Indicator Of Breast Cancer Risk Research Articles

Association Between Radio-pathological Breast Tumor Characteristics and Mammographic Breast Density

Background: Despite mammographic density being a strong indicator of breast cancer risk, it is unclear whether there is association between breast density and certain breast cancer subtypes. This study aimed to investigate the relation between radiologic breast density category and tumor characteristics. Methods: Patients with histologically proven breast cancer who had undergone diagnostic mammography were reviewed retrospectively from 2016 to 2019. The American College of Radiology BI-RADS mammographic density categories were recorded and grouped into low (a and b), and high (c and d). Patient characteristics as well as tumor size, border, pathology, ER, PR, Her2 immunohistochemistry was extracted from the mammography, ultrasonography, and core needle pathology reports. Binary logistic regression model was used to analyze the association between breast density and receptors, molecular subtypes, or tumor features. Results: The present study was comprised of 129 patients, with 7, 47, 41, and 34 patients in the density categories a, b, c, and d, respectively. Patients who had a higher breast density were significantly younger (p=0.001). Those with a lower density were more likely to have HER2, IHC 0 tumors (Odds ratio adjusted for age = 4.9, 95% CI 1.25-18.27, p=0.022). Mammographic density was not related to molecular subtypes and other tumor features. Conclusion: Mammographic dense breast may be associated with Her2 positive breast cancer.

Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Multi-view fusion-based local-global dynamic pyramid convolutional cross-tansformer network for density classification in mammography

Object. Breast density is an important indicator of breast cancer risk. However, existing methods for breast density classification do not fully utilise the multi-view information produced by mammography and thus have limited classification accuracy. Method. In this paper, we propose a multi-view fusion network, denoted local-global dynamic pyramidal-convolution transformer network (LG-DPTNet), for breast density classification in mammography. First, for single-view feature extraction, we develop a dynamic pyramid convolutional network to enable the network to adaptively learn global and local features. Second, we address the problem exhibited by traditional multi-view fusion methods, this is based on a cross-transformer that integrates fine-grained information and global contextual information from different views and thereby provides accurate predictions for the network. Finally, we use an asymmetric focal loss function instead of traditional cross-entropy loss during network training to solve the problem of class imbalance in public datasets, thereby further improving the performance of the model. Results. We evaluated the effectiveness of our method on two publicly available mammography datasets, CBIS-DDSM and INbreast, and achieved areas under the curve (AUC) of 96.73% and 91.12%, respectively. Conclusion. Our experiments demonstrated that the devised fusion model can more effectively utilise the information contained in multiple views than existing models and exhibits classification performance that is superior to that of baseline and state-of-the-art methods.

FGF1 supports glycolytic metabolism through the estrogen receptor in endocrine-resistant and obesity-associated breast cancer

BackgroundObesity increases breast cancer risk and breast cancer-specific mortality, particularly for people with estrogen receptor (ER)-positive tumors. Body mass index (BMI) is used to define obesity, but it may not be the best predictor of breast cancer risk or prognosis on an individual level. Adult weight gain is an independent indicator of breast cancer risk. Our previous work described a murine model of obesity, ER-positive breast cancer, and weight gain and identified fibroblast growth factor receptor (FGFR) as a potential driver of tumor progression. During adipose tissue expansion, the FGF1 ligand is produced by hypertrophic adipocytes as a stimulus to stromal preadipocytes that proliferate and differentiate to provide additional lipid storage capacity. In breast adipose tissue, FGF1 production may stimulate cancer cell proliferation and tumor progression.MethodsWe explored the effects of FGF1 on ER-positive endocrine-sensitive and resistant breast cancer and compared that to the effects of the canonical ER ligand, estradiol. We used untargeted proteomics, specific immunoblot assays, gene expression profiling, and functional metabolic assessments of breast cancer cells. The results were validated in tumors from obese mice and breast cancer datasets from women with obesity.ResultsFGF1 stimulated ER phosphorylation independently of estradiol in cells that grow in obese female mice after estrogen deprivation treatment. Phospho- and total proteomic, genomic, and functional analyses of endocrine-sensitive and resistant breast cancer cells show that FGF1 promoted a cellular phenotype characterized by glycolytic metabolism. In endocrine-sensitive but not endocrine-resistant breast cancer cells, mitochondrial metabolism was also regulated by FGF1. Comparison of gene expression profiles indicated that tumors from women with obesity shared hallmarks with endocrine-resistant breast cancer cells.ConclusionsCollectively, our data suggest that one mechanism by which obesity and weight gain promote breast cancer progression is through estrogen-independent ER activation and cancer cell metabolic reprogramming, partly driven by FGF/FGFR. The first-line treatment for many patients with ER-positive breast cancer is inhibition of estrogen synthesis using aromatase inhibitors. In women with obesity who are experiencing weight gain, locally produced FGF1 may activate ER to promote cancer cell metabolic reprogramming and tumor progression independently of estrogen.

