Stromal gene expression predicts clinical outcome in breast cancer.
Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.
Finak
Greg
G
McGill Centre for Bioinformatics, 3775 University Street, McGill University, Québec H3A 2B4, Canada.
Bertos
Nicholas
N
Pepin
Francois
F
Sadekova
Svetlana
S
Souleimanova
Margarita
M
Zhao
Hong
H
Chen
Haiying
H
Omeroglu
Gulbeyaz
G
Meterissian
Sarkis
S
Omeroglu
Atilla
A
Hallett
Michael
M
Park
Morag
M
D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers / Q000235:genetics; D006801:Humans; D007834:Lasers; D042282:Microdissection / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012044:Regression Analysis; D017154:Stromal Cells / Q000378:metabolism*
D005819:Genetic Markers