Circulating Tumor DNA Methylation Profiling for Early Diagnosis and Treatment Monitoring in Rhabdomyosarcoma: A Prospective Cohort Study

Circulating Tumor DNA Methylation Profiling for Early Diagnosis and Treatment Monitoring in Rhabdomyosarcoma: A Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Circulating Tumor DNA Methylation Profiling for Early Diagnosis and Treatment Monitoring in Rhabdomyosarcoma: A Prospective Cohort Study Quan Li, Shubin Luo, Sicong Jiang, Jiaxing Zhang, Hanming Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9067616/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Rhabdomyosarcoma (RMS) is the most prevalent soft tissue sarcoma in children and adolescents, with limited non-invasive tools available for early detection and real-time therapeutic monitoring. Circulating tumor DNA (ctDNA) methylation profiling via liquid biopsy represents a promising approach, yet its clinical utility in RMS remains inadequately characterized. Methods This prospective observational cohort study enrolled 86 patients aged 0 to 21 years with histopathologically confirmed RMS at a tertiary pediatric oncology center between January 2024 and December 2025. Plasma cfDNA was collected at diagnosis and at serial treatment time points. A targeted bisulfite sequencing panel interrogating five loci (RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13) was applied, and an aggregate ctDNA methylation score was derived. Associations with clinicopathological features, treatment response, and survival outcomes were analyzed. Results ctDNA methylation was detectable in 79 of 86 patients (91.9%) at diagnosis, with an AUC of 0.919 (95% CI 0.872 to 0.967). RASSF1A hypermethylation was identified in 68.6% of patients and was significantly associated with alveolar histology, metastatic disease, and high-risk classification. Molecular response by cycle 2 correlated strongly with imaging-based response (85.4% vs. 42.3%, p < 0.001). RASSF1A hypermethylation was independently prognostic for inferior event-free survival on multivariable analysis (hazard ratio 2.31, 95% CI 1.28 to 4.17, p = 0.006). The 18-month EFS was 44.2% versus 71.8% for RASSF1A-positive and RASSF1A-negative patients, respectively (log-rank p = 0.003). Conclusions Plasma ctDNA methylation profiling demonstrates high sensitivity for RMS detection and provides clinically meaningful prognostic and predictive information. Integration of this liquid biopsy approach into routine oncological care warrants prospective validation in larger multicenter cohorts. rhabdomyosarcoma circulating tumor DNA DNA methylation liquid biopsy RASSF1A early diagnosis treatment monitoring pediatric oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood and adolescence, accounting for approximately 3% to 4% of all pediatric malignancies [ 1 , 2 ]. In the United States, roughly 350 new cases are diagnosed annually, with a bimodal incidence distribution peaking in early childhood and again in early adolescence [ 3 ]. Histologically, RMS is categorized into embryonal, alveolar, and pleomorphic subtypes, with alveolar RMS carrying the most adverse prognosis, particularly in the context of PAX3/7-FOXO1 oncogenic fusions [ 4 , 5 ]. Despite multimodal therapeutic advances, overall survival ranges from greater than 90% in low-risk disease to less than 20% in high-risk and relapsed settings, and outcomes for patients with metastatic or fusion-positive disease remain substantially inferior [ 5 , 6 ]. Accurate molecular characterization is indispensable for risk stratification and treatment planning in RMS. Current standard practice relies on histopathological examination, immunohistochemistry, and FOXO1 fusion gene testing, the latter now recognized as the most important prognostic factor after metastatic status [ 5 , 6 ]. However, tissue biopsy captures only a spatially limited snapshot of tumor biology and cannot reflect dynamic changes during therapy [ 7 ]. Serial imaging with computed tomography and positron emission tomography carries cumulative radiation exposure in a pediatric population and lacks real-time molecular resolution [ 8 ]. These limitations highlight the clinical need for non-invasive, molecularly informative tools capable of continuous disease monitoring throughout the patient journey. Liquid biopsy through plasma cell-free DNA (cfDNA) analysis has emerged as a promising approach to tumor profiling and disease surveillance [ 9 , 10 ]. Circulating tumor DNA (ctDNA) carries genetic and epigenetic alterations reflective of the underlying malignancy, enabling applications spanning early diagnosis, risk stratification, treatment response monitoring, and detection of minimal residual disease [ 9 , 11 , 12 ]. Translation to pediatric oncology presents unique methodological challenges, as childhood tumors are more commonly characterized by chromosomal translocations and copy number alterations rather than the recurrent hotspot mutations exploited in adult ctDNA assays [ 11 , 13 ]. Among epigenetic targets amenable to plasma-based detection, aberrant promoter CpG island hypermethylation offers particular advantages. DNA methylation changes represent early and stable events in oncogenesis, are preserved in tumor-derived cfDNA, and can be sensitively quantified by bisulfite sequencing from low-input plasma samples [ 14 , 15 ]. The RASSF1A tumor suppressor, among the most frequently hypermethylated loci across pediatric solid tumors, has been identified in pediatric renal tumors and related entities where its promoter silencing correlates with aggressive biology and adverse outcomes [ 16 ]. The feasibility of detecting hypermethylated RASSF1A in plasma cfDNA from pediatric solid tumor patients has been established using droplet digital PCR, providing a foundation for broader multi-locus methylation panels [ 13 ]. Despite these signals, the clinical utility of a comprehensive multi-locus ctDNA methylation panel in RMS has not been prospectively evaluated. Prior liquid biopsy studies in RMS have been limited by small cohorts, single-marker approaches, or retrospective designs. The present prospective cohort study was therefore designed to evaluate the diagnostic performance, clinicopathological associations, prognostic significance, and longitudinal treatment monitoring utility of a targeted five-locus plasma ctDNA methylation panel interrogating RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13 in a pediatric RMS cohort. Materials and Methods Study Design and Patient Enrollment This prospective observational cohort study was conducted at a single tertiary pediatric oncology center between January 2024 and December 2025. Patients aged 0 to 21 years with newly diagnosed rhabdomyosarcoma confirmed by histopathological examination and immunohistochemistry were eligible for enrollment. Inclusion criteria required availability of adequate plasma samples at diagnosis and written informed consent obtained from patients or their legal guardians. Exclusion criteria included prior anticancer therapy, concurrent malignancy, active systemic infection known to interfere with cfDNA quantification, and insufficient sample volume precluding downstream molecular analysis. A total of 86 patients were ultimately enrolled after applying these criteria. The study was approved by the Institutional Review Board and Ethics Committee of the participating institution and was conducted in full accordance with the Declaration of Helsinki. All procedures involving human participants adhered to established ethical standards governing clinical research in pediatric oncology. Clinical Data Collection and Treatment Protocol Clinical Data Collection and Treatment Protocol Comprehensive clinicopathological data were systematically retrieved from electronic medical records, including patient age at diagnosis, sex, tumor site, tumor size, histological subtype, clinical risk group, TNM staging, and treatment allocation. Histological classification followed the International Classification of Rhabdomyosarcoma, distinguishing embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, and pleomorphic subtypes. Risk stratification was performed according to the criteria established by the European paediatric Soft tissue sarcoma Study Group, categorizing patients into low, intermediate, and high-risk groups. All patients received multimodal therapy consisting of standardized chemotherapy regimens based on vincristine, actinomycin-D, and ifosfamide, combined with local treatment comprising surgery and or radiotherapy as clinically indicated. Treatment response was evaluated according to Response Evaluation Criteria in Solid Tumors version 1.1 following every two cycles of chemotherapy, with responses classified as complete response, partial response, stable disease, or progressive disease. Plasma Sample Collection and cfDNA Extraction Peripheral blood samples of 8 to 10 mL were collected into EDTA-containing tubes at the following predefined time points: prior to initiation of any treatment at diagnosis, after completion of cycles 2, 4, and 6 of chemotherapy, within two weeks following definitive surgical resection where applicable, and at scheduled follow-up visits every three months thereafter until study completion or disease progression. Samples were processed within two hours of collection to minimize cfDNA degradation. Plasma was obtained by double centrifugation at 1,600 g for 10 minutes followed by 16,000 g for 10 minutes at 4 degrees Celsius and stored at minus 80 degrees Celsius until analysis. Cell-free DNA was extracted from 4 mL of plasma using the QIAamp Circulating Nucleic Acid Kit according to the manufacturer's protocol. Extracted cfDNA was quantified fluorometrically using the Qubit 4.0 Fluorometer with the dsDNA High Sensitivity Assay Kit, and fragment size distribution was assessed using the Agilent 2100 Bioanalyzer