Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Methylation in breast cancer vs. normal tissue

ABSTRACT: We applied a combination of Methyl-CpG Immunoprecipitation (MCIp) and Human CpG Island microarrays to identify aberrant DNA methylation in eight low-grade breast invasive carcinomas and two pre-invasive breast tumors against ten normal breast tissues. 10 breast tumor samples (8 invasive, 2 pre-invasive) and 10 normal breast tissues, paired randomly (except Array 10: matched pair)

ORGANISM(S): Homo sapiens

SUBMITTER: Marta Faryna

PROVIDER: E-GEOD-35263 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Genome-wide methylation screen in low-grade breast cancer identifies novel epigenetically altered genes as potential biomarkers for tumor diagnosis.

Faryna Marta M Konermann Carolin C Aulmann Sebastian S Bermejo Justo Lorenzo JL Brugger Markus M Diederichs Sven S Rom Joachim J Weichenhan Dieter D Claus Rainer R Rehli Michael M Schirmacher Peter P Sinn Hans-Peter HP Plass Christoph C Gerhauser Clarissa C

FASEB journal : official publication of the Federation of American Societies for Experimental Biology 20120828 12

Aberrant DNA methylation constitutes a well-established epigenetic marker for breast cancer. Changes in methylation early in cancer development may be clinically relevant for cancer detection and prognosis-based therapeutic decisions. In the present study, a combination of methyl-CpG immunoprecipitation (MCIp) and human CpG island (CGI) arrays was applied to compare genome-wide DNA methylation profiles in 10 low-grade in situ and invasive breast cancers against 10 normal breast samples. In total ...[more]

PMID: 22930747

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Project description:Introduction: Targeting of the tumor vasculature has evolved into an integral part of existing standard anti-cancer therapies. However, recent pre-clinical and clinical studies have shown that the efficacy of these treatments is temporary at most and frequently followed by the renewed tumor growth, indicating that these targets may not be robust. Methods: We transcriptionally profiled laser capture microdissection isolated microvessels from tumor and matching adjacent normal tissue samples obtained from breast cancer patients diagnosed with infiltrating ductal carcinoma. Gene expression profiles were generated and analyzed using Genespring and the web-based WebGestalt bioinformatics Toolkit. Several differentially expressed genes were validated by immunohistochemistry. To further mine our data set we used a novel integrative systems biology network approach to identify a gene signature that could robustly stratify breast cancer patient populations. Results: Vascular enrichment of the LCM samples was confirmed by Q-PCR of CD31. Hierarchical two-dimensional clustering of the transcriptome data identified 219 significantly upregulated transcripts in at least 4/8 patient samples (cut-off >1.3-fold, p<0.05). Several genes AGRN, FLNA and ILR4 were selected and their protein overexpression was confirmed in the tumor tissue. Additional mining of the data set using the clinical information associated with these tissues by applying an integrative bioinformatics network method generated a 15-gene M-bM-^@M-^XVascular-Derived Prognostic PredictorM-bM-^@M-^Y that is able to stratify patient populations in to high- and low- risk groups. Conclusions: The overall heterogeneity observed in the vascular gene expression patterns in the infiltrating ductal carcinoma samples poses a significant problem for these individual genes to be used as a robust vascular breast cancer specific marker or therapeutic target. Using an integrative bioinformatics network approach allowed us to identify a 15-gene M-bM-^@M-^XVascular-Derived Prognostic PredictorM-bM-^@M-^Y that can robustly identify patients with an increased risk of local recurrence. Stratifying patient populations using this gene signature may lead to better patient-tailored treatment regimes. Two-color microarrays comparing laser-captured microvessels from the tumors and matching adjacent normal tissues of 8 breast cancer patients

Project description:Introduction: MicroRNAs (miRNAs) are small non-coding RNA that play a vital role in cancer progression. Neo-adjuvant chemotherapy (NAC) has become the standard of care for locally advanced breast cancer. The aim of this study was to evaluate miRNA alterations during NAC using multiple samples of tissue and serum to correlate miRNA expression with clinico-pathological features and patient outcomes. Methods: Tissue and serum samples were collected from patients with locally advanced breast cancer undergoing NAC at four time points: time of diagnosis, after the first and fourth cycle of doxorubicin/cyclophosphamide treatment, and after the fourth cycle of docetaxel administration. First, we evaluated the miRNA expression profiles in tissue and correlated expression with clinico-pathological features. Then, a panel of four miRNAs (miR-451, miR-3200, miR-21, and miR-205) in serum samples was further validated using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). The alterations in serum levels of miRNA, associations with clinical and pathological responses, correlation with clinico-pathological features, and survival outcomes were studied using Friedman, Mann-Whitney U, Spearman, Wilcoxon signed-ranks tests. Pâ¤0.05 was considered statistically significant. Results: We analyzed 32 tissue samples and 108 serum samples from 8 patients and 27 patients, respectively. MicroRNA expression profiling of tumor versus normal tissue revealed more than 100 differentially expressed miRNAs. Serum miR-451 levels were significantly decreased during treatment, and higher serum levels were associated with improved clinical and pathological responses and disease-free survival. The serum levels of miR-3200 declined during NAC, and higher serum levels were associated with lower residual breast cancer burden and relapse rates. Conclusion: Variations in serum miRNA levels during NAC treatment may be therapeutically significant for predicting response and survival outcomes. Serum and tissue from a total of 27 consecutive patients diagnosed with locally advanced breast cancer undergoing NAC treatment were collected however microarray data were generated from tissue biopsy of 5 patients at four timepoints for both Normal and tumor. Additionally 3 tissue from only tumor at four timepoints and 6 patients at two timepoint T1 and T4

Dataset Information

Methylation in breast cancer vs. normal tissue

Publications

Genome-wide methylation screen in low-grade breast cancer identifies novel epigenetically altered genes as potential biomarkers for tumor diagnosis.

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