You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Current Oncology
Download PDF
  • Review
  • Open Access

22 October 2025

ctDNA in Pancreatic Adenocarcinoma: A Critical Appraisal

,
,
and
1
Department of Internal Medicine, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
2
Division of Hematology and Oncology, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
Curr. Oncol.2025, 32(11), 589;https://doi.org/10.3390/curroncol32110589
This article belongs to the Section Oncology Biomarkers
Version Notes
Order Reprints

Simple Summary

Detection of cancer derived mutations in blood could offer a more accurate assessment of patient prognosis, their response to therapies, and a better way to monitor the biology of the patients’ cancer and select more appropriate treatments for individual patients. However, despite the promise and progress in the field of testing circulating tumor DNA (ctDNA) in patients with cancers, concrete evidence that shows that ctDNA testing benefits the clinical outcomes of patients with pancreatic ductal adenocarcinoma (PDAC) is still lacking. More rigorous validation studies within specific clinical contexts are needed before the true potential of ctDNA can be realized for patients with PDAC.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies due to late diagnosis and limited treatment options. Circulating tumor DNA (ctDNA) is a promising, minimally invasive biomarker that could improve the clinical outcomes of patients with PDAC by enabling early disease detection, minimal residual disease (MRD) assessment, precise prognostication, and accurate treatment monitoring. CtDNA has prognostic as well as predictive value in both resectable and metastatic settings, with serial measurements enhancing risk stratification and recurrence prediction beyond CA19-9. However, despite the promise, the true potential of ctDNA has not yet been fulfilled in patients with PDAC. The current limitations include a low sensitivity of ctDNA assays in early stage PDAC, challenges in the assay interpretation due to the specific nature of ctDNA shedding in PDAC, inter-patient heterogeneity, and technical variability. As precision oncology advances, ctDNA will be a powerful tool for personalized care in PDAC, but rigorous validation of its use within specific clinical contexts is still needed before the true potential of ctDNA is realized for patients with PDAC.

1. Introduction

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies worldwide, with a 5-year survival rate of less than 15% []. Surgical resection is the only curative treatment option, but most patients present late with locally advanced unresectable disease or metastatic disease. Although there are well-recognized risk factors for the development of PDAC, including inherited pathogenic mutations in cancer susceptibility genes such as STK11 and BRCA1/2, chronic diseases such as chronic pancreatitis and diabetes, and lifestyle factors such as tobacco (15–30% of cases) and alcohol use, currently, existing biomarkers like CA19-9 and imaging modalities lack sufficient sensitivity for early stage detection or precise dynamic disease monitoring [,]. Thus, new improved disease specific markers are needed for early detection and making better clinical decisions.
Recently, circulating tumor DNA (ctDNA), a subset of cell-free DNA (cfDNA), has emerged as a promising biomarker that offers real-time, noninvasive insights into disease burden, treatment response, and disease recurrence [,,,]. First identified in the 1970s, the technological advances made in recent decades, including next-generation sequencing (NGS), digital PCR, and tumor-informed assays, have dramatically improved ctDNA detection [,,]. ctDNA, a cornerstone of liquid biopsy, has now emerged as a crucial biomarker for the detection of minimal residual disease (MRD) and recurrence risk stratification in solid tumors, with the most robust evidence shown to date in colorectal cancer (CRC) []. Recent improvements in assay sensitivity and the integration of artificial intelligence (AI) have further broadened ctDNA applications in early detection and treatment planning [,]. In 2020, the U.S. Food and Drug Administration (FDA) approved ctDNA-based liquid biopsies to detect actionable mutations such as pathogenic mutations in BRCA1 and BRCA 2 in patients with ovarian and prostate cancers, ALK rearrangement in non-small cell lung cancer, and PIK3CA mutations in breast cancer, with Medicare coverage implemented since 2021. These ctDNA based tests currently guide treatment decisions in oncology clinics.
Compared with tissue biopsy, liquid biopsy is less invasive, associated with fewer complications, and offers faster turnaround times. As NGS becomes more accessible and affordable, ctDNA-based liquid biopsies are expected to expand into broader clinical applications in oncology. However, despite all the advances we have seen in the ctDNA research field, there is currently no FDA approved ctDNA-based assay for use in patients with PDAC. Thus, perhaps the time is ripe for a critical appraisal of the status of ctDNA as a biomarker in this disease. This review summarizes key aspects of ctDNA clinical applications across PDAC stages, offering oncologists, researchers, and trainees an evidence-based appraisal.

2. Current Evidence on ctDNA Clinical Utility in PDAC

2.1. ctDNA as a Novel Prognostic Marker in Advanced PDAC

In patients with advanced metastatic disease, ctDNA has robust data supporting its use as a prognostic biomarker. Multiple prospective, retrospective studies, and metanalyses have demonstrated that the presence of detectable ctDNA at the baseline in patients with metastatic PDAC is associated with shorter overall survival (OS) and progression free survival (PFS) compared with ctDNA negative patients, even after adjusting for tumor burden, CA19-9 levels, and other risk factors [,,,,,]. Thus, ctDNA is an independent prognostic biomarker in advanced PDAC.
Previous studies have found that ctDNA and CA 19-9, the only FDA approved tumor marker in PDAC, are positively correlated and offer complementary prognostic information [,,,]. High CA19-9 levels are associated with an increased likelihood of ctDNA detection, and both markers are independently associated with worse survival outcomes [,,,]. In a large cohort of unresectable PDAC patients, the baseline ctDNA and CA19-9 correlated positively, however, remained independently associated with time to progression along with OS in multivariate analysis []. Additionally, ctDNA was detectable in some patients without CA 19-9 elevation, underscoring its non-overlapping utility. In a large colorectal cancer cohort, Botta et al. demonstrated that personalized ctDNA assays outperformed traditional tumor markers in predicting recurrence and remained independently prognostic even after adjusting for CA 19-9 and CEA []. While not all studies are PDAC-specific, these findings support the potential for ctDNA to complement or surpass CA19-9 in prognostic utility []. Similarly, a prospective ancillary study of the PANACHE01-PRODIGE48 trial found that both high CA19-9 and ctDNA positivity at the time of diagnosis were independently associated with worse OS. When combined, the biomarkers have superior risk stratification compared with either alone, with the worst outcomes in patients positive for both [].
The diagnostic accuracy of CA19-9 and ctDNA depends on the stage of disease and the type of assay. CA19-9 remains the most used biomarker but is limited by false positives in benign disease and false negatives in Lewis antigen-negative patients. On the other hand, ctDNA offers higher specificity, but lower sensitivity in early stage disease. Table 1 summarizes the evidence behind the sensitivity and specificity of these biomarkers. The potential of ctDNA as a screening tool and for early detection is discussed in the next section.
Table 1. Sensitivity and specificity of circulating biomarkers in PDAC.

2.2. ctDNA as a Tool for PDAC Screening/Early Detection

PDAC is deadly as it is notoriously difficult to diagnose in its early stages due to the absence of specific symptoms, limited risk stratification tools, and challenges in tissue accessibility. Noninvasive screening strategies are scarce, and only 20% are diagnosed with resectable disease []. An ideal screening test would be a blood test that reliably detects a marker or markers specifically produced by PDAC cells at an early stage, prior to metastasis, that can survive hepatic filtration with a high sensitivity and specificity.
As KRAS mutations are found in over 90% of PDAC cases, the detection of KRAS mutations in ctDNA is a key target for screening assays [,]. Notably, KRAS mutations are early fundamental biological events in PDAC oncogenesis and may develop several years before clinical diagnosis, offering a potential window for early intervention. However, the pancreas drains primarily into the portal venous circulation, causing significant hepatic filtration (first-pass effect) of tumor DNA before reaching peripheral blood. This effect contributes to a low sensitivity of peripheral ctDNA assays in early stage PDAC. Studies have demonstrated higher ctDNA detection rates in portal blood compared with peripheral blood, particularly in early or low-volume disease [,]. A prospective study by Nitschke et al. analyzed ctDNA from both peripheral and portal venous blood in resectable PDAC patients [] and found that KRAS-mutant ctDNA detection was significantly associated with shorter recurrence-free survival (p < 0.015). Notably, portal venous blood yielded a higher sensitivity for KRAS-mutant ctDNA detection and stronger prognostic significance than peripheral sampling []. These findings highlight a key limitation of using a ctDNA peripheral assay as a screening tool in PDAC, although they still reinforce the potential clinical value of postoperative ctDNA in detecting MRD and risk-stratifying patients for recurrence before radiographic evidence emerges [,,,].
The GRAIL’s Circulating Cell-Free Genome Atlas (CCGA) study enrolled over 15,000 participants to evaluate ctDNA-based multi-cancer early detection using a targeted methylation assay []. In its 2020 analysis, for PDAC, the sensitivity was 63% in Stage I and nearly 100% in Stage IV disease. While sensitivity remains a critical factor in screening, the high specificity of ctDNA is particularly valuable []. Accordingly, the American Society of Clinical Oncology and the College of American Pathologists opines that ctDNA is not yet validated for early detection or population screening, though it holds promise for treatment and MRD monitoring []. On the other hand, studies have shown that combining CA19-9, the only tumor marker validated to date in PDAC, with ctDNA (or other assays such as protein or methylation-based markers) improve early detection, signaling hope for the future [,,].

