Second-line treatment for metastatic clear cell renal cell cancer: experts' consensus algorithms.

Rothermundt, C; von Rappard, J; Eisen, T; Escudier, B; Grünwald, V; Larkin, J; McDermott, D; Oldenburg, J; Porta, C; Rini, B; Schmidinger, M; Sternberg, C N; Putora, Paul Martin (2017). Second-line treatment for metastatic clear cell renal cell cancer: experts' consensus algorithms. World journal of urology, 35(4), pp. 641-648. Springer 10.1007/s00345-016-1903-6

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BACKGROUND Second-line systemic treatment options for metastatic clear cell renal cell cancer (mccRCC) are diverse and treatment strategies are variable among experts. Our aim was to investigate the approach for the second-line treatment after first-line therapy with a tyrosine kinase inhibitor (TKI). Recently two phase III trials have demonstrated a potential role for nivolumab (NIV) and cabozantinib (CAB) in this setting. We aimed to estimate the impact of these trials on clinical decision making. MATERIALS AND METHODS Eleven international experts were asked to provide their treatment strategies for second-line systemic therapy for mccRCC in the current setting and once NIV and CAB will be approved and available. The treatment strategies were analyzed with the objective consensus approach. RESULTS The analysis of the decision trees revealed everolimus (EVE), axitinib (AXI), NIV and TKI switch (sTKI) as therapeutic options after first-line TKI therapy in the current situation and mostly NIV and CAB in the future setting. The most commonly used criteria for treatment decisions were duration of response, TKI tolerance and zugzwang a composite of several related criteria. CONCLUSION In contrast to the first-line setting, recommendations for second-line systemic treatment of mccRCC among experts were not as heterogeneous. The agents mostly used after disease progression on a first-line TKI included: EVE, AXI, NIV and sTKI. In the future setting of NIV and CAB availability, NIV was the most commonly chosen drug, whereas several experts identified situations where CAB would be preferred.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Putora, Paul Martin


600 Technology > 610 Medicine & health








Beatrice Scheidegger

Date Deposited:

07 Mar 2018 17:29

Last Modified:

26 Oct 2019 16:43

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Algorithm Consensus Diagnostic nodes Renal cell cancer




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