Increased matrix stiffness enhances pro-tumorigenic traits in a physiologically relevant breast tissue- monocyte 3D model.

ABSTRACT

High mammographic density, associated with increased tissue stiffness, is a strong risk factor for breast cancer per se. In postmenopausal women there is no differences in the occurrence of ductal carcinoma in situ (DCIS) depending on breast density. Preliminary data suggest that dense breast tissue is associated with a pro-inflammatory microenvironment including infiltrating monocytes. However, the underlying mechanism(s) remains largely unknown. A major roadblock to understanding this risk factor is the lack of relevant in vitro models. A biologically relevant 3D model with tunable stiffness was developed by cross-linking hyaluronic acid. Breast cancer cells were cultured with and without freshly isolated human monocytes. In a unique clinical setting, extracellular proteins were sampled using microdialysis in situ from women with various breast densities. We show that tissue stiffness resembling high mammographic density increases the attachment of monocytes to the cancer cells, increase the expression of adhesion molecules and epithelia-mesenchymal-transition proteins in estrogen receptor (ER) positive breast cancer. Increased tissue stiffness results in increased secretion of similar pro-tumorigenic proteins as those found in human dense breast tissue including inflammatory cytokines, proteases, and growth factors. ER negative breast cancer cells were mostly unaffected suggesting that diverse cancer cell phenotypes may respond differently to tissue stiffness. We introduce a biological relevant model with tunable stiffness that resembles the densities found in normal breast tissue in women. The model will be key for further mechanistic studies. Additionally, our data revealed several pro-tumorigenic pathways that may be exploited for prevention and therapy against breast cancer. STATEMENT OF SIGNIFICANCE: Women with mammographic high-density breasts have a 4-6-fold higher risk of breast cancer than low-density breasts. Biological mechanisms behind this increase are not fully understood and no preventive therapeutics are available. One major reason being a lack of suitable experimental models. Having such models available would greatly enhance the discovery of relevant targets for breast cancer prevention. We present a biologically relevant 3D-model for studies of human dense breasts, providing a platform for investigating both biophysical and biochemical properties that may affect cancer progression. This model will have a major scientific impact on studies for identification of novel targets for breast cancer prevention.