Appointment of Antonio Alcaraz, Group leader
Knowledge of the molecular bases of urological cancers will allow us to improve patient diagnosis and treatment alike, with the personalisation of existing treatments and the development of new ones aimed at molecular targets

Current research

Problem

Prostate, bladder and kidney cancers are the three most common urological tumours suffered by society today. Existing diagnostic techniques are invasive and non-sensitive and/or non-specific.

In addition, current prognostic factors make no distinction between those patients with tumour recurrence and those patients cured by surgery who require no adjuvant therapy. Therefore, if the group is able to identify non-invasive biomarkers with high diagnostic precision that can also predict disease outcome and treatment response, this would be a great step forward in clinical practice.

Approach

Technological breakthroughs in molecular biology over the last twenty years and the development of bioinformatics tools have enabled the group to make progress in the study of the genetic changes that take place in diverse urological cancers. The group uses these new approaches to identify changes in the genetic material of bladder, prostate and kidney tumours and thus develop the diagnostic, prognostic and treatment-response biomarkers required to improve current urological clinical practice.

Impact

The group has identified various biomarkers in urine in order to monitor patients with bladder cancer, thus avoiding (or lengthening the time between) invasive diagnostic techniques such as cystoscopies. In relation to this disease, the group has also identified tumour dissemination that is detectable in the blood, using non-invasive sampling techniques that are very useful for monitoring first tumour recurrences.

In prostate cancer, this group has identified diagnostic biomarkers in urine that might make it unnecessary to perform a prostatic biopsy and, lastly, in renal carcinoma, the group is working on the identification of biomarkers to predict the evolution of the disease after tumour removal.