Is breast cancer a result of epigenetic responses to traffic-related air pollution? A review of the latest evidence.

Environmental toxicants can exert adverse health effects via epigenetic regulation. We conducted a review of studies assessing traffic-related air pollution (TRAP) exposure and breast cancer (BC) risk, and the evidence for epigenetic mediation. 14 epidemiological studies demonstrated associations between TRAP exposure and BC risk, in which a total of 26 comparisons were assessed. 11 of these comparisons reported a positive association; whereas 15 comparisons were negative. Five publications linked TRAP exposure to epigenetic alterations in genes that may be related to BC risk. One animal study provided evidence of TRAP-treatment inducing breast tumorigenesis. Associations between TRAP components polycyclic aromatic hydrocarbons (PAH) and nitrogen dioxide (NO2) and BC risk were more consistent. While evidence for epigenetic regulation remains limited, polycyclic aromatic hydrocarbons (PAH) and nitrogen dioxide (NO2) exposures may alter methylation of breast tumorigenic genes (e.g., EPHB2, LONP1). Future epigenomic studies with environmental measures are needed to interrogate the relationship between TRAP and BC risk.

Rare mutations in RINT1 predispose carriers to breast and Lynch syndrome-spectrum cancers.

UNLABELLED: Approximately half of the familial aggregation of breast cancer remains unexplained. A multiple-case breast cancer family exome-sequencing study identified three likely pathogenic mutations in RINT1 (NM_021930.4) not present in public sequencing databases: RINT1 c.343C>T (p.Q115X), c.1132_1134del (p.M378del), and c.1207G>T (p.D403Y). On the basis of this finding, a population-based case-control mutation-screening study was conducted that identified 29 carriers of rare (minor allele frequency < 0.5%), likely pathogenic variants: 23 in 1,313 early-onset breast cancer cases and six in 1,123 frequency-matched controls [OR, 3.24; 95% confidence interval (CI), 1.29-8.17; P = 0.013]. RINT1 mutation screening of probands from 798 multiple-case breast cancer families identified four additional carriers of rare genetic variants. Analysis of the incidence of first primary cancers in families of women carrying RINT1 mutations estimated that carriers were at increased risk of Lynch syndrome-spectrum cancers [standardized incidence ratio (SIR), 3.35; 95% CI, 1.7-6.0; P = 0.005], particularly for relatives diagnosed with cancer under the age of 60 years (SIR, 10.9; 95% CI, 4.7-21; P = 0.0003). SIGNIFICANCE: The work described in this study adds RINT1 to the growing list of genes in which rare sequence variants are associated with intermediate levels of breast cancer risk. Given that RINT1 is also associated with a spectrum of cancers with mismatch repair defects, these findings have clinical applications and raise interesting biological questions.

DNA glycosylases involved in base excision repair may be associated with cancer risk in BRCA1 and BRCA2 mutation carriers.

Single Nucleotide Polymorphisms (SNPs) in genes involved in the DNA Base Excision Repair (BER) pathway could be associated with cancer risk in carriers of mutations in the high-penetrance susceptibility genes BRCA1 and BRCA2, given the relation of synthetic lethality that exists between one of the components of the BER pathway, PARP1 (poly ADP ribose polymerase), and both BRCA1 and BRCA2. In the present study, we have performed a comprehensive analysis of 18 genes involved in BER using a tagging SNP approach in a large series of BRCA1 and BRCA2 mutation carriers. 144 SNPs were analyzed in a two stage study involving 23,463 carriers from the CIMBA consortium (the Consortium of Investigators of Modifiers of BRCA1 and BRCA2). Eleven SNPs showed evidence of association with breast and/or ovarian cancer at p<0.05 in the combined analysis. Four of the five genes for which strongest evidence of association was observed were DNA glycosylases. The strongest evidence was for rs1466785 in the NEIL2 (endonuclease VIII-like 2) gene (HR: 1.09, 95% CI (1.03-1.16), p = 2.7 × 10(-3)) for association with breast cancer risk in BRCA2 mutation carriers, and rs2304277 in the OGG1 (8-guanine DNA glycosylase) gene, with ovarian cancer risk in BRCA1 mutation carriers (HR: 1.12 95%CI: 1.03-1.21, p = 4.8 × 10(-3)). DNA glycosylases involved in the first steps of the BER pathway may be associated with cancer risk in BRCA1/2 mutation carriers and should be more comprehensively studied.

Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.

INTRODUCTION: Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. METHODS: We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). RESULTS: The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X4(2) = 7.2, P value 0.13) than BCRAT (HL X4(2) = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). CONCLUSIONS: Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.

The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects.

BACKGROUND: In molecular epidemiology studies biospecimen data are collected, often with the purpose of evaluating the synergistic role between a biomarker and another feature on an outcome. Typically, biomarker data are collected on only a proportion of subjects eligible for study, leading to a missing data problem. Missing data methods, however, are not customarily incorporated into analyses. Instead, complete-case (CC) analyses are performed, which can result in biased and inefficient estimates. METHODS: Through simulations, we characterized the performance of CC methods when interaction effects are estimated. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and auxiliary information may or may not exist. RESULTS: CC analyses were shown to result in considerable bias and efficiency loss. While MI reduced bias and increased efficiency over CC methods under specific conditions, it too resulted in biased estimates depending on the strength of the auxiliary data available and the nature of the missingness. In particular, CC performed better than MI when extreme values of the covariate were more likely to be missing, while MI outperformed CC when missingness of the covariate related to both the covariate and outcome. MI always improved performance when strong auxiliary data were available. In a real study, MI estimates of interaction effects were attenuated relative to those from a CC approach. CONCLUSIONS: Our findings suggest the importance of incorporating missing data methods into the analysis. If the data are MAR, standard MI is a reasonable method. Auxiliary variables may make this assumption more reasonable even if the data are NMAR. Under NMAR we emphasize caution when using standard MI and recommend it over CC only when strong auxiliary data are available. MI, with the missing data mechanism specified, is an alternative when the data are NMAR. In all cases, it is recommended to take advantage of MI's ability to account for the uncertainty of these assumptions.

