Abstract 1688: High-throughput immune cell phenotyping using GeoMx DSP reveals Non-Small Cell Lung Cancers (NSCLC) are divided into distinct immunological subtypes
Authors: Kim Youngmi , Danaher Patrick , Nelson Brenn , Griswold Maddy , Hoang Margaret , Houghton McGarry A. , Beechem Joseph M. 
Published: September 2020

Abstract
GeoMx DSP reveals NSCLCs are divided into distinct immunological subtypes, and local immune signatures of tumor sub-regions differentially affect neighboring tumor biology. Although cancer immunotherapy is considered an effective cancer-treatment option, means to measure its effect on sub-tumor regions are on demands. Despite its popularity, scRNA-seq only reveals cell populations found within a tissue but is mute on roles in tumor microenvironment (TME) like the impact of one cell type on another's behavior. To gain insights inaccessible to single-cell methods, we demonstrate a harmonized analysis of scRNA-seq and NanoString GeoMx™ data in lung tumors. This approach reveals the spatial distribution of cell populations defined via scRNA-seq, enabling rich descriptions of cells' responses to each other and to their locations within the tumor. GeoMx Digital Spatial Profiler (GeoMx DSP) is based on barcoding technology that enables spatially resolved, digital characterization of proteins or mRNA in a highly multiplexed (over 1,500-plex) assay. The oligonucleotide tags cleaved from discrete regions are quantitated by NGS, and counts are mapped back to tissue location, yielding a spatially-resolved digital profile of analyte abundance. To measure immune infiltrates in tumors, we employed machine learning techniques to classify gene sets of immune cell phenotypes and built an immune cell phenotyping panel for GeoMx DSP. Gene expression data from subregions of a tumor tissue were analyzed through this immune cell phenotyping pipeline to quantify immune infiltrates. We applied scRNA-seq to predefine the cell populations present in NSCLC tumors, and we applied the GeoMx platform to measure these populations across dozens of FFPE tumor sections. We found that there were three immunological subtypes; cold, myeloid enriched and lymphoid enriched groups. This finding was validated using traditional methods, such as FACS or immunohistochemistry analysis. In addition, we also profiled immune infiltrates of sub-tumor regions within a tumor tissue and found that spatial profiling of subregions and their neighboring environment (TME) enabled us to measure how TME differentially affects tumor biology. These measurements enable us to describe the distribution of immune populations across spatial variables like tumor interior vs. margin, to catalog the degree to which immune populations traffic together within the tumor, and to correlate gene expression in tumor cells with neighboring immune populations. These analyses demonstrate the ability of spatial RNA profiling to reach conclusions inaccessible to single-cell data alone. “FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.”
Citation Format: Youngmi Kim, Patrick Danaher, Brenn Nelson, Maddy Griswold, Margaret Hoang, McGarry A. Houghton, Joseph M. Beechem. High-throughput immune cell phenotyping using GeoMx DSP reveals Non-Small Cell Lung Cancers (NSCLC) are divided into distinct immunological subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1688.