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Background Adherence to adjuvant tamoxifen therapy is suboptimal, and acceptance of tamoxifen for primary prevention is poor. Published results indicate effect of low-dose tamoxifen therapy. Using questionnaire data from a randomised controlled trial, we describe side effects of standard and low-dose tamoxifen in healthy women. Methods In the KARISMA trial, 1440 healthy women were randomised to 6 months of daily intake of 20, 10, 5, 2.5, 1 mg of tamoxifen or placebo. Participants completed a 48-item, five-graded Likert score symptom questionnaire at baseline and follow-up. Linear regression models were used to identify significant changes in severity levels across doses and by menopausal status. Results Out of 48 predefined symptoms, five were associated with tamoxifen exposure (hot flashes, night sweats, cold sweats, vaginal discharge and muscle cramps). When comparing these side effects in premenopausal women randomised to low doses (2.5, 5 mg) versus high doses (10, 20 mg), the mean change was 34% lower in the low-dose group. No dose-dependent difference was seen in postmenopausal women. Conclusions Symptoms related to tamoxifen therapy are influenced by menopausal status. Low-dose tamoxifen, in contrast to high-dose, was associated with less pronounced side effects, a finding restricted to premenopausal women. Our findings give new insights which may influence future dosing strategies of tamoxifen in both the adjuvant and preventive settings. Trial registration ClinicalTrials.gov ID: NCT03346200.

Background Despite a clear link between aging and cancer, there has been inconclusive evidence on how biological age (BA) may be associated with cancer incidence. Methods We studied 308,156 UK Biobank participants with no history of cancer at enrolment. Using 18 age-associated clinical biomarkers, we computed three BA measures (Klemera-Doubal method [KDM], PhenoAge, homeostatic dysregulation [HD]) and assessed their associations with incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, and melanoma) using Cox proportional-hazards models. Results A total of 35,426 incident cancers were documented during a median follow-up of 10.9 years. Adjusting for common cancer risk factors, 1-standard deviation (SD) increment in the age-adjusted KDM (hazard ratio = 1.04, 95% confidence interval = 1.03–1.05), age-adjusted PhenoAge (1.09, 1.07–1.10), and HD (1.02, 1.01–1.03) was significantly associated with a higher risk of any cancer. All BA measures were also associated with increased risks of lung and colorectal cancers, but only PhenoAge was associated with breast cancer risk. Furthermore, we observed an inverse association between BA measures and prostate cancer, although it was attenuated after removing glycated hemoglobin and serum glucose from the BA algorithms. Conclusions Advanced BA quantified by clinical biomarkers is associated with increased risks of any cancer, lung cancer, and colorectal cancer.

Women’s health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p

Background Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. Methods We conducted an extreme case–control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. Results In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07–2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2–35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. Conclusion Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.

Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10−04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.

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. Electronic supplementary material The online version of this article (10.1186/s13058-019-1099-y) contains supplementary material, which is available to authorized users.

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Purpose: Type 2 diabetes (T2D) has been reported to be associated with an elevated risk of breast cancer. It is unclear, however, whether this association is due to shared genetic factors.\ud Methods: We constructed a genetic risk score (GRS) using risk variants from 33 known independent T2D susceptibility loci and evaluated its relation to breast cancer risk using the data from two consortia, including 62,328 breast cancer patients and 83,817 controls of European ancestry. Unconditional logistic regression models were used to derive adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) to measure the association of breast cancer risk with T2D GRS or T2D-associated genetic risk variants. Meta-analyses were conducted to obtain summary ORs across all studies.\ud Results: The T2D GRS was not found to be associated with breast cancer risk, overall, by menopausal status, or for estrogen receptor positive or negative breast cancer. Three T2D associated risk variants were individually associated with breast cancer risk after adjustment for multiple comparisons using the Bonferroni method (at p < 0.001), rs9939609 (FTO) (OR 0.94, 95 % CI = 0.92–0.95, p = 4.13E−13), rs7903146 (TCF7L2) (OR 1.04, 95 % CI = 1.02–1.06, p = 1.26E−05), and rs8042680 (PRC1) (OR 0.97, 95 % CI = 0.95–0.99, p = 8.05E−04).\ud Conclusions: We have shown that several genetic risk variants were associated with the risk of both T2D and breast cancer. However, overall genetic susceptibility to T2D may not be related to breast cancer risk.

