Mammographic Density and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BACKGROUND

No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.

METHODS

We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.

RESULTS

The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.

CONCLUSION

BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).

Breast percent density changes in digital mammography pre- and post-radiotherapy

INTRODUCTION

Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.

METHODS

This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density.

CONCLUSIONS

This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Imaging Recommendations for Diagnosis, Staging, and Management of Hereditary Malignancies

Hereditary cancer syndromes, characterized by genetically distinct neoplasms developing in specific organs in more than one family members, predispose an individual to early onset of distinct site-specific tumors. Early age of onset, multiorgan involvement, multiple and bilateral tumors, advanced disease at presentation, and aggressive tumor histology are few characteristic features of hereditary cancer syndromes. A multidisciplinary approach to hereditary cancers has led to a paradigm shift in the field of preventive oncology and precision medicine. Imaging plays a pivotal role in the screening, testing, and follow-up of individuals and their first- and second-degree relatives with hereditary cancers. In fact, a radiologist is often the first to apprise the clinician about the possibility of an underlying hereditary cancer syndrome based on pathognomonic imaging findings. This article focuses on the imaging spectrum of few common hereditary cancer syndromes with specific mention of the imaging features of associated common and uncommon tumors in each syndrome. The screening and surveillance recommendations for each condition with specific management approaches, in contrast to sporadic cases, have also been described.

Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk

Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.

RASSF1A Suppression as a Potential Regulator of Mechano-Pathobiology Associated with Mammographic Density in BRCA Mutation Carriers

High mammographic density (MD) increases breast cancer (BC) risk and creates a stiff tissue environment. BC risk is also increased in BRCA1/2 gene mutation carriers, which may be in part due to genetic disruption of the tumour suppressor gene Ras association domain family member 1 (RASSF1A), a gene that is also directly regulated by tissue stiffness. High MD combined with BRCA1/2 mutations further increase breast cancer risk, yet BRCA1/2 mutations alone or in combination do not increase MD. The molecular basis for this additive effect therefore remains unclear. We studied the interplay between MD, stiffness, and BRCA1/2 mutation status in human mammary tissue obtained after prophylactic mastectomy from women at risk of developing BC. Our results demonstrate that RASSF1A expression increased in MCF10DCIS.com cell cultures with matrix stiffness up until ranges corresponding with BiRADs 4 stiffnesses (~16 kPa), but decreased in higher stiffnesses approaching malignancy levels (>50 kPa). Similarly, higher RASSF1A protein was seen in these cells when co-cultivated with high MD tissue in murine biochambers. Conversely, local stiffness, as measured by collagen I versus III abundance, repressed RASSF1A protein expression in BRCA1, but not BRCA2 gene mutated tissues; regional density as measured radiographically repressed RASSF1A in both BRCA1/2 mutated tissues. The combinatory effect of high MD and BRCA mutations on breast cancer risk may be due to RASSF1A gene repression in regions of increased tissue stiffness.

The TP53 mutation rate differs in breast cancers that arise in women with high or low mammographic density

Mammographic density (MD) influences breast cancer risk, but how this is mediated is unknown. Molecular differences between breast cancers arising in the context of the lowest and highest quintiles of mammographic density may identify the mechanism through which MD drives breast cancer development. Women diagnosed with invasive or in situ breast cancer where MD measurement was also available (n = 842) were identified from the Lifepool cohort of >54,000 women participating in population-based mammographic screening. This group included 142 carcinomas in the lowest quintile of MD and 119 carcinomas in the highest quintile. Clinico-pathological and family history information were recorded. Tumor DNA was collected where available (n = 56) and sequenced for breast cancer predisposition and driver gene mutations, including copy number alterations. Compared to carcinomas from low-MD breasts, those from high-MD breasts were significantly associated with a younger age at diagnosis and features associated with poor prognosis. Low- and high-MD carcinomas matched for grade, histological subtype, and hormone receptor status were compared for somatic genetic features. Low-MD carcinomas had a significantly increased frequency of TP53 mutations, higher homologous recombination deficiency, higher fraction of the genome altered, and more copy number gains on chromosome 1q and losses on 17p. While high-MD carcinomas showed enrichment of tumor-infiltrating lymphocytes in the stroma. The data demonstrate that when tumors were matched for confounding clinico-pathological features, a proportion in the lowest quintile of MD appear biologically distinct, reflective of microenvironment differences between the lowest and highest quintiles of MD.

Breast density, MR imaging biomarkers, and breast cancer risk

Mammographic breast density and various breast MRI features are imaging biomarkers that can predict a woman's future risk of breast cancer. While mammographic density (MD) has been established as an independent risk factor for the development of breast cancer, MD assessment methods need to be accurate and reproducible for widespread clinical use in stratifying patients based on their risk. In addition, a number of breast MRI biomarkers using contrast-enhanced and noncontrast-enhanced techniques are also being investigated as risk predictors. The validation and standardization of these breast MRI biomarkers will be necessary for population-based clinical implementation of patient risk stratification, as well. This review provides an update on MD assessment methods, breast MRI biomarkers, and their ability to predict breast cancer risk.

Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer

Background

Our aim is to study if mammographic density (MD) prior to neoadjuvant chemotherapy is a predictive factor in accomplishing a pathological complete response (pCR) in neoadjuvant-treated breast cancer patients.

