A gene-expression signature to predict survival in breast cancer across independent data sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-03

AUTHORS

A Naderi, A E Teschendorff, N L Barbosa-Morais, S E Pinder, A R Green, D G Powe, J F R Robertson, S Aparicio, I O Ellis, J D Brenton, C Caldas

ABSTRACT

Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0-11.9, P = 2.7e-07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the 'Adjuvant!' software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated. More... »

PAGES

1507

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/sj.onc.1209920

DOI

http://dx.doi.org/10.1038/sj.onc.1209920

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024977029

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16936776


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/2008/1112", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oncology and Carcinogenesis", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Breast Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cohort Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prognosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Array Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Survival Analysis", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Naderi", 
        "givenName": "A", 
        "id": "sg:person.01371052023.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01371052023.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Teschendorff", 
        "givenName": "A E", 
        "id": "sg:person.01317257236.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01317257236.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Lisbon", 
          "id": "https://www.grid.ac/institutes/grid.9983.b", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK", 
            "Faculty of Medicine, Institute of Molecular Medicine, University of Lisbon, Lisbon, Portugal"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Barbosa-Morais", 
        "givenName": "N L", 
        "id": "sg:person.0674725213.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674725213.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Department of Pathology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pinder", 
        "givenName": "S E", 
        "id": "sg:person.014576043464.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014576043464.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Nottingham", 
          "id": "https://www.grid.ac/institutes/grid.4563.4", 
          "name": [
            "Department of Molecular Medical Sciences, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Green", 
        "givenName": "A R", 
        "id": "sg:person.016416665412.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416665412.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Nottingham", 
          "id": "https://www.grid.ac/institutes/grid.4563.4", 
          "name": [
            "Department of Molecular Medical Sciences, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Powe", 
        "givenName": "D G", 
        "id": "sg:person.07527354646.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07527354646.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Nottingham", 
          "id": "https://www.grid.ac/institutes/grid.4563.4", 
          "name": [
            "Department of Medical and Surgical Sciences, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Robertson", 
        "givenName": "J F R", 
        "id": "sg:person.013163325712.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013163325712.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Aparicio", 
        "givenName": "S", 
        "id": "sg:person.016606422374.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016606422374.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Nottingham", 
          "id": "https://www.grid.ac/institutes/grid.4563.4", 
          "name": [
            "Department of Histopathology, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ellis", 
        "givenName": "I O", 
        "id": "sg:person.01054612302.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054612302.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brenton", 
        "givenName": "J D", 
        "id": "sg:person.01223360024.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223360024.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Caldas", 
        "givenName": "C", 
        "id": "sg:person.01072152660.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01072152660.47"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1073/pnas.1732912100", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000610606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2005.06.178", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001535254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0409462102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004658815"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bth469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008016941"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(05)17866-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014542455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(05)17866-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014542455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkg763", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019401433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa041588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022156409"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr1325", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023450491", 
          "https://doi.org/10.1186/bcr1325"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr1325", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023450491", 
          "https://doi.org/10.1186/bcr1325"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.1621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024060421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-5-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025733146", 
          "https://doi.org/10.1186/1471-2164-5-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01840834", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032983382", 
          "https://doi.org/10.1007/bf01840834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m402754200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033282293"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejca.2004.02.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035505159"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1385/mb:30:2:151", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036139232", 
          "https://doi.org/10.1385/mb:30:2:151"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.082099299", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037994416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa021967", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038600096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth756", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039053552", 
          "https://doi.org/10.1038/nmeth756"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth756", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039053552", 
          "https://doi.org/10.1038/nmeth756"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/415530a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043001094", 
          "https://doi.org/10.1038/415530a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/415530a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043001094", 
          "https://doi.org/10.1038/415530a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(05)17947-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047788005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.0020108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050418449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-5-94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051184694", 
          "https://doi.org/10.1186/1471-2164-5-94"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/0008-5472.can-04-3953", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052469408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2005.03.3845", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064204218"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-03", 
    "datePublishedReg": "2007-03-01", 
    "description": "Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0-11.9, P = 2.7e-07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the 'Adjuvant!' software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/sj.onc.1209920", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1097543", 
        "issn": [
          "0950-9232", 
          "1476-5594"
        ], 
        "name": "Oncogene", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "10", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "26"
      }
    ], 
    "name": "A gene-expression signature to predict survival in breast cancer across independent data sets", 
    "pagination": "1507", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "76fd026f7d28b5b151d5dec127473913bc2fd96c03fea2ad8938663447a3a181"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "16936776"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "8711562"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/sj.onc.1209920"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024977029"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/sj.onc.1209920", 
      "https://app.dimensions.ai/details/publication/pub.1024977029"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:14", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000361_0000000361/records_54008_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/1209920"
  }
]
 

