Abstract
IntroductionGlobal 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.MethodsFormalin-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.ResultsComparison 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.ConclusionsOur data indicate that the molecular composition of the immune response in a tumor may be a powerful predictor of cancer prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0530-2) contains supplementary material, which is available to authorized users.
Highlights
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
Expanding the stroma-specific gene set by identifying genes with correlating expression levels using tumor data from The Cancer Genome Atlas (TCGA) database gave rise to 16 gene signatures of stromal genes with highly correlating gene expression
Isolation of RNA from laser capture-microdissected formalin-fixed paraffin-embedded (FFPE) breast tumors To obtain RNA of sufficient quality from tumor compartments isolated with laser capture microdissection (LCM), sectioning, mounting, deparaffinization, and staining steps were performed with the highest possible purity to avoid RNase contamination
Summary
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. Tumors are heterogeneous, and homogenates will consist of several different cell types. Gene expression profiling has enabled novel classification of breast cancer into different subgroups. Gene expression analyses of tumor samples are generally performed on whole tumor homogenates and thereby represent a pattern that reflects the expression from all cell types present in the tumor. We have established a procedure for global-gene expression analysis using LCM on FFPE triple-negative breast cancers. Isolation of stromal and cancer cell compartments of the tumor with subsequent analysis of the global mRNA expression revealed compartment-specific gene expression. Expanding the stroma-specific gene set by identifying genes with correlating expression levels using tumor data from The Cancer Genome Atlas (TCGA) database gave rise to 16 gene signatures of stromal genes with highly correlating gene expression. Two signatures, consisting of genes related to an immune response, are of particular interest since they are prognostic in multivariate Cox proportional hazard models in several breast cancer data sets and data sets of other cancers
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