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Background: Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. Methods: We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. Results: The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18–1.57), 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram. Conclusion: Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines often use predicted lifetime risk from birth to define criteria for making decisions regarding breast cancer screening rather than thresholds based on absolute 5-year risk from current age.Methods: We used the Prospective Family Cohort Study of 14 657 women without breast cancer at baseline in which, during a median follow-up of 10 years, 482 women were diagnosed with invasive breast cancer. We examined the performances of the International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk models when using the alternative thresholds by comparing predictions based on 5-year risk with those based on lifetime risk from birth and remaining lifetime risk. All statistical tests were 2-sided.Results: Using IBIS, the areas under the receiver-operating characteristic curves were 0.66 (95% confidence interval = 0.63 to 0.68) and 0.56 (95% confidence interval = 0.54 to 0.59) for 5-year and lifetime risks, respectively (Pdiff < .001). For equivalent sensitivities, the 5-year incidence almost always had higher specificities than lifetime risk from birth. For women aged 20-39 years, 5-year risk performed better than lifetime risk from birth. For women aged 40 years or older, receiver-operating characteristic curves were similar for 5-year and lifetime IBIS risk from birth. Classifications based on remaining lifetime risk were inferior to 5-year risk estimates. Results were similar using BOADICEA.Conclusions: Our analysis shows that risk stratification using clinical models will likely be more accurate when based on predicted 5-year risk compared with risks based on predicted lifetime and remaining lifetime, particularly for women aged 20-39 years. [ABSTRACT FROM AUTHOR]

Background: Parity is associated with decreased risk of invasive ovarian cancer; however, the relationship between incomplete pregnancies and invasive ovarian cancer risk is unclear. This relationship was examined using 15 case-control studies from the Ovarian Cancer Association Consortium (OCAC). Histotype-specific associations, which have not been examined previously with large sample sizes, were also evaluated.Methods: A pooled analysis of 10 470 invasive epithelial ovarian cancer cases and 16 942 controls was conducted. Odds ratios (ORs) and 95% confidence intervals (CIs) for the association between incomplete pregnancies and invasive epithelial ovarian cancer were estimated using logistic regression. All models were conditioned on OCAC study, race and ethnicity, age, and education level and adjusted for number of complete pregnancies, oral contraceptive use, and history of breastfeeding. The same approach was used for histotype-specific analyses.Results: Ever having an incomplete pregnancy was associated with a 16% reduction in ovarian cancer risk (OR = 0.84, 95% CI = 0.79 to 0.89). There was a trend of decreasing risk with increasing number of incomplete pregnancies (2-sided Ptrend < .001). An inverse association was observed for all major histotypes; it was strongest for clear cell ovarian cancer.Conclusions: Incomplete pregnancies are associated with a reduced risk of invasive epithelial ovarian cancer. Pregnancy, including incomplete pregnancy, was associated with a greater reduction in risk of clear cell ovarian cancer, but the result was broadly consistent across histotypes. Future work should focus on understanding the mechanisms underlying this reduced risk. [ABSTRACT FROM AUTHOR]

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10−8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk. Mammographic density represents one the strongest predictors of breast cancer risk. Here the authors perform genome-wide association study meta-analysis of women screened with full-field digital mammography and identify 31 previously unreported loci associated with mammographic density phenotypes. [ABSTRACT FROM AUTHOR]

The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%-56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models. [ABSTRACT FROM AUTHOR]

Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility. The authors perform a meta-analysis of cutaneous squamous cell carcinoma, identifying causal variants within skin-specific regulatory elements. [ABSTRACT FROM AUTHOR]

