Unveiling the Prognostic Power of Circulating Tumor DNA in Multiple Myeloma: A Systematic Review and Updated Meta‐Analysis

ABSTRACT

Circulating tumor DNA (ctDNA) serves as a non‐invasive liquid biopsy technique, offering potential utility in the prognostic evaluation of multiple myeloma (MM). In this study, we performed a systematic review and updated meta‐analysis to analyze the association between ctDNA and the prognosis of patients diagnosed with MM. A comprehensive literature search was performed across PubMed, Embase, Web of Science, and the Cochrane Library to retrieve relevant literature published up to May 29, 2025, assessing the impact of ctDNA on survival outcomes in patients with MM. The survival outcomes included overall survival (OS), progression‐free survival (PFS), and time to progression (TTP). A total of 948 MM patients from 14 studies were included in the analysis. Patients with detectable ctDNA exhibited a significantly increased risk of aggressive disease progression (hazard ratio [HR], 3.21; 95% confidence interval [CI], 1.98–5.18) and reduced OS (HR, 3.97; 95% CI, 2.24–7.04). Subgroup analyses consistently indicated that ctDNA positivity was associated with accelerated disease progression, irrespective of race, disease status, treatment, or ctDNA detection method. In subgroups defined by relapsed or refractory MM (RRMM) and Oceanian or Caucasian race, ctDNA positivity was significantly correlated with poorer OS. However, the associations were not significant in newly diagnosed MM (NDMM) or among Asian patients. Therefore, we conclude that ctDNA is a potential predictor of inferior survival outcomes in MM patients.

1. Introduction

Multiple myeloma (MM) is the second most common hematologic malignancy, characterized by abnormal proliferation of clonal plasma cells in the bone marrow [1]. Over the past few decades, the advent of novel drugs has improved the prognosis of MM patients; however, survival outcomes remain considerably heterogeneous across patients [2]. Therefore, prognosis assessment and risk stratification are imperative for the precise treatment of MM. Currently, the widely used Revised International Staging System (R‐ISS) is based on high‐risk cytogenetics, β₂ microglobulin, albumin, and lactate dehydrogenase [3]. In addition, factors such as primary plasma cell leukemia, extramedullary lesions, high tumor burden, and inadequate treatment response are indicative of a poor prognosis [4]. Traditional efficacy assessments by the International Myeloma Working Group (IMWG) and monitoring of minimal residual disease (MRD) require invasive bone marrow puncture examinations, which can cause patients discomfort and pain [5]. Moreover, a single bone marrow aspirate may not adequately capture the full spatial heterogeneity of the disease [6]. Consequently, existing stratification systems and efficacy assessments continue to have unmet clinical needs, and it is imperative to identify and validate novel biomarkers to address current limitations.

Liquid biopsy, a non‐invasive technology, has revolutionized cancer management, with applications in early diagnosis, treatment response monitoring, and prognostic assessment [7]. At present, liquid biopsies primarily involve circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Cell‐free DNA (cfDNA) refers to fragments released into the bloodstream through active secretion by cells or as a result of apoptosis and necrosis. The fraction of cfDNA originating from tumor cells and carrying tumor‐specific mutations is specifically termed ctDNA [8, 9]. Owing to the minimal invasiveness of peripheral blood collection, ctDNA analysis offers high repeatability, rendering it suitable for dynamic disease monitoring. Furthermore, ctDNA analysis overcomes the spatial limitations of single‐site bone marrow biopsies [10]. By enabling comprehensive genomic profiling and precise detection of tumor‐specific mutations, it provides critical information to guide personalized treatment [11]. Several studies have explored the utility of ctDNA in MM for disease monitoring and prognostic assessment. Nonetheless, inconsistencies in detection methods, along with variations in patient cohorts and treatments, preclude definitive conclusions. Although a prior meta‐analysis linked elevated cfDNA levels to poorer overall survival (OS) and progression‐free survival (PFS) in MM patients, this conclusion was limited by the inclusion of only five studies, thereby limiting its statistical power [12]. This updated meta‐analysis aims to evaluate the application and prognostic significance of ctDNA detection in MM. This update extends the time range of the search by four years, thereby capturing the most recent outcomes in the context of advancements in diagnosis, treatment, and detection technologies. Furthermore, for the first time, we have conducted subgroup analyses based on different disease stages, detection methods, and races, which were not addressed in previous meta‐analyses.

