Guarin, J. | bioRxiv Science

bioRxiv2020

Obesity-driven changes in ECM composition promote local invasion and metastasis of breast tumors

The extracellular matrix (ECM) is a major component of the tumor microenvironment that supports cellular growth, promotes local invasion from the primary tumor, and contributes to metastatic outgrowth in sites of colonization. Obesity is a systemic disease that causes chronic inflammation which can lead to ECM deposition and ultimately fibrosis in adipose tissues such as the mammary gland. Overweight breast cancer patients have increased metastasis to the lung and liver, exhibit resistance to chemotherapy and have worse outcomes. We found that ECM isolated from the mammary gland of both tumor-bearing and obese mice increased invasion of breast cancer cells and set out to investigate whether obesity-driven changes in ECM could identify novel drivers of invasion and metastasis in breast cancer. We performed proteomics of the mammary fat pads of both lean and obese mice and identified the entire landscape of obesity-driven ECM changes. In particular, we focused on Collagen VI, an ECM protein secreted by adipocytes in mammary tissues. Collagen VI is upregulated in the ECM of obese and tumor-bearing mice and is associated with poor outcome in human breast cancer. We found that Collagen VI drives adhesion, migration and invasion of several human breast cancer cell lines via crosstalk between the adhesion receptor NG2 and the receptor tyrosine kinase EGFR, and activation of MAPK signaling. Overall, these studies demonstrate that obesity can have profound effects on the ECM composition of tissues, which in turn can promote local invasion and metastasis.

cancer biology↗

bioRxiv2020

Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Metastasis, the leading cause of death in cancer patients, requires the invasion of tumor cells through the stroma in response to migratory cues, such as those provided by the extracellular matrix (ECM). Recent advances in proteomics have led to the identification of hundreds of ECM proteins which are more abundant in tumors relative to healthy tissue. Our goal was to develop a pipeline to easily predict which of these ECM proteins is more likely to have an effect on cancer invasion and metastasis. We evaluated the effect of 4 ECM proteins upregulated in breast tumor tissue in multiple human breast cancer cell lines in 3 assays. We found there was no linear relationship between the 11 cell shape parameters we quantified when cells adhere to ECM proteins and 2D cell migration speed, persistence or 3D invasion. We then used classifiers and partial-least squares regression analysis to identify which metrics best predicted ECM-driven 2D migration and 3D invasion responses. ECM-driven 2D cell migration speed or persistence did not correlate with or predict 3D invasion in response to that same cue. However, cell adhesion, and in particular cell elongation and irregularity accurately predicted the magnitude of ECM-driven 2D migration and 3D invasion in all cell lines. Testing predictions revealed that our models are good at predicting the effect of novel ECM proteins within a given cell line, but that ECM responses are cell-line specific. Overall, our studies identify the cell morphological features that determine 3D invasion responses to individual ECM proteins. This platform will help provide insight into the functional role of ECM proteins abundant tumor tissue and help prioritize strategies for targeting tumor-ECM interactions to treat metastasis. FundingThis work was supported by the National Institutes of Health [R00-CA207866-04 to M.J.O.]; Tufts University [Start-up funds from the School of Engineering to M.J.O.] and funds from NSF REU to A.W. Conflict-of-interest: None. Insight BoxMetastasis, the dissemination of tumor cells, is driven by the interaction of invading tumor cells with their local environment, in particular with the ECM, which provides structure and support to our tissues. This study presents an integrated approach to predict the effect of individual ECM proteins on 3D invasion and metastasis based on simple adhesion assays which quantify cell shape. Machine learning classification and partial-least squares regression models reveal that ECM-driven 2D cell migration metrics are not predictive of 3D invasion, and that cell shape of cells adhered to ECM can predict that proteins effect on 3D invasion. These data provide a pipeline for predicting the effect of ECM proteins on breast cancer cell invasion and metastasis.

bioengineering↗