Previous studies using different exposure methods to assess air pollution and breast cancer risk among primarily whites have been inconclusive. Air pollutant exposures of particulate matter and oxides of nitrogen were estimated by kriging (NOx, NO2, PM10, PM2.5), land use regression (LUR, NOx, NO2) and California Line Source Dispersion model (CALINE4, NOx, PM2.5) for 57,589 females from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993–1996) through 2010. Cox proportional hazards models were used to examine the associations between time‐varying air pollution and breast cancer incidence adjusting for confounding factors. Stratified analyses were conducted by race/ethnicity and distance to major roads. Among all women, breast cancer risk was positively but not significantly associated with NOx (per 50 parts per billion [ppb]) and NO2 (per 20 ppb) determined by kriging and LUR and with PM2.5 and PM10 (per 10 μg/m3) determined by kriging. However, among women who lived within 500 m of major roads, significantly increased risks were observed with NOx (hazard ratio [HR] = 1.35, 95% confidence interval [95% CI]: 1.02–1.79), NO2 (HR = 1.44, 95% CI: 1.04–1.99), PM10 (HR = 1.29, 95% CI: 1.07–1.55) and PM2.5 (HR = 1.85, 95% CI: 1.15–2.99) determined by kriging and NOx (HR = 1.21, 95% CI:1.01–1.45) and NO2 (HR = 1.26, 95% CI: 1.00–1.59) determined by LUR. No overall associations were observed with exposures assessed by CALINE4. Subgroup analyses suggested stronger associations of NOx and NO2 among African Americans and Japanese Americans. Further studies of multiethnic populations to confirm the effects of air pollution, particularly near‐roadway exposures, on the risk of breast cancer is warranted. What's new? Prior studies of air pollution and breast cancer generally have employed one method of exposure assessment and have focused on white women, with inconsistent results. Here, three exposure assessment methods, kriging interpolation, land use regression (LUR), and California Line Source Dispersion model (CALINE4), were used to investigate associations between long‐term air pollutant exposure and breast cancer risk in the Southern California Multiethnic Cohort. Breast cancer risk was increased in association with air pollution exposure among women residing near major roads according to kriging and LUR measures. [ABSTRACT FROM AUTHOR]

Breast density is a modifiable factor that is strongly associated with breast cancer risk. We sought to understand the influence of newer technologies of full-field digital mammography (FFDM) on breast density research and to determine whether results are comparable across studies using FFDM and previous studies using traditional film-screen mammography. We studied 24,840 screening-age (40–74 years) non-Hispanic white women who were participants in the Research Program on Genes, Environment and Health of Kaiser Permanente Northern California and underwent screening mammography with either Hologic (Hologic, Inc. Marlborough, Massachusetts) or General Electric (General Electric Company, Boston, Massachusetts) FFDM machines between 2003 and 2013. We estimated the associations of parity, age at first birth, age at menarche, and menopausal status with percent density and dense area as measured by a single radiological technologist using Cumulus software (Canto Software, Inc. San Francisco, California). We found that associations between reproductive factors and mammographic density measured using processed FFDM images were generally similar in magnitude and direction to those from prior studies using film mammography. Estimated associations for both types of FFDM machines were in the same direction. There was some evidence of heterogeneity in the magnitude of the effect sizes by machine type, which we accounted for using random-effects meta-analysis when combining results. Our findings demonstrate the robustness of quantitative mammographic density measurements across FFDM and film mammography platforms. [ABSTRACT FROM AUTHOR]

Genome-wide sequencing enables evaluation of associations between traits and combinations of variants in genes and pathways. But such evaluation requires multi-locus association tests with good power, regardless of the variant and trait characteristics. And since analyzing families may yield more power than analyzing unrelated individuals, we need multi-locus tests applicable to both related and unrelated individuals. Here we describe such tests, and we introduce SKAT-X, a new test statistic that uses genome-wide data obtained from related or unrelated subjects to optimize power for the specific data at hand. Simulations show that: a) SKAT-X performs well regardless of variant and trait characteristics; and b) for binary traits, analyzing affected relatives brings more power than analyzing unrelated individuals, consistent with previous findings for single-locus tests. We illustrate the methods by application to rare unclassified missense variants in the tumor suppressor gene BRCA2, as applied to combined data from prostate cancer families and unrelated prostate cancer cases and controls in the Multi-ethnic Cohort (MEC). The methods can be implemented using open-source code for public use as the R-package GATARS (Genetic Association Tests for Arbitrarily Related Subjects) . [ABSTRACT FROM AUTHOR]