2. Methods

2.1. Search Strategies

The study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD420251062451. A comprehensive literature search was conducted on May 29, 2025, across PubMed, Embase, Web of Science, and the Cochrane Library. Key search terms included ’Multiple Myeloma’ and “Circulating Tumor DNA” with their respective synonyms.

2.2. Inclusion and Exclusion Criteria

Studies were included if they met the following criteria: (a) Patients were diagnosed with MM; (b) The detection of ctDNA or cfDNA was performed using peripheral blood samples; (c) The association between the ctDNA or cfDNA and survival outcomes of MM patients was evaluated; (d) Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported directly or could be estimated. Exclusion criteria were: (a) reviews, case reports, letters, and abstracts; (b) duplicate or unavailable data; and (c) studies not related to ctDNA or cfDNA.

2.3. Data Extraction and Study Quality Assessment

Based on the previously mentioned criteria and full‐text assessment, eligible studies were included for final analysis. If the original research did not report the HRs or their corresponding 95% CIs, the method described by Tierney et al. was used for estimation [13]. The following data were extracted independently by two researchers: first author details, publication year, country, race, sample size, disease stage, median age, blood sample, treatment, detection method of ctDNA or cfDNA, detection item, cut‐off value of ctDNA or cfDNA, survival outcome, and HR type. For assessment of study quality, the Newcastle‐Ottawa Quality Assessment Scale (NOS) was used [14]. The total scale score was 9, and studies with NOS scores of 6 or more were considered high‐quality.

2.4. Statistical Analysis

HRs and 95% CIs were collected and combined to explore the association between ctDNA and survival outcomes. If a study reported both univariable and multivariable analyses, data from the multivariable models were prioritized. Meta‐analyses were performed using Stata 16.0 software (StatCorp LP, College Station, TX). Heterogeneity between studies was assessed by the I ² statistic and the p‐value of Cochran's Q test. A fixed‐effect model was chosen when the I ² statistic was less than 50% and the p‐value of Cochran's Q test was greater than 0.05; otherwise, a random‐effect model was used. A pooled HR > 1 with a 95% CI excluding 1 was considered indicative of worse prognosis. Subgroup analyses were conducted according to disease stage, detection method, race, and treatment. Sensitivity analysis was performed using the leave‐one‐out method to examine the stability of results. Funnel plot and Egger's test were used to evaluate publication bias, with p > 0.05 considered to indicate no significant bias.

3. Results

3.1. Search Results

A total of 1720 records were found initially after searching. After removing the duplicates, 1187 records remained. Subsequent to a review of titles and abstracts, 37 articles were selected for full‐text assessment. Finally, 14 studies were eligible for inclusion in this meta‐analysis [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] (Figure 1).

FIGURE 1.

PRISMA flow diagram. Flow diagram describing the study selection process and number of studies at each stage according to the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines.

3.2. Characteristics of Included Studies and Quality Assessment

The characteristics of the studies are detailed in Table 1. The 14 included studies, published between 2018 and 2025, encompassed a total of 948 patients. Among the included studies, 5 studies [16, 17, 18, 19, 24], reported OS results, and 10 (1 study reported time to progression [TTP]) studies [15, 19, 20, 21, 22, 23, 25, 26, 27, 28], reported disease progression results, respectively. Eleven studies [15, 16, 19, 20, 21, 22, 24, 25, 26, 27, 28], used next‐generation sequencing (NGS), whole genome sequencing (WGS), or cancer personalized profiling by deep sequencing (CAPP‐Seq) for ctDNA detection, whereas 3 studies [17, 18, 23], used polymerase chain reaction (PCR). Except for the study by Park et al. [28], which did not report the blood sample, all other studies used plasma specimens. The cut‐off value for positive ctDNA or cfDNA was heterogeneous. The HRs for disease progression or OS were available in 10 studies [15, 18, 19, 20, 21, 22, 25, 26, 27, 28], and were estimated in 4 studies [16, 17, 23, 24]. In Deshpande's study, one HR for PFS and one for OS were extracted [19]. The NOS scores of the included studies ranged from 6 to 9, indicating the included studies were of high quality (Table 2).

TABLE 1.

The characteristics of the 14 included studies.

