Age-associated gene expression in normal breast tissue mirrors qualitative age-at-incidence patterns for breast cancer

The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study

BACKGROUND

Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity).

METHODS

We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays.

RESULTS

Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences.

CONCLUSION

Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

Delineation of Pathogenomic Insights of Breast Cancer in Young Women

The prognosis of breast cancer (BC) in young women (BCYW) aged ≤40 years tends to be poorer than that in older patients due to aggressive phenotypes, late diagnosis, distinct biologic, and poorly understood genomic features of BCYW. Considering the estimated predisposition of only approximately 15% of the BC population to BC-promoting genes, the underlying reasons for an increased occurrence of BCYW, at large, cannot be completely explained based on general risk factors for BC. This underscores the need for the development of next-generation of tissue- and body fluid-based prognostic and predictive biomarkers for BCYW. Here, we identified the genes associated with BCYW with a particular focus on the age, intrinsic BC subtypes, matched normal or normal breast tissues, and BC laterality. In young women with BC, we observed dysregulation of age-associated cancer-relevant gene sets in both cancer and normal breast tissues, sub-sets of which substantially affected the overall survival (OS) or relapse-free survival (RFS) of patients with BC and exhibited statically significant correlations with several gene modules associated with cellular processes such as the stroma, immune responses, mitotic progression, early response, and steroid responses. For example, high expression of COL1A2, COL5A2, COL5A1, NPY1R, and KIAA1644 mRNAs in the BC and normal breast tissues from young women correlated with a substantial reduction in the OS and RFS of BC patients with increased levels of these exemplified genes. Many of the genes upregulated in BCYW were overexpressed or underexpressed in normal breast tissues, which might provide clues regarding the potential involvement of such genes in the development of BC later in life. Many of BCYW-associated gene products were also found in the extracellular microvesicles/exosomes secreted from breast and other cancer cell-types as well as in body fluids such as urine, saliva, breast milk, and plasma, raising the possibility of using such approaches in the development of non-invasive, predictive and prognostic biomarkers. In conclusion, the findings of this study delineated the pathogenomics of BCYW, providing clues for future exploration of the potential predictive and prognostic importance of candidate BCYW molecules and research strategies as well as a rationale to undertake a prospective clinical study to examine some of testable hypotheses presented here. In addition, the results presented here provide a framework to bring out the importance of geographical disparities, to overcome the current bottlenecks in BCYW, and to make the next quantum leap for sporadic BCYW research and treatment.

Oncobiology and treatment of breast cancer in young women

Female breast cancer emerged as the leading cancer type in terms of incidence globally in 2020. Although mortality due to breast cancer has improved during the past three decades in many countries, this trend has reversed in women less than 40 years since the past decade. From the biological standpoint, there is consensus among experts regarding the clinically relevant definition of breast cancer in young women (BCYW), with an age cut-off of 40 years. The idea that breast cancer is an aging disease has apparently broken in the case of BCYW due to the young onset and an overall poor outcome of BCYW patients. In general, younger patients exhibit a worse prognosis than older pre- and postmenopausal patients due to the aggressive nature of cancer subtypes, a high percentage of cases with advanced stages at diagnosis, and a high risk of relapse and death in younger patients. Because of clinically and biologically unique features of BCYW, it is suspected to represent a distinct biologic entity. It is unclear why BCYW is more aggressive and has an inferior prognosis with factors that contribute to increased incidence. However, unique developmental features, adiposity and immune components of the mammary gland, hormonal interplay and crosstalk with growth factors, and a host of intrinsic and extrinsic risk factors and cellular regulatory interactions are considered to be the major contributing factors. In the present article, we discuss the status of BCYW oncobiology, therapeutic interventions and considerations, current limitations in fully understanding the basis and underlying cause(s) of BCYW, understudied areas of BCYW research, and postulated advances in the coming years for the field.

Characterization of transcriptome diversity and in vitro behavior of primary human high-risk breast cells

Biology and transcriptomes of non-cancerous human mammary epithelial cells at risk for breast cancer development were explored following primary isolation utilizing conditional reprogramming cell technology from mastectomy tissue ipsilateral to invasive breast cancer. Cultures demonstrated consistent categorizable behaviors. Relative viability and mammosphere formation differed between samples but were stable across three different mammary-specific media. E2F cell cycle target genes expression levels were positively correlated with viability and advancing age was inversely associated. Estrogen growth response was associated with Tissue necrosis factor signaling and Interferon alpha response gene enrichment. Neoadjuvant chemotherapy exposure significantly altered transcriptomes, shifting them towards expression of genes linked to mammary stem cell formation. Breast cancer prognostic signature sets include genes that in normal development are limited to specific stages of pregnancy or the menstrual cycle. Sample transcriptomes were queried for stage specific gene expression patterns. All cancer samples and a portion of high-risk samples showed overlapping stages reflective of abnormal gene expression patterns, while other high-risk samples exhibited more stage specific patterns. In conclusion, at-risk cells preserve behavioral and transcriptome diversity that could reflect different risk profiles. It is possible that prognostic platforms analogous to those used for breast cancer could be developed for high-risk mammary cells.

