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Expression Analysis of Microdissected Inflammatory Breast Cancer

ABSTRACT: Inflammatory breast cancer (IBC) is a unique clinical entity characterized by rapid onset of erythema and swelling of the breast often without an obvious breast mass. Many studies have examined and compared gene expression between IBC and non-IBC (nIBC), repeatedly finding clusters associated with receptor subtype, but no consistent gene signature associated with IBC has been validated. Here we examined microdissected IBC tumor cells compared to microdissected nIBC tumor cells matched based on estrogen and HER-2/neu receptor status. Gene expression profiling of 20 inflammatory breast cancer (IBC), 20 non-IBC and 5 normal was studied.

ORGANISM(S): Homo sapiens

SUBMITTER: Woodward WA

PROVIDER: S-ECPF-GEOD-45581 | biostudies-other | 2013 Apr

REPOSITORIES: biostudies-other

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Genomic and expression analysis of microdissected inflammatory breast cancer.

Project description:Inflammatory breast cancer (IBC) is a unique clinical entity characterized by rapid onset of erythema and swelling of the breast often without an obvious breast mass. Many studies have examined and compared gene expression between IBC and non-IBC (nIBC), repeatedly finding clusters associated with receptor subtype, but no consistent gene signature associated with IBC has been validated. Here we compared microdissected IBC tumor cells to microdissected nIBC tumor cells matched based on estrogen and HER-2/neu receptor status. Gene expression analysis and comparative genomic hybridization were performed. An IBC gene set and genomic set were identified using a training set and validated on the remaining data. The IBC gene set was further tested using data from IBC consortium samples and publicly available data. Receptor driven clusters were identified in IBC; however, no IBC-specific gene signature was identified. Fifteen genes were correlated between increased genomic copy number and gene overexpression data. An expression-guided gene set upregulated in the IBC training set clustered the validation set into two clusters independent of receptor subtype but segregated only 75 % of samples in each group into IBC or nIBC. In a larger consortium cohort and in published data, the gene set failed to optimally enrich for IBC samples. However, this gene set had a high negative predictive value for excluding the diagnosis of IBC in publicly available data (100 %). An IBC enriched genomic data set accurately identified 10/16 cases in the validation data set. Even with microdissection, no IBC-specific gene signature distinguishes IBC from nIBC. Using microdissected data, a validated gene set was identified that is associated with IBC tumor cells. Inflammatory breast cancer comparative genomic hybridization data are presented, but a validated genomic data set that identifies IBC is not demonstrated.

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Expression Analysis of Microdissected Inflammatory Breast Cancer

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Genomic Analysis of Microdissected Inflammatory Breast Cancer

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Expression Analysis and Genomic Analysis of Microdissected Inflammatory Breast Cancer

Project description:This SuperSeries is composed of the SubSeries listed below.

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Genomic Analysis of Microdissected Inflammatory Breast Cancer

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Expression Analysis and Genomic Analysis of Microdissected Inflammatory Breast Cancer

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Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes.

Project description:Angiogenesis represents a potential therapeutic target in breast cancer. However, responses to targeted antiangiogenic therapies have been reported to vary among patients. This suggests that the tumor vasculature may be heterogeneous and that an appropriate choice of treatment would require an understanding of these differences.To investigate whether and how the breast tumor vasculature varies between individuals, we isolated tumor-associated and matched normal vasculature from 17 breast carcinomas by laser-capture microdissection, and generated gene-expression profiles. Because microvessel density has previously been associated with disease course, tumors with low (n = 9) or high (n = 8) microvessel density were selected for analysis to maximize heterogeneity for this feature.We identified differences between tumor and normal vasculature, and we describe two subtypes present within tumor vasculature. These subtypes exhibit distinct gene-expression signatures that reflect features including hallmarks of vessel maturity. Potential therapeutic targets (MET, ITGAV, and PDGFR?) are differentially expressed between subtypes. Taking these subtypes into account has allowed us to derive a vascular signature associated with disease outcome.Our results further support a role for tumor microvasculature in determining disease progression. Overall, this study provides a deeper molecular understanding of the heterogeneity existing within the breast tumor vasculature and opens new avenues toward the improved design and targeting of antiangiogenic therapies.

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Distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene-expression analysis.

Project description:The present study aims at a platform-independent confirmation of previously obtained cDNA microarray results on inflammatory breast cancer (IBC) using Affymetrix chips. Gene-expression data of 19 IBC and 40 non-IBC specimens were subjected to clustering and principal component analysis. The performance of a previously identified IBC signature was tested using clustering and gene set enrichment analysis. The presence of different cell-of-origin subtypes in IBC was investigated and confirmed using immunohistochemistry on a TMA. Differential gene expression was analysed using SAM and topGO was used to identify the fingerprints of a pro-metastatic-signalling pathway. IBC and non-IBC have distinct gene-expression profiles. The differences in gene expression between IBC and non-IBC are captured within an IBC signature, identified in a platform-independent manner. Part of the gene-expression differences between IBC and non-IBC are attributable to the differential presence of the cell-of-origin subtypes, since IBC primarily segregated into the basal-like or ErbB2-overexpressing group. Strikingly, IBC tumour samples more closely resemble the gene-expression profile of T1/T2 tumours than the gene-expression profile or T3/T4 tumours. We identified the insulin-like growth factor-signalling pathway, potentially contributing to the biology of IBC. Our previous results have been validated in a platform-independent manner. The distinct biological behaviour of IBC is reflected in a distinct gene-expression profile. The fact that IBC tumours are quickly arising tumours might explain the close resemblance of the IBC gene-expression profile to the expression profile of T1/T2 tumours.

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Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.

Project description:Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ?10,000 peptides corresponding to ?1,800 proteins from sub-microgram amounts of protein extracted from ?4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor ? positive or negative (ER+/-) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers.

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