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Three Dimensional Single Cell Analysis of the Cancer Stem Cell Inducing Epithelial-Mesenchymal Transition Signaling Networks in Breast Cancer by Mass Cytometry

Final Report Summary - SIGNALING 3D (Three Dimensional Single Cell Analysis of the Cancer Stem Cell Inducing Epithelial-Mesenchymal Transition Signaling Networks in Breast Cancer by Mass Cytometry)

Tumor metastasis is the main cause of cancer patient mortality. In breast cancer, a process called the epithelial-mesenchymal transition (EMT) is implicated in the generation of treatment-resistant cancer stem cells and in driving the metastatic dissemination of tumor cells. The EMT is controlled by regulatory circuits that upon activation orchestrate the phenotypic changes caused by the process. Since EMT is a heterogeneous process, single-cell analysis approaches are needed to study it. We use mass cytometry to study proteins and their phosphorylation sites on the single-cell level. In mass cytometry, metal isotopes are used as reporters to mark cellular components of interest and currently about 50 cellular components can be measured simultaneously. Using this approach, we comprehensively studied the structure of the regulatory EMT circuits from induction to the conclusion of this dynamic process and under perturbation conditions. This has led to an understanding of the regulatory circuit of EMT to the extent that we can now predict how to block the process at distinct points and even how to accelerate it. These predictions were experimentally verified in cancer models. We furthermore studied, for the first time, how genomic aberrations, such as gene amplification, shape cellular signaling networks.

To study cell-to-cell communication and the cellular activation of EMT in breast cancer tissues, we developed a new imaging technology that we termed imaging mass cytometry. In this technology, a laser ablation system is coupled to a mass cytometer and 50 markers can be imaged at subcellular resolution on cancer tissues in 2D and 3D. To analyze highly multiplexed tissue images, we have developed a novel data analysis toolbox, which we called histoCAT. Using these imaging approaches, we have analyzed hundreds of breast cancer patient samples to generate a comprehensive map of the single-cell pathology and processes of breast cancer and reveal EMT in a subset of breast cancer patients. In summary, we have developed a novel tissue imaging technology and used single-cell systems biology analysis to understand the regulatory circuits that govern breast cancer signaling networks and to map of the single-cell pathology of breast cancer.