinogenesis for DNA-reactive and non-DNA-reactive carcinogens: Arsenic as an example. (30th May 2021)

• Record Type: Journal Article
• Authors: Cohen, Joel M.; Beck, Barbara D.; Rhomberg, Lorenz R.
• Is Part Of: Toxicology. Volume 456(2021)
• Publication Date: 2021-05-30
• Subjects: Arsenic -- Cancer risk assessment -- Threshold -- Carcinogenesis -- Mode of action -- Low-dose linearity; Toxicology -- Periodicals; Chemicals -- Physiological effect -- Periodicals; 615.9005
• DOI: 10.1016/j.tox.2021.152783
• Languages: English
• ISSNs: 0300-483X

Abstract

Abstract: Our understanding of the etiology of cancer has developed significantly over the past fifty years, beginning with a single-hit linear no-threshold (LNT) conceptual model based on early studies conducted in Drosophila . Over the past several decades, multiple lines of evidence have accumulated to support a contemporary model of chemical carcinogenesis: a multi-hit model involving a prolonged stress environment that over time may drive the mutation of multiple cells into an injured state that ultimately could lead to uncontrolled proliferation via clonal expansion of mutation-carrying daughter cells. Arsenic carcinogenicity offers a useful case study for further exploration of advanced conceptual models for chemical carcinogenesis. A threshold for arsenic carcinogenicity is supported by its mode of action, characterized by repeating cycles of cytotoxicity and cellular regeneration. Furthermore, preliminary meta-analyses of epidemiology dose-response data for inorganic arsenic (iAs) and bladder cancer, correlated to dose-response data measured in vitro, support a threshold of effect in humans on the order of 50−100 μg/L in drinking water. In light of recent developments in our understanding of cancer etiology, we urge strong consideration of the existing mode-of-action evidence supporting a threshold of effect for arsenic carcinogenicity, as well as consideration of the potential methodological pitfalls in evaluating epidemiology dose-response data that could potentially bias in the direction of low-dose linearity.