2.3. Differentiating Benign and Pancreatic Cysts with Malignant Potential

In the field of gastrointestinal oncology, pancreatic cysts also present diagnostic and management challenges. While most cysts are benign, some can carry a malignant potential. Given the poor prognosis of PDAC, being diagnosed with a pancreatic cyst often triggers patient anxiety and can lead to costly, invasive, and sometimes unnecessary interventions. Plasma ctDNA, including KRAS mutations, may correlate with tumor burden, but its sensitivity for early or premalignant disease is low and not recommended for cyst evaluation or screening [,,,]. In contrast, cyst fluid DNA analysis, despite its low cellularity, can provide useful insights, including actionable mutations, and improve risk stratification when combined with imaging and clinical data [,,].

2.4. Measuring Minimal Residual Disease

Multiple systematic reviews and metanalyses have consistently demonstrated that postoperative ctDNA positivity is a strong, independent predictor of overall survival in both curative and palliative cohorts of patients with solid tumors. In one metanalysis, encompassing over 3500 patients, ctDNA-positive individuals had a pooled hazard ratio of 7.27 for recurrence following curative-intent resection, with concordant findings across both tumor-informed and tumor-agonist assays [,,]. Prospective and large cohort studies show that ctDNA can detect recurrence months before clinical or radiographic evidence, with lead times of 7–12 months [,]. ctDNA also outperforms traditional biomarkers like CEA [,,,]. Undetectable ctDNA postoperatively carries a high negative predictive value, supporting its role in adjuvant therapy de-escalation [,]. While most evidence is in CRC, similar applications are being explored in PDAC, with ongoing clinical trials assessing ctDNA-guided treatment strategies. Although other MRD biomarkers like CA19-9, circulating tumor cells (CTCs), and mRNA have been explored, ctDNA remains the most extensively studied. The optimal timing of ctDNA measurement varies across studies in the literature, however, typically, measurements are taken preoperatively, followed by the early postoperative period (generally 2 to 12 weeks after surgery), and then additionally for long-term follow up. Presence of detectable ctDNA is significantly associated with worse OS and PFS [,,,,]. In resected PDAC patients, postoperative ctDNA positivity has demonstrated a high sensitivity (90%) and a high specificity (88%) for predicting disease recurrence []. Table 2 summarizes the clinical studies, systematic reviews, and meta-analyses evaluating MRD monitoring in PDAC. Current guidelines recommend adjuvant therapy for all resected PDAC patients who are fit; however, decisions about timing and regimen depend largely on recovery status and expected responsiveness. ctDNA trends from the neoadjuvant and early postoperative setting may help inform these decisions. Especially persistently elevated or rising postoperative ctDNA levels may support the need for aggressive adjuvant therapy.
Table 2. Studies that evaluated the value of ctDNA for MRD monitoring after surgery in PDAC.

2.5. ctDNA as a Tumor Marker to Monitor Chemotherapy Response

Monitoring Therapy in Neoadjuvant and Palliative Settings

Neoadjuvant chemotherapy is commonly used in resectable and borderline resectable PDAC with treatment response traditionally assessed by CT, PET/CT, or MRI, although these modalities can struggle to distinguish viable tumor from post-treatment inflammation or fibrosis. Advanced imaging methods like PET/MRI and diffusion-weighted MRI, along with radiomics, show promise, but are not yet standard and still require further validation [,,].
ctDNA provides a complimentary, real-time measure of tumor burden and responsiveness. In a prospective cohort of localized PDAC, serial ctDNA analysis revealed that patients who had detectable ctDNA after neoadjuvant chemotherapy had a higher CA19-9 level along with worse PFS. Additionally, the presence of KRAS mutations in ctDNA at the time of diagnosis independently predicted inferior outcomes. ctDNA clearance during therapy was also associated with improved prognosis []. Another prospective study used digital droplet PCR and found that the clearance of KRAS mutations in ctDNA during the neoadjuvant therapy period was associated with significantly improved OS, however, persistent or new detectable ctDNA after treatment resulted in poor outcomes []. Postoperative ctDNA positivity is also a strong predictor of early relapse and decreased survival, especially when combined with elevated CA19-9 levels []. Longitudinal ctDNA profiling has demonstrated that changes in ctDNA allele correlate with clinical response and disease burden.
Serial monitoring of ctDNA during neoadjuvant therapy may provide an early indication of tumor response; declining levels are associated with radiographic regression and may help guide timing for surgical resection. For example, patients who show strong ctDNA clearance with less intensive regimens such as gemcitabine may avoid escalation to mFOLFIRINOX, particularly if the postoperative functional status is marginal. Conversely, lack of ctDNA clearance or rising levels during or after neoadjuvant therapy may prompt a change in adjuvant strategy. This additional insight may help guide both the treatment selection and prediction of neoadjuvant therapy response.
In advanced PDAC, ctDNA monitoring provides earlier and more sensitive detection of disease progression along with treatment response when compared with imaging and serum tumor markers. Multiple studies in advanced PDAC and other solid tumors have shown that dynamic changes in the level of ctDNA correlate with clinical outcomes and often precede radiologic evidence, up to a median of 19–23 days [,,,]. Quantitative ctDNA mirrored the disease state, with the disappearance of ctDNA after initial chemotherapy correlating with longer PFS []. In particular, in metastatic CRC, ctDNA was more sensitive than CEA for detecting disease progression and responded more rapidly to changes in tumor burden []. Similarly, these findings have also been reported in other solid tumors, where ctDNA use during therapy predicted the time to treatment failure and other clinical outcomes, often preceding clinical or radiographic signs of progression [,,].

2.6. Patient Selection for Precision Medicine

Unresectable and metastatic PDACs account for nearly 80% of new PDAC diagnoses and present unique opportunities for precision medicine []. ctDNA serves as a noninvasive alternative for real-time genomic profiling, particularly when tumor tissue is limited or insufficient. In this setting, ctDNA has the potential to expand access to biomarker-driven treatments in PDAC by enabling real-time, noninvasive genomic profiling. ctDNA can be utilized to identify actionable mutations such as alterations in KRAS, BRCA, EGFR, BRAF, PIK3CA, which may inform targeted therapy selection. In a large cohort study, ctDNA identified relevant alterations for precision medicine in nearly half of the patients with advanced disease []. The strong agreement between ctDNA results and tissue mutation profiling results by NGS reinforces the utility of ctDNA guiding targeted therapies []. Furthermore, longitudinal tracking of ctDNA profiling can identify clonal evolution along with emerging resistance, allowing for a refined approach to precision medicine [,].
While most of the evidence comes from other related gastrointestinal malignancies, the findings are increasingly applicable to PDAC, where tissue samples are difficult to obtain. In a study evaluating advanced biliary tract cancers, ctDNA showed a high concordance (85%) with tissue based NGS and successfully identified actionable alterations. Some alterations like FGFR2 fusions and IDH1 mutations were reported with ctDNA while missing out on tissue analysis [,]. A recent prospective cohort study of 30 patients with resectable or borderline PDAC demonstrated the feasibility of the detection of pathogenic variants in baseline plasma using a tumor agnostic approach []. A large prospective study conducted by the National Center for Precision Medicine in France evaluated the utility of ctDNA sequencing across a cohort of 1772 patients. Their study provides compelling evidence that ctDNA profiling can facilitate timely and personalized treatment strategies, particularly when the tissue sample is limited or inaccessible []. In another retrospective cohort study of 259 inoperable PDAC patients, researchers evaluated the clinical potential of analyzing plasma-derived cfDNA using a two stage approach by combining droplet digital PCR (ddPCR) and targeted deep sequencing. KRAS mutations were detected by ddPCR in 58.9% of the cases, and subsequent cfDNA sequencing in a subset of 48 KRAS-mutant plasma samples identified potential actionable mutations in 29.2% of the patients. This demonstrates that ctDNA can uncover alterations beyond KRAS, expanding the therapeutic landscape for precision medicine [].

