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In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40–65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.

Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer. Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health-AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96-1.04) for breast cancer and 1.08 (95% CI: 0.97-1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11-1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57-0.59), 0.59 (95% CI: 0.56-0.63), and 0.68 (95% CI: 0.66-0.70) for the breast, ovarian, and endometrial models, respectively. These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races. Please see later in the article for the Editors' Summary.

Esophageal adenocarcinoma (EA) is characterized by a strong male predominance. Sex steroid hormones have been hypothesized to underlie this sex disparity, but no population-based study to date has examined this potential association. Using mass spectrometry and ELISA, we quantitated sex steroid hormones and sex hormone binding globulin, respectively, in plasma from males- 172 EA cases and 185 controls-within the Factors Influencing the Barrett/Adenocarcinoma Relationship (FINBAR) Study, a case-control investigation conducted in Northern Ireland and Ireland. Multivariable adjusted logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between circulating hormones and EA. Higher androgen:estrogen ratio metrics were associated with increased odds of EA (e.g., testosterone:estradiol ratio ORQ4 v. Q1 = 2.58, 95%CI = 1.23-5.43; Ptrend = 0.009). All estrogens and androgens were associated with significant decreased odds of EA. When restricted to individuals with minimal to no decrease in body mass index, the size of association for the androgen:estrogen ratio was not greatly altered. This first study of sex steroid hormones and EA provides tentative evidence that androgen:estrogen balance may be a factor related to EA. Replication of these findings in prospective studies is needed to enhance confidence in the causality of this effect.

Background Limited evidence, mostly from studies in Western populations, suggests that the prognostic effects of lifestyle-related risk factors may be molecular subtype-dependent. Here, we examined whether pre-diagnostic lifestyle-related risk factors for breast cancer are associated with clinical outcomes by molecular subtype among patients from an understudied Asian population. Methods In this population-based case series, we evaluated breast cancer risk factors in relation to 10-year all-cause mortality (ACM) and 5-year recurrence by molecular subtype among 3012 women with invasive breast cancer in Sarawak, Malaysia. A total of 579 deaths and 314 recurrence events occurred during a median follow-up period of ~ 24 months. Subtypes (luminal A-like, luminal B-like, HER2-enriched, triple-negative) were defined using immunohistochemical markers for hormone receptors and human epidermal growth factor receptor 2 (HER2) in conjunction with histologic grade. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between risk factors and ACM/recurrence were estimated in subtype-specific Cox regression models. Results We observed heterogeneity in the relationships between parity/breastfeeding, age at first full-term pregnancy (FFP), family history, body mass index (BMI), and tumor subtype ( p value < 0.05). Among luminal A-like patients only, older age at menarche [HR (95% CI) ≥15 vs ≤ 12 years  = 2.28 (1.05, 4.95)] and being underweight [HR BMI < 18.5kg/m 2 vs. 18.5–24.9kg/m 2  = 3.46 (1.21, 9.89)] or overweight [HR 25–29.9kg/m 2 vs. 18.5–24.9kg/m 2 = 3.14 (1.04, 9.50)] were associated with adverse prognosis, while parity/breastfeeding [HR breastfeeding vs nulliparity  = 0.48 (0.27, 0.85)] and older age at FFP [HR > 30 vs < 21 years  = 0.20 (0.04, 0.90)] were associated with good prognosis. For these women, the addition of age at menarche, parity/breastfeeding, and BMI, provided significantly better fit to a prognostic model containing standard clinicopathological factors alone [LRχ 2 (8 df ) = 21.78; p value = 0.005]. Overall, the results were similar in relation to recurrence. Conclusions Our finding that breastfeeding and BMI were associated with prognosis only among women with luminal A-like breast cancer is consistent with those from previously published data in Western populations. Further prospective studies will be needed to clarify the role of lifestyle modification, especially changes in BMI, in improving clinical outcomes for women with luminal A-like breast cancer.

We propose a model for high dimensional mediation analysis that includes latent variables. We describe our model in the context of an epidemiologie study for incident breast cancer with one exposure and a large number of biomarkers (i.e., potential mediators). We assume that the exposure directly influences a group of latent, or unmeasured, factors which are associated with both the outcome and a subset of the biomarkers. The biomarkers associated with the latent factors linking the exposure to the outcome are considered \"mediators.\" We derive the likelihood for this model and develop an expectation-maximization algorithm to maximize an L1-penalized version of this likelihood to limit the number of factors and associated biomarkers. We show that the resulting estimates are consistent and that the estimates of the nonzero parameters have an asymptotically normal distribution. In simulations, procedures based on this new model can have significantly higher power for detecting the mediating biomarkers compared with the simpler approaches. We apply our method to a study that evaluates the relationship between body mass index, 481 metabolic measurements, and estrogen-receptor positive breast cancer.

