Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression profiling of response to first-line aromatase inhibitor therapy

ABSTRACT: Fresh-frozen primary cancer specimens of 107 breast cancer patients whose metastases were treated with first-line aromatase inhibitors were evaluated for their whole genome mRNA expression profiles. Primary breast tumors (107) were analyzed against a common breast cancer reference pool (BRP).

ORGANISM(S): Homo sapiens

SUBMITTER: Paul Roepman

PROVIDER: E-GEOD-41994 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer.

Jansen Maurice P H M MP Knijnenburg Theo T Reijm Esther A EA Simon Iris I Kerkhoven Ron R Droog Marjolein M Velds Arno A van Laere Steven S Dirix Luc L Alexi Xanthippi X Foekens John A JA Wessels Lodewyk L Linn Sabine C SC Berns Els M J J EM Zwart Wilbert W

Cancer research 20131101 22

Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor α (ERα), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ...[more]

PMID: 24242068

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Project description:Extracellular, cancer-specific methylated DNA has been shown to be a prognostic marker when detected in serum or plasma. In this study we investigated the effect of treating cancer cells with differentially methylated CpG DNA. When breast cancer cell lines were treated with methylated CpG DNA, a consistent upregulation of CHAC1 mRNA expression was observed. CHAC1 was recently described to be a novel component of the unfolded protein response pathway. To elucidate the role of CHAC1 mRNA expression in cancer in more detail, we analyzed expression of this gene in breast (n=107) and ovarian cancer (n=107) and found a strong correlation with tumor differentiation. Poorly differentiated tumors exhibited higher CHAC1 expression levels (p=0.004 for breast and p=0.031 for ovarian cancer). Additionally, hormone receptor (HR)-negative breast cancers (p<0.001) and advanced stage disease ovarian cancers (p=0.026) also demonstrated high CHAC1 mRNA levels. mRNA expression analysis of the two known CHAC1 isoforms showed a strong association of expression above the median with poor outcome in breast cancer patients in a multivariate analysis (isoform a: relative risk (RR) of death 3.2 (95% CI 1.6-6.5; p<0.01); RR of relapse 3.9 (95% CI 1.6-9.8; p<0.01); isoform b: relative risk (RR) of death 3.5 (95% CI 1.6-7.3; p<0.01); RR of relapse 6.6 (95% CI 2.4-18.5; p<0.01)). Univariate analysis in ovarian cancer showed that CHAC1 mRNA expression above the median was associated with a poor relapse free survival (p=0.03). In younger ovarian cancer patients (age < median age), a high CHAC1 mRNA expression was associated with overall survival (p=0.007) and relapse free survival (p=0.015). Finally, we show that downregulation of CHAC1 by small interfering RNA suppressed breast cancer cell migration and proliferation, whereas overexpression resulted in an observed increase in these cellular behaviours. This is the first report demonstrating that a gene (CHAC1) whose expression is triggered by methylated, but not unmethylated DNA, is involved in tumour biology. Human breast cancer cell lines (BT-20, Hs578T) were treated with methylated and unmethylated DNA oligos, and differentially expressed genes were identified between treatments with methylated and un-methylated oligos.

Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions. Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate. Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes. Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

Dataset Information

Expression profiling of response to first-line aromatase inhibitor therapy

Publications

Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer.

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