Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes.

African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P < 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations.

Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction.

We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization.

PURPOSE: Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS: We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS: Genetically predicted BMI was positively associated with non-dense area (IVW: ß = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: ß = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (ß = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (ß = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION: Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

A comparative analysis of risk factor associations with interval and screen-detected breast cancers: A large UK prospective study.

The associations of certain factors, such as age and menopausal hormone therapy, with breast cancer risk are known to differ for interval and screen-detected cancers. However, the extent to which associations of other established breast cancer risk factors differ by mode of detection is unclear. We investigated associations of a wide range of risk factors using data from a large UK cohort with linkage to the National Health Service Breast Screening Programme, cancer registration, and other health records. We used Cox regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs) for associations between risk factors and breast cancer risk. A total of 9421 screen-detected and 5166 interval cancers were diagnosed in 517,555 women who were followed for an average of 9.72 years. We observed the following differences in risk factor associations by mode of detection: greater body mass index (BMI) was associated with a smaller increased risk of interval (RR per 5 unit increase 1.07, 95% CI 1.03-1.11) than screen-detected cancer (RR 1.27, 1.23-1.30); having a first-degree family history was associated with a greater increased risk of interval (RR 1.81, 1.68-1.95) than screen-detected cancer (RR 1.52, 1.43-1.61); and having had previous breast surgery was associated with a greater increased risk of interval (RR 1.85, 1.72-1.99) than screen-detected cancer (RR 1.34, 1.26-1.42). As these differences in associations were relatively unchanged after adjustment for tumour grade, and are in line with the effects of these factors on mammographic density, they are likely to reflect the effects of these risk factors on screening sensitivity.

Mosquito control exposures and breast cancer risk: analysis of 1071 cases and 2096 controls from the Ghana Breast Health Study.

Epidemiologic data on insecticide exposures and breast cancer risk are inconclusive and mostly from high-income countries. Using data from 1071 invasive pathologically confirmed breast cancer cases and 2096 controls from the Ghana Breast Health Study conducted from 2013 to 2015, we investigated associations with mosquito control products to reduce the spread of mosquito-borne diseases, such as malaria. These mosquito control products were insecticide-treated nets, mosquito coils, repellent room sprays, and skin creams for personal protection against mosquitos. Multivariable and polytomous logistic regression models were used to estimate odds ratios (ORadj) and 95% confidence intervals (CI) with breast cancer risk-adjusted for potential confounders and known risk factors. Among controls, the reported use of mosquito control products were mosquito coils (65%), followed by insecticide-treated nets (56%), repellent room sprays (53%), and repellent skin creams (15%). Compared to a referent group of participants unexposed to mosquito control products, there was no significant association between breast cancer risk and mosquito coils. There was an association in breast cancer risk with reported use of insecticide-treated nets; however, that association was weak and not statistically significant. Participants who reported using repellent sprays were at elevated risks compared to women who did not use any mosquito control products, even after adjustment for all other mosquito control products (OR = 1.42, 95% CI=1.15-1.75). We had limited power to detect an association with repellent skin creams. Although only a few participants reported using repellent room sprays weekly/daily or < month-monthly, no trends were evident with increased frequency of use of repellent sprays, and there was no statistical evidence of heterogeneity by estrogen receptor (ER) status (p-het > 0.25). Our analysis was limited when determining if an association existed with repellent skin creams; therefore, we cannot conclude an association. We found limited evidence of risk associations with widely used mosquito coils and insecticide-treated nets, which are reassuring given their importance for malaria prevention. Our findings regarding specific breast cancer risk associations, specifically those observed between repellent sprays, require further study.

Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer.

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02-1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04-1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09-1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.

Quest markup for developing FAIR questionnaire modules for epidemiologic studies.

BACKGROUND: Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies. RESULTS: We developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study. CONCLUSION: A markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools.

PRScalc, a privacy-preserving calculation of raw polygenic risk scores from direct-to-consumer genomics data.

