Gene Expression Analysis of In Vitro Cocultures to Study Interactions between Breast Epithelium and Stroma

The interactions between breast epithelium and stroma are fundamental to normal tissue homeostasis and for tumor initiation and progression. Gene expression studies of in vitro coculture models demonstrate that in vitro models have relevance for tumor progression in vivo. For example, stromal gene expression has been shown to vary in association with tumor subtype in vivo, and analogous in vitro cocultures recapitulate subtype-specific biological interactions. Cocultures can be used to study cancer cell interactions with specific stromal components (e.g., immune cells, fibroblasts, endothelium) and different representative cell lines (e.g., cancer-associated versus normal-associated fibroblasts versus established, immortalized fibroblasts) can help elucidate the role of stromal variation in tumor phenotypes. Gene expression data can also be combined with cell-based assays to identify cellular phenotypes associated with gene expression changes. Coculture systems are manipulable systems that can yield important insights about cell-cell interactions and the cellular phenotypes that occur as tumor and stroma co-evolve

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity

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

Complete age-related regression of mammary epithelium, often termed post-menopausal 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 (NBS). High-resolution digital images of normal breast Hematoxylin & Eosin stained slides were partitioned into epithelium, adipose tissue, and non-fatty 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 when these area measures reached a steady state. While 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

Alcohol intake and invasive breast cancer risk by molecular subtype and race in the Carolina Breast Cancer Study

Alcohol is an established breast cancer risk factor, but there is little evidence on whether the association differs between African Americans and whites

African American Women's Perspectives on Breast Cancer: Implications for Communicating Risk of Basal-like Breast Cancer

African American women suffer a higher burden of basal-like breast cancer, an aggressive subtype that has no targeted therapy. While epidemiologic research has identified key prevention strategies, little is known about how best to communicate risk to this population. This study explored women’s knowledge, beliefs, and attitudes about breast cancer to learn about risk perceptions. Six focus groups with 57 women (ages 18–49) women were conducted in North Carolina. Findings revealed that age, race (especially perceptions of cancer as a “White disease”), and lack of family history of breast cancer contributed to women’s perceptions of low breastcancer susceptibility. Perceptions of low risk were also attributed to conflicting risk information from family, media, and health providers. Women had little knowledge about breast cancer subtypes, but emphasized that health communications should be personally relevant, culturally appropriate, and convenient. These study findings will assist in developing health communication tools that encourage prevention

Basal-like Breast Cancer Cells Induce Phenotypic and Genomic Changes in Macrophages

Basal-like breast cancer (BBC) is an aggressive subtype of breast cancer that has no biologically-targeted therapy. The interactions of BBCs with stromal cells are important determinants of tumor biology, with inflammatory cells playing well-recognized roles in cancer progression. Despite the fact that macrophage-BBC communication is bidirectional, important questions remain about how BBCs affect adjacent immune cells. This study investigated monocyte-to-macrophage differentiation and polarization, and gene expression in response to coculture with basal-like versus luminal breast cancer cells. Changes induced by coculture were compared to changes observed under classical differentiation and polarization conditions. Monocytes (THP-1 cells) exposed to BBC cells in coculture had altered gene expression with upregulation of both M1 and M2 macrophage markers. Two sets of M1 and M2 markers were selected from the PCR profiles and used for dual immunofluorescence staining of BBC versus luminal cocultured THP-1s, and cancer-adjacent, benign tissue sections from patients diagnosed with BBC or luminal breast cancer confirming the differential expression patterns. Relative to luminal breast cancers, BBCs also increased differentiation of monocytes to macrophages and stimulated macrophage migration. Consistent with these changes in cellular phenotype, a distinct pattern of cytokine secretion was evident in macrophage-BBC cocultures, including upregulation of NAP-2, Osteoprotegerin, MIG, MCP-1, MCP-3 and IL-1β. Application of IL-1 receptor antagonist (IL-1RA) to cocultures attenuated BBC-induced macrophage migration. These data contribute to an understanding of the BBC-mediated activation of the stromal immune response, implicating specific cytokines that are differentially expressed in basal-like microenvironments and suggesting plausible targets for modulating immune responses to BBC

