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. 2025 Aug 26;47:e00500. doi: 10.1016/j.plabm.2025.e00500

Tumor-informed circulating tumor DNA detection for personalized monitoring of treatment response in epithelial ovarian cancer

Sung Wan Kang a, Ok-Ju Kang a, Young-Jae Lee b, Hee Jung Jung c, Min-Seo Lee a, Ji-Young Lee a, Yong-Man Kim a, Shin-Wha Lee a,⁎
PMCID: PMC12454446  PMID: 40994830

Abstract

Background

Circulating tumor DNA (ctDNA) has emerged as a valuable biomarker in liquid biopsies for monitoring treatment responses in cancer patients. However, detecting ctDNA in epithelial ovarian cancer (EOC) is challenging due to its high heterogeneity and the absence of hotspot driver mutations. Therefore, a personalized approach to ctDNA analysis is essential, tailored to the specific tumor mutations of each EOC patient. In this study, we aimed to evaluate a droplet digital PCR (ddPCR) method targeting various genetic alterations in ctDNA identified through a targeted next-generation sequencing (NGS) panel in EOC tumors.

Methods

EOC tumor tissues were sequenced using a targeted NGS panel to identify oncogenic mutations. ddPCR assays were subsequently designed and optimized to detect these tumor-specific mutations in ctDNA. ctDNA levels were monitored and compared with CA-125 for EOC.

Results

Fourteen pathogenic mutations, including TP53, PIK3CA, PTEN, KRAS, and RB1, were identified in 13 patients with EOC and selected as targets for ctDNA detection. The performance of ddPCR assays was validated for 10 mutations, and mutated ctDNA was successfully detected for 8 mutations in 7 patients. In most cases, ctDNA levels showed trends consistent with CA-125 levels, reflecting the treatment response. However, in one case, PTEN (E91∗) mutated ctDNA was detected during recurrence, while CA-125 levels remained within the normal range.

Conclusion

This study demonstrates the clinical utility of ddPCR for monitoring treatment responses in EOC by targeting patient-specific mutations. Integrating ddPCR with NGS-based mutation identification offers an effective approach for assessing therapeutic outcomes in EOC patients.

Keywords: Circulating tumor DNA, Epithelial ovarian cancer, Droplet digital PCR, Treatment response monitoring, Next-generation sequencing

Highlights

  • •

    A ddPCR-based method was assessed for the detection of ctDNA to monitor the treatment response in ovarian cancer patients.

  • •

    Using ddPCR, 8 of 10 tumor-specific mutations in ctDNA were detected, showing patterns consistent with CA-125 and treatment response.

  • •

    Combining ddPCR with deep sequencing could provide a rapid, accurate, and cost-effective approach to assess ovarian cancer therapy.

1. Introduction

Liquid biopsy has emerged as a non-invasive technique for cancer diagnosis, predicting treatment response, and monitoring disease progression [1]. Several components of tumors found in blood plasma have been identified as biomarkers. Among these, circulating tumor DNA (ctDNA) is a DNA fragment released from tumor cells that reflects the molecular features of the tumor, including specific genetic mutations [2,3]. ctDNA is more specific to cancer than traditional protein biomarkers and can more rapidly monitor minimal residual disease, treatment response, and recurrence following treatment [4].

Epithelial ovarian cancer (EOC) is the most lethal form of gynecological cancer, with 50–60 % of cases diagnosed at an advanced stage due to the absence of distinct symptoms as the disease progresses. Recurrence occurs in approximately 80 % of patients with advanced-stage EOC [5,6]. Serum cancer antigen 125 (CA-125) is currently the most widely utilized protein marker for monitoring treatment response and detecting recurrence in EOC [7]. However, early intervention based on CA-125 elevations has not been shown to improve overall survival rates. Additionally, CA-125 is insufficient as a predictive marker for recurrence, exhibiting a sensitivity range of 62–94 % [8,9]. Therefore, there is a pressing need for improved biomarkers. Recent studies have focused on detecting ctDNA that targets TP53 mutations in the plasma of patients with high-grade serous ovarian cancer (HGSOC) using next-generation sequencing (NGS) and digital PCR (dPCR) techniques [[10], [11], [12], [13], [14]]. TP53 mutations have been identified in over 90 % of HGSOC cases and can serve as targets for droplet digital PCR (ddPCR), one of the most sensitive methods for ctDNA analysis. However, the locations and types of TP53 somatic mutations exhibit considerable variability [15]. Furthermore, EOC subtypes, such as endometrioid carcinoma and clear cell carcinoma (CCC), often involve gene mutations beyond TP53 [16]. Consequently, personalized analytical approaches are necessary for the tumor mutations of each EOC patient.