Motivation: Currently, the Polygenic Score (PGS) Catalog curates over 400 publications on over 500 traits corresponding to over 3000 polygenic risk scores (PRSs). To assess the feasibility of privately calculating the underlying multivariate relative risk for individuals with consumer genomics data, we developed an in-browserPRS calculator for genomic data that does not circulate any data or engage in any computation outside of the user's personal device. Results: A prototype personal risk score calculator, created for research purposes, was developed to demonstrate how the PGS Catalog can be privately and readily applied to readily available direct-to-consumer genetic testing services, such as 23andMe. No software download, installation, or configuration is needed. The PRS web calculator matches individual PGS catalog entries with an individual's 23andMe genome data composed of 600k to 1.4 M single-nucleotide polymorphisms (SNPs). Beta coefficients provide researchers with a convenient assessment of risk associated with matched SNPs. This in-browser application was tested in a variety of personal devices, including smartphones, establishing the feasibility of privately calculating personal risk scores with up to a few thousand reference genetic variations and from the full 23andMe SNP data file (compressed or not). Availability and implementation: The PRScalc web application is developed in JavaScript, HTML, and CSS and is available at GitHub repository (https://episphere.github.io/prs) under an MIT license. The datasets were derived from sources in the public domain: [PGS Catalog, Personal Genome Project].

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.

Objective: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, such as the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face serious limitations in portability and privacy due to their need for circulating user data in remote servers for operation. Our objective was to overcome these limitations. Materials and Methods: We refactored R-iCARE into a Python package (Py-iCARE) then compiled it to WebAssembly (Wasm-iCARE): a portable web module, which operates entirely within the privacy of the user's device. Results: We showcase the portability and privacy of Wasm-iCARE through two applications: for researchers to statistically validate risk models, and to deliver them to end-users. Both applications run entirely on the client-side, requiring no downloads or installations, and keeps user data on-device during risk calculation. Conclusions: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

A new method for multiancestry polygenic prediction improves performance across diverse populations.

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival.

BACKGROUND: Breast cancer (BC) patients with a germline CHEK2 c.1100delC variant have an increased risk of contralateral BC (CBC) and worse BC-specific survival (BCSS) compared to non-carriers. AIM: To assessed the associations of CHEK2 c.1100delC, radiotherapy, and systemic treatment with CBC risk and BCSS. METHODS: Analyses were based on 82,701 women diagnosed with a first primary invasive BC including 963 CHEK2 c.1100delC carriers; median follow-up was 9.1 years. Differential associations with treatment by CHEK2 c.1100delC status were tested by including interaction terms in a multivariable Cox regression model. A multi-state model was used for further insight into the relation between CHEK2 c.1100delC status, treatment, CBC risk and death. RESULTS: There was no evidence for differential associations of therapy with CBC risk by CHEK2 c.1100delC status. The strongest association with reduced CBC risk was observed for the combination of chemotherapy and endocrine therapy [HR (95% CI): 0.66 (0.55-0.78)]. No association was observed with radiotherapy. Results from the multi-state model showed shorter BCSS for CHEK2 c.1100delC carriers versus non-carriers also after accounting for CBC occurrence [HR (95% CI): 1.30 (1.09-1.56)]. CONCLUSION: Systemic therapy was associated with reduced CBC risk irrespective of CHEK2 c.1100delC status. Moreover, CHEK2 c.1100delC carriers had shorter BCSS, which appears not to be fully explained by their CBC risk.

Analysis of Several Common APOBEC-type Mutations in Bladder Tumors Suggests Links to Viral Infection.

FGFR3 and PIK3CA are among the most frequently mutated genes in bladder tumors. We hypothesized that recurrent mutations in these genes might be caused by common carcinogenic exposures such as smoking and other factors. We analyzed 2,816 bladder tumors with available data on FGFR3 and/or PIK3CA mutations, focusing on the most recurrent mutations detected in ≥10% of tumors. Compared to tumors with other FGFR3/PIK3CA mutations, FGFR3-Y375C was more common in tumors from smokers than never-smokers (P = 0.009), while several APOBEC-type driver mutations were enriched in never-smokers: FGFR3-S249C (P = 0.013) and PIK3CA-E542K/PIK3CA-E545K (P = 0.009). To explore possible causes of these APOBEC-type mutations, we analyzed RNA sequencing (RNA-seq) data from 798 bladder tumors and detected several viruses, with BK polyomavirus (BKPyV) being the most common. We then performed IHC staining for polyomavirus (PyV) Large T-antigen (LTAg) in an independent set of 211 bladder tumors. Overall, by RNA-seq or IHC-LTAg, we detected PyV in 26 out of 1,010 bladder tumors with significantly higher detection (P = 4.4 × 10-5), 25 of 554 (4.5%) in non-muscle-invasive bladder cancers (NMIBC) versus 1 of 456 (0.2%) of muscle-invasive bladder cancers (MIBC). In the NMIBC subset, the FGFR3/PIK3CA APOBEC-type driver mutations were detected in 94.7% (18/19) of PyV-positive versus 68.3% (259/379) of PyV-negative tumors (P = 0.011). BKPyV tumor positivity in the NMIBC subset with FGFR3- or PIK3CA-mutated tumors was also associated with a higher risk of progression to MIBC (P = 0.019). In conclusion, our results support smoking and BKPyV infection as risk factors contributing to bladder tumorigenesis in the general patient population through distinct molecular mechanisms. PREVENTION RELEVANCE: Tobacco smoking likely causes one of the most common mutations in bladder tumors (FGFR3-Y375C), while viral infections might contribute to three others (FGFR3-S249C, PIK3CA-E542K, and PIK3CA-E545K). Understanding the causes of these mutations may lead to new prevention and treatment strategies, such as viral screening and vaccination.

