Identification of NHLRC1 as a Novel AKT Activator from a Lung Cancer Epigenome-Wide Association Study (EWAS).

Changes in DNA methylation identified by epigenome-wide association studies (EWAS) have been recently linked to increased lung cancer risk. However, the cellular effects of these differentially methylated positions (DMPs) are often unclear. Therefore, we investigated top differentially methylated positions identified from an EWAS study. This included a putative regulatory region of NHLRC1. Hypomethylation of this gene was recently linked with decreased survival rates in lung cancer patients. HumanMethylation450 BeadChip array (450K) analysis was performed on 66 lung cancer case-control pairs from the European Prospective Investigation into Cancer and Nutrition Heidelberg lung cancer EWAS (EPIC HD) cohort. DMPs identified in these pre-diagnostic blood samples were then investigated for differential DNA methylation in lung tumor versus adjacent normal lung tissue from The Cancer Genome Atlas (TCGA) and replicated in two independent lung tumor versus adjacent normal tissue replication sets with MassARRAY. The EPIC HD top hypermethylated DMP cg06646708 was found to be a hypomethylated region in multiple data sets of lung tumor versus adjacent normal tissue. Hypomethylation within this region caused increased mRNA transcription of the closest gene NHLRC1 in lung tumors. In functional assays, we demonstrate attenuated proliferation, viability, migration, and invasion upon NHLRC1 knock-down in lung cancer cells. Furthermore, diminished AKT phosphorylation at serine 473 causing expression of pro-apoptotic AKT-repressed genes was detected in these knock-down experiments. In conclusion, this study demonstrates the powerful potential for discovery of novel functional mechanisms in oncogenesis based on EWAS DNA methylation data. NHLRC1 holds promise as a new prognostic biomarker for lung cancer survival and prognosis, as well as a target for novel treatment strategies in lung cancer patients.

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.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10-6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

Mitochondrial DNA copy number variation, leukocyte telomere length, and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

BACKGROUND: Leukocyte telomere length (LTL) and mitochondrial genome (mtDNA) copy number and deletions have been proposed as risk markers for various cancer types, including breast cancer (BC). METHODS: To gain a more comprehensive picture on how these markers can modulate BC risk, alone or in conjunction, we performed simultaneous measurements of LTL and mtDNA copy number in up to 570 BC cases and 538 controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. As a first step, we measured LTL and mtDNA copy number in 96 individuals for which a blood sample had been collected twice with an interval of 15 years. RESULTS: According to the intraclass correlation (ICC), we found very good stability over the time period for both measurements, with ICCs of 0.63 for LTL and 0.60 for mtDNA copy number. In the analysis of the entire study sample, we observed that longer LTL was strongly associated with increased risk of BC (OR 2.71, 95% CI 1.58-4.65, p = 3.07 × 10- 4 for highest vs. lowest quartile; OR 3.20, 95% CI 1.57-6.55, p = 1.41 × 10- 3 as a continuous variable). We did not find any association between mtDNA copy number and BC risk; however, when considering only the functional copies, we observed an increased risk of developing estrogen receptor-positive BC (OR 2.47, 95% CI 1.05-5.80, p = 0.04 for highest vs. lowest quartile). CONCLUSIONS: We observed a very good correlation between the markers over a period of 15 years. We confirm a role of LTL in BC carcinogenesis and suggest an effect of mtDNA copy number on BC risk.

Identifying and correcting epigenetics measurements for systematic sources of variation.

Background: Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results: A sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions: The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.

Tumor-associated autoantibodies as early detection markers for ovarian cancer? A prospective evaluation.

