Zie ook in gerelateerde artikelen of zie artikelen op onze website met bloedtest in de titel.
18 april 2026: Bron: University of California Los Angeles d.d. 6 april 2026
Een bloedtest, Methylscan genoemd, ontdekt verschillende vormen van kanker in vroeg stadium door het meten van het DNA van in bloed circulerend dood tumorweefsel. Het verschil tussen een algemeen gebruikte ctDNA bloedtest en de Methylscan bloedtest is dat deze methylscan bloedtest het DNA van alle afgestorven celweefsel kan ontdekken. ctDNA meet meestal het DNA van afgestorven tumorweefsel en is dus beperkter in wat te vinden.
De onderzoekers kwamen tot opmerkelijke resultaten door celvrij DNA (cfDNA) te analyseren. Celvrij DNA (cfDNA) zijn kleine fragmenten genetisch materiaal die vrijkomen in het bloed wanneer cellen afsterven. Omdat cellen van elk orgaan DNA afgeven aan de bloedbaan, draagt cfDNA moleculaire signalen die weergeven wat er in het lichaam gebeurt.
Om de nauwkeurigheid van Methylscan bloedtest te testen, analyseerden de onderzoekers bloedmonsters van 1061 mensen, 460 kankerpatiënten en 601 mensen zonder aantoonbare kanker op moment dat de bloedtest werd uitgevoerd. Onder de kankerpatiënten waren er vier groepen met patiënten, te weten met leverkanker, longkanker, eierstokkanker en maagkanker; plus personen met leveraandoeningen zoals hepatitis B, hepatitis C, alcoholgerelateerde leverziekte en metabole leverziekte; mensen met goedaardige longknobbeltjes; en als controlegroep werd dus het Celvrij DNA (cfDNA) van gezonde deelnemers getest. Machine learning-algoritmen werden vervolgens toegepast om de complexe methyleringsgegevens te analyseren.
Voor de ontdekking van meerdere kankersoorten behaalde de bloedtest Methylscan een hoge mate van algehele nauwkeurigheid. Met een specificiteit van 98%, wat betekent dat er weinig vals-positieve resultaten waren. Ook ontdekte de bloedtest ongeveer 63% van de vormen van kanker in alle stadia en ongeveer 55% van de gevonden vormen van kanker waren in een nog vroeg stadium I. Belangrijk natuurlijk want bijna alle vormen van kanker in stadium I zijn operabel en in principe genezend te behandelen. Als de kanker eenmaal is uitgezaaid is genezing veel moeilijker.
De bloedtest presteerde ook goed bij de screening op leverkanker bij risicogroepen, waaronder mensen met levercirrose of HBV, waarbij bijna 80% van de gevallen werd ontdekt met een specificiteit van iets meer dan 90%, wat betekent dat er minder dan 10% vals-positieve resultaten waren.
Naast het simpelweg opsporen van kanker, hielpen de methyleringspatronen ook bij het identificeren van de bron van een signaal in het lichaam, oftewel het weefsel van het orgaan waar het dode celweefsel oorspronkelijk vandaan kwam.
Het volledige studierapport is gratis in te zien of te downloaden. Klik daarvoor op de titel van het abstract:
Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test
Edited by Jens Nielsen, BioInnovation Institute, Hellerup, Denmark; received July 14, 2025; accepted January 25, 2026
Significance
Cell-free DNA (cfDNA) in blood carries molecular signals from multiple organs, offering a powerful, noninvasive way to detect disease and monitor health. Current cfDNA methylation tests are costly and usually focus on a single condition. We developed MethylScan, a low-cost assay that sequences cfDNA methylome from blood. In over 1,000 individuals, MethylScan shows robust performance across a range of clinical applications, including multicancer detection in the general population, liver cancer surveillance in high-risk individuals, liver disease classification, organ injury detection, and ancestry prediction, all from one blood sample. This versatile approach enables affordable, wide-ranging cfDNA tests that can identify various health conditions simultaneously, with the potential to transform early disease detection and health monitoring across diverse clinical settings.