Spectrum and Frequency of Germline FANCM Protein-Truncating Variants in 44,803 European Female Breast Cancer Cases.

FANCM germline protein truncating variants (PTVs) are moderate-risk factors for ER-negative breast cancer. We previously described the spectrum of FANCM PTVs in 114 European breast cancer cases. In the present, larger cohort, we report the spectrum and frequency of four common and 62 rare FANCM PTVs found in 274 carriers detected among 44,803 breast cancer cases. We confirmed that p.Gln1701* was the most common PTV in Northern Europe with lower frequencies in Southern Europe. In contrast, p.Gly1906Alafs*12 was the most common PTV in Southern Europe with decreasing frequencies in Central and Northern Europe. We verified that p.Arg658* was prevalent in Central Europe and had highest frequencies in Eastern Europe. We also confirmed that the fourth most common PTV, p.Gln498Thrfs*7, might be a founder variant from Lithuania. Based on the frequency distribution of the carriers of rare PTVs, we showed that the FANCM PTVs spectra in Southwestern and Central Europe were much more heterogeneous than those from Northeastern Europe. These findings will inform the development of more efficient FANCM genetic testing strategies for breast cancer cases from specific European populations.

Increase in power by obtaining 10 or more controls per case when type-1 error is small in large-scale association studies.

BACKGROUND: The rule of thumb that there is little gain in statistical power by obtaining more than 4 controls per case, is based on type-1 error α = 0.05. However, association studies that evaluate thousands or millions of associations use smaller α and may have access to plentiful controls. We investigate power gains, and reductions in p-values, when increasing well beyond 4 controls per case, for small α. METHODS: We calculate the power, the median expected p-value, and the minimum detectable odds-ratio (OR), as a function of the number of controls/case, as α decreases. RESULTS: As α decreases, at each ratio of controls per case, the increase in power is larger than for α = 0.05. For α between 10-6 and 10-9 (typical for thousands or millions of associations), increasing from 4 controls per case to 10-50 controls per case increases power. For example, a study with power = 0.2 (α = 5 × 10-8) with 1 control/case has power = 0.65 with 4 controls/case, but with 10 controls/case has power = 0.78, and with 50 controls/case has power = 0.84. For situations where obtaining more than 4 controls per case provides small increases in power beyond 0.9 (at small α), the expected p-value can decrease by orders-of-magnitude below α. Increasing from 1 to 4 controls/case reduces the minimum detectable OR toward the null by 20.9%, and from 4 to 50 controls/case reduces by an additional 9.7%, a result which applies regardless of α and hence also applies to "regular" α = 0.05 epidemiology. CONCLUSIONS: At small α, versus 4 controls/case, recruiting 10 or more controls/cases can increase power, reduce the expected p-value by 1-2 orders of magnitude, and meaningfully reduce the minimum detectable OR. These benefits of increasing the controls/case ratio increase as the number of cases increases, although the amount of benefit depends on exposure frequencies and true OR. Provided that controls are comparable to cases, our findings suggest greater sharing of comparable controls in large-scale association studies.

epiDonate - distributed serverless data infrastructure for epidemiological studies.

Motivation: Epidemiological studies face two important challenges: the need to ingest ever more complex data types, and mounting concerns about participant privacy and data governance. These two challenges are compounded by the expectation that data infrastructure will eventually need to facilitate cross-registration of participants by multiple epidemiological studies. Implementation: The portable web-service epiDonate was developed using the serverless model known as FaaS (Function-as-a-Service). The reference implementation uses nodejs. The implementation relies on a simple tokenization scheme, mediated by a public API, that a) distinguishes admin from participant roles, with b) extensible permission configuration operating a read/write structure. General Features: The critical design feature of epiDonate is the absence of business logic on the server-side (the web service). The simplicity removes the need to customize virtual machines and enables ecosystems of multiple web Applications backed by one or more data donation deployments. Availability: https://episphere.github.io/donate.