In this study, we aimed to evaluate a ddPCR method targeting various genetic alterations in ctDNA identified in EOC tumors through a targeted NGS panel. By analyzing ctDNA from serial plasma samples of EOC patients, we confirmed whether the personalized ddPCR method effectively monitors treatment responses for each patient.

2. Materials and methods

2.1. Patients and sample collection

The study recruited 20 patients suspected of having ovarian cancer who underwent surgery at Asan Medical Center in Korea between July 2018 and January 2019. Of these, primary tumor tissues from 13 patients with EOC were reviewed by a pathologist and sequenced using a targeted NGS panel. These EOC patients received chemotherapy following surgery and provided informed consent. The study was approved by the Institutional Review Board of Asan Medical Center (IRB No. 2018-0754). Blood samples were collected at the time of surgery or before, during, or after chemotherapy using K2 EDTA tubes (BD Bioscience, Franklin Lakes, NJ, USA). Plasma was isolated within 1 h after venipuncture. The blood was subjected to a two-step centrifugation: initially for 15 min at 1300 g, followed by a second centrifugation for 10 min at 16,000 g to isolate the plasma supernatants, which were subsequently stored at −80 °C until the extraction of cell-free DNA (cfDNA) was performed within a few days.

2.2. DNA extraction

Genomic DNA was extracted from archived formalin-fixed, paraffin-embedded (FFPE) tissue specimens using the NEXprep FFPE Tissue Kit (Genes Laboratories, Seongnam-si, Gyeonggi-do, Korea) for targeted NGS, as previously described [17]. Hematoxylin and eosin staining was performed on the FFPE tissue slides to assess tumor cellularity. For the extraction of cfDNA from plasma, the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany) was utilized according to the manufacturer's instructions. The cfDNA was subsequently eluted in 50 μl of elution buffer, and its concentration was quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA).

2.3. Targeted NGS analysis

Targeted NGS was performed using the MiSeq platform (Illumina, San Diego, CA, USA) with OncoPanel AMC version 3, which captured 383 cancer-related genes, as previously described [17,18]. Briefly, genomic DNA was fragmented through mechanical sonication to an average size of 250 bp. A DNA library was prepared using the SureSelectXT Reagent Kit (Agilent Technologies, Santa Clara, CA, USA), incorporating sample-specific barcodes and hybrid capture. The sequenced reads were aligned to the human reference genome GRCh37/hg19 (NCBI build 37, February 2009) using the BWA. PCR duplicates were removed using the MarkDuplicates tool. Reads were realigned at indel sites and base qualities were recalibrated using GATK tools. Somatic variants were identified using MuTect and SomaticIndelocator, filtering out germline/common variants with dbSNP, ExAC, KRGDB, and an in-house normal panel. The final variants were annotated using the Variant Effect Predictor and converted to Mutation Annotation Format. False positives were reviewed in the Integrative Genomics Viewer (IGV). Identified variants were classified into tiers 1, 2, 3, 4, and R1/R2 based on the precision oncology knowledge base (OncoKB) [19]. BRCA1/2 variants were interpreted as pathogenic, likely pathogenic, variant of uncertain significance (VUS), likely benign, or benign according to the American College of Medical Genetics and Genomics (ACMG) guidelines [20].