Immuno-proteomic screening has identified several tumor-associated autoantibodies (AAb) that may have diagnostic capacity for invasive epithelial ovarian cancer, with AAbs to P53 proteins and cancer-testis antigens (CTAGs) as prominent examples. However, the early detection potential of these AAbs has been insufficiently explored in prospective studies. We performed ELISA measurements of AAbs to CTAG1A, CTAG2, P53 and NUDT11 proteins, for 194 patients with ovarian cancer and 705 matched controls from the European EPIC cohort, using serum samples collected up to 36 months prior to diagnosis under usual care. CA125 was measured using electrochemo-luminiscence. Diagnostic discrimination statistics were calculated by strata of lead-time between blood collection and diagnosis. With lead times ≤6 months, ovarian cancer detection sensitivity at 0.98 specificity (SE98) varied from 0.19 [95% CI 0.08-0.40] for CTAG1A, CTAG2 and NUDT1 to 0.23 [0.10-0.44] for P53 (0.33 [0.11-0.68] for high-grade serous tumors). However, at longer lead-times, the ability of these AAb markers to distinguish future ovarian cancer cases from controls declined rapidly; at lead times >1 year, SE98 estimates were close to zero (all invasive cases, range: 0.01-0.11). Compared to CA125 alone, combined logistic regression scores of AAbs and CA125 did not improve detection sensitivity at equal level of specificity. The added value of these selected AAbs as markers for ovarian cancer beyond CA125 for early detection is therefore limited.

A comprehensive analysis of polymorphic variants in steroid hormone and insulin-like growth factor-1 metabolism and risk of in situ breast cancer: Results from the Breast and Prostate Cancer Cohort Consortium.

We assessed the association between 1,414 single nucleotide polymorphisms (SNPs) in genes involved in synthesis and metabolism of steroid hormones and insulin-like growth factor 1, and risk of breast cancer in situ (BCIS), with the aim of determining whether any of these were disease specific. This was carried out using 1,062 BCIS cases and 10,126 controls as well as 6,113 invasive breast cancer cases from the Breast and Prostate Cancer Cohort Consortium (BPC3). Three SNPs showed at least one nominally significant association in homozygous minor versus homozygous major models. ACVR2A-rs2382112 (ORhom = 3.05, 95%CI = 1.72-5.44, Phom = 1.47 × 10-4 ), MAST2-rs12124649 (ORhom = 1.73, 95% CI =1.18-2.54, Phom = 5.24 × 10-3 ), and INSR-rs10500204 (ORhom = 1.96, 95% CI = 1.44-2.67, Phom =1.68 × 10-5 ) were associated with increased risk of BCIS; however, only the latter association was significant after correcting for multiple testing. Furthermore, INSR-rs10500204 was more strongly associated with the risk of BCIS than invasive disease in case-only analyses using the homozygous minor versus homozygous major model (ORhom = 1.78, 95% CI = 1.30-2.44, Phom = 3.23 × 10-4 ). The SNP INSR-rs10500204 is located in an intron of the INSR gene and is likely to affect binding of the promyelocytic leukemia (PML) protein. The PML gene is known as a tumor suppressor and growth regulator in cancer. However, it is not clear on what pathway the A-allele of rs10500204 could operate to influence the binding of the protein. Hence, functional studies are warranted to investigate this further.

Body mass index and breast cancer survival: a Mendelian randomization analysis.

Background: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer. Methods: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis. Results: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01-1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89-1.13, P = 0.95). Conclusions: Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.

Association analysis identifies 65 new breast cancer risk loci.

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

Gene-environment interactions involving functional variants: Results from the Breast Cancer Association Consortium.

Investigating the most likely causal variants identified by fine-mapping analyses may improve the power to detect gene-environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine-scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR-rs7558475 and current smoking (ORint = 0.77, 95% CI: 0.67-0.88, pint = 1.8 × 10-4 ). The interaction with the strongest statistical evidence was found between 5q14-rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16-1.59, pint = 1.9 × 10-5 ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10-4 ) and between 8q23-rs13267382 and age at first full-term pregnancy (ORint = 0.89, 95% CI: 0.83-0.95, pint = 5.2 × 10-4 ). While these results do not suggest any strong gene-environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.

Genetic variation in the ADIPOQ gene, adiponectin concentrations and risk of colorectal cancer: a Mendelian Randomization analysis using data from three large cohort studies.

