Zie ook in gerelateeerde artikelen hiernaast of hieronder

Zie ook dit artikel: https://kanker-actueel.nl/NL/bloedtest-met-vier-tumormarkers-ontdekt-alvleesklierkanker-al-in-vroeg-stadium-met-een-nauwkeurigheid-van-87-tot-91-procent-dit-geeft-hoop-op-een-vroegere-diagnose-en-een-betere-overlevingskans-van-alvleesklierkanker.html

19 september 2026: Bron: Nature medicin d.d. 16 september 2026

PANXEON, een bloedtest die drie verschillende manieren van testen gebruikt en combineert in de prognose, voorspelt het ontstaan van alvleesklierkanker in een heel vroeg stadium in stadium I en II. 
PANXEON combineerde circulerende microRNA's, exosomale microRNA's en de marker CA19-9 om gegevens te verzamelen om een prognose te doen. Vervolgens combineert de bloedtest PANXEON deze drie waarden met behulp van AI - kunstmatige intelligentie tot één enkele score die het risico inschat voor de patiënt om al alvleesklierkanker te hebben.

De resultaten waren veelbelovend. De bloedtest identificeerde in 87% van de gevallen correct alvleesklierkanker in stadium 1 en 2, terwijl het percentage fout-positieve uitslagen slechts 3% bedroeg bij groepen met een laag risico en 16% bij groepen met een hoog risico.

PANXEON ontdekte ook in meer dan 64% van de gevallen hooggradige dysplasie; dit is een vergevorderd voorstadium van kanker dat vaak wordt beschouwd als alvleesklierkanker in 'stadium 0'. Deze bevinding zou artsen kunnen helpen beter vast te stellen welke cysten in de alvleesklier actieve monitoring of ingrijpen vereisen, nog voordat er invasieve kanker ontstaat in met name slodarm, darmen en maag.

Alvleesklierkanker heeft de laagste overlevingscijfers van alle vormen van kanker; slechts 14% van de patiënten met alvleesklierkanker is vijf jaar na de diagnose nog in leven ondanks operatie en andere behandelingen. Bij ongeveer 90% van deze patiënten wordt de tumor pas in een gevorderd stadium ontdekt, wanneer de kanker zich al heeft verspreid. PANXEON hanteert een vernieuwende testmethode met de drie manieren en zou er daarmee voor kunnen zorgen dat het moment waarop alvleesklierkanker wordt ontdekt en behandeld, ingrijpend te veranderen.

Het studieverslag is in Nature gepubliceerd en is onder bepaalde voorwaarden of tegen betaling in te zien of te downloaden.

  • Article
  • Published:

Liquid biopsy for early detection of pancreatic ductal adenocarcinoma

Nature Medicine (2026) Cite this article

Save article

Abstract

There is no clinically relevant blood-based assay for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC), a solid malignancy characterized by poor outcomes. Here we developed, validated and tested a blood-based microRNA (miRNA) assay (which included hsa-miR-142-3p, hsa-miR-30c-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-200b-3p, hsa-miR-1260b, hsa-miR-145-3p, hsa-miR-145-5p, hsa-miR-429 and hsa-miR-200a-3p) and a composite score, PANXEON (PANcreatic cancer eXosome Early detectiON), that integrates the miRNA signature with carbohydrate antigen 19-9 for the detection of early-stage PDAC. We conducted an international, multicenter, observational, prospective biomarker study that involved 1,785 individuals with and without PDAC from four countries. The miRNA signature achieved an area under the receiver operating characteristic curve of 88.6% in the testing cohort, with a sensitivity of 83.8% for early-stage PDAC, while showing minimal cross-reactivity with other gastrointestinal cancers. In a cohort of 19 individuals, the miRNA signature levels decreased during neoadjuvant chemotherapy and after surgery and increased before disease recurrence. When combined with carbohydrate antigen 19-9 levels, this blood assay demonstrated a sensitivity of 86.8% for stage I–II PDAC, false-positive rates of 3.2% in low-risk controls and 15.6% in high-risk controls in the testing cohort. PANXEON demonstrates potential for detecting high-grade dysplasia in individuals with high-risk pancreatic cysts (64.3%). Collectively, we present a composite biomarker that may complement existing strategies for the detection of early-stage PDAC and warrants further large-scale prospective studies. ClinicalTrials.gov registration: NCT06388967.

