Abstract 1908: Using hierarchical clustering of differential gene expression patterns to study the contribution of the extracellular matrix in breast cancer

Breast cancer is the most frequently invasive cancer type among women worldwide, accounting for over 2 million new cases annually and 25% of all cancers diagnosed in women. Considering significant advances in the diagnostics and treatment of breast cancer it remains a leading cause of cancer mortality worldwide. The extracellular matrix (ECM) is a meshwork of fibrous proteins and proteoglycans that is essential for providing a physical framework and facilitating biochemical cues to maintain tissue homeostasis. However, the ECM also has a well-established role in tumour development and progression. Increasing evidence suggests that the ECM may impede targeted therapies and novel approaches that target the composition of the ECM may improve drug delivery and efficacy. Using the xCELLigence real time analysis platform, we monitored the growth of several breast cancer cell lines on the major fibrous proteins of the ECM to elucidate the contribution of individual ECM components on cell behaviour. Coupling this with endpoint assays, we have identified diverse adhesion, proliferation and migration profiles for each cell line on each substrate. RNA was extracted from cells in 2-Dimensional culture on 1) collagen, 2) fibronectin, 3) laminin and 4) stimulated by various growth factors and from cells in 3-Dimensional culture. Using qRT-PCR, we examined the expression of 40 genes that code for proteins known to directly or indirectly regulate the composition of the ECM. We determined that there is significant differential gene expression amongst each cell line in response to individual ECM components. Using a hierarchical clustering analysis of our gene panel, we have identified a strong relationship amongst cell lines maintained on collagen and fibronectin. We used STRING[1] analysis to help us refine a gene signature and are currently examining our gene set in patient tissue samples. Our hypothesis is that the composition of the ECM regulates gene expression and this correlates with advanced cancer stage. This work has the potential to create a clinically relevant gene signature to be used in the prediction of invasive cancers. Understanding how the ECM influences these proteins may provide information on the design of novel ECM targeted therapies in breast cancer. 1. Szklarczyk, D., et al., The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic acids research, 2017. 45(D1):p. D362-D368. Citation Format