Relationship of mammographic density and gene expression: analysis of normal breast tissue surrounding breast cancer

Adipose-enriched peri-tumoral stroma, in contrast to myofibroblast-enriched stroma, prognosticates poorer survival in breast cancers

Despite our understanding of the genetic basis of intra-tumoral heterogeneity, the role of stromal heterogeneity arising from an altered tumor microenvironment in affecting tumorigenesis is poorly understood. In particular, extensive study on the peri-tumoral stroma in the morphologically normal tissues surrounding the tumor is lacking. Here, we examine the heterogeneity in tumors and peri-tumoral stroma from 8 ER+/PR+/HER2- invasive breast carcinomas, through multi-region transcriptomic profiling by microarray. We describe the regional heterogeneity observed at the intrinsic molecular subtype, pathway enrichment, and cell type composition levels within each tumor and its peri-tumoral region, up to 7 cm from the tumor margins. Moreover, we identify a pro-inflammatory adipose-enriched peri-tumoral subtype which was significantly associated with poorer overall survival in breast cancer patients, in contrast to an adaptive immune cell- and myofibroblast-enriched subtype. These data together suggest that peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.

Hormone and receptor activator of NF-κB (RANK) pathway gene expression in plasma and mammographic breast density in postmenopausal women

Background

Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women.

Methods

We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density.

Results

One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0–15%, p value = 0.05), 10% (95% CI 0–20%, p value = 0.04) and % (95% CI 0–9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4–19%, p value = 0.003) and 7% (95% CI 0–13%, p value = 0.04) lower non-dense volume, respectively.

Conclusion

These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01522-2.

Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy

Background

Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established.

Methods

Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm²)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section.

Results

Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p < 0.001) and with local percent mammographic density volume at both the biopsy target (p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p < 0.05).

Conclusions

Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01482-z.

The mammographic breast density distribution of Finnish women with breast cancer and comparison of breast density reporting using the 4th and 5th editions of the Breast Imaging-Reporting and Data System

PURPOSE

To examine the breast density distribution in patients diagnosed with breast cancer in an eastern Finnish population and to examine the changes in breast density reporting patterns between the 4^th and 5^th editions of the Breast Imaging-Reporting and Data System (BI-RADS).

METHOD

821 women (mean age 62.8 ± 12.2 years, range 28-94 years) with breast cancer were included in this retrospective study and their digital mammographic examinations were assessed semi-automatically and then visually by two radiologists in accordance with the 4^th and 5^th editions of the BI-RADS. Intraclass correlation coefficients (ICCs) were used to evaluate interobserver reproducibility. Chi-square tests were used to examine the associations between the breast density distribution and age or body mass index (BMI).

RESULTS

Interobserver reproducibility of the visual assessment was excellent, with an ICCr = 0.93. The majority of breast cancers occurred in fatty breasts (93.8 %) when density was assessed according to the 4^th edition of the BI-RADS. The distributions remained constant after correction for age and BMI. Using the 5^th edition, there was an overall 50.2 % decrease in almost entirely fatty (p <  0.001), 19.4 % increase in scattered fibroglandular (p <  0.001), 28.7 % increase in heterogeneously dense (p <  0.001), and 2.1 % increase in extremely dense (p <  0.001) categories.

CONCLUSIONS

Most breast cancers in eastern Finland occur in fatty breasts with an area density of < 50 %. Assessing breast density using the 5^th edition of the BI-RADS greatly increased denser assessments.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Extracellular Matrix-Bound FGF2 Mediates Estrogen Receptor Signaling and Therapeutic Response in Breast Cancer

The extracellular matrix (ECM) is often unaccounted for in studies that consider the stromal contribution to cancer cell signaling and response to treatment. To investigate the influence of a fibrotic microenvironment, we use fibroblast-derived ECM scaffolds as a cell culture platform. We uncover that estrogen receptor-positive (ER⁺) breast cancer cells cultured within ECM-scaffolds have an increase in ER signaling that occurs via an MAPK-dependent, but estrogen-independent manner. The ECM acts as a reservoir by binding, enriching, and presenting growth factors to adjacent epithelial cells. We identified FGF2 as a specific ECM-bound factor that drives ER signaling. ER⁺ cells cultured on ECM matrices have reduced sensitivity to ER-targeted therapies. The sensitivity to ER-targeted therapy can be restored by inhibiting FGF2-FGFR1 binding. ECM-FGF2 complexes promote Cyclin D1 induction that prevents G₁ arrest even in the presence of antiestrogens. This work demonstrates that the ECM can drive ER signaling and resistance to endocrine therapy, and suggests that patients with ER⁺ breast cancer that have high mammographic breast density may benefit from existing FGFR-targeted therapies. IMPLICATIONS: This work uncovers how the ECM may mediate signaling between growth factors and ER⁺ breast cancer cells to promote estrogen-independent ER signaling and resistance to endocrine therapy.

The use of patient-derived breast tissue explants to study macrophage polarization and the effects of environmental chemical exposure

Ex vivo mammary explant systems are an excellent model to study interactions between epithelium and stromal cell types because they contain physiologically relevant heterotypic interactions in the background of genetically diverse patients. The intact human mammary tissue, termed patient-derived explant (PDE), can be used to investigate cellular responses to a wide variety of external stimuli in situ. For this study, we examined the impact of cytokines or environmental chemicals on macrophage phenotypes. We demonstrate that we can polarize macrophages within human breast tissue PDEs toward M1 or M2 through the addition of interferon-γ (IFNγ) + lipopolysaccharide (LPS) or interleukin (IL)-4 + IL-13, respectively. Elevated expression levels of M(IFNγ + LPS) markers (HLADRA and CXCL10) or M(IL-4 + IL-13) markers (CD209 and CCL18) were observed in cytokine-treated tissues. We also examined the impact of the endocrine-disrupting chemical, benzophenone-3, on PDEs and measured significant, yet varying effects on macrophage polarization. Furthermore, a subset of the PDEs respond to IL-4 + IL-13 through downregulation of E-cadherin and upregulation of vimentin which is reminiscent of epithelial-to-mesenchymal transition (EMT) changes. Finally, we were able to show immortalized nonmalignant breast epithelial cells can exhibit EMT characteristics when exposed to growth factors secreted by M(IL-4 + IL-13) macrophages. Taken together, the PDE model system is an outstanding preclinical model to study early tissue-resident immune responses and effects on epithelial and stromal responses to stimuli found both endogenously in the breast and exogenously as a result of exposures.

