Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq
Oncogenic driver mutations are identified in single cells by a transposon-based sequencing method. A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how...
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Veröffentlicht in: | Nature biotechnology 2016-09, Vol.34 (9), p.962-972 |
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creator | Mann, Karen M Newberg, Justin Y Black, Michael A Jones, Devin J Amaya-Manzanares, Felipe Guzman-Rojas, Liliana Kodama, Takahiro Ward, Jerrold M Rust, Alistair G van der Weyden, Louise Yew, Christopher Chin Kuan Waters, Jill L Leung, Marco L Rogers, Keith Rogers, Susan M McNoe, Leslie A Selvanesan, Luxmanan Navin, Nicholas Jenkins, Nancy A Copeland, Neal G Mann, Michael B |
description | Oncogenic driver mutations are identified in single cells by a transposon-based sequencing method.
A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline,
Sleeping Beauty
(SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes. |
doi_str_mv | 10.1038/nbt.3637 |
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A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline,
Sleeping Beauty
(SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.</description><identifier>ISSN: 1087-0156</identifier><identifier>EISSN: 1546-1696</identifier><identifier>DOI: 10.1038/nbt.3637</identifier><identifier>PMID: 27479497</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>38/91 ; 45/23 ; 45/47 ; 631/1647/334/1874/345 ; 631/1647/514/2254 ; 631/208/212/2305 ; 631/67/1990/283/1897 ; 631/67/69 ; 64/110 ; 64/60 ; Agriculture ; Algorithms ; Animals ; Bioinformatics ; Biomarkers, Tumor - genetics ; Biomedical Engineering/Biotechnology ; Biomedicine ; Biotechnology ; Cancer cells ; Cells ; DNA sequencing ; DNA Transposable Elements ; DNA, Neoplasm - genetics ; Female ; Gene expression ; Genes, Neoplasm - genetics ; Genetic aspects ; Health aspects ; Heterogeneity ; High-Throughput Nucleotide Sequencing - methods ; In Situ Hybridization - methods ; Leukemia ; Leukemia, Myeloid - genetics ; Leukemia, Myeloid - pathology ; Life Sciences ; Male ; Methods ; Mice ; Mutagenesis, Insertional - genetics ; Myeloid leukemia ; Neoplasm Proteins - genetics ; Oncology ; Sequence Analysis, DNA - methods ; Software ; Subpopulations ; Transposases - genetics ; Tumors</subject><ispartof>Nature biotechnology, 2016-09, Vol.34 (9), p.962-972</ispartof><rights>Springer Nature America, Inc. 2016</rights><rights>COPYRIGHT 2016 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Sep 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c639t-3abfbd62cf98aedcee30150782cabadb4f29cabb9659cf0e2daca26727c03a523</citedby><cites>FETCH-LOGICAL-c639t-3abfbd62cf98aedcee30150782cabadb4f29cabb9659cf0e2daca26727c03a523</cites><orcidid>0000-0002-7515-6515 ; 0000-0003-4364-1273</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27479497$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mann, Karen M</creatorcontrib><creatorcontrib>Newberg, Justin Y</creatorcontrib><creatorcontrib>Black, Michael A</creatorcontrib><creatorcontrib>Jones, Devin J</creatorcontrib><creatorcontrib>Amaya-Manzanares, Felipe</creatorcontrib><creatorcontrib>Guzman-Rojas, Liliana</creatorcontrib><creatorcontrib>Kodama, Takahiro</creatorcontrib><creatorcontrib>Ward, Jerrold M</creatorcontrib><creatorcontrib>Rust, Alistair G</creatorcontrib><creatorcontrib>van der Weyden, Louise</creatorcontrib><creatorcontrib>Yew, Christopher Chin Kuan</creatorcontrib><creatorcontrib>Waters, Jill L</creatorcontrib><creatorcontrib>Leung, Marco L</creatorcontrib><creatorcontrib>Rogers, Keith</creatorcontrib><creatorcontrib>Rogers, Susan M</creatorcontrib><creatorcontrib>McNoe, Leslie A</creatorcontrib><creatorcontrib>Selvanesan, Luxmanan</creatorcontrib><creatorcontrib>Navin, Nicholas</creatorcontrib><creatorcontrib>Jenkins, Nancy A</creatorcontrib><creatorcontrib>Copeland, Neal G</creatorcontrib><creatorcontrib>Mann, Michael B</creatorcontrib><title>Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq</title><title>Nature biotechnology</title><addtitle>Nat Biotechnol</addtitle><addtitle>Nat Biotechnol</addtitle><description>Oncogenic driver mutations are identified in single cells by a transposon-based sequencing method.
