Classification et signatures moléculaires des cancers du sein en 2017 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-04

AUTHORS

N. Joyon, F. Penault-Llorca, M. Lacroix-Triki

ABSTRACT

Les cancers du sein sont subdivisés selon leur degré d’expression des récepteurs hormonaux et du gène HER2. La classification moléculaire a bouleversé cette conception simpliste en mettant en lumière de multiples profils de pronostics différents. C’est dans ce contexte, et devant la nécessité d’employer des traitements ciblés que sont nées les signatures moléculaires. Bien qu’elles diffèrent par les méthodes employées (qRT-PCR, microarray ou dérivés type n-counter), elles ont les mêmes objectifs: calculer un score pronostique, fondé sur les niveaux d’expression de gènes impliqués dans la cancérogenèse, et si possible prédire la réponse au traitement. Applicables essentiellement aux tumeurs luminales RE+, elles ont prouvé leur valeur pronostique dans de vastes essais prospectifs, et les experts souhaitent les intégrer dans la décision thérapeutique, actuellement établie sur les critères clinicopathologiques. Par ailleurs, comparativement aux coûts d’une chimiothérapie, les signatures moléculaires apportent un réel bénéfice financier et permettent d’équilibrer la balance bénéfice/risque en diminuant le recours à des traitements agressifs parfois inefficaces. More... »

PAGES

64-70

References to SciGraph publications

Journal

TITLE

Oncologie

ISSUE

3-4

VOLUME

19

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10269-017-2700-6

DOI

http://dx.doi.org/10.1007/s10269-017-2700-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085038727


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", 
    "author": [
      {
        "affiliation": {
          "name": [
            "D\u00e9partement de pathologie, Gustave-Roussy Cancer Campus, F-94805, Villejuif Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Joyon", 
        "givenName": "N.", 
        "id": "sg:person.010501722563.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010501722563.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre Jean Perrin", 
          "id": "https://www.grid.ac/institutes/grid.418113.e", 
          "name": [
            "D\u00e9partement de pathologie, centre Jean-Perrin, F-63011, Clermont-Ferrand, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Penault-Llorca", 
        "givenName": "F.", 
        "id": "sg:person.0643603315.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643603315.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "D\u00e9partement de pathologie, Gustave-Roussy Cancer Campus, F-94805, Villejuif Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lacroix-Triki", 
        "givenName": "M.", 
        "id": "sg:person.01344636706.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344636706.13"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/bjc.2015.98", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004463892", 
          "https://doi.org/10.1038/bjc.2015.98"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4137/bmi.s6184", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004651390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jamaoncol.2015.4377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005237043"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1245/s10434-016-5329-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009335050", 
          "https://doi.org/10.1245/s10434-016-5329-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1245/s10434-016-5329-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009335050", 
          "https://doi.org/10.1245/s10434-016-5329-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr2192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009887926", 
          "https://doi.org/10.1186/bcr2192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18632/oncotarget.10485", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013178406"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0017163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016421066"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1078-0432.ccr-10-1282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021940935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2407-14-177", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024727892", 
          "https://doi.org/10.1186/1471-2407-14-177"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa052933", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024869935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djj052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030644591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/annonc/mdu498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030885951"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1078-0432.ccr-07-5026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034298668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmoldx.2013.10.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034838516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/annonc/mdv221", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036102989"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa1602253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036168635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa1510764", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036616787"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molonc.2015.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040686523"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr2124", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042187964", 
          "https://doi.org/10.1186/bcr2124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2015.65.2289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045222004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0007-4551(15)31216-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051675141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/annonc/mdw262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059394789"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djw050", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059821668"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-04", 
    "datePublishedReg": "2017-04-01", 
    "description": "Les cancers du sein sont subdivis\u00e9s selon leur degr\u00e9 d\u2019expression des r\u00e9cepteurs hormonaux et du g\u00e8ne HER2. La classification mol\u00e9culaire a boulevers\u00e9 cette conception simpliste en mettant en lumi\u00e8re de multiples profils de pronostics diff\u00e9rents. C\u2019est dans ce contexte, et devant la n\u00e9cessit\u00e9 d\u2019employer des traitements cibl\u00e9s que sont n\u00e9es les signatures mol\u00e9culaires. Bien qu\u2019elles diff\u00e8rent par les m\u00e9thodes employ\u00e9es (qRT-PCR, microarray ou d\u00e9riv\u00e9s type n-counter), elles ont les m\u00eames objectifs: calculer un score pronostique, fond\u00e9 sur les niveaux d\u2019expression de g\u00e8nes impliqu\u00e9s dans la canc\u00e9rogen\u00e8se, et si possible pr\u00e9dire la r\u00e9ponse au traitement. Applicables essentiellement aux tumeurs luminales RE+, elles ont prouv\u00e9 leur valeur pronostique dans de vastes essais prospectifs, et les experts souhaitent les int\u00e9grer dans la d\u00e9cision th\u00e9rapeutique, actuellement \u00e9tablie sur les crit\u00e8res clinicopathologiques. Par ailleurs, comparativement aux co\u00fbts d\u2019une chimioth\u00e9rapie, les signatures mol\u00e9culaires apportent un r\u00e9el b\u00e9n\u00e9fice financier et permettent d\u2019\u00e9quilibrer la balance b\u00e9n\u00e9fice/risque en diminuant le recours \u00e0 des traitements agressifs parfois inefficaces.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10269-017-2700-6", 
    "inLanguage": [
      "fr"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1020974", 
        "issn": [
          "1292-3818", 
          "1765-2839"
        ], 
        "name": "Oncologie", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Classification et signatures mol\u00e9culaires des cancers du sein en 2017", 
    "pagination": "64-70", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "69a452fa88fd32464c4c7d915d2c71ddd0689e59342a4ebc7daf3673bac292dd"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10269-017-2700-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1085038727"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10269-017-2700-6", 
      "https://app.dimensions.ai/details/publication/pub.1085038727"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:03", 
    "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/v90/0000000347_0000000347/records_89824_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10269-017-2700-6"
  }
]
 