Breast Mammographic Density: Stromal Implications on Breast Cancer Detection and Therapy

Current evidences state clear that both normal development of breast tissue as well as its malignant progression need many-sided local and systemic communications between epithelial cells and stromal components. During development, the stroma, through remarkably regulated contextual signals, affects the fate of the different mammary cells regarding their specification and differentiation. Likewise, the stroma can generate tumour environments that facilitate the neoplastic growth of the breast carcinoma. Mammographic density has been described as a risk factor in the development of breast cancer and is ascribed to modifications in the composition of breast tissue, including both stromal and glandular compartments. Thus, stroma composition can dramatically affect the progression of breast cancer but also its early detection since it is mainly responsible for the differences in mammographic density among individuals. This review highlights both the pathological and biological evidences for a pivotal role of the breast stroma in mammographic density, with particular emphasis on dense and malignant stromas, their clinical meaning and potential therapeutic implications for breast cancer patients.

Relationship of Serum Progesterone and Progesterone Metabolites with Mammographic Breast Density and Terminal Ductal Lobular Unit Involution among Women Undergoing Diagnostic Breast Biopsy

The association of progesterone/progesterone metabolites with elevated mammographic breast density (MBD) and delayed age-related terminal duct lobular unit (TDLU) involution, strong breast cancer risk factors, has received limited attention. Using a reliable liquid chromatography-tandem mass-spectrometry assay, we quantified serum progesterone/progesterone metabolites and explored cross-sectional relationships with MBD and TDLU involution among women, ages 40–65, undergoing diagnostic breast biopsy. Quantitative MBD measures were estimated in pre-biopsy digital mammograms. TDLU involution was quantified in diagnostic biopsies. Adjusted partial correlations and trends across MBD/TDLU categories were calculated. Pregnenolone was positively associated with percent MBD-area (MBD-A, rho: 0.30; p-trend = 0.01) among premenopausal luteal phase women. Progesterone tended to be positively associated with percent MBD-A among luteal phase (rho: 0.26; p-trend = 0.07) and postmenopausal (rho: 0.17; p-trend = 0.04) women. Consistent with experimental data, implicating an elevated 5α-pregnanes/3α-dihydroprogesterone (5αP/3αHP) metabolite ratio in breast cancer, higher 5αP/3αHP was associated with elevated percent MBD-A among luteal phase (rho: 0.29; p-trend = 0.08), but not postmenopausal women. This exploratory analysis provided some evidence that endogenous progesterone and progesterone metabolites might be correlated with MBD, a strong breast cancer risk factor, in both pre- and postmenopausal women undergoing breast biopsy. Additional studies are needed to understand the role of progesterone/progesterone metabolites in breast tissue composition and breast cancer risk.

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

Collagen and PAPP-A in the Etiology of Postpartum Breast Cancer

Pregnancy has a dual effect on the risk of breast cancer. On one hand, pregnancy at a young age is known to be protective. However, pregnancy is also associated with a transient increased risk of breast cancer. For women that have children after the age of 30, the risk remains higher than women who never had children for decades. Involution of the breast has been identified as a window of mammary development associated with the adverse effect of pregnancy. In this review, we summarize the current understanding of the role of involution and describe the role of collagen in this setting. We also discuss the role of a collagen-dependent protease, pappalysin-1, in postpartum breast cancer and its role in activating both insulin-like growth factor signaling and discoidin domain collagen receptor 2, DDR2. Together, these novel advances in our understanding of postpartum breast cancer open the way to targeted therapies against this aggressive breast cancer sub-type.

Vascular density of histologically benign breast tissue from women with breast cancer: associations with tissue composition and tumor characteristics

In breast tumors, it is well established that intratumoral angiogenesis is crucial for malignant progression, but little is known about the vascular characteristics of extratumoral, cancer-adjacent breast. Genome-wide transcriptional data suggest that extratumoral microenvironments may influence breast cancer phenotypes; thus, histologic features of cancer-adjacent tissue may also have clinical implications. To this end, we developed a digital algorithm to quantitate vascular density in approximately 300 histologically benign tissue specimens from breast cancer patients enrolled in the UNC Normal Breast Study (NBS). Specimens were stained for CD31, and vascular content was compared to demographic variables, tissue composition metrics, and tumor molecular features. We observed that the vascular density of cancer-adjacent breast was significantly higher in older and obese women, and was strongly associated with breast adipose tissue content. Consistent with observations that older and heavier women experience higher frequencies of ER+ disease, higher extratumoral vessel density was also significantly associated with positive prognostic tumor features such as lower stage, negative nodal status, and smaller size (<2 cm). These results reveal biological relationships between extratumoral vascular content and body size, breast tissue composition, and tumor characteristics, and suggest biological plausibility for the relationship between weight gain (and corresponding breast tissue changes) and breast cancer progression.

Associations between mammographic density and tumor characteristics in Chinese women with breast cancer

PURPOSE

Mammographic density (MD) is a strong risk factor for breast cancer, yet its relationship with tumor characteristics is not well established, particularly in Asian populations.

