Determination of the Subclonal Tumor Structure in Childhood Acute Myeloid Leukemia and Acral Melanoma by Next-Generation Sequencing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-09

AUTHORS

G. S. Krasnov, L. G. Ghukasyan, I. S. Abramov, T. V. Nasedkina

ABSTRACT

Intratumoral heterogeneity and clonal variability are among the central problems of clinical oncology, leading to resistance to therapy, relapse, and metastasis. High-throughput sequencing of the tumor exome makes it possible to investigate the subclonal tumor organization. Target panel, clinical exome, and complete exome sequencing data were compared in tumors with different mutational burden, acute myeloid leukemia (AML) in children and acral melanoma. Targeted sequencing of AML samples detected more than one potential driver mutation in the signaling pathway genes KIT, NRAS, KRAS, CBL, and FLT3 in one patient, reflecting the complex clonal structure of the tumor substrate. Clusters of mutant alleles corresponding to different populations of leukemic cells in a sample were isolated based on exome sequencing data from the same AML patients. A comparison of the mutation profile for a primary AML sample and samples obtained in remission and relapse made it possible to trace the dynamic changes in the clonal composition of the tumor. The subclonal tumor structure was investigated in an acral melanoma case as an example. Mutant alleles present in the sample with close frequencies were clustered using the SciClone and ClonEvol packages. The results were used to predict the intratumoral clonal composition and to assume a clonal evolution model, which described the changes in the clonal composition of the tumor during metastasis, including the appearance of new mutations that might be associated with further disease progression. The approach used in the work is suitable for identifying the mutations that cause the formation of new tumor clones, which may have a proliferative advantage, in particular, in conditions of antitumor therapy. More... »

PAGES

727-741

References to SciGraph publications

  • 1982-11. Mechanism of activation of a human oncogene in NATURE
  • 2015-10-01. Clonal dynamics in a single AML case tracked for 9 years reveals the complexity of leukemia progression in LEUKEMIA
  • 2020-06-24. Clonal dominance is an adverse prognostic factor in acute myeloid leukemia treated with intensive chemotherapy in LEUKEMIA
  • 2016-10-31. The genomic landscape of core-binding factor acute myeloid leukemias in NATURE GENETICS
  • 2020-10-16. Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity in NATURE COMMUNICATIONS
  • 2012-01-18. Clonal evolution in cancer in NATURE
  • 2020-07-01. CD96, a new immune checkpoint, correlates with immune profile and clinical outcome of glioma in SCIENTIFIC REPORTS
  • 2015-06-15. Somatic mutational landscape of AML with inv(16) or t(8;21) identifies patterns of clonal evolution in relapse leukemia in LEUKEMIA
  • 2019-07. Somatic Mutations Associated with Metastasis in Acral Melanoma in MOLECULAR BIOLOGY
  • 2012-04-29. Absolute quantification of somatic DNA alterations in human cancer in NATURE BIOTECHNOLOGY
  • 2012-09-11. ASXL1 exon 12 mutations are frequent in AML with intermediate risk karyotype and are independently associated with an adverse outcome in LEUKEMIA
  • 2016-08-18. Genetic hierarchy and temporal variegation in the clonal history of acute myeloid leukaemia in NATURE COMMUNICATIONS
  • 2013-02-10. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples in NATURE BIOTECHNOLOGY
  • 2013-06-20. The importance of relative mutant level for evaluating impact on outcome of KIT, FLT3 and CBL mutations in core-binding factor acute myeloid leukemia in LEUKEMIA
  • 2018-10-08. Vav1 mutations identified in human cancers give rise to different oncogenic phenotypes in ONCOGENESIS
  • 2020-10-28. Single-cell mutation analysis of clonal evolution in myeloid malignancies in NATURE
  • 2013-09-18. The causes and consequences of genetic heterogeneity in cancer evolution in NATURE
  • 2014-01-09. High number of additional genetic lesions in acute myeloid leukemia with t(8;21)/RUNX1-RUNX1T1: frequency and impact on clinical outcome in LEUKEMIA
  • 2014-03-28. MutationTaster2: mutation prediction for the deep-sequencing age in NATURE METHODS
  • 2010-04. A method and server for predicting damaging missense mutations in NATURE METHODS
  • 2015-12-03. SIFT missense predictions for genomes in NATURE PROTOCOLS
  • 2020-09-08. FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples in NATURE COMMUNICATIONS
  • 2017-04-07. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma in NPJ GENOMIC MEDICINE
  • 2020-05. Driver Mutations in Acute Myeloid Leukemia with Inversion of Chromosome 16 in MOLECULAR BIOLOGY
  • 2015-01-09. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes in NATURE COMMUNICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1134/s0026893321040051

