Cell-free DNA (cfDNA) has emerged as a pivotal biomarker in precision oncology, enabling non-invasive cancer diagnosis, personalized treatment, and real-time monitoring of tumour dynamics. cfDNA provides insights into tumor burden, genetic alterations, and epigenetic modifications, offering advantages over traditional tissue biopsies. Recent advancements in detection technologies have enhanced the sensitivity and specificity of cfDNA analysis, facilitating early cancer detection, tracking minimal residual disease, and guiding treatment decisions. This review explores the various applications of cfDNA in oncology, highlighting its potential in revolutionizing cancer care and advancing liquid biopsy methodologies for routine clinical use.
Introduction
Precision oncology has fundamentally reshaped cancer diagnosis, treatment, and surveillance protocols in contemporary clinical practice. A particularly consequential development in this evolving landscape has been the emergence of cfDNA as a clinically actionable biomarker. These circulating DNA fragments in blood and other biological fluids yield molecular insights into tumour biology previously accessible only through invasive procedures. This review critically examines the multidimensional utility of cfDNA in modern oncology, focusing particularly on its applications in tumour evolution characterization.
Characterization and Oncological Relevance of cfDNA
(1) Definition and Origins
Cell-free DNA encompasses extracellular DNA fragments detectable in various biological fluids, including plasma, urine, and saliva. These fragments primarily originate from the apoptosis of normal cells and both apoptotic and necrotic processes in tumour tissues. The tumour-derived fraction, designated as circulating tumour DNA (ctDNA), mirrors the genetic architecture of the originating malignancy. Predominantly, cfDNA fragments range between 150-200 base pairs- corresponding to nucleosomal DNA packaging units- with exceptionally brief half-lives spanning 5-150 minutes. These distinctive biophysical properties render cfDNA particularly suitable for capturing rapid fluctuations in tumour dynamics.
(2) cfDNA in Cancer Contexts
In oncological environments, cfDNA functions as a molecular surrogate marker for tumor burden and genomic alterations. During cellular turnover, tumor cells release ctDNA, harbouring specific mutations, copy number variations, and characteristic methylation signatures reflective of the primary lesion.
The minimally invasive nature of cfDNA acquisition offers significant advantages over conventional tissue biopsies, which frequently suffer limitations, including procedural invasiveness, complication risks, and inadequate representation of tumour heterogeneity. Consequently, cfDNA analysis has become intrinsic to liquid biopsy methodologies, particularly in contexts where tissue acquisition is technically challenging or diagnostically insufficient.
Figure 1. cfDNA kinetics.(Â Moser, Tina et al, 2023)
cfDNA Detection Technologies and Data Processing
(1) Extraction and Sequencing Techniques
Numerous methodological approaches have been developed for cfDNA isolation from plasma specimens. These techniques target distinct cfDNA subpopulations, including total cfDNA, extended double-stranded fragments, and truncated single-stranded components. Recent advancements in sequencing technologies have significantly improved the detection of subtle cfDNA alterations. Enzyme-mediated methylation sequencing (EM-seq) and TAPS (Tet-assisted pyridine borane sequencing) offer notable advantages over conventional bisulfite sequencing, demonstrating superior sensitivity and specificity for detecting epigenetic modifications while preserving DNA integrity.
(2) Data Processing Strategies
Low-coverage sequencing of cfDNA introduces substantial analytical challenges, including compromised sensitivity and elevated false-positive rates. Robust pre-processing algorithms remain essential for optimizing diagnostic accuracy. Researchers recently demonstrated that an Autoencoder deep learning architecture substantially improves tumour detection capability through analysis of cfDNA transcription start site coverage patterns while minimizing sequencing artefacts.
(3) Correcting GC Content Bias
GC content bias represents a persistent analytical challenge in cfDNA analysis, potentially introducing distortions in sequencing outputs and systematic errors. Computational frameworks, including deepTools and Griffin, have been specifically developed to address these biases. In contexts involving rare variant detection in early-stage malignancies, these correctional approaches become particularly crucial for reliable variant calling.