METHODS

MD was assessed from a total of 2001 Chinese breast cancer patients using Breast Imaging Reporting and Data System (BI-RADS) categories. Molecular subtypes were defined using immunohistochemical status on ER, PR, HER2, and Ki-67, as well as tumor grade. Multinomial logistic regression was used to test associations between MD and molecular subtype (luminal A = reference) adjusting for age, body mass index (BMI), menopausal status, parity, and nodal status.

RESULTS

The mean age at diagnosis was 51.7 years (SD = 10.7) and the average BMI was 24.7 kg/m² (SD = 3.8). The distribution of BI-RADS categories was 7.4% A = almost entirely fat, 24.2% B = scattered fibroglandular dense, 49.4% C = heterogeneously dense, and 19.0% D = extremely dense. Compared to women with BI-RADS = A/B, women with BI-RADS = D were more likely to have HER2-enriched tumors (OR = 1.81, 95% CI 1.08-3.06, p = 0.03), regardless of menopausal status. The association was only observed in women with normal (< 25 kg/m²) BMI (OR = 2.43, 95% CI 1.24-4.76, p < 0.01), but not among overweight/obese women (OR: 0.98, 95% CI 0.38-2.52, p = 0.96).

CONCLUSIONS

Among Chinese women with normal BMI, higher breast density was associated with HER2-enriched tumors. The results may partially explain the higher proportion of HER2+ tumors previously reported in Asian women.

A Longitudinal Study of the Association between Mammographic Density and Gene Expression in Normal Breast Tissue

High mammographic density (MD) is associated with a 4-6 times increase in breast cancer risk. For post-menopausal women, MD often decreases over time, but little is known about the underlying biological mechanisms. MD reflects breast tissue composition, and may be associated with microenvironment subtypes previously identified in tumor-adjacent normal tissue. Currently, these subtypes have not been explored in normal breast tissue. We obtained biopsies from breasts of healthy women at two different time points several years apart and performed microarray gene expression analysis. At time point 1, 65 samples with both MD and gene expression were available. At time point 2, gene expression and MD data were available from 17 women, of which 11 also had gene expression data available from the first time point. We validated findings from our previous study; negative correlation between RBL1 and MD in post-menopausal women, indicating involvement of the TGFβ pathway. We also found that breast tissue samples from women with a large decrease in MD sustained higher expression of genes in the histone family H4. In addition, we explored the previously defined active and inactive microenvironment subtypes and demonstrated that normal breast samples of the active subtype had characteristics similar to the claudin-low breast cancer subtype. Breast biopsies from healthy women are challenging to obtain, but despite a limited sample size, we have identified possible mechanisms relevant for changes in breast biology and MD over time that may be of importance for breast cancer risk and tumor initiation.

Sterol regulatory element binding protein 1 couples mechanical cues and lipid metabolism

Sterol regulatory element binding proteins (SREBPs) are a family of transcription factors that regulate lipid biosynthesis and adipogenesis by controlling the expression of several enzymes required for cholesterol, fatty acid, triacylglycerol and phospholipid synthesis. In vertebrates, SREBP activation is mainly controlled by a complex and well-characterized feedback mechanism mediated by cholesterol, a crucial bio-product of the SREBP-activated mevalonate pathway. In this work, we identified acto-myosin contractility and mechanical forces imposed by the extracellular matrix (ECM) as SREBP1 regulators. SREBP1 control by mechanical cues depends on geranylgeranyl pyrophosphate, another key bio-product of the mevalonate pathway, and impacts on stem cell fate in mouse and on fat storage in Drosophila. Mechanistically, we show that activation of AMP-activated protein kinase (AMPK) by ECM stiffening and geranylgeranylated RhoA-dependent acto-myosin contraction inhibits SREBP1 activation. Our results unveil an unpredicted and evolutionary conserved role of SREBP1 in rewiring cell metabolism in response to mechanical cues.

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

BACKGROUND

Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.

METHODS

Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics.

RESULTS

BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively).

CONCLUSIONS

When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

Molecular mechanisms linking high body mass index to breast cancer etiology in post-menopausal breast tumor and tumor-adjacent tissues

PURPOSE

In post-menopausal women, high body mass index (BMI) is an established breast cancer risk factor and is associated with worse breast cancer prognosis. We assessed the associations between BMI and gene expression of both breast tumor and adjacent tissue in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) diseases to help elucidate the mechanisms linking obesity with breast cancer biology in 519 post-menopausal women from the Nurses' Health Study (NHS) and NHSII.

METHODS

Differential gene expression was analyzed separately in ER+ and ER- disease both comparing overweight (BMI ≥ 25 to < 30) or obese (BMI ≥ 30) women to women with normal BMI (BMI < 25), and per 5 kg/m² increase in BMI. Analyses controlled for age and year of diagnosis, physical activity, alcohol consumption, and hormone therapy use. Gene set enrichment analyses were performed and validated among a subset of post-menopausal cases in The Cancer Genome Atlas (for tumor) and Polish Breast Cancer Study (for tumor-adjacent).

RESULTS

No gene was differentially expressed by BMI (FDR < 0.05). BMI was significantly associated with increased cellular proliferation pathways, particularly in ER+ tumors, and increased inflammation pathways in ER- tumor and ER- tumor-adjacent tissues (FDR < 0.05). High BMI was associated with upregulation of genes involved in epithelial-mesenchymal transition in ER+ tumor-adjacent tissues.

CONCLUSIONS

This study provides insights into molecular mechanisms of BMI influencing post-menopausal breast cancer biology. Tumor and tumor-adjacent tissues provide independent information about potential mechanisms.