The DNA damage repair landscape in Black women with breast cancer

BACKGROUND

Estrogen receptor positive (ER+) breast cancer is one of the most commonly diagnosed malignancies in women irrespective of their race or ethnicity. While Black women with ER+ breast cancer are 42% more likely to die of their disease than White women, molecular mechanisms underlying this disparate outcome are understudied. Recent studies identify DNA damage repair (DDR) genes as a new class of endocrine therapy resistance driver that contributes to poor survival among ER+ breast cancer patients. Here, we systematically analyze DDR regulation in the tumors and normal breast of Black women and its impact on survival outcome.

METHOD

Mutation and up/downregulation of 104 DDR genes in breast tumor and normal samples from Black patients relative to White counterparts was assessed. For DDR genes that were differently regulated in the tumor samples from Black women in multiple datasets associations with survival outcome were tested.

RESULTS

Overall, Black patient tumors upregulate or downregulate RNA levels of a wide array of single strand break repair (SSBR) genes relative to their white counterparts and uniformly upregulate double strand break repair (DSBR) genes. This DSBR upregulation was also detectable in samples of normal breast tissue from Black women. Eight candidate DDR genes were reproducibly differently regulated in tumors from Black women and associated with poor survival. A unique DDR signature comprised of simultaneous upregulation of homologous recombination gene expression and downregulation of SSBR genes was enriched in Black patients. This signature associated with cell cycle dysregulation (p < 0.001), a hallmark of endocrine therapy resistance, and concordantly, with significantly worse survival outcomes in all datasets analyzed (hazard ratio of 9.5, p < 0.001).

CONCLUSION

These results constitute the first systematic analysis of DDR regulation in Black women and provide strong rationale for refining biomarker profiles to ensure precision medicine for underserved populations.

Age-associated genes in human mammary gland drive human breast cancer progression

Background

Aging is a comorbidity of breast cancer suggesting that aging-associated transcriptome changes may promote breast cancer progression. However, the mechanism underlying the age effect on breast cancer remains poorly understood.

Method

We analyzed transcriptomics of the matched normal breast tissues from the 82 breast cancer patients in The Cancer Genome Atlas (TCGA) dataset with linear regression for genes with age-associated expression that are not associated with menopause. We also analyzed differentially expressed genes between the paired tumor and non-tumor breast tissues in TCGA for the identification of age and breast cancer (ABC)-associated genes. A few of these genes were selected for further investigation of their malignancy-regulating activities with in vitro and in vivo assays.

Results

We identified 148 upregulated and 189 downregulated genes during aging. Overlapping of tumor-associated genes between normal and tumor tissues with age-dependent genes resulted in 14 upregulated and 24 downregulated genes that were both age and breast cancer associated. These genes are predictive in relapse-free survival, indicative of their potential tumor promoting or suppressive functions, respectively. Knockdown of two upregulated genes (DYNLT3 and P4HA3) or overexpression of the downregulated ALX4 significantly reduced breast cancer cell proliferation, migration, and clonogenicity. Moreover, knockdown of P4HA3 reduced growth and metastasis whereas overexpression of ALX4 inhibited the growth of xenografted breast cancer cells in mice.

Conclusion

Our study suggests that transcriptome alterations during aging may contribute to breast tumorigenesis. DYNLT3, P4HA3, and ALX4 play significant roles in breast cancer progression.

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.

Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.

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.

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling

Background

Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer.

Results

A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling.

Materials and Methods

MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410).

Conclusions

Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.