to confirm the characteristic nucleosomal ladder pattern indicative of apoptotic cell-free DNA. Bisulfite Conversion and Targeted Methylation Sequencing Bisulfite conversion of extracted cfDNA was performed using the EZ DNA Methylation-Gold Kit, with conversion efficiency verified by assessing bisulfite conversion controls incorporated into each batch. A custom targeted methylation sequencing panel was designed to interrogate promoter CpG regions of genes previously implicated in rhabdomyosarcoma pathogenesis, including RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13. Panel design utilized bisulfite-converted reference sequences with amplicons ranging from 80 to 150 base pairs to accommodate the fragmented nature of circulating cfDNA. Library preparation was performed using the KAPA HyperPlus Kit optimized for bisulfite-treated input material. Sequencing was carried out on the Illumina NovaSeq 6000 platform with paired-end 150 base pair reads, achieving a minimum target coverage of 1,000-fold per CpG site to ensure reliable detection of low-frequency methylation signals. Raw sequencing data underwent adapter trimming and quality filtering using Trim Galore, followed by alignment to the bisulfite-converted human reference genome GRCh38 using Bismark. Methylation calling was performed with the Bismark methylation extractor, and CpG methylation levels were expressed as beta values ranging from 0 to 1 representing the proportion of methylated reads at each interrogated site. ctDNA Methylation Quantification and Threshold Determination A healthy donor cohort comprising 40 age-matched pediatric controls without malignant or inflammatory conditions was recruited to establish baseline cfDNA methylation profiles and define diagnostic thresholds. ctDNA methylation positivity at each locus was defined as a beta value exceeding the mean plus three standard deviations observed in the healthy control population. An aggregate ctDNA methylation score was computed by integrating weighted methylation signals across all panel loci, with weighting coefficients determined by the degree of differential methylation between RMS patients and healthy controls as assessed in a preliminary training subset of 20 patients. The optimal aggregate score cutoff for clinical classification was determined using receiver operating characteristic curve analysis with Youden index maximization. For longitudinal monitoring, relative ctDNA methylation change between consecutive time points was calculated and a reduction exceeding 50 percent from baseline was defined as a molecular response, analogous to previously published frameworks in pediatric solid tumor ctDNA studies. Survival Analysis and Statistical Methods The primary clinical endpoints were event-free survival, defined as the interval from diagnosis to the first occurrence of disease progression, relapse, secondary malignancy, or death from any cause, and overall survival, defined as the interval from diagnosis to death from any cause. Patients without events were censored at the date of last follow-up. Survival curves were generated using the Kaplan-Meier method and compared between groups using the log-rank test. Univariable and multivariable Cox proportional hazards regression analyses were conducted to identify independent prognostic factors, with results expressed as hazard ratios with 95 percent confidence intervals. Correlation between ctDNA methylation parameters and categorical clinicopathological variables was evaluated using the chi-square test or Fisher exact test as appropriate. Continuous variables were compared between groups using the Mann-Whitney U test given non-normal data distributions confirmed by the Shapiro-Wilk test. Diagnostic performance of ctDNA methylation markers was assessed by computing sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve. Longitudinal ctDNA methylation trajectories were analyzed using linear mixed-effects models to account for repeated measures within individual patients. All statistical analyses were performed using R software version 4.3.2, and a two-sided p value less than 0.05 was considered statistically significant. Results Patient Characteristics and Enrollment Between January 2024 and December 2025, a total of 91 patients with newly diagnosed rhabdomyosarcoma were screened for eligibility. Five patients were excluded due to insufficient plasma volume at diagnosis (n = 3) or prior exposure to corticosteroid therapy affecting cfDNA integrity (n = 2), yielding a final analytic cohort of 86 patients. The baseline clinicopathological characteristics of the enrolled cohort are summarized in Table 1 . The median age at diagnosis was 7.3 years (interquartile range 3.8 to 13.1 years), with a slight male predominance (54 males, 62.8%). The most common primary tumor sites were the head and neck region (31 patients, 36.0%), followed by the genitourinary tract (22 patients, 25.6%), extremities (18 patients, 20.9%), and other or parameningeal locations (15 patients, 17.4%). Regarding histological subtypes, embryonal rhabdomyosarcoma was the most prevalent (49 patients, 57.0%), followed by alveolar rhabdomyosarcoma (31 patients, 36.0%) and pleomorphic subtype (6 patients, 7.0%). Risk stratification revealed that 18 patients (20.9%) were classified as low risk, 39 patients (45.3%) as intermediate risk, and 29 patients (33.7%) as high risk. Metastatic disease at diagnosis was documented in 34 patients (39.5%). Table 1 Baseline Clinicopathological Characteristics of the Study Cohort (n = 86) Characteristic n (%) or Median (IQR) Age at diagnosis, years 7.3 (3.8 to 13.1) Sex, male 54 (62.8%) Primary tumor site Head and neck 31 (36.0%) Genitourinary 22 (25.6%) Extremity 18 (20.9%) Other or parameningeal 15 (17.4%) Histological subtype Embryonal 49 (57.0%) Alveolar 31 (36.0%) Pleomorphic 6 (7.0%) Risk group Low 18 (20.9%) Intermediate 39 (45.3%) High 29 (33.7%) Metastatic disease at diagnosis 34 (39.5%) Tumor size greater than 5 cm 51 (59.3%) Surgical resection performed 61 (70.9%) Diagnostic Performance of ctDNA Methylation at Initial Diagnosis ctDNA methylation markers were detectable in 79 of 86 patients, corresponding to an overall sensitivity of 91.9% (95% confidence interval 84.0% to 96.7%). In the healthy control cohort of 40 pediatric donors, only one individual demonstrated a borderline elevation at the CDKN2A locus, yielding a panel specificity of 97.5% (95% CI 87.1% to 99.9%). The area under the receiver operating characteristic curve for the aggregate methylation score was 0.919 (95% CI 0.872 to 0.967), indicating robust discriminative capacity between RMS patients and healthy controls (Fig. 1 ). Among individual loci, RASSF1A hypermethylation demonstrated the highest detection rate, being present in 59 of 86 patients (68.6%), followed by MYOD1 (52 patients, 60.5%), CDKN2A (41 patients, 47.7%), PAX3 (38 patients, 44.2%), and CDH13 (29 patients, 33.7%). The distribution of methylation positivity across loci is presented in Table 2 . Notably, the combination of RASSF1A and MYOD1 methylation jointly detected ctDNA in 71 patients (82.6%), demonstrating that these two markers collectively captured the majority of cases with adequate sensitivity even without full panel deployment. Table 2 ctDNA Methylation Detection Rates at Individual Loci at Diagnosis Methylation Locus Positive Cases Detection Rate (%) Median Beta Value in Positive Cases (IQR) RASSF1A 59/86 68.6% 0.34 (0.19 to 0.57) MYOD1 52/86 60.5% 0.28 (0.14 to 0.49) CDKN2A 41/86 47.7% 0.22 (0.11 to 0.41) PAX3 38/86 44.2% 0.19 (0.09 to 0.38) CDH13 29/86 33.7% 0.17 (0.08 to 0.31) Any single locus positive 79/86 91.9% RASSF1A and MYOD1 combined 71/86 82.6% Association Between ctDNA Methylation and Clinicopathological Features RASSF1A hypermethylation was significantly more prevalent in patients with alveolar histology compared to embryonal subtype (83.9% vs. 57.1%, p = 0.009). Metastatic disease at diagnosis was strongly associated with ctDNA methylation positivity across the full panel, with detection rates of 97.1% in metastatic patients versus 82.7% in those with localized disease (p = 0.031). High-risk patients demonstrated significantly elevated aggregate methylation scores compared to low-risk patients (median score 0.61 vs. 0.29, p = 0.003). Tumor size exceeding 5 cm was associated with higher RASSF1A beta values (median 0.41 vs. 0.23, p = 0.017). No significant association was observed between ctDNA methylation positivity and patient sex or primary tumor site after correction for multiple comparisons. These associations are detailed in Table 3 . Table 3 Association Between ctDNA Methylation Status and Clinicopathological Variables Variable RASSF1A Positive n (%) RASSF1A Negative n (%) p value Histological subtype 0.009 Alveolar (n = 31) 26 (83.9%) 5 (16.1%) Embryonal (n = 49) 28 (57.1%) 21 (42.9%) Pleomorphic (n = 6) 5 (83.3%) 1 (16.7%) Metastatic disease 0.031 Yes (n = 34) 27 (79.4%) 7 (20.6%) No (n = 52) 32 (61.5%) 20 (38.5%) Risk group 0.003 High (n = 29) 24 (82.8%) 5 (17.2%) Intermediate (n = 39) 27 (69.2%) 12 (30.8%) Low (n = 18) 8 (44.4%) 10 (55.6%) Tumor size 0.017 Greater than 5 cm (n = 51) 39 (76.5%) 12 (23.5%) 5 cm or less (n = 35) 20 (57.1%) 15 (42.9%) Dynamic ctDNA Methylation Changes During Treatment Among the 79 patients with detectable ctDNA methylation at diagnosis, serial plasma samples were available for longitudinal analysis in 74 patients. The temporal trajectory of aggregate ctDNA methylation scores across treatment time points is illustrated in Fig. 2 . Following two cycles of chemotherapy, molecular response defined as a reduction exceeding 50% from baseline aggregate methylation score was observed in 48 of 74 patients (64.9%). Patients achieving molecular response by cycle 2 demonstrated significantly superior clinical response rates at subsequent imaging assessment compared