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 [h2SNP]) 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). h2SNP (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 h2SNP to previously observed narrow-sense h2 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 h2SNP/h2 ratios varying between dense and nondense MD components. Electronic supplementary material The online version of this article (10.1186/s13058-018-0954-6) contains supplementary material, which is available to authorized users.

Large childhood body size has been consistently shown to be associated with decreased breast cancer risk. However, it is important to consider the effects of a large childhood body size on other adult diseases. It is not clear if the associations between childhood body size and adult diseases will persist if they later attain healthy weight. The associations between body size at age 7 and 17 adverse outcomes in adulthood were examined using Cox models in a Swedish study of 65,057 women. Large body size at age 7, when compared to small body size, was associated with decreased risk for breast cancer (HR [95% CI]: 0.81 [0.70–0.93]) and increased risks for anorexia (2.13 [1.63–2.77]) and bulimia (1.91 [1.35–2.70]). Neither adjusting for adult BMI nor restricting the dataset to lean adults (BMI 2) attenuated the associations. While large body size at age 7 by itself was positively associated with increased risks of diabetes (1.34 [1.16–1.55]), PCOS (1.69 [1.13–2.51]) and hypertension (before age 60), the associations were no longer significant after controlling for adult BMI. No clear associations were found with the remaining adverse outcomes (cervical, uterine, melanoma, colon cancer, depression, ovarian cyst, stroke, hyperlipidemia, heart failure, myocardial infarction, and angina pectoris).

Women with contralateral breast cancer (CBC) have significantly worse prognosis compared to women with unilateral cancer. A possible explanation of the poor prognosis of patients with CBC is that in a subset of patients, the second cancer is not a new primary tumor but a metastasis of the first cancer that has potentially obtained aggressive characteristics through selection of treatment. Exome and whole-genome sequencing of solid tumors has previously been used to investigate the clonal relationship between primary tumors and metastases in several diseases. In order to assess the relationship between the first and the second cancer, we performed exome sequencing to identify somatic mutations in both first and second cancers, and compared paired normal tissue of 25 patients with metachronous CBC. For three patients, we identified shared somatic mutations indicating a common clonal origin thereby demonstrating that the second tumor is a metastasis of the first cancer, rather than a new primary cancer. Accordingly, these patients all developed distant metastasis within 3 years of the second diagnosis, compared with 7 out of 22 patients with non-shared somatic profiles. Genomic profiling of both tumors help the clinicians distinguish between true CBCs and subsequent metastases.

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1. ispartof: Nature Genetics vol:47 issue:4 pages:373-80 ispartof: location:United States status: published

To assess whether children born to primiparous women around the time of a breast cancer diagnosis have an increased mortality risk. From the merged Swedish Multi-Generation and Cancer Registers, we identified 49,750 eligible children whose mother was diagnosed with breast cancer between 1958 and 2010. Mortality rates in offspring were compared to the background population using standardized mortality ratios (SMR), adjusted for calendar year of birth, attained age, and sex, and calculated for each category of timing of delivery (before, around, or after mother's diagnosis) and mother's parity status. Hazard ratios were assessed using a Cox proportional hazards model and adjusted for socioeconomic status, year of birth and mother's age at birth. Children born to a primiparous woman around a breast cancer diagnosis had a mortality rate five times greater than the background population (SMR 5.26, 95 % CI 1.93-11.5), whereas children born to a multiparous woman had a twofold increase (SMR 2.40, 95 % CI 1.10-4.55). Children of primiparous women born around diagnosis had an adjusted hazard ratio fourfold to that of children of primiparous women born before their mother's diagnosis (HR 4.29, 95 % CI 1.68-8.91), whereas hazard ratios for children of primiparous or multiparous women born at other times were not statistically significant. Children born to primiparous women around a breast cancer diagnosis have an increased relative mortality risk. Although relative risk is increased, in absolute terms children born from a cancer complicated pregnancy do relatively well. Additional investigations are needed to elucidate the reason(s) underlying this observation before the information can be used to inform patient counseling and clinical care.

