Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Obesity-driven changes in breast tissue exhibit a pro-angiogenic extracellular matrix signature

Obesity has reached epidemic proportions in the United States, emerging as a risk factor for the onset of breast cancer and a harbinger of unfavorable outcomes [1], [2], [3]. Despite limited understanding of the precise mechanisms, both obesity and breast cancer are associated with extracellular matrix (ECM) rewiring [4], [5], [6]. Utilizing total breast tissue proteomics, we analyzed normal-weight (18.5 to < 25 kg/m2), overweight (25 to < 30 kg/m2), and obese (≥30 kg/m2) individuals to identify potential ECM modifying proteins for cancer development and acceleration. Obese individuals exhibited substantial ECM alterations, marked by increased basement membrane deposition, angiogenic signatures, and ECM-modifying proteins. Notably, the collagen IV crosslinking enzyme peroxidasin (PXDN) emerged as a potential mediator of the ECM changes in individuals with an elevated body mass index (BMI), strongly correlating with angiogenic and basement membrane signatures. Furthermore, glycan-binding proteins galectin-1 (LGALS1) and galectin-3 (LGALS3), which play crucial roles in matrix interactions and angiogenesis, also strongly correlate with ECM modifications. In breast cancer, elevated PXDN, LGALS1, and LGALS3 correlate with reduced relapse-free and distant-metastatic-free survival. These proteins were significantly associated with mesenchymal stromal cell markers, indicating adipocytes and fibroblasts may be the primary contributors of the obesity-related ECM changes. Our findings unveil a pro-angiogenic ECM signature in obese breast tissue, offering potential targets to inhibit breast cancer development and progression.

Percentage mammographic density or absolute breast density for risk stratification in breast screening: Possible implications for socioeconomic health disparity

OBJECTIVES

Obesity levels and mortality from breast cancer are higher in more deprived areas of the UK, despite lower breast cancer incidence. Supplemental imaging for women with dense breasts has been proposed as a potential improvement to screening, but it is not clear how stratification by percentage mammographic density (%MD) would be reflected across socioeconomic groups. This study aims to clarify the associations between breast composition (dense and fatty tissue) and socioeconomic status in a multi-ethnic screening population.

METHODS

Demographic characteristics were collected for 62,913 participants in a UK breast screening programme (age, ethnicity, Index of Multiple Deprivation (IMD)). Automated mammographic measurements were derived: dense volume (DV), non-dense volume (NDV) and percent density (%MD). Correlations between deprivation and mammographic composition were examined before and after adjustment for age, ethnicity and NDV, using non-dense breast volume as a proxy for body mass index (BMI).

RESULTS

There was negligible correlation between deprivation and DV (r = 0.017; P < 0.001 in all cases), but NDV increased with increasing deprivation (Pearson r = 0.101). Correlations were weaker in the Asian and Chinese ethnic groups. %MD decreased with deprivation (r = -0.094) and adjustment for ethnicity did not alter the association between %MD and IMD (relative change, most to least deprived quintile IMD: 1.18; 95% confidence interval: 1.16, 1.21).

CONCLUSIONS

Deprivation-related differences in %MD in the screening population are largely explained by differences in breast fat volume (NDV) which reflects BMI. Women in more deprived areas, where obesity and breast cancer mortality rates are higher, have increased breast adiposity and may miss out on risk-adapted screening if stratification is based solely on %MD or BIRADS classification.

The distribution of breast density in women aged 18 years and older

PURPOSE

Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI.

METHODS

Breast density measures were estimated for 1,961 Australian women aged 18-97 years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI.

RESULTS

The mean age was 38 years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI.

CONCLUSION

This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

Are mammography image acquisition factors, compression pressure and paddle tilt, associated with breast cancer detection in screening?

OBJECTIVES

To assess the associations between objectively measured mammographic compression pressure and paddle tilt and breast cancer (BC) detected at the same ("contemporaneous") screen, subsequent screens, or in-between screens (interval cancers).