Figure 2. Technologies and the analysis of cfDNA in precision oncology.(Â Zhang X et al. 2024)
Advances in Epigenetic cfDNA Liquid Biopsy
(1) DNA Methylation Detection
Multiple technical approaches have been validated for assessing methylation patterns in cfDNA, ranging from conventional PCR-based methods to advanced sequencing techniques such as Whole Genome Bisulfite Sequencing (WGBS) and Methylation-Specific PCR (MSP). These methods facilitate the detection of tumour-specific methylation signatures, which often emerge before genetic alterations during carcinogenesis.
(2) Analysis of cfDNA Fragmentation
The biophysical characteristics of cfDNA fragments, including size distribution profiles and terminal motifs, frequently reflect their cellular origins. Analytical methodologies, including Quantitative PCR (qPCR), Digital Droplet PCR (ddPCR), and Whole Genome Sequencing (WGS), enable the comprehensive characterization of these fragmentation patterns. Experimental evidence suggests that fragment size analysis considerably enhances early cancer detection sensitivity, particularly when integrated with genetic and epigenetic profiling.
(3) Chromatin Modifications and Tumor Epigenetics
Advanced techniques such as Chromatin Immunoprecipitation Sequencing (ChIP-seq), MNase-seq, and ATAC-seq enable comprehensive analysis of chromatin remodelling processes, including histone modifications and nucleosome positioning dynamics. These approaches offer deeper insights into tumour epigenetic landscapes, shedding light on the mechanisms driving tumorigenesis and therapeutic resistance.
Commercial cfDNA Detection Methods
(1) Immunoglobulin High-Throughput Sequencing (IgHTS)
IgHTS detects malignant lymphoid populations by identifying characteristic rearrangements in immunoglobulin and T-cell receptor genomic regions. This FDA-approved methodology has demonstrated significant clinical utility in diagnosing haematological malignancies, including B-cell Acute Lymphoblastic Leukemia, Multiple Myeloma, and Chronic Lymphocytic Leukemia. Despite its high analytical sensitivity, IgHTS applications are limited by specimen quality and potential contamination from non-malignant sources.
(2) Targeted Capture Assays
Targeted capture methodologies employ biotinylated oligonucleotide probes to isolate relevant genomic regions. Commercial platforms, including Guardant 360 and FoundationOne Liquid, have been optimized to simultaneously profile multiple cancer-associated genes, facilitating actionable mutation detection and recurrence monitoring when tissue biopsies prove impractical.
(3) Methylation-Based Detection
Methylation-focused approaches aim to identify aberrant epigenetic signatures associated with malignancy. These methodologies show significant promise for multi-cancer early detection (MCED), with reported sensitivities of 50–60% for lymphatic malignancies. However, their clinical utility in hematological malignancies remains under active investigation.
(4) PCR-Based and Amplification Techniques
Traditional PCR methodologies, qPCR, maintain widespread implementation due to cost-effectiveness and established protocols. These approaches demonstrate particular effectiveness in detecting specific alterations, exemplified by BCR-ABL1 fusion detection in Chronic Myelogenous leukemia. Recent methodological advances, including allele-specific PCR and digital droplet PCR, have substantially enhanced detection sensitivity for low-frequency variants.
Applications of cfDNA in Precision Oncology
(1) Early Cancer Detection
Perhaps the most transformative application of cfDNA analysis involves early cancer detection. By identifying genetic mutations and epigenetic modifications before clinical manifestation or radiographic visualization, cfDNA analysis provides unprecedented opportunities for early therapeutic intervention. This capability proves especially valuable for challenging malignancies, including pancreatic, ovarian, and certain pulmonary cancers, where early detection critically influences survival outcomes.
A prospective investigation involving 2,500 patients demonstrated that cfDNA methylation signatures could detect pancreatic adenocarcinoma approximately 14 months before conventional diagnostic methods, significantly improving surgical resectability rates.
(2) Monitoring Tumor Dynamics
Malignancies evolve continuously, often developing therapeutic resistance through the acquisition of specific genetic alterations. cfDNA analysis allows real-time monitoring of these evolutionary processes, offering insights into tumour heterogeneity and the emergence of resistant subclones. Longitudinal ctDNA monitoring enables clinicians to detect minimal residual disease and adjust treatment strategies accordingly.