Framework for Detecting Breast Cancer Risk Presence Using Deep Learning

Cancer is a complicated global health concern with a significant fatality rate. Breast cancer is among the leading causes of mortality each year. Advancements in prognoses have been progressively based primarily on the expression of genes, offering insight into robust and appropriate healthcare decisions, owing to the fast growth of advanced throughput sequencing techniques and the use of various deep learning approaches that have arisen in the past few years. Diagnostic-imaging disease indicators such as breast density and tissue texture are widely used by physicians and automated technology. The effective and specific identification of cancer risk presence can be used to inform tailored screening and preventive decisions. For several classifications and prediction applications, such as breast imaging, deep learning has increasingly emerged as an effective method. We present a deep learning model approach for predicting breast cancer risk primarily on this foundation. The proposed methodology is based on transfer learning using the InceptionResNetV2 deep learning model. Our experimental work on a breast cancer dataset demonstrates high model performance, with 91% accuracy. The proposed model includes risk markers that are used to improve breast cancer risk assessment scores and presents promising results compared to existing approaches. Deep learning models include risk markers that are used to improve accuracy scores. This article depicts breast cancer risk indicators, defines the proper usage, features, and limits of each risk forecasting model, and examines the increasing role of deep learning (DL) in risk detection. The proposed model could potentially be used to automate various types of medical imaging techniques.

Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis.

Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.

Single Nucleotide Polymorphisms (SNPs) of OGG1 (Ser326Cys) and APE1 (Asp148Glu; –141T/G) Genes and Breast Cancer Risk in Filipino Women

Base excision repair (BER) pathway involves repair of damaged DNA caused by spontaneous decay of DNA, reactive oxygen species, and ionizing radiation. Polymorphisms of genes involved in the pathway have been reported to be associated with cancer risk. In this study, the association of BER gene polymorphisms with breast cancer risk in Filipino women was assessed. The polymorphisms studied included X-ray repair cross-complementing group 1 (XRCC1, Arg399Gln), 8-oxoguanine DNA glycosylase (OGG1, Ser326Cys), and apurinic/apyrimidinic endonuclease 1 (APE1, Asp148Glu; –141T/G). A total of 186 participants (93 breast cancer cases and 93 breast cancer-free control) were recruited for the study. The genotyping of samples was performed using polymerase chain reaction with confronting two-pair primers (PCR-CTPP). The association of polymorphisms of BER genes with breast cancer risk and gene-environment interaction was determined using the unconditional logistic regression model. Distributions of OGG1 and APE1 genotypes were in Hardy-Weinberg equilibrium (HWE); however, XRCC1 genotype distribution deviated from HWE and was not further analyzed. The analysis showed that OGG1 Ser326Cys, APE1 Asp148Glu, and APE1 –141T/G had no significant association with breast cancer risk. Also, there is no significant interaction between the three BER gene variants and family history of cancer. Lastly, no significant increased risk was observed when the combined effects of risk alleles of the three BER gene variants were determined. In conclusion, the study suggests that OGG1 Ser326Cys, APE1 Asp148Glu, and APE1 –141T/G are not good indicators of breast cancer risk in Filipino women.

Mammographic Breast Density and Acculturation: Longitudinal Analysis in Chinese Immigrants.