Stroma modifies relationships between risk factor exposure and age-related epithelial involution in benign breast

Delayed age-related lobular involution has been previously associated with elevated breast cancer risk. However, intraindividual variability in epithelial involution status within a woman is undefined. We developed a novel measure of age-related epithelial involution, density of epithelial nuclei in epithelial areas using digital image analysis in combination with stromal characteristics (percentage of section area comprising stroma). Approximately 1800 hematoxylin and eosin stained sections of benign breast tissue were evaluated from 416 participants having breast surgery for cancer or benign conditions. Two to sixteen slides per woman from different regions of the breast were studied. Epithelial involution status varied within a woman and as a function of stromal area. Percentage stromal area varied between samples from the same woman (median difference between highest and lowest stromal area within a woman was 7.5%, but ranged from 0.01 to 86.7%). Restricting to women with at least 10% stromal area (N = 317), epithelial nuclear density decreased with age (-637.1 cells/mm² per decade of life after age 40, p < 0.0001), increased with mammographic density (457.8 cells/mm² per increasing BI-RADs density category p = 0.002), and increased non-significantly with recent parity, later age at first pregnancy, and longer and more recent oral contraceptive use. These associations were attenuated in women with mostly fat samples (<10% stroma (N = 99)). Thirty-one percent of women evaluated had both adequate stroma (≥10%) and mostly fat (<10% stroma) regions of breast tissue, with the probability of having both types increasing with the number breast tissue samplings. Several breast cancer risk factors are associated with elevated age-related epithelial content, but associations depend upon stromal context. Stromal characteristics appear to modify relationships between risk factor exposures and breast epithelial involution.

Intra-individual Gene Expression Variability of Histologically Normal Breast Tissue

Several studies have sought to identify novel transcriptional biomarkers in normal breast or breast microenvironment to predict tumor risk and prognosis. However, systematic efforts to evaluate intra-individual variability of gene expression within normal breast have not been reported. This study analyzed the microarray gene expression data of 288 samples from 170 women in the Normal Breast Study (NBS), wherein multiple histologically normal breast samples were collected from different block regions and different sections at a given region. Intra-individual differences in global gene expression and selected gene expression signatures were quantified and evaluated in association with other patient-level factors. We found that intra-individual reliability was relatively high in global gene expression, but differed by signatures, with composition-related signatures (i.e., stroma) having higher intra-individual variability and tumorigenesis-related signatures (i.e., proliferation) having lower intra-individual variability. Histological stroma composition was the only factor significantly associated with heterogeneous breast tissue (defined as > median intra-individual variation; high nuclear density, odds ratio [OR] = 3.42, 95% confidence interval [CI] = 1.15–10.15; low area, OR = 0.29, 95% CI = 0.10–0.86). Other factors suggestively influencing the variability included age, BMI, and adipose nuclear density. Our results underscore the importance of considering intra-individual variability in tissue-based biomarker development, and have important implications for normal breast research.

Correlated metabolomic, genomic, and histologic phenotypes in histologically normal breast tissue

Breast carcinogenesis is a multistep process accompanied by widespread molecular and genomic alterations, both in tumor and in surrounding microenvironment. It is known that tumors have altered metabolism, but the metabolic changes in normal or cancer-adjacent, nonmalignant normal tissues and how these changes relate to alterations in gene expression and histological composition are not well understood. Normal or cancer-adjacent normal breast tissues from 99 women of the Normal Breast Study (NBS) were evaluated. Data of metabolomics, gene expression and histological composition was collected by mass spectrometry, whole genome microarray, and digital image, respectively. Unsupervised clustering analysis determined metabolomics-derived subtypes. Their association with genomic and histological features, as well as other breast cancer risk factors, genomic and histological features were evaluated using logistic regression. Unsupervised clustering of metabolites resulted in two main clusters. The metabolite differences between the two clusters suggested enrichment of pathways involved in lipid metabolism, cell growth and proliferation, and migration. Compared with Cluster 1, subjects in Cluster 2 were more likely to be obese (body mass index ≥30 kg/m2, p<0.05), have increased adipose proportion (p<0.01) and associated with a previously defined Active genomic subtype (p<0.01). By the integrated analyses of histological, metabolomics and transcriptional data, we characterized two distinct subtypes of non-malignant breast tissue. Further research is needed to validate our findings, and understand the potential role of these alternations in breast cancer initiation, progression and recurrence.

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.

Review of quantitative multiscale imaging of breast cancer

Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

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 Cancers Activate Stromal Fibroblast-Induced Suppression of Progenitors in Adjacent Normal Tissue

Human breast cancer cells are known to activate adjacent “normal-like” cells to enhance their own growth, but the cellular and molecular mechanisms involved are poorly understood. We now show by both phenotypic and functional measurements that normal human mammary progenitor cells are significantly under-represented in the mammary epithelium of patients' tumor-adjacent tissue (TAT). Interestingly, fibroblasts isolated from TAT samples showed a reduced ability to support normal EGF-stimulated mammary progenitor cell proliferation in vitro via their increased secretion of transforming growth factor β. In contrast, TAT fibroblasts promoted the proliferation of human breast cancer cells when these were co-transplanted in immunodeficient mice. The discovery of a common stromal cell-mediated mechanism that has opposing growth-suppressive and promoting effects on normal and malignant human breast cells and also extends well beyond currently examined surgical margins has important implications for disease recurrence and its prevention.

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Alterations to the breast tissue extend as far as 6 cm away from the primary tumors

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The matching contralateral non-tumor-bearing breast tissue remains unaltered

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Tumor-adjacent breast tissue contained significantly diminished progenitor pool

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Extending surgical margins may not be effective in reducing risk of tumor recurrence

From K-space to Nucleotide: Insights Into the Radiogenomics of Brain Tumors

Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.

DEEP LEARNING-BASED ASSESSMENT OF TUMOR-ASSOCIATED STROMA FOR DIAGNOSING BREAST CANCER IN HISTOPATHOLOGY IMAGES

Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor associated stroma as a diagnostic biomarker.

