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Abstract We identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity.
Abstract We propose a method for creating personalized regulatory networks, enabling the development of "virtual avatars" for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data.
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