References

  1. Rahib, L. et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74, 2913–2921 (2014).

    Article CAS PubMed Google Scholar 

  2. Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74, 12–49 (2024).

    PubMed Google Scholar 

  3. Placido, D. et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat. Med. 29, 1113–1122 (2023).

    Article CAS PubMed PubMed Central Google Scholar 

  4. Dbouk, M. et al. The multicenter cancer of pancreas screening study: impact on stage and survival. J. Clin. Oncol. 40, 3257–3266 (2022).

    Article CAS PubMed PubMed Central Google Scholar 

  5. Vasen, H. et al. Benefit of surveillance for pancreatic cancer in high-risk individuals: outcome of long-term prospective follow-up studies from three European expert centers. J. Clin. Oncol. 34, 2010–2019 (2016).

    Article CAS PubMed Google Scholar 

  6. Blackford, A. L. et al. Pancreatic cancer surveillance and survival of high-risk individuals. JAMA Oncol. 10, 1087–1096 (2024).

    Article PubMed PubMed Central Google Scholar 

  7. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article CAS PubMed PubMed Central Google Scholar 

  8. Chen, Y. L. et al. Cellular 5-hydroxylmethylcytosine content determines tumorigenic potential and prognosis of pancreatic ductal adenocarcinoma. Am. J. Cancer Res. 8, 2548–2563 (2018).

    CAS PubMed PubMed Central Google Scholar 

  9. Guler, G. D. et al. Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA. Nat. Commun. 11, 5270 (2020).

    Article CAS PubMed PubMed Central Google Scholar 

  10. Chowdhury, S. et al. Analytical validation of an early detection pancreatic cancer test using 5-hydroxymethylation signatures. J. Mol. Diagn. 26, 888–896 (2024).

    Article CAS PubMed Google Scholar 

  11. Kartal, E. et al. A faecal microbiota signature with high specificity for pancreatic cancer. Gut 71, 1359–1372 (2022).

    Article CAS PubMed PubMed Central Google Scholar 

  12. Conroy, T. et al. Pancreatic cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann. Oncol. 34, 987–1002 (2023).

    Article CAS PubMed Google Scholar 

  13. US Preventive Services Task Force; Owens, D. K.et al. Screening for pancreatic cancer: US Preventive Services Task Force reaffirmation recommendation statement. JAMA 322, 438–444 (2019).

    Article PubMed Google Scholar 

  14. Henrikson, N. B. et al. Screening for pancreatic cancer: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 322, 445–454 (2019).

    Article PubMed PubMed Central Google Scholar 

  15. Aslanian, H. R., Lee, J. H. & Canto, M. I. AGA clinical practice update on pancreas cancer screening in high-risk individuals: expert review. Gastroenterology 159, 358–362 (2020).

    Article CAS PubMed Google Scholar 

  16. Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10–45 (2025).

    PubMed PubMed Central Google Scholar 

  17. Gonda, T. A., Everett, J. N., Wallace, M., Simeone, D. M. & Consortium, P. Recommendations for a more organized and effective approach to the early detection of pancreatic cancer from the PRECEDE (Pancreatic Cancer Early Detection) Consortium. Gastroenterology 161, 1751–1757 (2021).

    Article PubMed Google Scholar 

  18. Goggins, M. et al. Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium. Gut 69, 7–17 (2020).

    Article CAS PubMed Google Scholar 

  19. Fulton, A. J. et al. Identification of patients with pancreatic adenocarcinoma due to inheritable mutation: challenges of daily clinical practice. World J. Gastrointest. Oncol. 11, 102–116 (2019).

    Article PubMed PubMed Central Google Scholar 

  20. Saftoiu, A. et al. Role of gastrointestinal endoscopy in the screening of digestive tract cancers in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 52, 293–304 (2020).

    Article PubMed Google Scholar 

  21. Sawhney, M. S. et al. ASGE guideline on screening for pancreatic cancer in individuals with genetic susceptibility: summary and recommendations. Gastrointest. Endosc. 95, 817–826 (2022).

    Article PubMed Google Scholar 

  22. Daly, M. B. et al. NCCN Guidelines(R) Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2024. J. Natl Compr. Canc. Netw. 21, 1000–1010 (2023).

    Article CAS PubMed Google Scholar 

  23. Singhi, A. D., Koay, E. J., Chari, S. T. & Maitra, A. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology 156, 2024–2040 (2019).