Methods

Data on all neoadjuvant treated breast cancer patients in Southern Sweden (2005–2016) were retrospectively identified, with patient and tumor characteristics retrieved from their medical charts. Diagnostic mammograms were used to evaluate and score MD as categorized by breast composition with the Breast Imaging-Reporting and Data System (BI-RADS) 5th edition. Logistic regression was used in complete cases to assess the odds ratios (OR) for pCR compared to BI-RADS categories (a vs b-d), adjusting for patient and pre-treatment tumor characteristics.

Results

A total of 302 patients were included in the study population, of which 57 (18.9%) patients accomplished pCR following neoadjuvant chemotherapy. The number of patients in the BI-RADS category a, b, c, and d were separately 16, 120, 140, and 26, respectively. In comparison to patients with BI-RADS breast composition a, patients with denser breasts had a lower OR of accomplishing pCR: BI-RADS b 0.32 (95%CI 0.07–0.1.5), BI-RADS c 0.30 (95%CI 0.06–1.45), and BI-RADS d 0.06 (95%CI 0.01–0.56). These associations were measured with lower point estimates, but wider confidence interval, in premenopausal patients; OR of accomplishing pCR for BI-RADS d in comparison to BI-RADS a: 0.03 (95%CI 0.00–0.76).

Conclusions

The likelihood of accomplishing pCR is indicated to be lower in breast cancer patients with higher MD, which need to be analysed in future studies for improved clinical decision-making regarding neoadjuvant treatment.

BRCA1 mutation influences progesterone response in human benign mammary organoids

Background

Women, who carry a germline BRCA1 gene mutation, have a markedly increased risk of developing breast cancer during their lifetime. While BRCA1 carriers frequently develop triple-negative, basal-like, aggressive breast tumors, hormone signaling is important in the genesis of BRCA1 mutant breast cancers. We investigated the hormone response in BRCA1-mutated benign breast tissue using an in vitro organoid system.

Methods

Scaffold-free, multicellular human breast organoids generated from benign breast tissues from non-carrier or BRCA1 mutation carriers were treated in vitro with a stepwise menstrual cycle hormone regimen of estradiol (E2) and progesterone (P4) over the course of 28 days.

Results

Breast organoids exhibited characteristics of the native breast tissue, including expression of hormone receptors, collagen production, and markers of luminal and basal epithelium, and stromal fibroblasts. RNA sequencing analysis revealed distinct gene expression in response to hormone treatment in the non-carrier and BRCA1-mutated organoids. The selective progesterone receptor modulator, telapristone acetate (TPA), was used to identify specifically PR regulated genes. Specifically, extracellular matrix organization genes were regulated by E2+P4+TPA in the BRCA1-mutated organoids but not in the non-carrier organoids. In contrast, in the non-carrier organoids, known PR target genes such as the cell cycle genes were inhibited by TPA.

Conclusions

These data show that BRCA1 mutation influences hormone response and in particular PR activity which differs from that of non-carrier organoids. Our organoid model system revealed important insights into the role of PR in BRCA1-mutated benign breast cells and the critical paracrine actions that modify hormone receptor (HR)-negative cells. Further analysis of the molecular mechanism of BRCA1 and PR crosstalk is warranted using this model system.

A review of the influence of mammographic density on breast cancer clinical and pathological phenotype

PURPOSE

It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context.

METHODS

With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival.

RESULTS AND CONCLUSIONS

There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.

Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program : A retrospective cohort study

Objectives

The purpose of this study is to evaluate the predictive value of the amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE), measured at baseline on breast MRI, for breast cancer development and risk of false-positive findings in women at increased risk for breast cancer.

Methods

Negative baseline MRI scans of 1533 women participating in a screening program for women at increased risk for breast cancer between January 1, 2003, and January 1, 2014, were selected. Automated tools based on deep learning were used to obtain quantitative measures of FGT and BPE. Logistic regression using forward selection was used to assess relationships between FGT, BPE, cancer detection, false-positive recall, and false-positive biopsy.

Results

Sixty cancers were detected in follow-up. FGT was only associated to short-term cancer risk; BPE was not associated with cancer risk. High FGT and BPE did lead to more false-positive recalls at baseline (OR 1.259, p = 0.050, and OR 1.475, p = 0.003) and to more frequent false-positive biopsies at baseline (OR 1.315, p = 0.049, and OR 1.807, p = 0.002), but were not predictive for false-positive findings in subsequent screening rounds.

Conclusions

FGT and BPE, measured on baseline MRI, are not predictive for overall breast cancer development in women at increased risk. High FGT and BPE lead to more false-positive findings at baseline.

Key Points

• Amount of fibroglandular tissue is only predictive for short-term breast cancer risk in women at increased risk.

• Background parenchymal enhancement measured on baseline MRI is not predictive for breast cancer development in women at increased risk.

• High amount of fibroglandular tissue and background parenchymal enhancement lead to more false-positive findings at baseline MRI.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06020-2) contains supplementary material, which is available to authorized users.

Comparison of background parenchymal enhancement and fibroglandular density at breast magnetic resonance imaging between BRCA gene mutation carriers and non-carriers

OBJECTIVE

High background parenchymal enhancement and amount of fibroglandular tissue on breast magnetic resonance imaging are related to increased breast cancer risk. This study sought to compare these parameters between BRCA mutation carriers and non-carriers and to evaluate the potential implications of the findings for short term follow-up.

