Abstract 5633: Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics
Authors: Femel Julia , Neil Emily , Park Dongju , Yassouri Fabio El , Appelshoffer Anijutta , Lloyd Erica , DiBuono Michael , Sauer Henry , Lafayette Hanna , Smith Hsinyi , Wang Jinling , Mangiardi Dominic , Makrigiorgos Alex , Praveen Paurush , Rüberg Silvia , Müller Werner , Wantenaar Tanya , Pinard Robert , Bosio Andreas 
Published: April 2023

With the advent of immunotherapies such as immune checkpoint inhibitors and CAR T cells, the interest in the tumor microenvironment and tumor-infiltrating lymphocytes (TILs) cells has increased in recent years as these play a critical role in predicting response to therapy and clinical outcomes. We have reported on the identification of potential targets for CAR T cell therapies by analyzing a multitude of tumor samples with hundreds of antibodies by flow cytometry and a new spatial proteomics platform, the MACSima Imaging Platform (Schäfer et al. Nat. Commun., 2021; Kinkhabwala et.al. Sci. Rep., 2022). Here we report on the analysis of tumor microenvironment and tumor infiltrating immune cells using a novel combined spatial transcriptomics and proteomics approach. For protein expression analysis we generated a panel of 60 fluorochrome conjugated antibodies (REAscreen Immuno-oncology, human, FFPE, version 01) selected to identify immune cells, cancer associated fibroblasts, matrix, blood vessels, lymphatics and malignant epithelial cell populations. RNA analysis was based on a novel in situ hybridization and amplification method followed by a cyclic spatial decoding of transcripts (RNAsky). To standardize and further ease the process of protein and RNA detection we generated the antigen and RNA detection probes in a ready-to-use format, i.e., dried and sealed in 96 well plates ready to be inserted into the MACSima. FFPE specimens of different tumor entities reviewed by a pathologist were first hybridized to probes, which were amplified, and spatially decoded by fluorochrome conjugated oligonucleotides. Subsequently the same specimen was exposed to fluorescently labeled antibodies by cycles of antibody reaction, image acquisition and erasure of signal. Finally, specimens were stained with H&E to report on tissue integrity and correlate spatial orientation. The 2D image stacks were analyzed for transcript and antigen quantification and pattern recognition using both, pixel and segmented single-cell data (MACS iQ View Analysis Software). Across the specimens we identified more than 20 cell types including 12 immune cell subsets. The comparison of the tumor microenvironment, tumor-infiltrating immune cells (TILs) and their spatial organization in relation to the tumor cells allowed to identify differences and similarities across the tumor types. In summary we report here on a novel standardized and automated combined spatial RNA and protein expression analysis of the tumor microenvironment and tumor infiltrating immune cells across tumor types. The characterization dataset demonstrates the ability of our platform to perform standardized in-depth phenotyping of sample cohorts. This will enable discovery and further development of predictive and prognostic biomarkers critical to patient stratification for immunotherapy.

Citation Format: Julia Femel, Emily Neil, Dongju Park, Fabio El Yassouri, Anijutta Appelshoffer, Erica Lloyd, Michael DiBuono, Henry Sauer, Hanna Lafayette, Hsinyi Smith, Jinling Wang, Dominic Mangiardi, Alex Makrigiorgos, Paurush Praveen, Silvia Rüberg, Werner Müller, Tanya Wantenaar, Robert Pinard, Andreas Bosio. Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5633.