Associations of Circulating Estrogens and Estrogen Metabolites with Fecal and Oral Microbiome in Postmenopausal Women in the Ghana Breast Health Study.

The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (ß = 0.36, P = 0.03), 2-hydroxyestradiol (ß = 0.30, P = 0.02), 4-methoxyestrone (ß = 0.51, P = 0.01), and estriol (ß = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (ß = -0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed.

PREDICT validity for prognosis of breast cancer patients with pathogenic BRCA1/2 variants.

We assessed the PREDICT v 2.2 for prognosis of breast cancer patients with pathogenic germline BRCA1 and BRCA2 variants, using follow-up data from 5453 BRCA1/2 carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC). PREDICT for estrogen receptor (ER)-negative breast cancer had modest discrimination for BRCA1 carrier patients overall (Gönen & Heller unbiased concordance 0.65 in CIMBA, 0.64 in BCAC), but it distinguished clearly the high-mortality group from lower risk categories. In an analysis of low to high risk categories by PREDICT score percentiles, the observed mortality was consistently lower than the expected mortality, but the confidence intervals always included the calibration slope. Altogether, our results encourage the use of the PREDICT ER-negative model in management of breast cancer patients with germline BRCA1 variants. For the PREDICT ER-positive model, the discrimination was slightly lower in BRCA2 variant carriers (concordance 0.60 in CIMBA, 0.65 in BCAC). Especially, inclusion of the tumor grade distorted the prognostic estimates. The breast cancer mortality of BRCA2 carriers was underestimated at the low end of the PREDICT score distribution, whereas at the high end, the mortality was overestimated. These data suggest that BRCA2 status should also be taken into consideration with tumor characteristics, when estimating the prognosis of ER-positive breast cancer patients.

Associations of breast cancer etiologic factors with stromal microenvironment of primary invasive breast cancers in the Ghana Breast Health Study.

Background: Emerging data suggest that beyond the neoplastic parenchyma, the stromal microenvironment (SME) impacts tumor biology, including aggressiveness, metastatic potential, and response to treatment. However, the epidemiological determinants of SME biology remain poorly understood, more so among women of African ancestry who are disproportionately affected by aggressive breast cancer phenotypes. Methods: Within the Ghana Breast Health Study, a population-based case-control study in Ghana, we applied high-accuracy machine-learning algorithms to characterize biologically-relevant SME phenotypes, including tumor-stroma ratio (TSR (%); a metric of connective tissue stroma to tumor ratio) and tumor-associated stromal cellular density (Ta-SCD (%); a tissue biomarker that is reminiscent of chronic inflammation and wound repair response in breast cancer), on digitized H&E-stained sections from 792 breast cancer patients aged 17-84 years. Kruskal-Wallis tests and multivariable linear regression models were used to test associations between established breast cancer risk factors, tumor characteristics, and SME phenotypes. Results: Decreasing TSR and increasing Ta-SCD were strongly associated with aggressive, mostly high grade tumors (p-value < 0.001). Several etiologic factors were associated with Ta-SCD, but not TSR. Compared with nulliparous women [mean (standard deviation) = 28.9% (7.1%)], parous women [mean (standard deviation) = 31.3% (7.6%)] had statistically significantly higher levels of Ta-SCD (p-value = 0.01). Similarly, women with a positive family history of breast cancer [FHBC; mean (standard deviation) = 33.0% (7.5%)] had higher levels of Ta-SCD than those with no FHBC [mean (standard deviation) = 30.9% (7.6%); p-value = 0.01]. Conversely, increasing body size was associated with decreasing Ta-SCD [mean (standard deviation) = 32.0% (7.4%), 31.3% (7.3%), and 29.0% (8.0%) for slight, moderate, and large body sizes, respectively, p-value = 0.005]. These associations persisted and remained statistically significantly associated with Ta-SCD in mutually-adjusted multivariable linear regression models (p-value < 0.05). With the exception of body size, which was differentially associated with Ta-SCD by grade levels (p-heterogeneity = 0.04), associations between risk factors and Ta-SCD were not modified by tumor characteristics. Conclusions: Our findings raise the possibility that epidemiological factors may act via the SME to impact both risk and biology of breast cancers in this population, underscoring the need for more population-based research into the role of SME in multi-state breast carcinogenesis.

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization.

Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast's primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method-these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.

The sequence kernel association test for multicategorical outcomes.

Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data ( N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.