Ludmila Prokunina-Olsson, Thomas O'Brien and colleagues report the discovery of a new gene, INFL4 , encoding interferon-λ4, that is upstream of INFL3 ( IL28B ). A compound dinucleotide frameshift genetic variant in INFL4 creates the full-length INFL4 protein and is more strongly associated with hepatitis C virus clearance in individuals of African ancestry than rs12979860, a known variant associated with clearance. Chronic infection with hepatitis C virus (HCV) is a common cause of liver cirrhosis and cancer. We performed RNA sequencing in primary human hepatocytes activated with synthetic double-stranded RNA to mimic HCV infection. Upstream of IFNL3 ( IL28B ) on chromosome 19q13.13, we discovered a new transiently induced region that harbors a dinucleotide variant ss469415590 (TT or ΔG), which is in high linkage disequilibrium with rs12979860, a genetic marker strongly associated with HCV clearance. ss469415590[ΔG] is a frameshift variant that creates a novel gene, designated IFNL4 , encoding the interferon-λ4 protein (IFNL4), which is moderately similar to IFNL3. Compared to rs12979860, ss469415590 is more strongly associated with HCV clearance in individuals of African ancestry, although it provides comparable information in Europeans and Asians. Transient overexpression of IFNL4 in a hepatoma cell line induced STAT1 and STAT2 phosphorylation and the expression of interferon-stimulated genes. Our findings provide new insights into the genetic regulation of HCV clearance and its clinical management.

Abstract Study Objectives: Prediagnosis lifestyle factors can influence colorectal cancer (CRC) survival. Sleep deficiency is linked to metabolic dysfunction and chronic inflammation, which may contribute to higher mortality from cardiometabolic conditions and promote tumor progression. We hypothesized that prediagnosis sleep deficiency would be associated with poor CRC survival. No previous study has examined either nighttime sleep or daytime napping in relation to survival among men and women diagnosed with CRC. Methods: We examined self-reported sleep duration and napping prior to diagnosis in relation to mortality among 4869 CRC survivors in the NIH-AARP Diet and Health Study. Vital status was ascertained by linkage to the Social Security Administration Death Master File and the National Death Index. We examined the associations of sleep and napping with mortality using traditional Cox regression (total mortality) and Compositing Risk Regression (cardiovascular disease [CVD] and CRC mortality). Models were adjusted for confounders (demographics, cancer stage, grade and treatment, smoking, physical activity, and sedentary behavior) as well as possible mediators (body mass index and health status) in separate models. Results: Compared to participants reporting 7–8 hours of sleep per day, those who reported <5 hr had a 36% higher all-cause mortality risk (Hazard Ratio (95% Confidence Interval), 1.36 (1.08–1.72)). Short sleep (<5 hr) was also associated with a 54% increase in CRC mortality (Substitution Hazard Ratio (95% Confidence Interval), 1.54 (1.11–2.14)) after adjusting for confounders and accounting for competing causes of death. Compared to no napping, napping 1 hr or more per day was associated with significantly higher total and CVD mortality but not CRC mortality. Conclusion: Prediagnosis short sleep and long napping were associated with higher mortality among CRC survivors.

This study, involving long-term follow-up of women exposed in utero to diethylstilbestrol (DES) and unexposed controls, showed increased risks of adverse reproductive outcomes, cervical intraepithelial neoplasia of grade 2 or higher, and breast cancer in women exposed to DES. Soon after the first synthetic estrogen, diethylstilbestrol (DES), was developed in 1938, 1 it was used clinically to prevent complications of pregnancy. 2 In the early 1950s, four clinical trials revealed no evidence of efficacy, and DES use declined. 3 – 6 In the late 1960s, an unusual cluster of cases of clear-cell adenocarcinoma of the vagina and cervix in adolescent girls and young women was observed at one hospital. 7 The clinicians involved, working with the mothers of these women, 8 discovered a strong association between this cancer and in utero exposure to DES. 9 Subsequent clinical studies of women exposed to DES in utero showed . . .

Background Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). Methods Data from the Black Women’s Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. Results AUCs were 0.577 (95% CI 0.556–0.598) for the BWHS model and 0.584 (95% CI 0.563–0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603–0.644). Conclusions This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.