Study	Publication Year	Country	Race	Sample size	Disease stage	Median age	Blood sample	Treatment	Detection method	Detection item	Cut‐off	Outcome	HR
Biancon	2018	Italy	Caucasian	22	RR	65	plasma	PIs or/and IMiDs	NGS	cfDNA IGH gene rearrangements	4.70%	PFS	Reported
Mithraprabhu (1)	2019	Australia	Oceanian	52	RR	NR	plasma	NR	TAS	fractional abundance of ctDNA	1%	OS	Estimated
Mithraprabhu (2)	2019	Australia	Oceanian	20	RR	NR	plasma	PIs or/and IMiDs	ddPCR	fractional abundance of ctDNA	1%	OS	Estimated
Li	2020	China	Asian	17	ND	NR	plasma	NR	ddPCR	cfDNA mutation	undetectable vs. detectable	OS	Reported
Deshpande	2021	USA	Caucasian	52	ND	NR	plasma	PIs or/and IMiDs	ULP‐WGS	level of cfDNA	20.74 ng/mL	PFS/OS	Reported
Liu	2022	China	Asian	82	ND	62	plasma	PIs or/and IMiDs	NGS	molecular tumor burden index	3.25%	PFS	Reported
Dhakal	2022	USA	Caucasian	28	Post‐ASCT	67	plasma	PIs or/and IMiDs	mPCR‐NGS	ctDNA status	undetectable vs. detectable	PFS	Reported
Waldschmidt	2022	USA	Caucasian	45	RR	NR	plasma	Immunotherapy	LPWGS	cfDNA tumor fraction	10%	PFS	Reported
Shen	2022	China	Asian	56	ND	65	plasma	NR	qPCR	AUL247 fragment of cfDNA	low vs. high	PFS	Estimated
Quivoron	2024	France	Caucasian	45	RR	69	plasma	NR	NGS	cfDNA mutations	3	OS	Estimated
Kogure	2024	Japan	Asian	259	RR	74	plasma	PIs or/and IMiDs	NGS	ctDNA mutations	2	PFS	Reported
Martello	2024	Italy	Caucasian	150	ND	61	plasma	NR	WGS	ctDNA tumor fraction	12%	PFS	Reported
Hosoya	2025	USA	Caucasian	36	RR	65	plasma	Immunotherapy	CAPP‐seq	ctDNA‐MRD	undetectable vs. detectable	TTP	Reported
Park	2025	Korea	Asian	84	ND	65	NR	NR	NGS	TET2	undetectable vs. detectable	PFS	Reported

Abbreviations: cfDNA, cell‐free DNA; ctDNA, circulating tumor DNA; CAPP‐Seq, cancer personalized profiling by deep sequencing; HR, hazard ratio; IMiDs, immunomodulatory drugs; LPWGS, low‐pass whole genome sequencing; MRD, minimal residual disease; ND, newly diagnosed; NGS, next‐generation sequencing; NR, not reported; OS, overall survival; PFS, progression‐free survival; PCR, polymerase chain reaction; PIs, proteasome inhibitors; RR, relapsed or refractory; TAS, targeted deep amplicon sequencing; TTP, time to progression; ULP‐WGS, ultra‐low pass‐whole genome sequencing; WGS, whole genome sequencing.

TABLE 2.

The NOS scores of the included studies.

Study	Selection				Comparability		Outcome			Overall score
	Representativeness of the exposed cohort	Selection of non‐exposed cohort	Ascertainment of exposure	Outcome not present at start of study	Controls for level of age	Controls for disease stage	Assessment of outcome	Was follow‐up long enough	Adequacy of follow‐up
Biancon(2018)	1	1	1	1	0	1	1	0	1	7
Mithraprabhu (1)(2019)	1	1	1	1	0	0	1	0	1	6
Mithraprabhu (2)(2019)	1	1	1	1	0	0	1	0	1	6
Li(2020)	1	1	1	1	0	1	1	0	1	7
Deshpande(2021)	1	1	1	1	0	1	1	0	1	7
Liu(2022)	1	1	1	1	0	1	1	0	1	7
Dhakal(2022)	1	1	1	1	0	1	1	1	1	8
Waldschmidt(2022)	1	1	1	1	1	1	1	1	1	9
Shen(2022)	1	1	1	1	1	1	1	0	1	8
Quivoron(2024)	1	1	1	1	1	1	1	0	1	8
Kogure(2024)	1	1	1	1	0	1	1	0	1	7
Martello(2024)	1	1	1	1	0	1	1	0	1	7
Hosoya(2025)	1	1	1	1	0	1	1	0	1	7
Park(2025)	1	1	1	1	1	1	1	1	1	9

3.3. Association of ctDNA With Disease Progression

Ten studies investigated the association between ctDNA status and PFS or TTP in patients with MM. A random‐effects model revealed that ctDNA positivity was significantly associated with shorter PFS or TTP compared to ctDNA negativity (HR, 3.21; 95% CI, 1.98–5.18; I ²  = 81.3%; p < 0.001; Figure 2A).

