A key genomic subtype associated with lymphovascular invasion in invasive breast cancer.
Journal
British journal of cancer
ISSN: 1532-1827
Titre abrégé: Br J Cancer
Pays: England
ID NLM: 0370635
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
30
11
2018
accepted:
02
05
2019
revised:
24
04
2019
pubmed:
23
5
2019
medline:
19
3
2020
entrez:
23
5
2019
Statut:
ppublish
Résumé
Lymphovascular invasion (LVI) is associated with the development of metastasis in invasive breast cancer (BC). However, the complex molecular mechanisms of LVI, which overlap with other oncogenic pathways, remain unclear. This study, using available large transcriptomic datasets, aims to identify genes associated with LVI in early-stage BC patients. Gene expression data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort (n = 1565) was used as a discovery dataset, and The Cancer Genome Atlas (TCGA; n = 854) cohort was used as a validation dataset. Key genes were identified on the basis of differential mRNA expression with respect to LVI status as characterised by histological review. The relationships among LVI-associated genomic subtype, clinicopathological features and patient outcomes were explored. A 99-gene set was identified that demonstrated significantly different expression between LVI-positive and LVI-negative cases. Clustering analysis with this gene set further divided cases into two molecular subtypes (subtypes 1 and 2), which were significantly associated with pathology-determined LVI status in both cohorts. The 10-year overall survival of subtype 2 was significantly worse than that of subtype 1. This study demonstrates that LVI in BC is associated with a specific transcriptomic profile with potential prognostic value.
Sections du résumé
BACKGROUND
Lymphovascular invasion (LVI) is associated with the development of metastasis in invasive breast cancer (BC). However, the complex molecular mechanisms of LVI, which overlap with other oncogenic pathways, remain unclear. This study, using available large transcriptomic datasets, aims to identify genes associated with LVI in early-stage BC patients.
METHODS
Gene expression data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort (n = 1565) was used as a discovery dataset, and The Cancer Genome Atlas (TCGA; n = 854) cohort was used as a validation dataset. Key genes were identified on the basis of differential mRNA expression with respect to LVI status as characterised by histological review. The relationships among LVI-associated genomic subtype, clinicopathological features and patient outcomes were explored.
RESULTS
A 99-gene set was identified that demonstrated significantly different expression between LVI-positive and LVI-negative cases. Clustering analysis with this gene set further divided cases into two molecular subtypes (subtypes 1 and 2), which were significantly associated with pathology-determined LVI status in both cohorts. The 10-year overall survival of subtype 2 was significantly worse than that of subtype 1.
CONCLUSION
This study demonstrates that LVI in BC is associated with a specific transcriptomic profile with potential prognostic value.
Identifiants
pubmed: 31114020
doi: 10.1038/s41416-019-0486-6
pii: 10.1038/s41416-019-0486-6
pmc: PMC6738092
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1129-1136Références
J Natl Cancer Inst. 2006 Feb 15;98(4):262-72
pubmed: 16478745
Bioinformatics. 2004 Jun 12;20(9):1453-4
pubmed: 14871861
Breast Cancer Res. 2010;12(4):207
pubmed: 20804570
F1000Res. 2018 Mar 5;7:274
pubmed: 29983921
Nucleic Acids Res. 2017 Jul 3;45(W1):W130-W137
pubmed: 28472511
Br J Cancer. 2016 Feb 2;114(3):340-7
pubmed: 26766741
J Clin Oncol. 2018 Jun 1;36(16):1631-1641
pubmed: 29504847
PLoS One. 2014 Jun 06;9(6):e98787
pubmed: 24905342
J Hepatol. 2011 Dec;55(6):1325-31
pubmed: 21703203
Cancer Discov. 2012 May;2(5):401-4
pubmed: 22588877
Breast Cancer Res. 2011 Oct 24;13(5):R101
pubmed: 22023707
Tumour Biol. 2017 Jun;39(6):1010428317705573
pubmed: 28651487
Int J Oncol. 2016 May;48(5):1783-93
pubmed: 26892540
Clin Cancer Res. 2017 Jun 1;23(11):2630-2639
pubmed: 28572257
Am J Pathol. 2011 Feb;178(2):861-71
pubmed: 21281818
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W741-8
pubmed: 15980575
Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979
pubmed: 29531073
Mol Omics. 2018 Aug 6;14(4):218-236
pubmed: 29917034
Cancer Biol Ther. 2016 Jun 2;17(6):635-47
pubmed: 27260686
Cancer. 2012 Aug 1;118(15):3670-80
pubmed: 22180017
Thorac Cancer. 2014 Nov;5(6):500-8
pubmed: 26767044
Pathobiology. 2015 Sep;82(3-4):113-23
pubmed: 26330352
FASEB J. 2014 Sep;28(9):4055-67
pubmed: 24903273
J Clin Oncol. 2011 Jul 1;29(19):2619-27
pubmed: 21606424
J Clin Pathol. 2018 Sep;71(9):802-805
pubmed: 29599396
Anticancer Res. 2014 Mar;34(3):1355-66
pubmed: 24596383
PLoS One. 2014 Jan 16;9(1):e78644
pubmed: 24454679
Med Mol Morphol. 2017 Dec;50(4):185-194
pubmed: 28936553
Sci Signal. 2013 Apr 02;6(269):pl1
pubmed: 23550210
Algorithms Mol Biol. 2008 Jun 26;3:8
pubmed: 18578891
PLoS One. 2018 May 16;13(5):e0197162
pubmed: 29768462
Oncotarget. 2016 Apr 26;7(17):24688-99
pubmed: 27029057
J Clin Oncol. 2008 Mar 10;26(8):1275-81
pubmed: 18250347
J Clin Oncol. 2011 Jul 1;29(19):2628-34
pubmed: 21606433
Cell. 2015 Oct 8;163(2):506-19
pubmed: 26451490
Nature. 2012 Apr 18;486(7403):346-52
pubmed: 22522925
J Pathol. 2011 Feb;223(3):358-65
pubmed: 21171081