Clinical significance of circulating tumor DNA monitoring based on TP53 mutation in high-grade serous ovarian cancer patients

Introduction

Ovarian cancer is the most lethal gynecologic malignancy, largely due to its late-stage diagnosis and lack of effective screening methods (1). According to GLOBOCAN 2022 (2), over 300,000 new cases and 200,000 deaths occur annually worldwide, with a rising incidence in South Korea as well (3). High-grade serous ovarian cancer (HGSOC) is the most common and aggressive histologic subtype, accounting for 70–80% of epithelial ovarian cancers (4). It is characterized by extensive genomic instability and near-universal mutations in TP53, a gene that encodes the tumor suppressor protein p53, which plays a key role in DNA damage response, cell cycle regulation, and apoptosis (5-7).

Cancer antigen 125 (CA-125) is the most widely used biomarker for monitoring ovarian cancer, particularly in assessing treatment response and recurrence (8,9). However, its clinical utility is limited by low specificity, minimal elevation in early-stage disease, and suboptimal sensitivity for detecting recurrence at a stage that allows effective intervention (10). Studies have shown that initiating treatment solely based on rising CA-125 levels does not improve overall survival in patients with ovarian cancer (11,12).

Recent advances in precision oncology have emphasized the need for more specific and dynamic biomarkers. Circulating tumor DNA (ctDNA), released from tumor cells into the bloodstream, offers a minimally invasive tool for real-time monitoring of tumor burden, minimal residual disease, treatment response, and disease recurrence (13-16). In ovarian cancer, ctDNA is being explored as a potential tool for detecting residual disease after surgery, identifying recurrence, and capturing resistance mutations such as BRCA reversion (17-20). However, most prior studies have focused on single time-point assessments or post-treatment prognostic associations, with limited evaluation of ctDNA as a tool for longitudinal disease monitoring across the entire treatment course.

Effective clinical application of ctDNA requires highly sensitive and quantitative detection methods, particularly given the typically low abundance of tumor-derived DNA fragments in circulation. Among the available technologies, droplet digital PCR (ddPCR) has emerged as an accurate method for quantifying known mutations in ctDNA (21,22). Given that TP53 mutations are present in nearly all cases of HGSOC (23), targeting tumor-specific TP53 alterations represents a biologically rational strategy for individualized ctDNA-based monitoring. Despite this biological rationale, the translational utility of personalized TP53 mutation–informed ctDNA monitoring throughout surgery, chemotherapy, and post-treatment surveillance has not been fully established.

Therefore, this prospective observational study aimed to evaluate the clinical utility of patient-specific TP53 mutation-based ctDNA monitoring using ddPCR in patients with HGSOC undergoing primary debulking surgery followed by adjuvant chemotherapy. By directly comparing longitudinal changes in TP53 mutant allele count (TP53MAC) with conventional CA-125 measurements at predefined treatment milestones and during follow-up, we sought to assess the feasibility and complementary value of this personalized biomarker for treatment response assessment and recurrence surveillance.

We hypothesized that patient-specific TP53 mutation-based ctDNA levels would dynamically reflect treatment response and provide complementary information to CA-125 during longitudinal surveillance in patients with HGSOC. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0241/rc).

Methods

TP53-based ctDNA monitoring study design

This study was designed as a prospective, observational, single-center cohort study evaluating the longitudinal clinical utility of patient-specific TP53 mutation-based ctDNA monitoring in patients with HGSOC. The study followed a multi-step process involving tumor tissue sequencing, plasma ctDNA detection, and longitudinal clinical follow-up (Figure S1). A patient-specific ctDNA assay was developed by targeting unique TP53 mutations identified in each tumor, enabling precise and individualized monitoring. Serial blood samples were collected at predefined time points—before and after surgery, after the 3rd and 6th cycles of adjuvant chemotherapy, and at 3 and 6 months post-treatment—followed by regular surveillance. At each time point, TP53MAC and CA-125 were measured in parallel to allow direct comparison of their clinical performance.