to non-responders (partial or complete response in 85.4% vs. 42.3%, p < 0.001). In patients who underwent definitive surgical resection (n = 61), ctDNA methylation became undetectable within four weeks postoperatively in 39 patients (63.9%), whereas 22 patients (36.1%) retained detectable methylation signals despite macroscopically complete resection. Among those with persistent postoperative ctDNA methylation, 15 patients (68.2%) subsequently experienced disease relapse during the follow-up period, compared to only 8 of 39 patients (20.5%) with undetectable postoperative ctDNA (p = 0.001). In four patients, rising ctDNA methylation signals preceded radiologically confirmed disease progression by a median of 7.4 weeks (range 3.1 to 14.2 weeks), suggesting potential utility for anticipatory detection of relapse. Survival Analysis According to ctDNA Methylation Status With a median follow-up duration of 13.6 months (IQR 8.9 to 17.3 months) across the cohort, Kaplan-Meier survival analyses were performed according to baseline RASSF1A methylation status and molecular response status at cycle 2. The 18-month event-free survival rate for the entire cohort was 57.3% (95% CI 46.8% to 67.1%). Patients with RASSF1A hypermethylation at diagnosis had significantly inferior 18-month EFS compared to RASSF1A-negative patients (44.2% vs. 71.8%, log-rank p = 0.003, Fig. 3 ). The 18-month overall survival rates were 61.4% and 83.7% for RASSF1A-positive and RASSF1A-negative patients, respectively (p = 0.014). Stratification by molecular response at cycle 2 revealed a pronounced survival difference. Patients achieving molecular response demonstrated an 18-month EFS of 72.6% compared to 31.4% in non-responders (p < 0.001). On multivariable Cox regression analysis incorporating age, histological subtype, risk group, metastatic status, and RASSF1A methylation positivity, RASSF1A hypermethylation retained independent prognostic significance for EFS (hazard ratio 2.31, 95% CI 1.28 to 4.17, p = 0.006), as did metastatic disease at diagnosis (hazard ratio 2.87, 95% CI 1.59 to 5.18, p < 0.001) and alveolar histology (hazard ratio 1.94, 95% CI 1.07 to 3.51, p = 0.029). These multivariable results are summarized in Table 4 and Fig. 4 . Table 4 Multivariable Cox Regression Analysis for Event-Free Survival Variable Hazard Ratio 95% CI p value RASSF1A hypermethylation (positive vs. negative) 2.31 1.28 to 4.17 0.006 Metastatic disease at diagnosis (yes vs. no) 2.87 1.59 to 5.18 < 0.001 Alveolar histology (vs. embryonal) 1.94 1.07 to 3.51 0.029 High risk group (vs. low or intermediate) 1.73 0.94 to 3.19 0.078 Age greater than 10 years (vs. 10 or younger) 1.42 0.81 to 2.49 0.221 Tumor size greater than 5 cm (vs. 5 cm or less) 1.38 0.79 to 2.41 0.257 Discussion This prospective cohort study demonstrates that plasma ctDNA methylation profiling via a targeted five-locus bisulfite sequencing panel achieves high diagnostic sensitivity in pediatric RMS, provides robust prognostic stratification, and enables real-time monitoring of treatment response. The aggregate methylation score exhibited excellent discriminative capacity between RMS patients and healthy pediatric controls, with an area under the ROC curve consistent with the performance benchmarks increasingly reported for methylation-based liquid biopsies across multiple cancer types [ 17 , 18 ]. The diagnostic sensitivity of the five-locus panel observed in the present study compares favorably with prior single-locus approaches targeting RASSF1A alone. Van Zogchel et al. developed a droplet digital PCR assay for hypermethylated RASSF1A and demonstrated its feasibility in pediatric solid tumors including RMS, reporting detection in the majority of tested plasma samples at diagnosis [ 19 ]. Lak et al. subsequently analyzed cfDNA from patients enrolled in the EpSSG RMS-2005 study and confirmed that RASSF1A methylation in ctDNA was associated with inferior outcomes, with ctDNA levels decreasing during effective therapy and rising at relapse [ 20 ]. The present study extends these observations by integrating RASSF1A with four additional loci encoding myogenic and tumor suppressor regulatory proteins, thereby meaningfully improving sensitivity beyond what single-marker approaches can achieve. The combination of RASSF1A and MYOD1 methylation alone captured the majority of ctDNA-positive cases, suggesting a pragmatic two-locus screening strategy may be sufficient in settings where comprehensive panel sequencing is not feasible. The strong association between RASSF1A hypermethylation and alveolar histology observed in the present cohort aligns with established molecular biology of RMS. Alveolar RMS, predominantly characterized by PAX3/FOXO1 or PAX7/FOXO1 chromosomal translocations, is recognized as the more aggressive histological variant carrying inferior survival outcomes [ 21 ]. A French population-based registry study confirmed that PAX3-FOXO1 fusion status confers significantly higher mortality risk compared to PAX7-FOXO1 and fusion-negative tumors [ 22 ]. The enrichment of RASSF1A hypermethylation in alveolar versus embryonal histology observed here is biologically plausible: epigenetic silencing of tumor suppressor genes through promoter hypermethylation is a recognized hallmark of oncogenesis, and RASSF1A, as one of the most frequently inactivated tumor suppressor genes across human malignancies, is implicated in cell cycle regulation, apoptosis, and microtubule stability [ 23 ]. Hypermethylation of RASSF1A has been documented in pediatric renal tumors and associated with poor prognosis, further supporting its role as an epigenetic driver of aggressive phenotypes across pediatric cancers [ 24 ]. The independent prognostic significance of RASSF1A hypermethylation for event-free survival on multivariable analysis, retained after adjustment for established factors including metastatic status, histology, and risk group, represents one of the most clinically consequential findings of this study. Abbou et al. reported from the Children's Oncology Group that pretreatment ctDNA detectability was independently prognostic in intermediate-risk RMS, with fusion-negative patients carrying detectable ctDNA at diagnosis exhibiting markedly worse event-free and overall survival [ 25 ]. The present data complement these mutation-based findings by demonstrating that methylation-based ctDNA positivity, specifically RASSF1A hypermethylation, similarly refines risk stratification beyond conventional clinicopathological parameters. Importantly, the COG's 2023 research blueprint now formally incorporates longitudinal ctDNA sampling into prospective trial designs, acknowledging ctDNA's potential to refine molecular risk stratification and guide therapy intensification or de-escalation [ 26 ]. The current findings provide additional biological rationale for including methylation markers in such frameworks, as they may capture tumor biology not reflected by mutation-based or fusion-detection strategies alone, particularly in fusion-negative patients where actionable genomic targets are sparse. The longitudinal monitoring data constitute another significant contribution of this work. The strong concordance between early molecular response after two chemotherapy cycles and subsequent imaging-defined response supports the utility of ctDNA methylation dynamics as a real-time surrogate of treatment efficacy. This observation is consistent with data from Ewing sarcoma, where ctDNA quantification by digital droplet PCR tracked closely with tumor burden and response to chemotherapy, with undetectable ctDNA correlating with ongoing remission across multiple years of follow-up [ 27 ]. Parallel findings from sarcoma cohorts at other institutions have indicated that a substantial logarithmic reduction in ctDNA within the first two treatment cycles reliably distinguishes responders from progressors [ 28 ]. The ability to obtain meaningful molecular response data after just two chemotherapy cycles is of particular clinical importance in pediatric oncology, where minimizing radiation exposure from serial imaging is a recognized priority. The postoperative ctDNA data further underscore the clinical utility of the panel. Persistent ctDNA methylation signals after macroscopically complete resection were strongly associated with subsequent disease relapse, echoing a growing body of evidence in adult solid tumors that postoperative ctDNA positivity identifies minimal residual disease and predicts recurrence months before radiological detection [ 29 , 30 ]. A 2024 feasibility study demonstrated that tumor-informed ctDNA assays successfully detected radiographic disease in the vast majority of pediatric solid tumor patients with active disease, with all relapse events preceded by or concurrent with ctDNA positivity [ 31 ]. The median lead time of ctDNA rise before radiologically confirmed progression observed in the present cohort is consistent with intervals reported in other pediatric and adult series, and is clinically meaningful as it creates a potential window for pre-emptive therapeutic intervention. Several limitations of the present study warrant consideration. The single-center design and relatively modest cohort size constrain the generalizability of findings, particularly with respect to subgroup-specific analyses such as histological variants and risk groups. The study period of approximately 18 months limits the maturity of survival data, and longer follow-up will be required to fully characterize the prognostic value of ctDNA methylation for overall survival and late relapse events. The panel was designed and weighted using a preliminary training subset derived from the same institution, introducing potential overfitting that must be evaluated through external validation. Furthermore, the absence of fusion gene status profiling precludes direct comparison