Methylated circulating tumor DNA: technical challenges and clinical applications in non-small cell lung cancer patients—a narrative review

Introduction

Background

Lung cancer (LC) is one of the most common and deadliest cancers worldwide, with approximately 2.2 million new cases and 1.8 million deaths reported annually (1,2). Early diagnosis is essential for improving patient outcomes, but obtaining tissue biopsies can be invasive and sometimes unfeasible—particularly in cases of disease progression.

Rationale and knowledge gap

In recent years, liquid biopsy has emerged as a minimally invasive alternative, allowing the analysis of tumor-derived materials such as circulating tumor DNA (ctDNA) obtained from blood, respiratory fluids, cerebrospinal fluid, or urine (3-6). Today, the main use of ctDNA analysis is the identification of gene alterations that enable the use of targeted therapies (7-10). Numerous applications of ctDNA [such as monitoring treatment efficacy, detection of molecular residual disease (MRD), etc.] are currently under development (11-15). However, these approaches rely on the detection of genetic alterations, which is not always possible. In this context, epigenetic modifications—specifically DNA methylation—offer a promising complementary approach.

DNA methylation involves the addition of a methyl group to cytosines at CpG sites. It is a dynamic, lifelong process that regulates gene expression and chromatin organization, operating from embryonic development through cellular differentiation, senescence, and aging (16). Consequently, DNA methylation patterns change with age and are influenced by multiple factors, including environmental exposures, lifestyle, and accumulated molecular damage. Aberrant DNA methylation has been reported in numerous diseases, including endocrine, neurodegenerative, cardiovascular, and immune disorders (17,18). It is also frequently dysregulated in cancer (19). Global hypomethylation can promote genomic instability and oncogene activation, while hypermethylation of tumor suppressor genes can drive tumor progression (19-21). Importantly, DNA methylation patterns are preserved in ctDNA, making methylated ctDNA (Met-ctDNA) a potential biomarker for the non-invasive diagnosis and monitoring of LC (4,22,23). Nevertheless, several limitations remain, including the fact that ctDNA represents only a small and variable fraction of total circulating free DNA (cfDNA), depending on tumor stage and type. In addition, the existence of so-called non-shedder patients—whose tumors release little or no detectable ctDNA into biofluids—poses a significant challenge (24). Furthermore, non-cancer-related methylation changes may reduce assay specificity by introducing background noise. Consequently, methylation markers must be carefully selected and results interpreted with caution.

Objective

This review focuses on non-small cell lung cancer (NSCLC) and aims to: (I) describe the various analytical methods used to assess Met-ctDNA and their limitations; and (II) evaluate its clinical utility for diagnosis, prognosis, treatment response, and detection of minimal residual disease (MRD) or recurrence. We present this article in accordance with the Narrative Review reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1321/rc).

Methods

The search strategy is presented in Table 1. Briefly, databases (PubMed, ResearchGate, and Google Scholar) were searched for relevant articles using queries constructed with keywords and their combinations, including “ctDNA”, “liquid biopsies”, “methylation”, “non-small cell lung cancer”, “diagnosis”, “prognosis”, and “treatment”. Studies referring to cfDNA or circulating markers were included under the term met-ctDNA when they involved methylation analysis of tumor-derived DNA, since ctDNA is a tumor-specific subset of cfDNA and terminology often varies across studies. An illustration is presented in Table S1. To ensure a comprehensive review, no publication timeframe was imposed due to the recent emergence of ctDNA methylation studies. Only peer-reviewed articles published in English were included. Conflicting results were interpreted in the context of study design, sample size, tumor stage, and assay methodology. Methylation markers were emphasized based on their frequency of reporting in the literature and their potential clinical relevance in NSCLC; when available, markers supported by larger cohorts or validation studies were highlighted. Important information from the most relevant studies is summarized in Table S2.

DNA methylation detection

Several methods have been developed to detect ctDNA methylation. Some rely on bisulfite conversion, while others are bisulfite-independent (Figure 1) (23,25,26). These methods differ markedly in analytical sensitivity, DNA input requirements, throughput, and suitability for routine clinical deployment.