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.10.1038/sj.onc.1209920'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/sj.onc.1209920'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/sj.onc.1209920'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/sj.onc.1209920'


 

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

261 TRIPLES      21 PREDICATES      61 URIs      30 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/sj.onc.1209920 schema:about N445875abc9004cdc9c43ef0ca34304f8
2 N4f107c0c22ed4136a7b35cb8111ccea4
3 N501fa8845106452bb0d71b1f710f2db0
4 N67ad3e1e80b64afe9ffb788468a7941e
5 N69ccd1567c724f8a9acc100f06c2cdaa
6 N722f91bb4e934e7cbc4d3a590099b537
7 Na55311457c144afba894845dc051d8d1
8 Nb16dd47bd302440a8c8fd8e2eb43cc6c
9 Ndbf0e123f4ab469d9bd9bcb7e60b7dd2
10 anzsrc-for:11
11 anzsrc-for:1112
12 schema:author N35a541b08a004a2bb73c6477013faa52
13 schema:citation sg:pub.10.1007/bf01840834
14 sg:pub.10.1038/415530a
15 sg:pub.10.1038/nmeth756
16 sg:pub.10.1186/1471-2164-5-9
17 sg:pub.10.1186/1471-2164-5-94
18 sg:pub.10.1186/bcr1325
19 sg:pub.10.1385/mb:30:2:151
20 https://doi.org/10.1002/sim.1621
21 https://doi.org/10.1016/j.ejca.2004.02.025
22 https://doi.org/10.1016/s0140-6736(05)17866-0
23 https://doi.org/10.1016/s0140-6736(05)17947-1
24 https://doi.org/10.1056/nejmoa021967
25 https://doi.org/10.1056/nejmoa041588
26 https://doi.org/10.1073/pnas.0409462102
27 https://doi.org/10.1073/pnas.082099299
28 https://doi.org/10.1073/pnas.1732912100
29 https://doi.org/10.1074/jbc.m402754200
30 https://doi.org/10.1093/bioinformatics/bth469
31 https://doi.org/10.1093/nar/gkg763
32 https://doi.org/10.1158/0008-5472.can-04-3953
33 https://doi.org/10.1200/jco.2005.03.3845
34 https://doi.org/10.1200/jco.2005.06.178
35 https://doi.org/10.1371/journal.pbio.0020108
36 schema:datePublished 2007-03
37 schema:datePublishedReg 2007-03-01
38 schema:description Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0-11.9, P = 2.7e-07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the 'Adjuvant!' software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf Nd99b563208ab4171b72183aa563b6fe1
43 Ne5392f4c3de8405b8d096d2a1a39f24f
44 sg:journal.1097543
45 schema:name A gene-expression signature to predict survival in breast cancer across independent data sets
46 schema:pagination 1507
47 schema:productId N3c1a3fdc337c450ea7997af72d709287
48 N7712f4ca8ba640809ca16786a02be522
49 N7efed9e97cad4d1ba5e549e7190d7be9
50 Na379aaaef7654569b9d107342d430438
51 Nd9e4ea697daf4d9f87717f05cd043d3a
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024977029
53 https://doi.org/10.1038/sj.onc.1209920
54 schema:sdDatePublished 2019-04-11T12:14
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher Ne1808ccaefb34e1387e97558d926076c
57 schema:url https://www.nature.com/articles/1209920
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N25ef47de58db433f921588e5e7b24e2a rdf:first sg:person.01317257236.92
62 rdf:rest N81cf3f9863034cf6827d7334ad1cb4c1
63 N35a541b08a004a2bb73c6477013faa52 rdf:first sg:person.01371052023.82
64 rdf:rest N25ef47de58db433f921588e5e7b24e2a
65 N3c1a3fdc337c450ea7997af72d709287 schema:name doi
66 schema:value 10.1038/sj.onc.1209920
67 rdf:type schema:PropertyValue
68 N445875abc9004cdc9c43ef0ca34304f8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Prognosis
70 rdf:type schema:DefinedTerm
71 N49058c8da4f343f186d32accf408a923 rdf:first sg:person.01054612302.65
72 rdf:rest Nda4cf169bb204ca784df760b16b67a3c
73 N4f107c0c22ed4136a7b35cb8111ccea4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Gene Expression Profiling
75 rdf:type schema:DefinedTerm
76 N501fa8845106452bb0d71b1f710f2db0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Survival Analysis
78 rdf:type schema:DefinedTerm
79 N60d40e80169142e9b1bd07e232be248f rdf:first sg:person.013163325712.61
80 rdf:rest N9e310de4d41846a78136ba502eabafb8
81 N67ad3e1e80b64afe9ffb788468a7941e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Reproducibility of Results
83 rdf:type schema:DefinedTerm
84 N69ccd1567c724f8a9acc100f06c2cdaa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Humans
86 rdf:type schema:DefinedTerm
87 N7222262d5a06460eb2c43c6a6d4eda4c rdf:first sg:person.01072152660.47
88 rdf:rest rdf:nil
89 N722f91bb4e934e7cbc4d3a590099b537 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Breast Neoplasms
91 rdf:type schema:DefinedTerm
92 N7712f4ca8ba640809ca16786a02be522 schema:name readcube_id
93 schema:value 76fd026f7d28b5b151d5dec127473913bc2fd96c03fea2ad8938663447a3a181
94 rdf:type schema:PropertyValue
95 N7efed9e97cad4d1ba5e549e7190d7be9 schema:name dimensions_id
96 schema:value pub.1024977029
97 rdf:type schema:PropertyValue
98 N81cf3f9863034cf6827d7334ad1cb4c1 rdf:first sg:person.0674725213.39
99 rdf:rest Ndd69d6b66e424ef3ac0aa4560f04469d
100 N9e310de4d41846a78136ba502eabafb8 rdf:first sg:person.016606422374.30
101 rdf:rest N49058c8da4f343f186d32accf408a923
102 Na379aaaef7654569b9d107342d430438 schema:name pubmed_id
103 schema:value 16936776
104 rdf:type schema:PropertyValue
105 Na55311457c144afba894845dc051d8d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Cohort Studies
107 rdf:type schema:DefinedTerm
108 Nb16dd47bd302440a8c8fd8e2eb43cc6c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Protein Array Analysis
110 rdf:type schema:DefinedTerm
111 Nd99b563208ab4171b72183aa563b6fe1 schema:issueNumber 10
112 rdf:type schema:PublicationIssue
113 Nd9e4ea697daf4d9f87717f05cd043d3a schema:name nlm_unique_id
114 schema:value 8711562
115 rdf:type schema:PropertyValue
116 Nda4cf169bb204ca784df760b16b67a3c rdf:first sg:person.01223360024.03
117 rdf:rest N7222262d5a06460eb2c43c6a6d4eda4c
118 Ndbf0e123f4ab469d9bd9bcb7e60b7dd2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Female
120 rdf:type schema:DefinedTerm
121 Ndcde2b4db9d343f48f48a57734615325 rdf:first sg:person.016416665412.02
122 rdf:rest Nf0c03f5ad1f24e868acb4c704764d37f
123 Ndd69d6b66e424ef3ac0aa4560f04469d rdf:first sg:person.014576043464.09
124 rdf:rest Ndcde2b4db9d343f48f48a57734615325
125 Ne1808ccaefb34e1387e97558d926076c schema:name Springer Nature - SN SciGraph project
126 rdf:type schema:Organization
127 Ne5392f4c3de8405b8d096d2a1a39f24f schema:volumeNumber 26
128 rdf:type schema:PublicationVolume
129 Nf0c03f5ad1f24e868acb4c704764d37f rdf:first sg:person.07527354646.92
130 rdf:rest N60d40e80169142e9b1bd07e232be248f
131 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
132 schema:name Medical and Health Sciences
133 rdf:type schema:DefinedTerm
134 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
135 schema:name Oncology and Carcinogenesis
136 rdf:type schema:DefinedTerm
137 sg:journal.1097543 schema:issn 0950-9232
138 1476-5594
139 schema:name Oncogene
140 rdf:type schema:Periodical
141 sg:person.01054612302.65 schema:affiliation https://www.grid.ac/institutes/grid.4563.4
142 schema:familyName Ellis
143 schema:givenName I O
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054612302.65
145 rdf:type schema:Person
146 sg:person.01072152660.47 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
147 schema:familyName Caldas
148 schema:givenName C
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01072152660.47
150 rdf:type schema:Person
151 sg:person.01223360024.03 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
152 schema:familyName Brenton
153 schema:givenName J D
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223360024.03
155 rdf:type schema:Person
156 sg:person.013163325712.61 schema:affiliation https://www.grid.ac/institutes/grid.4563.4
157 schema:familyName Robertson
158 schema:givenName J F R
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013163325712.61
160 rdf:type schema:Person
161 sg:person.01317257236.92 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
162 schema:familyName Teschendorff
163 schema:givenName A E
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01317257236.92
165 rdf:type schema:Person
166 sg:person.01371052023.82 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
167 schema:familyName Naderi
168 schema:givenName A
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01371052023.82
170 rdf:type schema:Person
171 sg:person.014576043464.09 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
172 schema:familyName Pinder
173 schema:givenName S E
174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014576043464.09
175 rdf:type schema:Person
176 sg:person.016416665412.02 schema:affiliation https://www.grid.ac/institutes/grid.4563.4
177 schema:familyName Green
178 schema:givenName A R
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416665412.02
180 rdf:type schema:Person
181 sg:person.016606422374.30 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
182 schema:familyName Aparicio
183 schema:givenName S
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016606422374.30
185 rdf:type schema:Person
186 sg:person.0674725213.39 schema:affiliation https://www.grid.ac/institutes/grid.9983.b
187 schema:familyName Barbosa-Morais
188 schema:givenName N L
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674725213.39
190 rdf:type schema:Person
191 sg:person.07527354646.92 schema:affiliation https://www.grid.ac/institutes/grid.4563.4