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

Age, estrogen, and immune response in breast adenocarcinoma and adjacent normal tissue

Chronic inflammation promotes breast tumor growth and invasion by accelerating angiogenesis and tissue remodeling in the tumor microenvironment. There is a complex relationship between inflammation and estrogen, which drives the growth of 70 percent of breast tumors. While low levels of estrogen exposure stimulate macrophages and other inflammatory cell populations, very high levels are immune suppressive. Breast tumor incidence is increased by obesity and age, which interact to influence inflammatory cell populations in normal breast tissue. To characterize the impact of these factors on tumors and the tumor microenvironment, we measured gene expression in 195 breast adenocarcinomas and matched adjacent normal breast tissue samples collected at Akershus University Hospital (AHUS). Age and Body Mass Index (BMI) were independently associated with inflammation in adjacent normal tissue but not tumors. Estrogen Receptor (ER)-negative tumors had elevated macrophage expression compared with matched normal tissue, but ER-positive tumors showed an unexpected decrease in macrophage expression. We found an inverse relationship between the increase in tumor estrogen pathway expression compared with adjacent normal tissue and tumor macrophage score. We validated this finding in 126 breast tumor-normal pairs from the previously published METABRIC cohort. We developed a novel statistic, the Rewiring Coefficient, to quantify the rewiring of gene co-expression networks at the level of individual genes. Differential correlation analysis demonstrated distinct pathways were rewired during tumorigenesis. Our data support an immune suppressive effect of high doses of estrogen signaling in breast tumor microenvironment, suggesting that this effect contributes to the greater presence of prognostic and therapeutically relevant immune cells in ER-negative tumors.

Integrative analysis of pathway deregulation in obesity

Obesity is a pandemic disease, linked to the onset of type 2 diabetes and cancer. Transcriptomic data provides a picture of the alterations in regulatory and metabolic activities associated with obesity, but its interpretation is typically blurred by noise. Here, we solve this problem by collecting publicly available transcriptomic data from adipocytes and removing batch effects using singular value decomposition. In this way we obtain a gene expression signature of 38 genes associated to obesity and identify the main pathways involved. We then show that similar deregulation patterns can be detected in peripheral markers, in type 2 diabetes and in breast cancer. The integration of different data sets combined with the study of pathway deregulation allows us to obtain a more complete picture of gene-expression patterns associated with obesity, breast cancer, and diabetes.

The worldwide increase in obesity is extremely worrisome, especially because this condition is associated with a higher risk for diseases such as type 2 diabetes and cancer. Identifying alterations in regulatory and metabolic activities associated with obesity is complicated due to the presence of noise. A team lead by Caterina La Porta from the University of Milan addressed the question from the point of view of big data and extracted a signature of 38 genes associated to obesity from the combination of publicly available gene expression data from obese and lean subjects. The results revealed a similarity between the deregulation patterns observed in obesity and those found in breast cancer and diabetes, providing a clearer picture of the role of obesity in these diseases.

Geography of breast cancer incidence according to age & birth cohorts

PURPOSE

Geographic variation in breast cancer incidence across Connecticut was examined according to age and birth cohort -specific groups.

METHODS

We assigned each of 60,937 incident breast cancer cases diagnosed in Connecticut, 1986-2009, to one of 828 census tracts around the state. Global and local spatial statistics estimated rate variation across the state according to age and birth cohorts.

RESULTS

We found the global distribution of incidence rates across places to be more heterogeneous for younger women and later birth cohorts. Concurrently, the spatial scan identified more locations with significantly high rates that pertained to larger proportions of at-risk women within these groups. Geographic variation by age groups was more pronounced than by birth cohorts.

CONCLUSION

Geographic patterns of cancer incidence exhibit differences within and across age and birth cohorts. With the continued insights from descriptive epidemiology, our capacity to effectively limit spatial disparities in cancer will improve.

Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.

p16 loss rescues functional decline of Brca1-deficient mammary stem cells

Recent evidence indicates that the accumulation of endogenous DNA damage can induce senescence and limit the function of adult stem cells. It remains elusive whether deficiency in DNA damage repair is associated with the functional alteration of mammary stem cells. In this article, we reported that senescence was induced in mammary epithelial cells during aging along with increased expression of p16Ink4a (p16), an inhibitor of CDK4 and CKD6. Loss of p16 abrogated the age-induced senescence in mammary epithelial cells and significantly increased mammary stem cell function. We showed that loss of Brca1, a tumor suppressor that functions in DNA damage repair, in the mammary epithelium induced senescence with induction of p16 and a decline of stem cell function, which was rescued by p16 loss. These data not only answer the question as to whether deficiency in DNA damage repair is associated with the functional decline of mammary stem cells, but also identify the role of p16 in suppressing Brca1-deficient mammary stem cell function.

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.

Cell Fate Decisions During Breast Cancer Development

During the formation of breast cancer, many genes become altered as cells evolve progressively from normal to a pre-malignant to a malignant state of growth. How mutations in genes lead to specific subtypes of human breast cancer is only partially understood. Here we review how initial genetic or epigenetic alterations within mammary epithelial cells (MECs) can alter cell fate decisions and put pre-malignant cells on a path towards cancer development with specific phenotypes. Understanding the early stages of breast cancer initiation and progression and how normal developmental processes are hijacked during transformation has significant implications for improving early detection and prevention of breast cancer. In addition, insights gleaned from this understanding may also be important for developing subtype-specific treatment options.

