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Abstract Background The 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Methods We explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction. Results The mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment. Conclusions Our results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.

Abstract Background Tissue stiffness, dictated by organisation of interstitial fibrillar collagens, increases breast cancer risk and contributes to cancer progression. Tamoxifen is a standard treatment for receptor-positive breast cancer and is also aproved for primary prevention. We investigated the effect of tamoxifen and its main metabolites on the breast tissue collagen organisation as a proxy for stiffness and explored the relationship between mammographic density (MD) and collagen organisation. Material and methods This sub-study of the double-blinded dose-determination trial, KARISMA, included 83 healthy women randomised to 6 months of 20, 10, 5, 2.5, and 1 mg of tamoxifen or placebo. Ultrasound-guided core-needle breast biopsies collected before and after treatment were evaluated for collagen organisation by polarised light microscopy. Results Tamoxifen reduced the amount of organised collagen and overall organisation, reflected by a shift from heavily crosslinked thick fibres to thinner, less crosslinked fibres. Collagen remodelling correlated with plasma concentrations of tamoxifen metabolites. MD change was not associated with changes in amount of organised collagen but was correlated with less crosslinking in premenopausal women. Conclusions In this study of healthy women, tamoxifen decreased the overall organisation of fibrillar collagens, and consequently, the breast tissue stiffness. These stromal alterations may play a role in the well-established preventive and therapeutic effects of tamoxifen. Trial registration ClinicalTrials.gov ID: NCT03346200. Registered November 1st, 2017. Retrospectively registered.

Abstract Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for patients with a possible melanoma. However, the 7PCL method only considers seven meta-features to calculate a risk score and is only relevant for patients with suspected melanoma. There are limited studies on the extensive use of patient metadata for the detection of all skin cancer subtypes. This study investigates artificial intelligence (AI) models that utilise patient metadata consisting of 23 attributes for suspicious skin lesion detection. We have identified a new set of most important risk factors, namely “C4C risk factors”, which is not just for melanoma, but for all types of skin cancer. The performance of the C4C risk factors for suspicious skin lesion detection is compared to that of the 7PCL and the Williams risk factors that predict the lifetime risk of melanoma. Our proposed AI framework ensembles five machine learning models and identifies seven new skin cancer risk factors: lesion pink, lesion size, lesion colour, lesion inflamed, lesion shape, lesion age, and natural hair colour, which achieved a sensitivity of $$80.46\pm 2.50\%$$ 80.46 ± 2.50 % and a specificity of $$62.09\pm 1.90\%$$ 62.09 ± 1.90 % in detecting suspicious skin lesions when evaluated using the metadata of 53,601 skin lesions collected from different skin cancer diagnostic clinics across the UK, significantly outperforming the 7PCL-based method (sensitivity $$68.09\pm 2.10\%$$ 68.09 ± 2.10 % , specificity $$61.07\pm 0.90\%$$ 61.07 ± 0.90 % ) and the Williams risk factors (sensitivity $$66.32\pm 1.90\%$$ 66.32 ± 1.90 % , specificity $$61.71\pm 0.6\%$$ 61.71 ± 0.6 % ). Furthermore, through weighting the seven new risk factors we came up with a new risk score, namely “C4C risk score”, which alone achieved a sensitivity of $$76.09\pm 1.20\%$$ 76.09 ± 1.20 % and a specificity of $$61.71\pm 0.50\%$$ 61.71 ± 0.50 % , significantly outperforming the 7PCL-based risk score (sensitivity $$73.91\pm 1.10\%$$ 73.91 ± 1.10 % , specificity $$49.49\pm 0.50\%$$ 49.49 ± 0.50 % ) and the Williams risk score (sensitivity $$60.68\pm 1.30\%$$ 60.68 ± 1.30 % , specificity $$60.87\pm 0.80\%$$ 60.87 ± 0.80 % ). Finally, fusing the C4C risk factors with the 7PCL and Williams risk factors achieved the best performance, with a sensitivity of $$85.24\pm 2.20\%$$ 85.24 ± 2.20 % and a specificity of $$61.12\pm 0.90\%$$ 61.12 ± 0.90 % . We believe that fusing these newly found risk factors and new risk score with image data will further boost the AI model performance for suspicious skin lesion detection. Hence, the new set of skin cancer risk factors has the potential to be used to modify current skin cancer referral guidelines for all skin cancer subtypes, including melanoma.

