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Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer.

ABSTRACT: The goal of this study was to determine whether gene expression differences exist between inflammatory breast cancers (IBC) and T stage-matched non-IBC patients stratified by hormone receptor and HER2 status. We used Affymetrix GeneChips to analyze 82 tumor samples (25 T4d patients, and 57 T4a-c patients) of newly diagnosed breast cancers. Genes that were differentially expressed between the IBC and non-IBC specimens were identified using the t test, and differential expression of gene sets was assessed using gene set analysis. Three distinct clinical subtypes of IBC and non-IBC were compared: ER-positive/HER2-normal, HER2-amplified, and ER-negative/HER2-normal. When we compared expression data from all IBC with all non-IBC, we found no significant differences after adjusting for multiple testing. When IBC and non-IBC tumors were compared by clinical subtype, however, significant differences emerged. Complement and immune system-related pathways were overexpressed in ER-positive/HER2-normal IBC. Protein translation and mTOR signaling were overexpressed in HER2-amplified IBC. Apoptosis-, neural-, and lipid metabolism-related pathways were overexpressed in ER-negative/HER2-normal IBC compared with non-IBC of the same receptor phenotype. In this T stage-matched case-control study, the survival curves of patients with IBC and non-IBC were similar for all three clinical subtypes. IBC tumors can be divided into molecular and clinical subtypes similar to those of non-IBC. Clinical subtypes of IBC show molecular differences compared with similar subtypes of non-IBC.

SUBMITTER: Iwamoto T

PROVIDER: S-EPMC4109066 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer.

Iwamoto Takayuki T Bianchini Giampaolo G Qi Yuan Y Cristofanilli Massimo M Lucci Anthony A Woodward Wendy A WA Reuben James M JM Matsuoka Junji J Gong Yun Y Krishnamurthy Savitri S Valero Vicente V Hortobagyi Gabriel N GN Robertson Fredika F Symmans W Fraser WF Pusztai Lajos L Ueno Naoto T NT

Breast cancer research and treatment 20101209 3

The goal of this study was to determine whether gene expression differences exist between inflammatory breast cancers (IBC) and T stage-matched non-IBC patients stratified by hormone receptor and HER2 status. We used Affymetrix GeneChips to analyze 82 tumor samples (25 T4d patients, and 57 T4a-c patients) of newly diagnosed breast cancers. Genes that were differentially expressed between the IBC and non-IBC specimens were identified using the t test, and differential expression of gene sets was ...[more]

PMID: 21153052

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Project description:BackgroundGene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms.ResultsExpression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan Gene Expression assays.ConclusionWe have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

Project description:BackgroundAlthough the conversion of clinically used breast cancer biomarkers such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) between primary tumors and metastatic lesions is well recognized, data on whether receptor conversion has an effect on therapy management and survival in patients with metastatic breast cancer is limited. This study aimed to investigate the clinical implications of receptor conversion throughout tumor progression.MethodsIn total, 2450 patients diagnosed with metastatic breast cancer in Tianjin Medical University Cancer Institute and Hospital were analyzed and 426 female patients with available biopsy results from both primary and metastatic sites were included in this study. We investigated the alteration of ER, PR and HER2 during breast cancer progression and evaluated the therapy management and prognostic value of receptor conversion.ResultsThe conversion rates of ER, PR, and HER2 between primary tumors and metastasis were 21.1% (McNemar's test p < 0.001), 33.2% (p < 0.001), and 11.6% (p = 0.868), respectively. Evaluation of ER, PR, and HER2 status in multiple consecutive metastases revealed a change in 19.1% (p > 0.05), 23.5% (p = 0.021), and 9.8% (p > 0.05) of patients, respectively. Adjuvant therapy (chemotherapy/endocrine therapy) was related to hormone receptor conversion (p < 0.05). A statistically significant differential survival associated with hormone receptor (ER/PR) conversion (log-rank p < 0.05) was observed. In the multivariate analysis, ER conversion was an independent influence factor of survival (p < 0.05). Molecular typing conversion in primary and metastatic lesions also had a significant effect on survival (p < 0.05). We found that changing treatment based on the receptor conversion could affect clinical outcomes (p < 0.05).ConclusionsOur findings indicated that receptor conversion during breast tumor progression had a significant effect on survival. Most importantly, our findings proved that patients with receptor conversion benefited from a change in therapy.

Project description:BACKGROUND: Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. METHODS: Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen ? and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided. RESULTS: SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (? = 0.65-0.70) and SSPs (? = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value. CONCLUSION: Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

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Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer.

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Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer.

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