    DOI

    http://dx.doi.org/10.1134/s0026893321040051

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1142004036


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biochemistry and Cell Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0607", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Plant Biology", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia", 
              "id": "http://www.grid.ac/institutes/grid.418899.5", 
              "name": [
                "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Krasnov", 
            "givenName": "G. S.", 
            "id": "sg:person.01004716151.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01004716151.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia", 
              "id": "http://www.grid.ac/institutes/grid.418899.5", 
              "name": [
                "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ghukasyan", 
            "givenName": "L. G.", 
            "id": "sg:person.012063052123.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012063052123.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia", 
              "id": "http://www.grid.ac/institutes/grid.418899.5", 
              "name": [
                "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Abramov", 
            "givenName": "I. S.", 
            "id": "sg:person.01302725235.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302725235.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia", 
              "id": "http://www.grid.ac/institutes/grid.418899.5", 
              "name": [
                "Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nasedkina", 
            "givenName": "T. V.", 
            "id": "sg:person.01133150100.87", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133150100.87"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/s41525-017-0013-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084537792", 
              "https://doi.org/10.1038/s41525-017-0013-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2015.264", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049637664", 
              "https://doi.org/10.1038/leu.2015.264"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1134/s0026893319040022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1120404589", 
              "https://doi.org/10.1134/s0026893319040022"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2014.4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012311625", 
              "https://doi.org/10.1038/leu.2014.4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-020-18988-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1131775868", 
              "https://doi.org/10.1038/s41467-020-18988-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms6901", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019589194", 
              "https://doi.org/10.1038/ncomms6901"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-020-2864-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1132055867", 
              "https://doi.org/10.1038/s41586-020-2864-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.3709", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041260635", 
              "https://doi.org/10.1038/ng.3709"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12625", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024549098", 
              "https://doi.org/10.1038/nature12625"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/300143a0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051255174", 
              "https://doi.org/10.1038/300143a0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10762", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053038796", 
              "https://doi.org/10.1038/nature10762"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41375-020-0932-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128717520", 
              "https://doi.org/10.1038/s41375-020-0932-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2203", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010515132", 
              "https://doi.org/10.1038/nbt.2203"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms12475", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008017504", 
              "https://doi.org/10.1038/ncomms12475"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2015.141", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000328933", 
              "https://doi.org/10.1038/leu.2015.141"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2012.262", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031979360", 
              "https://doi.org/10.1038/leu.2012.262"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth0410-248", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007489634", 
              "https://doi.org/10.1038/nmeth0410-248"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2013.186", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003846917", 
              "https://doi.org/10.1038/leu.2013.186"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41389-018-0091-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107450414", 
              "https://doi.org/10.1038/s41389-018-0091-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2015.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021631236", 
              "https://doi.org/10.1038/nprot.2015.123"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1134/s0026893320030073", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128554195", 
              "https://doi.org/10.1134/s0026893320030073"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-020-18169-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1130670114", 
              "https://doi.org/10.1038/s41467-020-18169-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2890", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020782180", 
              "https://doi.org/10.1038/nmeth.2890"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-020-66806-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128876618", 