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Background Interval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability. Methods From a population-based cohort of invasive breast cancer cases in Stockholm-Gotland, Sweden, diagnosed from 2001 to 2008, we analyzed the contralateral mammogram at the preceding negative screening of 394 interval cancer cases and 1009 screen-detected cancers. We examined 32 different image features in digitized film mammograms, based on three alternative dense area identification methods, by a set of logistic regression models adjusted for percent density with interval cancer versus screen-detected cancer as the outcome. Features were forward-selected into a multiple logistic regression model adjusted for mammographic percent density, age, BMI and use of hormone replacement therapy. The associations of the identified features were assessed also in a sample from an independent cohort. Results Two image features, ‘skewness of the intensity gradient’ and ‘eccentricity’, were associated with the risk of interval compared with screen-detected cancer. For the first feature, the per-standard deviation odds ratios were 1.32 (95 % CI: 1.12 to 1.56) and 1.21 (95 % CI: 1.04 to 1.41) in the primary and validation cohort respectively. For the second feature, they were 1.20 (95 % CI: 1.04 to 1.39) and 1.17 (95%CI: 0.98 to 1.39) respectively. The first feature was associated with the tumor size at screen detection, while the second feature was associated with the tumor size at interval detection. Conclusions We identified two novel mammographic features in screening mammograms of the normal breast that differentiated between future interval cancers and screen-detected cancers. We present a starting point for further research into features beyond percent density that might be relevant for interval cancer, and suggest ways to use this information to improve screening. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0761-x) contains supplementary material, which is available to authorized users.

Mammographic density (MD) is a well-established risk factor for breast cancer. Whether MD influences the tumor phenotype remains to be clarified. Previous studies are highly inconsistent and most lack important covariate information. This is a case-only study within a population-based case-control study. Cases were all postmenopausal women, aged 50-74 years, with incident breast cancer, diagnosed 1993-1995, and with no history of previous cancer (n = 2,720). 1,747 women with mammograms and information on tumor characteristics were included in analyses. MD was assessed using a computer-assisted thresholding technique. We used linear, logistic, and multinomial logistic regression, adjusting for possible confounders, to study density and tumor characteristics. PD was only statistically significantly associated with tumor size in our study (regression coefficient 0.031, p = 0.017). The effect of PD on tumor size was greater when mode of detection was excluded from the model (regression coefficient 0.043, p = 0.001). No other associations between PD and the tumor characteristics studied (lymph node metastasis, ER-status, PR-status, grade, and histopathological classification) were observed. In summary, PD was positively associated with tumor size in postmenopausal women. However, the relationship was at least partially confounded by mode of detection. Although there may be a true biological relationship between MD and more highly proliferative tumors, it also seems that part of this relationship is due to masking delaying diagnosis. In conclusion, PD does not seem to be differentially associated with tumor phenotype, except for tumor size, after taking mode of detection into consideration.

BACKGROUND: Gene expression profiling has led to a subclassification of breast cancers independent of established clinical parameters, such as the Sorlie–Perou subtypes. Mammographic density (MD) is one of the strongest risk factors for breast cancer, but it is unknown if MD is associated with molecular subtypes of this carcinoma. METHODS: We investigated whether MD was associated with breast cancer subtypes in 110 women with breast cancer, operated in Stockholm, Sweden, during 1994 to 1996. Subtypes were defined using expression data from HGU133A þB chips. The MD of the unaffected breast was measured using the Cumulus software. We used multinomial logistic models to investigate the relationship between MD and Sorlie–Perou subtypes. RESULTS: Although the distribution of molecular subtypes differed in women with high vs low MD, this was statistically non-significant (P ¼0.249), and further analyses revealed no association between the MD and Sorlie–Perou subtypes as a whole, nor with individual subtypes. CONCLUSION: These findings suggest that although MD is one of the strongest risk factors for breast cancer, it does not seem to be differentially associated with breast cancer molecular subtypes. However, larger studies with more comprehensive covariate information are needed to confirm these results.