Breast cancer is the most common cancer in women. Asian American women have experienced steadily increasing breast cancer incidence rates over the past several decades. The increased rate might be in part due to acculturation. We tested the hypothesis that higher level of acculturation was associated with higher mammographic breast density (MBD), an indicator of breast cancer risk, in a cohort of 425 premenopausal Chinese immigrant women in Philadelphia. Generalized estimating equations accounted for repeated observations and adjusted for age, type of mammographic image, body mass index, months of breastfeeding, number of live births, age at first birth, and menopausal stage (pre, early peri, late peri, post). Results indicated that acculturation level was not associated with any of the MBD measures. Findings were contrary to our hypothesis and previous, cross-sectional studies. In this study population, reproductive factors had a greater effect on MBD than acculturation-related behaviors in adulthood.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB. 135 cases were matched to 69 controls by age and type of benign change: non-proliferative or proliferation without atypia (PDWA). Whole-exome sequencing (WES) was performed for the BBB. Germline DNA (available from n = 26 participants) was utilized to develop a mutation-calling pipeline, to allow differentiation of somatic from germline variants. Among the 204 subjects, two known mutational signatures were identified, along with a currently uncatalogued signature that was significantly associated with triple negative BC (TNBC) (p = 0.007). The uncatalogued mutational signature was validated in 109 TNBCs from TCGA (p = 0.001). Compared to non-proliferative samples, PDWA harbors more abundant mutations at PIK3CA pH1047R (p < 0.001). Among the 26 BBB whose somatic copy number variation could be assessed, deletion of MLH3 is significantly associated with the mismatch repair mutational signature (p < 0.001). Matched BBB-cancer pairs were available for ten cases; several mutations were shared between BBB and cancers. This initial study of WES of BBB shows its potential for the identification of genetic alterations that portend breast oncogenesis. In future larger studies, robust personalized breast cancer risk indicators leading to novel interception paradigms can be assessed.

Abstract D007: Mammographic breast density and acculturation: Longitudinal analysis in Chinese immigrants

Abstract Breast cancer is the most common cancer in women, regardless of race or ethnicity. Unlike other racial and ethnic groups, breast cancer rates in Asian American women have steadily increased over the past several decades. The increase among recent migrants might be partly due to the adoption of the behaviors and values of the mainstream culture. We tested the hypothesis that higher level of acculturation was associated with higher mammographic breast density (MBD), an indicator of breast cancer risk, using data from a 2006-2010 longitudinal study of 426 premenopausal Chinese immigrant women in Philadelphia. MBD, including dense area, non-dense area, and percent density, was calculated using a computer assisted method. An abridged version of the General Ethnicity Questionnaire – American version provided a measure of acculturation. We used generalized estimating equations (GEE) to account for repeated observations and adjusted for age, type of mammographic image (film or digital), body mass index, months of breastfeeding, number of live births, age at first birth, and menopausal stage (pre, early peri, late peri, and post). At baseline, the mean age of participants was 43.9 years (SD=4.5), mean length of U.S. residence was 7.5 years (SD=4.8), and mean age at migration was 36.4 years (SD=6.4). GEE models using 1,110 observations from the 426 participants showed no significant associations between acculturation and MBD (percent density β=0.32, p=0.54; dense area β=0.28, p=0.57; and non-dense area β=-0.42, p=0.60). Our findings are consistent with an emerging body of research suggesting that acculturation measures are insufficient to distinguish levels of breast cancer risk in immigrant women, given changes in immigration patterns and economic development in sending countries. Future work should consider other factors, including behavioral, environmental and epigenetic factors or changes in reproductive patterns, that may have a greater effect on MBD than acculturation-related behaviors in adulthood. Citation Format: Rebeca Almeida, Celia Byrne, Carolyn Y. Fang, Marilyn Tseng. Mammographic breast density and acculturation: Longitudinal analysis in Chinese immigrants [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr D007.

Breast density analysis based on glandular tissue segmentation and mixed feature extraction

Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose a classification system for breast density, which can appropriately segment the glandular tissue from the whole breast and to achieve a better classification result. A new threshold method is applied to segment the breast glandular tissue. The gray level co-occurrence matrix (GLCM) is implemented to extract the texture features of the glandular tissue. Meanwhile, we obtain three statistical features (mean, skewness, kurtosis). In addition, the calculated breast density that is served as a new feature is added to the feature vectors. The mixed feature vectors are classified by Support Vector Machine (SVM) and Ultimate Learning Machine (ELM). Ten-fold cross-validation is used to verify the classifier performance. The system using the SVM achieves 96.19% accuracy for three density types in the MIAS database and achieves 96.35% accuracy of four density types in the DDSM database. The accuracy in the database mixed with the local database was 95.01% and there are three density types in the mixed database. The experimental results indicate that the system proposed has a better performance in breast density classification. The system proposed in this paper can be considered to help the physician to classify breast density.

