» Articles » PMID: 23568481

Genomic and 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 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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References
1.
Van Laere S, Van der Auwera I, Van den Eynden G, Van Hummelen P, van Dam P, Marck E . Distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene-expression analysis. Br J Cancer. 2007; 97(8):1165-74. PMC: 2360452. DOI: 10.1038/sj.bjc.6603967. View

2.
Bertucci F, Finetti P, Rougemont J, Charafe-Jauffret E, Nasser V, Loriod B . Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy. Cancer Res. 2004; 64(23):8558-65. DOI: 10.1158/0008-5472.CAN-04-2696. View

3.
Dawood S, Merajver S, Viens P, Vermeulen P, Swain S, Buchholz T . International expert panel on inflammatory breast cancer: consensus statement for standardized diagnosis and treatment. Ann Oncol. 2010; 22(3):515-523. PMC: 3105293. DOI: 10.1093/annonc/mdq345. View

4.
Van Laere S, Van der Auwera I, Van den Eynden G, Elst H, Weyler J, Harris A . Nuclear factor-kappaB signature of inflammatory breast cancer by cDNA microarray validated by quantitative real-time reverse transcription-PCR, immunohistochemistry, and nuclear factor-kappaB DNA-binding. Clin Cancer Res. 2006; 12(11 Pt 1):3249-56. DOI: 10.1158/1078-0432.CCR-05-2800. View

5.
Van der Auwera I, Bovie C, Svensson C, Limame R, Trinh X, van Dam P . Quantitative assessment of DNA hypermethylation in the inflammatory and non-inflammatory breast cancer phenotypes. Cancer Biol Ther. 2009; 8(23):2252-9. DOI: 10.4161/cbt.8.23.10133. View