MATERIALS AND METHODS

Magnetic resonance imaging studies of known BRCA mutation carriers, were compared to age-matched non-carrier studies performed in the same center during the same period. The groups were compared for qualitative background parenchymal enhancement and amount of fibroglandular tissue using the Breast Imaging Reporting and Data System (BI-RADS).

RESULTS

Breast parenchymal enhancement was high in up to one-third of the cohort: 22% of carriers and 33% of controls (p = 0.013). These results were sustained on separate analysis of menstrual-cycle-timed examinations. Amount of fibroglandular tissue was high in most cases: 62% of carriers and 75% of controls (p = 0.004). A BI-RADS final assessment score of 3 was more common in patients with high parenchymal enhancement, especially controls.

CONCLUSION

BRCA mutation carriers demonstrated lower levels of breast parenchymal enhancement and amount of fibroglandular tissue than age-matched non-carriers. These differences are probably influenced by hormonal status, as well as highlight different risks in distinctive subgroups of breast cancer (hormone-enriched, mutation-associated defective DNA damage repair), affecting considerations of preventive medical treatment. Differences in the indications for imaging between the carrier and non-carrier groups (screening for mutations and breast cancer evaluation, respectively) probably accounted for the higher rate of BI-RADS 3 in the control group.

Personalized prevention in high risk individuals: Managing hormones and beyond

Increasing numbers of women are being identified at 'high-risk' of breast cancer, defined by The National Institute of Health and Care Excellence (NICE) as a 10-year risk of ≥8%. Classically women have been so identified through family history based risk algorithms or genetic testing of high-risk genes. Recent research has shown that assessment of mammographic density and single nucleotide polymorphisms (SNPs), when combined with established risk factors, trebles the number of women reaching the high risk threshold. The options for risk reduction in such women include endocrine chemoprevention with the selective estrogen receptor modulators tamoxifen and raloxifene or the aromatase inhibitors anastrozole or exemestane. NICE recommends offering anastrozole to postmenopausal women at high-risk of breast cancer as cost effectiveness analysis showed this to be cost saving to the National Health Service. Overall uptake to chemoprevention has been disappointingly low but this may improve with the improved efficacy of aromatase inhibitors, particularly the lack of toxicity to the endometrium and thrombogenic risks. Novel approaches to chemoprevention under investigation include lower dose and topical tamoxifen, denosumab, anti-progestins and metformin. Although oophorectomy is usually only recommended to women at increased risk of ovarian cancer it has been shown in numerous studies to reduce breast cancer risks in the general population and in those with mutations in BRCA1/2. However, recent evidence from studies that have confined analysis to true prospective follow up have cast doubt on the efficacy of oophorectomy to reduce breast cancer risk in BRCA1 mutation carriers, at least in the short-term.

Breast tissue density change after oophorectomy in BRCA mutation carrier patients using visual and volumetric analysis

OBJECTIVE

BRCA1/2 mutations account for 30-50% of hereditary breast cancers and bilateral oophorectomy is associated with a reduced risk of breast cancer in these patients. Breast density is a well-established breast cancer risk factor and is also associated with increased risk in BRCA carriers. The aim of the study was to evaluate the impact of oophorectomy on mammographic breast density and to assess which method of breast density assessment is more sensitive to change over time.

METHODS

Retrospective study of 50 BRCA1/2 patients who underwent bilateral oophorectomy and had at least a baseline and post-surgery mammogram. Mammographic breast density was determined by Volpara and consensus visual assessment by two radiologists. The primary endpoint was change in density between baseline and the first mammogram post-surgery.

RESULTS

At baseline, there was a non-significant trend for decreased density with increasing age. Volumetric breast density (VBD) significantly decreased after oophorectomy from a median VBD of 12.5% at baseline to 10.2% post-surgery which was driven by a reduction in fibroglandular volume. There was a higher absolute decrease in VBD in patients aged between 40-50 (p < 0.01). Using Volpara Density Grades (analogous to BI-RADS 4th edition density categories), 84% of females displayed a decrease in density category over the study period compared to only 76% using the radiologists' visual classification (p < 0.001) Conclusion: Oophorectomy is associated with a decrease in breast density and younger patients exhibit a larger absolute decrease. Volpara is more sensitive to identify change over time compared to visual assessment. Advances in knowledge: Oophorectomy is associated with a significant decrease in VBD in patients with BRCA mutations and Volpara Density Grades were more sensitive to identify decreases in density compared to visually assessed BI-RADS categories. Decreases in breast density following oophorectomy surgery in BRCA patients may be one of the mechanisms contributing to the observed decreased breast cancer risk after surgery. However, further studies are needed to investigate the relationship between breast density, oophorectomy and breast cancer risk in BRCA patients.

Stromal characteristics may hold the key to mammographic density: the evidence to date

There is strong epidemiological data indicating a role for increased mammographic density (MD) in predisposing to breast cancer, however, the biological mechanisms underlying this phenomenon are less well understood. Recently, studies of human breast tissues have started to characterise the features of mammographically dense breasts, and a number of in-vitro and in-vivo studies have explored the potential mechanisms through which dense breast tissue may exert this tumourigenic risk. This article aims to review both the pathological and biological evidence implicating a key role for the breast stromal compartment in MD, how this may be modified and the clinical significance of these findings. The epidemiological context will be briefly discussed but will not be covered in detail.

