GOAL: An inverse toxicity-related algorithm for daily clinical practice decision making in advanced kidney cancer

Metastatic renal cell carcinoma (mRCC), considered almost an orphan disease only six years ago, appears today a very dynamic pathology. The recently switch to the actual overcrowded scenario defined by seven active drugs has driven physicians to an incertitude status, due to difficulties in defining the best possible treatment strategy. This situation is mainly related to the absence of predictive biomarkers for any available or new therapy. Such issue, associated with the nearly absence of published face-to-face studies, draws a complex picture frame. In order to solve this dilemma, decisional algorithms tailored on drug efficacy data and patient profile are recognized as very useful tools. These approaches try to select the best therapy suitable for every patient profile. On the contrary, the present review has the "goal" to suggest a reverse approach: basing on the pivotal studies, post-marketing surveillance reports and our experience, we defined the polarizing toxicity (the most frequent toxicity in the light of clinical experience) for every single therapy, creating a new algorithm able to identify the patient profile, mainly comorbidities, unquestionably unsuitable for each single agent presently available for either the first- or the second-line therapy. The GOAL inverse decision-making algorithm, proposed at the end of this review, allows to select the best therapy for mRCC by reducing the risk of limiting toxicities. (C) 2013 Elsevier Ireland Ltd. All rights reserved.