192 schema:familyName Powe
193 schema:givenName D G
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07527354646.92
195 rdf:type schema:Person
196 sg:pub.10.1007/bf01840834 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032983382
197 https://doi.org/10.1007/bf01840834
198 rdf:type schema:CreativeWork
199 sg:pub.10.1038/415530a schema:sameAs https://app.dimensions.ai/details/publication/pub.1043001094
200 https://doi.org/10.1038/415530a
201 rdf:type schema:CreativeWork
202 sg:pub.10.1038/nmeth756 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039053552
203 https://doi.org/10.1038/nmeth756
204 rdf:type schema:CreativeWork
205 sg:pub.10.1186/1471-2164-5-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025733146
206 https://doi.org/10.1186/1471-2164-5-9
207 rdf:type schema:CreativeWork
208 sg:pub.10.1186/1471-2164-5-94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051184694
209 https://doi.org/10.1186/1471-2164-5-94
210 rdf:type schema:CreativeWork
211 sg:pub.10.1186/bcr1325 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023450491
212 https://doi.org/10.1186/bcr1325
213 rdf:type schema:CreativeWork
214 sg:pub.10.1385/mb:30:2:151 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036139232
215 https://doi.org/10.1385/mb:30:2:151
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1002/sim.1621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024060421
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1016/j.ejca.2004.02.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035505159
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1016/s0140-6736(05)17866-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014542455
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1016/s0140-6736(05)17947-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047788005
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1056/nejmoa021967 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038600096
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1056/nejmoa041588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022156409
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1073/pnas.0409462102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004658815
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1073/pnas.082099299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037994416
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1073/pnas.1732912100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000610606
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1074/jbc.m402754200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033282293
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1093/bioinformatics/bth469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008016941
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1093/nar/gkg763 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019401433
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1158/0008-5472.can-04-3953 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052469408
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1200/jco.2005.03.3845 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064204218
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1200/jco.2005.06.178 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001535254
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1371/journal.pbio.0020108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050418449
248 rdf:type schema:CreativeWork
249 https://www.grid.ac/institutes/grid.4563.4 schema:alternateName University of Nottingham
250 schema:name Department of Histopathology, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK
251 Department of Medical and Surgical Sciences, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK
252 Department of Molecular Medical Sciences, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, UK
253 rdf:type schema:Organization
254 https://www.grid.ac/institutes/grid.5335.0 schema:alternateName University of Cambridge
255 schema:name Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK
256 Department of Pathology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK
257 rdf:type schema:Organization
258 https://www.grid.ac/institutes/grid.9983.b schema:alternateName University of Lisbon
259 schema:name Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Center, University of Cambridge, Cambridge, UK
260 Faculty of Medicine, Institute of Molecular Medicine, University of Lisbon, Lisbon, Portugal
261 rdf:type schema:Organization
 




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


...