of methylation-based and mutation-based ctDNA strategies within the same cohort, a gap that future studies should address. The healthy control cohort, while appropriately age-matched, was of limited size, and a larger normative dataset would enhance the precision of threshold determination. Finally, although the panel achieved high sensitivity at the aggregate level, the seven patients in whom ctDNA was not detectable at diagnosis represent a biologically important subset whose molecular characteristics should be interrogated in future studies using more comprehensive profiling approaches such as whole-methylome sequencing. Future research should prioritize multicenter prospective validation in larger, geographically diverse cohorts, and should explore whether methylation-based ctDNA response data can prospectively inform treatment adaptation, particularly the de-intensification of therapy in molecular responders and escalation in non-responders. Integration of methylation profiling with fusion gene detection and copy number alteration analysis within a unified liquid biopsy framework may offer the most complete molecular portrait of tumor burden and dynamics across the full biological spectrum of RMS subtypes [ 32 , 33 ]. Conclusion Plasma ctDNA methylation profiling using a targeted five-locus panel demonstrates high sensitivity and specificity for RMS detection at diagnosis, and RASSF1A hypermethylation emerges as an independent prognostic biomarker for inferior event-free survival. Dynamic methylation changes during treatment provide early molecular response assessment that strongly anticipates imaging-based outcomes, while persistent postoperative ctDNA signals identify patients at elevated risk of relapse. These findings support the prospective integration of ctDNA methylation profiling into multicenter pediatric oncology trials to validate its clinical utility for risk-adapted therapeutic decision-making in RMS. Declarations Funding None. Conflicts of Interest The authors declared that they have no conflicts of interest regarding this work. Clinical trial number Not applicable. Ethics approval and consent to participate This study was approved by the Institutional Review Board and Ethics Committee of The Second Affiliated Hospital of Nanchang University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study (or from their legal guardians for minors). References Kaseb, H., Kuhn, J., Gasalberti, D. P., & Babiker, H. M. (2024). Rhabdomyosarcoma. In StatPearls [Internet] . StatPearls Publishing. Zarrabi, A., Perrin, D., Kavoosi, M., Sommer, M., Sezen, S., Mehrbod, P., … Ghavami, S. (2023). 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Liquid biopsies: Potential and challenges. International Journal of Cancer , 148 (3), 528–545. Bartolomucci, A., Nobrega, M., Ferrier, T., Dickinson, K., Kaorey, N., Nadeau, A., … Burnier, J. V. (2025). Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology. NPJ precision oncology , 9 (1), 84. van Zogchel, L. M., Lak, N. S., Verhagen, O. J., Tissoudali, A., Gussmalla Nuru, M., Gelineau, N. U., … Tytgat, G. A. (2021). Novel circulating hypermethylated RASSF1A ddPCR for liquid biopsies in patients with pediatric solid tumors. JCO precision oncology , 5 , 1738–1748. Li, L., & Sun, Y. (2024). Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges. MedComm , 5 (11), e766. Wang, B., Wang, M., Lin, Y., Zhao, J., Gu, H., & Li, X. (2024). Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management. Clinical Chemistry and Laboratory Medicine (CCLM) , 62 (11), 2111–2127. Ueno-Yokohata, H., Okita, H., Nakasato, K., & Kiyokawa, N. (2023). Hypermethylation of RASSF1A gene in pediatric rhabdoid tumor of the kidney and clear cell sarcoma of the kidney. Pediatric Blood & Cancer , 70 (2), e30058. Li, L., & Sun, Y. (2024). Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges. MedComm , 5 (11), e766. Janssen, F. W., Lak, N. S., Janda, C. Y., Kester, L. A., Meister, M. T., Merks, J. H., … Looijenga, L. H. (2024). A comprehensive overview of liquid biopsy applications in pediatric solid tumors. NPJ precision oncology , 8 (1), 172. van Zogchel, L. M., Lak, N. S., Verhagen, O. J., Tissoudali, A., Gussmalla Nuru, M., Gelineau, N. U., … Tytgat, G. A. (2021). Novel circulating hypermethylated RASSF1A ddPCR for liquid biopsies in patients with pediatric solid tumors. JCO precision oncology , 5 , 1738–1748. Lak, N. S., van Zogchel, L. M., Zappeij-Kannegieter, L., Javadi, A., Van Paemel, R., Vandeputte, C., … Stutterheim, J. (2023). Cell-free DNA as a diagnostic and prognostic biomarker in pediatric rhabdomyosarcoma. JCO Precision Oncology , 7 , e2200113. Dehner, C. A., Rudzinski, E. R., & Davis, J. L. (2024). Rhabdomyosarcoma: Updates on classification and the necessity of molecular testing beyond immunohistochemistry. Human Pathology , 147 , 72–81. Raze, T., Lapouble, E., Lacour, B., Guissou, S., Defachelles, A. S., Gaspar, N., … Desandes, E. (2023). PAX–FOXO1 fusion status in children and adolescents with alveolar rhabdomyosarcoma: Impact on clinical, pathological, and survival features. Pediatric Blood & Cancer , 70 (4), e30228. Ueno-Yokohata, H., Okita, H., Nakasato, K., & Kiyokawa, N. (2023). Hypermethylation of RASSF1A gene in pediatric rhabdoid tumor of the kidney and clear cell sarcoma of the kidney. Pediatric Blood & Cancer , 70 (2), e30058. Gelineau, N. U., van Barneveld, A., Samim, A., Van Zogchel, L., Lak, N., Tas, M. L., … Tytgat, G. (2023). Case series on clinical applications of liquid biopsy in pediatric solid tumors: towards improved diagnostics and disease monitoring. Frontiers in oncology , 13 , 1209150. Abbou, S., Klega, K., Tsuji, J., Tanhaemami, M., Hall, D., Barkauskas, D. A., … Crompton, B. D. (2023). Circulating tumor DNA is prognostic in intermediate-risk rhabdomyosarcoma: a report from the Children's Oncology Group. Journal of Clinical Oncology , 41 (13), 2382–2393. Oberoi, S., Crane, J. N., Haduong, J. H., Rudzinski, E. R., Wolden, S. L., Dasgupta, R., … Children's Oncology Group Soft Tissue Sarcoma Committee. (2023). Children's Oncology Group's 2023 blueprint for research: soft tissue sarcomas. Pediatric blood & cancer , 70 , e30556. Seidel, M. G., Kashofer, K., Moser, T., Thueringer, A., Liegl-Atzwanger, B., Leithner, A., … Heitzer, E. (2022). Clinical implementation of plasma cell-free circulating tumor DNA quantification by digital droplet PCR for the monitoring of Ewing sarcoma in children and adolescents. Frontiers in Pediatrics , 10 , 926405. Bui, N. Q., Nemat-Gorgani, N., Subramanian, A., Torres, I. A., Lohman, M., Sears, T. J., … Moding, E. J. (2023). Monitoring sarcoma response to immune checkpoint inhibition and local cryotherapy with circulating tumor DNA analysis. Clinical Cancer Research , 29 (14), 2612–2620. Zhu, L., Xu, R., Yang, L., Shi, W., Zhang, Y., Liu, J., … Bing, P. (2023). Minimal residual disease (MRD) detection in solid tumors using circulating tumor DNA: a systematic review. Frontiers in Genetics , 14 , 1172108. Chen, H., & Zhou, Q. (2023). Detecting liquid remnants of solid tumors treated with curative intent: Circulating tumor DNA as a biomarker of minimal residual disease. Oncology Reports , 49 (5), 106. Mitchell, M. (2024). Feasibility of tumor-informed circulating tumor DNA (ctDNA) for molecular residual disease (MRD) assessment in pediatric patients with solid tumors. de Traux de Wardin, H., Dermawan, J. K., Merlin, M. S., Wexler, L. H., Orbach, D., Vanoli, F., … Antonescu, C. R. (2023). Sequential genomic analysis using a multisample/multiplatform approach to better define rhabdomyosarcoma progression and relapse. NPJ Precision Oncology , 7 (1), 96. Tombolan, L., Rossi, E., Binatti, A., Zin, A., Manicone, M., Facchinetti, A., … Bisogno, G. (2022). Clinical significance of circulating tumor cells and cell-free DNA in pediatric rhabdomyosarcoma. Molecular Oncology , 16 (10), 2071–2085. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Shubin","middleName":"","lastName":"Luo","suffix":""},{"id":616717935,"identity":"ae861fc4-f46c-4a9d-a8d0-9f372b921711","order_by":2,"name":"Sicong Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIie3PMQrCQBCF4Q0LppmwbRZyiASLIAT1KJFAKg+QciCSSrBdLyKWs6Rd9AC5xAZbC1sbYdJZ7FfPD2+ECIJ/FF88+a4CpZCbABXWuDbThrhJul+PyWmscqyZRWlETQk+IRcU+fnISLIJifR9glKi1NcbZ1dmkQo3wQZpJRNWkjaCDsMDcqrZSSvIDrQkAScsuga0sT3zl/gsX+9uu1Oqt37mJF8iXHYfBEEQ/PYBnTc66RGaGWsAAAAASUVORK5CYII=","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":true,"prefix":"","firstName":"Sicong","middleName":"","lastName":"Jiang","suffix":""},{"id":616717936,"identity":"9212a67c-7b7f-4991-be97-a8b178b85d47","order_by":3,"name":"Jiaxing Zhang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxing","middleName":"","lastName":"Zhang","suffix":""},{"id":616717937,"identity":"a421f94b-fc35-45f5-b650-146d2cdec6e5","order_by":4,"name":"Hanming Lin","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Hanming","middleName":"","lastName":"Lin","suffix":""},{"id":616717938,"identity":"765d3a25-d01b-405b-ad6a-4709ebbbb638","order_by":5,"name":"Yiman Xie","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yiman","middleName":"","lastName":"Xie","suffix":""},{"id":616717939,"identity":"a8d03e30-c8f6-4812-b519-d7ea3d4ed5c6","order_by":6,"name":"Mengmeng Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-03-09 02:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9067616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9067616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106382794,"identity":"3a819083-45d2-42c3-a5dd-70eb1806fbd3","added_by":"auto","created_at":"2026-04-08 05:30:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for cDNA Methylation Panel\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9067616/v1/eb2333eca3f4384eccc3ef69.png"},{"id":106382792,"identity":"447989e7-80cb-4a1f-b94b-5dcfc0b830ab","added_by":"auto","created_at":"2026-04-08 