3. Limitations and Challenges

Despite promising preliminary results, ctDNA faces several limitations that hinder routine clinical implementation. The current evidence indicates that in early stage PDAC, ctDNA concentrations are often below the threshold for detection, reducing its sensitivity for screening or early detection. This low sensitivity is due to several reasons. Tumor biology is different across patients, and not all tumors shed detectable levels of ctDNA into circulation [,,]. An additional biological factor limiting ctDNA detection is hepatic filtration, especially in patients who have localized disease, as it can further reduce the detectability [,,]. Furthermore, some rare driver mutations can be missed if they fall outside standard assay panels [,,]. As a result, a negative ctDNA result cannot confidently exclude the presence of disease, especially in early stage PDAC.
In terms of measuring MRD, ctDNA positivity postoperatively clearly indicates a high risk of recurrence and poor prognosis. However, it remains uncertain how positive ctDNA results should be acted upon. Currently, no sufficient data are available to recommend intensification or the switching of adjuvant therapies in these patients. Further studies are needed to understand the best treatment strategies for these patients. Similarly, how we should use ctDNA in disease monitoring during neoadjuvant, adjuvant, and palliative chemotherapy is not clear at present. Although it is logical to use ctDNA in CA19-9 negative patients for disease monitoring, in those with detectable CA19-9, how best we should use ctDNA remains unanswered. Most studies that have attempted to answer these questions were exploratory in nature, and further rigorous validation studies will be needed before the role of ctDNA can be clearly defined in improving patient outcomes.
There also remains unresolved technical challenges that include inconsistent sensitivity across the different assays and discrepancies between the tissue-based NGS and ctDNA findings. An additional challenge is setting the appropriate variant allele frequency thresholds and distinguishing true mutations from background noise or low-level artifacts [].
False positives from clonal hematopoiesis, the lack of assay standardization, variability in insurance coverage, high out-of-pocket costs for patients, and unclear reimbursement policies remain barriers to widespread adoption [,]. Reimbursement policies are inconsistent, and regulatory oversight is still evolving. A joint review from the American Society of Clinical Oncology and College of American Pathologists by Merker et al. emphasized the need for rigorous assay validation, clinical-context-specific interpretation, and harmonized standards before ctDNA can be fully integrated into routine care []. Clinical uncertainty persists in the world of ctDNA due to the lack of standardized algorithms to guide clinical decision-making based on ctDNA results outside of the context of clinical trials. Continued technological advances and regulatory guidance will be essential to overcome these challenges [].

4. Discussion and Future Directions

Currently, ctDNA offers a unique and minimally invasive way to monitor tumor biology and molecular characteristics throughout all stages of PDAC. Its most established clinical applications are for monitoring for recurrence and guiding molecular targeted therapies, especially when sufficient tumor tissue is not available for genomic testing. However, its broad integration into areas such as early detection and treatment decisions regarding adjuvant therapy remains more nuanced, requiring more active investigation. Important considerations include how ctDNA can be effectively combined with imaging techniques and establish markers such as CA19-9, how low-level or equivocal ctDNA marker results should be interpreted, and ways to achieve consistent testing approaches across various institutions.
These limitations are beginning to be addressed by emerging strategies. These include integrating ctDNA with clinical history, radiological imaging, and traditional serum biomarkers, supported by artificial intelligence to create dynamic and personalized treatment plans [,,,]. As technology advances, the establishment of standardized protocols and clear regulatory approvals will be essential for widespread clinical adoptions. However, as ctDNA testing becomes more routine, ethical and practical issues are bound to arise. Some of the challenges include incidental findings (such as clonal hematopoiesis or germline mutations), interpreting uncertain results, and patient anxiety associated with a positive liquid biopsy without any radiographic correlation. Recent guidance emphasizes that the utility of ctDNA varies by disease subtype, suggesting that PDAC specific assay sensitivities and shedding thresholds may be required, rather than applying a generalized gastrointestinal cancer framework []. Furthermore, the cost effectiveness of ctDNA, particularly in early stage PDAC, is limited and remains essential for broader reimbursement and clinical acceptance.
ctDNA is also gaining attention as a valuable tool for detecting micrometastatic disease in resectable or localized PDAC. Despite absences in radiographic abnormalities, ctDNA positivity following neoadjuvant therapy or surgery may indicate residual subclinical disease, often seen in the peritoneum or liver that may evade conventional imaging. This insight can be utilized to justify intensified adjuvant therapy or surveillance. Additionally, ctDNA clearance may be used for treatment de-escalation, improving patient quality of life by potentially minimizing overtreatment. However, hepatic filtration can reduce the ctDNA detectability from peritoneal micrometastases. This emphasizes the necessity of a multimodal monitoring approach and presents a potential for future prospective clinical trials exploration.
We summarize the potential clinical utility of ctDNA in patients with PDAC in Figure 1. Currently, there are several clinical trials that are actively evaluating ctDNA in PDAC, particularly focusing on key applications such as early detection, MRD monitoring, and assessing treatment response [,,,,]. In parallel, multiple studies have confirmed the diagnostic and prognostic value of CA19-9 as the most validated biomarker in PDAC, with consistent sensitivity and specificity reported across cohorts [,,,]. These findings reinforce CA19-9 as the benchmark serum marker while also discussing its limitations and need for complementary biomarker tools like ctDNA. Clinical trials that are evaluating ctDNA’s role in PDAC include early detection trials such as NCT03524677 and NCT04246203, which are evaluating mutation panels in high-risk populations [,,]. In molecularly selected subgroups, trials such as APOLLO (NCT04858334) are evaluating ctDNA to guide targeted therapy choices []. On ClinicalTrials.gov, over 40 ctDNA-related PDAC studies are currently active. These include plasma-based genomic signature trials, surveillance studies to guide adjuvant therapy, and serial ctDNA tracking trials for resistance profiling [,,]. Several studies are exploring the integration of ctDNA with imaging or radiomics into composite clinical prediction models [].
Figure 1. Utility of ctDNA through different phases of clinical care in PDAC.
Furthermore, ctDNA’s incorporation into clinical trials as an exploratory endpoint or stratification tool is increasingly being implemented. Although ctDNA is not yet validated as a surrogate marker for OS or PFS, it holds promise in early phase trials for monitoring treatment efficacy and early resistance []. These recommendations align with our proposal to expand ctDNA’s role within adaptive clinical trial designs that personalize treatment in real-time. Several prospective studies and meta-analyses reinforce the role of perioperative ctDNA as an independent predictor of recurrence and survival in PDAC. These studies underscore the prognostic value of ctDNA in postoperative settings, which supports its integration into future clinical trials [,,,,].
As research progresses, positive trial outcomes will help facilitate the inclusion of ctDNA testing in clinical guidelines, standardize the use of ctDNA, and clarify reimbursement policies. Future directions involve integrating ctDNA assays with other innovative approaches such as methylation profiles, fragmentomics analysis, and AI-driven prediction models. Other uses for ctDNA include evaluating ctDNA as a predictive marker for immunotherapy response, tracking resistance in real-time, and assessing tumor heterogeneity across metastatic sites through a noninvasive approach. The hope is that with increasing implementation of ctDNA, we can use it to guide decisions to reduce therapy intensity in patients who have achieved sustained responses and track treatment effects in early phase clinical studies. Furthermore, identifying early signs of organ-specific recurrence (particularly the liver) is an ongoing interest. Nevertheless, these applications still need prospective validation. They, however, highlight ctDNA’s growing potential in precision oncology.

5. Conclusions

ctDNA holds significant promise in transforming the management of PDAC by enabling earlier detection, individualized treatment strategies, and improved clinical outcomes. Despite this potential, broader adoption in clinical practice will depend on ongoing advances in assay technology, integration with complementary biomarkers, and the establishment of standardized protocols. Addressing the challenges associated with low ctDNA shedding in early stage disease and tumor heterogeneity, along with assay standardization, is crucial. Additionally, assessing the cost-effectiveness and clinical impact of routine ctDNA implementation, especially in early detection or screening scenarios, remains essential. Prospective clinical trials are needed for validating ctDNA’s utility across various stages of PDAC care.