Abstract
Plasma cell-free DNA (cfDNA), originating from multiple organs, holds significant potential for noninvasive diagnostics and prognostics. Current cfDNA methylation assays primarily focus on single clinical indications by targeting specific genomic loci. In contrast, comprehensive profiling of cfDNA methylome can enable simultaneous detection of multiple diseases by capturing organ-specific methylation signatures, thereby offering a holistic view of health, when disease etiology is unclear or when conventional biochemical diagnostics are unavailable. However, deep sequencing required for sensitive detection of methylation abnormalities remains prohibitively expensive, limiting widespread clinical use. To overcome this barrier, we developed MethylScan, a highly cost-effective approach for cfDNA methylome sequencing. We demonstrated its broad clinical utility in a cohort of 1,061 individuals across diverse applications, including multicancer detection in general population, liver cancer surveillance in high-risk individuals, liver disease classification, identification of organ abnormalities, and race prediction from cfDNA. In multicancer detection (liver, lung, ovarian, and stomach cancers), MethylScan achieved an area under the receiver operating characteristic curve (AUROC) of 0.938 (95% CI: 0.920 to 0.954), with a sensitivity of 63.3% (95% CI: 58.9 to 67.9%) at 98.0% specificity for all cancer stages. For early-stage cancers, the AUROC was 0.916 (95% CI: 0.890 to 0.940), with 55.3% sensitivity (95% CI: 49.1 to 62.1%) at the same specificity. In liver cancer surveillance, MethylScan achieved an AUROC of 0.927 (95% CI: 0.889 to 0.959), with 79.6% sensitivity (95% CI: 70.6 to 87.8%) at 90.4% specificity. The assay also demonstrated strong performance in additional diagnostic tasks, supporting its potential as a versatile platform for comprehensive cfDNA-based health monitoring.
Acknowledgments
This work was supported by the National Cancer Institute (Grant no. U01CA285010 to X.J.Z., W.L., W.Z. and S.L., Grant no. U01CA230705 to X.J.Z., S.W.F., and S.-H.B.H., Grant no. R01CA246329 to X.J.Z., W.L., and S.M.D., Grant no. U01CA237711 to W.L., Grant no. R01CA264864 to X.J.Z., Grant no. R43CA246941 to X.N., Grant no. R01CA210360 to D.R.A., Grant no. U01CA214182 to D.R.A. and S.M.D., Grant no. U2CCA271898 to S.M.D. and W.H., Grant no R01CA276917 to E.B.G., Grant no. R01CA253651 to H.-R.T., V.A. and Y. Zhu, Grant nos. R01CA246304 and R21CA240887 to V.A., and Grant no. K99CA290092 to S.L., and Grant no. R01CA255727 to Y. Zhu), the National Human Genome Research Institute (Grant no. UM1HG011593 to F.A.), the NSF (Grant no. DMS 2310788 for W.H.W.), the NSF Graduate Research Fellowship (Grant no. DGE-1418060 to M.L.S.), and the California Tobacco-Related Disease Research Program of the University of California (Grant no. T32DT4721 to R.H.). This work was supported by the V Foundation Translational Award to X.J.Z. and W.H. and a gift from Kathy and Brian Schultz via the Connie Frank Foundation to X.J.Z. This work was supported by a Stand Up To Cancer-LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Cancer Research Grant (Grant number: SU2C-AACR-DT23–17 to S.M.D.). Stand Up To Cancer is a division of the Entertainment Industry Foundation. Data and specimen collection were partially supported by the Integrated Diagnostics Program, Department of Radiological Sciences and Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA. Research Grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. Research funding from the Department of Veteran Affairs to S.M. (Grant no. 1I01BX006019-01A2 and I01BX6411-02). We are grateful to the UCLA DGSOM and UCLA Health for their support of the IPH and its ATLAS community health and biobanking initiative, and to TPCL for providing biospecimens used in this study. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. The Genotype-Tissue Expression (GTEx) project kindly supported our study by providing tissue DNA samples. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 08/26/2019 and/or dbGaP accession number phs000424.v8.p2. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH, and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. We thank Ms. Lucy Shi for assistance with figure preparation and refinement.
Author contributions
X.J.Z. designed research; Weihua Zeng, C.-C.L., S.L., Y. Zhou, M.L.S., Y.X., A.Y., A.M., B.T., Z.N., M.Y., D.A., S.G., P.A., I.R., C.W., S. Malik, Y. Zhu, H.-R.T., E.B.G., S.W.F., C.E.M., S.M. Dry, C.M.L., D.G., G.C., S.S., A.S., C.R.W., K.G.K., D.S.L., S.S.R., X.X., K.S., L.M., S. Memarzadeh, W.H., K.K., S.M. Dubinett, D.R.A., V.A., S.-H.B.H., W.H.W., X.N., W.L., and X.J.Z. performed research; Weihua Zeng, C.-C.L., S.L., X.N., W.L., and X.J.Z. contributed new reagents/analytic tools; Weihua Zeng, R.H., C.T., Q.L., Wanwen Zeng, D.Y.L., J.Z., Y.L., F.A., W.H.W., W.L., and X.J.Z. analyzed data; A.Y., A.M., B.T., Z.N., M.Y., D.A., S.G., P.A., I.R., C.W., S. Malik, Y. Zhu, H.-R.T., E.B.G., S.W.F., C.E.M., S.M. Dry, C.M.L., D.G., G.C., S.S., A.S., C.R.W., K.G.K., D.S.L., S.S.R., X.X., K.S., L.M., S. Memarzadeh, W.H., K.K., S.M. Dubinett, D.R.A., V.A., and S.-H.B.H. contributed to patient enrollment or provided patient samples; C.-C.L., M.L.S., Y.X., and X.N. provided and analyzed data from commercial vendors; and Weihua Zeng, C.-C.L., S.L., W.L., and X.J.Z. wrote the paper.
Competing interests
X.J.Z., W.H.W., and W.L. are co-founders and board members of EarlyDiagnostics, Inc. X.J.Z. has an executive leadership position at EarlyDiagnostics, Inc. M.L.S., X.N., and C.-C.L. are employees of EarlyDiagnostics, Inc and S.M.D. was a scientific advisor to EarlyDiagnostics, Inc. W.L., W.Z., and Y. Zhou are consultants for EarlyDiagnostics, Inc. X.J.Z., W.L., W.H.W., and F.A. are stockholders of EarlyDiagnostics, Inc. M.L.S., W.Z., S.L., C.-C.L., Y.Zhou, Q.L., X.N. have stock options with EarlyDiagnostics, Inc. X.J.Z., C.-C.L. X.N., M.L.S., and W.Z. are inventors on a patent application submitted by the Regents of the University of California and EarlyDiagnostics, Inc. (Patent No. WO2023283591A2). The other authors have no competing interests to declare.
References
1
M. L. Stackpole et al., Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer. Nat. Commun. 13, 5566 (2022).
2
M. C. Liu et al., Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).
3
S. Li et al., Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Proc. Natl. Acad. Sci. U. S. A. 120, e2305236120 (2023).
4
L. Zhang, J. Li, Unlocking the secrets: The power of methylation-based cfDNA detection of tissue damage in organ systems. Clin. Epigenetics 15, 168 (2023).
5
R. H. Miller et al., Tissue-specific DNA methylation variability and its potential clinical value. Front. Genet. 14, 1125967 (2023).
6
Y. Bergman, H. Cedar, DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).
7
P. P. Anglim, T. A. Alonzo, I. A. Laird-Offringa, DNA methylation-based biomarkers for early detection of non-small cell lung cancer: An update. Mol. Cancer 7, 81 (2008).
8
K. D. Robertson, DNA methylation and human disease. Nat. Rev. Genet. 6, 597–610 (2005).
9
V. V. Levenson, DNA methylation as a universal biomarker. Expert Rev. Mol. Diagn. 10, 481–488 (2010).
10
R. G. Urdinguio, J. V. Sanchez-Mut, M. Esteller, Epigenetic mechanisms in neurological diseases: Genes, syndromes, and therapies. Lancet Neurol. 8, 1056–1072 (2009).
