Mechanosensitive hormone signaling promotes mammary progenitor expansion and breast cancer risk.

Tissue stem-progenitor cell frequency has been implicated in tumor risk and progression, but tissue-specific factors linking these associations remain ill-defined. We observed that stiff breast tissue from women with high mammographic density, who exhibit increased lifetime risk for breast cancer, associates with abundant stem-progenitor epithelial cells. Using genetically engineered mouse models of elevated integrin mechanosignaling and collagen density, syngeneic manipulations, and spheroid models, we determined that a stiff matrix and high mechanosignaling increase mammary epithelial stem-progenitor cell frequency and enhance tumor initiation in vivo. Augmented tissue mechanics expand stemness by potentiating extracellular signal-related kinase (ERK) activity to foster progesterone receptor-dependent RANK signaling. Consistently, we detected elevated phosphorylated ERK and progesterone receptors and increased levels of RANK signaling in stiff breast tissue from women with high mammographic density. The findings link fibrosis and mechanosignaling to stem-progenitor cell frequency and breast cancer risk and causally implicate epidermal growth factor receptor-ERK-dependent hormone signaling in this phenotype.

A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer.

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

Immunosuppressive glycoproteins associate with breast tumor fibrosis and aggression.

Tumors feature elevated sialoglycoprotein content. Sialoglycoproteins promote tumor progression and are linked to immune suppression via the sialic acid-Siglec axis. Understanding factors that increase sialoglycoprotein biosynthesis in tumors could identify approaches to improve patient response to immunotherapy. We quantified higher levels of sialoglycoproteins in the fibrotic regions within human breast tumor tissues. Human breast tumor subtypes, which are more fibrotic, similarly featured increased sialoglycoprotein content. Further analysis revealed the breast cancer cells as the primary cell type synthesizing and secreting the tumor tissue sialoglycoproteins and confirmed that the more aggressive, fibrotic breast cancer subtypes expressed the highest levels of sialoglycoprotein biosynthetic genes. The more aggressive breast cancer subtypes also featured greater infiltration of immunosuppressive SIGLEC7, SIGLEC9, and SIGLEC10-pos myeloid cells, indicating that triple-negative breast tumors had higher expression of both immunosuppressive Siglec receptors and their cognate ligands. The findings link sialoglycoprotein biosynthesis and secretion to tumor fibrosis and aggression in human breast tumors. The data suggest targeting of the sialic acid-Siglec axis may comprise an attractive therapeutic target particularly for the more aggressive HER2+ and triple-negative breast cancer subtypes.

Derivation of a nuclear heterogeneity image index to grade DCIS.

Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity.

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.