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Project

Molecular profiling of clear-cell renal cell carcinoma and prediction of response to targeted therapies against the vascular endothelial growth factor receptor, immune checkpoint inhibitors and mTOR inhibitors in the metastatic setting.

Over the last 10 years, several new therapies became available for the treatment of metastaticclear-cell kidney tumors: blood vessel inhibitors, mTOR-inhibitors and new generation
immunotherapy. Although these therapies have globally improved therapeutic outcome, individual
patient responses are highly variable. There is an urgent need to know in advance which therapy
will be the best for each patient in order to improve therapeutic efficacy, prevent unnecessary side
effects and optimize the use of public resources.
Recently, based on fresh frozen tumor samples, our research group has discovered that clear-cell
kidney tumors can be classified into four molecular subgroups. Two subgroups are highly sensitive
to the blood vessel inhibitor sunitinib, two other subgroups are less or not sensitive. We now aim
to strengthen this model in order to understand better clear-cell kidney tumors and to improve
patient outcome. (A) We will validate our classification on paraphin embedded samples, which are
more largely available than fresh frozen samples. (B) We will deepen the molecular
characterization of the subtypes through the analysis of micro-RNAs and mutations, and study the
stability of this classification throughout disease evolution. (C) We will study the impact of the
classification on patients treated with other blood vessel inhibitors such as pazopanib and axitinib, with mTOR-inhibitors, with new generation immunotherapy and with surgical resection of
metastases.

Date:1 Oct 2016  →  Today
Keywords:predictive markers, clear cell renal cell carcinoma
Disciplines:Laboratory medicine, Palliative care and end-of-life care, Regenerative medicine, Other basic sciences, Other health sciences, Nursing, Other paramedical sciences, Other translational sciences, Other medical and health sciences
Project type:PhD project