Overexpression of lipid metabolism genes and PBX1 in the contralateral breasts of women with estrogen receptor-negative breast cancer

Risk biomarkers for estrogen receptor (ER)-negative breast cancer have clear value for breast cancer prevention. We previously reported a set of lipid metabolism (LiMe) genes with high expression in the contralateral unaffected breasts (CUBs) of ER-negative cancer cases. We now further examine LiMe gene expression in both tumor and CUB, and investigate the role of Pre-B-cell leukemia homeobox-1 (PBX1) as a candidate common transcription factor for LiMe gene expression. mRNA was extracted from laser-capture microdissected epithelium from tumor and CUB of 84 subjects (28 ER-positive cases, 28 ER-negative cases, 28 healthy controls). Gene expression was quantitated by qRT-PCR. Logistic regression models were generated to predict ER status of the contralateral cancer. Protein expression of HMGCS2 and PBX1 was measured using immunohistochemistry. The effect of PBX1 on LiMe gene expression was examined by overexpressing PBX1 in MCF10A cells with or without ER, and by suppressing PBX1 in MDA-MB-453 cells. The expression of DHRS2, HMGCS2, UGT2B7, UGT2B11, ALOX15B, HPGD, UGT2B28 and GLYATL1 was significantly higher in ER-negative versus ER-positive CUBs, and predicted ER status of the tumor in test and validation sets. In contrast, LiMe gene expression was significantly lower in ER-negative than ER-positive tumors. PBX1 overexpression in MCF10A cells up-regulated most LiMe genes, but not in MCF10A cells overexpressing ER. Suppressing PBX1 in MDA-MB-453 cells resulted in decrease of LiMe gene expression. Four binding sites of PBX1 and cofactor were identified in three lipid metabolism genes using ChIP-qPCR. These data suggest a novel role for PBX1 in the regulation of lipid metabolism genes in benign breast, which may contribute to ER-negative tumorigenesis.

Correlation of mammographic density and serum calcium levels in patients with primary breast cancer

Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well‐known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness‐of‐fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention.

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.

Genomic Changes in Normal Breast Tissue in Women at Normal Risk or at High Risk for Breast Cancer

Sporadic breast cancer develops through the accumulation of molecular abnormalities in normal breast tissue, resulting from exposure to estrogens and other carcinogens beginning at adolescence and continuing throughout life. These molecular changes may take a variety of forms, including numerical and structural chromosomal abnormalities, epigenetic changes, and gene expression alterations. To characterize these abnormalities, a review of the literature has been conducted to define the molecular changes in each of the above major genomic categories in normal breast tissue considered to be either at normal risk or at high risk for sporadic breast cancer. This review indicates that normal risk breast tissues (such as reduction mammoplasty) contain evidence of early breast carcinogenesis including loss of heterozygosity, DNA methylation of tumor suppressor and other genes, and telomere shortening. In normal tissues at high risk for breast cancer (such as normal breast tissue adjacent to breast cancer or the contralateral breast), these changes persist, and are increased and accompanied by aneuploidy, increased genomic instability, a wide range of gene expression differences, development of large cancerized fields, and increased proliferation. These changes are consistent with early and long-standing exposure to carcinogens, especially estrogens. A model for the breast carcinogenic pathway in normal risk and high-risk breast tissues is proposed. These findings should clarify our understanding of breast carcinogenesis in normal breast tissue and promote development of improved methods for risk assessment and breast cancer prevention in women.

Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues

Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement in regulating breast tissue composition.

Neutrophils drive accelerated tumor progression in the collagen-dense mammary tumor microenvironment

Background

High mammographic density has been correlated with a 4-fold to 6-fold increased risk of developing breast cancer, and is associated with increased stromal deposition of extracellular matrix proteins, including collagen I. The molecular and cellular mechanisms responsible for high breast tissue density are not completely understood.

Methods

We previously described accelerated tumor formation and metastases in a transgenic mouse model of collagen-dense mammary tumors (type I collagen-α1 (Col1α1)^tm1Jae and mouse mammary tumor virus - polyoma virus middle T antigen (MMTV-PyVT)) compared to wild-type mice. Using ELISA cytokine arrays and multi-color flow cytometry analysis, we studied cytokine signals and the non-malignant, immune cells in the collagen-dense tumor microenvironment that may promote accelerated tumor progression and metastasis.

Results

Collagen-dense tumors did not show any alteration in immune cell populations at late stages. The cytokine signals in the mammary tumor microenvironment were clearly different between wild-type and collagen-dense tumors. Cytokines associated with neutrophil signaling, such as granulocyte monocyte-colony stimulated factor (GM-CSF), were increased in collagen-dense tumors. Depleting neutrophils with anti-Ly6G (1A8) significantly reduced the number of tumors, and blocked metastasis in over 80 % of mice with collagen-dense tumors, but did not impact tumor growth or metastasis in wild-type mice.

Conclusion

Our study suggests that tumor progression in a collagen-dense microenvironment is mechanistically different, with pro-tumor neutrophils, compared to a non-dense microenvironment.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0703-7) contains supplementary material, which is available to authorized users.

DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas

Recurrence rates after breast-conserving therapy may depend on genomic characteristics of cancer-adjacent, benign-appearing tissue. Studies have not evaluated recurrence in association with multiple genomic characteristics of cancer-adjacent breast tissue. To estimate the prevalence of DNA defects and RNA expression subtypes in cancer-adjacent, benign-appearing breast tissue at least 2 cm from the tumor margin, cancer-adjacent, pathologically well-characterized, benign-appearing breast tissue specimens from The Cancer Genome Atlas project were analyzed for DNA sequence, copy-number variation, DNA methylation, messenger RNA (mRNA) sequence, and mRNA/microRNA expression. Additional samples were also analyzed by at least one of these genomic data types and associations between genomic characteristics of normal tissue and overall survival were assessed. Approximately 40% of cancer-adjacent, benign-appearing tissues harbored genomic defects in DNA copy number, sequence, methylation, or in RNA sequence, although these defects did not significantly predict 10-year overall survival. Two mRNA/microRNA expression phenotypes were observed, including an active mRNA subtype that was identified in 40% of samples. Controlling for tumor characteristics and the presence of genomic defects, this active subtype was associated with significantly worse 10-year survival among estrogen receptor (ER)-positive cases. This multi-platform analysis of breast cancer-adjacent samples produced genomic findings consistent with current surgical margin guidelines, and provides evidence that extratumoral RNA expression patterns in cancer-adjacent tissue predict overall survival among patients with ER-positive disease.