    Article PubMed PubMed Central Google Scholar 

  24. Mahajan, U. M. et al. Validation of two plasma multimetabolite signatures for patients at risk of or with suspected pancreatic ductal adenocarcinoma (METAPAC): a prospective, multicentre, investigator-masked, enrichment design, phase 4 diagnostic study. Lancet Gastroenterol. Hepatol. 10, 634–647 (2025).

    Article CAS PubMed Google Scholar 

  25. Mannucci, A., Cavestro, G. M. & Goel, A. A DEF perspective on the METAPAC study. Lancet Gastroenterol. Hepatol. 10, 879 (2025).

    Article CAS PubMed Google Scholar 

  26. Klein, A. P. et al. Prospective risk of pancreatic cancer in familial pancreatic cancer kindreds. Cancer Res. 64, 2634–2638 (2004).

    Article CAS PubMed Google Scholar 

  27. Schneider, R. et al. German national case collection for familial pancreatic cancer (FaPaCa): ten years experience. Fam. Cancer 10, 323–330 (2011).

    Article PubMed Google Scholar 

  28. Petersen, G. M. Familial pancreatic cancer. Semin. Oncol. 43, 548–553 (2016).

    Article PubMed PubMed Central Google Scholar 

  29. Lal, G. et al. Inherited predisposition to pancreatic adenocarcinoma: role of family history and germ-line p16, BRCA1, and BRCA2 mutations. Cancer Res. 60, 409–416 (2000).

    CAS PubMed Google Scholar 

  30. de Pretis, N. et al. Pancreatic cysts: diagnostic accuracy and risk of inappropriate resections. Pancreatology 17, 267–272 (2017).

    Article PubMed Google Scholar 

  31. Ohno, E. et al. Natural history of pancreatic cystic lesions: a multicenter prospective observational study for evaluating the risk of pancreatic cancer. J. Gastroenterol. Hepatol. 33, 320–328 (2018).

    Article PubMed Google Scholar 

  32. Schweber, A. B., Agarunov, E., Brooks, C., Hur, C. & Gonda, T. A. Prevalence, incidence, and risk of progression of asymptomatic pancreatic cysts in large sample real-world data. Pancreas 50, 1287–1292 (2021).

    Article CAS PubMed Google Scholar 

  33. Vege, S. S. & Chari, S. T. Chronic pancreatitis. N. Engl. J. Med. 386, 869–878 (2022).

    Article PubMed Google Scholar 

  34. Cortez, M. A. et al. MicroRNAs in body fluids-the mix of hormones and biomarkers. Nat. Rev. Clin. Oncol. 8, 467–477 (2011).

    Article CAS PubMed PubMed Central Google Scholar 

  35. Nakamura, K. et al. A liquid biopsy signature for the detection of patients with early-onset colorectal cancer. Gastroenterology 163, 1242–1251 (2022).

    Article CAS PubMed PubMed Central Google Scholar 

  36. Pardini, B. et al. A fecal MicroRNA signature by small RNA sequencing accurately distinguishes colorectal cancers: results from a multicenter study. Gastroenterology 165, 582–599 (2023).

    Article CAS PubMed Google Scholar 

  37. So, J. B. Y. et al. Development and validation of a serum microRNA biomarker panel for detecting gastric cancer in a high-risk population. Gut 70, 829–837 (2021).

    Article CAS PubMed Google Scholar 

  38. Miyoshi, J. et al. Liquid biopsy to identify Barrett’s oesophagus, dysplasia and oesophageal adenocarcinoma: the EMERALD multicentre study. Gut 74, 169–181 (2024).

  39. Kalluri, R. & LeBleu, V. S. The biology, function, and biomedical applications of exosomes. Science 367, eaau6977 (2020).

    Article CAS PubMed PubMed Central Google Scholar 

  40. Li, K. et al. Salivary extracellular microRNAs for early detection and prognostication of esophageal cancer: a clinical study. Gastroenterology 165, 932–945 (2023).

    Article CAS PubMed Google Scholar 

  41. Li, Y., Sui, S. & Goel, A. Extracellular vesicles associated microRNAs: their biology and clinical significance as biomarkers in gastrointestinal cancers. Semin. Cancer Biol. 99, 5–23 (2024).

