A six-gene signature predicting breast cancer lung metastasis

• Published in: Cancer research (Chicago, Ill.) Vol. 68; no. 15; pp. 6092 - 6099
• Main Authors: Landemaine, Thomas, Jackson, Amanda, Bellahcène, Akeila, Rucci, Nadia, Sin, Soraya, Abad, Berta Martin, Sierra, Angels, Boudinet, Alain, Guinebretière, Jean-Marc, Ricevuto, Enrico, Noguès, Catherine, Briffod, Marianne, Bièche, Ivan, Cherel, Pascal, Garcia, Teresa, Castronovo, Vincent, Teti, Anna, Lidereau, Rosette, Driouch, Keltouma
• Format: Journal Article Web Resource
• Language: English
• Published: United States American Association for Cancer Research, Inc. (AACR) 01.08.2008
• Subjects: Biochemistry, biophysics & molecular biology; Biochimie, biophysique & biologie moléculaire; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Breast Neoplasms/genetics/pathology; Cohort Studies; Female; Gene Expression Profiling; Human health sciences; Humans; Immunohistochemistry; Life sciences; Lung Neoplasms - pathology; Lung Neoplasms - secondary; Lung Neoplasms/pathology/secondary; Neoplasm Metastasis; Oligonucleotide Array Sequence Analysis; Oncologie; Oncology; Prognosis; Reverse Transcriptase Polymerase Chain Reaction; Sciences de la santé humaine; Sciences du vivant
• ISSN: 0008-5472; 1538-7445; 1538-7445
• DOI: 10.1158/0008-5472.CAN-08-0436

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.