There is considerable evidence that birth weight is positively associated with breast cancer risk, and seven single-nucleotide polymorphisms (SNPs) have been conclusively associated with this risk. We have hypothesized that breast cancer susceptibility loci may have a greater influence on breast cancer risk among women with higher birth weight, who are expected to have a larger pool of mammary stem cells that are susceptible to malignant transformation.In the context of a nationwide, population-based case-control study in Sweden, we retrieved recorded birth weight for 693 breast cancer cases and 747 control women who were also genotyped for most or all of the seven recently documented breast cancer susceptibility SNPs: rs2981582, rs12443621, rs8051542, rs3803662, rs889312, rs13281615, and rs3817198.We grouped heterozygotes with homozygotes for the wild-type allele, and we found a marginally significant interaction (p~0.07) between birth weight and rs2981582 (FGFR2), the genotype repeatedly identified as the top hit in genome-wide association studies. There were similar, though not significant, patterns for the other six SNPs.Although our findings require confirmation, we found suggestive evidence that genetic susceptibility modifies the positive association of birth weight with breast cancer.

High body mass index (BMI) and use of hormone replacement therapy (HRT) increase the risk of postmenopausal breast cancer. It has been shown that BMI modifies the effect of HRT, as its influence is most pronounced in lean women. We investigated the influence of BMI and HRT on prognosis in 2640 postmenopausal women diagnosed with breast cancer in Sweden in 1993–1995, taking into account HRT and mammography before diagnosis. Logistic and Cox regression were used. In non-users of HRT, obese women (BMI >30) compared with normal weight women (BMI

Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08-1.13, P = 4.1 x 10(-23)) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92-0.97, P = 1.4 x 10(-8)). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q.

Introduction A large body size is associated with larger breast cancer tumours at diagnosis. Standard regression models for tumour size at diagnosis are not sufficient for unravelling the mechanisms behind the association. Methods Using Swedish case-control data, we identified 1352 postmenopausal women with incident invasive breast cancer diagnosed between 1993 and 1995. We used a novel continuous tumour growth model, which models tumour sizes at diagnosis through three submodels: for tumour growth, time to symptomatic detection, and screening sensitivity. Tumour size at other time points is thought of as a latent variable. Results We quantified the relationship between body size with tumour growth and time to symptomatic detection. High body mass index and large breast size are, respectively, significantly associated with fast tumour growth rate and delayed time to symptomatic detection (combined P value = 5.0 × 10−5 and individual P values = 0.089 and 0.022). We also quantified the role of mammographic density in screening sensitivity. Conclusions The times at which tumours will be symptomatically detected may vary substantially between women with different breast sizes. The proposed tumour growth model represents a novel and useful approach for quantifying the effects of breast cancer risk factors on tumour growth and detection.

Introduction Mammographic density (MD) is a strong heritable and intermediate phenotype for breast cancer, but much of its genetic variation remains unexplained. We performed a large-scale genetic association study including 8,419 women of European ancestry to identify MD loci. Methods Participants of three Swedish studies were genotyped on a custom Illumina iSelect genotyping array and percent and absolute mammographic density were ascertained using semiautomated and fully automated methods from film and digital mammograms. Linear regression analysis was used to test for SNP-MD associations, adjusting for age, body mass index, menopausal status and six principal components. Meta-analyses were performed by combining P values taking sample size, study-specific inflation factor and direction of effect into account. Results Genome-wide significant associations were observed for two previously identified loci: ZNF365 (rs10995194, P = 2.3 × 10−8 for percent MD and P = 8.7 × 10−9 for absolute MD) and AREG (rs10034692, P = 6.7 × 10−9 for absolute MD). In addition, we found evidence of association for two variants at 6q25.1, both of which are known breast cancer susceptibility loci: rs9485370 in the TAB2 gene (P = 4.8 × 10−9 for percent MD and P = 2.5 × 10−8 for absolute MD) and rs60705924 in the CCDC170/ESR1 region (P = 2.2 × 10−8 for absolute MD). Both regions have been implicated in estrogen receptor signaling with TAB2 being a potential regulator of tamoxifen response. Conclusions We identified two novel MD loci at 6q25.1. These findings underscore the importance of 6q25.1 as a susceptibility region and provide more insight into the mechanisms through which MD influences breast cancer risk. Electronic supplementary material The online version of this article (doi:10.1186/s13058-015-0591-2) contains supplementary material, which is available to authorized users.