Raised mammographic density: causative mechanisms and biological consequences

High mammographic density is the most important risk factor for breast cancer, after ageing. However, the composition, architecture, and mechanical properties of high X-ray density soft tissues, and the causative mechanisms resulting in different mammographic densities, are not well described. Moreover, it is not known how high breast density leads to increased susceptibility for cancer, or the extent to which it causes the genomic changes that characterise the disease. An understanding of these principals may lead to new diagnostic tools and therapeutic interventions.

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities

Elevated mammographic density (MD) is an established breast cancer risk factor. Reduced involution of terminal duct lobular units (TDLU), the histologic source of most breast cancers, has been associated with higher MD and breast cancer risk. We investigated relationships of TDLU involution with area and volumetric MD, measured throughout the breast and surrounding biopsy targets (perilesional). Three measures inversely related to TDLU involution (TDLU count/mm(2), median TDLU span, median acini count/TDLU) assessed in benign diagnostic biopsies from 348 women, ages 40-65, were related to MD area (quantified with thresholding software) and volume (assessed with a density phantom) by analysis of covariance, stratified by menopausal status and adjusted for confounders. Among premenopausal women, TDLU count was directly associated with percent perilesional MD (P trend = 0.03), but not with absolute dense area/volume. Greater TDLU span was associated with elevated percent dense area/volume (P trend<0.05) and absolute perilesional MD (P = 0.003). Acini count was directly associated with absolute perilesional MD (P = 0.02). Greater TDLU involution (all metrics) was associated with increased nondense area/volume (P trend ≤ 0.04). Among postmenopausal women, TDLU measures were not significantly associated with MD. Among premenopausal women, reduced TDLU involution was associated with higher area and volumetric MD, particularly in perilesional parenchyma. Data indicating that TDLU involution and MD are correlated markers of breast cancer risk suggest that associations of MD with breast cancer may partly reflect amounts of at-risk epithelium. If confirmed, these results could suggest a prevention paradigm based on enhancing TDLU involution and monitoring efficacy by assessing MD reduction.

Leukocyte telomere length and its association with mammographic density and proliferative diagnosis among women undergoing diagnostic image-guided breast biopsy

BACKGROUND

Elevated mammographic density (MD) is a strong breast cancer risk factor but the mechanisms underlying the association are poorly understood. High MD and breast cancer risk may reflect cumulative exposures to factors that promote epithelial cell division. One marker of cellular replicative history is telomere length, but its association with MD is unknown. We investigated the relation of telomere length, a marker of cellular replicative history, with MD and biopsy diagnosis.

METHODS

One hundred and ninety-five women, ages 40-65, were clinically referred for image-guided breast biopsies at an academic facility in Vermont. Relative peripheral blood leukocyte telomere length (LTL) was measured using quantitative polymerase chain reaction. MD volume was quantified in cranio-caudal views of the breast contralateral to the primary diagnosis in digital mammograms using a breast density phantom, while MD area (cm(2)) was measured using thresholding software. Associations between log-transformed LTL and continuous MD measurements (volume and area) were evaluated using linear regression models adjusted for age and body mass index. Analyses were stratified by biopsy diagnosis: proliferative (hyperplasia, in-situ or invasive carcinoma) or non-proliferative (benign or other non-proliferative benign diagnoses).

RESULTS

Mean relative LTL in women with proliferative disease (n = 141) was 1.6 (SD = 0.9) vs. 1.2 (SD = 0.6) in those with non-proliferative diagnoses (n = 54) (P = 0.002). Mean percent MD volume did not differ by diagnosis (P = 0.69). LTL was not associated with MD in women with proliferative (P = 0.89) or non-proliferative (P = 0.48) diagnoses. However, LTL was associated with a significant increased risk of proliferative diagnosis (adjusted OR = 2.46, 95% CI: 1.47, 4.42).

CONCLUSIONS

Our analysis of LTL did not find an association with MD. However, our findings suggest that LTL may be a marker of risk for proliferative pathology among women referred for biopsy based on breast imaging.

Digital histologic analysis reveals morphometric patterns of age-related involution in breast epithelium and stroma

Complete age-related regression of mammary epithelium, often termed postmenopausal involution, is associated with decreased breast cancer risk. However, most studies have qualitatively assessed involution. We quantitatively analyzed epithelium, stroma, and adipose tissue from histologically normal breast tissue of 454 patients in the Normal Breast Study. High-resolution digital images of normal breast hematoxylin and eosin-stained slides were partitioned into epithelium, adipose tissue, and nonfatty stroma. Percentage area and nuclei per unit area (nuclear density) were calculated for each component. Quantitative data were evaluated in association with age using linear regression and cubic spline models. Stromal area decreased (P = 0.0002), and adipose tissue area increased (P < 0.0001), with an approximate 0.7% change in area for each component, until age 55 years when these area measures reached a steady state. Although epithelial area did not show linear changes with age, epithelial nuclear density decreased linearly beginning in the third decade of life. No significant age-related trends were observed for stromal or adipose nuclear density. Digital image analysis offers a high-throughput method for quantitatively measuring tissue morphometry and for objectively assessing age-related changes in adipose tissue, stroma, and epithelium. Epithelial nuclear density is a quantitative measure of age-related breast involution that begins to decline in the early premenopausal period.