05:30:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic cDNA Methylation Score During Treatment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9067616/v1/6c4c2b8959862a25a66c8059.png"},{"id":106382795,"identity":"da66426b-5719-4577-bccc-f60da2cd08e1","added_by":"auto","created_at":"2026-04-08 05:30:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier EFS Curves by RASSF1A Methylation Status\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9067616/v1/6dc33b2484015dda06e1eb0c.png"},{"id":106382793,"identity":"0fc66150-745e-4d2f-bf2b-aa2d3b1f8add","added_by":"auto","created_at":"2026-04-08 05:30:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of ctDNA Methylation Beta Values Across Five Panel Loci at Diagnosis, Stratified by Histological Subtype and Risk Group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9067616/v1/8abd1e022d09ee8c33a98355.png"},{"id":106404514,"identity":"086cd673-9be7-47fc-8a7d-5d86a9edb5eb","added_by":"auto","created_at":"2026-04-08 09:16:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1139861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9067616/v1/0d965c0a-3e33-4aa9-84de-79bc60e481d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating Tumor DNA Methylation Profiling for Early Diagnosis and Treatment Monitoring in Rhabdomyosarcoma: A Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood and adolescence, accounting for approximately 3% to 4% of all pediatric malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the United States, roughly 350 new cases are diagnosed annually, with a bimodal incidence distribution peaking in early childhood and again in early adolescence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Histologically, RMS is categorized into embryonal, alveolar, and pleomorphic subtypes, with alveolar RMS carrying the most adverse prognosis, particularly in the context of PAX3/7-FOXO1 oncogenic fusions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite multimodal therapeutic advances, overall survival ranges from greater than 90% in low-risk disease to less than 20% in high-risk and relapsed settings, and outcomes for patients with metastatic or fusion-positive disease remain substantially inferior [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate molecular characterization is indispensable for risk stratification and treatment planning in RMS. Current standard practice relies on histopathological examination, immunohistochemistry, and FOXO1 fusion gene testing, the latter now recognized as the most important prognostic factor after metastatic status [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, tissue biopsy captures only a spatially limited snapshot of tumor biology and cannot reflect dynamic changes during therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Serial imaging with computed tomography and positron emission tomography carries cumulative radiation exposure in a pediatric population and lacks real-time molecular resolution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These limitations highlight the clinical need for non-invasive, molecularly informative tools capable of continuous disease monitoring throughout the patient journey.\u003c/p\u003e \u003cp\u003eLiquid biopsy through plasma cell-free DNA (cfDNA) analysis has emerged as a promising approach to tumor profiling and disease surveillance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Circulating tumor DNA (ctDNA) carries genetic and epigenetic alterations reflective of the underlying malignancy, enabling applications spanning early diagnosis, risk stratification, treatment response monitoring, and detection of minimal residual disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Translation to pediatric oncology presents unique methodological challenges, as childhood tumors are more commonly characterized by chromosomal translocations and copy number alterations rather than the recurrent hotspot mutations exploited in adult ctDNA assays [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong epigenetic targets amenable to plasma-based detection, aberrant promoter CpG island hypermethylation offers particular advantages. DNA methylation changes represent early and stable events in oncogenesis, are preserved in tumor-derived cfDNA, and can be sensitively quantified by bisulfite sequencing from low-input plasma samples [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The RASSF1A tumor suppressor, among the most frequently hypermethylated loci across pediatric solid tumors, has been identified in pediatric renal tumors and related entities where its promoter silencing correlates with aggressive biology and adverse outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The feasibility of detecting hypermethylated RASSF1A in plasma cfDNA from pediatric solid tumor patients has been established using droplet digital PCR, providing a foundation for broader multi-locus methylation panels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these signals, the clinical utility of a comprehensive multi-locus ctDNA methylation panel in RMS has not been prospectively evaluated. Prior liquid biopsy studies in RMS have been limited by small cohorts, single-marker approaches, or retrospective designs. The present prospective cohort study was therefore designed to evaluate the diagnostic performance, clinicopathological associations, prognostic significance, and longitudinal treatment monitoring utility of a targeted five-locus plasma ctDNA methylation panel interrogating RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13 in a pediatric RMS cohort.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patient Enrollment\u003c/h2\u003e \u003cp\u003eThis prospective observational cohort study was conducted at a single tertiary pediatric oncology center between January 2024 and December 2025. Patients aged 0 to 21 years with newly diagnosed rhabdomyosarcoma confirmed by histopathological examination and immunohistochemistry were eligible for enrollment. Inclusion criteria required availability of adequate plasma samples at diagnosis and written informed consent obtained from patients or their legal guardians. Exclusion criteria included prior anticancer therapy, concurrent malignancy, active systemic infection known to interfere with cfDNA quantification, and insufficient sample volume precluding downstream molecular analysis. A total of 86 patients were ultimately enrolled after applying these criteria. The study was approved by the Institutional Review Board and Ethics Committee of the participating institution and was conducted in full accordance with the Declaration of Helsinki. All procedures involving human participants adhered to established ethical standards governing clinical research in pediatric oncology.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Data Collection and Treatment Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eClinical Data Collection and Treatment Protocol\u003c/div\u003e \u003cp\u003eComprehensive clinicopathological data were systematically retrieved from electronic medical records, including patient age at diagnosis, sex, tumor site, tumor size, histological subtype, clinical risk group, TNM staging, and treatment allocation. Histological classification followed the International Classification of Rhabdomyosarcoma, distinguishing embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, and pleomorphic subtypes. Risk stratification was performed according to the criteria established by the European paediatric Soft tissue sarcoma Study Group, categorizing patients into low, intermediate, and high-risk groups. All patients received multimodal therapy consisting of standardized chemotherapy regimens based on vincristine, actinomycin-D, and ifosfamide, combined with local treatment comprising surgery and or radiotherapy as clinically indicated. Treatment response was evaluated according to Response Evaluation Criteria in Solid Tumors version 1.1 following every two cycles of chemotherapy, with responses classified as complete response, partial response, stable disease, or progressive disease.\u003c/p\u003e\n\u003ch3\u003ePlasma Sample Collection and cfDNA Extraction\u003c/h3\u003e\n\u003cp\u003ePeripheral blood samples of 8 to 10 mL were collected into EDTA-containing tubes at the following predefined time points: prior to initiation of any treatment at diagnosis, after completion of cycles 2, 4, and 6 of chemotherapy, within two weeks following definitive surgical resection where applicable, and at scheduled follow-up visits every three months thereafter until study completion or disease progression. Samples were processed within two hours of collection to minimize cfDNA degradation. Plasma was obtained by double centrifugation at 1,600 g for 10 minutes followed by 16,000 g for 10 minutes at 4 degrees Celsius and stored at minus 80 degrees Celsius until analysis. Cell-free DNA was extracted from 4 mL of plasma using the QIAamp Circulating Nucleic Acid Kit according to the manufacturer's protocol. Extracted cfDNA was quantified fluorometrically using the Qubit 4.0 Fluorometer with the dsDNA High Sensitivity Assay Kit, and fragment size distribution was assessed using the Agilent 2100 Bioanalyzer to confirm the characteristic nucleosomal ladder pattern indicative of apoptotic cell-free DNA.