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Advanced single-cell and spatial analysis with high-multiplex characterization of circulating tumor cells and tumor tissue in prostate cancer: Unveiling resistance mechanisms with the CoDuCo in situ assay.

Authors :
Bonstingl, Lilli
Zinnegger, Margret
Sallinger, Katja
Pankratz, Karin
Müller, Christin-Therese
Pritz, Elisabeth
Odar, Corinna
Skofler, Christina
Ulz, Christine
Oberauner-Wappis, Lisa
Borrás-Cherrier, Anatol
Somođi, Višnja
Heitzer, Ellen
Kroneis, Thomas
Bauernhofer, Thomas
El-Heliebi, Amin
Source :
Biomarker Research; 11/16/2024, Vol. 12 Issue 1, p1-19, 19p
Publication Year :
2024

Abstract

Background: Metastatic prostate cancer is a highly heterogeneous and dynamic disease and practicable tools for patient stratification and resistance monitoring are urgently needed. Liquid biopsy analysis of circulating tumor cells (CTCs) and circulating tumor DNA are promising, however, comprehensive testing is essential due to diverse mechanisms of resistance. Previously, we demonstrated the utility of mRNA-based in situ padlock probe hybridization for characterizing CTCs. Methods: We have developed a novel combinatorial dual-color (CoDuCo) assay for in situ mRNA detection, with enhanced multiplexing capacity, enabling the simultaneous analysis of up to 15 distinct markers. This approach was applied to CTCs, corresponding tumor tissue, cancer cell lines, and peripheral blood mononuclear cells for single-cell and spatial gene expression analysis. Using supervised machine learning, we trained a random forest classifier to identify CTCs. Image analysis and visualization of results was performed using open-source Python libraries, CellProfiler, and TissUUmaps. Results: Our study presents data from multiple prostate cancer patients, demonstrating the CoDuCo assay's ability to visualize diverse resistance mechanisms, such as neuroendocrine differentiation markers (SYP, CHGA, NCAM1) and AR-V7 expression. In addition, druggable targets and predictive markers (PSMA, DLL3, SLFN11) were detected in CTCs and formalin-fixed, paraffin-embedded tissue. The machine learning-based CTC classification achieved high performance, with a recall of 0.76 and a specificity of 0.99. Conclusions: The combination of high multiplex capacity and microscopy-based single-cell analysis is a unique and powerful feature of the CoDuCo in situ assay. This synergy enables the simultaneous identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes, the detection of CTC clusters, the visualization of CTC heterogeneity, as well as the spatial investigation of tumor tissue. This assay holds significant potential as a tool for monitoring dynamic molecular changes associated with drug response and resistance in prostate cancer. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20507771
Volume :
12
Issue :
1
Database :
Complementary Index
Journal :
Biomarker Research
Publication Type :
Academic Journal
Accession number :
180936208
Full Text :
https://doi.org/10.1186/s40364-024-00680-z