Increased breast tissue receptor activator of nuclear factor-κB ligand (RANKL) gene expression is associated with higher mammographic density in premenopausal women

Increased mammographic breast density is associated with a 4-6-fold increased risk of breast cancer, yet lifestyle factors that can reduce dense breasts are yet to be identified, and viable prevention strategies to reduce breast density-associated breast cancer development are yet to be developed. We investigated the associations of breast tissue receptor activator of nuclear factor-κB (RANK) pathway gene expression with mammographic density in 48 premenopausal women, with no previous history of cancer. Gene expression levels were measured in total RNA isolated from formalin-fixed paraffin-embedded breast tissue samples, using the NanoString nCounter platform. Mammographic density was classified based on the American College of Radiology Breast Imaging Reporting and Data (BI-RADS). Linear regression was used to evaluate associations between gene expression and mammographic density. The mean age of participants was 44.4 years. Women with higher breast tissue RANKL (TNFSF11) (p-value = 0.0076), and TNF (p-value = 0.007) gene expression had higher mammographic density. Our finding provides mechanistic support for a breast cancer chemoprevention trial with a RANKL inhibitor among high-risk premenopausal women with dense breasts.

Modifiers of breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers

Pathogenic mutations in BRCA1 and BRCA2 are associated with high risks of breast and ovarian cancer. However, penetrance estimates for mutation carriers have been found to vary substantially between studies, and the observed differences in risk are consistent with the hypothesis that genetic and environmental factors modify cancer risks for women with these mutations. Direct evidence that this is the case has emerged in the past decade, through large-scale international collaborative efforts. Here, we describe the methodological challenges in the identification and characterisation of these risk-modifying factors, review the latest evidence on genetic and lifestyle/hormonal risk factors that modify breast and ovarian cancer risks for women with BRCA1 and BRCA2 mutations and outline the implications of these findings for cancer risk prediction. We also review the unresolved issues in this area of research and identify strategies of clinical implementation so that women with BRCA1 and BRCA2 mutations are no longer counselled on the basis of 'average' risk estimates.

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

BACKGROUND

The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.

MAIN TEXT

The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.

CONCLUSIONS

The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Large genomic rearrangements in the familial breast and ovarian cancer gene BRCA1 are associated with an increased frequency of high risk features

Large genomic rearrangements (LGRs) account for at least 10% of the mutations in BRCA1 and 5% of BRCA2 mutations in outbred hereditary breast and ovarian cancer (HBOC) families. Data from some series suggest LGRs represent particularly penetrant mutations. 1,034 index cases from HBOC families underwent comprehensive BRCA1 and BRCA2 mutation testing, including screening for LGRs. The personal and family history of 254 identified mutation carriers were compared based on mutation type. Thirty-six LGRs were detected; 32/122 (26%) BRCA1 and 4/132 (3%) BRCA2 mutations. High risk features (bilateral breast cancer, diagnosis <40 years, ovarian cancer, male breast cancer) were more commonly associated with an LGR than a non-LGR mutation (p = 0.008), In families with a BRCA1 LGR the mean age of breast cancer diagnosis was younger than in families with a non-LGR BRCA1 mutation (42.5 vs. 46.1 years, p = 0.007). Across the entire group of mutation positive families the number of relatives affected by breast or ovarian cancer was increased [LGR 3.7 vs. non- LGR 2.8 per family, p value (adjusted for genotype) = 0.047]. Excluding index cases, the odds ratio for breast cancer in BRCA1 families with an LGR was 1.42 (95% CI 1.24-1.63) and for ovarian cancer 1.66 (95% CI 1.10-2.49). The increased cancer risk was reflected in significantly higher risk assessments by mutation prediction tools. LGRs are associated with higher cancer risks. If validated, LGRs could be included in cancer risk prediction tools to improve personalised cancer risk prediction estimates and may guide cost-minimising mutation screening strategies in some healthcare settings.

Mammographic density and breast cancer in women from high risk families

Introduction

Mammographic density (MD) is one of the strongest determinants of sporadic breast cancer (BC). In this study, we compared MD in BRCA1/2 mutation carriers and non-carriers from BRCA1/2 mutation-positive families and investigated the association between MD and BC among BRCA1/2 mutation carriers per type of mutation and tumor subtype.

Methods

The study was carried out in 1039 female members of BRCA1 and BRCA2 mutation-positive families followed at 16 Spanish Genetic Counseling Units. Participants’ density was scored retrospectively from available mammograms by a single blinded radiologist using a 5-category scale (<10 %, 10-25 %, 25-50 %, 50-75 %, >75 %). In BC cases, we selected mammograms taken prior to diagnosis or from the contralateral breast, whereas, in non-cases, the last screening mammogram was evaluated. MD distribution in carriers and non-carriers was compared using ordinal logistic models, and the association between MD and BC in BRCA1/2 mutation carriers was studied using logistic regression. Huber-White robust estimators of variance were used to take into account correlations between family members. A similar multinomial model was used to explore this association by BC subtype.