Introduction High physical activity has been shown to decrease the risk of breast cancer, potentially by a mechanism that also reduces mammographic density. We tested the hypothesis that the risk of developing breast cancer in the next 10 years according to the Tyrer-Cuzick prediction model influences the association between physical activity and mammographic density. Methods We conducted a population-based cross-sectional study of 38,913 Swedish women aged 40–74 years. Physical activity was assessed using the validated web-questionnaire Active-Q and mammographic density was measured by the fully automated volumetric Volpara method. The 10-year risk of breast cancer was estimated using the Tyrer-Cuzick (TC) prediction model. Linear regression analyses were performed to assess the association between physical activity and volumetric mammographic density and the potential interaction with the TC breast cancer risk. Results Overall, high physical activity was associated with lower absolute dense volume. As compared to women with the lowest total activity level (

High risks of familial chronic lymphatic leukemia for specific relatives: signposts for genetic discovery?

BACKGROUND: Mammographic density is a strong risk factor for breast cancer but the lack of valid fully automated methods for quantifying it has precluded its use in clinical and screening settings. We compared the performance of a recently developed automated approach, based on the public domain ImageJ programme, to the well-established semi-automated Cumulus method. METHODS: We undertook a case-control study within the intervention arm of the Age Trial, in which ∼54,000 British women were offered annual mammography at ages 40-49 years. A total of 299 breast cancer cases diagnosed during follow-up and 422 matched (on screening centre, date of birth and dates of screenings) controls were included. Medio-lateral oblique (MLO) images taken closest to age 41 and at least one year before the index case's diagnosis were digitised for each participant. Cumulus readings were performed in the left MLO and ImageJ-based readings in both left and right MLOs. Conditional logistic regression was used to examine density-breast cancer associations. RESULTS: The association between density readings taken from one single MLO and breast cancer risk was weaker for the ImageJ-based method than for Cumulus (age-body mass index-adjusted odds ratio (OR) per one s.d. increase in percent density (95% CI): 1.52 (1.24-1.86) and 1.61 (1.33-1.94), respectively). The ImageJ-based density-cancer association strengthened when the mean of left-right MLO readings was used: OR=1.61 (1.31-1.98). CONCLUSIONS: The mean of left-right MLO readings yielded by the ImageJ-based method was as strong a predictor of risk as Cumulus readings from a single MLO image. The ImageJ-based method, using the mean of two measurements, is a valid automated alternative to Cumulus for measuring density in analogue films.

INTRODUCTION: Mammographic density is a strong risk factor for breast cancer, but it is unknown whether density at first breast cancer diagnosis and changes during follow-up influences risk of non-simultaneous contralateral breast cancer (CBC). METHODS: We collected mammograms for CBC-patients (cases, N = 211) and unilateral breast cancer patients (controls, N = 211), individually matched on age and calendar period of first breast cancer diagnosis, type of adjuvant therapy and length of follow-up (mean follow-up time: 8.25 years). The odds of CBC as a function of changes of density during follow-up were investigated using conditional logistic regression, adjusting for non-dense area at diagnosis. RESULTS: Patients who experienced ≥10% absolute decrease in percent density had a 55% decreased odds of CBC (OR = 0.45 95% CI: 0.24 to 0.84) relative to patients who had little or no change in density from baseline to first follow-up mammogram (mean = 1.6 (SD = 0.6) years after diagnosis), whereas among those who experienced an absolute increase in percent density we could not detect any effect on the odds of CBC (OR = 0.83 95% CI: 0.24 to 2.87). CONCLUSION: Decrease of mammographic density within the first two years after first diagnosis is associated with a significantly reduced risk of CBC, this potential new risk predictor can thus contribute to decision-making in follow-up strategies and treatment.