Author Contributions

Conceptualization, S.O., W.S., Y.Z. and K.L.A.; Methodology, S.O., W.S., Y.Z. and K.L.A.; Software, S.O. and K.L.A.; Validation, S.O., W.S., Y.Z. and K.L.A.; Investigation, S.O., W.S. and K.L.A.; Resources, S.O., W.S., Y.Z. and K.L.A.; Writing—original draft preparation, S.O.; Writing—review and editing, S.O., W.S. and K.L.A.; Visualization, S.O., W.S., Y.Z. and K.L.A.; Supervision, K.L.A.; Project administration, K.L.A.; Funding acquisition, K.L.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Cancer Prevention and Research Institute of Texas (CPRIT), grant number RP210234 to K.L.A.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial intelligence
ALKAnaplastic lymphoma kinase
BRCA1/2Breast cancer gene 1 and 2
BRAFB-Raf proto-oncogene
CA19-9Carbohydrate antigen 19-9
CEACarcinoembryonic antigen
cfDNACell-free DNA
CRCColorectal cancer
CTComputed tomography
ctDNACirculating tumor DNA
ddPCRDroplet digital polymerase chain reaction
EGFREpidermal growth factor receptor
FDAU.S. Food and Drug Administration
FGFR2Fibroblast growth factor receptor 2
IDH1Isocitrate dehydrogenase 1
KRASKirsten rat sarcoma viral oncogene
MRIMagnetic resonance imaging
MRDMinimal residual disease
NGSNext-generation sequencing
OSOverall survival
PDACPancreatic ductal adenocarcinoma
PETPositron emission tomography
PFSProgression free survival (PFS)

