Early Lung Cancer Detection Using Nucleotide Transition Probabilities in Plasma Cell-Free DNA.
2/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
036 participants, we systematically analyzed cfDNA fragment ends to identify discriminative regions for cancer detection and trained a support vector machine (SVM) model leveraging the FOTP features.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
It captures key nucleotide dependencies at cfDNA fragment ends, enhancing sensitivity for early-stage lung cancer and other cancers. [IMPACT] This approach offers a scalable and generalizable strategy for early cancer screening.
OpenAlex 토픽 ·
Cancer Genomics and Diagnostics
Genomic variations and chromosomal abnormalities
Genetic factors in colorectal cancer
[BACKGROUND] Lung cancer is the most lethal malignancy worldwide and urgently requires effective early detection strategies.
APA
Jinwen Ji, Ruyue Xue, et al. (2026). Early Lung Cancer Detection Using Nucleotide Transition Probabilities in Plasma Cell-Free DNA.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. https://doi.org/10.1158/1055-9965.EPI-25-2034
MLA
Jinwen Ji, et al.. "Early Lung Cancer Detection Using Nucleotide Transition Probabilities in Plasma Cell-Free DNA.." Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2026.
PMID
41931548 ↗
Abstract 한글 요약
[BACKGROUND] Lung cancer is the most lethal malignancy worldwide and urgently requires effective early detection strategies. Current non-invasive approaches based on plasma cell-free DNA (cfDNA) fragmentomics often suffer from limited sensitivity in early-stage patients due to low tumor DNA fractions.
[METHODS] We developed a novel computational feature, First-Order Transition Probability (FOTP), to capture nucleotide sequential dependencies within cfDNA fragments. Using low-pass whole genome sequencing data from 1,036 participants, we systematically analyzed cfDNA fragment ends to identify discriminative regions for cancer detection and trained a support vector machine (SVM) model leveraging the FOTP features.
[RESULTS] Analysis revealed that the first 10 bp at the 5' end of cfDNA fragments contained the most discriminative information. The SVM model achieved an area under the ROC curve (AUC) of 0.942, with 73.9% sensitivity for stage I and 81.8% for stage II lung cancer at 95% specificity, significantly outperforming existing fragmentomic features. Nucleotide frequency stability and entropy patterns beyond the initial 10 bp supported the biological basis of the approach, reflecting nuclease cleavage biases and chromatin features. The method generalized robustly across independent cohorts and multi-cancer validation sets, showing potential for tissue-of-origin prediction.
[CONCLUSIONS] FOTP is a biologically interpretable and highly efficient feature for early cancer detection. It captures key nucleotide dependencies at cfDNA fragment ends, enhancing sensitivity for early-stage lung cancer and other cancers.
[IMPACT] This approach offers a scalable and generalizable strategy for early cancer screening.
[METHODS] We developed a novel computational feature, First-Order Transition Probability (FOTP), to capture nucleotide sequential dependencies within cfDNA fragments. Using low-pass whole genome sequencing data from 1,036 participants, we systematically analyzed cfDNA fragment ends to identify discriminative regions for cancer detection and trained a support vector machine (SVM) model leveraging the FOTP features.
[RESULTS] Analysis revealed that the first 10 bp at the 5' end of cfDNA fragments contained the most discriminative information. The SVM model achieved an area under the ROC curve (AUC) of 0.942, with 73.9% sensitivity for stage I and 81.8% for stage II lung cancer at 95% specificity, significantly outperforming existing fragmentomic features. Nucleotide frequency stability and entropy patterns beyond the initial 10 bp supported the biological basis of the approach, reflecting nuclease cleavage biases and chromatin features. The method generalized robustly across independent cohorts and multi-cancer validation sets, showing potential for tissue-of-origin prediction.
[CONCLUSIONS] FOTP is a biologically interpretable and highly efficient feature for early cancer detection. It captures key nucleotide dependencies at cfDNA fragment ends, enhancing sensitivity for early-stage lung cancer and other cancers.
[IMPACT] This approach offers a scalable and generalizable strategy for early cancer screening.
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