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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 (Signaling 3D)
Date du début: 1 mars 2014, Date de fin: 28 févr. 2019 PROJET  TERMINÉ 

Tumor metastases, relapse, and resistance to therapy are the main causes of death in cancer patients. Cancer stem cells (CSCs) drive cancer growth, are likely responsible for cancer reoccurrence, and provide the potential to colonize a metastatic site. A cell plasticity process called epithelial-mesenchymal transition (EMT) generates CSCs from epithelial cancer cells and simultaneously equips these cells with motility and invasiveness, features prerequisite for metastasis. Consequently, therapeutic strategies that target EMT and CSC signaling networks are highly attractive. However, the properties of these signaling networks and their dependency on the tumor microenvironment and cancer genotypes are poorly understood. Here we propose to generate quantitative, time-resolved biomarker signatures and network models of EMT stages and CSCs on the single-cell level and to define their dependency on breast cancer tumor microenvironments and genotypes. Using mass cytometry, a technology able to quantify up to 100 proteins and phosphorylation sites simultaneously in a single-cell, the signaling network structure of EMT and CSC state will be gauged by modulation of cancer-related and signaling genes. These data will be used to infer mathematical signaling network descriptions of EMT and the CSC states, and to determine their master regulators and regulatory sub-networks. By extending mass cytometry to spatially resolved measurements, the EMT/CSC network states and their microenvironment will be analyzed in three dimensions in patient samples, and data will be correlated with associated genomic and clinical information. Based on these in vivo data, follow-up experiments in mouse models will be performed to validate the identified network states and cell-to-cell interaction as therapeutic targets. Finally, the in vivo dataset and its correlation with genomic and clinical information will be used to identify biomarkers for personalized medicine approaches.