Patient and sample collection

Between 2013 and 2016, a total of 25 patients with histologically confirmed HGSOC who underwent treatment at Asan Medical Center were enrolled in this study. All patients received primary debulking surgery followed by six cycles of adjuvant chemotherapy. The median follow-up duration was 91.1 months (range, 19–132 months). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All patients provided written informed consent prior to study enrollment. The study was approved by the Institutional Review Board of Asan Medical Center (IRB No. 2013-0572; approval date: June 16, 2013).

Tumor tissues obtained during surgery were immediately frozen after collection and stored at −80 ℃ for subsequent DNA extraction. When fresh tumor tissue from surgery was unavailable, DNA was extracted from formalin-fixed paraffin-embedded (FFPE) specimens used for histopathological diagnosis. Blood specimens were collected at the time of surgery and subsequent blood collection time points during the patient’s treatment course using BD EDTA-treated tubes (BD Bioscience, CA, USA). Within 1 hour of collection, blood underwent two-step centrifugation: first at 1,300 g for 15 minutes to separate plasma, followed by second centrifugation at 16,000 g for 10 minutes. When not processed immediately, plasma was stored at −80 ℃ and processed within a few days.

Tumor DNA extraction and sequencing

Genomic DNA extraction from fresh frozen tissue was performed using the QIAamp DNA mini kit (Qiagen, CA, USA) according to the manufacturer’s instructions. Tumor DNA extraction from FFPE tissue was performed using the RecoverAll total nucleic acid isolation kit (Ambion, CA, USA) according to manufacturer’s instructions. DNA concentration was measured using a NanodropTM 2000 spectrophotometer (Thermo Fisher Scientific, MA, USA). For genomic sequencing analysis, Sanger sequencing technique was used. Primers targeting exons 2-11 of the TP53 gene were synthesized and amplified through polymerase chain reaction (PCR). DNA sequencing was performed using the Applied Biosystems 3730 DNA Analyzer (Applied Biosystems, CA, USA).

Cell-free DNA (cfDNA) sample preparation

To validate the efficacy of the ddPCR assay used in this study, fragmented artificial cfDNA reference specimens were prepared. These specimens consisted of mixtures of TP53 wild-type and TP53 mutant (R175H and R248W) DNA fragments. Wild-type DNA was prepared by randomly fragmenting human female genomic DNA (Promega, Madison, WI, USA) using a DNA fragmentation kit (Takara, Shiga, Japan) according to the manufacturer’s instructions. TP53 mutant fragments were synthesized using 100 bp synthetic oligonucleotides (Bioneer, Daejeon, Republic of Korea). Various genome equivalents (GEq) of mutant DNA were mixed in serial dilution ratios with a total of 3,030 GEq corresponding to 10 ng of wild-type TP53 DNA.

For cfDNA extraction from plasma, the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany) was used according to manufacturer’s instructions. cfDNA was eluted with 50 µL of elution buffer, and its concentration was measured using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA).

ddPCR analysis

To detect oncogenic mutations in ctDNA, custom TaqMan SNP genotyping assays were used, including 6-carboxyfluorescein (FAM)-labeled probes for mutant alleles and VIC-labeled probes for wild-type counterparts (Thermo Fisher Scientific). Quantification of target mutations was performed using the QX200 Droplet Digital PCR system (Bio-Rad, Hercules, CA, USA). DNA templates were encapsulated into approximately 20,000 nanoliter-sized droplets using the Automated Droplet Generator (Bio-Rad). PCR amplification was performed under the following thermal cycling conditions: initial denaturation at 95 ℃ for 10 minutes, followed by 40 cycles of denaturation at 94 ℃ for 15 seconds and annealing/extension at 58–60 ℃ for 60 seconds, and final enzyme inactivation at 98 ℃ for 10 minutes. After amplification, droplets were analyzed using the QX200 Droplet Reader, which detects fluorescence signals for each droplet. Data collection and analysis were performed using QuantaSoft v1.7 software (Bio-Rad).

