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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1777

Title: Phenotypic heterogeneity of breast tumors
Authors: Vamvakidou, Alexandra P.
Keywords: Biomedical engineering;Breast--Tumors;Cancer--Treatment
Issue Date: 5-Sep-2007
Abstract: Breast tumors are typically heterogeneous and contain diverse subpopulations of tumor cells with differing phenotypic properties. In order to identify potential biomarkers of relapse in breast cancer, we have analyzed relapse related microarray data from five different laboratories and identified a list of significantly altered genes for each dataset. KEGG pathways for MAPK signaling, focal adhesion and cytokine-mediated signaling have been enriched for relapse in breast cancer in four out of the five microarray databases. We found that a set of genes coding for extracellular proteins are consistently upregulated in breast cancer patients with relapse in a multitude of microarray databases. A significant subset of these genes code for proteins found in the serum and as such could be potential candidates for relapse biomarkers. In addition, our work presents an in vitro coculture-based three-dimensional heterogeneous breast tumor model that can be used in drug resistance and drug delivery investigations. Breast cancer cell lines of different phenotypes (MDAMB231, MCF7 and ZR751) were cocultured in a rotating wall vessel (RWV) bioreactor to form a large number of heterogeneous tumoroids in a single cell culture experiment. In vitro tumoroids developed in this study recapture important features of the temporal-spatial organization of solid tumors, including the presence of necrotic areas at the center and higher levels of cell division at the tumor periphery. E-cad positive MCF7 cells form larger tumoroids than E-cad negative MDAMB231 cells. In addition, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Histologic cross sections of breast tumoroids developed in coculture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development.
URI: http://hdl.handle.net/1860/1777
Appears in Collections:Drexel Theses and Dissertations

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