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.
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
Andrew E
AE
Teschendorff
Ahmad
A
Miremadi
Sarah E
SE
Pinder
Ian O
IO
Ellis
Carlos
C
Caldas
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