Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study

PURPOSE Anti–PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti–PD-1 antibody treatment for advanced RCC by integrating genetic and clinical data using machine learning (ML). METHODS Clinical and single-nucleotide polymorphism (SNP) data obtained in the SNPs in nivolumab PD-1 inhibitor for RCC study, which enrolled Japanese patients treated with nivolumab monotherapy for advanced clear cell RCC, were used. A point-wise linear (PWL) algorithm, logistic regression with elastic-net regularization, and eXtreme Gradient Boosting were used in this study. AUC values for objective response and C-indices for progression-free survival (PFS) were calculated to evaluate the utility of the models. RESULTS Among the three ML algorithms, the AUC values to predict objective response were highest for the PWL algorithm among all the data sets. Three predictive models (clinical model, small SNP model, and large SNP model) were created by the PWL algorithm using the clinical data alone and using eight and 49 SNPs in addition to the clinical data. C-indices for PFS by the clinical model, small SNP model, and large SNP model were 0.522, 0.600, and 0.635, respectively. CONCLUSION The results demonstrated that the SNP models created by ML produced excellent predictions of tumor response to nivolumab monotherapy for advanced clear cell RCC and will be helpful in treatment decisions.