Prediction of breast cancer risk with volatile biomarkers in breath.

Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

Abstract P6-07-04: Rapamycin inhibits the stemness of mammary epithelial cells in the premalignant tissues of MMTV-ErbB2 transgenic mice

Abstract Rapamycin, a well-studied mTOR inhibitor, has been demonstrated to inhibit mammary carcinogenesis at multiple stages, including initiation, invasion, and metastasis, in preclinical animal models. Nevertheless, the cancer preventative potential and underlying mechanisms remain unclear, especially in individual breast cancer subtypes like ErbB2/Her2-positive breast cancers. ErbB2 amplification/overexpression is a particular clinical concern because it occurs in approximately one-third of human breast cancers and is associated with poor prognosis. Therefore, we used MMTV-ErbB2 transgenic mice as our model system to test the efficacy of rapamycin in the prevention of ErbB2-mediated mammary tumor development. Our initial data provided proof of concept regarding the anti-cancer effects of rapamycin in vivo. Indeed, rapamycin (1.5 mg/kg/day for 12 days) significantly reduced the volume and weight of syngeneic 78617 cell-derived mammary tumors in MMTV-ErbB2 mice, despite observed decreases in CD4+ and CD8+ immune cells. Since advanced mammary gland development can serve as an indicator of breast cancer risk, we investigated the effects of rapamycin on mammary gland development in MMTV-ErbB2 mice that were treated with low-dose rapamycin (1 mg/kg/day) between weeks 10 and 20 of age. As such, rapamycin significantly attenuated mammary morphogenesis at 20 weeks of age, as indicated by decreased branching density, ductal elongation, and proliferative index of the premalignant mammary glands. Flow cytometric analysis of isolated primary mammary epithelial cells (MECs) was performed using CD24 and CD49f markers to identify MEC populations. We found that rapamycin has a significant impact on MEC stemness based on changes in luminal (CD24highCD49flow), mammary stem cell (MaSC)-enriched (CD24highCD49fhigh), and myoepithelial/basal (CD24low/highCD49fhigh) MEC populations. We also used CD61 and CD49f markers to identify a population enriched with luminal progenitor cells (CD61highCD49fhigh) that was selectively inhibited by rapamycin. Consistent with our flow cytometric analyses, rapamycin inhibited the luminal progenitor cell-enriched population, self-renewal, and anchorage-independent cell growth of primary MECs, as demonstrated by colony-forming cell, mammosphere, and 3D culture assays, respectively. These functional stem cell assays further corroborate that rapamycin suppresses the stemness of primary MECs. Molecular analysis of MECs demonstrated that rapamycin inhibited mTOR signaling, as expected. Importantly, rapamycin also significantly suppressed the receptor tyrosine kinase/ErbB2, estrogen receptor, Wnt/β-catenin, and TGFβ/Smad3 signaling pathways prior to malignant transformation. Collectively, our study provides evidence that rapamycin has potential cancer preventative effects in the mammary glands of MMTV-ErbB2 mice during the premalignant risk window. These rapamycin-induced anti-cancer effects ultimately highlight the promising clinical significance of rapamycin for the prevention and treatment of human ErbB2-overexpressing breast cancers. Citation Format: Yang X, Zhao Q, Parris AB, Howard EW, Zhao M, Guo Z, Xing Y, Ma Z. Rapamycin inhibits the stemness of mammary epithelial cells in the premalignant tissues of MMTV-ErbB2 transgenic mice [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-07-04.

Waist circumference and risk of breast cancer in Korean women: A nationwide cohort study.

Although postmenopausal breast cancer (BC) risk has been linked to adiposity, associations between adiposity and premenopausal BC remain unclear. To address this question, we investigated the association of BC risk with measures of adiposity, including body mass index (BMI) and waist circumference (WC), in a large cohort of Asian women. We used a nationwide cohort of adult Korean women selected from the National Health Insurance Corporation database merged with national health examination data from 2009 to 2015. A total of 11,227,948 women were tracked to retrospectively identify incident cases of BC. Our analysis used Cox proportional hazards models to calculate hazard ratios and assess the association of BC risk with BMI and/or WC in both pre- and postmenopausal women. BMI and WC were robustly associated with increased risk for postmenopausal BC (ptrend <0.001 for both BMI and WC) but not with premenopausal BC. Association between WC and premenopausal BC was only statistically significant when considering BMI (ptrend =0.044). In contrast, postmenopausal BC was negatively associated with WC when considering BMI (ptrend =0.011). In premenopausal women, WC may predict increased BC risk when considering BMI. However, in postmenopausal women, WC is not superior to BMI as an indicator of BC risk.

Breast-density measurement using photon-counting spectral mammography.

To evaluate a method for measuring breast density using photon-counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. The measurement method employs the spectral difference in x-ray attenuation between adipose and fibro-glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue-equivalent material (slabs and breast-shaped phantoms) and using clinical data from a screening population (n=1329). A state-of-the-art nonspectral method for breast-density assessment was used for benchmarking. The precision of the spectral method was estimated to be 1.5-1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7-2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast-density measurement on an individual basis such as treatment monitoring and personalized screening.

Association between expression of inflammatory markers in normal breast tissue and mammographic density among premenopausal and postmenopausal women.

Inflammatory markers may be associated with breast cancer risk. We assessed the association between expression levels of proinflammatory (interleukin 6, tumor necrosis factor-α, C-reactive protein, cyclooxygenase 2, leptin, serum amyloid A1, interleukin 8, and signal transducer and activator of transcription 3) and anti-inflammatory markers (transforming growth factor-β, interleukin 10, and lactoferrin) in normal breast tissue with mammographic density, a strong breast cancer risk indicator, among 163 breast cancer patients. The expression of inflammatory markers was visually evaluated on immunohistochemistry stained slides. The percent mammographic density (PMD) was estimated by a computer-assisted method in the contralateral cancer-free breast. We used generalized linear models to estimate means of PMD by median expression levels of the inflammatory markers while adjusting for age and waist circumference. Higher expression levels (above median) of the proinflammatory marker interleukin 6 were associated with higher PMD among all women (24.1% vs 18.5%, P = 0.007). Similarly, higher expression levels (above median) of the proinflammatory markers (interleukin 6, tumor necrosis factor-α, C-reactive protein, and interleukin 8) were associated with higher PMD among premenopausal women (absolute difference in the PMD of 8.8% [P = 0.006], 7.7% [P = 0.022], 6.7% [P = 0.037], and 16.5% [P = 0.032], respectively). Higher expression levels (above median) of the anti-inflammatory marker transforming growth factor-β were associated with lower PMD among all (18.8% vs 24.3%, P = 0.005) and postmenopausal women (14.5% vs 20.7%, P = 0.013). Our results provide support for the hypothesized role of inflammatory markers in breast carcinogenesis through their effects on mammographic density. Inflammatory markers could be targeted in future breast cancer prevention interventions.

Intermittent energy restriction induces changes in breast gene expression and systemic metabolism.

BackgroundObservational studies suggest weight loss and energy restriction reduce breast cancer risk. Intermittent energy restriction (IER) reduces weight to the same extent as, or more than equivalent continuous energy restriction (CER) but the effects of IER on normal breast tissue and systemic metabolism as indicators of breast cancer risk are unknown.MethodsWe assessed the effect of IER (two days of 65 % energy restriction per week) for one menstrual cycle on breast tissue gene expression using Affymetrix GeneChips, adipocyte size by morphometry, and systemic metabolism (insulin resistance, lipids, serum and urine metabolites, lymphocyte gene expression) in 23 overweight premenopausal women at high risk of breast cancer. Unsupervised and supervised analyses of matched pre and post IER biopsies in 20 subjects were performed, whilst liquid and gas chromatography mass spectrometry assessed corresponding changes in serum and urine metabolites in all subjects after the two restricted and five unrestricted days of the IER.ResultsWomen lost 4.8 % (±2.0 %) of body weight and 8.0 % (±5.0 %) of total body fat. Insulin resistance (homeostatic model assessment (HOMA)) reduced by 29.8 % (±17.8 %) on the restricted days and by 11 % (±34 %) on the unrestricted days of the IER. Five hundred and twenty-seven metabolites significantly increased or decreased during the two restricted days of IER. Ninety-one percent of these returned to baseline after 5 days of normal eating. Eleven subjects (55 %) displayed reductions in energy restriction-associated metabolic gene pathways including lipid synthesis, gluconeogenesis and glycogen synthesis. Some of these women also had increases in genes associated with breast epithelial cell differentiation (secretoglobulins, milk proteins and mucins) and decreased collagen synthesis (TNMD, PCOLCE2, TIMP4). There was no appreciable effect of IER on breast gene expression in the other nine subjects. These groups did not differ in the degree of changes in weight, total body fat, fat cell size or serum or urine metabolomic markers. Corresponding gene changes were not seen in peripheral blood lymphocytes.ConclusionThe transcriptional response to IER is variable in breast tissue, which was not reflected in the systemic response, which occurred in all subjects. The mechanisms of breast responsiveness/non-responsiveness require further investigation.Trial registrationISRCTN77916487 31/07/2012.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0714-4) contains supplementary material, which is available to authorized users.