References

  1. Li, Q.; Feng, Z.; Miao, R.; Liu, X.; Liu, C.; Liu, Z. Prognosis and survival analysis of patients with pancreatic cancer: Retrospective experience of a single institution. World J. Surg. Oncol. 2022, 20, 11. [Google Scholar] [CrossRef] [PubMed]
  2. Zanini, S.; Renzi, S.; Limongi, A.R.; Bellavite, P.; Giovinazzo, F.; Bermano, G. A review of lifestyle and environment risk factors for pancreatic cancer. Eur. J. Cancer 2021, 145, 53–70. [Google Scholar] [CrossRef] [PubMed]
  3. Lai, E.; Ziranu, P.; Spanu, D.; Dubois, M.; Pretta, A.; Tolu, S.; Camera, S.; Liscia, N.; Mariani, S.; Persano, M.; et al. BRCA-mutant pancreatic ductal adenocarcinoma. Br. J. Cancer 2021, 125, 1321–1332. [Google Scholar] [CrossRef]
  4. Stejskal, P.; Goodarzi, H.; Srovnal, J.; Hajdúch, M.; van ’t Veer, L.J.; Magbanua, M.J.M. Circulating tumor nucleic acids: Biology, release mechanisms, and clinical relevance. Mol. Cancer 2023, 22, 15. [Google Scholar] [CrossRef]
  5. Sánchez-Herrero, E.; Serna-Blasco, R.; Robado de Lope, L.; González-Rumayor, V.; Romero, A.; Provencio, M. Circulating Tumor DNA as a Cancer Biomarker: An Overview of Biological Features and Factors That may Impact on ctDNA Analysis. Front. Oncol. 2022, 12, 943253. [Google Scholar] [CrossRef]
  6. Wan, J.C.M.; Massie, C.; Garcia-Corbacho, J.; Mouliere, F.; Brenton, J.D.; Caldas, C.; Pacey, S.; Baird, R.; Rosenfeld, N. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat. Rev. Cancer 2017, 17, 223–238. [Google Scholar] [CrossRef]
  7. Bettegowda, C.; Sausen, M.; Leary, R.J.; Kinde, I.; Wang, Y.; Agrawal, N.; Bartlett, B.R.; Wang, H.; Luber, B.; Alani, R.M.; et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med. 2014, 6, 224ra24. [Google Scholar] [CrossRef]
  8. Newman, A.M.; Lovejoy, A.F.; Klass, D.M.; Kurtz, D.M.; Chabon, J.J.; Scherer, F.; Stehr, H.; Liu, C.L.; Bratman, S.V.; Say, C.; et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 2016, 34, 547–555. [Google Scholar] [CrossRef]
  9. Heitzer, E.; Haque, I.S.; Roberts, C.E.S.; Speicher, M.R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 2019, 20, 71–88. [Google Scholar] [CrossRef]
  10. Zheng, J.; Qin, C.; Wang, Q.; Tian, D.; Chen, Z. Circulating tumour DNA-Based molecular residual disease detection in resectable cancers: A systematic review and meta-analysis. eBioMedicine 2024, 103, 105109. [Google Scholar] [CrossRef] [PubMed]
  11. Shen, S.Y.; Singhania, R.; Fehringer, G.; Chakravarthy, A.; Roehrl, M.H.A.; Chadwick, D.; Zuzarte, P.C.; Borgida, A.; Wang, T.T.; Li, T.; et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 2018, 563, 579–583. [Google Scholar] [CrossRef] [PubMed]
  12. Eledkawy, A.; Hamza, T.; El-Metwally, S. Precision cancer classification using liquid biopsy and advanced machine learning techniques. Sci. Rep. 2024, 14, 5841. [Google Scholar] [CrossRef] [PubMed]
  13. Pietrasz, D.; Wang-Renault, S.; Taieb, J.; Dahan, L.; Postel, M.; Durand-Labrunie, J.; Le Malicot, K.; Mulot, C.; Rinaldi, Y.; Phelip, J.-M.; et al. Prognostic value of circulating tumour DNA in metastatic pancreatic cancer patients: Post-hoc analyses of two clinical trials. Br. J. Cancer 2022, 126, 440–448. [Google Scholar] [CrossRef]
  14. Lapin, M.; Edland, K.H.; Tjensvoll, K.; Oltedal, S.; Austdal, M.; Garresori, H.; Rozenholc, Y.; Gilje, B.; Nordgård, O. Comprehensive ctDNA Measurements Improve Prediction of Clinical Outcomes and Enable Dynamic Tracking of Disease Progression in Advanced Pancreatic Cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2023, 29, 1267–1278. [Google Scholar] [CrossRef]
  15. Uesato, Y.; Sasahira, N.; Ozaka, M.; Sasaki, T.; Takatsuki, M.; Zembutsu, H. Evaluation of circulating tumor DNA as a biomarker in pancreatic cancer with liver metastasis. PLoS ONE 2020, 15, e0235623. [Google Scholar] [CrossRef]
  16. Strijker, M.; Soer, E.C.; de Pastena, M.; Creemers, A.; Balduzzi, A.; Beagan, J.J.; Busch, O.R.; van Delden, O.M.; Halfwerk, H.; van Hooft, J.E.; et al. Circulating tumor DNA quantity is related to tumor volume and both predict survival in metastatic pancreatic ductal adenocarcinoma. Int. J. Cancer 2020, 146, 1445–1456. [Google Scholar] [CrossRef]
  17. Guan, S.; Deng, G.; Sun, J.; Han, Q.; Lv, Y.; Xue, T.; Ding, L.; Yang, T.; Qian, N.; Dai, G. Evaluation of circulating tumor DNA as a prognostic biomarker for metastatic pancreatic adenocarcinoma. Front. Oncol. 2022, 12, 926260. [Google Scholar] [CrossRef]
  18. Guven, D.C.; Sahin, T.K.; Yildirim, H.C.; Aktepe, O.H.; Dizdar, O.; Yalcin, S. A systematic review and meta-analysis of the association between circulating tumor DNA (ctDNA) and prognosis in pancreatic cancer. Crit. Rev. Oncol. Hematol. 2021, 168, 103528. [Google Scholar] [CrossRef]
  19. Shah, D.; Wells, A.; Cox, M.; Dawravoo, K.; Abad, J.; D’Souza, A.; Suh, G.; Bayer, R.; Chaudhry, S.; Zhang, Q.; et al. Prospective Evaluation of Circulating Tumor DNA Using Next-generation Sequencing as a Biomarker During Neoadjuvant Chemotherapy in Localized Pancreatic Cancer. Ann. Surg. 2025, 281, 997–1005. [Google Scholar] [CrossRef]
  20. Chen, I.; Raymond, V.M.; Geis, J.A.; Collisson, E.A.; Jensen, B.V.; Hermann, K.L.; Erlander, M.G.; Tempero, M.; Johansen, J.S. Ultrasensitive plasma ctDNA KRAS assay for detection, prognosis, and assessment of therapeutic response in patients with unresectable pancreatic ductal adenocarcinoma. Oncotarget 2017, 8, 97769–97786. [Google Scholar] [CrossRef] [PubMed]
  21. Pinson, J.; Henriques, J.; Beaussire, L.; Sarafan-Vasseur, N.; Sa Cunha, A.; Bachet, J.-B.; Vernerey, D.; Di Fiore, F.; Schwarz, L.; PANACHE01-PRODIGE48 Group. New Biomarkers to Define a Biological Borderline Situation for Pancreatic Adenocarcinoma: Results of an Ancillary Study of the PANACHE01-PRODIGE48 Trial. Ann. Surg. 2024, 280, 734–744. [Google Scholar] [CrossRef] [PubMed]
  22. Arayici, M.E.; İnal, A.; Basbinar, Y.; Olgun, N. Evaluation of the diagnostic and prognostic clinical values of circulating tumor DNA and cell-free DNA in pancreatic malignancies: A comprehensive meta-analysis. Front. Oncol. 2024, 14, 1382369. [Google Scholar] [CrossRef]
  23. Kawamura, H.; Honda, M.; Takano, Y.; Kinuta, S.; Kamiga, T.; Saji, S.; Kono, K. Prognostic Role of Carcinoembryonic Antigen and Carbohydrate Antigen 19-9 in Stage IV Colorectal Cancer. Anticancer Res. 2022, 42, 3921–3928. [Google Scholar] [CrossRef]
  24. Botta, G.P.; Abdelrahim, M.; Drengler, R.L.; Aushev, V.N.; Esmail, A.; Laliotis, G.; Brewer, C.M.; George, G.V.; Abbate, S.M.; Chandana, S.R.; et al. Association of personalized and tumor-informed ctDNA with patient survival outcomes in pancreatic adenocarcinoma. Oncologist 2024, 29, 859–869. [Google Scholar] [CrossRef]
  25. Schwarz, L.; Vernerey, D.; Bachet, J.-B.; Tuech, J.-J.; Portales, F.; Michel, P.; Cunha, A.S. Resectable pancreatic adenocarcinoma neo-adjuvant FOLF(IRIN)OX-based chemotherapy-a multicenter, non-comparative, randomized, phase II trial (PANACHE01-PRODIGE48 study). BMC Cancer 2018, 18, 762. [Google Scholar] [CrossRef]
  26. Sefrioui, D.; Blanchard, F.; Toure, E.; Basile, P.; Beaussire, L.; Dolfus, C.; Perdrix, A.; Paresy, M.; Antonietti, M.; Iwanicki-Caron, I.; et al. Diagnostic value of CA19.9, circulating tumour DNA and circulating tumour cells in patients with solid pancreatic tumours. Br. J. Cancer 2017, 117, 1017–1025. [Google Scholar] [CrossRef]
  27. Luo, G.; Jin, K.; Deng, S.; Cheng, H.; Fan, Z.; Gong, Y.; Qian, Y.; Huang, Q.; Ni, Q.; Liu, C.; et al. Roles of CA19-9 in pancreatic cancer: Biomarker, predictor and promoter. Biochim. Biophys. Acta Rev. Cancer 2021, 1875, 188409. [Google Scholar] [CrossRef]
  28. Zhao, B.; Zhao, B.; Chen, F. Diagnostic value of serum carbohydrate antigen 19-9 in pancreatic cancer: A systematic review and meta-analysis. Eur. J. Gastroenterol. Hepatol. 2022, 34, 891–904. [Google Scholar] [CrossRef] [PubMed]
  29. Fahrmann, J.F.; Schmidt, C.M.; Mao, X.; Irajizad, E.; Loftus, M.; Zhang, J.; Patel, N.; Vykoukal, J.; Dennison, J.B.; Long, J.P.; et al. Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection. Gastroenterology 2021, 160, 1373–1383.e6. [Google Scholar] [CrossRef] [PubMed]
