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Pan-Cancer Detection Through DNA Methylation Profiling Using Enzymatic Conversion Library Preparation with Targeted Sequencing
Örebro University, School of Medical Sciences. Örebro University Hospital. Clinical Research Center.ORCID iD: 0000-0001-6688-947X
Örebro University, School of Medical Sciences. Department of Obstetrics and Gynecology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-7954-0696
Department of Laboratory Medicine, Örebro University Hospital, Örebro, Sweden.
Örebro University, School of Medical Sciences. Clinical Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-3887-9519
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2025 (English)In: International Journal of Molecular Sciences, ISSN 1661-6596, E-ISSN 1422-0067, Vol. 26, no 20, article id 10165Article in journal (Refereed) Published
Abstract [en]

We investigated differences in circulating cell-free DNA (cfDNA) methylation between patients with cancer and those presenting with severe, nonspecific symptoms. Plasma cfDNA from 229 patients was analyzed, of whom 37 were diagnosed with a wide spectrum of cancer types within 12 months. Samples underwent enzymatic conversion, library preparation, and enrichment using the NEBNext workflow and Twist pan-cancer methylation panel, followed by sequencing. Methylation analysis was performed with nf-core/methylseq. Differentially methylated regions (DMRs) were identified with DMRichR. Machine learning with cross-validation was used to classify cancer and controls. The classifier was applied to an external validation set of 144 controls previously unseen by the model. Cancer samples showed higher overall CpG methylation than controls (1.82% vs. 1.34%, p < 0.001). A total of 162 DMRs were detected, 95.7% being hypermethylated in cancer. Machine learning identified 20 key DMRs for classification between cancer and controls. The final model achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity), while mean cross-validation performance reached an AUC of 0.73 (57.1% sensitivity, 77.5% specificity). The specificity of the classifier on unseen control samples was 79.2%. Distinct methylation differences and DMR-based classification support cfDNA methylation as a robust biomarker for cancer detection in patients with confounding conditions.

Place, publisher, year, edition, pages
MDPI, 2025. Vol. 26, no 20, article id 10165
Keywords [en]
cfDNA, epigenetics, liquid biopsy, methylation, next-generation sequencing, pan-cancer
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-124686DOI: 10.3390/ijms262010165ISI: 001603744700001PubMedID: 41155454Scopus ID: 2-s2.0-105020281018OAI: oai:DiVA.org:oru-124686DiVA, id: diva2:2010458
Funder
Region Örebro CountyInsamlingsstiftelsen Lions Cancerforskningsfond Mellansverige Uppsala-Örebro
Note

Funding Agencies:

This research was funded by grants from the Örebro County Research Committee, the Lions Cancer Research Foundation (Lions Cancerforskningsfond Mellansverige Uppsala-Örebro) and by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement OLL-1019597 and OLL-993009. The APC was funded by Region Örebro Län.

Available from: 2025-10-30 Created: 2025-10-30 Last updated: 2026-01-23Bibliographically approved
In thesis
1. Chasing the code: Advancing Precision Diagnostics through Next Generation Sequencing
Open this publication in new window or tab >>Chasing the code: Advancing Precision Diagnostics through Next Generation Sequencing
2026 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Next generation sequencing (NGS) has revolutionized precision diagnostics by enabling high-throughput analysis of nucleic acids. This thesis combines technical validations with innovative applications of NGS across five studies.

Papers I–II focus on molecular autopsies, demonstrating that hybridisation-based whole-exome sequencing can be successfully applied to formalin-fixed paraffin-embedded (FFPE) tissue, even in severely fragmented samples. Using matched blood and FFPE samples, our complete workflow for variant detection achieved a sensitivity of 97% and a positive predictive value of 98%. Applied to clinical cases, 23 of 35 FFPE samples were successfully sequenced, and relevant variants were detected in previously unresolved cases of sudden unexplained death. Paper III expanded forensic analysis on blood using the same hybridisation-based NGS-technology.

Papers IV–V explore liquid biopsy for pan-cancer detection. Using enzymatic conversion and targeted methylation sequencing of plasma circulating cell-free DNA (cfDNA), we identified 162 differentially methylated regions (DMRs) and developed a classifier for pan-cancer detection with sensitivity and specificity of 83.8%. Fragmentomics analysis revealed cancer-associated patterns in cfDNA fragment length and end motifs: cancer samples exhibited shorter median fragment lengths and alterations in fragment end motifs. These findings highlight fragmentomics as a promising biomarker for cancer detection.

Together, these studies illustrate the versatility of NGS for precision diagnostics—from post-mortem genetic analysis to minimally invasive cancer screening—and underscore the importance of rigorous validation to bridge research and clinical implementation.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2026. p. 94
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 345
Keywords
next generation sequencing, precision diagnostics, genetic testing, sudden death, validation, circulating biomarkers, pan-cancer, methylation markers, fragmentomics
National Category
Other Basic Medicine
Identifiers
urn:nbn:se:oru:diva-124820 (URN)9789175297309 (ISBN)9789175297316 (ISBN)
Public defence
2026-01-30, Örebro universitet, Campus USÖ, hörsal X1, Södra Grev Rosengatan 32, Örebro, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2025-11-06 Created: 2025-11-06 Last updated: 2026-01-09Bibliographically approved

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Qvick, AlvidaAdolfsson, EmmaLindqvist, Carl MårtenCarlsson, JessicaStenmark, BiancaKarlsson, ChristinaHelenius, Gisela

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