Abstract
551 Background: Anti-angiogenic (AA) drugs are the cornerstone of first-line (FL) treatment in renal cell carcinoma (RCC). While several mechanisms of resistance to AA have been described, second line (SL) therapies are disappointing with short PFS reported. A recent strategy makes use of individual patient-derived tumor models in animals for drug testing simultaneously with that patient’s FL and SL treatments (AVATARs). The predictive value of each specific AVATAR for that same original patient will be key in order to predict SL treatment response in the clinic trying to help in decision making in this setting. Methods: We generated a unique panel of patient-derived mouse models of RCC based on the orthotopic implantation of primary renal tumor biopsies or metastasis directly obtained from patients. Sunitinib FL treatment followed by different SL treatments are evaluated in each mouse model. Response to treatments in these models and molecular profiling in search of predictive factors of response/resistance will be performed. Results: In this subset of 12 advanced RCC patients, tumor take rate in mice to develop AVATARs was 75% at 5 months after implantation. Previous data demonstrated a positive association of grafting capacity with tumor stage and Fuhrman grade (p < 0.0001). AVATAR models faithfully reproduced each patient’s histological characteristics and metastatic capacity. Time to tumor growth in AVATAR models significantly correlated to clinical outcome in each patient (spearman correlation, p < 0.0001). These advanced AVATAR models are treated FL with AA drugs and, at the moment of emergence of resistance to therapy, treatment is switched to known and experimental SL treatments in different animal cohorts. Updated results on AVATAR treatment and prediction of patient’s responses will be presented. Conclusions: Thorough characterization of these AVATAR models together with their response to AA treatments we will be able to predict response/resistance to new SL treatments for our RCC patients as a personalized cancer therapy approach.
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