An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer.
BACKGROUND
Estrogen receptor (ER)-negative breast cancer specimens are predominantly of high grade, have frequent p53 mutations, and are broadly divided into HER2-positive and basal subtypes. Although ER-negative disease has overall worse prognosis than does ER-positive breast cancer, not all ER-negative breast cancer patients have poor clinical outcome. Reliable identification of ER-negative tumors that have a good prognosis is not yet possible.


RESULTS
We apply a recently proposed feature selection method in an integrative analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in ER-negative breast cancer. We find a subclass of basal tumors, characterized by over-expression of immune response genes, which has a better prognosis than the rest of ER-negative breast cancers. Moreover, we show that, in contrast to ER-positive tumours, the majority of prognostic markers in ER-negative breast cancer are over-expressed in the good prognosis group and are associated with activation of complement and immune response pathways. Specifically, we identify an immune response related seven-gene module and show that downregulation of this module confers greater risk for distant metastasis (hazard ratio 2.02, 95% confidence interval 1.2-3.4; P = 0.009), independent of lymph node status and lymphocytic infiltration. Furthermore, we validate the immune response module using two additional independent datasets.


CONCLUSIONS
We show that ER-negative basal breast cancer is a heterogeneous disease with at least four main subtypes. Furthermore, we show that the heterogeneity in clinical outcome of ER-negative breast cancer is related to the variability in expression levels of complement and immune response pathway genes, independent of lymphocytic infiltration.
Teschendorff
Andrew E
AE
Breast Cancer Functional Genomics Laboratory, Cancer Research UK Cambridge Research Institute and Department of Oncology, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK. aet21@cam.ac.uk
Miremadi
Ahmad
A
Pinder
Sarah E
SE
Ellis
Ian O
IO
Caldas
Carlos
C
D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000235:genetics* / Q000276:immunology*; D030541:Databases, Genetic; D005260:Female; D015870:Gene Expression; D019398:Genes, BRCA1; D006801:Humans; D007109:Immunity / Q000235:genetics*; D008198:Lymph Nodes / Q000276:immunology; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis*
D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen