Dataset Information

Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer.

ABSTRACT: Triple negative breast cancers (TNBC) are difficult to treat due to a lack of targets and heterogeneity. Inhibition of angiogenesis is a promising therapeutic strategy, but has had limited effectiveness so far in breast cancer. To quantify heterogeneity in angiogenesis-related gene expression in breast cancer, we focused on two families--VEGFs and semaphorins--that compete for neuropilin co-receptors on endothelial cells. We compiled microarray data for over 2,600 patient tumor samples and analyzed the expression of VEGF- and semaphorin-related ligands and receptors. We used principal component analysis to identify patterns of gene expression, and clustering to group samples according to these patterns. We used available survival data to determine whether these clusters had prognostic as well as therapeutic relevance. TNBC was highly associated with dysregulation of VEGF- and semaphorin-related genes; in particular, it appeared that expression of both VEGF and semaphorin genes were altered in a pro-angiogenesis direction. A pattern of high VEGFA expression with low expression of secreted semaphorins was associated with 60% of triple-negative breast tumors. While all TNBC groups demonstrated poor prognosis, this signature also correlated with lower 5-year survival rates in non-TNBC samples. A second TNBC pattern, including high VEGFC expression, was also identified. These pro-angiogenesis signatures may identify cancers that are more susceptible to VEGF inhibition.

SUBMITTER: Bender RJ

PROVIDER: S-EPMC3648524 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer.

Bender R Joseph RJ Mac Gabhann Feilim F

PloS one 20130508 5

Triple negative breast cancers (TNBC) are difficult to treat due to a lack of targets and heterogeneity. Inhibition of angiogenesis is a promising therapeutic strategy, but has had limited effectiveness so far in breast cancer. To quantify heterogeneity in angiogenesis-related gene expression in breast cancer, we focused on two families--VEGFs and semaphorins--that compete for neuropilin co-receptors on endothelial cells. We compiled microarray data for over 2,600 patient tumor samples and analy ...[more]

PMID: 23667446

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Project description:BackgroundTriple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.MethodsLehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.ResultsWe identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets.ConclusionsOur study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Project description:BackgroundTriple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible.Methodology/principal findingsClinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04-5.06, Cox P=0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-? signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer.Conclusions/significanceGene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.

Project description:Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (Basal-like 1 (BL1); Basal-like 2 (BL2); Mesenchymal (M); Immunomodulatory (IM); Mesenchymal Stem like (MSL) and Luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458) and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA dataset. Results: we defined two signatures with the 120 and 81 top up- and down-regulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88,6% to 89,4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the up-regulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC datasets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several datasets, has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Dataset Information

Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer.

Publications

Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer.

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