Healthy Worker Survivor Bias in the Colorado Plateau Uranium Miners Cohort

Cohort mortality studies of underground miners have been used to estimate the number of lung cancer deaths attributable to radon exposure. However, previous studies of the radon–lung cancer association among underground miners may have been subject to healthy worker survivor bias, a type of time-varying confounding by employment status. We examined radon-mortality associations in a study of 4,124 male uranium miners from the Colorado Plateau who were followed from 1950 through 2005. We estimated the time ratio (relative change in median survival time) per 100 working level months (radon exposure averaging 130,000 mega-electron volts of potential α energy per liter of air, per working month) using G-estimation of structural nested models. After controlling for healthy worker survivor bias, the time ratio for lung cancer per 100 working level months was 1.168 (95% confidence interval: 1.152, 1.174). In an unadjusted model, the estimate was 1.102 (95% confidence interval: 1.099, 1.112)—39% lower. Controlling for this bias, we estimated that among 617 lung cancer deaths, 6,071 person-years of life were lost due to occupational radon exposure during follow-up. Our analysis suggests that healthy worker survivor bias in miner cohort studies can be substantial, warranting reexamination of current estimates of radon's estimated impact on lung cancer mortality

Down-regulation of sfrp1 in a mammary epithelial cell line promotes the development of a cd44high/cd24low population which is invasive and resistant to anoikis

<p>Abstract</p> <p>Background</p> <p>The Wnt family of secreted proteins is implicated in the regulation of cell fate during development, as well as in cell proliferation, morphology, and migration. Aberrant activation of the Wnt/β-catenin signaling pathway leads to the development of several human cancers, including breast cancer. Secreted frizzled-related protein 1 (SFRP1) antagonizes this pathway by competing with the Frizzled receptor for Wnt ligands resulting in an attenuation of the signal transduction cascade. Loss of SFRP1 expression is observed in breast cancer, along with several other cancers, and is associated with poor patient prognosis. However, it is not clear whether the loss of SFRP1 expression predisposes the mammary gland to tumorigenesis.</p> <p>Results</p> <p>When SFRP1 is knocked down in a non-malignant immortalized mammary epithelial cell line (76 N TERT), nuclear levels of β-catenin rise and the Wnt pathway is stimulated. The SFRP1 knockdown cells exhibit increased expression of the pro-proliferative Cyclin D1 gene and increased cellular proliferation, undergo a partial epithelial-mesenchymal transition (EMT), are resistant to anchorage-independent cell death, exhibit increased migration, are significantly more invasive, and exhibit a CD24^low/CD44^highcell surface marker expression pattern.</p> <p>Conclusion</p> <p>Our study suggests that loss of SFRP1 allows non-malignant cells to acquire characteristics associated with breast cancer cells.</p

Associations of Premenopausal Hysterectomy and Oophorectomy With Breast Cancer Among Black and White Women: The Carolina Breast Cancer Study, 1993–2001

Black women experience higher rates of hysterectomy than other women in the United States. Although research indicates that premenopausal hysterectomy with bilateral oophorectomy decreases the risk of breast cancer in black women, it remains unclear how hysterectomy without ovary removal affects risk, whether menopausal hormone therapy use attenuates inverse associations, and whether associations vary by cancer subtype. In the population-based, case-control Carolina Breast Cancer Study of invasive breast cancer in 1,391 black (725 cases, 666 controls) and 1,727 white (939 cases, 788 controls) women in North Carolina (1993–2001), we investigated the associations of premenopausal hysterectomy and oophorectomy with breast cancer risk. Compared with no history of premenopausal surgery, bilateral oophorectomy and hysterectomy without oophorectomy were associated with lower odds of breast cancer (for bilateral oophorectomy, multivariable-adjusted odds ratios = 0.60, 95% confidence interval: 0.47, 0.77; for hysterectomy without oophorectomy, multivariable-adjusted odds ratios = 0.68, 95% confidence interval: 0.55, 0.84). Estimates did not vary by race and were similar for hormone receptor–positive and hormone receptor–negative cancers. Use of estrogen-only menopausal hormone therapy did not attenuate the associations. Premenopausal hysterectomy, even without ovary removal, may reduce the long-term risk of hormone receptor–positive and hormone receptor–negative breast cancers. Varying rates of hysterectomy are a potentially important contributor to differences in breast cancer incidence among racial/ethnic groups

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics