Abstract

Abstract The recent advances in immunotherapies, such as immune checkpoint modulators, bispecific antibodies, and adoptive T-cell transfer, opens new opportunities for the treatment of cancer. Having this broad spectrum of new therapeutic agents available, the demand for predictive and robust preclinical models to minimize translational failures in immuno-oncology is increasing. Indivumed has successfully implemented the model of Precision Cut Cancer Tissue Slices (PCCTS) derived from viable human tumor tissue to serve as a platform for testing different applications such as chemotherapeutic agents, small molecules and antibodies. In this study, we investigated the effects of OKT3® (Muromonab), a therapeutic antibody against CD3, on PCCTS from a patient diagnosed with non-small cell lung cancer (NSCLC) in respect of gene expression changes using Spatial Transcriptomics. Spatial Transcriptomics (10x Genomics) is a technique to spatially resolve whole RNA-seq information. Tissue sections were prepared using a Krumdieck Tissue slicer and fresh tissue sections were incubated for 24 h with 10 µg/ml OKT3 followed by performing the Visium Spatial Gene Expression Workflow from 10x Genomics. For this purpose, a glass slide containing spatially barcoded capture probes were used to bind mRNAs of a tissue section. Following cDNA synthesis and library preparation, the libraries were sequenced using standard RNA-seq technology. Specific sequence barcodes allowed to assign gene expression data to the histological positions in the tissue. For visualization of gene expression patterns, the software Space Ranger and Loupe Browser were used. This method enabled the identification of cellular subpopulations in the spatial context before and after treatment with OKT3, whereas molecular identities and positional information of diverse cells would have been lost upon transcriptome analysis of bulk samples. Our data showed significant differences between untreated and OKT3 treated tissue slices especially in the microenvironment that encompasses inflammatory cells, extracellular matrix, and stromal cells interacting with tumor cells for cancer growth and progression. The combination of the PCCTS platform with Spatial Transcriptomics has been shown to be most valuable for the understanding of compound effects. The location of cells and their changes in gene expression patterns are important to understand tissue functionality and corresponding pathological changes. Citation Format: Nicole Kerstedt, Nicole Grabinski, Kristina Bernoth, Monika Schoeppler, Bita Motamedi-Baniassad, Jana Krueger, Moiken Petersen, Hartmut Juhl, Kerstin A. David. Spatial transcriptomics - a valuable tool to visualize compound effect in precision cut cancer tissue slices (PCCTS) [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 2705.

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