2.4. ddPCR analysis

To detect oncogenic mutations in ctDNA, custom TaqMan SNP genotyping assays were utilized, featuring FAM-labeled probes for mutant alleles and VIC-labeled probes for wild-type alleles (Thermo Fisher Scientific). The performance of the primers and probes was assessed using a mixture containing 1.0 % mutant DNA and wild-type DNA (3333 genome equivalents, GEq). The 100 bp mutant DNA oligomers were synthesized by Bioneer (Daejeon, Korea), while wild-type DNA fragments were prepared using a DNA fragmentation kit (Takara, Shiga, Japan) with female human genomic DNA (Promega, Madison, WI, USA), in accordance with the manufacturer's protocol.

Mutation quantification in DNA templates (10 ng), mutant DNA mixtures, and plasma cfDNA was performed using the QX200 ddPCR system (Bio-Rad, Hercules, CA, USA). The DNA templates were encapsulated into approximately 20,000 nL-sized oil droplets using the Automated Droplet Generator (Bio-Rad). PCR amplification was conducted under the following conditions: initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 94 °C for 15 s and annealing/extension at 58–60 °C for 60 s, with a final extension at 98 °C for 10 min. The resulting droplets were analyzed using a droplet reader, where each droplet was individually streamed through a detector. Data were processed and analyzed using QuantaSoft v1.7 software (Bio-Rad).

2.5. CA-125 measurement

CA-125 levels were measured using the ARCHITECT CA125 II reagent kit (Abbott Laboratories, Abbott Park, IL, USA) on the ARCHITECT i2000 immunoassay analyzer (Abbott Laboratories).

2.6. Cutoff setting and statistical analysis

To establish the cutoff for mutation-specific ctDNA positivity, the limit of blank (LOB) was calculated as the median plus two standard deviations (SDs) of fractional abundance (FA) values derived from plasma cfDNA in a mutation-negative population, in accordance with previous studies [21,22]. Conventionally, the cutoff value for CA-125 was set at 35 U/ml.

3. Results

3.1. Patient characteristics

A total of 13 patients with EOC participated in our study assessing ctDNA detection using ddPCR targeting oncogenic hotspot mutations identified through targeted NGS analysis (Fig. 1). The characteristics of these patients are detailed in Supplementary Table 1. Among them, 10 patients (76.9 %) had HGSOC, 2 patients (15.4 %) had a mixed subtype, and 1 patient (7.7 %) had CCC. All patients underwent primary surgery and subsequently received chemotherapy.

Fig. 1.

Fig. 1

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Schematic workflow of targeted NGS and ctDNA monitoring using ddPCR during the treatment of EOC patients. Oncogenic hotspot mutations were identified through a targeted NGS panel from FFPE tissues of EOC patients for the detection of plasma ctDNA. Mutation-specific primers and probes were designed for ddPCR-based detection, and the assay's analytical performance was optimized. ctDNA was isolated from plasma samples collected during treatment and follow-up and subsequently analyzed using ddPCR.

3.2. Identification of oncogenic mutations for ctDNA detection

To identify somatic mutations in tumors that could be targeted in ctDNA, we performed the OncoPanel AMC v3, which targets 383 cancer-related genes, using DNA extracted from the FFPE tissues of patients with EOC. The heatmap in Fig. 2 illustrates the somatic mutation profiles of these EOC patients. Notably, all 10 patients with HGSOC exhibited TP53 mutations, which are commonly associated with HGSOC [15]. Additionally, our cohort revealed various genomic alterations, including mutations in TP53, PTEN, KRAS, PIK3CA, and RB1, in tumor tissues from three other subtypes of EOC.

Fig. 2.

Fig. 2

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Heatmap showing somatic mutation profiles in patients with EOC. The heatmap presents somatic mutations identified in tumor samples from patients with EOC, analyzed using a targeted NGS panel that encompasses 383 cancer-related genes. Each column corresponds to an individual case sample, while each row represents histological types and mutated genes. The colors denote mutation and histological subtypes, as explained in the figure legend.