As a follow‐up to genome‐wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well‐known ovarian cancer risk factors and their interactions with 28 genome‐wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case–control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10−4). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46–0.60) compared to 0.71 (95%CI = 0.66–0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1–5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene–environment analysis in ovarian cancer. What's new? Genetic and environmental risk factors for ovarian cancer have been identified separately but interactions between both remain largely unexplored. The authors identified a new gene x environment interaction between oral contraceptive pill (OCP) use and a single nucleotide polymorphism in the PVT1 gene, a long‐noncoding RNA located on chromosome 8. The data suggest that the protective benefit of OCP use may be strongest in women with the T allele of PVT1 underscoring the need to tailor prevention strategies to individual genotypic profiles. [ABSTRACT FROM AUTHOR]

Purpose: Racial/ethnic minorities are often assumed to be less willing to participate in and provide biospecimens for biomedical research. We examined racial/ethnic differences in enrollment of women with breast cancer (probands) and their first-degree relatives in the Northern California site of the Breast Cancer Family Registry from 1996 to 2011.Methods: We evaluated participation in several study components, including biospecimen collection, for probands and relatives by race/ethnicity, cancer history, and other factors.Results: Of 4,780 eligible probands, 76% enrolled in the family registry by completing the family history and risk factor questionnaires and 68% also provided a blood or mouthwash sample. Enrollment was highest (81%) for non-Hispanic whites (NHWs) and intermediate (73-76%) for Hispanics, African Americans, and all Asian American subgroups, except Filipina women (66%). Of 4,279 eligible relatives, 77% enrolled in the family registry, and 65% also provided a biospecimen sample. Enrollment was highest for NHWs (87%) and lowest for Chinese (68%) and Filipinas (67%). Among those enrolled, biospecimen collection rates were similar for NHW, Hispanic, and African American women, both for probands (92-95%) and relatives (82-87%), but lower for some Asian-American subgroups (probands: 72-88%; relatives: 71-88%), foreign-born Asian Americans, and probands those who were more recent immigrants or had low English language proficiency.Conclusions: These results show that racial/ethnic minority populations are willing to provide biospecimen samples for research, although some Asian American subgroups in particular may need more directed recruitment methods. To address long-standing and well-documented cancer health disparities, minority populations need equal opportunities to contribute to biomedical research. [ABSTRACT FROM AUTHOR]

Objectives: Despite the epidemic of opioid overuse among American patients, there are limited data regarding the prevalence of such use among patients with head and neck cancer (HNC). Here, we report on the prevalence of persistent postoperative opioid (PPO) use and its risk factors among older patients with HNC undergoing surgery.Study Design: Retrospective cohort study.Setting: Surveillance, Epidemiology, and End Results (SEER)-Medicare linked cancer registry-claims database.Subjects and Methods: We identified patients aged 66 years or older who were diagnosed with HNC from 2008 to 2013, underwent primary surgical resection for their cancers, and met certain insurance and discharge criteria. The primary outcome was PPO use, defined as new opioid prescriptions 90 to 180 days postoperatively. We used multivariable logistic regression to evaluate associations between PPO use and factors such as demographics and postoperative treatment.Results: Of the 1190 eligible patients with HNC, 866 (72.8%) received opioid prescriptions attributable to their surgery. Among these 866 patients, the prevalence of PPO use was 33.3% overall; it was 48.3% among the 428 patients with preoperative opioid use compared to 18.5% among the 438 opioid-naive patients (adjusted odds ratio [OR], 3.96; 95% confidence interval [CI], 2.80-5.59). Other factors associated with PPO use include postoperative radiotherapy (OR, 1.99; 95%, CI 1.33-2.98) and Charlson comorbidity index (OR, 1.20; 95% CI, 1.03-1.41). Postoperative chemotherapy (OR, 1.19; 95% CI, 0.73-1.95) was not significantly associated with PPO use.Conclusions: PPO use is a substantial problem in older surgical patients with HNC, one that warrants consideration of alternative treatment strategies and continued examination of prescription guidelines for patients with HNC. [ABSTRACT FROM AUTHOR]

Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ . [ABSTRACT FROM AUTHOR]