Abstract Background Despite known benefits of physical activity in reducing breast cancer risk, its impact on mammographic characteristics remain unclear and understudied. This study aimed to investigate associations between pre-diagnostic physical activity and mammographic features at breast cancer diagnosis, specifically mammographic breast density (MBD) and mammographic tumor appearance (MA), as well as mode of cancer detection (MoD). Methods Physical activity levels from study baseline (1991–1996) and mammographic information from the time of invasive breast cancer diagnosis (1991–2014) of 1116 women enrolled in the Malmö Diet and Cancer Study cohort were used. Duration and intensity of physical activity were assessed according to metabolic equivalent of task hours (MET-h) per week, or World Health Organization (WHO) guideline recommendations. MBD was dichotomized into low-moderate or high, MA into spiculated or non-spiculated tumors, and MoD into clinical or screening detection. Associations were investigated through logistic regression analyses providing odds ratios (OR) with 95% confidence intervals (CI) in crude and multivariable-adjusted models. Results In total, 32% of participants had high MBD at diagnosis, 37% had non-spiculated MA and 50% had clinical MoD. Overall, no association between physical activity and MBD was found with increasing MET-h/week or when comparing women who exceeded WHO guidelines to those subceeding recommendations (ORadj 1.24, 95% CI 0.78–1.98). Likewise, no differences in MA or MoD were observed across categories of physical activity. Conclusions No associations were observed between pre-diagnostic physical activity and MBD, MA, or MoD at breast cancer diagnosis. While physical activity is an established breast cancer prevention strategy, it does not appear to modify mammographic characteristics or screening detection.

Abstract Background Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women. Methods In this case–control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression. Results We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42–2.99) for percent dense area and 2.21 (1.44–3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95–2.67) and 1.89 (1.22–2.96) for percent dense area and 1.24 (0.70–2.22) and 1.96 (1.19–3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume]. Conclusions This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals.

Abstract Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

Abstract Background Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

Abstract Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48–71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21–57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness—in addition to breast density—should be considered when assigning complementary screening modalities and personalized screening intervals.

Abstract 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.

Abstract Background Researchers have suggested that longitudinal trajectories of mammographic breast density (MD) can be used to understand changes in breast cancer (BC) risk over a woman’s lifetime. Some have suggested, based on biological arguments, that the cumulative trajectory of MD encapsulates the risk of BC across time. Others have tried to connect changes in MD to the risk of BC. Methods To summarize the MD–BC association, we jointly model longitudinal trajectories of MD and time to diagnosis using data from a large ( $$N = 40{,}087$$ N = 40 , 087 ) mammography cohort of Swedish women aged 40–80 years. Five hundred eighteen women were diagnosed with BC during follow-up. We fitted three joint models (JMs) with different association structures; Cumulative, current value and slope, and current value association structures. Results All models showed evidence of an association between MD trajectory and BC risk ( $$P < 0.001$$ P < 0.001 for current value of MD, $$P < 0.001$$ P < 0.001 and $$P =0.005$$ P = 0.005 for current value and slope of MD respectively, and $$P < 0.001$$ P < 0.001 for cumulative value of MD). Models with cumulative association structure and with current value and slope association structure had better goodness of fit than a model based only on current value. The JM with current value and slope structure suggested that a decrease in MD may be associated with an increased (instantaneous) BC risk. It is possible that this is because of increased screening sensitivity rather than being related to biology. Conclusion We argue that a JM with a cumulative association structure may be the most appropriate/biologically relevant model in this context.