Results

We identified and scored mammograms from 341 BRCA1, 350 BRCA2 mutation carriers and 229 non-carriers. Compared to non-carriers, MD was significantly lower among BRCA2 mutation carriers (odds ratio (OR) =0.71; P-value=0.04), but not among BRCA1 carriers (OR=0.84; P-value=0.33). MD was associated with subsequent development BC (OR per category of MD=1.45; 95 % confidence interval=1.18-1.78, P-value<0.001), with no significant differences between BRCA1 and BRCA2 mutation carriers (P-value=0.48). Finally, no statistically significant differences were observed in the association of MD with specific BC subtypes.

Conclusions

Our study, the largest to date on this issue, confirms that MD is an independent risk factor for all BC subtypes in either BRCA1 and BRCA2 mutation carriers, and should be considered a phenotype risk marker in this context.

Comparison of contrast enhancement and diffusion-weighted magnetic resonance imaging in healthy and cancerous breast tissue

OBJECTIVE

To measure background parenchymal enhancement (BPE) and compare with other contrast enhancement values and diffusion-weighted MRI parameters in healthy and cancerous breast tissue at the clinical level.

MATERIALS AND METHODS

This HIPAA-compliant, IRB approved retrospective study enrolled 77 patients (38 patients with breast cancer - mean age 51.8 ± 10.0 years; 39 high-risk patients for screening evaluation - mean age 46.3 ± 11.7 years), who underwent contrast-enhanced 3T breast MRI. Contrast enhanced MRI and diffusion-weighted imaging were performed to quantify BPE, lesion contrast enhancement, and apparent diffusion coefficient (ADC) metrics in fibroglandular tissue (FGT) and lesions.

RESULTS

BPE did not correlate with ADC values. Mean BPE for the lesion-bearing patients was higher (43.9%) compared to that of the high-risk screening patients (28.3%, p=0.004). Significant correlation (r=0.37, p<0.05) was found between BPE and lesion contrast enhancement.

CONCLUSION

No significant association was observed between parenchymal or lesion enhancement with conventional apparent diffusion metrics, suggesting that proliferative processes are not co-regulated in cancerous and parenchymal tissue.

High mammographic density is associated with an increase in stromal collagen and immune cells within the mammary epithelium

INTRODUCTION

Mammographic density (MD), after adjustment for a women's age and body mass index, is a strong and independent risk factor for breast cancer (BC). Although the BC risk attributable to increased MD is significant in healthy women, the biological basis of high mammographic density (HMD) causation and how it raises BC risk remain elusive. We assessed the histological and immunohistochemical differences between matched HMD and low mammographic density (LMD) breast tissues from healthy women to define which cell features may mediate the increased MD and MD-associated BC risk.

METHODS

Tissues were obtained between 2008 and 2013 from 41 women undergoing prophylactic mastectomy because of their high BC risk profile. Tissue slices resected from the mastectomy specimens were X-rayed, then HMD and LMD regions were dissected based on radiological appearance. The histological composition, aromatase immunoreactivity, hormone receptor status and proliferation status were assessed, as were collagen amount and orientation, epithelial subsets and immune cell status.

RESULTS

HMD tissue had a significantly greater proportion of stroma, collagen and epithelium, as well as less fat, than LMD tissue did. Second harmonic generation imaging demonstrated more organised stromal collagen in HMD tissues than in LMD tissues. There was significantly more aromatase immunoreactivity in both the stromal and glandular regions of HMD tissues than in those regions of LMD tissues, although no significant differences in levels of oestrogen receptor, progesterone receptor or Ki-67 expression were detected. The number of macrophages within the epithelium or stroma did not change; however, HMD stroma exhibited less CD206(+) alternatively activated macrophages. Epithelial cell maturation was not altered in HMD samples, and no evidence of epithelial-mesenchymal transition was seen; however, there was a significant increase in vimentin(+)/CD45(+) immune cells within the epithelial layer in HMD tissues.

CONCLUSIONS

We confirmed increased proportions of stroma and epithelium, increased aromatase activity and no changes in hormone receptor or Ki-67 marker status in HMD tissue. The HMD region showed increased collagen deposition and organisation as well as decreased alternatively activated macrophages in the stroma. The HMD epithelium may be a site for local inflammation, as we observed a significant increase in CD45(+)/vimentin(+) immune cells in this area.

Impact of the new density reporting laws: radiologist perceptions and actual behavior

RATIONALE AND OBJECTIVES

To assess radiologists' perceptions of how the new Breast Density Notification Act (BDNA) of Pennsylvania would affect their breast density reporting and their actual reporting patterns after implementation.

MATERIALS AND METHODS

Under an institutional review board-approved protocol, we surveyed 21 radiologists about how they believe the new law affected their breast density reporting patterns and analyzed actual changes for 16 respondents before and after the law took effect. Three hundred consecutive reports were assessed for each radiologist before and after the effective date. The distributions of reported density Breast Imaging Reporting and Data System (BI-RADS) (1-4) were compared using a type III test in the context of an ordinal mixed model accounting for between-reader variability and adjusting for age (PROC GLIMMIX, SAS, version 9.3) using a two-sided .05 significance level.

