Abstract A044: Immunotherapy biomarker assessment in RCC using IHC, gene expression profiling, and mutation burden assessment
Authors: Fang Peng , Yan Zhenyu , Wang Xiaodong , Chang Wes , Galderisi Chad , Spittle Cindy , Li Jin 
Published: August 2018

The field of immunotherapy includes multiple approaches that harness the power of the immune system to destroy cancer cells. Checkpoint inhibitors have been approved for the treatment of solid tumor and hematologic malignancies. While significant responses have been observed in a subset of patients, outcomes are variable and there is a need to identify additional predictive biomarkers beyond PD-L1 levels as measured by IHC. A more comprehensive assessment of the tumor and its microenvironment can be accomplished through gene expression profiling, mutation burden analysis, and immune repertoire profiling. Exploratory studies such as those presented here using targeted RNA and DNA sequencing will continue to expand our understanding of tumor immune cell interactions and how this information can be used to improve patient selection for checkpoint inhibitors and other immunotherapy. In this study, a set of 30 FFPE tumor samples including CRC, RCC, and NSCLC was analyzed using an IO Biomarker IHC Panel (PD-L1, CD8, CD3, and CD163). PD-L1 staining (% and intensity) was scored for both tumor cells (TC) and immune cells (IC). T-cell and macrophage markers were scored as high, medium, and low. Samples were also analyzed using the ThermoFisher Oncomine™ Immune Response Research Assay. This targeted RNA-sequencing panel measures the expression of 391 genes involved in tumor-immune cell interactions. RNA was extracted using RecoverAll and the quantity and quality was assessed using Qubit and RT-qPCR, respectively. A significant correlation was observed between the RT-qPCR quality score and mapped sequencing reads. A subset of 7 RCC samples with the highest RNA quality and sequencing QC metrics were selected for additional analysis. A trend was observed in the RNA expression level of individual genes such as PD-L1, PD1, CD8A, TNFRSF9, and LAG3 and a composite expression score based on 10 interferon gamma-related genes. Unsupervised clustering was performed using several published gene expression signatures that have been found to correlate with immune response (IR) and/or tumor inflammation. The gene content ranged between 4-13 genes and overlapped between some but not all signatures. The 13-gene signature classified six RCC samples as “high IR” and one sample as “low IR.” Interestingly, similar results were obtained when samples were classified using the other signatures. PD-L1 IHC positive staining patterns varied widely in the “high IR” samples and ranged from 40%TC/30%IC to 0%TC/5%IC. The “low IR” sample was PD-L1 negative in TC and IC. To further understand the relationship between the tumor’s genetic profile and the tumor microenvironment, mutation burden and immune repertoire analysis was performed using NGS-based methods. The results from the two panels will be presented. This exploratory study details how targeted RNA and DNA sequencing can identify patient subsets based on a multiplexed signature rather than a single marker. The use of both molecular and tissue-based assays will lead to a more comprehensive understanding of tumor and immune biology that may uncover new biomarkers for optimal stratification of patients for personalized immunotherapy treatment.

Citation Format: Peng Fang, Zhenyu Yan, Xiaodong Wang, Wes Chang, Chad Galderisi, Cindy Spittle, Jin Li. Immunotherapy biomarker assessment in RCC using IHC, gene expression profiling, and mutation burden assessment [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A044.