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

PREDICTION OF TOXICANT-SPECIFIC GENE EXPRESSION SIGNATURES FOLLOWING CHEMOTHERAPEUTIC TREATMENT OF BREAST CELL LINES

Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell-cycle–regulated genes and induction of DNA damage and oxidative stress–responsive genes. Our laboratory recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium after treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU would be unique to each compound and might reflect the underlying mechanisms of action of these agents. Therefore, we employed supervised analyses (significance analysis of microarrays) to identify genes that showed differential expression between DOX-treated and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX-treated than to 5FU-treated samples. These analyses also showed that toxicant-specific gene expression patterns, similar to general stress responses, vary according to cell type

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

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

Temporal Trends in the Inflammatory Cytokine Profile of Human Breastmilk

A longer lifetime duration of breastfeeding may decrease the risk of breast cancer by reducing breast inflammation and mitigating inflammatory cytokine expression during postlactational involution. However, little is known about how the inflammatory cytokine profile in human breastmilk changes over time. To study temporal trends in breastmilk cytokine expression, we measured 80 human cytokines in the whey fraction of breastmilk samples from 15 mothers at 1, 4, 8, and 12 weeks postpartum. We used mixed models to identify temporal changes in cytokine expression and investigated parity status (multiparous vs. primiparous) as a potential confounder. Nine cytokines (monocyte chemoattractant protein-1, epithelial-derived neutrophil-activating protein-78, hepatocyte growth factor, insulin-like growth factor-binding protein-1, interleukin-16, interleukin-8, macrophage colony-stimulating factor, osteoprotegerin, and tissue inhibitor of metallopeptidase-2) had significantly decreased expression with increasing breastfeeding duration; all nine have known roles in breast involution, inflammation, and cancer and may serve as biomarkers of changing breast microenvironment. No cytokine significantly increased in level over the study period. Total protein concentration significantly decreased over time (p<0.0001), which may mediate the association between length of breastfeeding and inflammatory cytokine expression. Parity status did not confound temporal trends, but levels of several cytokines were significantly higher among multiparous versus primiparous women. Our results suggest that inflammatory cytokine expression during lactation is dynamic, and expressed milk may provide a noninvasive window into the extensive biological changes that occur in the postpartum breast

Inverse-power-law behavior of cellular motility reveals stromal–epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy

The progression of breast cancer is known to be affected by stromal cells within the local microenvironment. Here we study the effect of stromal fibroblasts on the in-place motions (motility) of mammary epithelial cells within organoids in 3D co-culture, inferred from the speckle fluctuation spectrum using optical coherence tomography (OCT). In contrast to Brownian motion, mammary cell motions exhibit an inverse power-law fluctuation spectrum. We introduce two complementary metrics for quantifying fluctuation spectra: the power-law exponent and a novel definition of the motility amplitude, both of which are signal- and position-independent. We find that the power-law exponent and motility amplitude are positively (p<0.001) and negatively (p<0.01) correlated with the density of stromal cells in 3D co-culture, respectively. We also show how the hyperspectral data can be visualized using these metrics to observe heterogeneity within organoids. This constitutes a simple and powerful tool for detecting and imaging cellular functional changes with OCT

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 outcomes Luminal A breast cancers. These differences may reflect treatment or health care access issues, inherent biological differences, or both

Nuclear Localized LSR: A Novel Regulator of Breast Cancer Behavior and Tumorigenesis

Lipolysis Stimulated Lipoprotein Receptor (LSR) has been found in the plasma membrane and is believed to function in lipoprotein endocytosis and tight junctions. Given the impact of cellular metabolism and junction signaling pathways on tumor phenotypes and patient outcome, it is important to understand how LSR cellular localization mediates its functions. We conducted localization studies, evaluated DNA binding, and examined the effects of nuclear LSR in cells, xenografts, and clinical specimens. We found LSR within the membrane, cytoplasm, and the nucleus of breast cancer cells representing multiple intrinsic subtypes. Chromatin immunoprecipitation (ChIP) showed direct binding of LSR to DNA, and sequence analysis identified putative functional motifs and post-translational modifications of the LSR protein. While neither overexpression of transcript variants, nor pharmacological manipulation of post-translational modification significantly altered localization, inhibition of nuclear export enhanced nuclear localization, suggesting a mechanism for nuclear retention. Co-immunoprecipitation and proximal ligation assays indicated LSR-pericentrin interactions, presenting potential mechanisms for nuclear-localized LSR. The clinical significance of LSR was evaluated using data from over 1,100 primary breast tumors, which showed high LSR levels in basal-like tumors and tumors from African-Americans. In tumors histosections, nuclear localization was significantly associated with poor outcomes. Finally, in vivo xenograft studies revealed that basal-like breast cancer cells that over-express LSR exhibited both membrane and nuclear localization, and developed tumors with 100% penetrance, while control cells lacking LSR developed no tumors. These results show that nuclear LSR alters gene expression and may promote aggressive cancer phenotypes