Renal Cell Carcinoma (RCC) is the most common form of kidney tumor, accounting for approximately 3% of all adult malignancies. To date, RCC is still a difficult disease to diagnose and treat. Although the surgery is the standard therapy for localized tumors, one quarter of patients who underwent nephrectomy, relapse within three years. Moreover, one third of patients arrives with metastases at diagnosis. Unfortunately, the metastatic disease is generally characterized by therapy resistance and very poor outcomes. So far, the lack of valid preclinical RCC models has hampered the discovery of valuable diagnostic and prognostic biomarkers and predictive indicators of therapy response for improving patients' management. In the project, we focused our efforts on the optimization of new patient-derived preclinical models for RCC. We first isolated heterogeneous undifferentiated cell populations responsible for tumor propagation and cancer therapy resistance. By performing a phosphoproteomic analysis we identified a protein signature predictive of cancer progression that would help to select patients more likely to relapse after surgery and who may benefit of adjuvant therapy. We then established an orthotopic patient-derived xenograft (PDX) model that faithfully recapitulate grading, histology and molecular characteristics of the parental tumors. The PDX model proved to be an indicator of bad prognosis and patient tumor could be propagated for up to seventh generation in mice. These findings support the possibility to use PDXs as a platform for patient monitoring and for drug testing. Finally, we were able to establish and characterize, for the first time, long term organoid cultures from normal and tumor samples. All together, these three new models provide innovative and valuable tools for RCC research, suggesting many potential applications for reproducing disease progression models, for biomarkers discovery and drug testing.
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