              "https://doi.org/10.1038/s41598-020-66806-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2514", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000477771", 
              "https://doi.org/10.1038/nbt.2514"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-09", 
        "datePublishedReg": "2021-09-01", 
        "description": "Intratumoral heterogeneity and clonal variability are among the central problems of clinical oncology, leading to resistance to therapy, relapse, and metastasis. High-throughput sequencing of the tumor exome makes it possible to investigate the subclonal tumor organization. Target panel, clinical exome, and complete exome sequencing data were compared in tumors with different mutational burden, acute myeloid leukemia (AML) in children and acral melanoma. Targeted sequencing of AML samples detected more than one potential driver mutation in the signaling pathway genes KIT, NRAS, KRAS, CBL, and FLT3 in one patient, reflecting the complex clonal structure of the tumor substrate. Clusters of mutant alleles corresponding to different populations of leukemic cells in a sample were isolated based on exome sequencing data from the same AML patients. A comparison of the mutation profile for a primary AML sample and samples obtained in remission and relapse made it possible to trace the dynamic changes in the clonal composition of the tumor. The subclonal tumor structure was investigated in an acral melanoma case as an example. Mutant alleles present in the sample with close frequencies were clustered using the SciClone and ClonEvol packages. The results were used to predict the intratumoral clonal composition and to assume a clonal evolution model, which described the changes in the clonal composition of the tumor during metastasis, including the appearance of new mutations that might be associated with further disease progression. The approach used in the work is suitable for identifying the mutations that cause the formation of new tumor clones, which may have a proliferative advantage, in particular, in conditions of antitumor therapy.", 
        "genre": "article", 
        "id": "sg:pub.10.1134/s0026893321040051", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1371988", 
            "issn": [
              "0026-8933", 
              "1608-3245"
            ], 
            "name": "Molecular Biology", 
            "publisher": "Pleiades Publishing", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "55"
          }
        ], 
        "keywords": [
          "acute myeloid leukemia", 
          "myeloid leukemia", 
          "acral melanoma", 
          "AML samples", 
          "childhood acute myeloid leukemia", 
          "further disease progression", 
          "clonal composition", 
          "primary AML samples", 
          "exome sequencing data", 
          "tumor structure", 
          "new tumor clones", 
          "AML patients", 
          "potential driver mutations", 
          "disease progression", 
          "mutational burden", 
          "tumor substrate", 
          "Clinical Oncology", 
          "melanoma cases", 
          "leukemic cells", 
          "clinical exome", 
          "tumor clones", 
          "driver mutations", 
          "tumor organization", 
          "tumors", 
          "antitumor therapy", 
          "mutation profiles", 
          "intratumoral heterogeneity", 
          "next-generation sequencing", 
          "complex clonal structure", 
          "Targeted sequencing", 
          "relapse", 
          "patients", 
          "clonal evolution model", 
          "metastasis", 
          "therapy", 
          "leukemia", 
          "melanoma", 
          "proliferative advantage", 
          "tumor exomes", 
          "genes KIT", 
          "mutant alleles", 
          "new mutations", 
          "different populations", 
          "mutations", 
          "remission", 
          "exome", 
          "KRAS", 
          "FLT3", 
          "oncology", 
          "sequencing", 
          "NRAS", 
          "progression", 
          "alleles", 
          "sequencing data", 
          "burden", 
          "children", 
          "dynamic changes", 
          "kit", 
          "samples", 
          "cells", 
          "target panel", 
          "population", 
          "changes", 
          "clonal structure", 
          "clonal variability", 
          "Cbl", 
          "high-throughput sequencing", 
          "data", 
          "cases", 
          "clones", 
          "appearance", 
          "resistance", 
          "panel", 
          "heterogeneity", 
          "profile", 
          "SciClone", 
          "frequency", 
          "comparison", 
          "variability", 
          "results", 
          "conditions", 
          "composition", 
          "organization", 
          "model", 
          "formation", 
          "approach", 
          "determination", 
          "clusters", 
          "advantages", 
          "problem", 
          "package", 
          "structure", 
          "substrate", 
          "work", 
          "example", 
          "central problem", 
          "close frequencies", 
          "evolution model"
        ], 
        "name": "Determination of the Subclonal Tumor Structure in Childhood Acute Myeloid Leukemia and Acral Melanoma by Next-Generation Sequencing", 
        "pagination": "727-741", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1142004036"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1134/s0026893321040051"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1134/s0026893321040051", 
          "https://app.dimensions.ai/details/publication/pub.1142004036"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:31", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_896.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1134/s0026893321040051"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1134/s0026893321040051'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s0026893321040051'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s0026893321040051'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s0026893321040051'


     

    This table displays all metadata directly associated to this object as RDF triples.