11
R. Lehmann-Werman et al., Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc. Natl. Acad. Sci. 113, 201519286 (2016).
12
K. Sun et al., Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc. Natl. Acad. Sci. U.S.A. 112, E5503–E5512 (2015).
13
S. Guo et al., Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat. Genet. 49, 635–642 (2017).
14
R.-H. Xu et al., Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat. Mater. 16, 1155–1161 (2017), https://doi.org/10.1038/nmat4997.
15
S. Y. Shen et al., Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).
16
W. Li et al., CancerDetector: Ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 46, e89 (2018).
17
S. Kang et al., Cancerlocator: Non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 18, 53 (2017).
18
X. Chen et al., Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 11, 3475 (2020).
19
N. Liang et al., Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning. Nat. Biomed. Eng. 5, 586–599 (2021).
20
A. B. Sparks, C. A. Struble, E. T. Wang, K. Song, A. Oliphant, Noninvasive prenatal detection and selective analysis of cell-free DNA obtained from maternal blood: Evaluation for trisomy 21 and trisomy 18. Am. J. Obstet. Gynecol. 206, e1–e9 (2012).
21
R. W. K. Chiu et al., Non-invasive prenatal assessment of trisomy 21 by multiplexed maternal plasma DNA sequencing: Large scale validity study. BMJ 342, c7401 (2011).
22
I. De Vlaminck et al., Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl. Acad. Sci. U.S.A. 112, 13336–13341 (2015).
23
J. Moss et al., Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 5068 (2018).
24
R. L. Grossman et al., Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
25
A. Liberzon et al., Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
26
A. M. Moon, A. G. Singal, E. B. Tapper, Contemporary epidemiology of chronic liver disease and cirrhosis. Clin. Gastroenterol. Hepatol. 18, 2650–2666 (2020).
27
K. Tzartzeva et al., Surveillance imaging and alpha fetoprotein for early detection of hepatocellular carcinoma in patients with cirrhosis: A meta-analysis. Gastroenterology 154, 1706–1718.e1 (2018).
28
F. Gao et al., Global analysis of DNA methylation in hepatocellular carcinoma by a liquid hybridization capture-based bisulfite sequencing approach. Clin. Epigenetics 7, 86 (2015).
29
X. J. Sun, M. C. Wang, F. H. Zhang, X. Kong, An integrated analysis of genome-wide DNA methylation and gene expression data in hepatocellular carcinoma. FEBS Open Bio. 8, 1093–1103 (2018).
30
M. E. Ritchie et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
31
M. Ehrlich, DNA hypomethylation in cancer cells. Epigenomics 1, 239–259 (2009).
32
W. Timp, A. P. Feinberg, Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497–510 (2013).
33
K. C. A. Chan et al., Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl. Acad. Sci. U.S.A. 110, 18761–18768 (2013).
34
O. Alter, P. O. Brown, D. Botstein, Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. U.S.A. 97, 10101–10106 (2000).
35
L. Eldén, Matrix Methods in Data Mining and Pattern Recognition. (Society for Industrial and Applied Mathematics, 2007). https://doi.org/10.1137/1.9780898718867.
Gerelateerde artikelen
- Bloedtest - Methylscan ontdekt door dood celweefsel in het bloed circulerend verschillende vormen van kanker in heel vroeg stadium.
- Bloedtest TriOx vindt voor 95 procent met hulp van AI Kunstmatige Intelligentie kanker met solide tumoren in studievergelijking met mensen met en zonder vastgestelde kanker.
- Galleri bloedtest geeft uitstekende resultaten via ctDNA en voorspelt voor 85 procent plaats van primaire tumor en slechts paar procent vals positieve uitslag
- Bloedtest die methyl meet in cellen kan ruim van te voren voorspellen of iemand borstkanker gaat ontwikkelen. Blijkt uit jarenlang onderzoek. copy 1
Plaats een reactie ...
Reageer op "Bloedtest - Methylscan ontdekt door dood celweefsel in het bloed circulerend verschillende vormen van kanker in heel vroeg stadium."