RESULTS

Seventeen radiologists responded to the survey; however, one retired shortly after responding. Of the 16 respondents, 56% (nine of 16) did not favor the law, 13% (two of 16) were in favor, and 31% (five of 16) were neutral. The fraction perceived that after implementation, they rated more, equally, or less frequently breasts as scattered fibroglandular densities (BI-RADS 2) versus heterogeneously dense rating (BI-RADS 3) was 50% (eight of 16), 44% (seven of 16), and 6% (one of 16), respectively. In practice, 44% (seven of 16) performed differently than their survey answers. Fourteen of 16 radiologists increased the frequency of reported BI-RADS 2 scores after BDNA implementation with seven having statistically significant (P < .05) increases after adjusting for age differences.

CONCLUSIONS

Radiologists' reporting patterns changed, at least for a short duration, after the new density reporting law and for some of the radiologists in an unexpected way.

Mammographic density and breast cancer risk by family history in women of white and Asian ancestry

PURPOSE

Mammographic density, i.e., the radiographic appearance of the breast, is a strong predictor of breast cancer risk. To determine whether the association of breast density with breast cancer is modified by a first-degree family history of breast cancer (FHBC) in women of white and Asian ancestry, we analyzed data from four case-control studies conducted in the USA and Japan.

METHODS

The study population included 1,699 breast cancer cases and 2,422 controls, of whom 45% reported white (N = 1,849) and 40% Asian (N = 1,633) ancestry. To standardize mammographic density assessment, a single observer re-read all mammograms using one type of interactive thresholding software. Logistic regression was applied to estimate odds ratios (OR) while adjusting for confounders.

RESULTS

Overall, 496 (12%) of participants reported a FHBC, which was significantly associated with breast cancer risk in the adjusted model (OR 1.51; 95% CI 1.23-1.84). There was a statistically significant interaction on a multiplicative scale between FHBC and continuous percent density (per 10 % density: p = 0.03). The OR per 10% increase in percent density was higher among women with a FHBC (OR 1.30; 95% CI 1.13-1.49) than among those without a FHBC (OR 1.14; 1.09-1.20). This pattern was apparent in whites and Asians. The respective ORs were 1.45 (95% CI 1.17-1.80) versus 1.22 (95% CI 1.14-1.32) in whites, whereas the values in Asians were only 1.24 (95% CI 0.97-1.58) versus 1.09 (95% CI 1.00-1.19).

CONCLUSIONS

These findings support the hypothesis that women with a FHBC appear to have a higher risk of breast cancer associated with percent mammographic density than women without a FHBC.

A case-control study to assess the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk

Mammographic density is a strong risk factor for breast cancer, but its potential application in risk management is not clear, partly due to uncertainties about its interaction with other breast cancer risk factors. We aimed to quantify the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk of breast cancer (average lifetime risk of 23%), in particular in premenopausal women, and to investigate its relationship with other breast cancer risk factors in this population. We present the results from a case-control study nested with the FH01 cohort study of 6,710 women mostly aged 40-49 at intermediate familial risk of breast cancer. One hundred and three cases of breast cancer were age-matched to one or two controls. Density was measured by semiautomated interactive thresholding. Absolute density, but not percent density, was a significant risk factor for breast cancer in this population after adjusting for area of nondense tissue (OR per 10 cm(2) = 1.07, 95% CI 1.00-1.15, p = 0.04). The effect was stronger in premenopausal women, who made up the majority of the study population. Absolute density remained a significant predictor of breast cancer risk after adjusting for age at menarche, age at first live birth, parity, past or present hormone replacement therapy, and the Tyrer-Cuzick 10-year relative risk estimate of breast cancer. Absolute density can improve breast cancer risk stratification and delineation of high-risk groups alongside the Tyrer-Cuzick 10-year relative risk estimate.

Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

BACKGROUND

Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.

METHODS

Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).

RESULTS

548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.

CONCLUSIONS

Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

INTRODUCTION

Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers.

METHODS

We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject's digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject's belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model's discriminatory capacity.

RESULTS

In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density.

CONCLUSIONS

Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

Introduction

Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers.

Methods

We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity.

Results

In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density.

Conclusions

Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.

Measurement of breast density with digital breast tomosynthesis--a systematic review

Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54-0.94, whilst that of BI-RADs and MedDensity ranged from 0.48-0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred.

Ethnic background and genetic variation in the evaluation of cancer risk: a systematic review

The clinical use of genetic variation in the evaluation of cancer risk is expanding, and thus understanding how determinants of cancer susceptibility identified in one population can be applied to another is of growing importance. However there is considerable debate on the relevance of ethnic background in clinical genetics, reflecting both the significance and complexity of genetic heritage. We address this via a systematic review of reported associations with cancer risk for 82 markers in 68 studies across six different cancer types, comparing association results between ethnic groups and examining linkage disequilibrium between risk alleles and nearby genetic loci. We find that the relevance of ethnic background depends on the question. If asked whether the association of variants with disease risk is conserved across ethnic boundaries, we find that the answer is yes, the majority of markers show insignificant variability in association with cancer risk across ethnic groups. However if the question is whether a significant association between a variant and cancer risk is likely to reproduce, the answer is no, most markers do not validate in an ethnic group other than the discovery cohort's ancestry. This lack of reproducibility is not attributable to studies being inadequately populated due to low allele frequency in other ethnic groups. Instead, differences in local genomic structure between ethnic groups are associated with the strength of association with cancer risk and therefore confound interpretation of the implied physiologic association tracked by the disease allele. This suggest that a biological association for cancer risk alleles may be broadly consistent across ethnic boundaries, but reproduction of a clinical study in another ethnic group is uncommon, in part due to confounding genomic architecture. As clinical studies are increasingly performed globally this has important implications for how cancer risk stratifiers should be studied and employed.