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.

Genomic aberrations in young and elderly breast cancer patients

BACKGROUND

Age at breast cancer diagnosis is a known prognostic factor. Previously, several groups including ours have shown that young age at diagnosis is associated with higher prevalence of basal-like tumors and aggressive tumor phenotypes. Yet the impact of age at diagnosis on the genomic landscape of breast cancer remains unclear. In this study, we examined the pattern of somatic mutations, chromosomal copy number variations (CNVs) and transcriptomic profiles in young and elderly breast cancer patients.

METHODS

Analyses were performed on The Cancer Genome Atlas (TCGA) dataset. Patients with metastatic disease at diagnosis, classified as normal-like by PAM50 or had missing clinical information were excluded. Young patients were defined as ≤45 years of age, while elderly patients were those ≥70 years of age at breast cancer diagnosis. The remaining patients were classified as "intermediate". We evaluated the association between age at diagnosis and somatic mutations, CNV and gene expression in a logistic regression model adjusting for tumor size, nodal status, histology and breast cancer subtype. All analyses were corrected for multiple testing using the Benjamini-Hochberg approach.

RESULTS

In this study, 125, 486 and 169 patients were ≤45, 46-69 and ≥70 years of age, respectively. Older patients had more somatic mutations (n = 44 versus 35 versus 31; P = 0.0009) and more CNVs, especially in ductal tumors (P = 0.02). Eleven mutations were independently associated with age at diagnosis, of which only GATA3 was associated with young age (15.2% versus 8.2% versus 9%; P = 0.003). Only two CNV events were independently associated with age, with more chr18p losses in older patients and more chr6q27 deletions in younger ones. Younger age at diagnosis was associated with higher expression of gene signatures related to proliferation, stem cell features and endocrine resistance.

CONCLUSIONS

Age adds a layer of biological complexity beyond breast cancer molecular subtypes, classic pathological and clinical variables, worthy of further consideration in future drug development as we seek to refine therapeutic strategies in the era of personalized medicine.

Race-associated biological differences among Luminal A breast tumors

African-American (AA) women have higher breast cancer-specific mortality rates. A higher prevalence of the worse outcome Basal-like breast cancer subtype contributes to this, but AA women also have higher mortality even within the more favorable outcome Luminal A breast cancers. These differences may reflect treatment or health care access issues, inherent biological differences, or both. To identify potential biological differences by race among Luminal A breast cancers, gene expression data from 108 CAU and 57 AA breast tumors were analyzed. Race-associated genes were evaluated for associations with survival. Finally, expression of race- and survival-associated genes was evaluated in normal tissue of AA and CAU women. Six genes (ACOX2, MUC1, CRYBB2, PSPH, SQLE, TYMS) were differentially expressed by race among Luminal A breast cancers and were associated with survival (HR <0.8, HR >1.25). For all six genes, tumors in AA had higher expression of poor prognosis genes (CRYBB2, PSPH, SQLE, TYMS) and lower expression of good prognosis genes (ACOX2, MUC1). A score based on all six genes predicted survival in a large independent dataset (HR = 1.9 top vs. bottom quartile, 95% CI: 1.4-2.5). For four genes, normal tissue of AA and CAU women showed similar expression (ACOX2, MUC1, SQLE, TYMS); however, the poor outcome-associated genes CRYBB2 and PSPH were more highly expressed in AA versus CAU women's normal tissue. This analysis identified gene expression differences that may contribute to mortality disparities and suggests that among Luminal A breast tumors there are biological differences between AA and CAU patients. Some of these differences (CRYBB2 and PSPH) may exist from the earliest stages of tumor development, or may even precede malignancy.

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.

Role of HGF in epithelial-stromal cell interactions during progression from benign breast disease to ductal carcinoma in situ

Introduction

Basal-like and luminal breast cancers have distinct stromal–epithelial interactions, which play a role in progression to invasive cancer. However, little is known about how stromal–epithelial interactions evolve in benign and pre-invasive lesions.

Methods

To study epithelial–stromal interactions in basal-like breast cancer progression, we cocultured reduction mammoplasty fibroblasts with the isogenic MCF10 series of cell lines (representing benign/normal, atypical hyperplasia, and ductal carcinoma in situ). We used gene expression microarrays to identify pathways induced by coculture in premalignant cells (MCF10DCIS) compared with normal and benign cells (MCF10A and MCF10AT1). Relevant pathways were then evaluated in vivo for associations with basal-like subtype and were targeted in vitro to evaluate effects on morphogenesis.