    Article CAS PubMed PubMed Central Google Scholar 

  42. Nakamura, K. et al. An exosome-based transcriptomic signature for noninvasive, early detection of patients with pancreatic ductal adenocarcinoma: a multicenter cohort study. Gastroenterology 163, 1252–1266 (2022).

    Article CAS PubMed PubMed Central Google Scholar 

  43. Han, J. W. et al. A machine learning algorithm facilitates prognosis prediction and treatment selection for barcelona clinic liver cancer stage C hepatocellular carcinoma. Clin. Cancer Res. 30, 2812–2821 (2024).

    Article CAS PubMed Google Scholar 

  44. Lennon, A. M. et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369, eabb9601 (2020).

    Article CAS PubMed PubMed Central Google Scholar 

  45. Stoffel, E. M., Brand, R. E. & Goggins, M. Pancreatic cancer: changing epidemiology and new approaches to risk assessment, early detection, and prevention. Gastroenterology 164, 752–765 (2023).

    Article PubMed PubMed Central Google Scholar 

  46. Overbeek, K. A. et al. Timeline of development of pancreatic cancer and implications for successful early detection in high-risk individuals. Gastroenterology 162, 772–785 (2022).

    Article PubMed Google Scholar 

  47. Brand, R. E. et al. Detection of early-stage pancreatic ductal adenocarcinoma from blood samples: results of a multiplex biomarker signature validation study. Clin. Transl. Gastroenterol. 13, e00468 (2022).

    Article PubMed PubMed Central Google Scholar 

  48. Mannucci, A. & Goel, A. Advances in pancreatic cancer early diagnosis, prevention, and treatment: the past, the present, and the future. CA Cancer J. Clin. 76, e70035 (2025).

  49. Hruban, C. et al. Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer. Sci. Adv. 11, eads5002 (2025).

    Article CAS PubMed PubMed Central Google Scholar 

  50. Wang, X. et al. Development and prospective validation of a cell-free DNA-based model for the early detection of pancreatic cancer. Cancer Discov. 16, 66–80 (2025).

  51. Yin, L. et al. Development and validation of a cell-free DNA fragmentomics-based model for early detection of pancreatic cancer. J. Clin. Oncol. 43, 2863–2874 (2025).

    Article CAS PubMed Google Scholar 

  52. Wen, Y. H. et al. Cancer screening through a multi-analyte serum biomarker panel during health check-up examinations: results from a 12-year experience. Clin. Chim. Acta 450, 273–276 (2015).

    Article CAS PubMed Google Scholar 

  53. Boyd, L. N. C. et al. Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: a meta-analysis of aggregate and individual participant data. EClinicalMedicine 55, 101747 (2023).

    Article PubMed Google Scholar 

  54. Yew, P. Y. et al. Unraveling the multiple chronic conditions patterns among people with Alzheimer’s disease and related dementia: a machine learning approach to incorporate synergistic interactions. Alzheimers Dement. 20, 4818–4827 (2024).

    Article PubMed PubMed Central Google Scholar 

  55. Clift, A. K. et al. Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study. BMJ 381, e073800 (2023).

    Article PubMed PubMed Central Google Scholar 

  56. Khera, R. et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 6, 633–641 (2021).

    Article PubMed PubMed Central Google Scholar 

  57. Hubbell, E., Clarke, C. A., Aravanis, A. M. & Berg, C. D. Modeled reductions in late-stage cancer with a multi-cancer early detection test. Cancer Epidemiol. Biomarkers Prev. 30, 460–468 (2021).

    Article CAS PubMed Google Scholar 

  58. Mathios, D. et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat. Commun. 12, 5060 (2021).

    Article CAS PubMed PubMed Central Google Scholar 

  59. Choi, S. H. et al. Progression of unresected intraductal papillary mucinous neoplasms of the pancreas to cancer: a systematic review and meta-analysis. Clin. Gastroenterol. Hepatol. 15, 1509–1520.e4 (2017).

    Article PubMed Google Scholar 

  60. Overbeek, K. A. et al. Long-term yield of pancreatic cancer surveillance in high-risk individuals. Gut 71, 1152–1160 (2022).

    Article CAS PubMed Google Scholar 

  61. Caiming, X. & Mannucci, A. PCDC. Zenodo https://doi.org/10.5281/zenodo.15041773 (2026).

Download references

Acknowledgements

We thank S. Sui, Z. Zhu, Y. Li, T. Takahashi, and T. Noma for their thoughtful discussions and advice during this project. Although the participants were not directly involved in formulating the research question(s) and outcome measures, or in designing the study, we express our appreciation for the contributions of the study participants, their families and all those who assisted with the study.