Background: The immune system has been implicated in the pathophysiology of cutaneous squamous cell carcinoma (cSCC) as evidenced by the substantially increased risk of cSCC in immunosuppressed individuals. Associations between cSCC risk and single nucleotide polymorphisms (SNPs) in the HLA region have been identified by genome-wide association studies (GWAS). The translation of the associated HLA SNPs to structural amino acids changes in HLA molecules has not been previously elucidated.Methods: Using data from a GWAS that included 7238 cSCC cases and 56,961 controls of non-Hispanic white ancestry, we imputed classical alleles and corresponding amino acid changes in HLA genes. Logistic regression models were used to examine associations between cSCC risk and genotyped or imputed SNPs, classical HLA alleles, and amino acid changes.Results: Among the genotyped SNPs, cSCC risk was associated with rs28535317 (OR = 1.20, p = 9.88 × 10− 11) corresponding to an amino-acid change from phenylalanine to leucine at codon 26 of HLA-DRB1 (OR = 1.17, p = 2.48 × 10− 10). An additional independent association was observed for a threonine to isoleucine change at codon 107 of HLA-DQA1 (OR = 1.14, p = 2.34 × 10− 9). Among the classical HLA alleles, cSCC was associated with DRB1*01 (OR = 1.18, p = 5.86 × 10− 10). Conditional analyses revealed additional independent cSCC associations with DQA1*05:01 and DQA1*05:05. Extended haplotype analysis was used to complement the imputed haplotypes, which identified three extended haplotypes in the HLA-DR and HLA-DQ regions.Conclusions: Associations with specific HLA-DR and -DQ alleles are likely to explain previously observed GWAS signals in the HLA region associated with cSCC risk. [ABSTRACT FROM AUTHOR]

Background: Metabolomics is emerging as an important tool for detecting differences between diseased and non-diseased individuals. However, prospective studies are limited.Methods: We examined the detectability, reliability, and distribution of metabolites measured in pre-diagnostic plasma samples in a pilot study of women enrolled in the Northern California site of the Breast Cancer Family Registry. The study included 45 cases diagnosed with breast cancer at least one year after the blood draw, and 45 controls. Controls were matched on age (within 5 years), family status, BRCA status, and menopausal status. Duplicate samples were included for reliability assessment. We used a liquid chromatography/gas chromatography mass spectrometer platform to measure metabolites. We calculated intraclass correlations (ICCs) among duplicate samples, and coefficients of variation (CVs) across metabolites.Results: Of the 661 named metabolites detected, 338 (51%) were found in all samples, and 490 (74%) in more than 80% of samples. The median ICC between duplicates was 0.96 (25th - 75th percentile: 0.82-0.99). We observed a greater than 20% case-control difference in 24 metabolites (p < 0.05), although these associations were not significant after adjusting for multiple comparisons.Conclusions: These data show that assays are reproducible for many metabolites, there is a minimal laboratory variation for the same sample, and a large between-person variation. Despite small sample size, differences between cases and controls in some metabolites suggest that a well-powered large-scale study is likely to detect biological meaningful differences to provide a better understanding of breast cancer etiology. [ABSTRACT FROM AUTHOR]

Background: Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype. Methods: To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls). Results: Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06). Conclusion: The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation. [ABSTRACT FROM AUTHOR]

Motivation: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. [ABSTRACT FROM AUTHOR]

Background: Pubertal milestones, such as onset of breast development and menstruation, play an important role in breast cancer etiology. It is unclear if these milestones are different in girls with a first- or second-degree breast cancer family history (BCFH).Methods: In the LEGACY Girls Study (n = 1040), we examined whether three mother/guardian-reported pubertal milestones (having reached Tanner Stage 2 or higher (T2+) for breast and pubic hair development, and having started menstruation) differed by BCFH. We also examined whether associations between body size and race/ethnicity and pubertal milestones were modified by BCFH. We used mother/guardian reports as the primary measure of pubertal milestones, but also conducted sensitivity analyses using clinical Tanner measurements available for a subcohort (n = 204). We analyzed cross-sectional baseline data with logistic regression models for the entire cohort, and longitudinal data with Weibull survival models for the subcohort of girls that were aged 5-7 years at baseline (n = 258).Results: BCFH was modestly, but not statistically significantly, associated with Breast T2+ (odds ratio (OR) = 1.36, 95% confidence interval (CI) = 0.88-2.10), with a stronger association seen in the subcohort of girls with clinical breast Tanner staging (OR = 2.20, 95% CI = 0.91-5.32). In a longitudinal analysis of girls who were aged 5-7 years at baseline, BCFH was associated with a 50% increased rate of having early breast development (hazard ratio (HR) = 1.49, 95% CI = 1.0-2.21). This association increased to twofold in girls who were not overweight at baseline (HR = 2.04, 95% CI = 1.29-3.21). BCFH was not associated with pubic hair development and post-menarche status. The median interval between onset of breast development and menarche was longer for BCFH+ than BCFH- girls (2.3 versus 1.7 years), suggesting a slower developmental tempo for BCFH+ girls. Associations between pubertal milestones and body size and race/ethnicity were similar in girls with or without a BCFH. For example, weight was positively associated with Breast T2+ in both girls with (OR = 1.06 per 1 kg, 95% CI = 1.03-1.10) and without (OR = 1.14 per 1 kg, 95% CI = 1.04-1.24) a BCFH.Conclusions: These results suggest that BCFH may be related to earlier breast development and slower pubertal tempo independent of body size and race/ethnicity. [ABSTRACT FROM AUTHOR]