We selected 14 potential targets for oncogenic hotspot mutations in tumor suppressor genes and oncogenes from the tumor tissues of 13 patients with EOC to detect ctDNA that could monitor disease progression and response to treatment. Among the 10 patients with HGSOC, no additional cancer-driver gene mutations were identified other than those in the TP53 gene. Furthermore, the locations of the TP53 somatic mutations in these patients' tumor tissues were all distinct, consistent with previous reports indicating that the types of TP53 somatic point mutations are highly variable [15]. PTEN (E91∗) and KRAS (G12D) mutations were observed in one patient with adenocarcinoma exhibiting mixed histology of endometrioid and clear cell types. PIK3CA (H1047R) mutation was identified in another patient with CCC. RB1 (Y813Lfs∗2) mutation was observed in a patient with a malignant mixed Müllerian tumor (MMMT). Detailed information on each mutation is presented in Table 1.

Table 1.

NGS-defined oncogenic hotspot mutations.

Subject No. Cell type Gene RefSeq Category Exon Mutation Amino acid change Type Effect
S-01 High grade serous adenocarcinoma TP53 NM_000546.6 Tumor suppressor 7 c.712T > G p.C238G SNV Missense
S-03 High grade serous adenocarcinoma TP53 NM_000546.6 Tumor suppressor 7 c.681dup p.D228∗ INS Frameshift
S-04 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 7 c.707A > G p.Y236C SNV Missense
S-05 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 5 c.470T > A p.V157D SNV Missense
S-07 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 7 c.773A > G p.E258G SNV Missense
S-09 High grade serous adenocarcinoma TP53 NM_000546.6 Tumor suppressor 8 c.880G > T p.E294∗ SNV Nonsense
S-11 High grade serous adenocarcinoma TP53 NM_000546.6 Tumor suppressor 7 c.773A > C p.E258A SNV Missense
S-12 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 6 c.659A > G p.Y220C SNV Missense
S-13 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 8 c.817C > T p.R273C SNV Missense
S-16 High grade serous carcinoma TP53 NM_000546.6 Tumor suppressor 5 c.488A > G p.Y163C SNV Missense
S-17 Adenocarcinoma with mixed histology of endometrioid and clear cell PTEN NM_000314.8 Tumor suppressor 1 c.271G > T p.E91∗ SNV Missense
KRAS NM_004985.5 Oncogene 1 c.35G > A p.G12D SNV Missense
S-18 Clear cell carcinoma PIK3CA NM_006218.4 Oncogene 20 c.3140A > G p.H1047R SNV Missense
S-19 Malignant mixed mullerian tumor RB1 NM_000321.3 Tumor suppressor 23 c.2437dup p.Y813Lfs∗2 INS Frameshift
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3.3. Optimization of ddPCR for oncogenic mutations in ctDNA

Next, we designed primers and TaqMan probes labeled with FAM and VIC fluorophores for the selected 14 somatic mutations. Primer and probe sets for 10 of these mutations were successfully produced, while 4 sets were excluded due to functional defects: TP53 (C238G, Y236C, E294∗, Y220C, R273C, Y163C), PTEN (E91∗), KRAS (G12D), PIK3CA (H1047R), and RB1 (Y813Lfs∗2). Validation of the ddPCR using these primers and TaqMan probe sets confirmed the accurate separation and detection of mutant and wild-type DNA (Supplementary Figure 1).

Establishing mutation-specific cutoffs is crucial for the clinical application of the ddPCR platform in determining treatment regimens. A well-defined LOB as the cutoff for plasma ctDNA detection helps minimize false positives and improve assay accuracy [22,23]. To determine the ctDNA detection cutoff value in mutation-negative plasma cfDNA, we conducted ddPCR using cfDNA samples isolated from the plasma of EOC patients who did not have the corresponding mutations in this study (Supplementary Figure 2). The determined mutation-specific detection cutoff values are presented in Supplementary Table 3.

3.4. Dynamic monitoring of the oncogenic hotspot mutations in ctDNA

The ctDNA levels were quantified using ddPCR in plasma samples from 9 patients with EOC just before primary surgery and then monitored during and after first-line chemotherapy. We successfully detected ctDNA for four specific variants (C238G, Y236C, R273C, and Y163C) out of six targeted TP53 mutations in the blood of EOC patients prior to primary surgery and adjuvant chemotherapy. In addition to TP53, ctDNA for all targeted variants, including PIK3CA H1047R, PTEN E91∗, KRAS G12D, and RB1 Y813Lfs∗2, was also identified.

