639 Background: The MSKCC risk model, an established prognostic tool fo r metastatic RCC, integrates clinical + laboratory data, but is ignorant to tumor genomics. Mutations in BAP1, PBRM1, TP53, cumulatively found in over 50% of pts, have prognostic value in RCC. We sought to study the use of integrating mutation status into the MSKCC model using two large clinical trial datasets. Methods: Pts had received first line sunitinib or pazopanib on the phase III COMPARZ (training set, n = 357) or the phase II RECORD3 trial (validation set, n = 130). Genes were evaluated by next generation sequencing using archival tissue. Association of mutation status and overall survival (OS) was tested by multivariate Cox regression analysis (MVA) in the training set. An annotated model was constructed combining the original clinical variables and mutation status for the 3 genes. We compared risk group assignment and concordance index (c-index) for the original vs. new model in training and validation set. Results: Mutation status for each gene: BAP1, TP53 and PBRM1 independently correlated with OS on MVA (p≤0.0035). Comparing the original (clinical only) to the annotated (clinical + genomics) model, risk categories changed in 139 pts (39%). The C-index was improved with integration of genomic information (0.595 original model - > 0.628 new model). The independent validation cohort confirmed improvement of c-index for predicting OS with integration of genomic data (c-index 0.622 original model - > 0.641 new model). Conclusions: Mutation status for BAP1, PBRM1, and TP53 has prognostic value in pts with advanced RCC. The annotated risk model alters risk status in over 1/3 of pts and improves accuracy of estimating outcomes in patients receiving first-line therapy. Clinical trial information: NCT00720941. [Table: see text]
Benchmark Generator for Stratified Controllers of Tank Networks