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Deciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. Here, we undertake genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (408,786 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detect 21 genome-wide significant associations independent of previously reported results. Investigations of pleiotropy identify 12 cancer pairs exhibiting either positive or negative genetic correlations; 25 pleiotropic loci; and 100 independent pleiotropic variants, many of which are regulatory elements and/or influence cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility. Pleiotropic loci and genome-wide genetic correlations have identified shared heritability across some types of cancers. Here, the authors perform genome-wide association studies and characterize pan-cancer heritability and pleiotropy in individuals of European ancestry across 18 cancer types from two large cohorts.

The unexplained protective effect of childhood adiposity on breast cancer risk may be mediated via mammographic density (MD). Here, we investigate a complex relationship between adiposity in childhood and adulthood, puberty onset, MD phenotypes (dense area (DA), non-dense area (NDA), percent density (PD)), and their effects on breast cancer. We use Mendelian randomization (MR) and multivariable MR to estimate the total and direct effects of adiposity and age at menarche on MD phenotypes. Childhood adiposity has a decreasing effect on DA, while adulthood adiposity increases NDA. Later menarche increases DA/PD, but when accounting for childhood adiposity, this effect is attenuated. Next, we examine the effect of MD on breast cancer risk. DA/PD have a risk-increasing effect on breast cancer across all subtypes. The MD SNPs estimates are heterogeneous, and additional analyses suggest that different mechanisms may be linking MD and breast cancer. Finally, we evaluate the role of MD in the protective effect of childhood adiposity on breast cancer. Mediation MR analysis shows that 56% (95% CIs [32%–79%]) of this effect is mediated via DA. Our finding suggests that higher childhood adiposity decreases mammographic DA, subsequently reducing breast cancer risk. Understanding this mechanism is important for identifying potential intervention targets. Mammographic density is known to be linked to breast cancer risk. Here, the authors use Mendelian randomization to estimate the effects of childhood body size and age at menarche on density phenotypes and breast cancer risk.

Even distinct cancer types share biological hallmarks. Here, we investigate polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and UK Biobank (48,969 cases, 359,802 controls). Within cohorts, each PRS is evaluated in multivariable logistic regression models against all other cancer types. Results are then meta-analyzed across cohorts. Ten positive and one inverse cross-cancer associations are found after multiple testing correction. Two pairs show bidirectional associations; the melanoma PRS is positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS is positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS is inversely associated with lung cancer. Overall, we validate known, and uncover previously unreported, patterns of pleiotropy that have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies. While genetic loci shared between cancer types have been identified, cross-cancer relationships for polygenic risk scores have not been well studied. Here, the authors have developed polygenic risk scores for 16 cancers in two large cohorts and identified positive and inverse cross-cancer associations.

This large study found no increased risk of cardiovascular events in children and young adults using attention deficit–hyperactivity disorder (ADHD) drugs. Although the data are compatible with nearly a doubling of risk, the study was underpowered and the absolute event rate was quite low. Medications that are used to treat attention deficit–hyperactivity disorder (ADHD) are prescribed for more than 2.7 million children in the United States each year 1 and have been considered to be relatively safe. 2 – 5 However, reports of adverse events from Canada and the United States that have included cases of sudden death, myocardial infarction, and stroke in conjunction with the use of these drugs have raised concern about their safety. 6 , 7 Although case reports from adverse-event reporting systems can be an important source for identifying medication safety signals, they cannot reliably quantify risk. Thus, there is a compelling need to obtain . . .

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases. Conventional transcriptome-wide association study (TWAS) approaches predict genetically regulated gene expression at the tissue level. Here, the authors develop a framework for cell-type-aware TWAS that predicts cell-type level expression from genotype data and identifies disease-associated genes with cell-type-specific effects.

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.

