Citations

Abstract Introduction: Breast cancer genome-wide association studies (GWAS) have identified over 200 susceptibility loci, many replicated in diverse populations. However, cross-ancestry evaluation of breast cancer genetic architecture remains limited. We examined breast cancer genetic architecture using GWAS summary results from European (EUR; cases (ca) = 188,474, controls (co) = 96,201), East Asian (EAS; ca = 20,393, co = 86,329), African American (AA; ca = 9,235, co = 10,184), and US Hispanic/Latina and Latin American (H/L; ca = 2,396, co =7,468) studies. Methods: GWAS results were derived from the Breast Cancer Association Consortium, the African Ancestry Breast Cancer Genetics Consortium (AABCG), Biobank Japan, and a meta-analysis of five studies of H/L women with breast cancer. Linkage disequilibrium (LD) scores were generated for EUR, EAS, and AMR using the 1000 Genomes Project, while AA LD scores were derived from a subset of AABCG controls. Heritability was estimated for each ancestry group using LD score regression. Genetic correlations between populations were assessed via Popcorn. Polygenicity was analyzed using GENESIS; however, due to the limited sample size in the H/L studies, these estimates were restricted to AA, EAS, and EUR. Enrichment of heritability across regulatory elements was evaluated using stratified LD score regression across all populations. Results: The logit-scale heritability and corresponding standard error (SE) were 0.466 (0.066) for EAS, 0.501 (0.050) for EUR, 0.588 (0.360) for H/L, and 0.614 (0.095) for AA. The estimated number of independent susceptibility loci was 4,446 for EAS, 5,235 for EUR, and 8,308 for AA. Using a clumping and thresholding approach, an optimal set of common variants were projected to explain 38.6% (EUR), 39.4% (EAS), and 26.2% (AA) of genetic variance for samples of 100,000 cases and 100,000 controls with AUCs from corresponding estimated polygenic risk scores (PRSs) of 0.621 (EUR), 0.634 (EAS), and 0.611 (AA). Genetic correlations were strongest between EUR and EAS (ρ = 0.79, SE = 0.08) and EUR and H/L (ρ = 0.68, SE = 0.21), and weakest between AA and EAS (ρ = 0.42, SE = 0.14) and AA and H/L (ρ = 0.26, SE = 0.24). Among 73 genomic features, we found significant (P < 0.05/73) enrichment in heritability in EUR for ‘ancient promoter’ regions, transcription factor binding sites, H3K4me3, ‘super-enhancers’, H3K4me1, H3K27ac; in EAS for ‘super-enhancers’; and in AA for H3K27ac. No genomic features were significantly enriched in H/L, likely due to limited power. Conclusion: These findings suggest a shared breast cancer genetic architecture across diverse populations, as well as the potential for a similar level of breast cancer risk stratification by PRS in these populations. Expanding GWAS in underrepresented populations is essential to improve genetic risk predictions and foster equitable cancer prevention. Citation Format: James L. Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José P. Miranda, Devika Godbole, Julie-Alexia Dias, Leila Dorling, Wenlong C. Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Martin J. Zhang, Yan Zhang, Joe Dennis, Esther M. John, Gabriela Torres-Mejia, Larry Kushi, Jeffrey Weitzel, Susan L. Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy Wong, Kyriaki Michailidou, Qianqian Zhu, Diptavo Dutta, Thomas U. Ahearn, Haoyu Zhang. Genetic architecture of breast cancer across diverse populations: Assessing heritability, genetic correlation, and polygenicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2282.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3583.

Abstract Background There have been conflicting messages about the influence of female sex hormones on women's health, with historical messaging indicating that use of estrogen and/or progesterone in peri- or postmenopause poses a significant clinical risk to cisgender women. More recent guidance indicates that the benefit of hormone therapy (HT) outweighs the risks for symptomatic women. Exogenous estrogen use is also indicated for contraception and gender-affirming care. Despite the potential for broad applications, robust reference intervals for estradiol, progesterone, luteinizing hormone (LH) and follicle-stimulating hormone (FSH) are lacking, and guidelines indicate that measurement of 17-β-estradiol (E2), progesterone, LH, or FSH does not facilitate care in women who may be experiencing menopausal symptoms or women taking exogenous HT. Content Here we review the physiological roles of estrogen, progesterone, LH, and FSH. We examine the modes of administration for estrogen and progesterone, clarify the nomenclature related to exogenous hormone use, and comprehensively review the literature for studies evaluating normal concentrations of these female gonadal axis hormones during the menstrual cycle. The content primarily focuses on cisgender women, but some aspects of these hormones in transgender women will also be discussed. Summary Currently, E2, LH, FSH, and progesterone reference intervals for women remain incomplete. Although there are a variety of clinical indications that benefit women using HT, symptoms and shared decision-making should guide care. Collaborative efforts between clinicians and laboratory professionals to better define therapeutic or reference intervals for these hormones can advance women's health globally.

