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A method for early diagnosis of lung cancer from tumor originated DNA fragments using plasma cfDNA methylome and fragmentome profiles.

Authors :
Kim, Yeo Jin
Jeon, Hahyeon
Jeon, Sungwon
Lee, Sung-Hun
Kim, Changjae
Ahn, Ji-Hye
Um, Hyojin
Woo, Yeong Ju
Jeong, Seong-ho
Kim, Yeonkyung
Park, Ha-Young
Oh, Hyung-Joo
Cho, Hyun-Ju
Bae, Jin-Han
Kim, Ji-Hoon
An, Seolbin
Kang, Sung-Bong
Jho, Sungwoong
Biro, Orsolya
Kis, David
Source :
Molecular & Cellular Probes. Dec2022, Vol. 66, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Early detection is critical for minimizing mortality from cancer. Plasma cell-free DNA (cfDNA) contains the signatures of tumor DNA, allowing us to quantify the signature and diagnose early-stage tumors. Here, we report a novel tumor fragment quantification method, TOF (Tumor Originated Fragment) for the diagnosis of lung cancer by quantifying and analyzing both the plasma cfDNA methylation patterns and fragmentomic signatures. TOF utilizes the amount of ctDNA predicted from the methylation density information of each cfDNA read mapped on 6243 lung-tumor-specific CpG markers. The 6243 tumor-specific markers were derived from lung tumor tissues by comparing them with corresponding normal tissues and healthy blood from public methylation data. TOF also utilizes two cfDNA fragmentomic signatures: 1) the short fragment ratio, and 2) the 5′ end-motif profile. We used 298 plasma samples to analyze cfDNA signatures using enzymatic methyl-sequencing data from 201 lung cancer patients and 97 healthy controls. The TOF score showed 0.98 of the area under the curve in correctly classifying lung cancer from normal samples. The TOF score resolution was high enough to clearly differentiate even the early-stage non-small cell lung cancer patients from the healthy controls. The same was true for small cell lung cancer patients. • A novel tumor fragment quantification method for the diagnosis of lung cancer. • Machine learning models using ctDNA methylome and fragmentome profiles. • Tumor Originated Fragment (TOF) scoring system to diagnose early-stage lung cancer. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08908508
Volume :
66
Database :
Academic Search Index
Journal :
Molecular & Cellular Probes
Publication Type :
Academic Journal
Accession number :
160538385
Full Text :
https://doi.org/10.1016/j.mcp.2022.101873