Genome-wide associated studies have provided a first understanding of the genetics behind breast and prostate cancer risk. However, this methodology has limitations in that only previously detected variants can be interrogated, and thus it also is limited to variants with high frequency in a population. Genetic variants detected by genome-wide associated studies each commonly have a low impact on the risk of developing disease. Additionally, a handful of genes have been detected to confer a moderate risk of developing disease. With the advent of exome sequencing of the genome of various tumor types, it is becoming clear that very few somatically mutated genes (SMGs) are limited to a single tumor type, but rather that most SMGs are found in several tumor types, albeit in different frequencies. Here we outline a project aimed at investigating germline variation in 1,242 genes known to be somatically mutated in various cancer types, and their association to the risk of breast and prostate cancer. We have previously developed a methodology to perform a library preparation for sequencing, from genomic DNA to ligated adapters, in a single tube without purification steps (Neiman et al. 2012, submitted). Here we modified this protocol to a 96-plate format by ligating a specific barcode in each well in the plate, prior to pooling of wells in batches of 48 samples. This was followed by a sequence capture of the targeted 1,242 genes of interest. Based on previously collected sample sets where extensive questionnaire data are available for lifestyle factors, family history of cancer et cetera, we selected 480 breast cancer cases and 480 prostate cancer cases with a positive family history and/or young age of onset. Controls for the prostate cancer set were selected to be cancer-free, have a prostate specific antigen of < 1 ng/ml above the age of 56 years. For breast cancer, controls were selected to be cancer-free over the age of 68 years. This extreme design in case/control selection is aimed at enriching for genetic markers of intermediate penetrance for risk of breast and/or prostate cancer in this discovery step of the study.

Introduction Given the role of estrogen in breast carcinogenesis and the modification of estrogen receptor (ER) activity by its biochemical cofactors, we hypothesize that genetic variation within ER cofactor genes alters cellular response to estrogen exposure and consequently modifies the risk for ER-positive breast cancer. Methods We genotyped 790 tagging SNPs within 60 ER cofactor genes in 1,257 cases and 1,464 controls from Sweden and in 2,215 cases and 1,265 controls from Finland, and tested their associations with either ER-positive or ER-negative breast cancer. Results Seven SNPs showed consistent association with ER-positive breast cancer in the two independent samples, and six of them were located within PPARGC1B, encoding an ER co-activator, with the strongest association at rs741581 (odds ratio = 1.41, P = 4.84 × 10-5) that survived Bonferroni correction for multiple testing in the combined ER-positive breast cancer sample (Pcorrected = 0.03). Moreover, we also observed significant synergistic interaction (Pinteraction = 0.008) between the genetic polymorphisms within PPARGC1B and ESR1 in ER-positive breast cancer. By contrast, no consistent association was observed in ER-negative breast cancer. Furthermore, we found that administration of estrogen in the MCF-7 cell line induced PPARGC1B expression and enhanced occupancies of ER and RNA polymerase II within the region of SNP association, suggesting the upregulation of PPARGC1B expression by ESR1 activation. Conclusions Our study revealed that DNA polymorphisms of PPARGC1B, coding a bona fide ER co-activator, are associated with ER-positive breast cancer risk. The feed-forward transcriptional regulatory loop between PPARGC1B and ESR1 further augments their protein interaction, which provides a plausible mechanistic explanation for the synergistic genetic interaction between PPARGC1B and ESR1 in ER-positive breast cancer. Our study also highlights that biochemically and genomically informed candidate gene studies can enhance the discovery of interactive disease susceptibility genes.

INTRODUCTION: Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk. METHODS: We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases. RESULTS: Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer. CONCLUSIONS: ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.