    285 TRIPLES      22 PREDICATES      151 URIs      116 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1134/s0026893321040051 schema:about anzsrc-for:06
    2 anzsrc-for:0601
    3 anzsrc-for:0604
    4 anzsrc-for:0607
    5 schema:author N14a9cce2355a4e15a3b761cfb872da34
    6 schema:citation sg:pub.10.1038/300143a0
    7 sg:pub.10.1038/leu.2012.262
    8 sg:pub.10.1038/leu.2013.186
    9 sg:pub.10.1038/leu.2014.4
    10 sg:pub.10.1038/leu.2015.141
    11 sg:pub.10.1038/leu.2015.264
    12 sg:pub.10.1038/nature10762
    13 sg:pub.10.1038/nature12625
    14 sg:pub.10.1038/nbt.2203
    15 sg:pub.10.1038/nbt.2514
    16 sg:pub.10.1038/ncomms12475
    17 sg:pub.10.1038/ncomms6901
    18 sg:pub.10.1038/ng.3709
    19 sg:pub.10.1038/nmeth.2890
    20 sg:pub.10.1038/nmeth0410-248
    21 sg:pub.10.1038/nprot.2015.123
    22 sg:pub.10.1038/s41375-020-0932-8
    23 sg:pub.10.1038/s41389-018-0091-1
    24 sg:pub.10.1038/s41467-020-18169-2
    25 sg:pub.10.1038/s41467-020-18988-3
    26 sg:pub.10.1038/s41525-017-0013-8
    27 sg:pub.10.1038/s41586-020-2864-x
    28 sg:pub.10.1038/s41598-020-66806-z
    29 sg:pub.10.1134/s0026893319040022
    30 sg:pub.10.1134/s0026893320030073
    31 schema:datePublished 2021-09
    32 schema:datePublishedReg 2021-09-01
    33 schema:description Intratumoral heterogeneity and clonal variability are among the central problems of clinical oncology, leading to resistance to therapy, relapse, and metastasis. High-throughput sequencing of the tumor exome makes it possible to investigate the subclonal tumor organization. Target panel, clinical exome, and complete exome sequencing data were compared in tumors with different mutational burden, acute myeloid leukemia (AML) in children and acral melanoma. Targeted sequencing of AML samples detected more than one potential driver mutation in the signaling pathway genes KIT, NRAS, KRAS, CBL, and FLT3 in one patient, reflecting the complex clonal structure of the tumor substrate. Clusters of mutant alleles corresponding to different populations of leukemic cells in a sample were isolated based on exome sequencing data from the same AML patients. A comparison of the mutation profile for a primary AML sample and samples obtained in remission and relapse made it possible to trace the dynamic changes in the clonal composition of the tumor. The subclonal tumor structure was investigated in an acral melanoma case as an example. Mutant alleles present in the sample with close frequencies were clustered using the SciClone and ClonEvol packages. The results were used to predict the intratumoral clonal composition and to assume a clonal evolution model, which described the changes in the clonal composition of the tumor during metastasis, including the appearance of new mutations that might be associated with further disease progression. The approach used in the work is suitable for identifying the mutations that cause the formation of new tumor clones, which may have a proliferative advantage, in particular, in conditions of antitumor therapy.
    34 schema:genre article
    35 schema:inLanguage en
    36 schema:isAccessibleForFree false
    37 schema:isPartOf N2066e6fffd9243acb50bfe042d4b032e
    38 N5f42d09e044c40b3a0abededbf6bcdd2
    39 sg:journal.1371988
    40 schema:keywords AML patients
    41 AML samples
    42 Cbl
    43 Clinical Oncology
    44 FLT3
    45 KRAS
    46 NRAS
    47 SciClone
    48 Targeted sequencing
    49 acral melanoma
    50 acute myeloid leukemia
    51 advantages
    52 alleles
    53 antitumor therapy
    54 appearance
    55 approach
    56 burden
    57 cases
    58 cells
    59 central problem
    60 changes
    61 childhood acute myeloid leukemia
    62 children
    63 clinical exome
    64 clonal composition
    65 clonal evolution model
    66 clonal structure
    67 clonal variability
    68 clones
    69 close frequencies
    70 clusters
    71 comparison
    72 complex clonal structure
    73 composition
    74 conditions
    75 data
    76 determination
    77 different populations
    78 disease progression
    79 driver mutations
    80 dynamic changes
    81 evolution model
    82 example
    83 exome
    84 exome sequencing data
    85 formation
    86 frequency
    87 further disease progression
    88 genes KIT
    89 heterogeneity
    90 high-throughput sequencing
    91 intratumoral heterogeneity
    92 kit
    93 leukemia
    94 leukemic cells
    95 melanoma
    96 melanoma cases
    97 metastasis
    98 model
    99 mutant alleles
    100 mutation profiles
    101 mutational burden
