title: Second-line treatment for metastatic clear cell renal cell cancer: experts' consensus algorithms.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Haematology and Oncology, Kantonsspital St. Gallen, 9007, St. Gallen, Switzerland. christian.rothermundt@kssg.ch.
authors.forename: C
authors.initials: C
authors.lastname: Rothermundt
authors.affiliation: Department of Nephrology, University Hospital Bern, Bern, Switzerland.
authors.forename: J
authors.initials: J
authors.lastname: von Rappard
authors.affiliation: Department of Oncology, Cambridge University Hospitals NHS Foundation Cambridge, Cambridge, UK.
authors.forename: T
authors.initials: T
authors.lastname: Eisen
authors.affiliation: Gustave Roussy, Villejuif, France.
authors.forename: B
authors.initials: B
authors.lastname: Escudier
authors.affiliation: Klinik für Hämatologie, Hämostaseologie, Onkologie und Stammzelltransplantation, Medizinische Hochschule Hannover, Hannover, Germany.
authors.forename: V
authors.initials: V
authors.lastname: Grünwald
authors.affiliation: The Royal Marsden Hospital, London, UK.
authors.forename: J
authors.initials: J
authors.lastname: Larkin
authors.affiliation: Beth Israel Deaconess Medical Center, Boston, MA, USA.
authors.forename: D
authors.initials: D
authors.lastname: McDermott
authors.affiliation: Department of Medical Oncology, Norwegian Radium Hospital, Oslo, Norway.
authors.forename: J
authors.initials: J
authors.lastname: Oldenburg
authors.affiliation: Policlinico San Matteo Pavia Fondazione IRCCS, Pavia, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Porta
authors.affiliation: Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH, USA.
authors.forename: B
authors.initials: B
authors.lastname: Rini
authors.affiliation: Abteilung für Onkologie, Allgemeines Krankenhaus, Universitätskliniken, Vienna, Austria.
authors.forename: M
authors.initials: M
authors.lastname: Schmidinger
authors.affiliation: Department of Medical Oncology, San Camillo and Forlanini Hospitals, Rome, Italy.
authors.forename: C N
authors.initials: CN
authors.lastname: Sternberg
authors.affiliation: Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
authors.forename: P M
authors.initials: PM
authors.lastname: Putora
mesh_terms: D000465:Algorithms; D000813:Anilides / Q000627:therapeutic use; D000911:Antibodies, Monoclonal / Q000627:therapeutic use; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D000077784:Axitinib; D002292:Carcinoma, Renal Cell / Q000188:drug therapy* / Q000556:secondary; D032921:Consensus; D003661:Decision Support Techniques; D003663:Decision Trees; D000068338:Everolimus / Q000627:therapeutic use; D006801:Humans; D007093:Imidazoles / Q000627:therapeutic use; D007191:Indazoles / Q000627:therapeutic use; D007680:Kidney Neoplasms / Q000473:pathology*; D000077594:Nivolumab; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use*; D011725:Pyridines / Q000627:therapeutic use; D017211:Treatment Failure
chemical_list: D000813:Anilides; D000911:Antibodies, Monoclonal; D000970:Antineoplastic Agents; D007093:Imidazoles; D007191:Indazoles; D047428:Protein Kinase Inhibitors; D011725:Pyridines; C558660:cabozantinib; D000077594:Nivolumab; D000068338:Everolimus; D000077784:Axitinib
keywords: Algorithm; Consensus; Diagnostic nodes; Renal cell cancer