Abstract Background Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. Methods We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes’ coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. Results In European ancestry samples, 14 genes were significantly associated (q

Abstract Background Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

IntroductionMammographic breast density (MBD) is an established breast cancer risk factor, yet the underlying molecular mechanisms remain to be deciphered. Fibroblast growth factor receptor 1 (FGFR1) amplification is associated with breast cancer development and aberrant FGF signaling found in the biological processes related to both high mammographic density and breast cancer microenvironment. The aim of this study was to investigate the FGF/FGFR1 expression in-between paired tumor-adjacent and tumor tissues from the same patient, and its associations with MBD and tumor characteristics.MethodsFGFR1 expression in paired tissues from 426 breast cancer patients participating in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort study was analyzed by immunohistochemistry. FGF ligand expression was obtained from RNA-sequencing data for 327 of the included patients.ResultsFGFR1 levels were differently expressed in tumor-adjacent and tumor tissues, with increased FGFR1 levels detected in 58% of the tumors. High FGFR1 expression in tumor tissues was associated with less favorable tumor characteristics; high histological grade (OR=1.86, 95% CI 1.00–3.44), high Ki67 proliferative index (OR=2.18, 95% CI 1.18–4.02) as well as tumors of Luminal B-like subtype (OR=2.56, 95%CI 1.29–5.06). While no clear association between FGFR1 expression and MBD was found, FGF ligand (FGF1, FGF11, FGF18) expression was positively correlated with MBD.DiscussionTaken together, these findings support a role of the FGF/FGFR1 system in early breast cancer which warrants further investigation in the MBD–breast cancer context.

Abstract Background Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. Methods We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. Results The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47–2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. Conclusions These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.

Abstract Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values

Abstract Background Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. Methods We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. Results We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p

Dennis et al. investigate potential breast cancer associations with rare germline copy number variants (CNVs) by conducting a genome-wide analysis in a large breast cancer case-control dataset. The authors detected associations with exonic deletions in established breast cancer susceptibility genes and suggestive associations for a number of non-coding CNVs.

Abstract Background Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. Methods Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate

Abstract Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10−8 and 4.42 × 10−8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

Abstract Background Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients. Methods We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP

Breast cancer risk for BRCA1/BRCA2 mutation carriers varies depending on other genetic factors. Here, the authors perform a case-only genome-wide association study and highlight novel loci associated with breast cancer risk for BRCA1/BRCA2 mutation carriers.

Objectives: To assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR). Methods: We prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014–2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics. Results: The overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT. Conclusions: The majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Abstract Background Cancer patients have a highly increased risk of psychiatric disorders following diagnosis, compared with cancer-free individuals. Inflammation is involved in the development of both cancer and psychiatric disorders. The role of non-steroidal anti-inflammatory drugs (NSAIDs) in the subsequent risk of psychiatric disorders after cancer diagnosis is however unknown. Methods We performed a cohort study of all patients diagnosed with a first primary malignancy between July 2006 and December 2013 in Sweden. Cox proportional hazards models were used to assess the association of NSAID use during the year before cancer diagnosis with the risk of depression, anxiety, and stress-related disorders during the first year after cancer diagnosis. Results Among 316,904 patients identified, 5613 patients received a diagnosis of depression, anxiety, or stress-related disorders during the year after cancer diagnosis. Compared with no use of NSAIDs, the use of aspirin alone was associated with a lower rate of depression, anxiety, and stress-related disorders (hazard ratio [HR], 0.88; 95% confidence interval [CI], 0.81 to 0.97), whereas the use of non-aspirin NSAIDs alone was associated with a higher rate (HR, 1.24; 95% CI, 1.15 to 1.32), after adjustment for sociodemographic factors, comorbidity, indications for NSAID use, and cancer characteristics. The association of aspirin with reduced rate of depression, anxiety, and stress-related disorders was strongest for current use (HR, 0.84; 95% CI, 0.75 to 0.93), low-dose use (HR, 0.88; 95% CI, 0.80 to 0.98), long-term use (HR, 0.84; 95% CI, 0.76 to 0.94), and among patients with cardiovascular disease (HR, 0.81; 95% CI, 0.68 to 0.95) or breast cancer (HR, 0.74; 95% CI, 0.56 to 0.98). Conclusion Pre-diagnostic use of aspirin was associated with a decreased risk of depression, anxiety, and stress-related disorders during the first year following cancer diagnosis.

Abstract Background A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations. Methods Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year. Results Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17–25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2–7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60–300% worse than using mean change at 1year. Conclusions All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis.