METHODS

Automated pressure and paddle tilt estimates were derived for 80,495 mammographic examinations in a UK population-based screening programme. Adjusted logistic regression models were fitted to estimate the associations of compression parameters with BC detected at contemporaneous screen (777 cases).Nested case-control designs were used to estimate associations of pressure and tilt with: (a) interval cancer (148 cases/625 age-matched controls) and (b) subsequent screen-detected cancer (344/1436), via conditional logistic regression.

RESULTS

Compression pressure was negatively associated with odds of BC at contemporaneous screen (odds ratio (OR) for top versus bottom third of the pressure distribution: 0.74; 95% CI 0.60, 0.92; P-for-linear-trend (Pt) = 0.007). There was weak evidence that moderate pressure at screening was associated with lower odds of interval cancer (OR for middle versus bottom third: 0.63; 95% CI 0.38, 1.05; p = 0.079), but no association was found between pressure and the odds of BC at subsequent screen. There was no evidence that paddle tilt was associated with the odds of contemporaneous, subsequent screen or interval cancer detection.

CONCLUSIONS

Findings are consistent with compression pressure, but not paddle tilt, affecting the performance of mammographic screening by interfering with its ability to detect cancers.

ADVANCES IN KNOWLEDGE

Inadequate or excessive compression pressure at screening may contribute to a reduced ability to detect cancers, resulting in a greater number of interval cancer cases.

Alternative methods to measure breast density in younger women

BACKGROUND

Breast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18-40.

METHODS

Breast tissue composition (percent water, collagen, and lipid content) was measured in 539 women aged 18-40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density.

RESULTS

Over 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV.

CONCLUSIONS

OBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18-40 years.

Mammography biomarkers of cardiovascular and musculoskeletal health: A review

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.

Association of body composition fat parameters and breast density in mammography by menopausal status

We investigated the relationship between body fat-driven obesity and breast fat density in mammography according to menopausal status. We retrospectively analyzed 8537 women (premenopausal, n = 4351; postmenopausal, n = 4186). Body fat parameters included BMI (body mass index), waist circumference (WC), waist-hip ratio (WHR), fat mass index (FMI), Percentage of body fat (PBF), and visceral fat area (VFA). Body fat-driven obesity was defined as follows: overall obesity, BMI ≥ 25 kg/m²; central obesity, WC > 85 cm; abdominal obesity, WHR > 0.85; excessive FMI, the highest quartile (Q4) of FMI; excessive PBF, the highest quartile (Q4) of VFA; visceral obesity, and the highest quartile (Q4) of VFA). Breast density was classified according to BI-RADS (grade a, b, c, and d), which defined as an ordinal scale (grade a = 1, grade b = 2, grade c = 3, and grade d = 4). All body fat-driven obesity parameters were negatively associated with the grade of breast density in both groups of women (p < 0.001): The more fatty parameters are, the less dense breast is. In multivariable binary logistic regression, all body fat-driven obesity parameters also showed a negative association with grade d density (vs. grade a, b, or c). In premenopausal women, BMI was a more associated parameter with grade d density than those of the other fat-driven parameters (OR 0.265, CI 0.204-0.344). In postmenopausal women, WC was more associated with grade d density than the others (OR 0.315, CI 0.239-0.416). We found that BMI, WC, WHR, FMI, PBF and VFA were negatively correlated with dense breast, and the association degree pattern between body fat-driven obesity and dense breast differs according to menopausal status.

Black Women Are Less Likely to Be Classified as High-Risk for Breast Cancer Using the Tyrer-Cuzick 8 Model

BACKGROUND

Breast cancer risk assessment is a powerful tool that guides recommendations for supplemental breast cancer screening and genetic counseling. The Tyrer-Cuzick 8 (TC8) model is widely used for calculating breast cancer risk and thus helps determine if women qualify for supplemental screening or genetic counseling. However, the TC8 model may underestimate breast cancer risk in Black women. This study sought to assess this disparity.