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.10.1007/s10269-017-2700-6'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10269-017-2700-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10269-017-2700-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10269-017-2700-6'


 

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

145 TRIPLES      20 PREDICATES      48 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10269-017-2700-6 schema:author Nb46425011d74493285c868ca9e01fbf3
2 schema:citation sg:pub.10.1038/bjc.2015.98
3 sg:pub.10.1186/1471-2407-14-177
4 sg:pub.10.1186/bcr2124
5 sg:pub.10.1186/bcr2192
6 sg:pub.10.1245/s10434-016-5329-6
7 https://doi.org/10.1001/jamaoncol.2015.4377
8 https://doi.org/10.1016/j.jmoldx.2013.10.008
9 https://doi.org/10.1016/j.molonc.2015.11.004
10 https://doi.org/10.1016/s0007-4551(15)31216-9
11 https://doi.org/10.1056/nejmoa052933
12 https://doi.org/10.1056/nejmoa1510764
13 https://doi.org/10.1056/nejmoa1602253
14 https://doi.org/10.1093/annonc/mdu498
15 https://doi.org/10.1093/annonc/mdv221
16 https://doi.org/10.1093/annonc/mdw262
17 https://doi.org/10.1093/jnci/djj052
18 https://doi.org/10.1093/jnci/djw050
19 https://doi.org/10.1158/1078-0432.ccr-07-5026
20 https://doi.org/10.1158/1078-0432.ccr-10-1282
21 https://doi.org/10.1200/jco.2015.65.2289
22 https://doi.org/10.1371/journal.pone.0017163
23 https://doi.org/10.18632/oncotarget.10485
24 https://doi.org/10.4137/bmi.s6184
25 schema:datePublished 2017-04
26 schema:datePublishedReg 2017-04-01
27 schema:description Les cancers du sein sont subdivisés selon leur degré d’expression des récepteurs hormonaux et du gène HER2. La classification moléculaire a bouleversé cette conception simpliste en mettant en lumière de multiples profils de pronostics différents. C’est dans ce contexte, et devant la nécessité d’employer des traitements ciblés que sont nées les signatures moléculaires. Bien qu’elles diffèrent par les méthodes employées (qRT-PCR, microarray ou dérivés type n-counter), elles ont les mêmes objectifs: calculer un score pronostique, fondé sur les niveaux d’expression de gènes impliqués dans la cancérogenèse, et si possible prédire la réponse au traitement. Applicables essentiellement aux tumeurs luminales RE+, elles ont prouvé leur valeur pronostique dans de vastes essais prospectifs, et les experts souhaitent les intégrer dans la décision thérapeutique, actuellement établie sur les critères clinicopathologiques. Par ailleurs, comparativement aux coûts d’une chimiothérapie, les signatures moléculaires apportent un réel bénéfice financier et permettent d’équilibrer la balance bénéfice/risque en diminuant le recours à des traitements agressifs parfois inefficaces.
28 schema:genre research_article
29 schema:inLanguage fr
30 schema:isAccessibleForFree false
31 schema:isPartOf N3951164d16f54bfca94718871961e4d8
32 Nb991953ae9e8410abc82875c7a172baf
33 sg:journal.1020974
34 schema:name Classification et signatures moléculaires des cancers du sein en 2017
35 schema:pagination 64-70
36 schema:productId N98f98e4ad5a54e6f9af61bc7e8ee0102
37 Na8031c80372945efb56a69b85d1a6854
38 Ndc8d4bd2545f4962afcb85526fb4a544
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085038727
40 https://doi.org/10.1007/s10269-017-2700-6
41 schema:sdDatePublished 2019-04-11T10:03
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher Nb0d91c5db1a540559f60ab048d43424b
44 schema:url https://link.springer.com/10.1007%2Fs10269-017-2700-6
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N24df6252722840a184696484b1cde1e5 schema:name Département de pathologie, Gustave-Roussy Cancer Campus, F-94805, Villejuif Cedex, France
49 rdf:type schema:Organization
50 N3168dcdcfb0d47158198ab0dcf7530dc rdf:first sg:person.0643603315.57
51 rdf:rest Na34c77373cc34afdaeace5c9975b2613
52 N3951164d16f54bfca94718871961e4d8 schema:volumeNumber 19
53 rdf:type schema:PublicationVolume
54 N98f98e4ad5a54e6f9af61bc7e8ee0102 schema:name readcube_id
55 schema:value 69a452fa88fd32464c4c7d915d2c71ddd0689e59342a4ebc7daf3673bac292dd
56 rdf:type schema:PropertyValue
57 Na34c77373cc34afdaeace5c9975b2613 rdf:first sg:person.01344636706.13
58 rdf:rest rdf:nil
59 Na8031c80372945efb56a69b85d1a6854 schema:name doi
60 schema:value 10.1007/s10269-017-2700-6
61 rdf:type schema:PropertyValue
62 Nb0d91c5db1a540559f60ab048d43424b schema:name Springer Nature - SN SciGraph project
63 rdf:type schema:Organization
64 Nb46425011d74493285c868ca9e01fbf3 rdf:first sg:person.010501722563.41
65 rdf:rest N3168dcdcfb0d47158198ab0dcf7530dc
66 Nb991953ae9e8410abc82875c7a172baf schema:issueNumber 3-4
67 rdf:type schema:PublicationIssue