Abstract Background Despite the biological link between thyroid hormones and breast cancer cell proliferation shown in experimental studies, little is known about the association between hyperthyroidism and breast cancer, as well as its association with the most common mammographic and genetic risk predictors for breast cancer. Methods This study estimates the incidence rate ratios (IRRs) of breast cancer among women diagnosed with hyperthyroidism, compared to those who are not, using two cohorts: a Swedish national cohort of the general female population (n = 3,793,492, 2002–2011) and the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA, n = 69,598, 2002–2017). We used logistic regression to estimate the odds ratios (ORs) of hyperthyroidism according to the mammographic and genetic risk predictors for breast cancer. Results An increased risk of breast cancer was observed in patients in the national cohort with hyperthyroidism (IRR = 1.23, 95% CI = 1.12–1.36), particularly for toxic nodular goiter (IRR = 1.38, 95% CI = 1.16–1.63). Hyperthyroidism was associated with higher body mass index, early age at first birth, and lower breastfeeding duration. Higher mammographic density was observed in women with toxic nodular goiter, compared to women without hyperthyroidism. Additionally, among genotyped women without breast cancer in the KARMA cohort (N = 11,991), hyperthyroidism was associated with a high polygenic risk score (PRS) for breast cancer overall (OR = 1.98, 95% CI = 1.09–3.60) and for estrogen receptor-positive specific PRS (OR = 1.90, 95% CI = 1.04–3.43). Conclusion Hyperthyroidism is associated with an increased risk of breast cancer, particularly for patients with toxic nodular goiter. The association could be explained by higher mammographic density among these women, as well as pleiotropic genetic variants determining shared hormonal/endocrine factors leading to the pathology of both diseases.

Abstract Background Mammographic density (MD) is a strong risk factor for breast cancer. We examined how endogenous plasma hormones are associated with average MD area (cm2) and annual MD change (cm2/year). Methods This study within the prospective KARMA cohort included analyses of plasma hormones of 1040 women. Hormones from the progestogen (n = 3), androgen (n = 7), oestrogen (n = 2) and corticoid (n = 5) pathways were analysed by ultra-performance supercritical fluid chromatography-tandem mass spectrometry (UPSFC-MS/MS), as well as peptide hormones and proteins (n = 2). MD was measured as a dense area using the STRATUS method (mean over the left and right breasts) and mean annual MD change over time. Results Greater baseline mean MD was associated with overall higher concentrations of progesterone (average + 1.29 cm2 per doubling of hormone concentration), 17OH-progesterone (+ 1.09 cm2), oesterone sulphate (+ 1.42 cm2), prolactin (+ 2.11 cm2) and SHBG (+ 4.18 cm2), and inversely associated with 11-deoxycortisol (− 1.33 cm2). The association between MD and progesterone was confined to the premenopausal women only. The overall annual MD change was − 0.8 cm2. Hormones from the androgen pathway were statistically significantly associated with MD change. The annual MD change was − 0.96 cm2 and − 1.16 cm2 lesser, for women in the highest quartile concentrations of testosterone and free testosterone, respectively, compared to those with the lowest concentrations. Conclusions Our results suggest that, whereas hormones from the progestogen, oestrogen and corticoid pathways drive baseline MD, MD change over time is mainly driven by androgens. This study emphasises the complexity of risk factors for breast cancer and their mechanisms of action.

In cancer many gene variants may contribute to disease etiology, but the impact of a given gene variant may have varied effect size. Here, the authors analyse summary statistics of genome-wide association studies from fourteen cancers, and show the utility of polygenic risk scores may vary depending on cancer type.

