Arsenic, Cancer, Risk assessment, Model, Multiple lines of evidence
A new article by Gradient scientists discusses some of the ways our understanding of cancer has changed over the past 50 years, with help from increasingly more complex models. This article uses arsenic to exemplify these developments.
Articleavailable for free until June 13, 2021. No registration required.
Article from Toxicology, Volume 456:
Historical Perspective on the Role of Cell Proliferation in Carcinogenesis for DNA-Reactive and Non-DNA-Reactive Carcinogens: Arsenic as an Example
Joel M. Cohen, Barbara D. Beck, Lorenz R. Rhomberg
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.