In patient S-01 with HGSOC, ctDNA targeting the TP53 C238G mutation was successfully detected. Following surgery, the ctDNA level decreased below the cutoff, along with CA-125 levels, and remained negative during chemotherapy, which was consistent with the CT imaging results (Fig. 3A and B).

Fig. 3.

Fig. 3

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Plasma ctDNA monitoring in patients S01 and S18. Two cases of EOC patients are shown, with the mutated ctDNA FA (%) and CA-125 (U/ml) values presented in the upper panel, and CT scan images in the lower panel. The levels of mutated ctDNA in (A) S01 (TP53 C238G) and (B) S18 (PIK3CA H1047R) are shown at the indicated time points, in comparison with CA-125 levels. Purple dashed lines represent mutation detection cutoffs, and blue dashed lines represent CA-125 cutoffs. Clinical outcomes, as revealed by CT scans, are marked at each assessment time (blue arrowheads). Red boxes highlight tumor regions. Preoperative CT scans showed detectable ovarian tumor masses, which were no longer visible after surgery, confirming complete resection. In S01, ctDNA levels decreased below the detection cutoff following debulking surgery and remained undetectable during chemotherapy, consistent with CA-125 levels and the absence of visible tumor recurrence on imaging. In S18, preoperative CA-125 levels were within the normal range despite the presence of a visible tumor mass on CT, whereas PIK3CA-mutated ctDNA was detected in plasma. After surgery, no residual tumor was observed in follow-up CT scans, and ctDNA levels dropped below the detection threshold, while CA-125 levels fluctuated but remained within normal limits. (FA, fractional abundance; CT, computed tomography).

In patient S-18, who had CCC of the ovary, we tracked the levels of PIK3CA H1047R mutated ctDNA and CA-125 in plasma. Pre-operative CA-125 levels were below the cutoff and did not accurately reflect the tumor burden of EOC observed in CT imaging. In contrast, PIK3CA H1047R mutation-specific ctDNA was detected at positive levels before surgery. After surgery, CA-125 levels increased above the cutoff but subsequently decreased during chemotherapy, ultimately remaining at negative levels. Meanwhile, ctDNA levels remained negative throughout chemotherapy, which corresponded with CT imaging results that showed no visible tumor (Fig. 3C and D).

In patient S-17 with mixed-type EOC, ctDNA was monitored for the PTEN E91∗ and KRAS G12D mutations. As shown in Fig. 4, recurrence was confirmed following primary chemotherapy, which prompted a change in the treatment regimen to pegylated liposomal doxorubicin (PLD). The ctDNA analysis targeting the PTEN mutation yielded a positive result, while the CA-125 levels were negative, indicating that ctDNA is more sensitive in monitoring treatment response. However, both KRAS mutant ctDNA and CA-125 levels were negative.

Fig. 4.

Fig. 4

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ctDNA monitoring of patient S17. ctDNA levels were monitored in serial plasma samples from patient S17, targeting two oncogenic hotspot mutations: PTEN E91∗ and KRAS G12D. Mutant ctDNA levels for (A) PTEN E91∗ and (B) KRAS G12D are shown at the indicated time points, compared with CA-125 levels. Purple dashed lines denote the mutation detection cutoffs, while blue dashed lines represent the CA-125 cutoff levels. (C) Representative CT images were obtained from patient S17 at different assessment time points. Tumor regions are highlighted with red boxes. The preoperative CT scan revealed an ovarian tumor mass, which was no longer visible after debulking surgery, confirming successful resection. ctDNA levels for both mutations dropped below the detection threshold, and CA-125 levels also remained within normal limits postoperatively. During follow-up, CT imaging detected tumor recurrence anterior to the inferior vena cava (IVC). This recurrence was accompanied by detection of PTEN-mutated ctDNA in plasma, whereas CA-125 levels remained within the normal range at that time point. Following the initiation of treatment for recurrence, ctDNA levels appeared to decline more rapidly than CA-125, indicating a favorable response to treatment and suggesting its potential for utility in monitoring treatment response. (FA, fractional abundance; CT, computed tomography).

