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
Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, 8010, Graz, Austria.
Center for Biomarker Research in Medicine (CBmed), 8010, Graz, Austria.
Biomark Res. 2024 Nov 16;12(1):140. doi: 10.1186/s40364-024-00680-z.
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.
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.
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.
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.
转移性前列腺癌是一种高度异质性和动态性的疾病，迫切需要实用的患者分层和耐药性监测工具。循环肿瘤细胞（CTC）和循环肿瘤DNA的液体活检分析很有前景，然而，由于耐药机制的多样性，全面检测至关重要。此前，我们证明了基于mRNA的原位锁式探针杂交在表征CTC方面的实用性。
我们开发了一种用于原位mRNA检测的新型组合双色（CoDuCo）检测方法，具有增强的多重检测能力，能够同时分析多达15种不同的标志物。该方法应用于CTC、相应的肿瘤组织、癌细胞系和外周血单核细胞，用于单细胞和空间基因表达分析。使用监督式机器学习，我们训练了一个随机森林分类器来识别CTC。使用开源Python库、CellProfiler和TissUUmaps进行图像分析和结果可视化。
我们的研究展示了来自多名前列腺癌患者的数据，证明了CoDuCo检测方法能够可视化多种耐药机制，如神经内分泌分化标志物（SYP、CHGA、NCAM1）和AR-V7表达。此外，在CTC以及福尔马林固定、石蜡包埋的组织中检测到了可靶向治疗的靶点和预测性标志物（PSMA、DLL3、SLFN11）。基于机器学习的CTC分类具有很高的性能，召回率为0.76，特异性为0.99。
高多重检测能力与基于显微镜的单细胞分析相结合是CoDuCo原位检测方法的独特且强大的特征。这种协同作用能够同时识别和表征具有上皮、上皮-间质和神经内分泌表型的CTC，检测CTC簇，可视化CTC异质性，以及对肿瘤组织进行空间研究。作为监测前列腺癌中与药物反应和耐药性相关的动态分子变化的工具，该检测方法具有巨大潜力。