FIGURE 2.

Forest plots showing the prognostic role of ctDNA on disease progression and subgroup analyses. (A) Meta‐analysis of ctDNA on disease progression. (B) Subgroup meta‐analysis of detection methods. (C) Subgroup meta‐analysis of races. (D) Subgroup meta‐analysis of disease stages. (E) Subgroup meta‐analysis of treatments.

To investigate the sources of heterogeneity, we stratified the studies by detection method into a high‐throughput group (including NGS, WGS, and CAPP‐seq) and a low‐throughput group (PCR‐based methods). Subgroup analysis revealed that heterogeneity was substantially reduced in the high‐throughput group (I ²  = 21.6%, p = 0.251), with a pooled HR of 3.38 (95% CI, 2.48–4.59). The low‐throughput group, which contained only one study, exhibited no heterogeneity. A significant inter‐subgroup difference was observed (p < 0.001), indicating that the detection method is a key factor accounting for the heterogeneity (Figure 2B).

Moreover, we conducted subgroup analyses according to race, disease stage and treatment. The race subgroup results showed that among Caucasian and Asian patients, the pooled HRs were 3.64 (95% CI, 2.51–5.27) and 2.37 (95% CI, 1.20–4.69) (Figure 2C). The disease stage subgroup results showed that in newly diagnosed MM (NDMM) patients, relapsed or refractory MM (RRMM) patients, and post‐autologous stem cell transplantation (ASCT) patients, the pooled HRs were 2.78 (95% CI, 2.36–4.71), 3.33 (95% CI, 2.36–4.71), and 5.60 (95% CI, 1.82–17.21), respectively (Figure 2D). Besides, the treatment subgroup results showed that in the proteasome inhibitors (PIs) and/or immunomodulatory drugs (IMiDs) era or in the immunotherapy era, the pooled HRs were 3.63 (95% CI, 2.14–6.15) and 3.19 (95% CI, 1.97–5.17), respectively (Figure 2E). The results demonstrated that ctDNA positivity correlated with accelerated disease progression, irrespective of race, disease status or treatment.

3.4. Association of ctDNA With Overall Survival

A fixed‐effect meta‐analysis of five studies showed a significant association between ctDNA positivity and reduced OS in patients with MM (HR, 3.97; 95% CI, 2.24–7.04; I ²  = 40.1%; p = 0.154; Figure 3A).

FIGURE 3.

Forest plots showing the prognostic role of ctDNA on OS and subgroup analyses. (A) Meta‐analysis of ctDNA on OS. (B) Subgroup meta‐analysis of races. (C) Subgroup meta‐analysis of disease stages.

Subgroup analyses by race and disease stage revealed that ctDNA positivity was significantly associated with worse OS in Caucasian (HR, 3.34; 95% CI, 1.61–6.93) and Oceanian patients (HR, 8.63; 95% CI, 3.15–23.59), as well as in patients with RR disease (HR, 4.14; 95% CI, 2.15–7.95). However, the associations were not statistically significant in the Asian (HR, 0.40; 95% CI, 0.04–3.94) subgroup (comprising one study), nor in the ND patients (HR, 2.04; 95% CI, 0.11–36.35) (Figure 3B,C). Thus, the OS prognostic value of ctDNA was confirmed in specific subgroups, whereas its role in Asian and ND populations requires further investigation in larger cohorts.

3.5. Sensitivity Analysis

Sensitivity analyses were performed to assess the stability of the meta‐analysis results for disease progression and OS by iteratively excluding each study. The pooled HRs and 95% CIs remained consistent throughout this process, confirming the robustness of our findings (Figure 4A,B).

FIGURE 4.

Sensitivity analyses to assess the robustness of the pooled results of disease progression (A) and OS (B).