The effect of change in body mass index on volumetric measures of mammographic density.

Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear. We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. In cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (β, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (β, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.

Analysis of differentially expressed novel post-translational modifications of plasma apolipoprotein E in Taiwanese females with breast cancer

APOE ε2 or ε4 alleles being used as indicators of breast cancer risk are controversial in Taiwanese females. We provide a concept for relative comparisons of post-translational modifications (PTMs) of plasma apolipoprotein E (ApoE) between normal controls and breast cancer patients to investigate the association of ApoE with breast cancer risk. APOE polymorphisms (ApoE isoforms) were not assessed in this study. The relative modification ratio (%) of 15 targeted and 21 modified peptides were evaluated by 1D SDS-PAGE, in-gel digestion, and label-free nano-LC/MS to compare normal controls with breast cancer patients. Plasma levels of the ApoE protein did not significantly differ between normal controls and breast cancer patients. Eleven sites with novel PTMs were identified from 7 pairs of differentially expressed targeted and modified peptides according to the relative modification ratio including methylation at the E3 (↑1.45-fold), E7 (↑1.45-fold), E11 (↑1.19-fold), E77 (↑2.02-fold), E87 (↑2.02-fold), and Q98 (↑1.62-fold) residues; dimethylation at the Q187 (↑1.44-fold) residue; dihydroxylation at the R92 (↑1.25-fold), K95 (↑1.25-fold), and R103 (↑1.25-fold) residues; and glycosylation at the S129 (↑1.14-fold) residue. The clustered methylation and dihydroxylation of plasma ApoE proteins may play a role in breast cancer.

The Effect of Weight Change on Volumetric Measures of Mammographic Density

Abstract The association between changing body mass index (BMI) and mammographic breast density is important to better evaluate how to adjust for BMI gain/loss in longitudinal studies of density and breast cancer risk. Increasing BMI has been associated with decreasing percent dense area but the effect on absolute dense area is unclear. No studies have explored a longitudinal association using volumetric density measurement. Methods: We examined the association between change in BMI and change in volumetric breast density among 24,556 women who received breast imaging at the San Francisco Mammography Registry from 2007–2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry (SXA) volumetric measurement. The cross-sectional and longitudinal associations between BMI and absolute dense volume (DV) and percent dense volume (PDV) were assessed using multivariable adjusted regression. Results: Women were primarily Caucasian (66%) or Asian (25%) and most were postmenopausal (64%) at time of first mammogram. In cross-sectional analysis, BMI was positively associated with DV (β = 2.95 cm3, 95% CI, 2.69–3.21) and inversely associated with PDV (β = −2.03%, 95% CI, −2.09–−1.98). In longitudinal analysis, an annual increase in BMI was associated with an annual decrease in both DV (β = −1.01 cm3/year, 95% CI, −1.59–−0.42) and PDV (β = −1.17%/year, 95% CI, −1.31–−1.04). Findings were consistent between pre- and postmenopausal women. The annual decrease in DV was strongest among premenopausal women who were initially overweight or obese (P &amp;lt; 0.01 for interaction by initial BMI). Conclusion: Our findings support an inverse association between change in BMI and change in PDV. Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should consider adjusting for change in BMI. The association between increasing BMI and decreasing DV is unexpected and will require confirmation using volumetric methods.