Additionally, decreased levels of ctDNA and CA-125 were observed for TP53 mutations (Y236C, R273C, Y163C) and the RB1 Y813Lfs∗2 mutation, which corresponded to the responses to chemotherapy in the respective EOC patients (Fig. 5A–D). However, ctDNA for the TP53 mutations E294∗ and Y220C was undetectable in patients S-09 and S-12 (Fig. 5E–F).

Fig. 5.

Fig. 5

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Dynamic monitoring of ctDNA. Six cases of EOC patients are presented, showing the mutated ctDNA fractional abundance (FA, %) alongside CA-125 levels (U/ml). The levels of mutated ctDNA were monitored over time for (A) S19 (RB1 Y813Lfs∗2), (B) S04 (TP53 Y236C), (C) S13 (TP53 R273C), (D) S16 (TP53 Y163C), (E) S09 (TP53 E294∗), and (F) S12 (TP53 Y220C), and were compared with CA-125 levels. The purple dashed lines indicate the mutation detection cutoffs, while the blue dashed lines represent the CA-125 cutoff levels. In most cases, ctDNA levels closely followed the trends of CA-125, with decreases in both correlating with treatment response. In cases S09 and S12, ctDNA remained undetectable despite the presence of known TP53 mutations. (FA, fractional abundance; CT, computed tomography).

4. Discussion

In this study, we evaluated a personalized monitoring platform utilizing the ddPCR technique, which continuously tracks ctDNA levels in plasma for tumor-specific gene mutations identified through a targeted NGS panel in patients with EOC. We established a detection cutoff for each mutation and confirmed their presence in the plasma ctDNA of each EOC patient. Additionally, we quantified ctDNA levels during chemotherapy and compared them with CA-125 levels.

EOC is a highly heterogeneous disease characterized by diverse histological and molecular features [24]. Genomic alterations in EOC exhibit distinct patterns across different histological subtypes. However, high-frequency oncogenic driver mutations are largely absent, with the exception of TP53 mutations, which are predominant in HGSOC. Mutations in other genes, such as BRCA1/2, ARID1A, PIK3CA, KRAS, PTEN, and NF1, were observed in less than 20 % of cases, each demonstrating substantial heterogeneity in mutation types and loci, including variations within TP53 [[25], [26], [27]]. Another study identified relatively high frequencies of somatic mutations in several oncogenes, including TP53, PIK3CA, KRAS, PTEN, FBXW7, and RB1, in tumor tissues from EOC patients [28]. In our cohort, somatic mutations were consistently observed in these genes. Additionally, none of the detected somatic mutations, including single nucleotide variations and insertions/deletions (indels), were concordant, indicating a high degree of heterogeneity (Fig. 2 and Supplementary Table 2). This genetic heterogeneity in EOC complicates the detection of ctDNA and poses challenges for clinical applications. For ctDNA analysis, screening for patient-specific actionable hotspot mutations may be crucial for effective detection in individual cases of EOC.

An ultra-sensitive detection technique is essential for the precise and effective analysis of low-frequency mutations in plasma ctDNA. ddPCR is a rapid and highly sensitive method that facilitates the detection and monitoring of ctDNA. Recent studies have demonstrated the clinical utility of ddPCR for ctDNA analysis and the monitoring of minimal residual disease in early-stage cancer [[29], [30], [31]]. Using ddPCR, we successfully detected ctDNA for eight targeted variants, including TP53, PIK3CA, PTEN, KRAS, and RB1 (Fig. 3, Fig. 4, Fig. 5). In clinical monitoring, ctDNA levels generally correlated with trends in CA-125; however, in one case, ctDNA provided a more sensitive indication of recurrence. Patient S17 exhibited an increase in PTEN-mutated ctDNA while CA-125 levels remained within normal limits, suggesting the potential of ctDNA for early relapse detection even in the absence of conventional biomarker elevation. The detection of ctDNA may reflect minimal residual disease preceding clinically detectable recurrence. Additionally, during treatment for recurrence, ctDNA levels declined more rapidly than CA-125, aligning with a favorable response to chemotherapy and indicating its potential role in tracking treatment response with greater sensitivity.