  30. Mason, J.; Lundberg, E.; Jonsson, P.; Nyström, H.; Franklin, O.; Lundin, C.; Naredi, P.; Antti, H.; Sund, M.; Öhlund, D. A Cross-Sectional and Longitudinal Analysis of Pre-Diagnostic Blood Plasma Biomarkers for Early Detection of Pancreatic Cancer. Int. J. Mol. Sci. 2022, 23, 12969. [Google Scholar] [CrossRef]
  31. Zheng, Z.; Lu, Z.; Yan, F.; Song, Y. The role of novel biomarkers in the early diagnosis of pancreatic cancer: A systematic review and meta-analysis. PLoS ONE 2025, 20, e0322720. [Google Scholar] [CrossRef]
  32. Ben-Ami, R.; Wang, Q.-L.; Zhang, J.; Supplee, J.G.; Fahrmann, J.F.; Lehmann-Werman, R.; Brais, L.K.; Nowak, J.; Yuan, C.; Loftus, M.; et al. Protein biomarkers and alternatively methylated cell-free DNA detect early stage pancreatic cancer. Gut 2024, 73, 639–648. [Google Scholar] [CrossRef]
  33. Lennerz, J.K.; Stenzinger, A. Allelic Ratio of KRAS Mutations in Pancreatic Cancer. Oncologist 2015, 20, e8–e9. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, Z.; Zhang, H.; Liao, X.; Tsai, H. KRAS mutation: The booster of pancreatic ductal adenocarcinoma transformation and progression. Front. Cell Dev. Biol. 2023, 11, 1147676. [Google Scholar] [CrossRef]
  35. Nitschke, C.; Markmann, B.; Walter, P.; Badbaran, A.; Tölle, M.; Kropidlowski, J.; Belloum, Y.; Goetz, M.R.; Bardenhagen, J.; Stern, L.; et al. Peripheral and Portal Venous KRAS ctDNA Detection as Independent Prognostic Markers of Early Tumor Recurrence in Pancreatic Ductal Adenocarcinoma. Clin. Chem. 2023, 69, 295–307. [Google Scholar] [CrossRef] [PubMed]
  36. Taback, B.; Saha, S.; Hoon, D.S.B. Comparative analysis of mesenteric and peripheral blood circulating tumor DNA in colorectal cancer patients. Ann. N. Y. Acad. Sci. 2006, 1075, 197–203. [Google Scholar] [CrossRef] [PubMed]
  37. Lee, J.-S.; Han, Y.; Yun, W.-G.; Kwon, W.; Kim, H.; Jeong, H.; Seo, M.-S.; Park, Y.; Cho, S.I.; Kim, H.; et al. Parallel Analysis of Pre- and Postoperative Circulating Tumor DNA and Matched Tumor Tissues in Resectable Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study. Clin. Chem. 2022, 68, 1509–1518. [Google Scholar] [CrossRef]
  38. Groot, V.P.; Mosier, S.; Javed, A.A.; Teinor, J.A.; Gemenetzis, G.; Ding, D.; Haley, L.M.; Yu, J.; Burkhart, R.A.; Hasanain, A.; et al. Circulating Tumor DNA as a Clinical Test in Resected Pancreatic Cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2019, 25, 4973–4984. [Google Scholar] [CrossRef]
  39. Jiang, J.; Ye, S.; Xu, Y.; Chang, L.; Hu, X.; Ru, G.; Guo, Y.; Yi, X.; Yang, L.; Huang, D. Circulating Tumor DNA as a Potential Marker to Detect Minimal Residual Disease and Predict Recurrence in Pancreatic Cancer. Front. Oncol. 2020, 10, 1220. [Google Scholar] [CrossRef]
  40. Ueberroth, B.E.; Jones, J.C.; Bekaii-Saab, T.S. Circulating tumor DNA (ctDNA) to evaluate minimal residual disease (MRD), treatment response, and posttreatment prognosis in pancreatic adenocarcinoma. Pancreatol. Off. J. Int. Assoc. Pancreatol. IAP Al 2022, 22, 741–748. [Google Scholar] [CrossRef]
  41. The Circulating Cell-Free Genome Atlas (CCGA) Study. GRAIL. Available online: https://grail.com/clinical-studies/ccga-study/ (accessed on 21 April 2025).
  42. Merker, J.D.; Oxnard, G.R.; Compton, C.; Diehn, M.; Hurley, P.; Lazar, A.J.; Lindeman, N.; Lockwood, C.M.; Rai, A.J.; Schilsky, R.L.; et al. Circulating Tumor DNA Analysis in Patients with Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2018, 36, 1631–1641. [Google Scholar] [CrossRef]
  43. Sellahewa, R.; Moghaddam, S.M.; Lundy, J.; Jenkins, B.J.; Croagh, D. Circulating Tumor DNA Is an Accurate Diagnostic Tool and Strong Prognostic Marker in Pancreatic Cancer. Pancreas 2023, 52, e188–e195. [Google Scholar] [CrossRef]
  44. Abdallah, R.; Taly, V.; Zhao, S.; Pietrasz, D.; Bachet, J.-B.; Basile, D.; Mas, L.; Zaanan, A.; Laurent-Puig, P.; Taieb, J. Plasma circulating tumor DNA in pancreatic adenocarcinoma for screening, diagnosis, prognosis, treatment and follow-up: A systematic review. Cancer Treat. Rev. 2020, 87, 102028. [Google Scholar] [CrossRef]
  45. Wang, Z.-Y.; Ding, X.-Q.; Zhu, H.; Wang, R.-X.; Pan, X.-R.; Tong, J.-H. KRAS Mutant Allele Fraction in Circulating Cell-Free DNA Correlates with Clinical Stage in Pancreatic Cancer Patients. Front. Oncol. 2019, 9, 1295. [Google Scholar] [CrossRef]
  46. Gonda, T.A.; Cahen, D.L.; Farrell, J.J. Pancreatic Cysts. N. Engl. J. Med. 2024, 391, 832–843. [Google Scholar] [CrossRef]
  47. Laquière, A.E.; Lagarde, A.; Napoléon, B.; Bourdariat, R.; Atkinson, A.; Donatelli, G.; Pol, B.; Lecomte, L.; Curel, L.; Urena-Campos, R.; et al. Genomic profile concordance between pancreatic cyst fluid and neoplastic tissue. World J. Gastroenterol. 2019, 25, 5530–5542. [Google Scholar] [CrossRef] [PubMed]
  48. Elta, G.H.; Enestvedt, B.K.; Sauer, B.G.; Lennon, A.M. ACG Clinical Guideline: Diagnosis and Management of Pancreatic Cysts. Am. J. Gastroenterol. 2018, 113, 464–479. [Google Scholar] [CrossRef]
  49. Chidharla, A.; Rapoport, E.; Agarwal, K.; Madala, S.; Linares, B.; Sun, W.; Chakrabarti, S.; Kasi, A. Circulating Tumor DNA as a Minimal Residual Disease Assessment and Recurrence Risk in Patients Undergoing Curative-Intent Resection with or without Adjuvant Chemotherapy in Colorectal Cancer: A Systematic Review and Meta-Analysis. Int. J. Mol. Sci. 2023, 24, 10230. [Google Scholar] [CrossRef]
  50. Hoang, T.; Choi, M.K.; Oh, J.H.; Kim, J. Utility of circulating tumor DNA to detect minimal residual disease in colorectal cancer: A systematic review and network meta-analysis. Int. J. Cancer 2025, 157, 593–799. [Google Scholar] [CrossRef]
  51. Ryoo, S.-B.; Heo, S.; Lim, Y.; Lee, W.; Cho, S.H.; Ahn, J.; Kang, J.-K.; Kim, S.Y.; Kim, H.-P.; Bang, D.; et al. Personalised circulating tumour DNA assay with large-scale mutation coverage for sensitive minimal residual disease detection in colorectal cancer. Br. J. Cancer 2023, 129, 374–381. [Google Scholar] [CrossRef] [PubMed]
  52. Slater, S.; Bryant, A.; Aresu, M.; Begum, R.; Chen, H.-C.; Peckitt, C.; Lazaro-Alcausi, R.; Carter, P.; Anandappa, G.; Khakoo, S.; et al. Tissue-Free Liquid Biopsies Combining Genomic and Methylation Signals for Minimal Residual Disease Detection in Patients with Early Colorectal Cancer from the UK TRACC Part B Study. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2024, 30, 3459–3469. [Google Scholar] [CrossRef]
  53. Tarazona, N.; Gimeno-Valiente, F.; Gambardella, V.; Zuñiga, S.; Rentero-Garrido, P.; Huerta, M.; Roselló, S.; Martinez-Ciarpaglini, C.; Carbonell-Asins, J.A.; Carrasco, F.; et al. Targeted next-generation sequencing of circulating-tumor DNA for tracking minimal residual disease in localized colon cancer. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2019, 30, 1804–1812. [Google Scholar] [CrossRef]
  54. Negro, S.; Pulvirenti, A.; Trento, C.; Indraccolo, S.; Ferrari, S.; Scarpa, M.; Urso, E.D.L.; Bergamo, F.; Pucciarelli, S.; Deidda, S.; et al. Circulating Tumor DNA as a Real-Time Biomarker for Minimal Residual Disease and Recurrence Prediction in Stage II Colorectal Cancer: A Systematic Review and Meta-Analysis. Int. J. Mol. Sci. 2025, 26, 2486. [Google Scholar] [CrossRef]
  55. Henriksen, T.V.; Demuth, C.; Frydendahl, A.; Nors, J.; Nesic, M.; Rasmussen, M.H.; Reinert, T.; Larsen, O.H.; Jaensch, C.; Løve, U.S.; et al. Unraveling the potential clinical utility of circulating tumor DNA detection in colorectal cancer-evaluation in a nationwide Danish cohort. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2024, 35, 229–239. [Google Scholar] [CrossRef]
  56. Parikh, A.R.; Chee, B.H.; Tsai, J.; Rich, T.A.; Price, K.S.; Patel, S.A.; Zhang, L.; Ibrahim, F.; Esquivel, M.; Van Seventer, E.E.; et al. Minimal Residual Disease using a Plasma-Only Circulating Tumor DNA Assay to Predict Recurrence of Metastatic Colorectal Cancer Following Curative Intent Treatment. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2024, 30, 2964–2973. [Google Scholar] [CrossRef]
