Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography

Genetic architecture of mammographic density as a risk factor for breast cancer: a systematic review

BACKGROUND

Mammography Density (MD) is a potential risk marker that is influenced by genetic polymorphisms and can subsequently modulate the risk of breast cancer. This qualitative systematic review summarizes the genes and biological pathways involved in breast density and discusses the potential clinical implications in view of the genetic risk profile for breast density.

METHODS

The terms related to "Common genetic variations" and "Breast density" were searched in Scopus, PubMed, and Web of Science databases. Gene pathways analysis and assessment of protein interactions were also performed.

RESULTS

Eighty-six studies including 111 genes, reported a significant association between mammographic density in different populations. ESR1, IGF1, IGFBP3, and ZNF365 were the most prevalent genes. Moreover, estrogen metabolism, signal transduction, and prolactin signaling pathways were significantly related to the associated genes. Mammography density was an associated phenotype, and eight out of 111 genes, including COMT, CYP19A1, CYP1B1, ESR1, IGF1, IGFBP1, IGFBP3, and LSP1, were modifiers of this trait.

CONCLUSION

Genes involved in developmental processes and the evolution of secondary sexual traits play an important role in determining mammographic density. Due to the effect of breast tissue density on the risk of breast cancer, these genes may also be associated with breast cancer risk.

The epidemiologic factors associated with breast density: A review

In recent years, some studies have evaluated the epidemiologic factors associated with breast density. However, the variant and inconsistent results exist. In addition, breast density has been proved to be a significant risk factor associated with breast cancer. Our review summarized the published studies and emphasized the crucial factors including epidemiological factors associated with breast density. In addition, we also discussed the potential reasons for the discrepant results with risk factors. To decrease the incidence and mortality rates for breast cancer, in clinical practice, breast density should be included for clinical risk models in addition to epidemiological factors, and physicians should get more concentrate on those women with risk factors and provide risk-based breast cancer screening regimens.

Mutation of NEKL-4/NEK10 and TTLL genes suppress neuronal ciliary degeneration caused by loss of CCPP-1 deglutamylase function

Ciliary microtubules are subject to post-translational modifications that act as a "Tubulin Code" to regulate motor traffic, binding proteins and stability. In humans, loss of CCP1, a cytosolic carboxypeptidase and tubulin deglutamylating enzyme, causes infantile-onset neurodegeneration. In C. elegans, mutations in ccpp-1, the homolog of CCP1, result in progressive degeneration of neuronal cilia and loss of neuronal function. To identify genes that regulate microtubule glutamylation and ciliary integrity, we performed a forward genetic screen for suppressors of ciliary degeneration in ccpp-1 mutants. We isolated the ttll-5(my38) suppressor, a mutation in a tubulin tyrosine ligase-like glutamylase gene. We show that mutation in the ttll-4, ttll-5, or ttll-11 gene suppressed the hyperglutamylation-induced loss of ciliary dye filling and kinesin-2 mislocalization in ccpp-1 cilia. We also identified the nekl-4(my31) suppressor, an allele affecting the NIMA (Never in Mitosis A)-related kinase NEKL-4/NEK10. In humans, NEK10 mutation causes bronchiectasis, an airway and mucociliary transport disorder caused by defective motile cilia. C. elegans NEKL-4 localizes to the ciliary base but does not localize to cilia, suggesting an indirect role in ciliary processes. This work defines a pathway in which glutamylation, a component of the Tubulin Code, is written by TTLL-4, TTLL-5, and TTLL-11; is erased by CCPP-1; is read by ciliary kinesins; and its downstream effects are modulated by NEKL-4 activity. Identification of regulators of microtubule glutamylation in diverse cellular contexts is important to the development of effective therapies for disorders characterized by changes in microtubule glutamylation. By identifying C. elegans genes important for neuronal and ciliary stability, our work may inform research into the roles of the tubulin code in human ciliopathies and neurodegenerative diseases.