Funding

This work was supported by CA72851, CA181572, CA184792, CA187956, CA202797, CA214254 and CA271443 grants from the National Cancer Institute (National Institutes of Health), and by Start-Up grant no. 32233 from the Italian Association for Cancer Research (AIRC Foundation). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Caiming Xu, Alessandro Mannucci.

Authors and Affiliations

  1. Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Monrovia, CA, USA

    Caiming Xu, Alessandro Mannucci, Haiyong Han & Ajay Goel

  2. Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital, Milan, Italy

    Alessandro Mannucci & Giulia Martina Cavestro

  3. The Translational Genomics Research Institute, Phoenix, AZ, USA

    Haiyong Han, Ruben M. Munoz, Derek Cridebring, Michael J. Demeure & Daniel Von Hoff

  4. Hoag Family Center Institute, Newport Beach, CA, USA

    Sourat Darabi, Michael J. Demeure & Carlos Becerra

  5. Department of Gastrointestinal and Pediatric Surgery, Division of Reparative Medicine, Institute of Life Sciences, Mie University Graduate School of Medicine, Mie, Japan

    Yuji Toiyama & Yoshinaga Okugawa

  6. Department of Genomic Medicine, Mie University Hospital, Mie, Japan

    Yoshinaga Okugawa

  7. Department of Surgical Oncology, City of Hope National Medical Center, Duarte, CA, USA

    Gagandeep Singh, Mustafa Raoof & Laleh Melstrom

  8. Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Joon Oh Park

  9. Division of Gastroenterology, City of Hope National Medical Center, Duarte, CA, USA

    James Lin & Gregory Idos

  10. HonorHealth Research Institute, Scottsdale, AZ, USA

    Erkut Borazanci

  11. Department of Surgery, The Medical College of Wisconsin, Milwaukee, WI, USA

    Douglas B. Evans

  12. Department of Biochemistry and Molecular Biology, Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

    Susan Tsai

  13. Division of Surgical Oncology, Department of Surgery, Ohio State University Comprehensive Cancer Center, Columbus, USA

    Eunsung Jun

  14. Baylor Scott and White Research Institute, Baylor University Medical Center, Dallas, TX, USA

    Scott A. Celinski

  15. Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA

    Vincent Chung

  16. Department of General Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China

    Guixin Zhang

  17. Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China

    Dong Shang

  18. Allegheny Health Network Cancer Institute, Allegheny Health Network, Pittsburgh, PA, USA

    David L. Bartlett & Ali H. Zaidi

  19. Department of Gastroenterological Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Mitsuro Kanda

  20. Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

    Song Cheol Kim

  21. The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Valsamo Anagnastou

  22. Department of Surgery, Ochsner Clinic Foundation, New Orleans, LA, USA

    John S. Bolton

  23. City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    Ajay Goel

Contributions

C.X., A.M., H.H., D.V.H. and A.G. conceptualized and designed the study. The laboratory experiments and formal analyses were conducted by C.X. and A.M. and supervised by H.H., D.V.H. and A.G. Software analyses were conducted by C.X. and A.M. Clinical data were curated by C.X., A.M., H.H., R.M.M., D.C., S.D., Y.T., Y.O., D.B.E., M.J.D., E.B., S.T., M.K., J.O.P., S.C.K. and A.H.Z. Funding acquisition was led by A.G. and D.V.H. The project was administered by D.V.H. and A.G. and supervised by H.H., D.V.H. and A.G. C.X. and A.M. produced the figures and the first draft of the manuscript, which was edited and revised by D.V.H. and A.G. All authors critically revised the paper for intellectual content. All authors read and approved the final version of the paper.

Corresponding author

Correspondence to Ajay Goel.