  57. Malla, M.; Loree, J.M.; Kasi, P.M.; Parikh, A.R. Using Circulating Tumor DNA in Colorectal Cancer: Current and Evolving Practices. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2022, 40, 2846–2857. [Google Scholar] [CrossRef]
  58. Chakrabarti, S.; Kasi, A.K.; Parikh, A.R.; Mahipal, A. Finding Waldo: The Evolving Paradigm of Circulating Tumor DNA (ctDNA)-Guided Minimal Residual Disease (MRD) Assessment in Colorectal Cancer (CRC). Cancers 2022, 14, 3078. [Google Scholar] [CrossRef]
  59. Sogbe, M.; Aliseda, D.; Sangro, P.; de la Torre-Aláez, M.; Sangro, B.; Argemi, J. Prognostic value of circulating tumor DNA in different cancer types detected by ultra-low-pass whole-genome sequencing: A systematic review and patient-level survival data meta-analysis. Carcinogenesis 2025, 46, bgae073. [Google Scholar] [CrossRef] [PubMed]
  60. Gracie, L.; Pan, Y.; Atenafu, E.G.; Ward, D.G.; Teng, M.; Pallan, L.; Stevens, N.M.; Khoja, L. Circulating tumour DNA (ctDNA) in metastatic melanoma, a systematic review and meta-analysis. Eur. J. Cancer 2021, 158, 191–207. [Google Scholar] [CrossRef]
  61. Gandini, S.; Zanna, I.; De Angelis, S.P.; Cocorocchio, E.; Queirolo, P.; Lee, J.H.; Carlino, M.S.; Mazzarella, L.; Achutti Duso, B.; Palli, D.; et al. Circulating tumour DNA and melanoma survival: A systematic literature review and meta-analysis. Crit. Rev. Oncol. Hematol. 2021, 157, 103187. [Google Scholar] [CrossRef]
  62. Fan, G.; Zhang, K.; Yang, X.; Ding, J.; Wang, Z.; Li, J. Prognostic value of circulating tumor DNA in patients with colon cancer: Systematic review. PLoS ONE 2017, 12, e0171991. [Google Scholar] [CrossRef]
  63. Ocaña, A.; Díez-González, L.; García-Olmo, D.C.; Templeton, A.J.; Vera-Badillo, F.; José Escribano, M.; Serrano-Heras, G.; Corrales-Sánchez, V.; Seruga, B.; Andrés-Pretel, F.; et al. Circulating DNA and Survival in Solid Tumors. Cancer Epidemiol. Biomark. Prev. 2016, 25, 399–406. [Google Scholar] [CrossRef]
  64. Yanala, U.R.; Martos, M.P.; Dickey, E.M.; Corona, A.M.; Ezenwajiaku, N.; Pizzolato, J.F.; Terrero, G.; Hester, C.A.; Merchant, N.B.; Datta, J.; et al. Utility of circulating tumor DNA (ctDNA) for the detection of minimal residual disease (MRD) after curative-intent therapy for patients with localized pancreatic adenocarcinoma (PDAC): A single institution series and meta-analysis. J. Clin. Oncol. 2024, 42 (Suppl. 3), 695. [Google Scholar] [CrossRef]
  65. Lee, B.; Lipton, L.; Cohen, J.; Tie, J.; Javed, A.A.; Li, L.; Goldstein, D.; Burge, M.; Cooray, P.; Nagrial, A.; et al. Circulating tumor DNA as a potential marker of adjuvant chemotherapy benefit following surgery for localized pancreatic cancer. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2019, 30, 1472–1478. [Google Scholar] [CrossRef]
  66. Lee, J.-S.; Rhee, T.-M.; Pietrasz, D.; Bachet, J.-B.; Laurent-Puig, P.; Kong, S.-Y.; Takai, E.; Yachida, S.; Shibata, T.; Lee, J.W.; et al. Circulating tumor DNA as a prognostic indicator in resectable pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Sci. Rep. 2019, 9, 16971. [Google Scholar] [CrossRef]
  67. Vidal, L.; Pando, E.; Blanco, L.; Fabregat-Franco, C.; Castet, F.; Sierra, A.; Macarulla, T.; Balsells, J.; Charco, R.; Vivancos, A. Liquid biopsy after resection of pancreatic adenocarcinoma and its relation to oncological outcomes. Systematic review and meta-analysis. Cancer Treat. Rev. 2023, 120, 102604. [Google Scholar] [CrossRef] [PubMed]
  68. Hata, T.; Mizuma, M.; Motoi, F.; Ohtsuka, H.; Nakagawa, K.; Morikawa, T.; Unno, M. Prognostic impact of postoperative circulating tumor DNA as a molecular minimal residual disease marker in patients with pancreatic cancer undergoing surgical resection. J. Hepato-Biliary-Pancreat. Sci. 2023, 30, 815–824. [Google Scholar] [CrossRef] [PubMed]
  69. Zhang, Y.; Huang, Z.-X.; Song, B. Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma. World J. Gastroenterol. 2021, 27, 3037–3049. [Google Scholar] [CrossRef] [PubMed]
  70. Soloff, E.V.; Al-Hawary, M.M.; Desser, T.S.; Fishman, E.K.; Minter, R.M.; Zins, M. Imaging Assessment of Pancreatic Cancer Resectability After Neoadjuvant Therapy: AJR Expert Panel Narrative Review. AJR Am. J. Roentgenol. 2022, 218, 570–581. [Google Scholar] [CrossRef]
  71. Panda, A.; Garg, I.; Truty, M.J.; Kline, T.L.; Johnson, M.P.; Ehman, E.C.; Suman, G.; Anaam, D.A.; Kemp, B.J.; Johnson, G.B.; et al. Borderline Resectable and Locally Advanced Pancreatic Cancer: FDG PET/MRI and CT Tumor Metrics for Assessment of Pathologic Response to Neoadjuvant Therapy and Prediction of Survival. AJR Am. J. Roentgenol. 2021, 217, 730–740. [Google Scholar] [CrossRef]
  72. Vitello, D.J.; Shah, D.; Wells, A.; Masnyk, L.; Cox, M.; Janczewski, L.M.; Abad, J.; Dawravoo, K.; D’Souza, A.; Suh, G.; et al. Mutant KRAS in Circulating Tumor DNA as a Biomarker in Localized Pancreatic Cancer in Patients Treated with Neoadjuvant Chemotherapy. Ann. Surg. 2024. [Google Scholar] [CrossRef]
  73. Kitahata, Y.; Kawai, M.; Hirono, S.; Okada, K.-I.; Miyazawa, M.; Motobayashi, H.; Ueno, M.; Hayami, S.; Miyamoto, A.; Yamaue, H. Circulating Tumor DNA as a Potential Prognostic Marker in Patients with Borderline-Resectable Pancreatic Cancer Undergoing Neoadjuvant Chemotherapy Followed by Pancreatectomy. Ann. Surg. Oncol. 2022, 29, 1596–1605. [Google Scholar] [CrossRef]
  74. Lyskjær, I.; Kronborg, C.S.; Rasmussen, M.H.; Sørensen, B.S.; Demuth, C.; Rosenkilde, M.; Johansen, A.F.B.; Knudsen, M.; Vang, S.; Krag, S.R.P.; et al. Correlation between early dynamics in circulating tumour DNA and outcome from FOLFIRI treatment in metastatic colorectal cancer. Sci. Rep. 2019, 9, 11542. [Google Scholar] [CrossRef]
  75. Kim, S.; Lim, Y.; Kang, J.-K.; Kim, H.-P.; Roh, H.; Kim, S.Y.; Lee, D.; Bang, D.; Jeong, S.-Y.; Park, K.J.; et al. Dynamic changes in longitudinal circulating tumour DNA profile during metastatic colorectal cancer treatment. Br. J. Cancer 2022, 127, 898–907. [Google Scholar] [CrossRef]
  76. Ghidini, M.; Hahne, J.C.; Senti, C.; Heide, T.; Proszek, P.Z.; Shaikh, R.; Carter, P.; Hubank, M.; Trevisani, F.; Garrone, O.; et al. Circulating Tumor DNA Dynamics and Clinical Outcome in Metastatic Colorectal Cancer Patients Undergoing Front-Line Chemotherapy. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2025, 31, 707–718. [Google Scholar] [CrossRef]
  77. Sugimori, M.; Sugimori, K.; Tsuchiya, H.; Suzuki, Y.; Tsuyuki, S.; Kaneta, Y.; Hirotani, A.; Sanga, K.; Tozuka, Y.; Komiyama, S.; et al. Quantitative monitoring of circulating tumor DNA in patients with advanced pancreatic cancer undergoing chemotherapy. Cancer Sci. 2020, 111, 266–278. [Google Scholar] [CrossRef] [PubMed]
  78. Zou, D.; Day, R.; Cocadiz, J.A.; Parackal, S.; Mitchell, W.; Black, M.A.; Lawrence, B.; Fitzgerald, S.; Print, C.; Jackson, C.; et al. Circulating tumor DNA is a sensitive marker for routine monitoring of treatment response in advanced colorectal cancer. Carcinogenesis 2020, 41, 1507–1517. [Google Scholar] [CrossRef]
  79. Gouda, M.A.; Huang, H.J.; Piha-Paul, S.A.; Call, S.G.; Karp, D.D.; Fu, S.; Naing, A.; Subbiah, V.; Pant, S.; Dustin, D.J.; et al. Longitudinal Monitoring of Circulating Tumor DNA to Predict Treatment Outcomes in Advanced Cancers. JCO Precis. Oncol. 2022, 6, e2100512. [Google Scholar] [CrossRef]
  80. Pellini, B.; Madison, R.W.; Childress, M.A.; Miller, S.T.; Gjoerup, O.; Cheng, J.; Huang, R.S.P.; Krainock, M.; Gupta, P.; Zou, W.; et al. Circulating Tumor DNA Monitoring on Chemo-immunotherapy for Risk Stratification in Advanced Non-Small Cell Lung Cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2023, 29, 4596–4605. [Google Scholar] [CrossRef]
  81. Satoi, S.; Yamamoto, T.; Matsui, Y. Conversion surgery in patients with initially unresectable pancreatic ductal adenocarcinoma: Where do we stand in 2018? J. Pancreatol. 2018, 1, 25–29. [Google Scholar] [CrossRef]
