title: Prognostic stromal gene signatures in breast cancer.
abstract: BACKGROUND
  Global gene expression analysis of tumor samples has been a valuable tool to subgroup tumors and has the potential to be of prognostic and predictive value. However, tumors are heterogeneous, and homogenates will consist of several different cell types. This study was designed to obtain more refined expression data representing different compartments of the tumor.
  
  
  METHODS
  Formalin-fixed paraffin-embedded stroma-rich triple-negative breast cancer tumors were laser-microdissected, and RNA was extracted and processed to enable microarray hybridization. Genes enriched in stroma were identified and used to generate signatures by identifying correlating genes in publicly available data sets. The prognostic implications of the signature were analyzed.
  
  
  RESULTS
  Comparison of the expression pattern from stromal and cancer cell compartments from three tumors revealed a number of genes that were essentially specifically expressed in the respective compartments. The stroma-specific genes indicated contribution from fibroblasts, endothelial cells, and immune/inflammatory cells. The gene set was expanded by identifying correlating mRNAs using breast cancer mRNA expression data from The Cancer Genome Atlas. By iterative analyses, 16 gene signatures of highly correlating genes were characterized. Based on the gene composition, they seem to represent different cell types. In multivariate Cox proportional hazard models, two immune/inflammatory signatures had opposing hazard ratios for breast cancer recurrence also after adjusting for clinicopathological variables and molecular subgroup. The signature associated with poor prognosis consisted mainly of C1Q genes and the one associated with good prognosis contained HLA genes. This association with prognosis was seen for other cancers as well as in other breast cancer data sets.
  
  
  CONCLUSIONS
  Our data indicate that the molecular composition of the immune response in a tumor may be a powerful predictor of cancer prognosis.
authors.forename: Sofia
authors.initials: S
authors.lastname: Winslow
authors.forename: Karin
authors.initials: K
authors.lastname: Leandersson
authors.forename: Anders
authors.initials: A
authors.lastname: Edsjö
authors.forename: Christer
authors.initials: C
authors.lastname: Larsson
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics* / Q000276:immunology / Q000401:mortality / Q000473:pathology*; D016000:Cluster Analysis; D019295:Computational Biology; D004847:Epithelial Cells / Q000378:metabolism / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D060787:Neoplasm Grading; D009928:Organ Specificity / Q000235:genetics; D011379:Prognosis; D016016:Proportional Hazards Models; D017154:Stromal Cells / Q000378:metabolism* / Q000473:pathology; D059467:Transcriptome
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