Abstract Background Increased knowledge of breast cancer risk factors has meant that we are currently exploring risk-based screening, i.e. determining screening strategies based on women’s varying levels of risk. This also enables risk management through primary prevention strategies, e.g. a lifestyle programme or risk-reducing medication. However, future implementation of risk-based screening and prevention will warrant significant changes in current practice and policy. The present study explores women’s perceptions of the implementation and organisation of risk-based breast cancer screening and prevention to optimise acceptability and uptake. Methods A total of 143 women eligible for breast cancer screening in the Netherlands, the United Kingdom, and Sweden participated in focus group discussions. The focus group discussions were transcribed verbatim and the qualitative data was analysed using thematic analysis. Results Women from all three countries generally agreed on the overall proceedings, e.g. a risk assessment after which the risk estimate is communicated via letter (for below average and average risk) or consultation (for moderate and high risk). However, discrepancies in information needs, preferred risk communication format and risk counselling professional were identified between countries. Additionally, a need to educate healthcare professionals on all aspects of the risk-based screening and prevention programme was established. Conclusion Women’s insights identified the need for country-specific standardised protocols regarding the assessment and communication of risk, and the provision of heterogeneous screening and prevention recommendations, monitoring the principle of solidarity in healthcare policy.

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Background: There is a rising concern about treatment-associated cardiotoxicities in breast cancer patients. This study aimed to determine the time- and treatment-specific incidence of arrhythmia, heart failure, and ischemic heart disease in women diagnosed with breast cancer. Methods: A register-based matched cohort study was conducted including 8015 breast cancer patients diagnosed from 2001 to 2008 in the Stockholm-Gotland region and followed up until 2017. Time-dependent risks of arrhythmia, heart failure, and ischemic heart disease in breast cancer patients were assessed using flexible parametric models as compared to matched controls from general population. Treatment-specific effects were estimated in breast cancer patients using Cox model. Results: Time-dependent analyses revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis. Hazard ratios (HRs) within the first year of diagnosis were 2.14 (95% CI = 1.63–2.81) for arrhythmia and 2.71 (95% CI = 1.70–4.33) for heart failure. HR more than 10 years following diagnosis was 1.42 (95% CI = 1.21–1.67) for arrhythmia and 1.28 (95% CI = 1.03–1.59) for heart failure. The risk for ischemic heart disease was significantly increased only during the first year after diagnosis (HR = 1.45, 95% CI = 1.03–2.04). Trastuzumab and anthracyclines were associated with increased risk of heart failure. Aromatase inhibitors, but not tamoxifen, were associated with risk of ischemic heart disease. No increased risk of heart disease was identified following locoregional radiotherapy. Conclusions: Administration of systemic adjuvant therapies appears to be associated with increased risks of heart disease. The risk estimates observed in this study may aid adjuvant therapy decision-making and patient counseling in oncology practices. Funding: This work was supported by the Swedish Research Council (grant no: 2018-02547); Swedish Cancer Society (grant no: CAN-19-0266); and FORTE (grant no: 2016-00081).

In breast cancer the contribution of different genetic variants to disease heritability is complex and not fully understood. Here, the authors present a network-based analysis in 84,567 patients studying ~7.3 million variants, identifying gene modules associated with breast cancer survival.

Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Interval cancer patients are more likely to carry rare gene mutations than screen-detected breast cancer patients. Here, the authors report that interval cancer patients are more likely cancer survivors and are at a greater risk of developing other non-breast tumors.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract Purpose Breast cancer is a common disease with a relatively good prognosis. Therefore, understanding the spectrum of diseases and mortality among breast cancer patients is important, though currently incomplete. We systematically examined the incidence and mortality of all diseases following a breast cancer diagnosis, as well as the sequential association of disease occurrences (trajectories). Methods In this national cohort study, 57,501 breast cancer patients (2001–2011) were compared to 564,703 matched women from the general Swedish population and followed until 2012. The matching criteria included year of birth, county of residence, and socioeconomic status. Based on information from the Swedish Patient and Cause of Death Registries, hazard ratios (HR) were estimated for disease incidence and mortality. Conditional logistic regression models were used to identify disease trajectories among breast cancer patients. Results Among 225 diseases, 45 had HRs > 1.5 and p

Abstract Background Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk. Methods Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies. Results Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38–1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28–1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45–1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile. Conclusions The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Over 170 susceptibility loci have been identified by genome-wide association studies in breast cancer. Here, the authors interrogated the role of risk-associated variants from non-breast tissue, and using expression quantitative trait loci, identify potential target genes of known breast cancer susceptibility variants, as well as 11 regions not previously known to be associated with breast cancer risk.