Current and Future Methods for Measuring Breast Density: A Brief Comparative Review

Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment.

Relationship of serum estrogens and metabolites with area and volume mammographic densities

Elevated mammographic density is a breast cancer risk factor, which has a suggestive, but unproven, relationship with increased exposure to sex steroid hormones. We examined associations of serum estrogens and estrogen metabolites with area and novel volume mammographic density measures among 187 women, ages 40-65, undergoing diagnostic breast biopsies at an academic facility in Vermont. Serum parent estrogens, estrone and estradiol, and their 2-, 4-, and 16-hydroxylated metabolites were measured using liquid chromatography-tandem mass spectrometry. Area mammographic density was measured in the breast contralateral to the biopsy using thresholding software; volume mammographic density was quantified using a density phantom. Linear regression was used to estimate associations of estrogens with mammographic densities, adjusted for age and body mass index, and stratified by menopausal status and menstrual cycle phase. Weak, positive associations between estrogens, estrogen metabolites, and mammographic density were observed, primarily among postmenopausal women. Among premenopausal luteal phase women, the 16-pathway metabolite estriol was associated with percent area (p = 0.04) and volume (p = 0.05) mammographic densities and absolute area (p = 0.02) and volume (p = 0.05) densities. Among postmenopausal women, levels of total estrogens, the sum of parent estrogens, and 2-, 4- and 16-hydroxylation pathway metabolites were positively associated with area density measures (percent: p = 0.03, p = 0.04, p = 0.01, p = 0.02, p = 0.07; absolute: p = 0.02, p = 0.02, p = 0.01, p = 0.02, p = 0.03, respectively) but not volume density measures. Our data suggest that serum estrogen profiles are weak determinants of mammographic density and that analysis of different density metrics may provide complementary information about relationships of estrogen exposure to breast tissue composition.

Tumor intrinsic subtype is reflected in cancer-adjacent tissue

INTRODUCTION

Overall survival of early-stage breast cancer patients is similar for those who undergo breast-conserving therapy (BCT) and mastectomy; however, 10% to 15% of women undergoing BCT suffer ipsilateral breast tumor recurrence. The risk of recurrence may vary with breast cancer subtype. Understanding the gene expression of the cancer-adjacent tissue and the stromal response to specific tumor subtypes is important for developing clinical strategies to reduce recurrence risk.

METHODS

We utilized two independent datasets to study gene expression data in cancer-adjacent tissue from invasive breast cancer patients. Complementary in vitro cocultures were used to study cell-cell communication between fibroblasts and specific breast cancer subtypes.

RESULTS

Our results suggest that intrinsic tumor subtypes are reflected in histologically normal cancer-adjacent tissue. Gene expression of cancer-adjacent tissues shows that triple-negative (Claudin-low or basal-like) tumors exhibit increased expression of genes involved in inflammation and immune response. Although such changes could reflect distinct immune populations present in the microenvironment, altered immune response gene expression was also observed in cocultures in the absence of immune cell infiltrates, emphasizing that these inflammatory mediators are secreted by breast-specific cells. In addition, although triple-negative breast cancers are associated with upregulated immune response genes, luminal breast cancers are more commonly associated with estrogen-response pathways in adjacent tissues.

CONCLUSIONS

Specific characteristics of breast cancers are reflected in the surrounding histologically normal tissue. This commonality between tumor and cancer-adjacent tissue may underlie second primaries and local recurrences.

IMPACT

Biomarkers derived from cancer-adjacent tissue may be helpful in defining personalized surgical strategies or in predicting recurrence risk.

Benign breast tissue composition in breast cancer patients: association with risk factors, clinical variables, and gene expression

BACKGROUND

Breast tissue composition (epithelium, non-fatty stroma, and adipose) changes qualitatively and quantitatively throughout the lifespan, and may mediate relationships between risk factors and breast cancer initiation. We sought to identify relationships between tissue composition, risk factors, tumor characteristics, and gene expression.

METHODS

Participants were 146 patients from the Polish Breast Cancer Study, with data on risk factor and clinicopathological characteristics. Benign breast tissue composition was evaluated using digital image analysis of histologic sections. Whole-genome microarrays were performed on the same tissue blocks.

RESULTS

Mean epithelial, non-fatty stromal, and adipose proportions were 8.4% (SD = 4.9%), 27.7% (SD = 24.0%), and 64.0% (SD = 24.0%), respectively. Among women <50 years old, stroma proportion decreased and adipose proportion increased with age, with approximately 2% difference per year (P < 0.01). The variation in epithelial proportion with age was modest (0.1% per year). Higher epithelial proportion was associated with obesity (7.6% in nonobese vs. 10.1% in obese; P = 0.02) and with poorly differentiated tumors (7.8% in well/moderate vs. 9.9% in poor; P = 0.05). Gene expression signatures associated with epithelial and stromal proportion were identified and validated. Stroma-associated genes were in metabolism and stem cell maintenance pathways, whereas epithelial genes were enriched for cytokine and immune response pathways.

CONCLUSIONS

Breast tissue composition was associated with age, body mass index, and tumor grade, with consequences for breast gene expression.

IMPACT

Breast tissue morphologic factors may influence breast cancer etiology. Composition and gene expression may act as biomarkers of breast cancer risk and progression.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Introduction

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

Methods

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

Results

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

Conclusions

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

Electronic supplementary material

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

MicroRNA profile in very young women with breast cancer

Background

Breast cancer is rarely diagnosed in very young women (35years old or younger), and it often presents with distinct clinical-pathological features related to a more aggressive phenotype and worse prognosis when diagnosed at this early age. A pending question is whether breast cancer in very young women arises from the deregulation of different underlying mechanisms, something that will make this disease an entity differentiated from breast cancer diagnosed in older patients.

