Yeo Jin Kim , Hahyeon Jeon , Sungwon Jeon , Sung-Hun Lee , Changjae Kim , Ji-Hye Ahn , Hyojin Um , Yeong Ju Woo , Seong-ho Jeong , Yeonkyung Kim , Ha-Young Park , Hyung-Joo Oh , Hyun-Ju Cho , Jin-Han Bae , Ji-Hoon Kim , Seolbin An , Sung-Bong Kang , Sungwoong Jho , Orsolya Biro , David Kis , In-Jae Oh
{"title":"利用血浆cfDNA甲基组和片段组图谱从肿瘤来源的DNA片段中早期诊断肺癌的方法","authors":"Yeo Jin Kim , Hahyeon Jeon , Sungwon Jeon , Sung-Hun Lee , Changjae Kim , Ji-Hye Ahn , Hyojin Um , Yeong Ju Woo , Seong-ho Jeong , Yeonkyung Kim , Ha-Young Park , Hyung-Joo Oh , Hyun-Ju Cho , Jin-Han Bae , Ji-Hoon Kim , Seolbin An , Sung-Bong Kang , Sungwoong Jho , Orsolya Biro , David Kis , In-Jae Oh","doi":"10.1016/j.mcp.2022.101873","DOIUrl":null,"url":null,"abstract":"<div><p><span><span><span>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 </span>cfDNA methylation patterns and fragmentomic signatures. TOF utilizes the amount of </span>ctDNA<span> 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 </span></span>small cell lung cancer patients.</p></div>","PeriodicalId":49799,"journal":{"name":"Molecular and Cellular Probes","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A method for early diagnosis of lung cancer from tumor originated DNA fragments using plasma cfDNA methylome and fragmentome profiles\",\"authors\":\"Yeo Jin Kim , Hahyeon Jeon , Sungwon Jeon , Sung-Hun Lee , Changjae Kim , Ji-Hye Ahn , Hyojin Um , Yeong Ju Woo , Seong-ho Jeong , Yeonkyung Kim , Ha-Young Park , Hyung-Joo Oh , Hyun-Ju Cho , Jin-Han Bae , Ji-Hoon Kim , Seolbin An , Sung-Bong Kang , Sungwoong Jho , Orsolya Biro , David Kis , In-Jae Oh\",\"doi\":\"10.1016/j.mcp.2022.101873\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span><span>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 </span>cfDNA methylation patterns and fragmentomic signatures. TOF utilizes the amount of </span>ctDNA<span> 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. 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A method for early diagnosis of lung cancer from tumor originated DNA fragments using plasma cfDNA methylome and fragmentome profiles
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.
期刊介绍:
MCP - Advancing biology through–omics and bioinformatic technologies wants to capture outcomes from the current revolution in molecular technologies and sciences. The journal has broadened its scope and embraces any high quality research papers, reviews and opinions in areas including, but not limited to, molecular biology, cell biology, biochemistry, immunology, physiology, epidemiology, ecology, virology, microbiology, parasitology, genetics, evolutionary biology, genomics (including metagenomics), bioinformatics, proteomics, metabolomics, glycomics, and lipidomics. Submissions with a technology-driven focus on understanding normal biological or disease processes as well as conceptual advances and paradigm shifts are particularly encouraged. The Editors welcome fundamental or applied research areas; pre-submission enquiries about advanced draft manuscripts are welcomed. Top quality research and manuscripts will be fast-tracked.