68 Nd85912563fc441f595de4f4121696318 schema:name Département de pathologie, Gustave-Roussy Cancer Campus, F-94805, Villejuif Cedex, France
69 rdf:type schema:Organization
70 Ndc8d4bd2545f4962afcb85526fb4a544 schema:name dimensions_id
71 schema:value pub.1085038727
72 rdf:type schema:PropertyValue
73 sg:journal.1020974 schema:issn 1292-3818
74 1765-2839
75 schema:name Oncologie
76 rdf:type schema:Periodical
77 sg:person.010501722563.41 schema:affiliation N24df6252722840a184696484b1cde1e5
78 schema:familyName Joyon
79 schema:givenName N.
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010501722563.41
81 rdf:type schema:Person
82 sg:person.01344636706.13 schema:affiliation Nd85912563fc441f595de4f4121696318
83 schema:familyName Lacroix-Triki
84 schema:givenName M.
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344636706.13
86 rdf:type schema:Person
87 sg:person.0643603315.57 schema:affiliation https://www.grid.ac/institutes/grid.418113.e
88 schema:familyName Penault-Llorca
89 schema:givenName F.
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643603315.57
91 rdf:type schema:Person
92 sg:pub.10.1038/bjc.2015.98 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004463892
93 https://doi.org/10.1038/bjc.2015.98
94 rdf:type schema:CreativeWork
95 sg:pub.10.1186/1471-2407-14-177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024727892
96 https://doi.org/10.1186/1471-2407-14-177
97 rdf:type schema:CreativeWork
98 sg:pub.10.1186/bcr2124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042187964
99 https://doi.org/10.1186/bcr2124
100 rdf:type schema:CreativeWork
101 sg:pub.10.1186/bcr2192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009887926
102 https://doi.org/10.1186/bcr2192
103 rdf:type schema:CreativeWork
104 sg:pub.10.1245/s10434-016-5329-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009335050
105 https://doi.org/10.1245/s10434-016-5329-6
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1001/jamaoncol.2015.4377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005237043
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.jmoldx.2013.10.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034838516
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.molonc.2015.11.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040686523
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/s0007-4551(15)31216-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051675141
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1056/nejmoa052933 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024869935
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1056/nejmoa1510764 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036616787
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1056/nejmoa1602253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036168635
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1093/annonc/mdu498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030885951
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1093/annonc/mdv221 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036102989
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1093/annonc/mdw262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059394789
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1093/jnci/djj052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030644591
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1093/jnci/djw050 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059821668
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1158/1078-0432.ccr-07-5026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034298668
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1158/1078-0432.ccr-10-1282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021940935
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1200/jco.2015.65.2289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045222004
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1371/journal.pone.0017163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016421066
138 rdf:type schema:CreativeWork
139 https://doi.org/10.18632/oncotarget.10485 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013178406
140 rdf:type schema:CreativeWork
141 https://doi.org/10.4137/bmi.s6184 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004651390
142 rdf:type schema:CreativeWork
143 https://www.grid.ac/institutes/grid.418113.e schema:alternateName Centre Jean Perrin
144 schema:name Département de pathologie, centre Jean-Perrin, F-63011, Clermont-Ferrand, France
145 rdf:type schema:Organization
 




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


...