  82. Botrus, G.; Kosirorek, H.; Sonbol, M.B.; Kusne, Y.; Uson Junior, P.L.S.; Borad, M.J.; Ahn, D.H.; Kasi, P.M.; Drusbosky, L.M.; Dada, H.; et al. Circulating Tumor DNA-Based Testing and Actionable Findings in Patients with Advanced and Metastatic Pancreatic Adenocarcinoma. Oncologist 2021, 26, 569–578. [Google Scholar] [CrossRef]
  83. Keane, F.; Saadat, L.V.; O’Connor, C.A.; Chou, J.F.; Bowman, A.S.; Xu, F.; Crowley, F.; Debnath, N.; Schoenfeld, J.D.; Singhal, A.; et al. Clinical utility and tissue concordance of circulating tumor DNA in pancreatic ductal adenocarcinoma. J. Natl. Cancer Inst. 2025, 117, djaf139. [Google Scholar] [CrossRef]
  84. Sivapalan, L.; Thorn, G.J.; Gadaleta, E.; Kocher, H.M.; Ross-Adams, H.; Chelala, C. Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer. BMC Cancer 2022, 22, 369. [Google Scholar] [CrossRef]
  85. Sivapalan, L.; Kocher, H.M.; Ross-Adams, H.; Chelala, C. Molecular profiling of ctDNA in pancreatic cancer: Opportunities and challenges for clinical application. Pancreatol. Off. J. Int. Assoc. Pancreatol. IAP Al 2021, 21, 363–378. [Google Scholar] [CrossRef]
  86. Hwang, S.; Woo, S.; Kang, B.; Kang, H.; Kim, J.S.; Lee, S.H.; Kwon, C.I.; Kyung, D.S.; Kim, H.-P.; Kim, G.; et al. Concordance of ctDNA and tissue genomic profiling in advanced biliary tract cancer. J. Hepatol. 2025, 82, 649–657. [Google Scholar] [CrossRef]
  87. Awosika, J.A.; Monge, C.; Greten, T.F. Integration of circulating tumor DNA in biliary tract cancer: The emerging landscape. Hepatic Oncol. 2024, 11, 2403334. [Google Scholar] [CrossRef]
  88. Labiano, I.; Huerta, A.E.; Alsina, M.; Arasanz, H.; Castro, N.; Mendaza, S.; Lecumberri, A.; Gonzalez-Borja, I.; Guerrero-Setas, D.; Patiño-Garcia, A.; et al. Building on the clinical applicability of ctDNA analysis in non-metastatic pancreatic ductal adenocarcinoma. Sci. Rep. 2024, 14, 16203. [Google Scholar] [CrossRef]
  89. Bayle, A.; Belcaid, L.; Aldea, M.; Vasseur, D.; Peyraud, F.; Nicotra, C.; Geraud, A.; Sakkal, M.; Seknazi, L.; Cerbone, L.; et al. Clinical utility of circulating tumor DNA sequencing with a large panel: A National Center for Precision Medicine (PRISM) study. Ann. Oncol. 2023, 34, 389–396. [Google Scholar] [CrossRef]
  90. Takai, E.; Totoki, Y.; Nakamura, H.; Kato, M.; Shibata, T.; Yachida, S. Clinical Utility of Circulating Tumor DNA for Molecular Assessment and Precision Medicine in Pancreatic Cancer. In Circulating Nucleic Acids in Serum and Plasma–CNAPS IX; Gahan, P.B., Fleischhacker, M., Schmidt, B., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 13–17. [Google Scholar] [CrossRef]
  91. Andersen, L.; Kisistók, J.; Henriksen, T.V.; Bramsen, J.B.; Reinert, T.; Øgaard, N.; Mattesen, T.B.; Birkbak, N.J.; Andersen, C.L. Exploring the biology of ctDNA release in colorectal cancer. Eur. J. Cancer Oxf. Engl. 1990 2024, 207, 114186. [Google Scholar] [CrossRef]
  92. Bredno, J.; Lipson, J.; Venn, O.; Aravanis, A.M.; Jamshidi, A. Clinical correlates of circulating cell-free DNA tumor fraction. PLoS ONE 2021, 16, e0256436. [Google Scholar] [CrossRef]
  93. Cho, M.-S.; Park, C.H.; Lee, S.; Park, H.S. Clinicopathological parameters for circulating tumor DNA shedding in surgically resected non-small cell lung cancer with EGFR or KRAS mutation. PLoS ONE 2020, 15, e0230622. [Google Scholar] [CrossRef]
  94. Koudahl Conrad, J.B.; Vesterman Henriksen, T.; Berg Nors, J.; Heilskov Rasmussen, M.; Worm Ørntoft, M.-B.; Hallundbæk Schlesinger, N.; Vadgaard Andersen, P.; Andersson Gotschalck, K.; Andersen, C.L. The role of renal and liver function in clinical ctDNA testing. PLoS ONE 2025, 20, e0319194. [Google Scholar] [CrossRef]
  95. Pommergaard, H.C.; Yde, C.W.; Ahlborn, L.B.; Andersen, C.L.; Henriksen, T.V.; Hasselby, J.P.; Rostved, A.A.; Sørensen, C.L.; Rohrberg, K.S.; Nielsen, F.C.; et al. Personalized circulating tumor DNA in patients with hepatocellular carcinoma: A pilot study. Mol. Biol. Rep. 2022, 49, 1609–1616. [Google Scholar] [CrossRef]
  96. Yu, L.; Lopez, G.; Rassa, J.; Wang, Y.; Basavanhally, T.; Browne, A.; Huang, C.-P.; Dorsey, L.; Jen, J.; Hersey, S. Direct comparison of circulating tumor DNA sequencing assays with targeted large gene panels. PLoS ONE 2022, 17, e0266889. [Google Scholar] [CrossRef]
  97. Deveson, I.W.; Gong, B.; Lai, K.; LoCoco, J.S.; Richmond, T.A.; Schageman, J.; Zhang, Z.; Novoradovskaya, N.; Willey, J.C.; Jones, W.; et al. Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology. Nat. Biotechnol. 2021, 39, 1115–1128. [Google Scholar] [CrossRef]
  98. Boscolo Bielo, L.; Trapani, D.; Repetto, M.; Crimini, E.; Valenza, C.; Belli, C.; Criscitiello, C.; Marra, A.; Subbiah, V.; Curigliano, G. Variant allele frequency: A decision-making tool in precision oncology? Trends Cancer 2023, 9, 1058–1068. [Google Scholar] [CrossRef]
  99. Dang, D.K.; Park, B.H. Circulating tumor DNA: Current challenges for clinical utility. J. Clin. Investig. 2022, 132, e154941. [Google Scholar] [CrossRef]
  100. Douglas, M.P.; Ragavan, M.V.; Chen, C.; Kumar, A.; Gray, S.W.; Blakely, C.M.; Phillips, K.A. Private Payer and Medicare Coverage Policies for Use of Circulating Tumor DNA Tests in Cancer Diagnostics and Treatment. J. Natl. Compr. Cancer Netw. 2023, 21, 609–616.e4. Available online: https://jnccn.org/view/journals/jnccn/21/6/article-p609.xml (accessed on 17 June 2025). [CrossRef]
  101. Duffy, M.J.; Crown, J. Circulating Tumor DNA as a Biomarker for Monitoring Patients with Solid Cancers: Comparison with Standard Protein Biomarkers. Clin. Chem. 2022, 68, 1381–1390. [Google Scholar] [CrossRef]
  102. Arisi, M.F.; Dotan, E.; Fernandez, S.V. Circulating Tumor DNA in Precision Oncology and Its Applications in Colorectal Cancer. Int. J. Mol. Sci. 2022, 23, 4441. [Google Scholar] [CrossRef]
  103. Corcoran, R.B.; Chabner, B.A. Application of Cell-free DNA Analysis to Cancer Treatment. N. Engl. J. Med. 2018, 379, 1754–1765. [Google Scholar] [CrossRef] [PubMed]
  104. Boonstra, P.A.; Wind, T.T.; van Kruchten, M.; Schuuring, E.; Hospers, G.A.P.; van der Wekken, A.J.; de Groot, D.-J.; Schröder, C.P.; Fehrmann, R.S.N.; Reyners, A.K.L. Clinical utility of circulating tumor DNA as a response and follow-up marker in cancer therapy. Cancer Metastasis Rev. 2020, 39, 999–1013. [Google Scholar] [CrossRef] [PubMed]
  105. Rajdev, L.; King, G.G.; Lieu, C.H.; Cohen, S.A.; Pant, S.; Uboha, N.V.; Deming, D.; Malla, M.; Kasi, A.; Klute, K.; et al. Incorporating Circulating Tumor DNA Testing into Clinical Trials: A Position Paper by the National Cancer Institute GI Oncology Circulating Tumor DNA Working Group. JCO Precis. Oncol. 2025, 9, e2400489. [Google Scholar] [CrossRef]
  106. Antolino, L. Mutation of K-RAS, CDKN2A, SMAD4 and TP53 in Pancreatic Cancer: Role of Liquid Biopsy in Preoperative Diagnosis. Clinicaltrials.Gov. 2019. Available online: https://clinicaltrials.gov/study/NCT03524677 (accessed on 17 June 2025).
  107. Westphalen, B. Prognostic Role of Circulating Tumor DNA in Resectable Pancreatic Cancer. Clinicaltrials.Gov. 2025. Available online: https://clinicaltrials.gov/study/NCT04246203 (accessed on 17 June 2025).
  108. Grunvald, M.W.; Jacobson, R.A.; Kuzel, T.M.; Pappas, S.G.; Masood, A. Current Status of Circulating Tumor DNA Liquid Biopsy in Pancreatic Cancer. Int. J. Mol. Sci. 2020, 21, 7651. [Google Scholar] [CrossRef]
  109. National Cancer Institute (NCI) APOLLO: A Randomized Phase II Double-Blind Study of Olaparib Versus Placebo Following Curative Intent Therapy in Patients with Resected Pancreatic Cancer and a Pathogenic BRCA1, BRCA2 or PALB2 Mutation; clinicaltrials.gov. 2025. Available online: https://clinicaltrials.gov/study/NCT04858334 (accessed on 17 June 2025).
  110. University Hospital, Essen Identification of Novel Inflammation-related Biomarkers for Early Detection of Anthracycline-induced Cardiotoxicity in Breast Cancer Patients; clinicaltrials.gov. 2022. Available online: https://clinicaltrials.gov/study/NCT05298072 (accessed on 17 June 2025).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Previous
Comparative Efficacy of Photodynamic Therapy and Cold Knife Conization for Cervical High-Grade Squamous Intraepithelial Lesions
Next