Breast Cancer Gene Expression Analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Clarke C
PROVIDER: S-ECPF-GEOD-42568 | biostudies-other | 2013 Oct
REPOSITORIES: biostudies-other
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Project description:Recurrent mutations in histone modifying enzymes in multiple cancer types imply key roles in tumorigenesis. However, the functional relevance of these mutations remains unknown. Here we show that the JARID1B histone H3 lysine 4 demethylase is frequently amplified and overexpressed in luminal breast tumors and a somatic point mutation of JARID1B leads to the gain of luminal-specific gene expression programs. Downregulation of JARID1B in luminal breast cancer cells induces the expression of basal cell-specific genes and growth arrest, which is partially rescued by the inhibition of TGFBR thereby indicating a key role for TGFb signaling. Integrated genome-wide analysis of JARID1B chromatin binding, histone H3 lysine trimethyl (H3K4me3) and dimethyl (H3K4me2) patterns, and gene expression profiles in luminal and basal-like breast cancer cells suggest a key role for JARID1B in luminal cell-specific gene expression programs. A significant fraction of JARID1B binding-sites overlaps with CTCF in both luminal and basal-like breast cancer cells. CTCF also co-immunoprecipitates with JARID1B and it may influence its histone demethylase (HDM) activity as the H3K4me3/me2 ratio is lower at the CTCF-overlapping compared to JARID1B-unique sites. Additionally, a heterozygous JARID1B missense mutation (K1435R) in the HCC2157 basal-like breast cancer cell line is associated with unique JARID1B chromatin-binding and gene expression patterns implying gain of luminal features. In line with this, exogenous expression of this mutant in basal-like breast cancer cells leads to a gain of JARID1B binding at many luminal-specific genes. A PARADIGM score reflecting JARID1B activity in luminal breast cancer cells is associated with poor clinical outcome in patients with luminal breast tumors. Together, our data imply that JARID1B is a luminal lineage-driving oncogene and that its therapeutic targeting may represent a novel therapeutic strategy in treatment-resistant luminal breast tumors. RNA-Seq in breast cancer cell-lines transfected with JARID1B/CTCF/control siRNA. 50 cycles of sequencing on Illumina platform.
| S-ECPF-GEOD-45732 | biostudies-other
Project description:While genetic mutation is a hallmark of cancer, many cancers also acquire epigenetic alterations during tumorigenesis including aberrant DNA hypermethylation of tumor suppressors as well as changes in chromatin modifications as caused by genetic mutations of the chromatin-modifying machinery. However, the extent of epigenetic alterations in cancer cells has not been fully characterized. Here, we describe the first complete methylome maps at single nucleotide resolution of a low-passage breast cancer cell line and primary human mammary epithelial cells. We find widespread DNA hypomethylation in the cancer cell, primarily at partially methylated domains (PMDs) in normal breast cells. Unexpectedly, genes within these regions are largely silenced in cancer cells. The loss of DNA methylation in these regions is accompanied by formation of repressive chromatin, with a significant fraction displaying allelic DNA methylation where one allele is DNA methylated while the other allele is occupied by histone modifications H3K9me3 or H3K27me3. Our results show a mutually exclusive and complementary relationship between DNA methylation and H3K9me3 or H3K27me3. These results suggest that global DNA hypomethylation in breast cancer is tightly linked to the formation of repressive chromatin domains and gene silencing, thus identifying a potential epigenetic pathway for gene regulation in cancer cells and suggesting a possible new approach toward the development of cancer therapeutics. mRNA-Seq of polyA-selected RNA from breast cancer HCC1954 and normal breast HMEC. 36 cycles of sequencing on Illumina platform.
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Project description:The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics.The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.
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Project description:BackgroundThe survival of patients with breast cancer is highly sporadic, from a few months to more than 15 years. In recent studies, the gene expression profiling of tumors has been used as a promising means of predicting prognosis factors.MethodsIn this study, we used gene expression datasets of tumors to identify prognostic factors in breast cancer. We conducted log-rank tests and used unsupervised clustering methods to find reciprocally expressed gene sets associated with worse survival rates. Prognosis prediction scores were determined as the ratio of gene expressions.ResultsAs a result, four prognosis prediction gene set modules were constructed. The four prognostic gene sets predicted worse survival rates in three independent gene expression data sets. In addition, we found that cancer patient with poor prognosis, i.e., triple-negative cancer, HER2-enriched, TP53 mutated and high-graded patients had higher prognosis prediction scores than those with other types of breast cancer.ConclusionsIn conclusion, based on a gene expression analysis, we suggest that our well-defined scoring method of the prediction of survival outcome may be useful for developing prognostic factors in breast cancer.
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Project description:Angiogenesis is essential for solid tumour growth, whilst the molecular profiles of tumour blood vessels have been reported to be different between cancer types. Although presently available anti-angiogenic strategies are providing some promise for the treatment of some cancers it is perhaps not surprisingly that, none of the anti-angiogenic agents available work on all tumours. Thus, the discovery of novel anti-angiogenic targets, relevant to individual cancer types, is required. Using Affymetrix microarray analysis of laser-captured, CD31-positive blood vessels we have identified 63 genes that are upregulated significantly (5-72 fold) in angiogenic blood vessels associated with human invasive ductal carcinoma (IDC) of the breast as compared with blood vessels in normal human breast. We tested the angiogenic capacity of a subset of these genes. Genes were selected based on either their known cellular functions, their enriched expression in endothelial cells and/or their sensitivity to anti-VEGF treatment; all features implicating their involvement in angiogenesis. For example, RRM2, a ribonucleotide reductase involved in DNA synthesis, was upregulated 32-fold in IDC-associated blood vessels; ATF1, a nuclear activating transcription factor involved in cellular growth and survival was upregulated 23-fold in IDC-associated blood vessels and HEX-B, a hexosaminidase involved in the breakdown of GM2 gangliosides, was upregulated 8-fold in IDC-associated blood vessels. Furthermore, in silico analysis confirmed that AFT1 and HEX-B also were enriched in endothelial cells when compared with non-endothelial cells. None of these genes have been reported previously to be involved in neovascularisation. However, our data establish that siRNA depletion of Rrm2, Atf1 or Hex-B had significant anti-angiogenic effects in VEGF-stimulated ex vivo mouse aortic ring assays. Overall, our results provide proof-of-principle that our approach can identify a cohort of potentially novel anti-angiogenic targets that are likley to be, but not exclusivley, relevant to breast cancer.