A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline,
Sleeping Beauty
(SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.</description><subject>38/91</subject><subject>45/23</subject><subject>45/47</subject><subject>631/1647/334/1874/345</subject><subject>631/1647/514/2254</subject><subject>631/208/212/2305</subject><subject>631/67/1990/283/1897</subject><subject>631/67/69</subject><subject>64/110</subject><subject>64/60</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Animals</subject><subject>Bioinformatics</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Cancer cells</subject><subject>Cells</subject><subject>DNA sequencing</subject><subject>DNA Transposable Elements</subject><subject>DNA, Neoplasm - genetics</subject><subject>Female</subject><subject>Gene expression</subject><subject>Genes, Neoplasm - genetics</subject><subject>Genetic aspects</subject><subject>Health aspects</subject><subject>Heterogeneity</subject><subject>High-Throughput Nucleotide Sequencing - methods</subject><subject>In Situ Hybridization - methods</subject><subject>Leukemia</subject><subject>Leukemia, Myeloid - genetics</subject><subject>Leukemia, Myeloid - pathology</subject><subject>Life Sciences</subject><subject>Male</subject><subject>Methods</subject><subject>Mice</subject><subject>Mutagenesis, Insertional - genetics</subject><subject>Myeloid leukemia</subject><subject>Neoplasm Proteins - genetics</subject><subject>Oncology</subject><subject>Sequence Analysis, DNA - methods</subject><subject>Software</subject><subject>Subpopulations</subject><subject>Transposases 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L</au><au>Leung, Marco L</au><au>Rogers, Keith</au><au>Rogers, Susan M</au><au>McNoe, Leslie A</au><au>Selvanesan, Luxmanan</au><au>Navin, Nicholas</au><au>Jenkins, Nancy A</au><au>Copeland, Neal G</au><au>Mann, Michael B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq</atitle><jtitle>Nature biotechnology</jtitle><stitle>Nat Biotechnol</stitle><addtitle>Nat Biotechnol</addtitle><date>2016-09-01</date><risdate>2016</risdate><volume>34</volume><issue>9</issue><spage>962</spage><epage>972</epage><pages>962-972</pages><issn>1087-0156</issn><eissn>1546-1696</eissn><abstract>Oncogenic driver mutations are identified in single cells by a transposon-based sequencing method.
A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline,
Sleeping Beauty
(SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>27479497</pmid><doi>10.1038/nbt.3637</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-7515-6515</orcidid><orcidid>https://orcid.org/0000-0003-4364-1273</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 38/91 45/23 45/47 631/1647/334/1874/345 631/1647/514/2254 631/208/212/2305 631/67/1990/283/1897 631/67/69 64/110 64/60 Agriculture Algorithms Animals Bioinformatics Biomarkers, Tumor - genetics Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cancer cells Cells DNA sequencing DNA Transposable Elements DNA, Neoplasm - genetics Female Gene expression Genes, Neoplasm - genetics Genetic aspects Health aspects Heterogeneity High-Throughput Nucleotide Sequencing - methods In Situ Hybridization - methods Leukemia Leukemia, Myeloid - genetics Leukemia, Myeloid - pathology Life Sciences Male Methods Mice Mutagenesis, Insertional - genetics Myeloid leukemia Neoplasm Proteins - genetics Oncology Sequence Analysis, DNA - methods Software Subpopulations Transposases - genetics Tumors |
title | Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T23%3A08%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analyzing%20tumor%20heterogeneity%20and%20driver%20genes%20in%20single%20myeloid%20leukemia%20cells%20with%20SBCapSeq&rft.jtitle=Nature%20biotechnology&rft.au=Mann,%20Karen%20M&rft.date=2016-09-01&rft.volume=34&rft.issue=9&rft.spage=962&rft.epage=972&rft.pages=962-972&rft.issn=1087-0156&rft.eissn=1546-1696&rft_id=info:doi/10.1038/nbt.3637&rft_dat=%3Cgale_pubme%3EA462831202%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1822135483&rft_id=info:pmid/27479497&rft_galeid=A462831202&rfr_iscdi=true |