Ethics declarations

Competing interests

H.H. is a scientific advisor for Stromatis Pharma and ImproveBio, and a board member for Atom Therapeutics. V.A. receives research funding to Johns Hopkins University from AstraZeneca and Labcorp/Personal Genome Diagnostics, has received research funding to Johns Hopkins University from Bristol Myers Squibb and Delfi Diagnostics in the past 5 years, and is an advisory board member for AstraZeneca and Neogenomics (compensated) and receives honoraria from Foundation Medicine, Guardant Health, Roche, Thermo Fisher and Labcorp/Personal Genome Diagnostics; these arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. V.A. is an inventor on patent applications (63/276,525, 17/779,936, 16/312,152, 16/341,862, 17/047,006 and 17/598,690) submitted by Johns Hopkins University related to cancer genomic analyses, circulating tumor DNA therapeutic response monitoring and immunogenomic features of response to immunotherapy that have been licensed to one or more entities. All other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks George Calin, Elisa Giovannetti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Ranzoni in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Table 1 Clinicopathologic Characteristics of samples from training and validation cohort
Full size table
Extended Data Table 2 Clinicopathologic characteristics of samples from testing and pre–/post–surgery cohort
Full size table
Extended Data Table 3 Clinicopathologic Characteristics of samples from gastrointestinal cancer cohorts
Full size table

Extended Data Fig. 1 PDAC risk at different miRNA signature values and thresholds of positivity.

(a-b) Restricted cubic splines plots depict the odds ratios for the presence of PDAC based on miRNA signature levels in the training (a) and validation (b) cohorts. (c-d) Sensitivity (green) and specificity (blue) plots versus probability cutoff points for the miRNA signature in the training (c) and validation (d) cohorts.

Extended Data Fig. 2 The performance of the miRNA signature remains robust regardless of tumor location and country of origin.

(a-b) Raincloud plots with super-imposed box and whisker plot of the distribution of miRNA signature levels in NDCs and patients with head/uncinate process and body/tail PDAC in the training (a) and validation (b) cohorts. (c-d) ROC curve analysis in distinguishing patients with head/uncinate process or body/tail PDAC from NDC individuals in the training (c) and validation (d) cohorts. (e-f) ROC curve analysis in distinguishing patients with PDAC from NDC individuals from Japan, South Korea, and the USA in the training (e) and validation (f) cohorts. For box plots (a-b), the center line represents the median, the box bounds represent the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values within 1.5 times the interquartile range from the box bounds. Statistical significance was evaluated using a two-sided Student’s t-test for continuous variables with no adjustments for multiple comparisons. Abbreviations: AUROC, area under the receiver operating characteristic curve; CI, Confidence interval; S.Korea, South Korea; USA, United States of America.

Extended Data Fig. 3 Performance of the miRNA signature in the testing cohort, which comprises high-risk controls.

(a-c) ROC curve analysis evaluates the performance of the miRNA signature in distinguishing patients with PDAC/HGD from (a) average- and high-risk controls, (b) average-risk controls only, and (c) high-risk controls only. (d) Violin plots showing miRNA signature levels in controls at various degrees of risk for PDAC, compared with those with HGD or PDAC. (e) Raincloud plot demonstrating the miRNA signature levels in controls without PDAC versus patients of PDAC of Caucasian or African American ethnicity. (f-g) ROC curve analysis evaluates the performance of the miRNA signature in distinguishing patients with PDAC/HGD from controls within (f) Caucasian cohorts and (g) African-American cohorts. For box plots (d-e), the center line represents the median, the box bounds represent the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values within 1.5 times the interquartile range from the box bounds. Statistical significance was evaluated using a two-sided Student’s t-test with no adjustments for multiple comparisons. Abbreviations: AUROC, Area under the receiver operating characteristic curve; CP, chronic pancreatitis; FPC, Familial pancreatic cancer; HGD, High-grade dysplasia; HPC, Hereditary pancreatic cancer; IPMN, intraductal papillary mucinous neoplasm; PDAC, pancreatic ductal adenocarcinoma; ROC, receiver operating characteristic.

Extended Data Fig. 4 PANXEON demonstrates minimal cross-reactivity with other gastrointestinal cancers.

(a) ROC curve analysis compares the performance of PANXEON in detecting PDAC versus other gastrointestinal cancers (ie, ESCC, CRC, GC, HCC, and CCA). (b) Bar plots illustrate the sensitivity of PANXEON in detecting PDAC and other gastrointestinal cancers. Abbreviations: AUROC, Area under the receiver operating characteristic curve; CCA, cholangiocarcinoma. CRC, colorectal cancer. ESCC, esophageal squamous cell carcinoma. GC, gastric cancer. GI, gastrointestinal cancer. HCC, hepatocellular carcinoma. NDC, non-disease control. PDAC, pancreatic ductal adenocarcinoma; ROC, receiver operating characteristic.