The benefits of radiotherapy for inoperable biliary tract cancer remain unclear due to the lack of randomized data. We evaluated the impact of radiotherapy on survival in elderly patients using the SEER-Medicare database. Patients in the SEER-Medicare database with inoperable biliary tract tumors diagnosed between 1998 and 2011 were included. We used multivariate logistic regression to evaluate factors associated with treatment selection, and multivariate Cox regression and propensity score matching to evaluate treatment selection in relation to subsequent survival. Of the 2343 patients included, 451 (19%) received radiotherapy within 4 months of diagnosis. The use of radiotherapy declined over time, and was influenced by receipt of chemotherapy and patient age, race, marital status, poverty status, and tumor stage and type. Median survival was 9.3 (95% CI 8.7-9.7) months among patients who did not receive radiation and 10.0 (95% CI 9.1-11.3) months among those who received radiation, conditional on having survived 4 months. In patients who received chemotherapy ( n = 1053), receipt of radiation was associated with improved survival, with an adjusted hazard ratio of 0.82 (95% 0.70-0.97, P = 0.02). In patients who did not receive chemotherapy ( n = 1290), receipt of radiation was not associated with improved survival, with an adjusted hazard ratio of 1.09 (95% 0.91-1.30, P = 0.34). Propensity-scored matched analyses showed similar results. Despite the survival benefit associated with the addition of radiotherapy to chemotherapy, the use of radiation for unresectable biliary tract cancers has declined over time. [ABSTRACT FROM AUTHOR]

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) <0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. [ABSTRACT FROM AUTHOR]

We propose a cost-effective sampling design and estimating procedure for validating personal risk models using right-censored cohort data. Validation involves using each subject's covariates, as ascertained at cohort entry, in a risk model (specified independently of the data) to assign him/her a probability of an adverse outcome within a future time period. Subjects are then grouped according to the magnitudes of their assigned risks, and within each group, the mean assigned risk is compared with the probability of outcome occurrence as estimated using the follow-up data. Such validation presents two complications. First, in the presence of right-censoring, estimating the probability of developing the outcomes before death requires competing risk analysis. Second, for rare outcomes, validation using the full cohort requires assembling covariates and assigning risks to thousands of subjects. This can be costly when some covariates involve analyzing biological specimens. A two-stage sampling design addresses this problem by assembling covariates and assigning risks only to those subjects most informative for estimating key parameters. We use this design to estimate the outcome probabilities needed to evaluate model performance and we provide theoretical and bootstrap estimates of their variances. We also describe how to choose two-stage designs with minimal efficiency loss for a parameter of interest when the quantities determining optimality are unknown at the time of design. We illustrate these methods by using subjects in the California Teachers Study to validate ovarian cancer risk models. We find that a design with optimal efficiency for one performance parameter need not be so for others, and trade-offs will be required. A two-stage design that samples all outcome-positive subjects and more outcome-negative than censored subjects will perform well in most circumstances. The methods are implemented in Risk Model Assessment Program, an R program freely available at http://med.stanford.edu/epidemiology/two-stage.html. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet "high-risk" criteria for MRI screening. Methods: We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided. Results: The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA. Conclusion: RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years). [ABSTRACT FROM AUTHOR]

