Multi-Cancer Detection Using Multiplex Droplet Digital PCR Methylation Assay

Background

Cancer is one of the leading causes of death worldwide, with nearly 10 million deaths reported in 2020. Although many cancers can be cured when detected and treated early, many patients are still diagnosed at advanced stages, leading to poor treatment outcomes. Currently, most Western countries have screening programs only for colorectal, breast, and cervical cancers, while other cancer types lack effective early detection methods. Therefore, the development of a tool capable of detecting multiple cancers simultaneously is of great significance.

DNA methylation is a biomarker that undergoes changes early in cancer development, offering high stability and consistency. Unlike mutations, methylation patterns exhibit significant differences between cancer cells and normal cells, and these differences emerge early in carcinogenesis. As a result, methylation detection is considered an ideal tool for early cancer diagnosis. In recent years, droplet digital PCR (ddPCR) technology has gained widespread attention in methylation detection due to its high sensitivity and absolute quantification capabilities. However, no previous studies have utilized multiplex ddPCR for multi-cancer detection.

Source of the Paper

This paper was completed by a research team from the University of Antwerp and Antwerp University Hospital in Belgium. The main authors include Isabelle Neefs, Nele De Meulenaere, Thomas Vanpoucke, and others. The paper was published online on September 6, 2024, in the journal Molecular Oncology, with the DOI 10.¹⁰⁰²⁄₁₈₇₈-0261.13708.

Research Process and Results

1. Target Selection and Experimental Design

Based on data from The Cancer Genome Atlas (TCGA), the research team screened 1,792 differentially methylated CpG sites and selected 40 sites with significant differences for further analysis. Ultimately, the team identified three methylation targets (target 1, target 2, and target 3) and developed two ddPCR assays: a triplex assay for simultaneously detecting target 1 and target 2, and a duplex assay for detecting target 3.

2. Sample Collection and Processing

The research team collected 103 tumor samples and 109 normal adjacent tissue samples, covering eight common cancer types: lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancers. All samples were microscopically examined by pathologists to confirm tissue type and tumor cell percentage (TCP). After DNA extraction, samples were treated with bisulfite conversion to distinguish methylated from non-methylated DNA.

3. Optimization of ddPCR Assays

The research team optimized the ddPCR assays through temperature gradients and probe concentration gradients. The optimal temperature for the triplex assay was 55°C, with probe concentrations of 450 nM (target 1), 680 nM (target 2 - FAM), and 1.4 µM (target 2 - SUN). The optimal temperature for the duplex assay was 58°C, with a probe concentration of 2.93 µM. The optimized assays demonstrated excellent repeatability and sensitivity in positive and negative controls.

4. Methylation Analysis

The research team used the optimized ddPCR assays to analyze methylation levels in 103 tumor samples and 109 normal adjacent tissue samples. The results showed significant methylation differences in all cancer types (except colorectal cancer) for target 1. Targets 2 and 3 also exhibited significant methylation differences across all cancer types, with lung and pancreatic cancers showing the most pronounced differences.

5. Sensitivity and Specificity Analysis

Using receiver-operator characteristic (ROC) analysis, the research team evaluated the sensitivity and specificity of each target. The results showed that target 1 alone had a sensitivity of 82.5% and a specificity of 98.2%, while target 2 alone had a sensitivity of 76.6% and a specificity of 93.6%. However, when combining targets 1 and 2, the sensitivity increased to 93.2%, with a specificity of 92.7%. Further inclusion of target 3 improved the overall sensitivity to 94.1%, with a specificity of 87.3%.

Conclusions and Significance

This study is the first to report a multi-cancer methylation detection method based on multiplex ddPCR technology. By combining three methylation targets, the method achieved high sensitivity and specificity in detecting eight cancer types, with an overall area under the curve (AUC) of 0.948. This achievement lays the foundation for the future development of liquid biopsy-based tools for early multi-cancer detection.

Research Highlights

1. Multi-Cancer Detection: This study is the first to utilize multiplex ddPCR technology for methylation detection across eight cancer types, demonstrating broad application potential.
2. High Sensitivity and Specificity: By combining multiple methylation targets, the detection sensitivity and specificity were significantly improved, particularly for lung and pancreatic cancers.
3. Optimized Detection Methods: The research team optimized the ddPCR assays through temperature and probe concentration gradients, providing a reference for developing more efficient multiplex detection methods in the future.

Future Prospects

The research team plans to further evaluate the method’s performance in formalin-fixed paraffin-embedded (FFPE) samples and liquid biopsies to validate its practical application in clinical diagnostics. Additionally, with the introduction of Bio-Rad’s QX600 system, future developments may enable multiplex detection of more targets, further enhancing sensitivity and specificity.

Acknowledgments

The research team thanks all patients and the Antwerp University Hospital Biobank for their contributions. Special thanks are extended to laboratory technician Anne Schepers for her work in sample processing. This research was supported by the University of Antwerp and the Research Foundation Flanders (FWO).

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This study provides a new tool and method for early cancer diagnosis, offering significant scientific and clinical value. In the future, with further technological advancements, this multi-cancer detection method is expected to be widely applied in clinical practice, helping more patients achieve early diagnosis and treatment.