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

Urology Biochemistry Cell Biology Oncology Medicine Life Sciences Immunology
Circulating Tumor Cells (CTCs) Metastatic Prostate Cancer Multiplex Padlock Probe In Situ Hybridization Single-Cell Gene Expression Liquid Biopsy Spatial Transcriptomics Neuroendocrine Transdifferentiation

Research Report on the Academic Paper

Title: 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: Lilli Bonstingl et al.

Journal: Biomarker Research (2024) 12:140

DOI: 10.1186/s40364-024-00680-z

Abstract

Background: Metastatic prostate cancer is a highly heterogeneous and dynamic disease, necessitating practical tools for patient stratification and resistance monitoring. Liquid biopsy analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) shows promise, but comprehensive testing is essential due to diverse resistance mechanisms. Previously, the utility of mRNA-based in situ padlock probe hybridization for characterizing CTCs was demonstrated.

Methods: A novel combinatorial dual-color (CODuco) assay for in situ mRNA detection was developed, 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. Supervised machine learning was used to train a random forest classifier for CTC identification. Image analysis and visualization were performed using open-source Python libraries, CellProfiler, and TissUUmaps.

Results: Data from multiple prostate cancer patients demonstrated the CODuco assay’s ability to visualize diverse resistance mechanisms, such as neuroendocrine differentiation markers (SYP, CHGA, NCAM1) and AR-V7 expression. 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 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, and 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.

Keywords: Circulating tumor cells (CTCs), Metastatic prostate cancer, Multiplex padlock probe in situ hybridization, Single-cell gene expression, Liquid biopsy, Spatial transcriptomics, Neuroendocrine transdifferentiation, Resistance monitoring, Image analysis

Background

Prostate cancer (PC) is the third most frequently diagnosed solid cancer worldwide, with an estimated 1.4 million new cases reported in 2020. The risk for developing invasive PC increases with age, and projections suggest that annual new PC cases will increase to 2.9 million by 2040. Despite continuous improvements in treatment options, advanced PC therapy is challenging due to the emergence of resistance mechanisms. Dysregulation of the androgen receptor (AR) pathway plays a key role in PC development and progression, with androgen deprivation therapy (ADT) being the most common treatment for advanced disease. However, patients often develop resistance to ADT, leading to castration-resistant prostate cancer (CRPC). Novel AR-targeted treatments, such as enzalutamide and abiraterone, are effective but almost all patients acquire secondary resistance. Resistance is frequently driven by AR signaling pathway aberrations, including AR gene amplification, mutations, and the expression of AR splice variants, particularly AR-V7. Additionally, AR-independent mechanisms like neuroendocrine transdifferentiation are on the rise.

There is an urgent need to investigate multiple resistance mechanisms, which could be exploited as predictive biomarkers. Liquid biopsy, using analytes such as CTCs and ctDNA, has gained attention as a minimally invasive way to monitor disease state. However, simultaneously investigating a broad spectrum of resistance mechanisms and predictive biomarkers in PC remains a major challenge.

Methods

Patient Sampling and Ethics: The study enrolled patients with advanced metastatic PC at the Division of Oncology, Department of Internal Medicine, Medical University of Graz, following the principles of the World Medical Association Declaration of Helsinki. The study was approved by the ethics committee, and written informed consent was obtained from all patients and healthy controls.

Cell Line and PBMC Sample Preparation: PC cell lines VCaP and PC-3, and non-small cell lung cancer cell line NCI-H1299 were cultured and harvested. Peripheral blood mononuclear cells (PBMCs) from healthy controls were isolated by density gradient centrifugation.

CTC Enrichment and Sample Preparation for In Situ Analysis: CTCs were enriched from blood samples using the Cytogen Smart Biopsy Cell Isolator. Cells were fixed and centrifuged onto microscope slides.

CODuco In Situ PLP Hybridization: In situ PLP hybridization with a novel CODuco staining approach was used to visualize transcripts in cells. Reverse transcription, PLP hybridization, ligation, rolling circle amplification, and fluorescently labeled readout detection probe hybridization were performed.

Selection of Genes: CODuco in situ hybridization was used to visualize hematopoietic, epithelial, prostate-specific, and neuroendocrine transcripts.

Probe Design: Reverse transcription primers and PLPs were designed using CLC Main Workbench software and a Python software package developed by the Mats Nilsson lab.

Buffers for In Situ Hybridization: Diethyl pyrocarbonate (DEPC) was used to remove RNase activity in ultrapure water.

In Situ Hybridization: Prefixed slides were thawed, fixed, and subjected to reverse transcription, PLP hybridization, ligation, rolling circle amplification, and bridge probe hybridization.

Tissue Preparation: Formalin-fixed, paraffin-embedded (FFPE) tissue sections were preprocessed for in situ hybridization.

Imaging: Slides were imaged using SlideView VS200 digital slide scanners.

Image Analysis of Cell-Based Samples: Original and background scan images were converted to TIFF format and analyzed using CellProfiler.

Machine Learning-Based Classification: A random forest classifier was trained and evaluated using a dataset of cells from healthy controls with and without spiked-in tumor cells.

Analysis of Tissue Sample: Original and background scan images were converted to TIFF format and analyzed using CellProfiler and TissUUmaps.

AdnaTest ProstateCancerSelect AR-V7: The AdnaTest ProstateCancerSelect AR-V7 was used according to the manufacturer’s guidelines.

Data Analysis and Visualization: Python libraries, including NumPy and Pandas, were used for data analysis and visualization.

Results

Decoding of CODuco In Situ Signals: The CODuco approach employs a two-color code for in situ signal detection, enabling the visualization of up to 11 markers.

Validation of CODuco for CTC Characterization: The CODuco in situ assay was validated on healthy control PBMCs, PC cell lines VCaP and PC-3, and non-small cell lung cancer cell line NCI-H1299.

Classifier Training and Evaluation: A random forest classifier was trained and evaluated on blood samples of healthy controls with and without spiked-in tumor cells.

Classifier Identified Patient CTCs with a Recall of 0.76 and Specificity of 0.99: The classifier was tested on three patient samples, achieving a recall of 0.76 and specificity of 0.99.

CODuco Revealed Interpatient CTC Heterogeneity and Captured Neuroendocrine CTCs and CTC Clusters: Interpatient heterogeneity was observed regarding CTC count, presence of CTC clusters, and expression patterns.

CODuco In Situ Revealed Intrapatient CTC Heterogeneity: Individual CTCs showed very heterogeneous expression patterns.

CODuco In Situ Showed High Concordance with Clinical Parameters and AdnaTest: The results of the in situ assay were in line with clinical parameters and AdnaTest results.

CODuco Analysis of Patient-Matched CTC and Tissue Samples: The CODuco in situ assay was successfully applied to FFPE tissue, enabling spatial analysis at the single-cell level.

Discussion

The CODuco in situ assay enables the identification and characterization of CTCs, targeting up to 11 markers in a multiplex fashion. The assay’s capability to detect neuroendocrine markers is of utmost importance, as this remains challenging with conventional antibody staining procedures. The assay provides single-cell resolution RNA expression data and images of cell morphology, facilitating the identification of single CTCs and CTC clusters and revealing intrapatient heterogeneity.

Conclusion

The CODuco in situ assay combines high multiplex capacity and microscopy-based single-cell analysis, enabling the simultaneous identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes. This assay holds significant potential as a tool for monitoring dynamic molecular changes associated with drug response and resistance in prostate cancer.