Davide Bimbatti, Cecilia Nasso, Domiziana Aspden, Sebastiano Buti, Ludovica Antonj, Alessio Signori, Elena Verzoni, Cristian Lolli, Marilena Di Napoli, Martina Fanelli, Miriam De Lucia, Cristina Masini, Giandomenico Roviello, Daniela Arduini, Roberto Filippi, Alessia Mennitto, Mariella Sorarù, Giovanni Bozza, Luigi Formisano, Carlo Messina, Lucia Bonomi, Annalisa Guida, Emanuela Fantinel, Sarah Scagliarini, Maria Giuseppa Vitale, Silvia Chiellino, Brigida Anna Maiorano, Filippo Maria Deppieri, Paolo Andrea Zucali, Alessia Cavo, Claudia Caserta, Francesca La Russa, Vincenza Conteduca, Francesca Maines, Silvia Zai, Francesco Pierantoni, Massimiliano Icardi, Pasquale Rescigno, Fabrizio Di Costanzo, Giuseppe Luigi Banna, Sara Elena Rebuzzi
Meet-URO 33 provides a real-world snapshot of 1L decision-making in mRCC. Distinct clinical patterns, including histology, metastatic sites, performance status, comorbidities, and prognostic risk, were associated with treatment selection. These findings characterize clinical phenotypes in routine practice and provide context for future effectiveness, safety, and treatment-sequencing analyses of 1L therapeutic strategies within the Meet-URO 33 cohort over time.
BACKGROUND: Despite the availability of several first-line (1L) systemic therapies for metastatic renal cell carcinoma (mRCC), no validated biomarkers currently guide the selection of 1L systemic treatment.
OBJECTIVES: To identify baseline clinical features associated with 1L treatment selection in a large real-world mRCC population.
DESIGN: Meet-URO 33-REGAL is a multicenter study with prospective and retrospective components, enrolling patients with mRCC receiving 1L systemic therapy since January 2021.
METHODS: Baseline patient and disease characteristics were analyzed to compare treatment selection among dual immune checkpoint inhibitor therapy (ICI-ICI), ICI plus tyrosine kinase inhibitor combinations (ICI-TKI), and TKI monotherapy. Multivariable logistic regression models were used to evaluate clinical features associated with pairwise treatment selection.
RESULTS: A total of 1695 patients from 58 centers were included: 248 (14.6%) received TKI monotherapy, 338 (20.0%) ICI-ICI, and 1109 (65.4%) ICI-TKI. ICI-ICI was more commonly selected in patients with sarcomatoid features (14.5% vs 7.4% for ICI-TKI, p < 0.001) and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) intermediate/poor-risk disease (92.0% vs 80.1%, p < 0.001). In multivariable analysis, compared with TKI monotherapy, ICI-TKI selection was associated with IMDC intermediate-risk disease (odds ratio (OR) 2.16, 95% confidence interval (CI) 1.25-3.74, p = 0.006), poor-risk disease (OR 2.89, 95% CI 1.38-6.06, p = 0.005), and bone metastases (OR 1.79, 95% CI 1.29-2.49, p = 0.001). In regimen-specific comparisons, nivolumab plus cabozantinib was associated with bone metastases and non-clear cell histology; pembrolizumab plus lenvatinib with eastern cooperative oncology group (ECOG) performance status 0 and pancreatic involvement; and nivolumab plus ipilimumab with cardiovascular and metabolic comorbidities and lymph node metastases. TKI monotherapy was more commonly selected in older patients and those with comorbidities, consistent with a more selected clinical phenotype in routine clinical practice.
CONCLUSION: Meet-URO 33 provides a real-world snapshot of 1L decision-making in mRCC. Distinct clinical patterns, including histology, metastatic sites, performance status, comorbidities, and prognostic risk, were associated with treatment selection. These findings characterize clinical phenotypes in routine practice and provide context for future effectiveness, safety, and treatment-sequencing analyses of 1L therapeutic strategies within the Meet-URO 33 cohort over time.