(3) Personalized Treatment Strategies
The core principle of precision oncology is the customization of therapeutic strategies based on the molecular characteristics of an individual’s tumour. cfDNA analysis supports this approach by identifying actionable mutations and biomarkers without the need for invasive tissue sampling. This molecular insight informs the selection of optimal treatment strategies, including targeted therapies, conventional cytotoxic agents, and immunotherapies.
In bronchogenic carcinoma, cfDNA analysis can detect EGFR mutations that predict responsiveness to tyrosine kinase inhibitors. Similarly, in breast cancer, the identification of HER2 amplifications through cfDNA analysis may indicate a potential benefit from HER2-targeted therapies.
(4) Detection of Minimal Residual Disease and Recurrence
Detecting minimal residual disease-malignant cells that persist after seemingly successful treatment remains a significant clinical challenge. cfDNA analysis addresses this by identifying low concentrations of tumour DNA in circulation, even when the disease is undetectable by conventional imaging. This capability facilitates earlier recurrence detection and timely therapeutic intervention.
(5) Prognosis and Predicting Treatment Response
cfDNA quantification and characterization offer valuable prognostic insights, as ctDNA levels typically correlate with overall tumor burden and disease stage. Elevated ctDNA concentrations are generally associated with advanced disease and poorer clinical outcomes. Additionally, monitoring ctDNA dynamics during treatment provides real-time insights into therapeutic efficacy, often predicting clinical response before radiographic changes become evident.
This proves particularly valuable in immunotherapeutic contexts, where conventional imaging may initially suggest disease progression due to immune cell infiltration rather than genuine tumour expansion.
Figure 3. Clinical utilities of cfDNA fragmentomics.(Â Ding, S.C et al. 2022)
Challenges in cfDNA-Based Precision Oncology
(1) Technical Limitations
Despite substantial progress, significant technical challenges persist in cfDNA analysis. Detecting ctDNA in early-stage disease or minimal tumor burden contexts remains challenging due to limited ctDNA concentrations. Advanced analytical methodologies, including digital PCR, next-generation sequencing, and sensitive methylation assays, are essential but frequently involve substantial costs and technical complexity.
(2) Biological Variability
cfDNA characteristics demonstrate considerable variability depending on tumour biology, treatment status, and individual patient factors, complicating standardized interpretation. Additionally, cfDNA derived from non-malignant sources may interfere with ctDNA detection, potentially generating false-positive or false-negative results.
Through systematic validation studies, specific confounding variables requiring consideration during result interpretation have been identified, particularly in inflammatory conditions and following certain therapeutic interventions.
(3) Standardization and Validation
The widespread clinical implementation of cfDNA-based diagnostic approaches requires rigorous standardization of collection, processing, and analytical protocols. This includes consistent specimen handling procedures and the establishment of universally accepted thresholds for mutation detection and treatment monitoring.
(4) Ethical and Regulatory Considerations
Clinical implementation of cfDNA analysis raises important ethical and regulatory questions, particularly regarding data privacy and early detection clinical utility. Obtaining regulatory approval for cfDNA-based diagnostic tests represents a substantial challenge for broader clinical integration.
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Conclusion
Cell-free DNA represents a transformative biomarker in precision oncology, offering unprecedented opportunities for non-invasive disease monitoring and treatment guidance. From early detection to therapy selection and recurrence monitoring, cfDNA analysis has fundamentally altered cancer management approaches. Nevertheless, addressing current methodological limitations through continued technological innovation and rigorous validation remains essential for maximizing clinical impact.
As liquid biopsy methodologies continue advancing, cfDNA analysis will increasingly integrate into routine clinical practice, enabling truly personalized cancer care.
References:
- Moser T, Kühberger S, Lazzeri I, et al. Bridging biological cfDNA features and machine learning approaches[J]. Trends in Genetics, 2023, 39(4): 285-307.https://doi.org/10.1016/j.tig.2023.01.004