Figure 1 Principal techniques used to study methylation of circulating tumor DNA. Illustration created with BioRender (https://app.biorender.com/). MBD-seq, methyl-CpG-binding domain sequencing; MeDIP-seq, methylated DNA immunoprecipitation sequencing; MS-PCR, methylation specific polymerase chain reaction; MSRE-PCR, methylation-sensitive restriction enzyme digestion polymerase chain reaction.

Bisulfite-conversion methods (Figure 1A)

Sodium bisulfite conversion remains the gold standard and the most widely used method (23,26). This treatment converts unmethylated cytosines to uracils, while methylated cytosines remain unchanged. These changes can then be detected using the techniques described below (23,26).

Methylation-specific polymerase chain reaction (MS-PCR)

MS-PCR allows the analysis of DNA methylation in specific genes. It requires two sets of primers: one specific to the methylated sequence and the other to the unmethylated sequence. Two separate PCR reactions are performed, and the methylation status is determined based on the results. Several advanced approaches may be employed for the PCR, including quantitative real time PCR, digital PCR (dPCR) (23). Quantitative MS-PCR enables the detection of multiple regions but has lower sensitivity in cases of low ctDNA concentrations (27). dPCR partitions samples into thousands of droplets, allowing absolute quantification with high sensitivity—ideal for low ctDNA concentrations (27,28). While MS-PCR-based approaches are technically robust and cost-effective, they are limited by restricted multiplexing (most platforms rely on 4 to 6 fluorescent channels and may require compensation matrices) and by their reliance on predefined methylation markers. dPCR is of particular interest due to its superior analytical sensitivity, especially in ctDNA settings. However, these techniques are confined to small gene panels and do not enable genome-wide methylome profiling.

Pyrosequencing

It uses a single primer set to amplify the bisulfite-converted DNA, and the methylation status of individual CpG sites is then quantified directly through sequencing. It requires specific equipment and generates short reads, reducing accuracy for CpG sites distant from primer’s 3' end (26). Enhancing performance often requires ultrasensitive methods and noise-reduction strategies, though these can be costly (27,29).

Next-generation sequencing (NGS)

To increase genome coverage, several sequencing approaches are available (Table 2). Some target specific CpG-rich regions, such as reduced representation bisulfite sequencing (RRBS), which requires a high DNA input (26). RRBS uses methylation-insensitive restriction enzymes that recognize specific sequences (e.g., CCGG) and applies size selection before bisulfite conversion and high-throughput sequencing (30,31,36).

Targeted bisulfite sequencing (TBS-seq) focuses on CpGs within selected genes and regulatory regions (23,25,26,31). Heatrich-bisulfite sequencing (Heatrich-BS) uses heat denaturation and avoids DNA-sequencing bias (25). Whole-genome bisulfite sequencing (WGBS) enables comprehensive analysis across nearly all cytosines, including low-CpG-density and non-CpG regions (23). For example, a multi-cancer early detection (MCED) test using WGBS has shown promise for early solid tumor detection and patient prognosis (31,36,37).

Microarray hybridization

Genome-wide methylation patterns can also be assessed by microarray-based approaches (23,26). Commercially available methylation arrays, such as the Illumina Infinium Human Methylation 450K have been used to study ctDNA methylation (38,39). These methods require relatively high DNA input (26). Therefore, sequencing-based approaches may be preferred for ctDNA analysis, as they generally require lower DNA input.

Mass spectrometry-based bisulfite sequencing

New technologies, such as EpiTYPER (Agena Bioscience, San Diego, CA, USA) utilize mass spectrometry following bisulfite treatment. This technique includes several steps: bisulfite conversion, PCR amplification, transcription to single-stranded RNA and analysis via matrix assisted laser desorption-ionization time of flight (MALDI-TOF) mass spectrometry (40).

Limitations of bisulfite-based methods

Despite their widespread use, bisulfite-based methods have limitations. Bisulfite treatment can degrade DNA, reducing available material for amplification (41). This can be minimized using milder protocols, such as shorter incubation times (41). Additionally, these methods cannot distinguish between 5-methylcytosine and 5-hydroxymethylcytosine, potentially leading to false positives. Incomplete conversion of unmethylated cytosine can also affect results (26). To overcome these limitations, bisulfite-free methods have been developed.

