Cancer effects of formaldehyde: a proposal for an indoor air guideline value

Formaldehyde is a ubiquitous indoor air pollutant that is classified as “Carcinogenic to humans (Group 1)” (IARC, Formaldehyde, 2-butoxyethanol and 1-tert-butoxypropanol-2-ol. IARC monographs on the evaluation of carcinogenic risks to humans, vol 88. World Health Organization, Lyon, pp 39–325, 2006). For nasal cancer in rats, the exposure–response relationship is highly non-linear, supporting a no-observed-adverse-effect level (NOAEL) that allows setting a guideline value. Epidemiological studies reported no increased incidence of nasopharyngeal cancer in humans below a mean level of 1 ppm and peak levels below 4 ppm, consistent with results from rat studies. Rat studies indicate that cytotoxicity-induced cell proliferation (NOAEL at 1 ppm) is a key mechanism in development of nasal cancer. However, the linear unit risk approach that is based on conservative (“worst-case”) considerations is also used for risk characterization of formaldehyde exposures. Lymphohematopoietic malignancies are not observed consistently in animal studies and if caused by formaldehyde in humans, they are high-dose phenomenons with non-linear exposure–response relationships. Apparently, these diseases are not reported in epidemiological studies at peak exposures below 2 ppm and average exposures below 0.5 ppm. At the similar airborne exposure levels in rodents, the nasal cancer effect is much more prominent than lymphohematopoietic malignancies. Thus, prevention of nasal cancer is considered to prevent lymphohematopoietic malignancies. Departing from the rat studies, the guideline value of the WHO (Air quality guidelines for Europe, 2nd edn. World Health Organization, Regional Office for Europe, Copenhagen, pp 87–91, 2000), 0.08 ppm (0.1 mg m−3) formaldehyde, is considered preventive of carcinogenic effects in compliance with epidemiological findings

Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium.

BACKGROUND: Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions. METHODS: Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions. RESULTS: Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth. CONCLUSIONS: Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry.

BACKGROUND: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. RESULTS: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. CONCLUSIONS: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs

Anthropometric factors and thyroid cancer risk by histological subtype: pooled analysis of 22 prospective studies

Background: Greater height and body mass index (BMI) have been associated with an increased risk of thyroid cancer, particularly papillary carcinoma, the most common and least aggressive subtype. Few studies have evaluated these associations in relation to other, more aggressive histologic types or thyroid cancer-specific mortality. Methods: This large pooled analysis of 22 prospective studies (833,176 men and 1,260,871 women) investigated thyroid cancer incidence associated with greater height, BMI at baseline and young adulthood, and adulthood BMI gain (difference between young-adult and baseline BMI), overall and separately by sex and histological subtype using multivariable Cox proportional hazards regression models. Associations with thyroid cancer mortality were investigated in a subset of cohorts (578,922 men and 774,373 women) that contributed cause of death information. Results: During follow-up, 2996 incident thyroid cancers and 104 thyroid cancer deaths were identified. All anthropometric factors were positively associated with thyroid cancer incidence: hazard ratios (HR) [confidence intervals (CIs)] for height (per 5 cm) = 1.07 [1.04–1.10], BMI (per 5 kg/m2) = 1.06 [1.02–1.10], waist circumference (per 5 cm) = 1.03 [1.01–1.05], young-adult BMI (per 5 kg/m2) = 1.13 [1.02–1.25], and adulthood BMI gain (per 5 kg/m2) = 1.07 [1.00–1.15]. Associations for baseline BMI and waist circumference were attenuated after mutual adjustment. Baseline BMI was more strongly associated with risk in men compared with women (p = 0.04). Positive associations were observed for papillary, follicular, and anaplastic, but not medullary, thyroid carcinomas. Similar, but stronger, associations were observed for thyroid cancer mortality. Conclusion: The results suggest that greater height and excess adiposity throughout adulthood are associated with higher incidence of most major types of thyroid cancer, including the least common but most aggressive form, anaplastic carcinoma, and higher thyroid cancer mortality. Potential underlying biological mechanisms should be explored in future studies