We describe a flexible family of tests for evaluating the goodness of fit (calibration) of a pre-specified personal risk model to the outcomes observed in a longitudinal cohort. Such evaluation involves using the risk model to assign each subject an absolute risk of developing the outcome within a given time from cohort entry and comparing subjects' assigned risks with their observed outcomes. This comparison involves several issues. For example, subjects followed only for part of the risk period have unknown outcomes. Moreover, existing tests do not reveal the reasons for poor model fit when it occurs, which can reflect misspecification of the model's hazards for the competing risks of outcome development and death. To address these issues, we extend the model-specified hazards for outcome and death, and use score statistics to test the null hypothesis that the extensions are unnecessary. Simulated cohort data applied to risk models whose outcome and mortality hazards agreed and disagreed with those generating the data show that the tests are sensitive to poor model fit, provide insight into the reasons for poor fit, and accommodate a wide range of model misspecification. We illustrate the methods by examining the calibration of two breast cancer risk models as applied to a cohort of participants in the Breast Cancer Family Registry. The methods can be implemented using the Risk Model Assessment Program, an R package freely available at http://stanford.edu/~ggong/rmap/. [ABSTRACT FROM AUTHOR]

Background: A previous Australian population-based breast cancer case-control study found indirect evidence that control participation, although high, was not random. We hypothesized that unaffected sisters may provide a more appropriate comparison group than unrelated population controls.Methods: Three population-based case-control-family studies of breast cancer in women of white European origin were carried out by the Australian, Ontario and Northern California sites of the Breast Cancer Family Registry. We compared risk factors between 3643 cases, 2444 of their unaffected sisters and 2877 population controls and conducted separate case-control analyses based on population and sister controls using unconditional multivariable logistic regression.Results: Compared with sister controls, population controls were more highly educated, had an earlier age at menarche, fewer births, their first birth at a later age and their last birth more recently. The established breast cancer associations detected using sister controls, but not detected using population controls, were decreasing risk with each of later age at menarche, more births, younger age at first birth and greater time since last birth.Conclusions: Since participation of population controls might be unintentionally related to some risk factors, we hypothesize that sister controls could provide more valid relative risk estimates and be recruited at lower cost. Given declining study participation by population controls, this contention is highly relevant to epidemiologic research. [ABSTRACT FROM AUTHOR]

The association of ovarian carcinoma risk with the polymorphism rs1271572 in the estrogen receptor beta (ESR2) gene was examined in 4946 women with primary invasive ovarian carcinoma and 6582 controls in a pooled analysis of ten casecontrol studies within the Ovarian Cancer Association Consortium (OCAC). All participants were non-Hispanic white women. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression adjusted for site and age. Women with the TT genotype were at increased risk of ovarian carcinoma compared to carriers of the G allele (OR = 1.10; 95%; CI: 1.01-1.21; p = 0.04); the OR was 1.09 (CI: 0.99-1.20; p = 0.07) after excluding data from the center (Hawaii) that nominated this SNP for OCAC genotyping A stronger association of rs1271572 TT versus GT/GG with risk was observed among women aged ≤50 years versus older women (OR = 1.35; CI: 1.12-1.62; p = 0.002; p for interaction = 0.02) that remained statistically significant after excluding Hawaii data (OR = 1.34; CI: 1.11-1.61; p = 0.009). No heterogeneity of the association was observed by study, menopausal status, gravidity, parity, use of contraceptive or menopausal hormones, tumor histological type, or stage at diagnosis. This pooled analysis suggests that rs1271572 might influence the risk of ovarian cancer, in particular among younger women. [ABSTRACT FROM AUTHOR]

