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Updated: Apr 5, 2026

Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients

Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients

Published on: September 9, 2020

Early Lung Cancer Detection Using Nucleotide Transition Probabilities in Plasma Cell-Free DNA.

Jinwen Ji¹, Ruyue Xue², Xu Zhang²

¹The State Key Laboratory of Neurology and Oncology Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing, China.

Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology

|April 3, 2026

Summary

A new computational feature, First-Order Transition Probability (FOTP), improves early lung cancer detection using cell-free DNA (cfDNA) fragmentomics. This method enhances sensitivity for early-stage cancers, offering a scalable screening strategy.

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Last Updated: Apr 5, 2026

Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients

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Published on: August 24, 2017

Area of Science:

Genomics
Computational Biology
Biomarker Discovery

Background:

Lung cancer is a leading cause of cancer death globally, necessitating improved early detection methods.
Current non-invasive plasma cell-free DNA (cfDNA) fragmentomics show limited sensitivity in early stages due to low tumor DNA fractions.

Purpose of the Study:

To develop a novel computational feature to enhance the sensitivity of cfDNA fragmentomics for early cancer detection.
To identify discriminative regions and nucleotide sequential dependencies within cfDNA fragments for improved cancer diagnosis.

Main Methods:

Development of the First-Order Transition Probability (FOTP) computational feature to analyze nucleotide sequential dependencies in cfDNA fragments.
Utilized low-pass whole genome sequencing data from 1,036 participants.
Trained a support vector machine (SVM) model using FOTP features on cfDNA fragment ends.

Main Results:

The initial 10 base pairs (bp) at the 5' end of cfDNA fragments provided the most discriminative information.
The SVM model achieved an AUC of 0.942, with 73.9% sensitivity for stage I and 81.8% for stage II lung cancer at 95% specificity.
The method demonstrated robustness across independent cohorts and multi-cancer validation sets, indicating potential for tissue-of-origin prediction.

Conclusions:

First-Order Transition Probability (FOTP) is a biologically interpretable and effective feature for early cancer detection.
This approach significantly enhances sensitivity for early-stage lung and other cancers by capturing key nucleotide dependencies.
The FOTP method presents a scalable and generalizable strategy for early cancer screening.