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.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

Genetic Modifiers of the Breast Tumor Microenvironment

Multiple nonmalignant cell types in the tumor microenvironment (TME) impact breast cancer risk, metastasis, and response to therapy, yet most heritable mechanisms that influence TME cell function and breast cancer outcomes are largely unknown. Breast cancer risk is ∼30% heritable and >170 genetic loci have been associated with breast cancer traits. However, the majority of candidate genes have poorly defined mechanistic roles in breast cancer biology. Research indicates that breast cancer risk modifiers directly impact cancer cells, yet it is equally plausible that some modifier alleles impact the nonmalignant TME. The objective of this review is to examine the list of current breast cancer candidate genes that may modify breast cancer risk and outcome through the TME.

Breast cancer: influence of tumour volume estimation method at MRI on prediction of pathological response to neoadjuvant chemotherapy

OBJECTIVE

Does method of tumour volume measurement on MRI influence prediction of treatment outcome in patients with primary breast cancer undergoing neoadjuvant chemotherapy (NAC)?.

METHOD

The study comprised of 136 women with biopsy-proven breast cancer scheduled for MRI monitoring during NAC treatment. Dynamic contrast-enhanced images were acquired at baseline (pre-NAC) and interim (post three NAC cycles) time points. Functional tumour volumes (FTVs), automatically derived using vendor software and enhancing tumour volumes (ETVs), user-derived using a semi-automated thresholding technique, were calculated at each time point and percentage changes calculated. Response, assessed using residual cancer burden (RCB) score on surgically resected specimens, was compared statistically with volumetric changes and receiver operating characteristic analysis performed.

RESULTS

Mean volumetric differences for each RCB response category were (FTV/ETV): pathological complete response (pCR) 95.5/96.8%, RCB-I 69.8/66.7%, RCB-II 64.0/65.5%, RCB-III 25.4/24.0%. Differences were significant between pCR and RCB-II/RCB-III categories (p < 0.040; unpaired t-test) using FTV measures and between pCR and RCB-I/RCB-II/RCB-III categories (p < 0.006; unpaired t-test) when ETV was used. Receiver operating characteristic analysis for pCR identification post-NAC yielded area under the curve for FTV/ETV of 0.834/0.920 respectively. Sensitivity and specificity for FTV was 80.0 and 76.8% for FTV and 81.0 and 91.8% for ETV.

CONCLUSION

ETV changes can identify patients likely to achieve a complete response to NAC. Potentially, this could impact patient management regarding the possible avoidance of post-NAC surgery. Advances in Knowledge: Interim changes in ETV are more useful than FTV in predicting final pathological response to NAC. ETV differentiates patients who will achieve a complete response from those who will have residual disease.

Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density

Background

Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density.

Methods

Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI).

Results

Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD.

Conclusions

Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0940-z) contains supplementary material, which is available to authorized users.

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.

Breast density: why all the fuss?

The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

PURPOSE

With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations.

METHODS

The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman- and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (r_G) and breast PD (r_PD), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images.

RESULTS

All density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, p_w < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman- and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (-0.2 ≤ r_G ≤ 0.2), weakly to moderately associated with breast PD (-0.6 ≤ r_PD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6).

CONCLUSIONS

Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations.

Is There an Association Between Bone Mineral Density and Mammographic Density? A Systematic Review

INTRODUCTION

Both bone mineral density (BMD) and breast density are related to reproductive hormone levels. This suggests that BMD and breast density could be meaningfully associated, and serve as surrogate markers for breast cancer risk. However, few studies have investigated the association of BMD with percent mammographic density, making it difficult to draw meaningful conclusions.

MATERIALS AND METHODS

We conducted a systematic review of studies published in electronic databases till April 2016 using the following search terms: "bone density," "bone mineral density," "mammographic breast density," "breast density," and "mammographic density." We identified 203 articles, of which 8 met the inclusion criteria for this review.

RESULTS

BMD does not appear to be associated with percent mammographic density. BMD at the spine was weakly positively associated with percent mammographic density among postmenopausal women who were not hormone users, while BMD at the hip and legs was positively associated with percent mammographic density among premenopausal women. On the other hand, one study reported an inverse association of BMD at the spine and hip with percent mammographic density among perimenopausal women.

CONCLUSION

In this review, we found no evidence of an association between BMD and percent mammographic density.

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density

BACKGROUND

Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures.

METHODS

Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided.

RESULTS

There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race.

CONCLUSIONS

After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.