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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.

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.

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.

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 . . .

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.

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.

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.

Background Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. Methods Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm 2 )) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. Results Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide ( p  < 0.001) and with local percent mammographic density volume at both the biopsy target ( p  = 0.035) and within a 2 mm perilesional ring ( p  = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another ( p  < 0.05). Conclusions Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

Background Emerging data indicate that variations in quantitative epithelial and stromal tissue composition and their relative abundance in benign breast biopsies independently impact risk of future invasive breast cancer. To gain further insights into breast cancer etiopathogenesis, we investigated associations between epidemiological factors and quantitative tissue composition metrics of the normal breast. Methods The study participants were 4108 healthy women ages 18–75 years who voluntarily donated breast tissue to the US-based Susan G. Komen Tissue Bank (KTB; 2008–2019). Using high-accuracy machine learning algorithms, we quantified the percentage of epithelial, stromal, adipose, and fibroglandular tissue, as well as the proportion of fibroglandular tissue that is epithelium relative to stroma (i.e., epithelium-to-stroma proportion, ESP) on digitized hematoxylin and eosin (H&E)-stained normal breast biopsy specimens. Data on epidemiological factors were obtained from participants using a detailed questionnaire administered at the time of tissue donation. Associations between epidemiological factors and square root transformed tissue metrics were investigated using multivariable linear regression models. Results With increasing age, the amount of stromal, epithelial, and fibroglandular tissue declined and adipose tissue increased, while that of ESP demonstrated a bimodal pattern. Several epidemiological factors were associated with individual tissue composition metrics, impacting ESP as a result. Compared with premenopausal women, postmenopausal women had lower ESP [ β (95% Confidence Interval (CI)) = −0.28 (− 0.43, − 0.13); P  < 0.001] with ESP peaks at 30–40 years and 60–70 years among pre- and postmenopausal women, respectively. Pregnancy [ β (95%CI) vs nulligravid  = 0.19 (0.08, 0.30); P  < 0.001] and increasing number of live births ( P -trend  < 0.001) were positively associated with ESP, while breastfeeding was inversely associated with ESP [ β (95%CI) vs no breastfeeding  = −0.15 (− 0.29, − 0.01); P  = 0.036]. A positive family history of breast cancer (FHBC) [ β (95%CI) vs no FHBC  = 0.14 (0.02–0.26); P  = 0.02], being overweight or obese [ β (95%CI) vs normal weight  = 0.18 (0.06–0.30); P  = 0.004 and 0.32 (0.21–0.44); P  < 0.001, respectively], and Black race [ β (95%CI) vs White  = 0.12 (− 0.005, 0.25); P  = 0.06] were positively associated with ESP. Conclusion Our findings revealed that cumulative exposure to etiological factors over the lifespan impacts normal breast tissue composition metrics, individually or jointly, to alter their dynamic equilibrium, with potential implications for breast cancer susceptibility and tumor etiologic heterogeneity.

Lung cancer in never-smokers (LCINS) is a leading cause of cancer death globally, but no screening programs for LCINS exist. To identify medical conditions that could serve as markers of LCINS risk, we conducted a nested case-control study within the United Kingdom’s Clinical Practice Research Datalink (CPRD-GOLD), consisting of 1581 LCINS cases and 14,318 never-smoking controls. Conditions significantly associated with LCINS 1-10 years before the index date were validated in an independent dataset, CPRD-Aurum (2188 LCINS cases, 19,597 never-smoking controls). These conditions include Chronic Obstructive Pulmonary Disease/Emphysema (COPD); gastroesophageal reflux disease (GERD); bronchitis and tracheitis; diabetes mellitus type 1; and gastritis and non-infective gastroenteritis and colitis. Adjusting for medication use only slightly attenuated these associations. Overall, inflammatory diseases appear to be important in LCINS pathogenesis although further studies need to confirm these associations. Conditions such as GERD or COPD could be considered as part of eligibility criteria for future LCINS screening programs. Inflammatory diseases may play an important role in lung cancer in never-smokers (LCINS) pathogenesis. Here, the authors show that conditions such as GERD or COPD could be considered as part of eligibility criteria for LCINS screening programs in the future.