Diffusion-weighted imaging of the high-risk breast: Apparent diffusion coefficient values and their relationship to breast density

PURPOSE

To document the apparent diffusion coefficient (ADC) of fibroglandular breast tissue in women at high-risk of developing breast cancer and investigate the relationship between ADC and breast density.

MATERIALS AND METHODS

Local research ethics approval was obtained. A total of 33 high-risk women including 17 BRCA1/2 mutation carriers (mean age, 43 years) and 16 women postmantle irradiation (mean age 40 years) underwent diffusion-weighted MRI between days 6 and 16 of their menstrual cycle. ADC histograms from a region of interest in fibroglandular tissue and mammographic breast density measurements were obtained. Mean, percentile ADC values (10th, 25th, 50th, 75th, 90th) and skew were compared for the two groups; ADC and mammographic breast density were correlated.

RESULTS

Mean ADC values (×10(-6) mm(2) /s) were 2017 ± 197 in postmantle irradiated women and 1827 ± 289 in BRCA1/2 mutation carriers (P = 0.035) with significant differences at all percentiles (P < 0.0001) but not skew (P = 0.44). ADC values showed weak positive correlation with mammographic breast density in BRCA1/2 mutation carriers (r = 0.51, P = 0.043) but not in postmantle radiotherapy patients (r = 0.49, P = 0.13).

CONCLUSION

Higher ADC values seen in fibroglandular tissue postmantle irradiation compared with BRCA1/2 mutation carriers has potential to improve tumor detection in these patients. Lack of correlation between ADC and breast density postmantle irradiation may be a result of microstructural changes.

High mammographic density in women of Ashkenazi Jewish descent

Introduction

Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry and adjusted PMD.

Methods

We selected self-identified Caucasian women in the California Pacific Medical Center Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of age-adjusted and body mass index-adjusted PMD. Our final dataset included 474 women with the highest adjusted PMD and 469 with the lowest genotyped on the Illumina 1 M platform. Principal component analysis (PCA) and identity-by-descent analyses allowed us to infer the women's genetic ancestry and correlate it with adjusted PMD.

Results

Women of Ashkenazi Jewish ancestry, as defined by the first principal component of PCA and identity-by-descent analyses, represented approximately 15% of the sample. Ashkenazi Jewish ancestry, defined by the first principal component of PCA, was associated with higher adjusted PMD (P = 0.004). Using multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity and use of postmenopausal hormone therapy, did not attenuate the association.

Conclusions

Women of Ashkenazi Jewish ancestry, based on genetic analysis, are more likely to have high age-adjusted and body mass index-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or environmental risk factors that increase mammographic density.

Breast cancer risk factors differ between Asian and white women with BRCA1/2 mutations

The prevalence and penetrance of BRCA1 and BRCA2 (BRCA1/2) mutations may differ between Asians and whites. We investigated BRCA1/2 mutations and cancer risk factors in a clinic-based sample. BRCA1/2 mutation carriers were enrolled from cancer genetics clinics in Hong Kong and California according to standardized entry criteria. We compared BRCA mutation position, cancer history, hormonal and reproductive exposures. We analyzed DNA samples for single-nucleotide polymorphisms reported to modify breast cancer risk. We performed logistic regression to identify independent predictors of breast cancer. Fifty Asian women and forty-nine white American women were enrolled. BRCA1 mutations were more common among whites (67 vs. 42 %, p = 0.02), and BRCA2 mutations among Asians (58 vs. 37 %, p = 0.04). More Asians had breast cancer (76 vs. 53 %, p = 0.03); more whites had relatives with breast cancer (86 vs. 50 %, p = 0.0003). More whites than Asians had breastfed (71 vs. 42 %, p = 0.005), had high BMI (median 24.3 vs. 21.2, p = 0.04), consumed alcohol (2 drinks/week vs. 0, p < 0.001), and had oophorectomy (61 vs. 34 %, p = 0.01). Asians had a higher frequency of risk-associated alleles in MAP3K1 (88 vs. 59 %, p = 0.005) and TOX3/TNRC9 (88 vs. 55 %, p = 0.0002). On logistic regression, MAP3K1 was associated with increased breast cancer risk for BRCA2, but not BRCA1 mutation carriers; breast density was associated with increased risk among Asians but not whites. We found significant differences in breast cancer risk factors between Asian and white BRCA1/2 mutation carriers. Further investigation of racial differences in BRCA1/2 mutation epidemiology could inform targeted cancer risk-reduction strategies.

Breast density as indicator for the use of mammography or MRI to screen women with familial risk for breast cancer (FaMRIsc): a multicentre randomized controlled trial

BACKGROUND

To reduce mortality, women with a family history of breast cancer often start mammography screening at a younger age than the general population. Breast density is high in over 50% of women younger than 50 years. With high breast density, breast cancer incidence increases, but sensitivity of mammography decreases. Therefore, mammography might not be the optimal method for breast cancer screening in young women. Adding MRI increases sensitivity, but also the risk of false-positive results. The limitation of all previous MRI screening studies is that they do not contain a comparison group; all participants received both MRI and mammography. Therefore, we cannot empirically assess in which stage tumours would have been detected by either test.The aim of the Familial MRI Screening Study (FaMRIsc) is to compare the efficacy of MRI screening to mammography for women with a familial risk. Furthermore, we will assess the influence of breast density.

