Reference : A six-gene signature predicting breast cancer lung metastasis.
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Human health sciences : Oncology
http://hdl.handle.net/2268/24059
A six-gene signature predicting breast cancer lung metastasis.
English
Landemaine, Thomas [> > > >]
Jackson, Amanda [> > > >]
Bellahcene, Akeila mailto [Université de Liège - ULg > Département des sciences biomédicales et précliniques > GIGA-R : Labo de recherche sur les métastases >]
Rucci, Nadia [> > > >]
Sin, Soraya [> > > >]
Abad, Berta Martin [> > > >]
Sierra, Angels [> > > >]
Boudinet, Alain [> > > >]
Guinebretiere, Jean-Marc [> > > >]
Ricevuto, Enrico [> > > >]
Nogues, Catherine [> > > >]
Briffod, Marianne [> > > >]
Bieche, Ivan [> > > >]
Cherel, Pascal [> > > >]
Garcia, Teresa [> > > >]
Castronovo, Vincenzo mailto [Université de Liège - ULg > Département des sciences biomédicales et précliniques > Biologie générale et cellulaire - GIGA-R : Labo de recherche sur les métastases >]
Teti, Anna [> > > >]
Lidereau, Rosette [> > > >]
Driouch, Keltouma [> > > >]
2008
Cancer Research
American Association for Cancer Research, Inc. (AACR)
68
15
6092-9
Yes (verified by ORBi)
International
0008-5472
1538-7445
Baltimore
MD
[en] Breast Neoplasms/genetics/pathology ; Cohort Studies ; Female ; Gene Expression Profiling ; Humans ; Immunohistochemistry ; Lung Neoplasms/pathology/secondary ; Neoplasm Metastasis ; Oligonucleotide Array Sequence Analysis ; Prognosis ; Reverse Transcriptase Polymerase Chain Reaction
[en] The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists of using tissue surgically resected from lung metastatic lesions and comparing their gene expression profiles with those from nonpulmonary sites, all coming from breast cancer patients. We show that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a 6-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the 6-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we show that the signature improves risk stratification independently of known standard clinical variables and a previously established lung metastasis signature based on an experimental breast cancer metastasis model.
Giga-Cancer
European Commission FPVI METABRE (LSHC-CT-2004-503049) ; La Ligue contre le cander des Hauts de Seine ; European Community withe the METABRE consortium ; Breast Cancer Research Foundation (USA) ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/24059
10.1158/0008-5472.CAN-08-0436

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