3.6. Publication Bias

To analyze the publication bias of meta‐analysis, funnel plot and Egger's test were conducted. The results of Egger's test indicated no evidence of publication bias for OS (p = 0.633), whereas a significant publication bias was suggested for disease progression (p < 0.001). The potential risk of publication bias was assessed visually using funnel plots (Figure 5A,B). To evaluate the impact of publication bias, a trim‐and‐fill analysis was performed. The analysis included 4 studies, and the adjusted pooled hazard ratio was notably attenuated to 2.25 (95% CI, 1.48–3.43) from the original estimate of 3.07 (95% CI, 2.04–4.63), suggesting that the initial result may have been overestimated (Figure 5C).

FIGURE 5.

(A) Funnel plots to assess publication bias of disease progression. (B) Funnel plots to assess publication bias of OS. (C) Trim‐and‐fill analysis funnel plot for assessing and adjusting for potential publication bias.

4. Discussion

Although the emergence of novel therapies has improved the survival of MM, the need for precise disease stratification remains. As a non‐invasive biomarker, ctDNA is expected to become a new prognostic tool to refine risk stratification. In this study, we found that ctDNA positivity was significantly associated with accelerated disease progression (HR, 3.21; 95% CI, 1.98–5.18) and reduced OS (HR, 3.97; 95% CI, 2.24–7.04) in MM. Our findings are consistent with previous studies, demonstrating a significant association between cfDNA and shorter PFS (HR, 4.78; 95% CI, 2.00–11.45) and OS (HR, 3.06; 95% CI, 1.66–5.63) in MM patients [12]. Furthermore, we performed subgroup analyses based on races, disease stages, and detection methods, thereby providing more comprehensive and robust evidence supporting our conclusions. The ctDNA positivity was consistently associated with shorter disease progression, irrespective of whether detection occurred pre‐treatment, post‐transplantation, or at relapse, and this association was observed in both Asian and Caucasian patient populations. The persistently high heterogeneity within the NDMM subgroup (I ²  = 81.5%) suggested that factors beyond disease stage, such as baseline characteristics, treatment regimens, and follow‐up duration might also influence the outcomes of the subgroup analysis. Furthermore, subgroup analyses of disease progression based on ctDNA detection methods revealed that the high heterogeneity originated from variations in detection techniques. Studies utilizing high‐throughput methods exhibited low heterogeneity (I ²  = 21.6%), indicating that their findings were relatively consistent and comparable. However, the prognostic significance of ctDNA positivity for OS subgroups was not universally applicable. A significant association with poorer OS was observed in subgroups of patients with RRMM and those of Oceanian or Caucasian race. In contrast, the association was not statistically significant in ND patients or Asian subgroups, potentially due to the limited number of included studies in these groups, which may have restricted the statistical power to detect a significant effect on OS.

We also focused on evaluating the association between ctDNA and standard laboratory parameters for MM to assess its clinical relevance. Hosoya et al., reported a significant association between ctDNA positivity and clinical parameters, including M‐protein and free light chain difference (dFLC) [27]. Similarly, Waldschmidt et al., observed a significant correlation between cfDNA positivity and involved serum‐free light chain (iFLC) [22]. In contrast, studies by Biancon et al., and Deshpande et al., found no significant correlation between cfDNA positivity and either M‐protein or FLC ratios [15] [19]., These inconsistent findings suggest that the association between ctDNA and clinical parameters is not absolute, and the inconsistent outcome across studies might be attributed to heterogeneity in study designs. Such contradiction has also been observed in studies investigating the association between ctDNA and cytogenetics. Liu et al., reported that specific ctDNA mutations are linked to distinct cytogenetics. In contrast, studies by Waldschmidt et al. and Hosoya et al. reported no statistically significant association between high‐risk cytogenetics and ctDNA or cfDNA [20, 22, 27]. Notably, Liu et al. demonstrated a positive correlation between a high molecular tumor burden index (mTBI) in ctDNA and elevated circulating plasma cells (CPCs) counts, suggesting underlying connections between different dimensions of liquid biopsy [20]. The prognostic value of CPCs was identified, with an increased number of CPCs associated with adverse survival outcomes in MM [29] [30]., In the context of MRD monitoring, Biancon et al. and Hosoya et al. found that ctDNA had a high level of correlation with bone marrow MRD [15] [27]., Furthermore, Dhakal et al. concluded that ctDNA demonstrated superior sensitivity and specificity, and provided a more accurate prediction of PFS and relapse risk compared to conventional flow cytometry‐based bone marrow MRD detection. Longitudinal ctDNA‐MRD analysis of two patients showed that an increase in ctDNA preceded imaging‐confirmed clinical relapse, suggesting the potential for ctDNA‐MRD to be a more sensitive biomarker [21]. Martello et al. reported the relevance of ctDNA in the context of imaging level and they observed that patients with higher ctDNA exhibited more significant features of bone marrow infiltration on PET‐CT imaging, with higher bone marrow Deauville Scores and SUVmax values, as well as a higher incidence of extramedullary lesions [26]. These findings demonstrate the clinical significance of ctDNA across multiple dimensions, including disease burden, prognostic assessment, and imaging. As a novel prognostic biomarker, ctDNA offers a more comprehensive foundation for clinical decision‐making.

