Digital PCR-based detection and quantification of circulating tumor DNA as a biomarker for early cancer relapse monitoring

Project Abstract

Digital PCR (dPCR) has emerged as a highly sensitive molecular technique for the detection and quantification of circulating tumor DNA (ctDNA) in peripheral blood, offering a minimally invasive approach for tracking tumor dynamics and informing early relapse monitoring. This study evaluates the diagnostic performance of dPCR–based ctDNA assays in patients with solid tumors undergoing curative-intent therapy, aiming to establish thresholds for relapse risk stratification and to compare its predictive value with conventional imaging and serological markers. A prospective cohort of cancer patients was enrolled, with serial plasma samples collected pre-treatment, during treatment, and at defined post-therapy intervals. ctDNA was enriched using tumor-informed and tumor-agnostic dPCR assays targeting patient-specific somatic mutations and common cancer-associated alterations, enabling absolute quantification of fractional abundance and copy number of ctDNA fragments. Analytical validation assessed limit of detection, linearity, precision, and robustness against pre-analytical variables such as blood collection tubes, processing delays, and cfDNA fragmentation. Clinical validation integrated ctDNA metrics with clinical outcomes, radiologic assessments, and disease-free survival over a 24-month follow-up, employing time-to-event analyses and joint modeling to capture longitudinal ctDNA trajectories. The results demonstrated that ctDNA positivity post-therapy was a significant predictor of relapse, with higher sensitivity observed in tumor-informed panels while tumor-agnostic approaches provided broader applicability across tumor types. Quantitative ctDNA dynamics, including sustained elevation, transient bursts, and clearance patterns, correlated with radiographic progression, occult metastasis, and minimal residual disease status, often preceding imaging-detected relapse by several weeks to months. Multivariate models incorporating ctDNA kinetics, baseline tumor burden, histology, and treatment response yielded superior relapse prediction compared with standard biomarkers alone, enabling individualized surveillance intervals and timely therapeutic interventions. The study also explored analytic parameters that influence clinical interpretability, such as variant allele frequency cutoffs, assay specificity for clonal hematopoiesis, and the impact of clonal evolution on longitudinal monitoring. Importantly, ctDNA monitoring identified subclinical relapse in a subset of patients who were radiographically negative at relapse suspicion, prompting early treatment modification and potentially improved outcomes. These findings support the integration of dPCR-based ctDNA assays into post-treatment surveillance workflows, providing a scalable, high-sensitivity tool for early relapse detection, risk stratification, and personalized management in diverse solid tumors. Limitations include heterogeneity in tumor mutational landscapes, potential false negatives in ctDNA-low tumors, and the need for standardization of pre-analytical and analytical protocols across centers. Future work will focus on large-scale multicenter validation, integration with imaging and clinical decision support systems, and cost-effectiveness analyses to optimize adoption in routine oncology care.

Project Overview

What This Project Is About

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A straightforward look at using a precise DNA measurement method to detect tiny fragments shed by cancer cells into the blood. The project tests whether this method can reliably identify and count these fragments to signal relapse earlier than current tests.

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The Problem It Addresses

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Cancer relapse is often detected after it has started to grow again, limiting treatment options. Standard tests may miss early signs. This project explores whether measuring circulating tumor DNA (ctDNA) with a sensitive technique can provide an earlier, noninvasive warning, helping patients receive timely care.

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Objectives of the Project

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1. Explain what ctDNA is and why it matters for relapse monitoring.
2. Describe the digital PCR method and its advantages for detecting small DNA amounts.
3. Assess the feasibility of using ctDNA levels as an early relapse marker in a simplified model or dataset.
4. Identify potential challenges and limitations in real-world use.
5. Propose a basic workflow for implementing ctDNA testing in a clinical or research setting.

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What You Will Do Step by Step

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Review background literature on ctDNA and digital PCR; design a small study or data analysis plan; learn or simulate the digital PCR workflow; analyze sample data to look for ctDNA signals; interpret results in relation to relapse timing; present findings and limitations.

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Expected Outcome

Expect to demonstrate that digital PCR can detect low levels of ctDNA and show how changes over time might indicate relapse earlier than some imaging tests. The project will outline practical steps for adoption and highlight what further research is needed to translate findings into clinical practice.