Contribution of CXCL12 secretion to invasion of breast cancer cells

INTRODUCTION: Neu (HER2/ErbB2) is overexpressed in 25% to 30% of human breast cancer, correlating with a poor prognosis. Researchers in previous studies who used the mouse mammary tumor virus Neu-transgenic mouse model (MMTV-Neu) demonstrated that the Neu-YB line had increased production of CXCL12 and increased metastasis, whereas the Neu-YD line had decreased metastasis. In this study, we examined the role of increased production of CXCL12 in tumor cell invasion and malignancy. METHODS: We studied invasion in the tumor microenvironment using multiphoton intravital imaging, in vivo invasion and intravasation assays. CXCL12 signaling was altered by using the CXCR4 inhibitor AMD3100 or by increasing CXCL12 expression. The role of macrophage signaling in vivo was determined using a colony-stimulating factor 1 receptor (CSF-1R) blocking antibody. RESULTS: The Neu-YD strain was reduced in invasion, intravasation and metastasis compared to the Neu-YB and Neu deletion mutant (activated receptor) strains. Remarkably, in the Neu-YB strain, in vivo invasion to epidermal growth factor was dependent on both CXCL12-CXCR4 and CSF1-CSF-1R signaling. Neu-YB tumors had increased macrophage and microvessel density. Overexpression of CXCL12 in rat mammary adenocarcinoma cells increased in vivo invasion as well as microvessel and macrophage density. CONCLUSIONS: Expression of CXCL12 by tumor cells results in increased macrophage and microvessel density and in vivo invasiveness

Sensitivity of Edge Detection Methods for Quantifying Cell Migration Assays

Quantitative imaging methods to analyze cell migration assays are not standardized. Here we present a suite of two-dimensional barrier assays describing the collective spreading of an initially-confined population of 3T3 fibroblast cells. To quantify the motility rate we apply two different automatic image detection methods to locate the position of the leading edge of the spreading population after [Image: see text], [Image: see text] and [Image: see text] hours. These results are compared with a manual edge detection method where we systematically vary the detection threshold. Our results indicate that the observed spreading rates are very sensitive to the choice of image analysis tools and we show that a standard measure of cell migration can vary by as much as 25% for the same experimental images depending on the details of the image analysis tools. Our results imply that it is very difficult, if not impossible, to meaningfully compare previously published measures of cell migration since previous results have been obtained using different image analysis techniques and the details of these techniques are not always reported. Using a mathematical model, we provide a physical interpretation of our edge detection results. The physical interpretation is important since edge detection algorithms alone do not specify any physical measure, or physical definition, of the leading edge of the spreading population. Our modeling indicates that variations in the image threshold parameter correspond to a consistent variation in the local cell density. This means that varying the threshold parameter is equivalent to varying the location of the leading edge in the range of approximately 1–5% of the maximum cell density