Higher levels of circulating adiponectin have been related to lower risk of colorectal cancer in several prospective cohort studies, but it remains unclear whether this association may be causal. We aimed to improve causal inference in a Mendelian Randomization meta-analysis using nested case-control studies of the European Prospective Investigation into Cancer and Nutrition (EPIC, 623 cases, 623 matched controls), the Health Professionals Follow-up Study (HPFS, 231 cases, 230 controls) and the Nurses' Health Study (NHS, 399 cases, 774 controls) with available data on pre-diagnostic adiponectin concentrations and selected single nucleotide polymorphisms in the ADIPOQ gene. We created an ADIPOQ allele score that explained approximately 3% of the interindividual variation in adiponectin concentrations. The ADIPOQ allele score was not associated with risk of colorectal cancer in logistic regression analyses (pooled OR per score-unit unit 0.97, 95% CI 0.91, 1.04). Genetically determined twofold higher adiponectin was not significantly associated with risk of colorectal cancer using the ADIPOQ allele score as instrumental variable (pooled OR 0.73, 95% CI 0.40, 1.34). In a summary instrumental variable analysis (based on previously published data) with higher statistical power, no association between genetically determined twofold higher adiponectin and risk of colorectal cancer was observed (0.99, 95% CI 0.93, 1.06 in women and 0.94, 95% CI 0.88, 1.01 in men). Thus, our study does not support a causal effect of circulating adiponectin on colorectal cancer risk. Due to the limited genetic determination of adiponectin, larger Mendelian Randomization studies are necessary to clarify whether adiponectin is causally related to lower risk of colorectal cancer.

DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility.

AIM OF THE STUDY: A vast majority of human malignancies are associated with ageing, and age is a strong predictor of cancer risk. Recently, DNA methylation-based marker of ageing, known as 'epigenetic clock', has been linked with cancer risk factors. This study aimed to evaluate whether the epigenetic clock is associated with breast cancer risk susceptibility and to identify potential epigenetics-based biomarkers for risk stratification. METHODS: Here, we profiled DNA methylation changes in a nested case-control study embedded in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (n = 960) using the Illumina HumanMethylation 450K BeadChip arrays and used the Horvath age estimation method to calculate epigenetic age for these samples. Intrinsic epigenetic age acceleration (IEAA) was estimated as the residuals by regressing epigenetic age on chronological age. RESULTS: We observed an association between IEAA and breast cancer risk (OR, 1.04; 95% CI, 1.007-1.076, P = 0.016). One unit increase in IEAA was associated with a 4% increased odds of developing breast cancer (OR, 1.04; 95% CI, 1.007-1.076). Stratified analysis based on menopausal status revealed that IEAA was associated with development of postmenopausal breast cancers (OR, 1.07; 95% CI, 1.020-1.11, P = 0.003). In addition, methylome-wide analyses revealed that a higher mean DNA methylation at cytosine-phosphate-guanine (CpG) islands was associated with increased risk of breast cancer development (OR per 1 SD = 1.20; 95 %CI: 1.03-1.40, P = 0.02) whereas mean methylation levels at non-island CpGs were indistinguishable between cancer cases and controls. CONCLUSION: Epigenetic age acceleration and CpG island methylation have a weak, but statistically significant, association with breast cancer susceptibility.

DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled = 4 × 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled = 2 × 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled = 7 × 10-16 and 1 × 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled = 2 × 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled = 5 × 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity ≤ 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

Epigenetic Signatures of Cigarette Smoking.

BACKGROUND: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. METHODS AND RESULTS: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10-7 (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10-7 (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. CONCLUSIONS: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.

IMPORTANCE: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. OBJECTIVE: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. DESIGN, SETTING, AND PARTICIPANTS: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality. EXPOSURES: Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors. MAIN OUTCOMES AND MEASURES: Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking). RESULTS: The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population. CONCLUSIONS AND RELEVANCE: This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

Tobacco smoking-associated genome-wide DNA methylation changes in the EPIC study.