\u003c/p\u003e\n\u003ch3\u003eBisulfite Conversion and Targeted Methylation Sequencing\u003c/h3\u003e\n\u003cp\u003eBisulfite conversion of extracted cfDNA was performed using the EZ DNA Methylation-Gold Kit, with conversion efficiency verified by assessing bisulfite conversion controls incorporated into each batch. A custom targeted methylation sequencing panel was designed to interrogate promoter CpG regions of genes previously implicated in rhabdomyosarcoma pathogenesis, including RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13. Panel design utilized bisulfite-converted reference sequences with amplicons ranging from 80 to 150 base pairs to accommodate the fragmented nature of circulating cfDNA. Library preparation was performed using the KAPA HyperPlus Kit optimized for bisulfite-treated input material. Sequencing was carried out on the Illumina NovaSeq 6000 platform with paired-end 150 base pair reads, achieving a minimum target coverage of 1,000-fold per CpG site to ensure reliable detection of low-frequency methylation signals. Raw sequencing data underwent adapter trimming and quality filtering using Trim Galore, followed by alignment to the bisulfite-converted human reference genome GRCh38 using Bismark. Methylation calling was performed with the Bismark methylation extractor, and CpG methylation levels were expressed as beta values ranging from 0 to 1 representing the proportion of methylated reads at each interrogated site.\u003c/p\u003e\n\u003ch3\u003ectDNA Methylation Quantification and Threshold Determination\u003c/h3\u003e\n\u003cp\u003eA healthy donor cohort comprising 40 age-matched pediatric controls without malignant or inflammatory conditions was recruited to establish baseline cfDNA methylation profiles and define diagnostic thresholds. ctDNA methylation positivity at each locus was defined as a beta value exceeding the mean plus three standard deviations observed in the healthy control population. An aggregate ctDNA methylation score was computed by integrating weighted methylation signals across all panel loci, with weighting coefficients determined by the degree of differential methylation between RMS patients and healthy controls as assessed in a preliminary training subset of 20 patients. The optimal aggregate score cutoff for clinical classification was determined using receiver operating characteristic curve analysis with Youden index maximization. For longitudinal monitoring, relative ctDNA methylation change between consecutive time points was calculated and a reduction exceeding 50 percent from baseline was defined as a molecular response, analogous to previously published frameworks in pediatric solid tumor ctDNA studies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis and Statistical Methods\u003c/h2\u003e \u003cp\u003eThe primary clinical endpoints were event-free survival, defined as the interval from diagnosis to the first occurrence of disease progression, relapse, secondary malignancy, or death from any cause, and overall survival, defined as the interval from diagnosis to death from any cause. Patients without events were censored at the date of last follow-up. Survival curves were generated using the Kaplan-Meier method and compared between groups using the log-rank test. Univariable and multivariable Cox proportional hazards regression analyses were conducted to identify independent prognostic factors, with results expressed as hazard ratios with 95 percent confidence intervals. Correlation between ctDNA methylation parameters and categorical clinicopathological variables was evaluated using the chi-square test or Fisher exact test as appropriate. Continuous variables were compared between groups using the Mann-Whitney U test given non-normal data distributions confirmed by the Shapiro-Wilk test. Diagnostic performance of ctDNA methylation markers was assessed by computing sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve. Longitudinal ctDNA methylation trajectories were analyzed using linear mixed-effects models to account for repeated measures within individual patients. All statistical analyses were performed using R software version 4.3.2, and a two-sided p value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics and Enrollment\u003c/h2\u003e \u003cp\u003eBetween January 2024 and December 2025, a total of 91 patients with newly diagnosed rhabdomyosarcoma were screened for eligibility. Five patients were excluded due to insufficient plasma volume at diagnosis (n\u0026thinsp;=\u0026thinsp;3) or prior exposure to corticosteroid therapy affecting cfDNA integrity (n\u0026thinsp;=\u0026thinsp;2), yielding a final analytic cohort of 86 patients. The baseline clinicopathological characteristics of the enrolled cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age at diagnosis was 7.3 years (interquartile range 3.8 to 13.1 years), with a slight male predominance (54 males, 62.8%). The most common primary tumor sites were the head and neck region (31 patients, 36.0%), followed by the genitourinary tract (22 patients, 25.6%), extremities (18 patients, 20.9%), and other or parameningeal locations (15 patients, 17.4%). Regarding histological subtypes, embryonal rhabdomyosarcoma was the most prevalent (49 patients, 57.0%), followed by alveolar rhabdomyosarcoma (31 patients, 36.0%) and pleomorphic subtype (6 patients, 7.0%). Risk stratification revealed that 18 patients (20.9%) were classified as low risk, 39 patients (45.3%) as intermediate risk, and 29 patients (33.7%) as high risk. Metastatic disease at diagnosis was documented in 34 patients (39.5%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Clinicopathological Characteristics of the Study Cohort (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%) or Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (3.8 to 13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther or parameningeal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbryonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlveolar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleomorphic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic disease at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size greater than 5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical resection performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance of ctDNA Methylation at Initial Diagnosis\u003c/h2\u003e \u003cp\u003ectDNA methylation markers were detectable in 79 of 86 patients, corresponding to an overall sensitivity of 91.9% (95% confidence interval 84.0% to 96.7%). In the healthy control cohort of 40 pediatric donors, only one individual demonstrated a borderline elevation at the CDKN2A locus, yielding a panel specificity of 97.5% (95% CI 87.1% to 99.9%). The area under the receiver operating characteristic curve for the aggregate methylation score was 0.919 (95% CI 0.872 to 0.967), indicating robust discriminative capacity between RMS patients and healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong individual loci, RASSF1A hypermethylation demonstrated the highest detection rate, being present in 59 of 86 patients (68.6%), followed by MYOD1 (52 patients, 60.5%), CDKN2A (41 patients, 47.7%), PAX3 (38 patients, 44.2%), and CDH13 (29 patients, 33.7%). The distribution of methylation positivity across loci is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, the combination of RASSF1A and MYOD1 methylation jointly detected ctDNA in 71 patients (82.6%), demonstrating that these two markers collectively captured the majority of cases with adequate sensitivity even without full panel deployment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ectDNA Methylation Detection Rates at Individual Loci at Diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylation Locus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian Beta Value in Positive Cases (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRASSF1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (0.19 to 0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYOD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (0.14 to 0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22 (0.11 to 0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.09 to 0.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.08 to 0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny single locus positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRASSF1A and MYOD1 combined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between ctDNA Methylation and Clinicopathological Features\u003c/h2\u003e \u003cp\u003eRASSF1A hypermethylation was significantly more prevalent in patients with alveolar histology compared to embryonal subtype (83.9% vs. 57.1%, p\u0026thinsp;=\u0026thinsp;0.009). Metastatic disease at diagnosis was strongly associated with ctDNA methylation positivity across the full panel, with detection rates of 97.1% in metastatic patients versus 82.7% in those with localized disease (p\u0026thinsp;=\u0026thinsp;0.031). High-risk patients demonstrated significantly elevated aggregate methylation scores compared to low-risk patients (median score 0.61 vs. 0.29, p\u0026thinsp;=\u0026thinsp;0.003). Tumor size exceeding 5 cm was associated with higher RASSF1A beta values (median 0.41 vs. 0.23, p\u0026thinsp;=\u0026thinsp;0.017). No significant association was observed between ctDNA methylation positivity and patient sex or primary tumor site after correction for multiple comparisons. These associations are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between ctDNA Methylation Status and Clinicopathological Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRASSF1A Positive n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRASSF1A Negative n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlveolar (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (83.