This study has several limitations. Firstly, the absence of a predetermined minimum sample size for the included studies resulted in the predominance of small‐scale and exploratory research, potentially compromising the reliability of the conclusions. Secondly, the short follow‐up duration characteristic of most studies limits the comprehensive evaluation of long‐term survival outcomes. Thirdly, variations in the content of ctDNA detection and the criteria for positive judgment were observed among the included studies. The standardization of ctDNA analysis is essential for ensuring the reproducibility and comparability of data across different studies and laboratories. Furthermore, the publication bias analysis for disease progression indicated funnel plot asymmetry with an Egger's test p‐value less than 0.001, suggesting the potential presence of publication bias. The presence of such bias may have resulted in an overestimation of the adverse prognostic effect of ctDNA positivity on survival outcomes, as studies with statistically significant positive results are more likely to be published and included than those with negative results. Nevertheless, after adjustment for publication bias, the positive correlation between the two remained statistically significant. This supports the existence of an association but suggests that its magnitude should be interpreted with caution. Future high‐quality studies, including those with negative results, are needed to verify the true strength of this association.

Although this study aimed to investigate the prognostic value of ctDNA, some of the incorporated studies actually measured total cfDNA concentration rather than tumor‐specific ctDNA. Since cfDNA encompasses background nucleic acids derived from non‐tumor cells and its concentration can be influenced by various non‐tumor factors, this approach may introduce confounding bias and dilute the tumor‐related signal, potentially leading to an underestimation of the prognostic strength of ctDNA. To address this, we performed subgroup analysis comparing the prognostic impact of ctDNA and cfDNA. The results showed that both ctDNA and cfDNA had a statistically significant impact on patient survival outcomes (Figure 6). This consistency is further supported by evidence from other malignancies. For instance, in the most extensive endometrial cancer cohort published to date, both high cfDNA levels (disease free survival [DFS]: HR, 3.91, p < 0.0001; disease specific survival [DSS]: HR, 6.54, p < 0.0001) and detectable ctDNA (DFS: HR, 3.63, p < 0.0001; DSS: HR, 3.91, p < 0.001) were significantly associated with poor prognosis. Notably, the combination of high cfDNA and ctDNA positivity yielded the highest HRs for DFS (9.25) and DSS (11.20), indicating that cfDNA and ctDNA provide complementary but directionally consistent prognostic information. Nonetheless, we acknowledge that the inclusion of cfDNA studies may introduce non‐tumor confounding and future meta‐analyses incorporating more studies specifically focused on ctDNA will be able to provide more precise conclusions [31].

FIGURE 6.

Forest plots showing the prognostic role of ctDNA or cfDNA on disease progression (A) and OS (B).

5. Conclusions

Our meta‐analysis demonstrates that ctDNA positivity is a robust prognostic biomarker in MM, associated with significantly shorter disease progression and reduced overall survival. Furthermore, the standardized assessment of ctDNA analysis in clinical practice is essential for advancing precise medicine and improving outcomes in MM.

Author Contributions

Wanting Ying: writing – original draft, formal analysis, data curation. Ran Li: writing – review and editing, formal analysis, data curation. Xiaoyan Qu: writing – review and editing, funding acquisition. Lei Fan: funding acquisition, writing – review and editing.

Funding

This work was supported by National Natural Science Foundation of China, 82570256, 82470186. Beijing Xisike Clinical Oncology Research Foundation, No. Y‐SYBLD2022RWR‐0013. The Natural Science Foundation of Jiangsu Province, BK20232039.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.