Abstract P4-02-08: Quantitative Characterization of 3D Vasculature Spatial Patterns Within Tumor Microenvironment of Breast Cancer Stem Cells

The sustainment characters of cancer stem cells (CSCs) such as self-renew and differentiation to other tumor cells greatly depend on the tumor microenvironment, which is composed of many components, e.g. vasculature, extracellular matrix, epithelial cells, stromal cells, as well as nutrients and oxygen. As vasculature is an important factor for the CSC and tumor development, the understanding of their spatial patterns is essential for calibrating the CSC-microenvironment interactions in mathematical modeling. In this study, we acquired the vasculature in side tumors and normal breast tissues by using two-photon fluorescence microscopy, which enables 3D in vivo imaging. We developed an advanced vasculature segmentation approach for an objective and accurate quantification of the vasculature in 3D image volumes. The approach integrates supervoxel analysis and the orientation guided hidden Markov random field (ori-HMRF) modeling together to compensate for low quality images, e.g., low signal-to-noise ratio (SNR) and uneven background. By constructing a new feature space that combines the CIELAB color space and the coordinates space, the supervoxel analysis divides an image volume into subregions with local similar intensity and restricted regular shape, boundaries of which can delineate the vasculature boundaries accurately even in low intensity contrast regions. We further designed a set of features for the separation of blood vessel regions from the background. To make use of the context information, i.e. the continuity of vasculature, the ori-HMRF model is used to incorporate the consistency of vasculatures' orientation in order to reduce the false positives and negatives. Experimental results on image volumes from both breast cancer and normal breast tissues show that the proposed method can effectively reconstruct the vasculature structure with the CSC embedded tumor microenvironment.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-02-08.

Mouse Mammary Tumor Expressing MMTV-SmoM2 as a Tool To Study Lung Metastasis

Background: The hedgehog signaling network regulates pattern formation, proliferation, cell fate and stem/progenitor cell self-renewal in many organs. Altered hedgehog signaling is implicated in 20-25% of all cancers, including breast cancer. We demonstrated previously that expression of activated human SMO (SmoM2) under the mouse mammary tumor virus (MMTV) promoter in transgenic mice leads to increased proliferation, altered differentiation, ductal dysplasias, and increase the proportion of mammosphere-forming cells, with reduction of stem cell pool, in virgin mice.Material and Methods: We isolated and established a tumor line from this mouse line (named TU505). To investigate the role of hedgehog signaling in mammary tumorigenesis and metastasis we transplanted TU505 fragments (1-2mm3) to a cleared fat pad of 3 week-old mice. After 10 days we removed the primary tumors and let the mice recover for another 3 weeks, which at that time the animals were sacrificed and the lungs removed under stereomicroscope. The lung metastases visible at naked eyes were snap frozen for RNA isolation, and the remaining lungs fixed in 4%PFA.Results: The TU505 is a tumor expressing MMTV-SmoM2, triple-negative (ER-, PR-, and HEB2-), hormone independent, highly metastatic to the lung, and with a robust expression of Ptch1, Ptch2, Smo, Gli2, and Gli3. The gene expression of primary tumor and metastasis are compared.Discussion: The hedgehog signaling networks has been associated with metastasis. The TU505 is a strong tool to study mammary gland tumorigenesis and metastasis.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2165.