Abstract Rationale Increased risk of coronavirus disease (COVID-19) hospitalization and death has been reported among patients with clinical lung disease. Objectives To test the association of objective measures of prepandemic lung function and structure with COVID-19 outcomes in U.S. adults. Methods Prepandemic obstruction (FEV1/FVC < 0.70) and restriction (FEV1/FVC ⩾ 0.7, FVC < 80%) were defined based on the most recent spirometry exam conducted in 11 prospective U.S. general population–based cohorts. Severe obstruction was classified by FEV1 < 50%. Percentage emphysema, percentage high-attenuation areas, and interstitial lung abnormalities were defined on computed tomography in a subset. Incident COVID-19 was ascertained via questionnaires, serosurvey, and medical records from 2020 to 2023 and classified as severe (hospitalized or fatal) or nonsevere. Cause-specific hazard models were adjusted for sociodemographics, anthropometry, smoking, comorbidities, and COVID-19 vaccination status. Measurements and Main Results Among 29,323 participants (mean age, 67 yr), there were 748 severe incident COVID-19 cases over median follow-up of 17.3 months from March 1, 2020. Greater hazards of severe COVID-19 were associated with severe obstruction (vs. normal; adjusted hazard ratio [aHR], 2.11; 95% confidence interval [CI], 1.02–1.27), restriction (vs. normal; aHR, 1.40; 95% CI, 1.12–1.76), and percentage emphysema (highest vs. lowest quartile; aHR, 1.64; 95% CI, 1.03–2.61), but not greater high-attenuation areas or interstitial lung abnormalities. COVID-19 vaccination provided greater absolute risk reduction in these groups. Results were similar in participants without smoking, obesity, or clinical cardiopulmonary disease. Conclusions Prepandemic severe spirometric obstruction, spirometric restriction, and greater percentage emphysema lung on computed tomography were associated with risk of severe COVID-19. These findings support enhanced COVID-19 risk mitigation for individuals with impaired lung health and warrant further mechanistic studies on interactions of lung function, structure, and vulnerability to acute respiratory illnesses.

Abstract Background: Prior research on the associations of p16, COX-2, and Ki67 immunopositivity in ductal carcinoma in situ (DCIS) tissue with the risk of subsequent ipsilateral invasive breast cancer (IBC) is limited. Methods: In a case–control study nested in a cohort of women diagnosed with DCIS, immunostaining for p16, COX-2, and Ki67 was performed on DCIS tissue from those who developed subsequent ipsilateral IBC (cases; n = 146) and on matched subjects who did not develop IBC (controls; n = 273). Conditional logistic regression was used to estimate ORs and 95% confidence intervals for the associations between immunopositivity for p16, COX-2, and Ki67 and the risk of subsequent ipsilateral IBC. Results: There was no association between p16, COX-2, and Ki67 immunopositivity, examined either individually or in combination, and a risk of ipsilateral IBC. Compared with all other groups, the multivariable OR (95% confidence interval) for women who were triple positive for the three markers was 1.16 (0.38–3.54). Conclusions: p16, COX-2, and Ki67 immunopositivity was not associated with altered risk of ipsilateral IBC in women with DCIS. Impact: p16, COX-2, and Ki67 may not be prognostic for ipsilateral IBC in women with DCIS.

Ophthalmic immune-related adverse events (Eye-irAEs) from immune checkpoint inhibitors can cause visual morbidity. The absence of standardized definitions for Eye-irAEs not only impedes the development of evidence-based treatments but also progress in translational research. The objective of this study was to develop consensus guidance for an approach to Eye-irAEs. Four ophthalmic physicians (uveitis specialists and neuro-ophthalmologists) drafted Eye-irAE consensus guidance and definitions, which were reviewed by the multidisciplinary Eye-irAE definition panel. The panel was divided into Group A (Neuro-ophthalmology/Orbital Disease) and Group B (Uveitis/Ocular Surface Disease). A modified Delphi consensus process was used, with two rounds of anonymous ratings by panelists and two meetings to discuss areas of controversy. For each disorder, five diagnostic components were evaluated: symptoms, examination findings, laboratory studies/imaging findings, diagnostic criteria, and treatment. Panelists rated content for usability, appropriateness and accuracy on 9-point scales in electronic surveys and provided free-text comments. Aggregated survey responses were incorporated into revised definitions. Consensus was based on numeric ratings using the RAND Corporation/ University of California Los Angeles Health Services Utilization Study (RAND/UCLA) Appropriateness Method with prespecified definitions. 29 panelists from 25 academic medical centers voted on 114 rating scales (66 neuro-ophthalmic/orbital disease components, 48 uveitis/ocular surface disease components); of these, 86.3% (57/66) in Group A and 89.6% (43/48) in Group B reached first-round consensus. After revisions, all items except 6.1% (4/66) in Group A and 1.6% (1/60) in Group B received second-round consensus. Consensus definitions were achieved for 10/11 neuro-ophthalmic/orbital disorders: optic neuritis, inflammatory optic disc edema, arteritic ischemic optic neuropathy, optic perineuritis, orbital inflammation, thyroid eye disease-like orbital inflammation, cavernous sinus syndrome, oculomotor mononeuritis, trochlear mononeuritis, and abducens mononeuritis. Consensus definitions were achieved for 9/10 uveitis/ocular surface disorders: anterior uveitis, intermediate uveitis, posterior uveitis, panuveitis, Vogt-Koyanagi-Harada-like syndrome, sarcoidosis-like syndrome, acute macular neuroretinopathy, dry eye disease, and scleritis. These disease definitions establish a standardized classification for Eye-irAE, highlighting differences between irAEs and other inflammatory disorders. Importantly, diagnostic certainty does not always align directly with the need to treat as an Eye-irAE. Given the consensus from this representative panel group, it is anticipated the definitions will be used broadly across clinical and research settings.

Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E (APOE) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.

Key Points Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation. Better performance was observed in most recently published studies and those with a low or unclear risk of bias. Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios. Background Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established. Methods PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model. Results Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I 2 >90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool. Conclusions Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.