Bisulfite-free methods (Figure 1B)

Methylation-sensitive restriction enzymes methods

These enzymes cleave DNA at specific CpG sites only if the cytosines are unmethylated. Because they recognize limited sequences, such methods are best for studying specific regions or genes (23,25). Their performance decreases in regions with intermediate methylation levels (23,25).

Enzymatic conversion of cytosines

This alternative uses specific enzymes to convert unmethylated cytosines to thymine followed by sequencing (enzymatic-methyl-seq) (25,32).

Enrichment-based methods

Also known as affinity-based techniques, these methods enrich methylated regions by removing unmethylated DNA (23,26) (Figure 1). Two main strategies exist. Methyl-CpG-binding domain sequencing (MBD-seq) uses proteins that bind methyl-CPG domains (31,33,42), methylated DNA immunoprecipitation sequencing (MeDIP-seq) uses anti-5 methyl cytosine antibodies for immunoprecipitation of methylated DNA (23,31,34,42,43). Both approaches are combined with NGS (23,25). MeDIP-seq seems to have lower performance and higher sequence bias than MBD-seq, potentially in link with the anti-methylcytosine antibodies used (42). Guardant Infinity^TM (Guardant Health, Palo Alto, CA) is a commercially available assay that employs a proprietary workflow incorporating bead-based methylation partitioning and targeted sequencing (44).

Nanopore-sequencing

This uses native DNA sequencing with Oxford Nanopore technology, which is constantly improving and provides performance equivalent to bisulfite sequencing (45,46). This technology is based on the different electrolytic currents generated by base modifications such as 5-methylcytosine (35,47). For example, a recent report using Nanopore sequencing of cerebrospinal fluid samples identified distinct methylation patterns in patients with LC brain metastases (48).

Table 2 summarizes the technical parameters of the main NGS-based DNA methylation profiling approaches, including commercially available reagents, DNA input requirements, coverage, sensitivity, and bioinformatic complexity. It provides a global overview, as some parameters may vary depending on the specific assay used, the genomic regions targeted, and the sequencing instrument. Some assays are currently being optimized for ctDNA and therefore may require lower DNA input.

Clinical applications

Diagnostic performance and tumor staging

Early detection of LC remains a challenge. Met-ctDNA represents a promising tool to overcome current diagnostic limitations. A meta-analysis, including 33 studies, highlighted the diagnostic potential of Met-ctDNA (49). Most studies used MS-PCR targeting genes such as SHOX2, RASSF1A and APC. Although sensitivity varied widely (8% to 93%; summary sensitivity: 46.9%), specificity was consistently high (69% to 100%; summary specificity: 92.9%) (49), underscoring its value as a confirmatory test. These discrepant results may be due to multiple factors, including analytical differences (e.g., use of serum instead of plasma in a few studies), methodological variability (dPCR, MS-PCR, pyrosequencing), geographical differences, and heterogeneity in patient staging. In addition, the type of control group and patient selection criteria may introduce bias, as most studies are retrospective.

To improve diagnostic accuracy, researchers have turned to ultra-sensitive technologies such as dPCR and artificial intelligence (AI) models. For example, Zhao et al. [2023] developed a multiplex droplet digital MS-PCR (mdMSP) platform targeting four gene promoters (SOX17, TAC1, CDO1, HOXA7), achieving a sensitivity of 90% and specificity of 82% for LC screening (50). Deep learning models, such as those proposed by Kim et al. (51) and the LUNG-TRAC framework (52), have also shown strong performance. Wang et al. [2023] introduced a dual-model system, lung cancer alertness by ctDNA methylation (LunaCAM), optimized separately for screening (LunaCAM-s) and diagnosis (LunaCAM-d), each trained and validated on hundreds of plasma samples (53).

Beyond diagnosis, Met-ctDNA may help assess tumor staging. For instance, p16 (CDKN2) methylation, RASSF1A hypermethylation and elevated RARB2 methylation levels have been associated with more advanced stages of NSCLC (54-56).