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbryonal (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleomorphic (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater than 5 cm (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 cm or less (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDynamic ctDNA Methylation Changes During Treatment\u003c/h2\u003e \u003cp\u003eAmong the 79 patients with detectable ctDNA methylation at diagnosis, serial plasma samples were available for longitudinal analysis in 74 patients. The temporal trajectory of aggregate ctDNA methylation scores across treatment time points is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Following two cycles of chemotherapy, molecular response defined as a reduction exceeding 50% from baseline aggregate methylation score was observed in 48 of 74 patients (64.9%). Patients achieving molecular response by cycle 2 demonstrated significantly superior clinical response rates at subsequent imaging assessment compared to non-responders (partial or complete response in 85.4% vs. 42.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn patients who underwent definitive surgical resection (n\u0026thinsp;=\u0026thinsp;61), ctDNA methylation became undetectable within four weeks postoperatively in 39 patients (63.9%), whereas 22 patients (36.1%) retained detectable methylation signals despite macroscopically complete resection. Among those with persistent postoperative ctDNA methylation, 15 patients (68.2%) subsequently experienced disease relapse during the follow-up period, compared to only 8 of 39 patients (20.5%) with undetectable postoperative ctDNA (p\u0026thinsp;=\u0026thinsp;0.001). In four patients, rising ctDNA methylation signals preceded radiologically confirmed disease progression by a median of 7.4 weeks (range 3.1 to 14.2 weeks), suggesting potential utility for anticipatory detection of relapse.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis According to ctDNA Methylation Status\u003c/h2\u003e \u003cp\u003eWith a median follow-up duration of 13.6 months (IQR 8.9 to 17.3 months) across the cohort, Kaplan-Meier survival analyses were performed according to baseline RASSF1A methylation status and molecular response status at cycle 2. The 18-month event-free survival rate for the entire cohort was 57.3% (95% CI 46.8% to 67.1%). Patients with RASSF1A hypermethylation at diagnosis had significantly inferior 18-month EFS compared to RASSF1A-negative patients (44.2% vs. 71.8%, log-rank p\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 18-month overall survival rates were 61.4% and 83.7% for RASSF1A-positive and RASSF1A-negative patients, respectively (p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratification by molecular response at cycle 2 revealed a pronounced survival difference. Patients achieving molecular response demonstrated an 18-month EFS of 72.6% compared to 31.4% in non-responders (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On multivariable Cox regression analysis incorporating age, histological subtype, risk group, metastatic status, and RASSF1A methylation positivity, RASSF1A hypermethylation retained independent prognostic significance for EFS (hazard ratio 2.31, 95% CI 1.28 to 4.17, p\u0026thinsp;=\u0026thinsp;0.006), as did metastatic disease at diagnosis (hazard ratio 2.87, 95% CI 1.59 to 5.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and alveolar histology (hazard ratio 1.94, 95% CI 1.07 to 3.51, p\u0026thinsp;=\u0026thinsp;0.029). These multivariable results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox Regression Analysis for Event-Free Survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRASSF1A hypermethylation (positive vs. negative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 to 4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic disease at diagnosis (yes vs. no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 to 5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlveolar histology (vs. embryonal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 to 3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk group (vs. low or intermediate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 to 3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge greater than 10 years (vs. 10 or younger)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 to 2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size greater than 5 cm (vs. 5 cm or less)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 to 2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective cohort study demonstrates that plasma ctDNA methylation profiling via a targeted five-locus bisulfite sequencing panel achieves high diagnostic sensitivity in pediatric RMS, provides robust prognostic stratification, and enables real-time monitoring of treatment response. The aggregate methylation score exhibited excellent discriminative capacity between RMS patients and healthy pediatric controls, with an area under the ROC curve consistent with the performance benchmarks increasingly reported for methylation-based liquid biopsies across multiple cancer types [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnostic sensitivity of the five-locus panel observed in the present study compares favorably with prior single-locus approaches targeting RASSF1A alone. Van Zogchel et al. developed a droplet digital PCR assay for hypermethylated RASSF1A and demonstrated its feasibility in pediatric solid tumors including RMS, reporting detection in the majority of tested plasma samples at diagnosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lak et al. subsequently analyzed cfDNA from patients enrolled in the EpSSG RMS-2005 study and confirmed that RASSF1A methylation in ctDNA was associated with inferior outcomes, with ctDNA levels decreasing during effective therapy and rising at relapse [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The present study extends these observations by integrating RASSF1A with four additional loci encoding myogenic and tumor suppressor regulatory proteins, thereby meaningfully improving sensitivity beyond what single-marker approaches can achieve. The combination of RASSF1A and MYOD1 methylation alone captured the majority of ctDNA-positive cases, suggesting a pragmatic two-locus screening strategy may be sufficient in settings where comprehensive panel sequencing is not feasible.\u003c/p\u003e \u003cp\u003eThe strong association between RASSF1A hypermethylation and alveolar histology observed in the present cohort aligns with established molecular biology of RMS. Alveolar RMS, predominantly characterized by PAX3/FOXO1 or PAX7/FOXO1 chromosomal translocations, is recognized as the more aggressive histological variant carrying inferior survival outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A French population-based registry study confirmed that PAX3-FOXO1 fusion status confers significantly higher mortality risk compared to PAX7-FOXO1 and fusion-negative tumors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The enrichment of RASSF1A hypermethylation in alveolar versus embryonal histology observed here is biologically plausible: epigenetic silencing of tumor suppressor genes through promoter hypermethylation is a recognized hallmark of oncogenesis, and RASSF1A, as one of the most frequently inactivated tumor suppressor genes across human malignancies, is implicated in cell cycle regulation, apoptosis, and microtubule stability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hypermethylation of RASSF1A has been documented in pediatric renal tumors and associated with poor prognosis, further supporting its role as an epigenetic driver of aggressive phenotypes across pediatric cancers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe independent prognostic significance of RASSF1A hypermethylation for event-free survival on multivariable analysis, retained after adjustment for established factors including metastatic status, histology, and risk group, represents one of the most clinically consequential findings of this study. Abbou et al. reported from the Children's Oncology Group that pretreatment ctDNA detectability was independently prognostic in intermediate-risk RMS, with fusion-negative patients carrying detectable ctDNA at diagnosis exhibiting markedly worse event-free and overall survival [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The present data complement these mutation-based findings by demonstrating that methylation-based ctDNA positivity, specifically RASSF1A hypermethylation, similarly refines risk stratification beyond conventional clinicopathological parameters. Importantly, the COG's 2023 research blueprint now formally incorporates longitudinal ctDNA sampling into prospective trial designs, acknowledging ctDNA's potential to refine molecular risk stratification and guide therapy intensification or de-escalation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The current findings provide additional biological rationale for including methylation markers in such frameworks, as they may capture tumor biology not reflected by mutation-based or fusion-detection strategies alone, particularly in fusion-negative patients where actionable genomic targets are sparse.\u003c/p\u003e \u003cp\u003eThe longitudinal monitoring data constitute another significant contribution of this work. The strong concordance between early molecular response after two chemotherapy cycles and subsequent imaging-defined response supports the utility of ctDNA methylation dynamics as a real-time surrogate of treatment efficacy. This observation is consistent with data from Ewing sarcoma, where ctDNA quantification by digital droplet PCR tracked closely with tumor burden and response to chemotherapy, with undetectable ctDNA correlating with ongoing remission across multiple years of follow-up [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Parallel findings from sarcoma cohorts at other institutions have indicated that a substantial logarithmic reduction in ctDNA within the first two treatment cycles reliably distinguishes responders from progressors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The ability to obtain meaningful molecular response data after just two chemotherapy cycles is of particular clinical importance in pediatric oncology, where minimizing radiation exposure from serial imaging is a recognized priority.