Analytical validation of ddPCR for TP53 mutation detection in ctDNA

To validate the performance of the ddPCR assay for TP53 mutation detection in ctDNA, we evaluated the ability to detect R175H and R248W TP53 hotspot mutations, which are among the most frequently altered codons in TP53 of epithelial ovarian cancer (24). Synthetic DNA mixtures containing various ratios of wild-type and mutant alleles (10%, 1%, 0.1%) were prepared, and ddPCR was performed using mutation-specific probes. The assay consistently detected TP53 mutations at all tested allele frequencies, with mutant allele fractions accurately quantified in a concentration-dependent manner, confirming the high sensitivity and quantitative precision of the assay (Figure S2).

Mutation-specific fluorescence thresholds were established based on the distribution of fluorescence amplitudes in no-template and wild-type controls. These thresholds were applied to distinguish true-positive droplets from background noise and to minimize false-positive signals during ctDNA analysis.

CA-125 measurement

Serum CA-125 levels were quantified using the ARCHITECT CA-125 II reagent kit (Abbott Laboratories, IL, USA) on the ARCHITECT i2000 immunoanalyzer (Abbott Laboratories).

Cut-off value determination

To establish the threshold for mutation-specific ctDNA positivity, the limit of blank (LOB) was defined as the median plus two standard deviations of copies per microliter values obtained from plasma cfDNA of mutation-negative populations, following previous studies (25,26). To determine the LOB for TP53 mutations, ddPCR was performed on cfDNA extracted from plasma specimens of HGSOC patients confirmed negative for R175H or R248W mutations. Based on this approach, the predefined cut-off value for TP53MAC was calculated as 0.631 copies/µL (median + 2 × SD) (Figure S3), consistent with previously reported methodologies (27). For CA-125, the established clinical cut-off value of 35 U/mL was applied.

Statistical analysis

The primary endpoint was longitudinal change in TP53MAC in relation to treatment response and disease recurrence. Secondary endpoints included concordance between tumor and plasma TP53 mutations and comparative performance of TP53MAC and CA-125 for recurrence detection. Mutation analysis in cfDNA was performed using QuantaSoft Software (Bio-Rad). TP53MAC and CA-125 levels at different time points (pre-treatment, post-surgery, post-chemotherapy, and recurrence) were compared using the Wilcoxon signed-rank test. Changes in biomarker levels throughout the treatment course were analyzed using generalized linear mixed models to account for repeated measures and interpatient variability.

Receiver operating characteristic (ROC) curve analyses were performed to compare the prognostic performance of TP53MAC and CA-125 at multiple time points. The area under the curve (AUC) was calculated for each biomarker, and differences in AUC were descriptively compared. Positive likelihood ratios (LR+) were computed from sensitivity and specificity values at each time point to assess clinical utility. Sensitivity at a fixed specificity level of 78–80%, as previously reported for CA-125 (28), was compared between TP53MAC and CA-125. Statistical analyses were performed using SPSS version 21.0 (SPSS, Inc., Chicago, IL, USA), and ROC curves were generated using GraphPad Prism version 5. A P value <0.05 was considered statistically significant.

Given the exploratory and translational nature of this prospective observational study, a formal sample size calculation was not performed. The sample size was determined by the availability of patients with complete longitudinal plasma sampling, confirmed tumor-specific TP53 mutations, and sufficient follow-up duration to evaluate longitudinal biomarker dynamics.

Results

Clinical characteristics of study subjects

A total of 25 patients with histologically confirmed HGSOC were included in the final analysis. All included patients had available tumor tissue for TP53 mutation analysis, longitudinal plasma samples for ctDNA assessment, and complete clinical follow-up data. The majority of patients (92.0%, n=23) had advanced-stage disease (FIGO stage III–IV). The mean age at diagnosis was 55.5 years (range, 43–73 years), and the mean follow-up duration was 91.1 months (range, 19–132 months). During follow-up, 18 patients (72.0%) experienced disease recurrence. The median progression-free survival (PFS) for the entire cohort was 48.1 months, and the mean PFS among patients with recurrence was 23.6 months (Table 1).