    102 mutations
    103 myeloid leukemia
    104 new mutations
    105 new tumor clones
    106 next-generation sequencing
    107 oncology
    108 organization
    109 package
    110 panel
    111 patients
    112 population
    113 potential driver mutations
    114 primary AML samples
    115 problem
    116 profile
    117 progression
    118 proliferative advantage
    119 relapse
    120 remission
    121 resistance
    122 results
    123 samples
    124 sequencing
    125 sequencing data
    126 structure
    127 substrate
    128 target panel
    129 therapy
    130 tumor clones
    131 tumor exomes
    132 tumor organization
    133 tumor structure
    134 tumor substrate
    135 tumors
    136 variability
    137 work
    138 schema:name Determination of the Subclonal Tumor Structure in Childhood Acute Myeloid Leukemia and Acral Melanoma by Next-Generation Sequencing
    139 schema:pagination 727-741
    140 schema:productId N2de53077ce4143e5af3b9782adf8d6cc
    141 Nec97e96cc8ce41e3a1bf0b4014ad9027
    142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1142004036
    143 https://doi.org/10.1134/s0026893321040051
    144 schema:sdDatePublished 2022-05-10T10:31
    145 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    146 schema:sdPublisher N98201d4c37074df798f51824b1c8ad6a
    147 schema:url https://doi.org/10.1134/s0026893321040051
    148 sgo:license sg:explorer/license/
    149 sgo:sdDataset articles
    150 rdf:type schema:ScholarlyArticle
    151 N14a9cce2355a4e15a3b761cfb872da34 rdf:first sg:person.01004716151.36
    152 rdf:rest Ne319377b33574b27bbb87aa01f72b264
    153 N2066e6fffd9243acb50bfe042d4b032e schema:volumeNumber 55
    154 rdf:type schema:PublicationVolume
    155 N2a6e235340984e9cb878ebb5d830710c rdf:first sg:person.01133150100.87
    156 rdf:rest rdf:nil
    157 N2de53077ce4143e5af3b9782adf8d6cc schema:name doi
    158 schema:value 10.1134/s0026893321040051
    159 rdf:type schema:PropertyValue
    160 N5f42d09e044c40b3a0abededbf6bcdd2 schema:issueNumber 5
    161 rdf:type schema:PublicationIssue
    162 N71b3510c7d4343c2a870731bf1b16704 rdf:first sg:person.01302725235.00
    163 rdf:rest N2a6e235340984e9cb878ebb5d830710c
    164 N98201d4c37074df798f51824b1c8ad6a schema:name Springer Nature - SN SciGraph project
    165 rdf:type schema:Organization
    166 Ne319377b33574b27bbb87aa01f72b264 rdf:first sg:person.012063052123.16
    167 rdf:rest N71b3510c7d4343c2a870731bf1b16704
    168 Nec97e96cc8ce41e3a1bf0b4014ad9027 schema:name dimensions_id
    169 schema:value pub.1142004036
    170 rdf:type schema:PropertyValue
    171 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    172 schema:name Biological Sciences
    173 rdf:type schema:DefinedTerm
    174 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
    175 schema:name Biochemistry and Cell Biology
    176 rdf:type schema:DefinedTerm
    177 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    178 schema:name Genetics
    179 rdf:type schema:DefinedTerm
    180 anzsrc-for:0607 schema:inDefinedTermSet anzsrc-for:
    181 schema:name Plant Biology
    182 rdf:type schema:DefinedTerm
    183 sg:journal.1371988 schema:issn 0026-8933
    184 1608-3245
    185 schema:name Molecular Biology
    186 schema:publisher Pleiades Publishing
    187 rdf:type schema:Periodical
    188 sg:person.01004716151.36 schema:affiliation grid-institutes:grid.418899.5
    189 schema:familyName Krasnov
    190 schema:givenName G. S.
    191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01004716151.36
    192 rdf:type schema:Person
    193 sg:person.01133150100.87 schema:affiliation grid-institutes:grid.418899.5
    194 schema:familyName Nasedkina
    195 schema:givenName T. V.
    196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01133150100.87
    197 rdf:type schema:Person
    198 sg:person.012063052123.16 schema:affiliation grid-institutes:grid.418899.5
    199 schema:familyName Ghukasyan
    200 schema:givenName L. G.
    201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012063052123.16
    202 rdf:type schema:Person
    203 sg:person.01302725235.00 schema:affiliation grid-institutes:grid.418899.5
    204 schema:familyName Abramov
    205 schema:givenName I. S.
    206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302725235.00
    207 rdf:type schema:Person
    208 sg:pub.10.1038/300143a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051255174
    209 https://doi.org/10.1038/300143a0
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1038/leu.2012.262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031979360
