Evaluation Of Tumor Microenvironment And Biomarkers Of Immune Checkpoint Inhibitor (Ici) Response In Metastatic Renal Cell Carcinoma (Mrcc).

2595 Background: ICIs are now standard of care for mRCC; however, there are few biomarkers to predict ICI response. Recent data from atezolizumab/bevacizumab trials in mRCC suggest tumors with high T eff high /PD-L1+ are more likely to respond to ICI. Here, we use two gene panels as well as other inflammation markers in the tumor microenvironment to correlate with ICI responses. Methods: This multicenter study evaluated 86 patients (pts) with mRCC treated with ICIs. FFPE tumor samples were evaluated by RNA sequencing for T eff status. Two gene panels were analyzed: a T eff Gene Panel (CD8, CD27, IFNG, GZMA, GZMB, PRF1, EOMES, CXCL9, CXCL10, CXCL11, CD274, CTLA4, FOXP3, TIGIT, IDO1, PSMB9, TAP1) and a 5-Gene panel (FOXP3, CCR4, KLRK1, ITK, and TIGIT) based on the gene expression pattern of tumors in our cohort. Objective response rates (ORRs, defined as CRs and PRs) were correlated with PD-L1 status (positivity was defined as ≥1% TPS based on Dako 22C3 IHC assay), and TMB (0-10, 10-20, ≥20 mut/Mb), and tumor inflammation (high CD8 expression compared to a large reference population). Best responses to ICI was determined by an expert radiologist using RECIST 1.1 criteria. Inflamed tumor status, T eff gene panel, 5-gene panel, PD-L1 status, and TMB were associated with ORR and tested using a chi-squared test with Yates’s continuity correction. Results: ORR was 50% (4/8) for PD-L1 positive pts and 14% (9/65) for PD-L1 negative pts (p = 0.042). The majority of tumors (95%, 82/86) had TMB < 10 mut/mb. 43 pts (50%) were classified as T eff high and 43 pts were classified as T eff low . ORR was 23% (10/43) in the T eff high cohort and 12% (5/43) in the T eff low cohort (p = 0.256). ORR was 31% (14/45) in the 5-Gene high cohort and 2% (1/41) in the 5-Gene low cohort (p = 0.001). Conclusions: TMB and tumor inflammation based on CD8 did not reliably predict for objective responses in this study of mRCC pts treated with ICIs. Gene expression signatures provide a more comprehensive evaluation of the tumor microenvironment and may lead to better predictive biomarkers for ICI response than individual biomarkers such as PD-L1, TMB, or CD8 expression.