The association of invasive ovarian carcinoma risk with the functional polymorphism rs2228570 ( aka rs10735810; FokI polymorphism) in the vitamin D receptor ( VDR) gene was examined in 1820 white non-Hispanic cases and 3479 controls in a pooled analysis of five population-based case-control studies within the Ovarian Cancer Association Consortium. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. Carriers of the rare T allele were at increased risk of ovarian carcinoma compared to women with the CC genotype in all studies combined; each copy of the T allele was associated with a modest 9% increased risk (OR = 1.09; 95% CI: 1.01-1.19; p = 0.04). No significant heterogeneity among studies was observed ( p = 0.37) and, after excluding the dataset from the Hawaii study, the risk association for rs2228570 among replication studies was unchanged (OR = 1.09; 95% CI: 1.00-1.19; p = 0.06). A stronger association of rs2228570 with risk was observed among younger women (aged < 50 years versus 50 years or older) ( p = 0.04). In all studies combined, the increased risk per copy of the T allele among younger women was 24% (OR = 1.24; 95% CI: 1.04-1.47; p = 0.02). This association remained statistically significant after excluding the Hawaii data (OR = 1.20; 95% CI: 1.01-1.43; p = 0.04). No heterogeneity of the association was observed by stage ( p = 0.46), tumor histology ( p = 0.98), or time between diagnosis and interview ( p = 0.94). This pooled analysis provides further evidence that the VDR rs2228570 polymorphism might influence ovarian cancer susceptibility. [ABSTRACT FROM AUTHOR]

Interest in targeted disease prevention has stimulated development of models that assign risks to individuals, using their personal covariates. We need to evaluate these models and quantify the gains achieved by expanding a model to include additional covariates. This paper reviews several performance measures and shows how they are related. Examples are used to show that appropriate performance criteria for a risk model depend upon how the model is used. Application of the performance measures to risk models for hypothetical populations and for US women at risk of breast cancer illustrate two additional points. First, model performance is constrained by the distribution of risk-determining covariates in the population. This complicates the comparison of two models when applied to populations with different covariate distributions. Second, all summary performance measures obscure model features of relevance to its utility for the application at hand, such as performance in specific subgroups of the population. In particular, the precision gained by adding covariates to a model can be small overall, but large in certain subgroups. We propose new ways to identify these subgroups and to quantify how much they gain by measuring the additional covariates. Those with largest gains could be targeted for cost-efficient covariate assessment. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment. [ABSTRACT FROM AUTHOR]

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission. Recent studies implicate rare, large, high-penetrance copy number variants in some cases, but the genes or biological mechanisms that underlie susceptibility are not known. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended major histocompatibility complex region on chromosome 6. We carried out a genome-wide association study of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium and SGENE data sets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10-9). This region includes a histone gene cluster and several immunity-related genes—possibly implicating aetiological mechanisms involving chromatin modification, transcriptional regulation, autoimmunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms. [ABSTRACT FROM AUTHOR]

Background: Recent studies have identified several single nucleotide polymorphisms (SNPs) in the population that are associated with variations in the risks of many different diseases including cancers such as breast, prostate and colorectal. For ovarian cancer, the known highly penetrant susceptibility genes (BRCA1 and BRCA2) are probably responsible for only 40% of the excess familial ovarian cancer risks, suggesting that other susceptibility genes of lower penetrance exist. Methods: We have taken a candidate approach to identifying moderate risk susceptibility alleles for ovarian cancer. To date, we have genotyped 340 SNPs from 94 candidate genes or regions, in up to 1,491 invasive epithelial ovarian cancer cases and 3,145 unaffected controls from three different population based studies from the UK, Denmark and USA. Results: After adjusting for population stratification by genomic control, 18 SNPs (5.3%) were significant at the 5% level, and 5 SNPs (1.5%) were significant at the 1% level. The most significant association was for the SNP rs2107425, located on chromosome 11p15.5, which has previously been identified as a susceptibility allele for breast cancer from a genome wide association study (P-trend = 0.0012). When SNPs/genes were stratified into 7 different pathways or groups of validation SNPs, the breast cancer associated SNPs were the only group of SNPs that were significantly associated with ovarian cancer risk (P-heterogeneity = 0.0003; P-trend = 0.0028; adjusted (for population stratification) P-trend = 0.006). We did not find statistically significant associations when the combined data for all SNPs were analysed using an admixture maximum likelihood (AML) experiment-wise test for association (P-heterogeneity = 0.051; P-trend = 0.068). Conclusion: These data suggest that a proportion of the SNPs we evaluated were associated with ovarian cancer risk, but that the effect sizes were too small to detect associations with individual SNPs. [ABSTRACT FROM AUTHOR]