METHODS/DESIGN

This Dutch multicentre, randomized controlled trial, with balanced randomisation (1:1) has a parallel grouped design. Women with a cumulative lifetime risk for breast cancer due to their family history of ≥20%, aged 30-55 years are eligible. Identified BRCA1/2 mutation carriers or women with 50% risk of carrying a mutation are excluded. Group 1 receives yearly mammography and clinical breast examination (n = 1000), and group 2 yearly MRI and clinical breast examination, and mammography biennially (n = 1000).Primary endpoints are the number and stage of the detected breast cancers in each arm. Secondary endpoints are the number of false-positive results in both screening arms. Furthermore, sensitivity and positive predictive value of both screening strategies will be assessed. Cost-effectiveness of both strategies will be assessed. Analyses will also be performed with mammographic density as stratification factor.

DISCUSSION

Personalized breast cancer screening might optimize mortality reduction with less over diagnosis. Breast density may be a key discriminator for selecting the optimal screening strategy for women < 55 years with familial breast cancer risk; mammography or MRI. These issues are addressed in the FaMRIsc study including high risk women due to a familial predisposition.

TRIAL REGISTRATION

Netherland Trial Register NTR2789.

Beyond prostate-specific antigen - future biomarkers for the early detection and management of prostate cancer

Prostate-specific antigen is currently commonly used as a screening biomarker for prostate cancer, but it has limitations in both sensitivity and specificity. The development of novel biomarkers for early cancer detection has the potential to improve survival, reduce unnecessary investigations and benefit the health economy. Here we review the use and limitations of prostate-specific antigen and its subtypes, urinary biomarkers including PCA3, alpha-methylacyl-CoA racemase, the TMPRSS2-ERG fusion gene and microseminoprotein-beta, and other novel markers in both serum and urine. Many of these biomarkers are at early stages of development and require evaluation in prospective trials to determine their potential usefulness in clinical practice. Genetic profiling may allow for the targeting of high-risk populations for screening and may offer the opportunity to combine biomarker results with genotype to aid risk assessment.

Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk

BACKGROUND

Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures.

METHODS

We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status.

RESULTS

Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07).

CONCLUSION

We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland.

IMPACT

We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association.

Unravelling modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers: update on genetic modifiers

Pathogenic mutations in the tumour suppressor genes BRCA1 and BRCA2 confer increased risks for breast and ovarian cancer and account for approximately 15% of the excess familial risk of breast cancer amongst first-degree relatives of patients with breast cancer. There is considerable evidence indicating that these risks vary by other genetic and environmental factors clustering in families. In the past few years, based on the availability of genome-wide association data and samples from large collaborative studies, several common alleles have been found to modify breast or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. These common alleles explain a small proportion of the genetic variability in breast or ovarian cancer risk for mutation carriers, suggesting more modifiers remain to be identified. We review the so far identified genetic modifiers of breast and ovarian cancer risk and consider the implications for risk prediction. BRCA1 and BRCA2 mutation carriers could be some of the first to benefit from clinical applications of common variants identified through genome-wide association studies. However, to be able to provide more individualized risk estimates, it will be important to understand how the associations vary with different tumour characteristics and their interactions with other genetic and environmental modifiers.

Mammographic breast density and breast cancer: evidence of a shared genetic basis

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Common variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 are associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers

INTRODUCTION

Several common alleles have been shown to be associated with breast and/or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. Recent genome-wide association studies of breast cancer have identified eight additional breast cancer susceptibility loci: rs1011970 (9p21, CDKN2A/B), rs10995190 (ZNF365), rs704010 (ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2).

METHODS

To evaluate whether these single nucleotide polymorphisms (SNPs) are associated with breast cancer risk for BRCA1 and BRCA2 carriers, we genotyped these SNPs in 12,599 BRCA1 and 7,132 BRCA2 mutation carriers and analysed the associations with breast cancer risk within a retrospective likelihood framework.

RESULTS

Only SNP rs10771399 near PTHLH was associated with breast cancer risk for BRCA1 mutation carriers (per-allele hazard ratio (HR) = 0.87, 95% CI: 0.81 to 0.94, P-trend = 3 × 10-4). The association was restricted to mutations proven or predicted to lead to absence of protein expression (HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3.1 × 10-5, P-difference = 0.03). Four SNPs were associated with the risk of breast cancer for BRCA2 mutation carriers: rs10995190, P-trend = 0.015; rs1011970, P-trend = 0.048; rs865686, 2df-P = 0.007; rs1292011 2df-P = 0.03. rs10771399 (PTHLH) was predominantly associated with estrogen receptor (ER)-negative breast cancer for BRCA1 mutation carriers (HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 4 × 10-5) and there was marginal evidence of association with ER-negative breast cancer for BRCA2 mutation carriers (HR = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049).

CONCLUSIONS

The present findings, in combination with previously identified modifiers of risk, will ultimately lead to more accurate risk prediction and an improved understanding of the disease etiology in BRCA1 and BRCA2 mutation carriers.