Nevertheless, ctDNA was not detected in 2 out of the 6 TP53 mutations. In a previous comparative study evaluating the suitability of two NGS-based panels for detecting TP53 mutations, the ctDNA detection rates using ddPCR were 62.5 % and 80.0 %, corresponding to the TP53 mutations identified in HGSOC tissue samples by the two respective NGS panels [11]. Another study reported that all TP53 mutations identified in HGSOC tissues through Sanger sequencing were subsequently detected in plasma ctDNA using ddPCR, achieving a detection rate of 100 % [13].

This discrepancy in detection rates may be attributed to the method used for mutation analysis in tumor tissues. Mutations identified in tumor DNA samples using low-sensitivity Sanger sequencing typically exhibit high variant allele frequencies (VAFs) exceeding 20 %, suggesting that targeting these variations could enhance ctDNA detection. Although whole-exome sequencing can identify mutations with low VAFs, below 5 %, it is susceptible to high false-positive rates due to variant-calling errors, and the mutation profile of the primary tumor tissue may not be accurately reflected in ctDNA [32,33]. In a study analyzing TP53 mutations across tumor tissues and blood ctDNA from patients with various cancers using NGS, a positive concordance rate of 45.0 % was observed [34]. In ultra-deep NGS analysis, the study reported a higher concordance of 87.5 % between paired tumor tissue and plasma samples, with a 91.4 % concordance validated by ddPCR [35]. Consequently, the application of highly accurate ultra-deep NGS should be considered for analyzing tumor-derived genetic alterations in ctDNA, facilitating more effective monitoring of treatment response and disease progression.

Our study has several limitations that should be acknowledged. It is not a longitudinal monitoring study, and the small sample size restricts the detection of various hotspot mutations characteristic of EOC. To implement our ddPCR-based ctDNA monitoring approach in clinical practice, it is essential to design and optimize a comprehensive set of probes targeting somatic variant hotspots identified in primary tumors through NGS. Although emerging NGS-based ctDNA detection methodologies have demonstrated high accuracy and the capability to identify a wide range of unknown mutations, ddPCR offers the advantages of being significantly more cost-effective and providing a markedly shorter turnaround time [36,37]. Integrating multiple approaches, such as NGS-based ctDNA analysis and ddPCR, for clinical applications may enhance the efficiency of ctDNA detection and monitoring by enabling the identification of mutations beyond TP53 in EOC.

In conclusion, our study demonstrates the clinical utility of our ddPCR-based approach, which targets patient-specific mutations as a methodology for monitoring treatment response and disease progression in patients with EOC. Integrating ddPCR with mutation identification through deep sequencing could provide a highly accurate, rapid, and cost-effective strategy for evaluating therapeutic outcomes in EOC.

CRediT authorship contribution statement

Sung Wan Kang: Writing – original draft, Validation, Investigation, Formal analysis. Ok-Ju Kang: Writing – original draft, Investigation. Young-Jae Lee: Project administration, Conceptualization. Hee Jung Jung: Methodology, Investigation. Min-Seo Lee: Writing – review & editing, Investigation. Ji-Young Lee: Investigation. Yong-Man Kim: Supervision, Conceptualization. Shin-Wha Lee: Writing – review & editing, Project administration, Funding acquisition, Conceptualization.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (grant number: RS-2021-NR063303). This study was also supported by a grant (grant number: 2024IP0090) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SHINWHA LEE reports financial support was provided by National Research Foundation of Korea. SHINWHA LEE reports financial support was provided by Asan Medical Center Asan Institute for Life Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We would like to express our gratitude to our patients and their caregivers, the research nurses, the members of the Department of Obstetrics and Gynecology at Asan Medical Center, and the equipment provided by the Asan Institute for Life Sciences.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.plabm.2025.e00500.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (484.2KB, docx)

Data availability

Data will be made available on request.

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