METHODS

Data on race, breast density, body mass index (BMI), and TC8 scores were retrospectively extracted from the electronic medical record (EMR). Logistic regressions were run to evaluate racial differences in TC8 scores. Summary and correlation statistics determined relationships between BMI, breast density, and race. Rank biserial correlations were employed to explore the impact of breast density and BMI on TC8 scores.

RESULTS

Of 15,356 patients, 5796 were White and 5813 were Black. Black patients had higher rates of BMI ≥ 27 compared with White women (79.2% vs. 45.7%), lower rates of breast density (35.1% vs. 56.2%), and lower rates of high-risk TC8 scores (10.7% vs. 17.5%, OR = 1.6646). There was an inverse relationship between TC8 score and BMI (r_rb = - 0.04) and a direct relationship between TC8 score and breast density (r_rb = 0.37).

DISCUSSION

Black women are less likely to have high-risk TC8 scores despite having only marginally lower breast cancer incidence rates and higher breast cancer mortality rates than White women. This suggests that the TC8 model underestimates breast cancer risk in Black women, possibly due to lower rates of breast density and higher BMIs among Black women.

Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older

Mammographic breast density and body mass index (BMI) are strong risk factors of breast cancer, but few studies have investigated these factors in older women. Our study assessed the association between breast density, BMI and the breast cancer risk among women aged ≥75 years. We included women who underwent breast cancer screening between 2009 and 2014 and were followed up until 2020. Breast density was measured using Breast Imaging Reporting and Data System. BMI was classified into three groups: <23, 23 to <25 and ≥25. Cox proportional hazards models were used to estimate the association of breast density and BMI with breast cancer risk. In 483 564 women, 1885 developed breast cancer. The 5-year incidence increased with an increase in breast density and BMI. Increase in breast density was associated with an increased breast cancer risk in all BMI categories: among women with BMI <23, those with heterogeneous/extreme density had a 2.98-fold (95% CI: 2.23-3.80) increased risk of breast cancer compared to those with entirely fatty breasts. An increase in BMI was associated with increased breast cancer risk in women with the same breast density in all density categories. When the combined associations of breast density and BMI on the risk of breast cancer were considered, women with a BMI ≥25 and heterogeneous/extreme breast density had a 5.35-fold (95% CI: 4.26-6.72) increased risk of breast cancer compared to women with a BMI <23 and fatty breasts. Women aged ≥75 years, with dense breasts, regardless of BMI status, might benefit from a tailored screening strategy for early detection of breast cancer.

Current regular aspirin use and mammographic breast density: a cross-sectional analysis considering concurrent statin and metformin use

PURPOSE

The nonsteroidal anti-inflammatory drug aspirin is an agent of interest for breast cancer prevention. However, it is unclear if aspirin affects mammographic breast density (MBD), a marker of elevated breast cancer risk, particularly in the context of concurrent use of medications indicated for common cardiometabolic conditions, which may also be associated with MBD.

METHODS

We used data from the New York Mammographic Density Study for 770 women age 40-60 years old with no history of breast cancer. We evaluated the association between current regular aspirin use and MBD, using linear regression for continuous measures of absolute and percent dense areas and absolute non-dense area, adjusted for body mass index (BMI), sociodemographic and reproductive factors, and use of statins and metformin. We assessed effect modification by BMI and reproductive factors.

RESULTS

After adjustment for co-medication, current regular aspirin use was only positively associated with non-dense area (β = 18.1, 95% CI: 6.7, 29.5). Effect modification by BMI and parity showed current aspirin use to only be associated with larger non-dense area among women with a BMI ≥ 30 (β = 28.2, 95% CI: 10.8, 45.7), and with lower percent density among parous women (β = -3.3, 95% CI: -6.4, -0.3).

CONCLUSIONS

Independent of co-medication use, current regular aspirin users had greater non-dense area with stronger estimates for women with higher BMI. We found limited support for an association between current aspirin use and mammographically dense breast tissue among parous women.

Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore

Background

Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density).

Methods

In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5‐year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified.

Results

Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8–3.1], adjusted HR: 1.9 [1.4–2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559–0.670], adjusted AUC: 0.703 [0.653–0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5‐year absolute risk [absolute risk ≥1.43%], were recalled, and/or mammographic density ≥50%).

Conclusion

The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.

Biomarkers of mammographic density in premenopausal women

BACKGROUND

While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density.

METHODS

A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers' Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors.

RESULTS

Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components.

CONCLUSIONS

We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

A Phenomic Perspective on Factors Influencing Breast Cancer Treatment: Integrating Aging and Lifestyle in Blood and Tissue Biomarker Profiling

Breast cancer is the most common malignancy among women worldwide. Over the last four decades, diagnostic and therapeutic procedures have improved substantially, giving patients with localized disease a better chance of cure, and those with more advanced cancer, longer periods of disease control and survival. However, understanding and managing heterogeneity in the clinical response exhibited by patients remains a challenge. For some treatments, biomarkers are available to inform therapeutic options, assess pathological response and predict clinical outcomes. Nevertheless, some measurements are not employed universally and lack sensitivity and specificity, which might be influenced by tissue-specific alterations associated with aging and lifestyle. The first part of this article summarizes available and emerging biomarkers for clinical use, such as measurements that can be made in tumor biopsies or blood samples, including so-called liquid biopsies. The second part of this article outlines underappreciated factors that could influence the interpretation of these clinical measurements and affect treatment outcomes. For example, it has been shown that both adiposity and physical activity can modify the characteristics of tumors and surrounding tissues. In addition, evidence shows that inflammaging and immunosenescence interact with treatment and clinical outcomes and could be considered prognostic and predictive factors independently. In summary, changes to blood and tissues that reflect aging and patient characteristics, including lifestyle, are not commonly considered clinically or in research, either for practical reasons or because the supporting evidence base is developing. Thus, an aim of this article is to encourage an integrative phenomic approach in oncology research and clinical management.

Menopausal Transition, Body Mass Index, and Prevalence of Mammographic Dense Breasts in Middle-Aged Women

The interrelationship between menopausal stage, excessive adiposity and dense breasts remains unclear. We aimed to investigate the relationship between menopausal stage and dense-breast prevalence in midlife women while considering a possible effect modification of being overweight. The present cross-sectional study comprised 82,677 Korean women, aged 35–65 years, who attended a screening exam. Menopausal stages were categorized based on the Stages of Reproductive Aging Workshop (STRAW + 10) criteria. Mammographic breast density was categorized according to Breast Imaging Reporting and Data System (BI-RADS). Dense breasts were defined as BI-RADS Breast Density category D (extremely dense). The prevalence of dense breasts decreased as menopausal stage increased (p-trend < 0.001), and this pattern was pronounced in overweight women than non-overweight women (p-interaction = 0.016). Compared with pre-menopause, the multivariable-adjusted prevalence ratios (and 95% confidence intervals) for dense breasts were 0.98 (0.96–1.00) in early transition, 0.89 (0.86–0.92) in late transition, and 0.55 (0.52–0.59) in post-menopause, among non-overweight women, while corresponding prevalence ratios were 0.92 (0.87–0.98), 0.83 (0.77–0.90) and 0.36 (0.31–0.41) among overweight women. The prevalence of dense breasts was inversely associated with increasing menopausal stages and significantly decreased from the late menopausal transition, with stronger declines among overweight women.

Left-right breast asymmetry and risk of screen-detected and interval cancers in a large population-based screening population

OBJECTIVES

To assess the associations between automated volumetric estimates of mammographic asymmetry and breast cancers detected at the same ("contemporaneous") screen, at subsequent screens, or in between (interval cancers).

