Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the de...
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| Veröffentlicht in: | Nature biomedical engineering 2021-06, Vol.5 (6), p.586-599 |
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| creator | Liang, Naixin Li, Bingsi Jia, Ziqi Wang, Chenyang Wu, Pancheng Zheng, Tao Wang, Yanyu Qiu, Fujun Wu, Yijun Su, Jing Xu, Jiayue Xu, Feng Chu, Huiling Fang, Shuai Yang, Xingyu Wu, Chengju Cao, Zhili Cao, Lei Bing, Zhongxing Liu, Hongsheng Li, Li Huang, Cheng Qin, Yingzhi Cui, Yushang Han-Zhang, Han Xiang, Jianxing Liu, Hao Guo, Xin Li, Shanqing Zhao, Heng Zhang, Zhihong |
| description | The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52–81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93–98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91–100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.
Deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of early cancers from plasma samples at dilution factors as low as 1/10,000. |
| doi_str_mv | 10.1038/s41551-021-00746-5 |
| format | Article |
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Deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of early cancers from plasma samples at dilution factors as low as 1/10,000.</description><identifier>ISSN: 2157-846X</identifier><identifier>EISSN: 2157-846X</identifier><identifier>DOI: 10.1038/s41551-021-00746-5</identifier><identifier>PMID: 34131323</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>45 ; 45/23 ; 631/114 ; 631/337 ; 631/61 ; 631/67 ; 692/53 ; Adult ; Biomarkers ; Biomarkers, Tumor - blood ; Biomarkers, Tumor - genetics ; Biomedical and Life Sciences ; Biomedical Engineering/Biotechnology ; Biomedicine ; Cancer ; Cancer screening ; Case-Control Studies ; Circulating Tumor DNA - blood ; Circulating Tumor DNA - genetics ; Classifiers ; Confidence intervals ; Deoxyribonucleic acid ; Dilution ; DNA ; DNA Methylation ; DNA sequencing ; Early Detection of Cancer - methods ; Female ; High-Throughput Nucleotide Sequencing ; Humans ; Learning algorithms ; Lung cancer ; Lung Neoplasms - blood ; Lung Neoplasms - diagnosis ; Lung Neoplasms - genetics ; Lung Neoplasms - pathology ; Machine learning ; Machine Learning - statistics & numerical data ; Male ; Medical screening ; Methylation ; Middle Aged ; Mutation ; Patients ; Sequence analysis ; Sequence Analysis, DNA - methods ; Subgroups ; Surgery ; Tumors</subject><ispartof>Nature biomedical engineering, 2021-06, Vol.5 (6), p.586-599</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Limited 2021. corrected publication 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature Limited 2021. corrected publication 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-aaaf130c5dc1e6104c735fcbae11bb8fd523b6f13c13d4c1e39ab30bdd35a08e3</citedby><cites>FETCH-LOGICAL-c375t-aaaf130c5dc1e6104c735fcbae11bb8fd523b6f13c13d4c1e39ab30bdd35a08e3</cites><orcidid>0000-0002-4320-8739 ; 0000-0001-8122-7992 ; 0000-0002-1454-1604</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34131323$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liang, Naixin</creatorcontrib><creatorcontrib>Li, Bingsi</creatorcontrib><creatorcontrib>Jia, Ziqi</creatorcontrib><creatorcontrib>Wang, Chenyang</creatorcontrib><creatorcontrib>Wu, Pancheng</creatorcontrib><creatorcontrib>Zheng, Tao</creatorcontrib><creatorcontrib>Wang, Yanyu</creatorcontrib><creatorcontrib>Qiu, Fujun</creatorcontrib><creatorcontrib>Wu, Yijun</creatorcontrib><creatorcontrib>Su, Jing</creatorcontrib><creatorcontrib>Xu, Jiayue</creatorcontrib><creatorcontrib>Xu, Feng</creatorcontrib><creatorcontrib>Chu, Huiling</creatorcontrib><creatorcontrib>Fang, Shuai</creatorcontrib><creatorcontrib>Yang, Xingyu</creatorcontrib><creatorcontrib>Wu, Chengju</creatorcontrib><creatorcontrib>Cao, Zhili</creatorcontrib><creatorcontrib>Cao, Lei</creatorcontrib><creatorcontrib>Bing, Zhongxing</creatorcontrib><creatorcontrib>Liu, Hongsheng</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><creatorcontrib>Huang, Cheng</creatorcontrib><creatorcontrib>Qin, Yingzhi</creatorcontrib><creatorcontrib>Cui, Yushang</creatorcontrib><creatorcontrib>Han-Zhang, Han</creatorcontrib><creatorcontrib>Xiang, Jianxing</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Guo, Xin</creatorcontrib><creatorcontrib>Li, Shanqing</creatorcontrib><creatorcontrib>Zhao, Heng</creatorcontrib><creatorcontrib>Zhang, Zhihong</creatorcontrib><title>Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning</title><title>Nature biomedical engineering</title><addtitle>Nat Biomed Eng</addtitle><addtitle>Nat Biomed Eng</addtitle><description>The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52–81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93–98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91–100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.
Deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of early cancers from plasma samples at dilution factors as low as 1/10,000.</description><subject>45</subject><subject>45/23</subject><subject>631/114</subject><subject>631/337</subject><subject>631/61</subject><subject>631/67</subject><subject>692/53</subject><subject>Adult</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - blood</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Cancer</subject><subject>Cancer screening</subject><subject>Case-Control Studies</subject><subject>Circulating Tumor DNA - blood</subject><subject>Circulating Tumor DNA - genetics</subject><subject>Classifiers</subject><subject>Confidence intervals</subject><subject>Deoxyribonucleic acid</subject><subject>Dilution</subject><subject>DNA</subject><subject>DNA Methylation</subject><subject>DNA sequencing</subject><subject>Early Detection of Cancer - 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Deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of early cancers from plasma samples at dilution factors as low as 1/10,000.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>34131323</pmid><doi>10.1038/s41551-021-00746-5</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-4320-8739</orcidid><orcidid>https://orcid.org/0000-0001-8122-7992</orcidid><orcidid>https://orcid.org/0000-0002-1454-1604</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2157-846X |
| ispartof | Nature biomedical engineering, 2021-06, Vol.5 (6), p.586-599 |
| issn | 2157-846X 2157-846X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2541785143 |
| source | MEDLINE; Alma/SFX Local Collection |
| subjects | 45 45/23 631/114 631/337 631/61 631/67 692/53 Adult Biomarkers Biomarkers, Tumor - blood Biomarkers, Tumor - genetics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Cancer Cancer screening Case-Control Studies Circulating Tumor DNA - blood Circulating Tumor DNA - genetics Classifiers Confidence intervals Deoxyribonucleic acid Dilution DNA DNA Methylation DNA sequencing Early Detection of Cancer - methods Female High-Throughput Nucleotide Sequencing Humans Learning algorithms Lung cancer Lung Neoplasms - blood Lung Neoplasms - diagnosis Lung Neoplasms - genetics Lung Neoplasms - pathology Machine learning Machine Learning - statistics & numerical data Male Medical screening Methylation Middle Aged Mutation Patients Sequence analysis Sequence Analysis, DNA - methods Subgroups Surgery Tumors |
| title | Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T08%3A22%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Ultrasensitive%20detection%20of%20circulating%20tumour%20DNA%20via%20deep%20methylation%20sequencing%20aided%20by%20machine%20learning&rft.jtitle=Nature%20biomedical%20engineering&rft.au=Liang,%20Naixin&rft.date=2021-06-01&rft.volume=5&rft.issue=6&rft.spage=586&rft.epage=599&rft.pages=586-599&rft.issn=2157-846X&rft.eissn=2157-846X&rft_id=info:doi/10.1038/s41551-021-00746-5&rft_dat=%3Cproquest_cross%3E2541785143%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2542128462&rft_id=info:pmid/34131323&rfr_iscdi=true |