AIM: Epigenetic changes may occur in response to environmental stressors, and an altered epigenome pattern may represent a stable signature of environmental exposure. MATERIALS & METHODS: Here, we examined the potential of DNA methylation changes in 910 prediagnostic peripheral blood samples as a marker of exposure to tobacco smoke in a large multinational cohort. RESULTS: We identified 748 CpG sites that were differentially methylated between smokers and nonsmokers, among which we identified novel regionally clustered CpGs associated with active smoking. Importantly, we found a marked reversibility of methylation changes after smoking cessation, although specific genes remained differentially methylated up to 22 years after cessation. CONCLUSION: Our study has comprehensively cataloged the smoking-associated DNA methylation alterations and showed that these alterations are reversible after smoking cessation.

Interactions between breast cancer susceptibility loci and menopausal hormone therapy in relationship to breast cancer in the Breast and Prostate Cancer Cohort Consortium.

Current use of menopausal hormone therapy (MHT) has important implications for postmenopausal breast cancer risk, and observed associations might be modified by known breast cancer susceptibility loci. To provide the most comprehensive assessment of interactions of prospectively collected data on MHT and 17 confirmed susceptibility loci with invasive breast cancer risk, a nested case-control design among eight cohorts within the NCI Breast and Prostate Cancer Cohort Consortium was used. Based on data from 13,304 cases and 15,622 controls, multivariable-adjusted logistic regression analyses were used to estimate odds ratios (OR) and 95 % confidence intervals (CI). Effect modification of current and past use was evaluated on the multiplicative scale. P values <1.5 × 10(-3) were considered statistically significant. The strongest evidence of effect modification was observed for current MHT by 9q31-rs865686. Compared to never users of MHT with the rs865686 GG genotype, the association between current MHT use and breast cancer risk for the TT genotype (OR 1.79, 95 % CI 1.43-2.24; P interaction = 1.2 × 10(-4)) was less than expected on the multiplicative scale. There are no biological implications of the sub-multiplicative interaction between MHT and rs865686. Menopausal hormone therapy is unlikely to have a strong interaction with the common genetic variants associated with invasive breast cancer.

Weight cycling and the risk of type 2 diabetes in the EPIC-Germany cohort.

AIMS/HYPOTHESIS: Studies on weight cycling and the risk of type 2 diabetes have revealed inconsistent results, possibly due to differences in the definition of weight fluctuations. Here, we investigated whether weight cycling during adulthood is related to diabetes risk in a large cohort study, using a complementary approach to define patterns of weight development. METHODS: Weight cycling, weight loss and weight gain were defined (1) a priori, using distinct categories, and (2) by functional principal component analysis (FPCA) to capture weight patterns in greater detail. Associations of weight cycling, weight loss and weight gain with the risk of type 2 diabetes were evaluated by Cox regression models. RESULTS: A priori defined weight cycling was associated with increased diabetes risk, compared with stable weight (HR 1.36 [95% CI 1.09, 1.68]). No significant association between FPCA-derived weight cycling and risk of diabetes was observed after adjustment for concurrent weight patterns (HR 1.19 [95% CI 0.89, 1.60]). Subgroup analyses showed that FPCA-derived weight cycling during net weight gain was associated with a higher risk of diabetes (HR 1.68 [95% CI 1.14, 2.48]). A priori defined weight gain (HR 2.08 [95% CI 1.60, 2.70]) was more clearly related to the risk of diabetes than FPCA-derived weight gain (HR 1.20 [95% CI 0.95, 1.51]), while no significant associations were observed for weight loss. CONCLUSIONS/INTERPRETATION: Overall, weight cycling may not be an independent risk factor for type 2 diabetes when accounting for concurrent patterns of weight development. However, weight cycling may pose a stronger risk of diabetes than non-cycling during net weight gain.

Genetic risk variants associated with in situ breast cancer.

INTRODUCTION: Breast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS. METHODS: To evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile. RESULTS: We found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11-1.38, P = 1.27 x 10(-4)) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99-1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09-1.42, P = 1.07 x 10(-3)). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies. CONCLUSIONS: Our study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.