METHODS

Automated measurements from mammographic images (N = 79,731) were used to estimate absolute asymmetry in breast volume (BV) and dense volume (DV) in a large ethnically diverse population of attendees of a UK breast screening programme. Logistic regression models were fitted to assess asymmetry associations with the odds of a breast cancer detected at contemporaneous screen (767 cases), adjusted for relevant confounders.Nested case-control investigations were designed to examine associations between asymmetry and the odds of: (a) interval cancer (numbers of cases/age-matched controls: 153/646) and (b) subsequent screen-detected cancer (345/1438), via conditional logistic regression.

RESULTS

DV, but not BV, asymmetry was positively associated with the odds of contemporaneous breast cancer (P-for-linear-trend (Pt) = 0.018). This association was stronger for first (prevalent) screens (Pt = 0.012). Both DV and BV asymmetry were positively associated with the odds of an interval cancer diagnosis (Pt = 0.060 and 0.030, respectively). Neither BV nor DV asymmetry were associated with the odds of having a subsequent screen-detected cancer.

CONCLUSIONS

Increased DV asymmetry was associated with the risk of a breast cancer diagnosis at a contemporaneous screen or as an interval cancer. BV asymmetry was positively associated with the risk of an interval cancer diagnosis.

ADVANCES IN KNOWLEDGE

The findings suggest that DV and BV asymmetry may provide additional signals for detecting contemporaneous cancers and assessing the likelihood of interval cancers in population-based screening programmes.

Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening

OBJECTIVES

To develop a breast cancer risk model to identify women at mammographic screening who are at higher risk of breast cancer within the general screening population.

METHODS

This retrospective nested case-control study used data from a population-based breast screening program (2009-2015). All women aged 40-75 diagnosed with screen-detected or interval breast cancer (n = 1882) were frequency-matched 3:1 on age and screen-year with women without screen-detected breast cancer (n = 5888). Image-derived risk factors from the screening mammogram (percent mammographic density [PMD], breast volume, age) were combined with core biopsy history, first-degree family history, and other clinical risk factors in risk models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Classifiers assigning women to low- versus high-risk deciles were derived from risk models. Agreement between classifiers was assessed using a weighted kappa.

RESULTS

The AUC was 0.597 for a risk model including only image-derived risk factors. The successive addition of core biopsy and family history significantly improved performance (AUC = 0.660, p < 0.001 and AUC = 0.664, p = 0.04, respectively). Adding the three remaining risk factors did not further improve performance (AUC = 0.665, p = 0.45). There was almost perfect agreement (kappa = 0.97) between risk assessments based on a classifier derived from image-derived risk factors, core biopsy, and family history compared with those derived from a model including all available risk factors.

CONCLUSIONS

Women in the general screening population can be risk-stratified at time of screen using a simple model based on age, PMD, breast volume, and biopsy and family history.

KEY POINTS

• A breast cancer risk model based on three image-derived risk factors as well as core biopsy and first-degree family history can provide current risk estimates at time of screen. • Risk estimates generated from a combination of image-derived risk factors, core biopsy history, and first-degree family history may be more valid than risk estimates that rely on extensive self-reported risk factors. • A simple breast cancer risk model can avoid extensive clinical risk factor data collection.

Do Body Mass Index and Breast Density Impact Cancer Risk Among Women with Lobular Carcinoma In Situ?

PURPOSE

Both body mass index (BMI) and breast density impact breast cancer risk in the general population. Whether obesity and density represent additive risk factors in women with lobular carcinoma in situ (LCIS) is unknown.

METHODS

Patients diagnosed with LCIS from 1988 to 2017 were identified from a prospectively maintained database. BMI was categorized by World Health Organization classification. Density was captured as the mammographic Breast Imaging Reporting and Data System (BIRADS) value. Other covariates included age at LCIS diagnosis, menopausal status, family history, chemoprevention, and prophylactic mastectomy. Cancer-free probability was estimated using the Kaplan-Meier method, and Cox regression models were used for univariable and multivariable analyses.