After diagnosis with prostate cancer, Black men in the United States have poorer survival than White men, even after controlling for differences in cancer stage. The extent to which these racial survival differences are due to biologic versus non-biologic factors is unclear, and it has been hypothesized that differences associated with socioeconomic status (SES) might account for much of the observed survival difference. The authors examined this hypothesis in a cohort study, using cancer registry and US Census data for White and Black men with incident prostate cancer (n = 23,334) who resided in 1,005 census tracts in the San Francisco Bay Area during 1973–1993. Separate analyses were conducted using two endpoints: death from prostate cancer and death from other causes. For each endpoint, death rate ratios (Blacks vs. Whites) were computed for men diagnosed at ages <65 years and at ages ≧65 years. These data suggest that differences associated with SES do not explain why Black men die from prostate cancer at a higher rate when compared with White men with this condition. However, among men with prostate cancer, SES-associated differences appear to explain almost all of the racial difference in risk of death from other causes. Am J Epidemiol 2000;151:409–16. [ABSTRACT FROM PUBLISHER]

When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed. [ABSTRACT FROM AUTHOR]

Recent studies based on genome-wide association, linkage, and admixture scan analysis have reported associations of various genetic variants in 8q24 with susceptibility to breast, prostate, and colorectal cancer. This locus lies within a 1.18- Mb region that contains no known genes but is bounded at its centromeric end by FAM84B and at its telomeric end by c-MYC, two candidate cancer susceptibility genes. To investigate the associations of specific loci within 8q24 with specific cancers, we genotyped the nine previously reported cancer-associated single-nucleotide polymorphisms across the region in four case-control sets of prostate (1854 case subjects and 1894 control subjects), breast (2270 case subjects and 2280 control subjects), colorectal (2299 case subjects and 2284 control subjects), and ovarian (1975 case subjects and 3411 control subjects) cancer. Five different haplotype blocks within this gene desert were specifically associated with risks of different cancers. One block was solely associated with risk of breast cancer, three others were associated solely with the risk of prostate cancer, and a fifth was associated with the risk of prostate, colorectal, and ovarian cancer, but not breast cancer. We conclude that there are at least five separate functional variants in this region. [ABSTRACT FROM AUTHOR]

This article reports on a study to determine the prevalence of the genetic mutation known as BRCA1 in five racial groups of women with breast cancer. The presence of the BRCA gene raises the risk of breast and ovarian cancer in those who carry it and is most often seen in people with multiple relatives who had or have cancer or in people who are of Ashkenazi Jewish heritage. This study compared the number of breast cancer patients with BRCA1 in Hispanic, African American and Asian American groups to those of non-Hispanic, white patients. The study found the highest incidence of BRCA1 is in Hispanic women and the lowest rate is in Asian American women, leading the authors to suspect there may be some unrecognized Jewish ancestry in the Hispanic population.

A total of 283 epithelial ovarian cancer families from the United Kingdom (UK) and the United States (US) were screened for coding sequence changes and large genomic alterations (rearrangements and deletions) in the BRCA1 and BRCA2 genes. Deleterious BRCA1 mutations were identified in 104 families (37%) and BRCA2 mutations in 25 families (9%). Of the 104 BRCA1 mutations, 12 were large genomic alterations; thus this type of change represented 12% of all BRCA1 mutations. Six families carried a previously described exon 13 duplication, known to be a UK founder mutation. The remaining six BRCA1 genomic alterations were previously unreported and comprised five deletions and an amplification of exon 15. One of the 25 BRCA2 mutations identified was a large genomic deletion of exons 19-20. The prevalence of BRCA1/2 mutations correlated with the extent of ovarian and breast cancer in families. Of 37 families containing more than two ovarian cancer cases and at least one breast cancer case with diagnosis at less than 60 years of age, 30 (81%) had a BRCA1/2 mutation. The mutation prevalence was appreciably less in families without breast cancer; mutations were found in only 38 out of 141 families (27%) containing two ovarian cancer cases only, and in 37 out of 59 families (63%) containing three or more ovarian cancer cases. These data indicate that BRCA1 and BRCA2 are the major susceptibility genes for ovarian cancer but that other susceptibility genes may exist. Finally, it is likely that these data will be of clinical importance for individuals in families with a history of epithelial ovarian cancer, in providing accurate estimates of their disease risks. Hum Mutat 28(12), 1207-1215, 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]