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Project description:Gene expression profiles were generated from 176 primary breast cancer patients and 12 normal breast samples. This data is used to investigate the clinical relevance of genes of interest from in vitro studies, including FABP5 and DDX1. This Series represents RNA isolated from 176 primary breast cancer patients and 10 normal breast samples. Gene expression is compared between various subgroups depending on their clinical parameters.
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Project description:Breast cancer is a heterogeneous disease, having multiple subtypes with different malignant phenotypes. The triple-negative breast cancer, or basal breast cancer, is highly aggressive, metastatic, and difficult to treat. Previously, we identified that key molecules (IL6, CSF2, CCL5, VEGFA, and VEGFC) secreted by tumor cells and stromal cells in basal breast cancer can promote metastasis. It remains to assess whether these molecules function similarly in other subtypes of breast cancer. Here, we characterize the relative gene expression of the five secreted molecules and their associated receptors (GP130, GMRA, GMRB, CCR5, VEGFR2, NRP1, VEGFR3, NRP2) in the basal, HER2 (human epidermal growth factor receptor 2) positive, luminal A, and luminal B subtypes using high throughput data from tumor samples in The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). IL6 and CCL5 gene expression are basal breast cancer specific, whereas high gene expression of GP130 was observed in luminal A/B. VEGFA/C and CSF2?mRNA are overexpressed in HER2 positive breast cancer, with VEGFA and CSF2 also overexpressed in basal breast cancer. Further study of the specific protein function of these factors within their associated cancer subtypes may yield personalized biomarkers and treatment modalities.
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Project description:Metastasis is the main cause of breast cancer‑related mortalities. The present study aimed to uncover the relevant molecular mechanisms of breast cancer metastasis and to explore potential biomarkers that may be used for prognosis. Expression profile microarray data GSE8977, which contained 22 stroma samples (15 were from normal breast and 7 were from invasive ductal carcinoma tumor samples), were obtained from the Gene Expression Omnibus database. Following data preprocessing, differentially expressed genes (DEGs) were selected based on analyses conducted using the linear models for microarray analysis package from R and Bioconductor software. The resulting data were used in subsequent function and pathway enrichment analyses, as well as protein‑protein interaction (PPI) network and subnetwork analyses. Transcription factors (TFs) and tumor‑associated genes were also identified among the DEGs. A total of 234 DEGs were identified, which were enriched in immune response, cell differentiation and cell adhesion‑related functions and pathways. Downregulated DEGs included TFs, such as the proto‑oncogene SPI1, pre‑B‑cell leukemia homeobox 3 (PBX3) and lymphoid enhancer‑binding factor 1 (LEF1), as well as tumor suppressors (TSs), such as capping actin protein, gelsolin like (CAPG) and tumor protein p53‑inducible nuclear protein 1 (TP53INP1). Upregulated DEGs also included TFs and tumor suppressors, consisting of transcription factor 7‑like 2 (TCF7L2) and pleiomorphic adenoma gene‑like 1 (PLAGL1). DEGs that were identified at the hub nodes in the PPI network and the subnetwork were epidermal growth factor receptor (EGFR) and spleen‑associated tyrosine kinase (SYK), respectively. Several genes crucial in the metastasis of breast cancer were identified, which may serve as potential biomarkers, many of which were associated with cell adhesion, proliferation or immune response, and may influence breast cancer metastasis by regulating these function or pathways.
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Project description:BackgroundYoung women with breast cancer experience inferior outcome and commonly manifest aggressive biological subtypes. Data is controversial regarding biological differences between breast tumors in young (diagnosed at <40 years of age) versus older women. We hypothesize there may be age-related expression differences in key genes for proliferation, invasion and metastasis within and across breast cancer subtypes, and that these differences correlate with outcome.MethodsUsing clinically-annotated gene expression data from 778 breast tumors from three public databases, we compared clinico-pathologic characteristics, mRNA expression of 17 selected genes, and outcome, as a function of age (< 40 years vs. ≥ 40 years).Results14 of 17 genes were differentially expressed in tumors of young vs. older women, 4 of which persisted after correction for subtype and grade (p ≤0.05). BUB1, KRT5, and MYCN were overexpressed and CXCL2 underexpressed in young women. In multivariate analysis, overexpression of cytokeratin genes predicted inferior DFS only for young women. Overexpression of ANGPTL4 strongly predicted inferior DFS in basal but not HER2-enriched tumors in young women. Overexpression of cytokeratin genes and MYBL2 and low SNAI1 expression correlated with inferior DFS in HER2-enriched tumors in younger women. Kaplan-Meier analysis within the basal and HER2-enriched subgroups showed that overexpression of cytokeratin genes was associated with inferior DFS for young, but not older women.ConclusionsThis preliminary study reveals age- and subtype-related differences in expression of key breast cancer genes for proliferation, invasion and metastasis, which correlate with prognostic differences in young women and suggest targeted therapies.
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