Tumor-agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell-free DNA fragmentomics.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
33 patients, ctDNA detection was performed in a tumor-agnostic fashion using DNA methylation, cfDNA fragment lengths, and 4-mer 5' end motifs.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
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O · Outcome 결과 / 결론
Our findings suggest that machine learning models based on DNA methylation, cfDNA fragment lengths, and cfDNA end motifs can estimate ctDNA levels and predict clinical outcomes in advanced pancreatic cancer.
We investigated whether DNA methylation and cell-free DNA (cfDNA) fragmentation patterns can improve circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer.
- p-value P < 0.001
- HR 1.9
APA
Lapin M, Tjensvoll K, et al. (2025). Tumor-agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell-free DNA fragmentomics.. Molecular oncology, 19(12), 3535-3547. https://doi.org/10.1002/1878-0261.70116
MLA
Lapin M, et al.. "Tumor-agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell-free DNA fragmentomics.." Molecular oncology, vol. 19, no. 12, 2025, pp. 3535-3547.
PMID
40857204 ↗
Abstract 한글 요약
We investigated whether DNA methylation and cell-free DNA (cfDNA) fragmentation patterns can improve circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer. In a cohort of 33 patients, ctDNA detection was performed in a tumor-agnostic fashion using DNA methylation, cfDNA fragment lengths, and 4-mer 5' end motifs. Machine learning models estimating ctDNA levels were built for each individual detection method and their combination. All models significantly differentiated ctDNA levels in patients from healthy individuals (P < 0.001). Using the highest estimated levels in healthy volunteers as cutoffs, ctDNA was detected in 79%, 67%, 67%, and 55% of patients using methylation, fragment length, end motifs, and the combined model, respectively. Univariable Cox regression showed that all ctDNA level estimates were associated with increased hazard ratios (HR, all P < 0.001) for progression-free survival (PFS) and overall survival (OS). Multivariable Cox regression confirmed ctDNA levels as an independent predictor of PFS (HR = 1.9, P < 0.001) and OS (HR = 2.7, P < 0.001). Our findings suggest that machine learning models based on DNA methylation, cfDNA fragment lengths, and cfDNA end motifs can estimate ctDNA levels and predict clinical outcomes in advanced pancreatic cancer.
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