Sense of coherence (SoC) is the origin of health according to Antonovsky. The link between SoC and risk of cancer has however rarely been assessed. We performed a cohort study of 46,436 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (Karma). Participants answered a SoC-13 questionnaire at recruitment to Karma and were subsequently followed up for incident breast cancer. Multivariate Cox models were used to assess the hazard ratios (HRs) of breast cancer in relation to SoC. We identified 771 incident cases of breast cancer during follow-up (median time: 5.2 years). No association was found between SoC, either as a categorical (strong vs. weak SoC, HR: 1.08, 95% CI: 0.90–1.29) or continuous (HR: 1.08; 95% CI: 1.00–1.17 per standard deviation increase of SoC) variable, and risk of breast cancer. In summary, we found little evidence to support an association between SoC and risk of breast cancer.

Abstract Background High mammographic density is associated with breast cancer and with delayed detection. We have examined whether localized density, at the site of the subsequent cancer, is independently associated with being diagnosed with a large-sized or interval breast cancer. Methods Within a prospective cohort of 63,130 women, we examined 891 women who were diagnosed with incident breast cancer. For 386 women, retrospective localized density assessment was possible. The main outcomes were interval cancer vs. screen-detected cancer and large (> 2 cm) vs. small cancer. In negative screening mammograms, overall and localized density were classified reflecting the BI-RADS standard. Density concordance probabilities were estimated through multinomial regression. The associations between localized density and the two outcomes were modeled through logistic regression, adjusted for overall density, age, body mass index, and other characteristics. Results The probabilities of concordant localized density were 0.35, 0.60, 0.38, and 0.32 for overall categories “A,” “B,” “C,” and “D.” Overall density was associated with large cancer, comparing density category D to A with OR 4.6 (95%CI 1.8–11.6) and with interval cancer OR 31.5 (95%CI 10.9–92) among all women. Localized density was associated with large cancer at diagnosis with OR 11.8 (95%CI 2.7–51.8) among all women and associated with first-year interval cancer with OR 6.4 (0.7 to 58.7) with a significant linear trend p = 0.027. Conclusions Overall density often misrepresents localized density at the site where cancer subsequently arises. High localized density is associated with interval cancer and with large cancer. Our findings support the continued effort to develop and examine computer-based measures of localized density for use in personalized breast cancer screening.

Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.

Abstract Background Breast cancer patients who have not previously attended mammography screening may be more likely to discontinue adjuvant hormone therapy and therefore have a worse disease prognosis. Methods We conducted a population-based cohort study using data from Stockholm Mammography Screening Program, Stockholm-Gotland Breast Cancer Register, Swedish Prescribed Drug Register, and Swedish Cause of Death Register. Women in Stockholm who were diagnosed with breast cancer between 2001 and 2008 were followed until December 31, 2015. Non-participants of mammography screening were defined as women who, prior to their breast cancer diagnosis, were invited for mammography screening but did not attend. Results Of the 5098 eligible breast cancer patients, 4156 were defined as screening participants and 942 as non-participants. Compared with mammography screening participants, non-participants were more likely to discontinue adjuvant hormone therapy, with an adjusted hazard ratio (HR) of 1.30 (95% CIs, 1.11 to 1.53). Breast cancer patients not participating in mammography screening were also more likely to have worse disease-free survival, even after adjusting for tumor characteristics and other covariates (adjusted HR 1.22 (95% CIs, 1.05 to 1.42 for a breast cancer event). Conclusions Targeted interventions to prevent discontinuation of adjuvant hormone therapy are needed to improve breast cancer outcomes among women not attending mammography screening.