Detection of TP53 mutations and concordance between tumor tissue and plasma

Tumor-specific TP53 mutations were identified in the tumor tissue of all enrolled patients. The same TP53 mutations were consistently detected in matched plasma ctDNA samples, demonstrating complete concordance between tumor tissue and plasma across the study cohort (Table 2).

Changes in TP53MAC and CA-125 during treatment

Plasma TP53MAC levels declined significantly following primary treatment. Compared with pretreatment levels, TP53MAC decreased significantly after primary debulking surgery (P=0.003) and further declined after completion of adjuvant chemotherapy (P=0.01). Serum CA-125 levels also showed significant reductions during the treatment course (P<0.001; Table S1).

When TP53MAC and CA-125 were evaluated in parallel at predefined treatment milestones, including the preoperative, postoperative, and post-chemotherapy time points, both biomarkers generally reflected treatment response. However, interpatient variability and discordant biomarker dynamics were observed, suggesting complementary behavior between TP53MAC and CA-125.

Performance of TP53MAC for recurrence detection

The predictive performance of TP53MAC for disease recurrence was evaluated using ROC curve analyses at multiple treatment and follow-up time points and directly compared with CA-125 (Figure 1). Overall, TP53MAC demonstrated AUC values comparable to those of CA-125 throughout the disease course.

Figure 1 Receiver operating characteristic curve analysis and diagnostic performance of TP53 mutant allele count versus CA-125 across different treatment time points. AUC, area under the curve; CA-125, cancer antigen 125; Chemo, chemotherapy; ctDNA, circulating tumor DNA; f/u, follow-up; post-OP, postoperative; pre-OP, preoperative.

At the preoperative time point, TP53MAC showed a higher AUC than CA-125 (0.659 vs. 0.508). After surgery, CA-125 demonstrated a slightly higher AUC than TP53MAC (0.664 vs. 0.656), with a more pronounced difference favoring CA-125 at the third cycle of chemotherapy (0.694 vs. 0.520). In contrast, after completion of chemotherapy, TP53MAC again showed higher AUC values compared with CA-125 (0.674 vs. 0.613). During surveillance, TP53MAC consistently demonstrated higher AUC values at 3 months (0.661 vs. 0.539) and 6 months (0.654 vs. 0.527) of follow-up.

To further assess clinical utility, positive LR+ were calculated for each biomarker at all evaluated time points (Table 3). TP53MAC showed higher LR+ values than CA-125 immediately after surgery and after completion of chemotherapy. In addition, sensitivity analyses at a fixed specificity level of 78–80% demonstrated higher sensitivity for TP53MAC than CA-125 at the postoperative and post-chemotherapy time points (Table S2).

Longitudinal biomarker dynamics during follow-up

Longitudinal monitoring revealed heterogeneous temporal patterns of TP53MAC and CA-125 across individual patients. Representative cases illustrating concordant and discordant biomarker dynamics are shown (Figure 2), and longitudinal biomarker profiles for all patients are presented (Figure S4). These overall longitudinal patterns were further examined through representative patient-level analyses, as described below.

Figure 2 Longitudinal dynamics of TP53 mutant allele count and CA-125 levels in representative patients with high-grade serous ovarian cancer. Time-course plots showing TP53MAC (pink, left y-axis, log scale) and CA125 (blue, right y-axis, log scale) measured at serial time points during and after treatment in selected patients with high-grade serous ovarian cancer. Treatment events including surgery, chemotherapy (Chemo), bevacizumab (Beva), olaparib (Ola), veliparib (Vel), durvalumab (Du) and recurrence (Recur) are indicated with distinct colored symbols. Colored dots represent ctDNA (TP53) concentrations, with intensity reflecting allele counts (copies/µL). (A) Representative cases demonstrating persistent postoperative TP53MAC elevation reflecting residual disease. TP53MAC remained above the predefined cut-off after surgery in selected patients and showed dynamic changes during treatment, with increases at recurrence, whereas CA-125 remained within the normal range in some cases. (B) A representative case showing concordant declines in both TP53MAC and CA-125 following treatment, consistent with sustained progression-free status during long-term follow-up. (C) Cases illustrating recurrence with normal CA-125 levels. TP53MAC demonstrated variable dynamics, including delayed elevation at recurrence or transient fluctuations despite clinically confirmed disease progression. (D) Cases demonstrating discordant biomarker dynamics. CA-125 showed prolonged elevation without corresponding TP53MAC increase in some patients, whereas TP53MAC increased prior to radiologic confirmation of recurrence in others.