\u003c/p\u003e \u003cp\u003eThe postoperative ctDNA data further underscore the clinical utility of the panel. Persistent ctDNA methylation signals after macroscopically complete resection were strongly associated with subsequent disease relapse, echoing a growing body of evidence in adult solid tumors that postoperative ctDNA positivity identifies minimal residual disease and predicts recurrence months before radiological detection [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A 2024 feasibility study demonstrated that tumor-informed ctDNA assays successfully detected radiographic disease in the vast majority of pediatric solid tumor patients with active disease, with all relapse events preceded by or concurrent with ctDNA positivity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The median lead time of ctDNA rise before radiologically confirmed progression observed in the present cohort is consistent with intervals reported in other pediatric and adult series, and is clinically meaningful as it creates a potential window for pre-emptive therapeutic intervention.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study warrant consideration. The single-center design and relatively modest cohort size constrain the generalizability of findings, particularly with respect to subgroup-specific analyses such as histological variants and risk groups. The study period of approximately 18 months limits the maturity of survival data, and longer follow-up will be required to fully characterize the prognostic value of ctDNA methylation for overall survival and late relapse events. The panel was designed and weighted using a preliminary training subset derived from the same institution, introducing potential overfitting that must be evaluated through external validation. Furthermore, the absence of fusion gene status profiling precludes direct comparison of methylation-based and mutation-based ctDNA strategies within the same cohort, a gap that future studies should address. The healthy control cohort, while appropriately age-matched, was of limited size, and a larger normative dataset would enhance the precision of threshold determination. Finally, although the panel achieved high sensitivity at the aggregate level, the seven patients in whom ctDNA was not detectable at diagnosis represent a biologically important subset whose molecular characteristics should be interrogated in future studies using more comprehensive profiling approaches such as whole-methylome sequencing.\u003c/p\u003e \u003cp\u003eFuture research should prioritize multicenter prospective validation in larger, geographically diverse cohorts, and should explore whether methylation-based ctDNA response data can prospectively inform treatment adaptation, particularly the de-intensification of therapy in molecular responders and escalation in non-responders. Integration of methylation profiling with fusion gene detection and copy number alteration analysis within a unified liquid biopsy framework may offer the most complete molecular portrait of tumor burden and dynamics across the full biological spectrum of RMS subtypes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePlasma ctDNA methylation profiling using a targeted five-locus panel demonstrates high sensitivity and specificity for RMS detection at diagnosis, and RASSF1A hypermethylation emerges as an independent prognostic biomarker for inferior event-free survival. Dynamic methylation changes during treatment provide early molecular response assessment that strongly anticipates imaging-based outcomes, while persistent postoperative ctDNA signals identify patients at elevated risk of relapse. These findings support the prospective integration of ctDNA methylation profiling into multicenter pediatric oncology trials to validate its clinical utility for risk-adapted therapeutic decision-making in RMS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflicts of interest regarding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board and Ethics Committee of The Second Affiliated Hospital of Nanchang University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study (or from their legal guardians for minors).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaseb, H., Kuhn, J., Gasalberti, D. P., \u0026amp; Babiker, H. M. (2024). Rhabdomyosarcoma. In \u003cem\u003eStatPearls [Internet]\u003c/em\u003e. StatPearls Publishing.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarrabi, A., Perrin, D., Kavoosi, M., Sommer, M., Sezen, S., Mehrbod, P., \u0026hellip; Ghavami, S. (2023). 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Rhabdomyosarcoma: Updates on classification and the necessity of molecular testing beyond immunohistochemistry. \u003cem\u003eHuman Pathology\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e, 72\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaze, T., Lapouble, E., Lacour, B., Guissou, S., Defachelles, A. S., Gaspar, N., \u0026hellip; Desandes, E. (2023). PAX\u0026ndash;FOXO1 fusion status in children and adolescents with alveolar rhabdomyosarcoma: Impact on clinical, pathological, and survival features. \u003cem\u003ePediatric Blood \u0026amp; Cancer\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(4), e30228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUeno-Yokohata, H., Okita, H., Nakasato, K., \u0026amp; Kiyokawa, N. (2023). 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Clinical significance of circulating tumor cells and cell-free DNA in pediatric rhabdomyosarcoma. \u003cem\u003eMolecular Oncology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(10), 2071\u0026ndash;2085.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"rhabdomyosarcoma, circulating tumor DNA, DNA methylation, liquid biopsy, RASSF1A, early diagnosis, treatment monitoring, pediatric oncology","lastPublishedDoi":"10.21203/rs.3.rs-9067616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9067616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRhabdomyosarcoma (RMS) is the most prevalent soft tissue sarcoma in children and adolescents, with limited non-invasive tools available for early detection and real-time therapeutic monitoring. Circulating tumor DNA (ctDNA) methylation profiling via liquid biopsy represents a promising approach, yet its clinical utility in RMS remains inadequately characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective observational cohort study enrolled 86 patients aged 0 to 21 years with histopathologically confirmed RMS at a tertiary pediatric oncology center between January 2024 and December 2025. Plasma cfDNA was collected at diagnosis and at serial treatment time points. A targeted bisulfite sequencing panel interrogating five loci (RASSF1A, MYOD1, PAX3, CDKN2A, and CDH13) was applied, and an aggregate ctDNA methylation score was derived. Associations with clinicopathological features, treatment response, and survival outcomes were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ectDNA methylation was detectable in 79 of 86 patients (91.9%) at diagnosis, with an AUC of 0.919 (95% CI 0.872 to 0.967). RASSF1A hypermethylation was identified in 68.6% of patients and was significantly associated with alveolar histology, metastatic disease, and high-risk classification. Molecular response by cycle 2 correlated strongly with imaging-based response (85.4% vs. 42.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RASSF1A hypermethylation was independently prognostic for inferior event-free survival on multivariable analysis (hazard ratio 2.31, 95% CI 1.28 to 4.17, p\u0026thinsp;=\u0026thinsp;0.006). The 18-month EFS was 44.2% versus 71.8% for RASSF1A-positive and RASSF1A-negative patients, respectively (log-rank p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlasma ctDNA methylation profiling demonstrates high sensitivity for RMS detection and provides clinically meaningful prognostic and predictive information. Integration of this liquid biopsy approach into routine oncological care warrants prospective validation in larger multicenter cohorts.\u003c/p\u003e","manuscriptTitle":"Circulating Tumor DNA Methylation Profiling for Early Diagnosis and Treatment Monitoring in Rhabdomyosarcoma: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 05:30:45","doi":"10.21203/rs.3.rs-9067616/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-29T02:49:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T15:48:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T07:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252998671923748519159019483265752382148","date":"2026-04-11T18:15:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256897303124275087476378038139298942122","date":"2026-04-11T12:12:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T00:57:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229593390875034363117666152314548398153","date":"2026-04-09T18:49:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337784285125370380237615608657873108811","date":"2026-04-09T14:14:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138372647384708256091205704954842283037","date":"2026-04-02T15:23:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T06:42:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T11:11:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T11:11:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-03-09T02:35:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6bf98ed5-4436-476f-b66e-445184cc6c3d","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T05:30:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 05:30:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9067616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9067616","identity":"rs-9067616","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}