BackgroundWe compared the results of hereditary cancer multigene panel testing among patients ≤ 45 years of age diagnosed with ductal carcinoma in situ (DCIS) versus invasive breast cancer (IBC) in a large integrated health care system.MethodsA retrospective cohort study of hereditary cancer gene testing among women ≤ 45 years of age diagnosed with DCIS or IBC at Kaiser Permanente Northern California between September 2019 and August 2020 was performed. During the study period, institutional guidelines recommended the above population be referred to genetic counselors for pretesting counseling and testing.ResultsA total of 61 DCIS and 485 IBC patients were identified. Genetic counselors met with 95% of both groups, and 86.4% of DCIS patients and 93.9% of IBC patients (p = 0.0339) underwent gene testing. Testing differed by race/ethnicity (p = 0.0372). Among those tested, 11.76% (n = 6) of DCIS patients and 16.71% (n = 72) of IBC patients had a pathogenic variant (PV) or likely pathogenic variant (LPV) based on the 36-gene panel (p = 0.3650). Similar trends were seen in 13 breast cancer (BC)-related genes (p = 0.0553). Family history of cancer was significantly associated with both BC-related and non-BC-related PVs in IBC, but not DCIS.ConclusionIn our study, 95% of patients were seen by a genetic counselor when age was used as an eligibility criterion for referral. While larger studies are needed to further compare the prevalence of PVs/LPVs among DCIS and IBC patients, our data suggest that even in younger patients, the prevalence of PVs/LPVs in BC-related genes is lower in DCIS patients.

Understanding the relative contributions of SARS-CoV-2 infection-induced and vaccine-induced seroprevalence is key to measuring overall population-level seroprevalence and help guide policy decisions. Using a series of six population-based cross-sectional surveys conducted among persons aged ≥7 years in a large health system with over 4.5 million members between May 2021 and April 2022, we combined data from the electronic health record (EHR), an electronic survey and SARS-CoV-2 spike antibody binding assay, to assess the relative contributions of infection and vaccination to population-level SARS-CoV-2 seroprevalence. EHR and survey data were incorporated to determine spike antibody positivity due to SARS-CoV-2 infection and COVID-19 vaccination. We used sampling and non-response weighting to create population-level estimates. We enrolled 4,319 persons over six recruitment waves. SARS-CoV-2 spike antibody seroprevalence increased from 83.3% (CI 77.0-88.9) in May 2021 to 93.5% (CI 89.5-97.5) in April 2022. By April 2022, 68.5% (CI 61.9-74.3) of the population was seropositive from COVID-19 vaccination only, 13.9% (10.7-17.9) from COVID-19 vaccination and prior diagnosed SARS-CoV-2 infection, 8.2% (CI 4.5-14.5) from prior diagnosed SARS-CoV-2 infection only and 2.9% (CI 1.1-7.6) from prior undiagnosed SARS-CoV-2 infection only. We found high agreement (≥97%) between EHR and survey data for ascertaining COVID-19 vaccination and SARS-CoV-2 infection status. By April 2022, 93.5% of persons had detectable SARS-CoV-2 spike antibody, predominantly from COVID-19 vaccination. In this highly vaccinated population and over 18 months into the pandemic, SARS-CoV-2 infection without COVID-19 vaccination was a small contributor to overall population-level seroprevalence.

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.

Purpose We examined the association between post-diagnosis statin use (3-hydroxy-3-methylglutaryl-coenzyme A [HMG-CoA] inhibitors) and risk of breast cancer recurrence. Materials and methods The study included 1945 early stage breast cancer survivors participating in the Life After Cancer Epidemiology (LACE) Study. Women who were diagnosed from 1997 to 2000 and identified from the Kaiser Permanente Northern California (KPNC) Cancer Registry entered the cohort on average 2 years post-diagnosis. Information on statin use was obtained from the KPNC pharmacy database. A total of 210 breast cancer recurrences were reported and verified by medical record review. Cox proportional hazard models were used to estimate rate ratios (RR) and 95% confidence intervals (CI). Results The mean duration of statin use in the cohort among those who initiated use post-diagnosis was 1.96 years, and lipophilic statins were mainly used (97.8%). Starting statins after diagnosis was suggestive of a decreased risk of breast cancer recurrence (RR = 0.67; 95% CI: 0.39–1.13). Risk of recurrence decreased with increasing duration of statin use after diagnosis ( p linear trend = 0.02). Conclusion Our findings provide initial support for an inverse association between post-diagnosis, lipophilic statin use and risk of breast cancer recurrence.