Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram‐based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen‐detected cases and 1197 matched controls; and 354 younger‐diagnosis cases and 944 controls frequency‐matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually‐ and frequency‐matched studies, respectively. We estimated measure‐specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen‐detected and younger‐diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41‐2.31]) and Cirrus (1.72 [1.38‐2.14]); Cirrus (1.49 [1.32‐1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27‐1.68]), respectively. The AUCs were: 0.73 [0.68‐0.77], 0.63 [0.60‐0.66], and 0.72 [0.69‐0.75], respectively. Combined, our new mammogram‐based measures have twice the risk gradient for screen‐detected and younger‐diagnosis breast cancer (P ≤ 10−12), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk‐based personalised breast screening.

Lipid dyshomeostasis is associated with the most common form of dementia, Alzheimer’s disease (AD). Substantial progress has been made in identifying positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers for AD, but they have limited use as front-line, non-invasive diagnostic tools. Small extracellular vesicles (EVs) are released by all cell types and contain an enriched subset of their parental cell molecular composition, including lipids. EVs are released from the brain into the periphery, providing a potential source of tissue and disease specific lipid biomarkers. However, the EV lipidome of the central nervous system (CNS) is currently unknown and the potential of brain-derived EVs (BDEVs) to inform on lipid dyshomeostasis in AD remains unclear. The aim of this study was to reveal the lipid composition of BDEVs in human frontal cortex tissue, and to determine whether BDEVs in AD have altered lipid profiles compared to age-matched neurological controls (NC). Here, using semi-quantitative mass spectrometry, we describe the BDEV lipidome, covering 4 lipid categories, 17 lipid classes and 692 lipid molecules. Frontal cortex-derived BDEVs were enriched in glycerophosphoserine (PS) lipids, a characteristic of small EVs. Here we report that BDEVs are enriched in ether-containing PS lipids. A novel finding that further establishes ether lipids as a feature of EVs. While no significant changes were detected in the frontal cortex in AD, the lipid profile of the BDEVs from this tissue exhibited disease related differences. AD BDEVs had altered glycerophospholipid (GP) and sphingolipid (SP) levels, specifically increased plasmalogen glycerophosphoethanolamine (PE-P) and decreased polyunsaturated fatty acyl containing lipids (PUFAs), and altered amide-linked acyl chain content in sphingomyelin (SM) and ceramide (Cer) lipids relative to vesicles from neurological control subjects. The most prominent alteration being a two-fold decrease in lipid species containing docosahexaenoic acid (DHA). The in-depth lipidome analysis provided in this study highlights the advantage of EVs over more complex tissues for improved detection of dysregulated lipids that may serve as potential biomarkers in the periphery.

Background: Age-related epigenetic dysregulations are associated with several diseases, including cancer. The number of stochastic epigenetic mutations (SEM) has been suggested as a biomarker of life-course accumulation of exposure-related DNA damage; however, the predictive role of SEMs in cancer has seldom been investigated. Methods: A SEM, at a given CpG site, was defined as an extreme outlier of DNA methylation value distribution across individuals. We investigated the association of the total number of SEMs with the risk of eight cancers in 4,497 case–control pairs nested in three prospective cohorts. Furthermore, we investigated whether SEMs were randomly distributed across the genome or enriched in functional genomic regions. Results: In the three-study meta-analysis, the estimated ORs per one-unit increase in log(SEM) from logistic regression models adjusted for age and cancer risk factors were 1.25; 95% confidence interval (CI), 1.11–1.41 for breast cancer, and 1.23; 95% CI, 1.07–1.42 for lung cancer. In the Melbourne Collaborative Cohort Study, the OR for mature B-cell neoplasm was 1.46; 95% CI, 1.25–1.71. Enrichment analyses indicated that SEMs frequently occur in silenced genomic regions and in transcription factor binding sites regulated by EZH2 and SUZ12 (P < 0.0001 and P = 0.0005, respectively): two components of the polycomb repressive complex 2 (PCR2). Finally, we showed that PCR2-specific SEMs are generally more stable over time compared with SEMs occurring in the whole genome. Conclusions: The number of SEMs is associated with a higher risk of different cancers in prediagnostic blood samples. Impact: We identified a candidate biomarker for cancer early detection, and we described a carcinogenesis mechanism involving PCR2 complex proteins worthy of further investigations.

Background Mammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus). Methods We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We reported risk gradients as change in odds ratio per standard deviation of controls after adjusting for age and body mass index (OPERA). For models involving multiple measures, we calculated the OPERA equivalent to the area under the receiver operating characteristic curve. Results For interval, screen-detected and younger-diagnosis cancer, the best fitting models (OPERAs [95% confidence intervals]) were: Cumulus (1.81 [1.41 to 2.31]) and Cirrus (1.7 [1.38 to 2.14]); Cirrus (1.49 [1.32 to 1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27 to 1.68]), respectively. Their OPERA equivalents were: 2.35, 1.58, and 2.28, respectively. Conclusions Our mammogram-based measures improved risk prediction beyond and, except for interval cancers, negated the influence of conventional mammographic density. Combined, these new mammogram-based risk measures are at least as accurate as the current polygenetic risk scores (OPERA ~ 1.6) in predicting, on a population basis, women who will be diagnosed with breast cancer.

Abstract Background We previously investigated the association between 5 “first-generation” measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. This study assessed cancer risk associations for 3 recently developed methylation-based biomarkers of aging: PhenoAge, GrimAge, and predicted telomere length. Methods We estimated rate ratios (RRs) for the association between these 3 age-adjusted measures and risk of colorectal (N = 813), gastric (N = 165), kidney (N = 139), lung (N = 327), mature B-cell (N = 423), prostate (N = 846), and urothelial (N = 404) cancer using conditional logistic regression models. We also assessed associations by time since blood draw and by cancer subtype, and we investigated potential nonlinearity. Results We observed relatively strong associations of age-adjusted PhenoAge with risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per SD was approximately 1.2-1.3). Similar findings were obtained for age-adjusted GrimAge, but the association with lung cancer risk was much larger (RR per SD = 1.82, 95% confidence interval [CI] = 1.44 to 2.30), after adjustment for smoking status, pack-years, starting age, time since quitting, and other cancer risk factors. Most associations appeared linear, larger than for the first-generation measures, and were virtually unchanged after adjustment for a large set of sociodemographic, lifestyle, and anthropometric variables. For cancer overall, the comprehensively adjusted rate ratio per SD was 1.13 (95% CI = 1.07 to 1.19) for PhenoAge and 1.12 (95% CI = 1.05 to 1.20) for GrimAge and appeared larger within 5 years of blood draw (RR = 1.29, 95% CI = 1.15 to 1.44 and 1.19, 95% CI = 1.06 to 1.33, respectively). Conclusions The methylation-based measures PhenoAge and GrimAge may provide insights into the relationship between biological aging and cancer and be useful to predict cancer risk, particularly for lung cancer.

ABSTRACT We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10−7, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10−7) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5–5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER− breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER− breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes. Fine-mapping of causal variants and integration of epigenetic and chromatin conformation data identify likely target genes for 150 breast cancer risk regions.