The Jerusalem Perinatal Study cohort, 1964-2005: methods and a review of the main results.

The Jerusalem Perinatal Study recorded information on population-based cohorts of 92 408 live- and stillbirths in 1964-76, and their parents, with active surveillance of infant deaths and birth defects. Data on maternal conditions, obstetric complications and interventions during labour and delivery were recorded for 92% of the births. Subsets were surveyed with antenatal interviews in 1965-68 (n = 11 467), paediatric admissions to hospital (n = 17 782) and postpartum interviews in 1975-76 (n = 16 912). Data from some offspring were linked to records of a health examination at age 17. The offspring, mothers and fathers have been traced recently, their vital status assessed, and the data linked to Israel's Cancer Registry and Psychiatric Registry. This paper describes the different types of data available, their sources, and some potential biases. Characteristics of this unique population are shown. Findings from the study are reviewed and a list of references is provided. The cohorts provide a unique source of data for a wide variety of studies.

Prostate cancer in fathers with fewer male offspring: the Jerusalem Perinatal Study cohort.

Recent studies have suggested the involvement of loci on the Y chromosome in prostate cancer. We studied the relative risk (RR) of prostate cancer in relation to sex ratio of offspring in a cohort of 38,934 Israeli men who were followed from the birth of their offspring (in 1964 through 1976) until 2005. Cox models were used to adjust for changes in incidence over time, age, the man's year of birth, and social and ethnic variables. A total of 712 men were diagnosed with prostate cancer. Compared with men who had at least one son, men with only daughters had an increased risk of prostate cancer (adjusted RR = 1.40, 95% confidence interval [CI] = 1.20 to 1.64, P<.0001). In men with one, two, or three or more offspring, the relative risks associated with absence of sons were 1.25 (95% CI = 1.00 to 1.56), 1.41 (95% CI = 1.04 to 1.91), and 1.60 (95% CI = 1.05 to 2.43), respectively. Men with no daughters showed no statistically significantly altered risk, compared with men who had offspring of both sexes. The relative risk of prostate cancer decreased as the number of sons increased (P(trend)<.0001) but did not change with the number of daughters. These findings suggest that a Y chromosome locus may be involved in prostate cancer risk in this population.

Estrogen metabolism and breast cancer.

BACKGROUND: Specific pathways involved in estrogen metabolism may play a role in the etiology of breast cancer. We used data from a large population-based case-control study to assess the association of the urinary estrogen metabolites 2-hydroxyestrone (2-OHE1), 16alpha-hydroxyestrone (16-OHE1), and their ratio (2/16) with both invasive and in situ breast cancer. METHODS: Study participants from the Long Island Breast Cancer Study Project provided a spot urine specimen and completed a comprehensive interviewer-administered questionnaire. Women who used exogenous hormones or who took tamoxifen in the 6 months before urine collection were excluded from the analysis, leaving 269 invasive cases, 158 in situ cases, and 326 controls. Unconditional logistic regression was used to obtain adjusted odds ratios (ORs) for invasive and in situ breast cancer, separately, in relation to tertiles of the individual metabolites (standardized for creatinine) and the 2/16 ratio, stratified by menopausal status. RESULTS: The OR for invasive breast cancer was inversely associated with the 2/16 ratio among premenopausal women (OR = 0.50 for extreme tertiles; 95% confidence interval = 0.25-1.01). ORs ranged from 0.32 to 0.60 when women were stratified by whether cases had received chemotherapy within 6 months before urine collection and by estrogen receptor status. In postmenopausal women, there was a slight reduction in the odds ratio for invasive cancer with high levels of the 2/16 ratio (OR = 0.78; 95% confidence interval = 0.46-1.33). Neither the individual metabolites nor the ratio were associated with in situ breast cancer. CONCLUSION: These data provide support for the hypothesis that the 2/16 ratio is associated with reduced breast cancer risk. The most consistent associations were observed with invasive cancer in premenopausal women.

Frequent promoter methylation of CDH1, DAPK, RARB, and HIC1 genes in carcinoma of cervix uteri: its relationship to clinical outcome.

BACKGROUND: Cervical cancer (CC), a leading cause of cancer-related deaths in women worldwide, has been causally linked to genital human papillomavirus (HPV) infection. Although a host of genetic alterations have been identified, molecular basis of CC development is still poorly understood. RESULTS: We examined the role of promoter hypermethylation, an epigenetic alteration that is associated with the silencing tumor suppressor genes in human cancer, by studying 16 gene promoters in 90 CC cases. We found a high frequency of promoter methylation in CDH1, DAPK, RARB, and HIC1 genes. Correlation of promoter methylation with clinical characteristics and other genetic changes revealed the following: a) overall promoter methylation was higher in more advanced stage of the disease, b) promoter methylation of RARB and BRCA1 predicted worse prognosis, and c) the HIC1 promoter methylation was frequently seen in association with microsatellite instability. Promoter methylation was associated with gene silencing in CC cell lines. Treatment with methylation or histone deacetylation-inhibiting agents resulted in profound reactivation of gene expression. CONCLUSIONS: These results may have implications in understanding the underlying epigenetic mechanisms in CC development, provide prognostic indicators, and identify important gene targets for treatment.