Extended Data Fig. 5 The performance of CA19-9 and our miRNA signature in detecting patients with pancreatic ductal adenocarcinoma.

(a-b) Performance of CA19-9 in detecting patients with PDAC, shown with raincloud plots (a) and ROC curves (b) across all stages of PDAC in the testing cohort. (c) Rain cloud plots for the miRNA signature in those with versus without PDAC in the testing cohorts, stratified by stages at PDAC diagnosis (d) ROC curve analysis reveals the performance of the miRNA risk score in individuals with CA19-9 levels below threshold value (37 U/mL) from the test cohort. For box plots (a, c), the center line represents the median, the box bounds represent the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values within 1.5 times the interquartile range from the box bounds. Statistical significance was evaluated using a two-sided Student’s t-test with no adjustments for multiple comparisons. ns p > 0.05; * p < 0.05; ** p < 0.01, *** p < 0.001. Abbreviations: AUROC, Area under the receiver operating characteristic curve. CA19-9: Carbohydrate antigen 19-9. CI, Confidence interval. HGD, High-grade dysplasia. PDAC, pancreatic ductal adenocarcinoma.

Extended Data Fig. 6 Population-level simulation of PANXEON vs. CA19-9 performance.

(a) Uncertainty distributions for the sensitivity and specificity of PANXEON and CA19-9 used as input parameters for the Monte Carlo simulation (b–g) Comparison of diagnostic performance metrics between PANXEON and CA19-9 in a hypothetical screening cohort of 100,000 high-risk individuals. Panels show the number of (b) additional true pancreatic cancer cases identified, (c) total false-positive results, (d) positive predictive value (PPV), (e) negative predictive value (NPV), (f) total true negatives correctly classified, and (g) overall diagnostic accuracy. For box plots (a-g), the center line represents the median, the box bounds represent the 25th and 75th percentiles, and the whiskers extend to the largest and smallest values within 1.5 times the interquartile range from the box bounds. Statistical significance was evaluated using a two-sided Student’s t-test with no adjustments for multiple comparisons.

Extended Data Fig. 7 Cost-effectiveness and clinical impact of PANXEON-based surveillance.

(a) Schematic representation of the Markov model chain illustrating health states and transitions for the surveillance of individuals with a familial history of PDAC or a hereditary predisposition to PDAC. (b–e) Comparison of clinical and economic outcomes across three strategies: annual PANXEON surveillance, annual imaging (MRCP/EUS), and no surveillance. Panels show (b) total deaths, (c) PDAC-specific deaths avoided, (d) cumulative expenditures in USD, and (e) health utility. (f) Incremental cost-effectiveness plane showing incremental costs versus incremental effectiveness. The efficiency frontier illustrates the trade-off between PANXEON-based surveillance and alternative modalities. (g) Stage shift distribution comparison illustrating the intercepted scenario (detected via surveillance) versus the pre-intercepted scenario (clinical diagnosis). Abbreviations: HGD, High-grade dysplasia; IPMN, intraductal papillary mucinous neoplasm; PDAC, pancreatic ductal adenocarcinoma.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, C., Mannucci, A., Han, H. et al. Liquid biopsy for early detection of pancreatic ductal adenocarcinoma. Nat Med (2026). https://doi.org/10.1038/s41591-026-04625-x

Download citation

  • Received

  • Accepted

  • Published

  • Version of record

  • DOIhttps://doi.org/10.1038/s41591-026-04625-x











alvleesklierkanker, bloedtest PANXEON, circulerende microRNA's, exosomale microRNA's, CA19-9, preventie


Plaats een reactie ...

Reageer op "PANXEON een bloedtest die drie verschillende markers meet en combineert - circulerende microRNA's, exosomale microRNA's en de marker CA19-9 kan alvleesklierkanker in een zeer vroeg stadium opsporen waardoor genezend behandelen nog goed mogelijk is."


Gerelateerde artikelen
 

Gerelateerde artikelen

PANXEON een bloedtest die >> Bloedtest met vier tumormarkers >> Eiwit xCT belangrijke schakel >> AI - Kunstmatige intelligentie >> Het EndoRotor-systeem blijkt >> Stijgende glucose waarden >> Aspirine kan verdere uitzaaiingen >> Foliumzuur binnengekregen >> Fruit en groenten lijken geen >> Vitamine D op normale waarden >>