Results

Our results show that premalignant MCF10DCIS cells express characteristic gene expression patterns of invasive basal-like microenvironments. Furthermore, while hepatocyte growth factor (HGF) secretion is upregulated (relative to normal, MCF10A levels) when fibroblasts are cocultured with either atypical (MCF10AT1) or premalignant (MCF10DCIS) cells, only MCF10DCIS cells upregulated the HGF receptor MET. In three-dimensional cultures, upregulation of HGF/MET in MCF10DCIS cells induced morphological changes suggestive of invasive potential, and these changes were reversed by antibody-based blocking of HGF signaling. These results are relevant to in vivo progression because high expression of a novel MCF10DCIS-derived HGF signature was correlated with the basal-like subtype, with approximately 86% of basal-like cancers highly expressing the HGF signature, and because high expression of HGF signature was associated with poor survival.

Conclusions

Coordinated and complementary changes in HGF/MET expression occur in epithelium and stroma during progression of pre-invasive basal-like lesions. These results suggest that targeting stroma-derived HGF signaling in early carcinogenesis may block progression of basal-like precursor lesions.

Pregnancy offers new insights into mechanisms of breast cancer risk and resistance

Pregnancy induces long-lasting changes in gene expression that are associated with a reduction in breast cancer risk. Although several mechanisms have been proposed to mediate the reduction in breast cancer risk among parous women, recent studies focus attention on progenitor cells as major targets. The results suggest new biomarkers that may improve risk prediction and provide endpoints for assessment of clinical responses to prophylactic therapies.

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.

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics

BACKGROUND

Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups.

METHODS

We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase PCR was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier.

RESULTS

In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% luminal A, 20.5% luminal B, 13.0% HER2-enriched, 9.8% basal-like, and 3.6% normal-like. Among low-risk endocrine-positive tumors (i.e., estrogen and progesterone receptor positive by immunohistochemistry, HER2 negative, and low histologic grade), only 76.5% were categorized as luminal A by PAM50. Continuous-scale luminal A, luminal B, HER2-enriched, and normal-like scores from PAM50 were mutually positively correlated. Basal-like score was inversely correlated with other subtypes. The proportion with non-luminal A subtype decreased with older age at diagnosis, P Trend < 0.0001. Compared with non-Hispanic Whites, African American women were more likely to have basal-like tumors, age-adjusted OR = 4.4 [95% confidence intervals (CI), 2.3-8.4], whereas Asian and Pacific Islander women had reduced odds of basal-like subtype, OR = 0.5 (95% CI, 0.3-0.9).

CONCLUSIONS

Our data indicate that over 50% of breast cancers treated in the community have luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathologic criteria to higher-risk subtypes.

IMPACT

Subtyping in a population-based cohort revealed distinct profiles by age and race.

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

PURPOSE

Previous studies of breast tissue gene expression have shown that the extratumoral microenvironment has substantial variability across individuals, some of which can be attributed to epidemiologic factors. To evaluate how mammographic density and breast tissue composition relate to extratumoral microenvironment gene expression, we used data on 121 patients with breast cancer from the population-based Polish Women's Breast Cancer Study.

EXPERIMENTAL DESIGN

Breast cancer cases were classified on the basis of a previously reported, biologically defined extratumoral gene expression signature with two subtypes: an Active subtype, which is associated with high expression of genes related to fibrosis and wound response, and an Inactive subtype, which has high expression of cellular adhesion genes. Mammographic density of the contralateral breast was assessed using pretreatment mammograms and a quantitative, reliable computer-assisted thresholding method. Breast tissue composition was evaluated on the basis of digital image analysis of tissue sections.

RESULTS

The Inactive extratumoral subtype was associated with significantly higher percentage mammographic density (PD) and dense area (DA) in univariate analysis (PD: P = 0.001; DA: P = 0.049) and in multivariable analyses adjusted for age and body mass index (PD: P = 0.004; DA: P = 0.049). Inactive/higher mammographic density tissue was characterized by a significantly higher percentage of stroma and a significantly lower percentage of adipose tissue, with no significant change in epithelial content. Analysis of published gene expression signatures suggested that Inactive/higher mammographic density tissue expressed increased estrogen response and decreased TGF-β signaling.

CONCLUSIONS

By linking novel molecular phenotypes with mammographic density, our results indicate that mammographic density reflects broad transcriptional changes, including changes in both epithelia- and stroma-derived signaling.