Introduction The difference in breast cancer incidence and prognosis between ethnic groups seeks an explanation. We have recently shown that Swedish women are two to three times more likely to be diagnosed with breast cancer compared with Singaporean women. In the present paper, we compare breast cancer survival in the two countries. Methods We compared the survival of 10,287 Singaporean women and 17,090 Swedish women with breast cancer. Relative survival ratios were used to describe the prognosis in the two populations. A Poisson regression model was used to calculate relative risks for different follow-up periods, age groups, time of diagnosis and disease stages. Results The majority of the Swedish women had local cancer (80%) compared with Singaporean women (51%). The overall 5-year relative survival of the Swedish women appeared better (80%) than that of the Singaporean women (70%). A similar survival pattern was observed, however, between the two countries in a stage-by-stage comparison. Survival improved for all women in Singapore over the two decades, but only in the premenopausal women in Stockholm. In 1980 to 1989, premenopausal Singaporean women had 27% increased risk of death compared with Swedish women, adjusted for stage and year of follow-up, while the postmenopausal women had 48% increased risk. In 1990 to 1999, this risk decreased by 19% and 22% for the premenopausal and postmenopausal Singaporean women compared with the Swedish women. Conclusions The stage-dependent prognosis was similar for Singaporean women and for Swedish women. Singaporean women, both premenopausal and postmenopausal, had pronounced improvement in prognosis over the calendar periods, probably contributed by marked economic improvement, leading to better medical facilities and management with increased awareness of patients to diagnosis and treatment, as well as improved treatment options. Improvement seen only in the premenopausal women in Stockholm was probably due to improved treatment options.

Introduction Oestrogen exposure is a central factor in the development of breast cancer. Oestrogen receptor alpha (ESR1) is the main mediator of oestrogen effect in breast epithelia and has also been shown to be activated by epidermal growth factor (EGF). We sought to determine if common genetic variation in the ESR1 and EGF genes affects breast cancer risk, tumour characteristics or breast cancer survival. Methods We genotyped 157 single nucleotide polymorphisms (SNPs) in ESR1 and 54 SNPs in EGF in 92 Swedish controls and selected haplotype tagging SNPs (tagSNPs) that could predict both single SNP and haplotype variation in the genes with an R2 of at least 0.8. The tagSNPs were genotyped in 1,590 breast cancer cases and 1,518 controls, and their association with breast cancer risk, tumour characteristics and survival were assessed using unconditional logistic regression models, Cox proportional hazard models and haplotype analysis. Results The single tagSNP analysis did not reveal association evidence for breast cancer risk, tumour characteristics, or survival. A multi-locus analysis of five adjacent tagSNPs suggested a region in ESR1 (between rs3003925 and rs2144025) for association with breast cancer risk (p = 0.001), but the result did not withstand adjustment for multiple comparisons (p = 0.086). A similar region was also implicated by haplotype analyses, but its significance needs to be verified by follow-up analysis. Conclusion Our results do not support a strong association between common variants in the ESR1 and EGF genes and breast cancer risk, tumour characteristics or survival.

Introduction A genetic component is well established in the etiology of breast cancer. It is not well known, however, whether genetic traits also influence prognostic features of the malignant phenotype. Methods We carried out a population-based cohort study in Sweden based on the nationwide Multi-Generation Register. Among all women with breast cancer diagnosed from 1961 to 2001, 2,787 mother-daughter pairs and 831 sister pairs with breast cancer were identified; we achieved complete follow-up and classified 5-year breast cancer-specific prognosis among proband (mother or oldest sister) into tertiles as poor, intermediary, or good. We used Kaplan-Meier estimates of survival proportions and Cox models to calculate relative risks of dying from breast cancer within 5 years depending on the proband's outcome. Results The 5-year survival proportion among daughters whose mothers died within 5 years was 87% compared to 91% if the mother was alive (p = 0.03). Among sisters, the corresponding proportions were 70% and 88%, respectively (p = 0.001). After adjustment for potential confounders, daughters and sisters of a proband with poor prognosis had a 60% higher 5-year breast cancer mortality compared to those of a proband with good prognosis (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.2 to 2.2; p for trend 0.002). This association was slightly stronger among sisters (HR, 1.8; 95% CI, 1.0 to 3.4) than among daughters (HR, 1.6; 95% CI, 1.1 to 2.3). Conclusion Breast cancer prognosis of a woman predicts the survival in her first-degree relatives with breast cancer. Our novel findings suggest that breast cancer prognosis might be inherited.