In several patients, TP53MAC and CA-125 demonstrated concordant declines after surgery and chemotherapy and increased at the time of recurrence. However, discordant biomarker dynamics were observed in a subset of patients. In these cases, TP53MAC increased earlier than CA-125 at recurrence, whereas isolated CA-125 elevation without a corresponding increase in TP53MAC was not consistently associated with radiologically confirmed recurrence.

Representative patient-level analyses of TP53MAC and CA-125

To further illustrate the heterogeneous longitudinal dynamics of TP53MAC and CA-125, representative patient-level analyses were examined. In patients P-0029 (TP53-E286K) and P-0032 (TP53-R273C), TP53MAC remained above the predefined cut-off after primary debulking surgery, in contrast to most patients in whom TP53MAC declined below the cut-off postoperatively. In P-0029, TP53MAC decreased below the cut-off following completion of adjuvant chemotherapy, whereas in P-0032 it remained persistently elevated during postoperative chemotherapy. Postoperative imaging identified subdiaphragmatic nodules in P-0029 and residual liver lesions in P-0032, consistent with persistent TP53MAC positivity reflecting residual tumor burden. At recurrence, TP53MAC increased further in P-0032 while CA-125 remained within the normal range (Figure 2A).

In patient P-0046 (TP53-R282W), both TP53MAC and CA-125 were elevated at baseline and subsequently declined below their respective cut-off values during treatment, consistent with sustained progression-free status during long-term follow-up (Figure 2B).

In patients P-0077 (TP53-S241F) and P-0084 (TP53-P190T), both of whom experienced recurrence, CA-125 remained within the normal range throughout both recurrence events. In P-0077, TP53MAC remained within the normal range during the first recurrence with minor fluctuations, but increased above the predefined cut-off during the second recurrence, subsequently declining following additional treatment (Figure 2C).

In patient P-0080 (TP53-R273C), CA-125 remained above the upper limit of normal for a prolonged period prior to radiologic confirmation of recurrence, whereas TP53MAC remained below the predefined cut-off during the same interval. In patient P-0089 (TP53-R273H), TP53MAC exceeded the cut-off prior to radiologic confirmation of recurrence. At the time of recurrence, both TP53MAC and CA-125 were above their respective cut-off values, after which TP53MAC declined to below the cut-off following treatment initiation (Figure 2D).

Discussion

In this prospective observational study, we evaluated the clinical utility of patient-specific TP53 mutation-based ctDNA monitoring using ddPCR in patients with HGSOC. We demonstrated complete concordance between tumor tissue and plasma TP53 mutations and showed that longitudinal changes in TP53MAC dynamically reflected treatment response across surgery, chemotherapy, and follow-up. Importantly, TP53MAC provided information complementary to CA-125, particularly during post-treatment surveillance, where discordant biomarker dynamics were frequently observed.

Although CA-125 remains the most widely used biomarker for monitoring ovarian cancer, its clinical utility is limited by suboptimal sensitivity and heterogeneous expression patterns (8,9). Consistent with prior reports and our cohort-level observations, CA-125 did not reliably identify recurrence in all patients, particularly during early stages of relapse (10-12). In contrast, TP53MAC demonstrated performance comparable to CA-125 across multiple time points and showed favorable sensitivity and likelihood ratios at selected post-treatment stages, supporting its role as a complementary biomarker rather than a replacement for CA-125.