Prognostic value of Met-ctDNA

Met-ctDNA has been explored in various biological fluids, including plasma, serum, pleural fluid and urine, and several studies have demonstrated its association with clinical outcomes such as overall survival (OS) and progression-free survival (PFS). Detailed information from the most pertinent publications is presented in the supplemental table.

Researchers have investigated specific gene methylation markers to predict prognosis. BRMS1 promoter methylation, for instance, was associated with shorter OS and disease-free interval (DFI) in operable NSCLC, and also with reduced OS and PFS in advanced disease (57). Similarly, DCLK1 methylation correlated with poorer survival, regardless of cancer stage (58). Other individual markers associated with adverse outcomes include p16 (59), KMT2C (60), APC, RASSF1A, CDH13, and/or CDKN2A (61). In contrast, hypomethylation of WIF1 was linked to better outcomes (62), and lower SHOX2 methylation levels were modestly associated with longer survival (63). In advanced-stage patients, methylation of SHP1P2 and SOX17 was correlated with worse prognosis (64,65), while HOXA9 promoter methylation was linked to lower OS and PFS in stage III–IV NSCLC patients (66,67). These single-gene panels are cost-effective and easier to translate into clinical practice, but relying on a single marker may not capture the full tumor heterogeneity and can limit precise clinical stratification.

Thus, combining biomarkers further enhanced prognostic stratification. For example, RASSF1A hypermethylation with concurrent p63 negativity predicted poorer recurrence-free survival in node-negative stage I–II NSCLC (68). Combined methylation of CDO1 and HOXA9 indicated a worse prognosis, whereas PTGDR and AJAP1 methylation was associated to favorable outcomes (69).

Multi-gene approaches can also be applied, as subtype-specific analyses have revealed differential methylation patterns. Indeed, in lung adenocarcinoma (LUAD), methylation of MGMT, BRCA1, RARβ, p16/INKα (20), and SMAD7 (70) has been reported, while in LUSC, relevant markers included DMRTA2, PAX3, BARHL2 and LINC01475 (71). Larger-scale analyses using panels of genes have also provided prognostic insight (72). For instance, Liu et al. [2024] identified that low methylation of small nucleolar RNA (snoRNA) genes was associated with longer PFS in patients treated with EGFR TKIs, while high SNORD3F methylation predicted poor outcomes (73).

To quantify these findings, methylation scores have been developed. The tumor methylation score (TMS) and the tumor-informed methylation-based MRD (timMRD) score have both been associated with DFS and PFS (74,75). Chen et al. [2023] also showed that high postoperative timMRD scores were predictive of shorter DFS in LUAD stage I patients (74). Although multi-locus scores and AI-based models can improve diagnostic and prognostic accuracy, as well as discriminative power by integrating complementary biomarkers and complex correlations, their clinical implementation still faces significant challenges. These include high background noise due to the fragmented nature of cfDNA, tumor heterogeneity, limited biomarker sensitivity, platform biases, and batch effects, as well as the need for substantial resources. It is also worth noting the lack of harmonized workflows and variations in DNA methylation quantification depending on the software used. Moreover, the clinical benefit of these scores must be demonstrated beyond established prognostic factors through prospective studies with external validation and large sample sizes (76).

Treatment response

Treatment response in LC is typically assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, which rely on radiologic evaluation of tumor size, primarily via CT imaging (77,78). Several serum protein markers—such as carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and squamous cell carcinoma (SCC) antigen—may also be employed but they lack specificity and are often insufficient for early assessment of treatment efficacy. In this context, ctDNA has emerged as a valuable alternative biomarker. Its short half-life and ability to reflect both primary and metastatic tumor burden offer distinct advantages for dynamic treatment monitoring (79). Mutation-based ctDNA assays are currently used to monitor resistance mutations and quantify actionable alterations, but they may not capture response dynamics in patients without targetable mutations. Methylation-based assays may complement mutation-based approaches by reflecting broader epigenetic changes. Therefore, several studies have explored the utility of Met-ctDNA as a predictive marker of treatment response, as summarized in Table S2.

