The inhalation of nickel-containing dust has been associated with an increased risk of respiratory cancer in workplaces that process and refine sulfidic nickel mattes, where workers are exposed to mixtures of sulfidic, oxidic, water-soluble, and metallic forms of nickel. Because there is great complexity in the physical and chemical properties of nickel species, it is of interest which specific nickel forms are associated with carcinogenic risk. A bioavailability model for tumor induction by nickel has been proposed, based on the results of animal inhalation bioassays conducted on four nickel-containing substances. The nickel ion bioavailability model holds that a nickel-containing substance must release nickel ions that become bioavailable at the nucleus of epithelial respiratory cells for the substance to be carcinogenic, and that the carcinogenic potency of the substance is proportional to the degree to which the nickel ions are bioavailable at that site. This hypothesis updates the nickel ion theory, which holds that exposure to any nickel-containing substance leads to an increased cancer risk. The bioavailability of nickel ions from nickel-containing substances depends on their respiratory toxicity, clearance, intracellular uptake, and both extracellular and intracellular dissolution. Although some data gaps were identified, a weight-of-evidence evaluation indicates that the nickel ion bioavailability model may explain the existing animal and in vitro data better than the nickel ion theory. Epidemiological data are not sufficiently robust for determining which model is most appropriate, but are consistent with the nickel ion bioavailability model. Information on nickel bioavailability should be incorporated into future risk assessments.

Early in 2013, the World Health Organization (WHO) released a 2012 update to the 2002 State of the Science of Endocrine Disrupting Chemicals. Several significant concerns have been identified that raise questions about conclusions reached in this report regarding endocrine disruption. First, the report is not a state-of-the-science review and does not follow the 2002 WHO recommended weight-of-evidence approach. Second, endocrine disruption is often presumed to occur based on exposure or a potential mechanism despite a lack of evidence to show that chemicals are causally established as endocrine disruptors. Additionally, causation is often inferred by the presentation of a series of unrelated facts, which collectively do not demonstrate causation. Third, trends in disease incidence or prevalence are discussed without regard to known causes or risk factors; endocrine disruption is implicated as the reason for such trends in the absence of evidence. Fourth, dose and potency are ignored for most chemicals discussed. Finally, controversial topics (i.e., low dose effects, non-monotonic dose response) are presented in a one-sided manner and these topics are important to understanding endocrine disruption. Overall, the 2012 report does not provide a balanced perspective, nor does it accurately reflect the state of the science on endocrine disruption.

Background: Inducible cyclooxgenase-2 (COX-2) is commonly overexpressed in breast tumors and is a target for cancer therapy. Here, we studied the association of COX-2 with breast cancer survival and how this association is influenced by tumor estrogen and HER2 receptor status and Akt pathway activation.Methods: Tumor COX-2, HER2 and estrogen receptor α (ER) expression and phosphorylation of Akt, BAD, and caspase-9 were analyzed immunohistochemically in 248 cases of breast cancer. Spearman's correlation and multivariable logistic regression analyses were used to examine the relationship between COX-2 and tumor characteristics. Kaplan-Meier survival and multivariable Cox proportional hazards regression analyses were used to examine the relationship between COX-2 and disease-specific survival.Results: COX-2 was significantly associated with breast cancer outcome in ER-negative [Hazard ratio (HR) = 2.72; 95% confidence interval (CI), 1.36-5.41; comparing high versus low COX-2] and HER2 overexpressing breast cancer (HR = 2.84; 95% CI, 1.07-7.52). However, the hazard of poor survival associated with increased COX-2 was highest among patients who were both ER-negative and HER2-positive (HR = 5.95; 95% CI, 1.01-34.9). Notably, COX-2 expression in the ER-negative and HER2-positive tumors correlated significantly with increased phosphorylation of Akt and of the two Akt targets, BAD at Ser136 and caspase-9 at Ser196.Conclusions: Up-regulation of COX-2 in ER-negative and HER2-positive breast tumors is associated with Akt pathway activation and is a marker of poor outcome. The findings suggest that COX-2-specific inhibitors and inhibitors of the Akt pathway may act synergistically as anticancer drugs in the ER-negative and HER2-positive breast cancer subtype.

IARC is reassessing the human carcinogenicity of nickel compounds in 2009. To address the inconsistencies among results from studies of water-soluble nickel compounds, we conducted a weight-of-evidence analysis of the relevant epidemiological, toxicological, and carcinogenic mode-of-action data. We found the epidemiological evidence to be limited, in that some, but not all, data suggest that exposure to soluble nickel compounds leads to increased cancer risk in the presence of certain forms of insoluble nickel. Although there is no evidence that soluble nickel acts as a complete carcinogen in animals, there is limited evidence that suggests it may act as a tumor promoter. The mode-of-action data suggest that soluble nickel compounds will not be able to cause genotoxic effects in vivo because they cannot deliver sufficient nickel ions to nuclear sites of target cells. Although the mode-of-action data suggest several possible non-genotoxic effects of the nickel ion, it is unclear whether soluble nickel compounds can elicit these effects in vivo or whether these effects, if elicited, would result in tumor promotion. The mode-of-action data equally support soluble nickel as a promoter or as not being a causal factor in carcinogenesis at all. The weight of evidence does not indicate that soluble nickel compounds are complete carcinogens, and there is only limited evidence that they could act as tumor promoters.

Background: The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives. Results: We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306–315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs. Conclusion: PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.