Angiogenic and T-effector subgroups identified by gene expression profiling (GEP) and propensity for PBRM1 and BAP1 alterations in clear cell renal cell carcinoma (ccRCC).

343 Background: With the emergence of multiple active treatment options in RCC, predictive biomarkers for optimal treatment selection are lacking. Gene expression data from IMmotion151 and Javelin Renal 101 clinical trials generated anti-angiogenic and immune signatures that warrant further validation. We aimed to describe the genomic and gene expression profiles in a multi-institutional database of patients with ccRCC, and its association with other biomarkers of interest. Methods: Whole transcriptome sequencing was performed for ccRCC patient samples submitted to a commercial CLIA-certified laboratory (Caris Life Sciences, Phoenix, AZ) from February 2019 to September 2020. Tumor GEP and hierarchical clustering based on the validated 66-gene signature (D’Costa et al, 2020) were used to identify patient subgroups. Samples from both primary tumors and metastatic sites were included. Results: A total of 316 patients with ccRCC, median age 62 (range 32-90), 71.8% men, were included. Tissue samples were obtained from primary tumor (46.5%), lung (12.3%), bone (9.5%), liver (4.7%) and other metastatic sites (27%). Gene expression analysis identified angiogenic, mixed and T-effector subgroups in 24.1%, 51.3% and 24.7%, respectively. Patients with angiogenic subgroup tumors compared to those with T-effector subgroup tumors were more likely to be older (63 versus 60 years, p=0.035), female (40.8% versus 16.7%, p=0.0009) and more frequently found in pancreatic/small bowel metastases (75% versus 12.5%, p=0.0103). Biomarkers of potential response to immunotherapy such as PD-L1 (p=0.0021), TMB (not significant), and dMMR/MSI-H status (not significant) were more frequent in the T-effector subgroup. PBRM1 mutations were more common in the angiogenic subgroup (62.0% vs 37.5%, p=0.0034) while BAP1 mutations were more common in the T-effector subgroup (18.6% versus 3.0%, p= 0.0035). Immune cell population abundance (e.g. NK cells, monocytes) and immune checkpoint gene expression (TIM-3, PD-L1, PD-L2, CTLA4) were also increased in the T-effector subgroup. Conclusions: Our hierarchical clustering results based on the 66-gene expression signature were concordant with results from prior studies. Patient subgroups identified by evaluation of angiogenic and T-effector signature scores exhibit significantly different mutations and immune profiles. These findings require prospective validation in future biomarker-selected clinical trials.

Duke Scholars

Author Tian Zhang Medicine, Medical Oncology