RESULTS

A total of 1222 women with LCIS were identified. At a median follow-up of 7 years, 179 women developed breast cancer (121 invasive, 58 ductal carcinoma in situ); 5- and 10-year cumulative incidences of breast cancer were 10% and 17%, respectively. In multivariable analysis, increased breast density (BIRADS C/D vs. A/B) was significantly associated with increased hazard of breast cancer (hazard ratio [HR] 2.42, 95% confidence interval [CI] 1.52-3.88), whereas BMI was not. On multivariable analysis, chemoprevention use was associated with a significantly decreased hazard of breast cancer (HR 0.49, 95% CI 0.29-0.84). Exploratory analyses did not demonstrate significant interaction between BMI and menopausal status, BMI and breast density, BMI and chemoprevention use, or breast density and chemoprevention.

CONCLUSIONS

Breast cancer risk among women with LCIS is impacted by breast density. These results aid in personalizing risk assessment among women with LCIS and highlight the importance of chemoprevention counseling for risk reduction.

Breast-Associated Adipocytes Secretome Induce Fatty Acid Uptake and Invasiveness in Breast Cancer Cells via CD36 Independently of Body Mass Index, Menopausal Status and Mammary Density

Breast adiposity is correlated with body mass index, menopausal status and mammary density. We here wish to establish how these factors influence the cross-talk between breast adipocytes and normal or malignant breast cells. Adipocyte-derived stem cells (ASCs) were obtained from healthy women and classified into six distinct groups based on body mass index, menopausal status and mammary density. The ASCs were induced to differentiate, and the influence of their conditioned media (ACM) was determined. Unexpectedly, there were no detectable differences in adipogenic differentiation and secretion between the six ASC groups, while their corresponding ACMs had no detectable influence on normal breast cells. In clear contrast, all ACMs profoundly influenced the proliferation, migration and invasiveness of malignant breast cells and increased the number of lipid droplets in their cytoplasm via increased expression of the fatty acid receptor CD36, thereby increasing fatty acid uptake. Importantly, inhibition of CD36 reduced lipid droplet accumulation and attenuated the migration and invasion of the breast cancer cells. These findings suggest that breast-associated adipocytes potentiate the invasiveness of breast cancer cells which, at least in part, is mediated by metabolic reprogramming via CD36-mediated fatty acid uptake.

Negative prognostic value of intra-ductal fat infiltrate in breast cancer

BACKGROUND

Recent studies showed a correlation between Body Mass Index and both breast cancer occurrence and progression. Nevertheless, no study reported an accurate evaluation of intra-ductal fat infiltrate. Therefore, the main aim of this study was to evaluate the putative association between intra-ductal fat infiltrate (IDFi) and breast cancer subtypes by using digital pathology.

METHODS

We retrospectively collected 220 breast biopsies. Paraffin serial sections were used for haematoxylin and eosin staining and immunohistochemical evaluation of the following markers: estrogen receptor (ER), progesterone receptor (PR), Ki67 and c-erb2. Three haematoxylin and eosin sections for each paraffin block were digitalized. Digital slides were used to evaluate the areas of IDFi. Five randomized areas were evaluated for each slide. By using GraphPad software IDFi areas was correlated with a) breast cancer histotype, b) presence of microcalcifications and c) biomarkers expression.

RESULTS

Breast biopsies were classified as follow: 20 normal breast, 50 benign lesions, and 150 malignant lesions (85 ductal in situ carcinomas; 65 ductal infiltrating carcinomas). Statistical analysis showed a significant increase of IDFi in malignant lesions as compared to both normal breast and benign lesions. We noted higher IDFi in breast ductal carcinomas as compared to lobular lesions. Significant differences were observed between breast lesions with microcalcifications respect to lesions without calcifications. Noteworthy, we also found a positive association between IDFi and the expression of both ER and Ki67.

CONCLUSION

Results of our study highlighted the possible role of fat in breast cancer progression suggesting a negative prognostic value of IDFi.