Methods

We performed a comprehensive study of miRNA expression using miRNA Affymetrix2.0 array on paraffin-embedded tumour tissue of 42 breast cancer patients 35 years old or younger, 17 patients between 45 and 65 years old and 29 older than 65 years. Data were statistically analyzed by t-test and a hierarchical clustering via average linkage method was conducted. Results were validated by qRT-PCR. Putative targeted pathways were obtained using DIANA miRPath online software.

Results

The results show a differential and unique miRNA expression profile of 121 miRNAs (p-value <0.05), 96 of those with a FDR-value <0.05. Hierarchical clustering grouped the samples according to their age, but not by subtype nor by tumour characteristics. We were able to validate by qRT-PCR differences in the expression of 6 miRNAs: miR-1228*, miR-3196, miR-1275, miR-92b, miR-139 and miR-1207. Moreover, all of the miRNAs maintained the expression trend. The validated miRNAs pointed out pathways related to cell motility, invasion and proliferation.

Conclusions

The study suggests that breast cancer in very young women appears as a distinct molecular signature. To our knowledge, this is the first time that a validated microRNA profile, distinctive to breast cancer in very young women, has been presented. The miRNA signature may be relevant to open an important field of research in order to elucidate the underlying mechanism in this particular disease, which in a more clinical setting, could potentially help to identify therapeutic targets in this particular set of patients.

Parity-related molecular signatures and breast cancer subtypes by estrogen receptor status

INTRODUCTION

Relationships of parity with breast cancer risk are complex. Parity is associated with decreased risk of postmenopausal hormone receptor-positive breast tumors, but may increase risk for basal-like breast cancers and early-onset tumors. Characterizing parity-related gene expression patterns in normal breast and breast tumor tissues may improve understanding of the biological mechanisms underlying this complex pattern of risk.

METHODS

We developed a parity signature by analyzing microRNA microarray data from 130 reduction mammoplasty (RM) patients (54 nulliparous and 76 parous). This parity signature, together with published parity signatures, was evaluated in gene expression data from 150 paired tumors and adjacent benign breast tissues from the Polish Breast Cancer Study, both overall and by tumor estrogen receptor (ER) status.

RESULTS

We identified 251 genes significantly upregulated by parity status in RM patients (parous versus nulliparous; false discovery rate = 0.008), including genes in immune, inflammation and wound response pathways. This parity signature was significantly enriched in normal and tumor tissues of parous breast cancer patients, specifically in ER-positive tumors.

CONCLUSIONS

Our data corroborate epidemiologic data, suggesting that the etiology and pathogenesis of breast cancers vary by ER status, which may have implications for developing prevention strategies for these tumors.

Weight Loss Reversed Obesity-Induced HGF/c-Met Pathway and Basal-Like Breast Cancer Progression

Epidemiologic studies demonstrate that obesity is associated with an aggressive subtype of breast cancer called basal-like breast cancer (BBC). Using the C3(1)-T_Ag murine model of BBC, we previously demonstrated that mice displayed an early onset of tumors when fed obesogenic diets in the adult window of susceptibility. Obesity was also shown to elevate mammary gland expression and activation of hepatocyte growth factor (HGF)/c-Met compared to lean controls, a pro-tumorigenic pathway associated with BBC in patients. Epidemiologic studies estimate that weight loss could prevent a large proportion of BBC. We sought to investigate whether weight loss in adulthood prior to tumor onset would protect mice from accelerated tumorigenesis observed in obese mice. Using a life-long model of obesity, C3(1)-T_Ag mice were weaned onto and maintained on an obesogenic high-fat diet. Obese mice displayed significant elevations in tumor progression, but not latency or burden. Tumor progression was significantly reversed when obese mice were induced to lose weight by switching to a control low-fat diet prior to tumor onset compared to mice maintained on obesogenic diet. We investigated the HGF/c-Met pathway known to regulate tumorigenesis. Importantly, HGF/c-Met expression in normal mammary glands and c-Met in tumors was elevated with obesity and was significantly reversed with weight loss. Changes in tumor growth could not be explained by measures of HGF action including phospho-AKT or phospho-S6. Other mediators associated with oncogenesis such as hyperinsulinemia and a high leptin:adiponectin ratio were elevated by obesity and reduced with weight loss. In sum, weight loss significantly blunted the obesity-responsive pro-tumorigenic HGF/c-Met pathway and improved several metabolic risk factors associated with BBC, which together may have contributed to the dramatic reversal of obesity-driven tumor progression. Future research aims to evaluate the role of obesity and the HGF/c-Met pathway in basal-like breast cancer progression.

Differential impact of body mass index on absolute and percent breast density: implications regarding their use as breast cancer risk biomarkers

Percent breast density (PBD), a commonly used biomarker of breast cancer risk (BCR), is confounded by the influence of non-dense breast tissue on its measurement and factors, such as BMI, which have an impact on non-dense tissue. Consequently, BMI, a potent BCR factor, is, paradoxically, negatively correlated with PBD. We propose that absolute breast density (ABD) is a more accurate biomarker of BCR. We used a volumetric method to compare the correlation between PBD and ABD with baseline demographics and dietary and physical activity variables in a group of 169 postmenopausal women enrolled in a clinical trial prior to any intervention. As expected, a strong negative correlation between PBD and BMI was observed (Rho = -0.5, p < 5e(-12)). In contrast, we observed a strong, previously not well established, positive correlation of BMI with ABD (Rho = 0.41, p < 2.5e(-8)), which supports the use of ABD as a more accurate indicator of BCR. Correction of PBD by BMI did not frequently provide the same information as ABD. In addition, because of the strong influence of BMI on ABD, many correlations between dietary variables and ABD did not emerge, until adjustment was made for BMI. ABD corrected by BMI should be the gold standard BD measurement. These findings identify the optimal measurement of BD when testing the influence of an intervention on BD as a biomarker of BCR.

Mammographic density-a review on the current understanding of its association with breast cancer

There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.