Abstract 175: Pathway modeling to translate the 27-gene immuno-oncology algorithm into bladder cancer
Authors: Robert S. Seitz, Tyler J. Nielsen, Brock L. Schweitzer, David R. Hout, Douglas T. Ross
Journal: Cancer Research (2021)

Abstract Background The 27-gene immuno-oncology (IO) algorithm has demonstrated an association with immune checkpoint inhibitor (ICI) response in TNBC, NSCLC, and metastatic urothelial carcinoma (mUC). The algorithm can be run on data generated from either a qPCR assay or from analysis of whole transcriptome RNA-seq data. It integrates gene expression information from infiltrating inflammatory cells with signatures from surrounding stroma and tumor cells to classify cases into likely responder versus non-responders. We hypothesized that because the algorithm derives its biologic signature from the tumor immune microenvironment (TIME), the classification function and thresholds might translate to other solid tissue types based upon biologic separation of inflammatory phenotypes. Methods Using NSCLC and breast cancer datasets from TCGA, we identified 939 genes that comprise the Mesenchymal (M), Mesenchymal Stem-like (MSL), and Immunomodulatory (IM) gene expression patterns centered around a previously described 101-gene signature (Ring, 2016). We applied this 939 gene set to 433 bladder samples from TCGA (UC) and k-means clustered the genes based upon each of the three centroids. Clinical cases were also organized by k-means clustering (k=3). Pathway analysis was performed (GSEA—UCSD/Broad). We assessed classification of UC cases by looking at enrichment of inflammatory pathways into the IM cluster compared to mesenchymal pathways into the M or MSL clusters. The threshold for responder classification using the 27-gene IO algorithm previously established in TNBC was assessed by quantitating the fraction of cases enriched into the IM cluster (potential responders) as opposed to the M or MSL clusters (potential non-responders). Results The 939 genes centered around the 101-gene signature encoded twenty different physiologic pathways. Ten of these pathways included at least one of the genes from the 27-gene IO algorithm. Significant enrichment of inflammatory cell pathways was seen into the IM cluster as opposed to mesenchymal and reactive fibroblast pathways enriched into the M and MSL clusters. Pathways containing therapeutic targets designed to overcome resistance to ICIs were enriched in the MSL gene expression centroid. The 27-gene IO algorithm threshold applied to the TCGA samples classified 79% as responders in the IM cluster as opposed 16% in the M and MSL. Discussion These results support the hypothesis that gene expression signatures discerning TIME physiology associated with ICI response are tissue agnostic and relevant in multiple solid tissue types. The dramatic enrichment of responders into the IM cluster using previously established thresholds is consistent with appropriate biologic classification of the cases and supports utilizing the 27-gene IO algorithm and established threshold for association with ICI response in treated mUC cohorts. Citation Format: Robert S. Seitz, Tyler J. Nielsen, Brock L. Schweitzer, David R. Hout, Douglas T. Ross. Pathway modeling to translate the 27-gene immuno-oncology algorithm into bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 175.