Response to chemotherapy could be assessed by tracking methylation dynamics, but prospective validation of these findings is urgently needed. Declining methylation levels of RARB2, RASSF1A, and SHOX2 after treatment have been observed in responders (55,63,80), and SHOX2 methylation level even reflected the degree of response (80). Methylation of 14-3-3σ was associated with better survival in patients treated with platinum-based regimens (81). Early demethylation of APC, RASSF1A, CDH13, or CDKN2A also predicted benefit from therapy (azacitidine, entinostat) (61), while persistent methylation of APC and RASSF1A as soon as 24 hours after treatment initiation was associated with increased cisplatin sensitivity (82). One study using bisulfite dPCR demonstrated that HOXA9 methylation was a poor prognostic factor, suggesting that patients with detectable HOXA9 methylation after the first treatment cycle could be considered for a switch to immunotherapy (66). Integrative model incorporating immune parameters, Met-ctDNA and chromosomal aneuploidy fraction could also predict pathological response (83).

In addition to chemotherapy, Met-ctDNA has also been evaluated for monitoring response to immunotherapy, particularly anti-PD-1 agents, which remains challenging in patients without actionable driver mutations (84). A combination approach integrating HOXA9 promoter methylation status with circulating interferon-gamma (IFN-γ) levels was used to stratify patients: patients with low IFN-γ concentrations and detectable methylated HOXA9 exhibited significantly shorter OS and PFS, whereas those with IFN-γ levels ≥250 pg/mL and undetectable HOXA9 methylation showed improved outcomes. These findings were further corroborated by radiological assessments, as CT scans performed after three cycles of treatment aligned with the methylation-based risk stratification (85). A more complex approach, using a TMS also demonstrated that variations in Met-ctDNA levels between baseline and 4–10 weeks after treatment initiation were sensitive predictors of therapeutic response (75).

In patients receiving EGFR tyrosine kinase inhibitors (TKIs), ctDNA profiling by NGS is commonly used to detect baseline mutations and emerging resistance mechanisms (86). In parallel, methylation-based biomarkers offer complementary insights, although they remain exploratory and require validation. For instance, unmethylated CHFR has been associated with prolonged survival in patients treated with EGFR TKIs (87), while elevated WIF1 methylation levels in ctDNA were linked to partial or absent responses to gefitinib (62). Additionally, methylation dynamics have been explored in the context of osimertinib, a third-generation TKI. Xia et al. [2019] developed a methylation ratio-based scoring model to stratify patients with stage IV LUAD into four groups based on their response. A significant decrease in methylation levels was indicative of therapeutic response, whereas increasing methylation levels were associated with resistance and disease progression (88). A methylated ctDNA signature has also been identified as a predictor of afatinib response (89). In ALK-positive NSCLC patients treated with ALK-directed TKI therapy, methylation scoring has also proven effective in tracking disease kinetics (22).

Despite promising results, optimal methylation marker panels are likely to be treatment-specific, since chemotherapy, immunotherapy, and targeted therapy probably induce distinct methylation dynamics. Moreover, most evidence remains exploratory and retrospective; therefore, prospective validation in larger cohorts with standardized assays and thresholds is required, along with demonstration of added clinical value beyond current radiological and mutation-based monitoring.

Met-ctDNA for detection of MRD

Recurrence is a major concern in surgically treated NSCLC patients, with reported rates ranging from 30% to 70%, depending on tumor stage and resection status (90). Even after complete resection, up to 45% of patients may experience relapse within five years (28). While radiologic imaging remains the standard method for monitoring disease progression (88), it often detects recurrence only after significant tumor burden has reaccumulated. Early identification of molecular relapse could greatly enhance postoperative management and enable more personalized follow-up strategies (28,90).

Met-ctDNA has been explored as a candidate biomarker for MRD detection and early relapse prediction. For example, dynamic changes in SHOX2 methylation levels have been shown to correlate with tumor size and radiological progression (63,80). More integrative methylation-based approaches, such as the TMS (75) and the timMRD score (74) have demonstrated high sensitivity in detecting molecular relapse, even before radiologic evidence appears. Prospective studies are needed to confirm these data and to establish the potential clinical impact.

