Abstract
When triple negative breast cancer (TNBC) are analyzed by gene expression profiling different subclasses are identified, at least one characterized by genes related to immune signaling mechanisms supporting the role of these genes in the cancers. In an earlier study we observed differences in TNBC cell lines with respect to their expression of the cytokine IL32. Our analyses showed that certain cell lines expressed higher levels of the cytokine compared to others. Because TNBC are heterogeneous and immune-related genes appear to play a pivotal role in these cancers, we chose to examine the transcriptomes of the different cell lines based on IL32 expression. We performed group analyses of TNBC cell lines demonstrating high IL32 compared to low IL32 levels and identified IL32, GATA3, MYBL1, ETS1, PTX3 and TMEM158 as differentially associated with a subpopulation of TNBC. The six candidate genes were validated experimental and in different patient datasets. The genes distinguished a subset of TNBC from other TNBC, and TNBC from normal, luminal A, luminal B, and HER2 patient samples. The current project serves as a preliminary study in which we outline the discovery and validation of our list of six candidate genes.
Highlights
Over the past few years significant amounts of research has been performed in efforts to characterize triple negative breast cancers (TNBC)
The study began with the observation that only particular basal-like\TNBC were positive for Interleukin 32 (IL32) gene expression while other cell lines showed lower to negligible levels
In an earlier study we found that the cytokine IL32 was over-expressed in particular TNBC and underexpressed in other TNBC cell lines
Summary
Over the past few years significant amounts of research has been performed in efforts to characterize triple negative breast cancers (TNBC) The goal of these studies was to identify genes that would serve as biomarkers and/or candidates for targeted therapies. Lehman et al [3] identified 2188 genes that defined six TNBC subclasses that were designated as basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL), and luminal androgen receptor (LAR) subclasses Related to these studies Ring et al [4] developed a ‘leaner algorithm’ based on the same data and identified 101 genes that were able to identify the TNBC subclasses and in addition, predict patient outcome, recapitulating and expanding upon the results observed within the larger set of candidate genes. Together these data (a) emphasize the heterogeneity of TNBC (b) show that smaller gene sets can define the breast cancer subclasses and (c) demonstrate the ability of the gene sets to predict patient outcome
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