Longitudinal and patient-level analyses revealed heterogeneous biomarker dynamics, underscoring the biological complexity of disease evolution in HGSOC. In several patients, TP53MAC increased earlier than CA-125 at recurrence, suggesting that ctDNA may capture molecular disease progression before biochemical or radiologic confirmation (13,14,17). Conversely, isolated CA-125 elevation without corresponding TP53MAC positivity was not consistently associated with confirmed recurrence, highlighting the potential value of integrating both biomarkers for surveillance rather than relying on a single marker.

Previous studies have explored ctDNA as a prognostic or predictive biomarker in ovarian cancer, primarily focusing on postoperative detection of minimal residual disease or relapse prediction (17-20,29,30). However, many of these studies relied on single time-point assessments or next-generation sequencing-based approaches, which are often associated with higher costs and longer turnaround times, limiting their feasibility for routine longitudinal monitoring. The present study extends existing evidence by demonstrating the feasibility of patient-specific TP53 mutation-informed ctDNA monitoring across the entire treatment course, including surgery, chemotherapy, and long-term follow-up.

Given that TP53 mutations are present in nearly all cases of HGSOC (23), targeting tumor-specific TP53 alterations represents a biologically rational strategy for individualized ctDNA monitoring. In this context, ddPCR enables sensitive and quantitative detection of predefined TP53 mutations in a cost-effective and rapid manner, supporting its potential applicability in routine clinical practice (22,29,30). Although ddPCR-based assays are not yet universally implemented in all clinical laboratories, they may represent a practical approach for longitudinal ctDNA monitoring, particularly when repeated next-generation sequencing is not feasible.

In some patients, TP53MAC remained detectable despite normalization of CA-125, suggesting its potential to identify residual or recurrent disease not captured by conventional biomarkers. This approach is based on a single, ubiquitous driver mutation and complements broader genomic profiling strategies that capture subclonal evolution and actionable alterations.

Several limitations of this study should be acknowledged. The relatively small sample size and single-center design may limit generalizability. In addition, this study was not powered to assess the independent prognostic value of TP53MAC for survival outcomes. Furthermore, we were unable to compare TP53MAC with KELIM, a validated CA-125-based kinetic biomarker, because CA-125 measurements during the early chemotherapy period were not sufficiently frequent to allow robust KELIM estimation. Intratumoral heterogeneity and clonal evolution may also affect ctDNA detectability, as ddPCR targets predefined mutations and may not capture newly emerging variants (20,30). Finally, although TP53MAC dynamics were qualitatively associated with radiologic findings in selected cases, a formal quantitative comparison with imaging-based tumor burden like RECIST measurements was not performed due to the lack of standardized radiologic assessments. Therefore, the correlation between ctDNA levels and tumor burden should be interpreted as descriptive.

Despite these limitations, this study provides translational evidence supporting the feasibility and clinical relevance of personalized TP53 mutation-based ctDNA monitoring in HGSOC. TP53MAC may serve as a useful adjunct to CA-125 for assessing treatment response and monitoring recurrence, particularly in patients with discordant biomarker dynamics. Further validation in larger, multicenter cohorts will be essential to define its role in routine ovarian cancer surveillance.

Conclusions

In conclusion, patient-specific TP53 mutation-based ctDNA monitoring using ddPCR was feasible in patients with HGSOC and dynamically reflected treatment response and disease burden over time. TP53MAC demonstrated performance comparable to CA-125 for recurrence detection and provided complementary information during post-treatment surveillance, particularly in patients with discordant biomarker dynamics. These findings support the potential role of TP53 mutation–informed ctDNA monitoring as an adjunct to conventional biomarkers rather than a replacement for CA-125.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0241/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0241/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0241/prf

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 No. RS-2021-NR063303). This study was also supported by a grant from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (grant No. 2023IP0145).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0241/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Institutional Review Board of Asan Medical Center (IRB No. 2013-0572; approval date: June 16, 2013). Written informed consent was obtained from all participants prior to study enrollment.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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