    212 https://doi.org/10.1038/leu.2012.262
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1038/leu.2013.186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003846917
    215 https://doi.org/10.1038/leu.2013.186
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1038/leu.2014.4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012311625
    218 https://doi.org/10.1038/leu.2014.4
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1038/leu.2015.141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000328933
    221 https://doi.org/10.1038/leu.2015.141
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1038/leu.2015.264 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049637664
    224 https://doi.org/10.1038/leu.2015.264
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1038/nature10762 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053038796
    227 https://doi.org/10.1038/nature10762
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1038/nature12625 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024549098
    230 https://doi.org/10.1038/nature12625
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1038/nbt.2203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010515132
    233 https://doi.org/10.1038/nbt.2203
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1038/nbt.2514 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000477771
    236 https://doi.org/10.1038/nbt.2514
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1038/ncomms12475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008017504
    239 https://doi.org/10.1038/ncomms12475
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1038/ncomms6901 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019589194
    242 https://doi.org/10.1038/ncomms6901
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1038/ng.3709 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041260635
    245 https://doi.org/10.1038/ng.3709
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1038/nmeth.2890 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020782180
    248 https://doi.org/10.1038/nmeth.2890
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1038/nmeth0410-248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007489634
    251 https://doi.org/10.1038/nmeth0410-248
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1038/nprot.2015.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021631236
    254 https://doi.org/10.1038/nprot.2015.123
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1038/s41375-020-0932-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1128717520
    257 https://doi.org/10.1038/s41375-020-0932-8
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1038/s41389-018-0091-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107450414
    260 https://doi.org/10.1038/s41389-018-0091-1
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1038/s41467-020-18169-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1130670114
    263 https://doi.org/10.1038/s41467-020-18169-2
    264 rdf:type schema:CreativeWork
    265 sg:pub.10.1038/s41467-020-18988-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131775868
    266 https://doi.org/10.1038/s41467-020-18988-3
    267 rdf:type schema:CreativeWork
    268 sg:pub.10.1038/s41525-017-0013-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084537792
    269 https://doi.org/10.1038/s41525-017-0013-8
    270 rdf:type schema:CreativeWork
    271 sg:pub.10.1038/s41586-020-2864-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1132055867
    272 https://doi.org/10.1038/s41586-020-2864-x
    273 rdf:type schema:CreativeWork
    274 sg:pub.10.1038/s41598-020-66806-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1128876618
    275 https://doi.org/10.1038/s41598-020-66806-z
    276 rdf:type schema:CreativeWork
    277 sg:pub.10.1134/s0026893319040022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1120404589
    278 https://doi.org/10.1134/s0026893319040022
    279 rdf:type schema:CreativeWork
    280 sg:pub.10.1134/s0026893320030073 schema:sameAs https://app.dimensions.ai/details/publication/pub.1128554195
    281 https://doi.org/10.1134/s0026893320030073
    282 rdf:type schema:CreativeWork
    283 grid-institutes:grid.418899.5 schema:alternateName Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia
    284 schema:name Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, Moscow, Russia
    285 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...