First-line treatment of metastatic clear cell renal cell carcinoma: a decision-making analysis among experts.

Aeppli, S; Schmaus, M; Eisen, T; Escudier, B; Grünwald, V; Larkin, J; McDermott, D; Oldenburg, J; Porta, C; Rini, B I; Schmidinger, M; Sternberg, C N; Rothermundt, C; Putora, P.M. (2021). First-line treatment of metastatic clear cell renal cell carcinoma: a decision-making analysis among experts. ESMO open, 6(1), p. 100030. BMJ 10.1016/j.esmoop.2020.100030

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BACKGROUND

The treatment landscape of metastatic clear cell renal cell carcinoma (mccRCC) has been transformed by targeted therapies with tyrosine kinase inhibitors (TKI) and more recently by the incorporation of immune checkpoint inhibitors (ICI). Today, a spectrum of single agent TKI to TKI/ICI and ICI/ICI combinations can be considered and the choice of the best regimen is complex.

MATERIALS AND METHODS

We performed an updated decision-making analysis among 11 international kidney cancer experts. Each expert provided their treatment strategy and relevant decision criteria in the first line treatment of mccRCC. After the collection of all input a list of unified decision criteria was determined and compatible decision trees were created. We used a methodology based on diagnostic nodes, which allows for an automated cross-comparison of decision trees, to determine the most common treatment recommendations as well as deviations.

RESULTS

Diverse parameters were considered relevant for treatment selection, various drugs and drug combinations were recommended by the experts. The parameters, chosen by the experts, were performance status, International Metastatic renal cell carcinoma Database Consortium (IMDC) risk group, PD-L1 status, zugzwang and contraindication to immunotherapy. The systemic therapies selected for first line treatment were sunitinib, pazopanib, tivozanib, cabozantinib, ipilimumab/nivolumab or pembrolizumab/axitinib.

CONCLUSION

A wide spectrum of treatment recommendations based on multiple decision criteria was demonstrated. Significant inter-expert variations were observed. This demonstrates how data from randomized trials are implemented differently when transferred into daily practice.

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