Abstract 4694: Evaluation of tumor-associated antigen expression with the MACSimaTM high-content imaging platform

• Published in: Cancer research (Chicago, Ill.) Ill.), 2019-07, Vol.79 (13_Supplement), p.4694-4694
• Main Authors: Herbel, Christoph, Dittmer, Vera, Martinez-Osuna, Manuel, Kuester, Laura Nadine, Schaefer, Daniel, Drewes, Jan, Kollet, Jutta, Mueller, Werner, Mallmann, Michael, Mallmann, Peter, Stroebel, Philipp, Hardt, Olaf, Eckardt, Dominik, Bosio, Andreas
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-4694

Here we report the use of the MACSima™ imaging platform to perform high-content imaging for the pre-clinical validation of tumor target expression on tumor and healthy human tissues indicative for potential on-target/off-tumor toxicity in vivo. Major advances have been achieved in cancer therapy in the past decade. In particular, targeted-immunotherapy has progressed and clinical benefits for patients have been achieved. However, on-target/off-tumor toxicity is a potential threat which has been shown to be more pronounced in solid than in liquid tumors. These findings highlight the need for better pre-clinical assays to improve the safety profile of immunotherapies. On-target/off-tumor toxicity is mainly based on the expression of tumor-associated antigens (TAA) in healthy tissues under physiological conditions. Currently, most prediction methods for on-target/off-tumor expression are based on bulk mRNA expression data of healthy tissue. These prediction models, however, have limitations, mainly poor predictable relation between RNA and protein level. Moreover, it is frequently unclear which cell types are affected by on-target/off-tumor effects. To overcome these limitations we employ multi-parameter imaging to analyze the expression of TAAs directly at the protein level. Additionally, we gain spatial information about tissue and cellular distribution of TAAs. Notably, our novel high-content imaging technology potentially allows for the analysis of hundreds of markers in a single experiment, paving the way for high-dimensional characterization of cells within complex solid tissues. We performed high-content imaging with the MACSima™ platform to validate the expression of known TAAs across tumor and healthy tissue samples. Subsequently, we employed unbiased cluster analysis revealing correlation patterns within the datasets to identify cell types at risk. In detail, we analyzed several high-grade serous ovarian carcinoma and pancreatic ductal adenocarcinoma for the expression of known TAAs, described tumor markers, and tissue lineage markers. Additionally, we evaluated the expression of these TAAs across several healthy human tissues. Next we performed pixel- and object-based data analysis for unbiased cluster analysis. Thereby we identified cell clusters that express TAAs in ovarian and pancreatic cancers, as well as in healthy tissues. We found that primarily epithelial cells express the analyzed TAAs in different tissues and that TAA expression shows in