Abstract Background Long-term insulin exposure has been implicated in breast cancer etiology, but epidemiological evidence remains inconclusive. The aims of this study were to investigate the association of insulin therapy with mammographic density (MD) as an intermediate phenotype for breast cancer and to assess associations with long-term elevated circulating insulin levels using a genetic score comprising 18 insulin-associated variants. Methods We used data from the KARolinska MAmmography (Karma) project, a Swedish mammography screening cohort. Insulin-treated patients with type 1 (T1D, n = 122) and type 2 (T2D, n = 237) diabetes were identified through linkage with the Prescribed Drug Register and age-matched to 1771 women without diabetes. We assessed associations with treatment duration and insulin glargine use, and we further examined MD differences using non-insulin-treated T2D patients as an active comparator. MD was measured using a fully automated volumetric method, and analyses were adjusted for multiple potential confounders. Associations with the insulin genetic score were assessed in 9437 study participants without diabetes. Results Compared with age-matched women without diabetes, insulin-treated T1D patients had greater percent dense (8.7% vs. 11.4%) and absolute dense volumes (59.7 vs. 64.7 cm3), and a smaller absolute nondense volume (615 vs. 491 cm3). Similar associations were observed for insulin-treated T2D, and estimates were not materially different in analyses comparing insulin-treated T2D patients with T2D patients receiving noninsulin glucose-lowering medication. In both T1D and T2D, the magnitude of the association with the absolute dense volume was highest for long-term insulin therapy (≥ 5 years) and the long-acting insulin analog glargine. No consistent evidence of differential associations by insulin treatment duration or type was found for percent dense and absolute nondense volumes. Genetically predicted insulin levels were positively associated with percent dense and absolute dense volumes, but not with the absolute nondense volume (percentage difference [95% CI] per 1-SD increase in insulin genetic score = 0.8 [0.0; 1.6], 0.9 [0.1; 1.8], and 0.1 [− 0.8; 0.9], respectively). Conclusions The consistency in direction of association for insulin treatment and the insulin genetic score with the absolute dense volume suggest a causal influence of long-term increased insulin exposure on mammographic dense breast tissue.

Endometrial cancer is the most common invasive gynaecological cancer in developed countries. Here a meta-analysis identifies an additional nine novel endometrial cancer risk loci and eQTL analysis reveals risk variants associate with reduced expression of negative regulators of oncogenic signal transduction proteins.

Abstract Epidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.

Abstract Background Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. Methods We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h 2 SNP]) in 4948 participants of the cohort. Results In total, three novel MD loci were identified (at P < 5 × 10− 8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h 2 SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h 2 SNP to previously observed narrow-sense h 2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. Conclusions These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h 2 SNP/h 2 ratios varying between dense and nondense MD components.

Abstract Background Breast cancer prognosis is strongly associated with tumor size at diagnosis. We aimed to identify factors associated with diagnosis of large (> 2 cm) compared to small tumors, and to examine implications for long-term prognosis. Methods We examined 2012 women with invasive breast cancer, of whom 1466 had screen-detected and 546 interval cancers that were incident between 2001 and 2008 in a population-based screening cohort, and followed them to 31 December 2015. Body mass index (BMI) was ascertained after diagnosis at the time of study enrollment during 2009. PD was measured based on the contralateral mammogram within 3 years before diagnosis. We used multiple logistic regression modeling to examine the association between tumor size and body mass index (BMI), mammographic percent density (PD), or hormonal and genetic risk factors. Associations between the identified risk factors and, in turn, the outcomes of local recurrence, distant metastases, and death (153 events in total) in women with breast cancer were examined using Cox regression. Analyses were carried out according to mode of detection. Results BMI and PD were the only factors associated with tumor size at diagnosis. For BMI (≥25 vs. < 25 kg/m2), the multiple adjusted odds ratios (OR) were 1.37 (95% CI 1.02–1.83) and 2.12 (95% CI 1.41–3.18), for screen-detected and interval cancers, respectively. For PD (≥20 vs. < 20%), the corresponding ORs were 1.72 (95% CI 1.29–2.30) and 0.60 (95% CI 0.40–0.90). Among women with interval cancers, those with high BMI had worse prognosis than women with low BMI (hazard ratio 1.70; 95% CI 1.04–2.77), but PD was not associated with the hazard rate. Among screen-detected cancers, neither BMI nor PD was associated with the hazard rate. Conclusions In conclusion, high BMI was associated with the risk of having a tumor larger than 2 cm at diagnosis. Among women with interval cancer, high BMI was associated with worse prognosis. We believe that women with high BMI should be especially encouraged to attend screening.