Results across studies remain heterogeneous, with several analyses reporting negative or inconclusive findings. For example, some studies using urinary samples found no correlation between methylation of CDO1 or SOX17 and recurrence (91) or did not confirm the results on Met-ctDNA (92). In contrast, several plasma-based analyses have identified potential markers of early relapse. These include RARB2 methylation (55), increased detection of MCED ctDNA within two years post-surgery (93), and RASSF1A hypermethylation combined with p63 loss in node-negative stage I–II NSCLC (68). Elevated methylation of snoRNA genes has also been linked to recurrence in patients with EGFR-mutated tumors (73). These discrepancies likely reflect differences in sample type (urine versus plasma), assay sensitivity, methodological heterogeneity across studies, and patient selection criteria.

Met-ctDNA has also been investigated as a surrogate marker of metastatic potential after surgery. For instance, high levels of SMAD7 methylation—a negative regulator of TGF-β signaling—were observed in metastatic LUAD cases (70). Moreover, methylation scoring has shown potential for identifying early lymph node involvement and reflecting residual tumor burden and invasiveness (74,94).

Conclusions

Challenges and perspectives for clinical translation

Although Met-ctDNA is a promising non-invasive biomarker which could reflect tumor heterogeneity, some limitations remain. To date, and to the best of our knowledge, no method for detecting Met-ctDNA has been implemented in routine clinical practice for the management of cancer patients. Nevertheless, some approaches appear to be more promising than others for eventual clinical translation.

Targeted approaches generally represent the most clinically translatable solutions for ctDNA methylation analysis because they offer a favorable balance between sensitivity, cost, and feasibility. In particular, TBS-seq and methylation-specific PCR (MSP) enable high sensitivity for predefined biomarker regions while requiring relatively low DNA input and generating smaller datasets that are easier to process which may facilitate future clinical implementation (95).

Other cost-effective targeted strategies include enrichment-based methods such as circulating free MeDIP-seq (cfMeDIP-seq) and circulating free MBD-seq (cfMBD-seq), which avoid bisulfite conversion, preserve DNA integrity, and reduce sequencing requirements by focusing on methylated regions of interest. By contrast, whole-genome approaches such as WGBS, although powerful for discovery and MCED, remain limited in clinical implementation due to high sequencing cost, large data volume, and complex bioinformatic requirements, and they still require extensive prospective validation. Finally, nanopore sequencing is emerging as a promising alternative because it enables direct methylation detection on native DNA but still required further validation and standardization before routine clinical use.

Technical issues exist, including pre-analytical variability (plasma vs. serum, sample volume, extraction kits) and differences in detection technologies, significantly impact sensitivity and specificity (26,27,29,96). Moreover, multi-locus and multi-omics approaches, while informative, require advanced bioinformatics and remain difficult to standardize (27), and desperately need standardize workflow. Interpretation is also challenging, as ctDNA levels do not always correlate with tumor burden and performance at early stages is limited (97,98). Unlike mutation-based ctDNA approaches, which are highly specific but restricted to patients harboring detectable genomic alterations, methylation-based assays can be applied more broadly across LC populations. However, methylation signatures are influenced by aging, smoking, and other environmental exposures, and some markers may be shared across multiple cancer types, which can reduce specificity (24,99). To overcome these limitations, studies should use carefully selected markers, include age- and smoking-matched control cohorts, and validate findings prospectively. A validation dataset linking real methylation data to gene expression would also be valuable for confirming biomarker relevance. Integrating methylome profiling could as well strengthen clinical relevance. For instance, the human methylome atlas generated from healthy samples may be used to filter out non-tumor methylation signals and reduce false-positives (16). NSCLC-specific methylome studies can identify differentially methylated regions useful for early detection and risk stratification (100). A combined analysis of healthy and NSCLC methylomes could thus help distinguish tumor-specific methylation from background epigenetic variation and facilitate translation into clinical assays. Beyond epigenetics, integrating genomic alterations when present can improve specificity and provide a more comprehensive molecular map of NSCLC. In practice, crossover of genomic and epigenomic data may allow help the selection of a small, highly accurate gene panel that minimizes the impact of environmental confounders and enhances NSCLC-specificity. Such a panel could be implemented using targeted methods (e.g., MS-PCR or targeted NGS), which are more feasible for routine clinical use.

Importantly, clonal hematopoiesis (CH) represents another major confounding factor that can generate false-positive results, particularly in older patients. CH, including CH of indeterminate potential (CHIP) is associated with somatic variants frequently observed in cancers, and is driven by mutations in epigenetic modifiers such as DNMT3A, TET2, and ASXL1, which may alter methylation signals (101,102). Because cfDNA mainly originates from hematopoietic cells, CH-derived DNA can confound tumor-derived signals, which may impact outcome interpretation. Although CH has been mainly discussed in the context of genomic alteration detection, its effects on methylation-based assays should not be overlooked. In clinical practice, CH can be addressed by direct methods such as sequencing matched leukocyte DNA. A similar approach could be envisaged to methylation assays. Future work using bioinformatic algorithms to identify CH-derived variants, and DNA fragment patterns (shorter ctDNA, C>T transitions) may help distinguish CH from tumor signals.

Despite these challenges, Met-ctDNA holds major potential, particularly for patients without mutations and without easily accessible routine follow-up options. Once validated on larger prospective studies, machine learning-based methylation scores may support personalized strategies (103,104). It can also serve diagnostic, prognostic, and predictive roles, enabling early detection of progression (23,89). Treatment monitoring also represents a promising avenue for integration into routine patient care. This approach is based on the hypothesis that the kinetics of ctDNA monitoring using methylation, as is well documented for mutation detection techniques (105-111), should be able to rapidly identify treatment effects (Figure 2A). We can imagine following patients longitudinally by quantifying the methylation level of validated marker(s). Ideally, the marker would be positive at diagnosis, decrease after first-line treatment, and then follow different trajectories after a second-line therapy. In one scenario (green curve), the methylation level remains low or continues to decrease, suggesting a durable response or complete remission. In another scenario (red curve), the methylation level rises again after the second treatment, indicating treatment resistance or disease progression. The idea is to adapt the patient’s treatment based on the dynamics of Met-ctDNA, as illustrated in Figure 2B. If Met-ctDNA becomes undetectable in the bloodstream, treatment could be maintained or a de-escalation strategy initiated, depending on clinical context and the patient’s overall risk profile. Conversely, if Met-ctDNA levels remain stable or increase over time, this may indicate residual disease or treatment failure, and a change in therapeutic strategy may be required. In such cases, clinicians could consider intensifying therapy by adding another agent, switching to a different treatment class, or initiating a more aggressive approach, with the goal of overcoming resistance and preventing progression.

Figure 2 Potential use of Met-ctDNA monitoring to adjust treatment. (A) Theoretical Met-ctDNA kinetics of two patients during their treatment course. Both patients experienced a rapid and significant decrease in Met-ctDNA concentration, which was associated with a clinical response to first-line treatment. The subsequent increase in Met-ctDNA levels in both cases indicates recurrence or relapse. One patient showed a complete response to the second-line treatment (green), while the other (red) exhibited primary resistance and did not respond to the second-line therapy. (B) Algorithm illustrating how Met-ctDNA monitoring could be used to adapt treatment strategy. During treatment, if the concentration of Met-ctDNA decreases (or becomes undetectable), the treatment could be maintained or discontinued. Conversely, if the concentration of Met-ctDNA remains stable or increases, combining the current treatment with another therapy could be considered, or the therapeutic strategy could be completely changed. ctDNA, circulating tumor DNA; Met-ctDNA, DNA methylation of ctDNA.

Of course, robust standardization and validation in large prospective clinical trials are required before implementation in routine practice.

Finally, beyond plasma, exosomes and circulating tumor cells provide additional stable sources of DNA and RNA information (112-114), while RNA methylation and epigenetic therapies targeting DNA methyltransferases represent emerging avenues (115,116). Multi-omics can be particularly helpful, as demonstrated by a recent study combining methylated ctDNA and extracellular vesicle-derived microRNAs, which better elucidates the complexity of immunotherapy response and enables improved patient stratification (117).

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1321/rc

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Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1321/coif). The authors have no conflicts of interest to declare.

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