Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation

Smith, David; Sætrom, Pål; Snøve, Ola; Lundberg, Cathryn; Rivas, Guillermo; Glackin, Carlotta A.; Larson, Garrett P.

doi:10.1186/1471-2105-9-63

Cited by 42 publications

(34 citation statements)

References 75 publications

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“…It typically involves ab initio discovery of a subset of genes and a subset of conditions that show strong association with each other. Such a gene set can then be subjected to functional characterizations (e.g., by examining its enrichment for GO terms, protein-protein interaction network colocalization, or even promoter motifs) (8)(9)(10). Biclusteringbased approaches first find a core gene set activated in multiple transcriptomic states and then test for associations with predefined modules.…”

Section: Discussionmentioning

confidence: 99%

“…No existing tools provide this functionality, although there are available methods for integrative analysis of multiple expression datasets. For example, "biclustering" tools attempt ab initio discovery of "gene modules" that display coordinated expression across experiments (8,9). Other tools additionally require these modules to have common promoter motifs (10)(11)(12).…”

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confidence: 99%

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New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior

Ament

Blatti²,

Alaux

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This problem occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that identifies statistically significant meta-associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on shared combinations of transcription factors. These relationships were revealed only when we considered complex and variable regulatory rules, suggesting that these shared transcription factors are used in distinct ways by different determinants. This regulatory code would have been missed by traditional gene coexpression or cis-regulatory analytic methods. We expect that our meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.honey bee | transcriptional regulation A hallmark of complex phenotypes is that they have many different intrinsic and extrinsic determinants, and phenotypic variation between individuals is shaped by the interplay between heritable genotypic factors and environmental conditions (1). Understanding a complex phenotype, therefore, requires knowing whether its different determinants are subserved by common or distinct molecular mechanisms. For a given phenotype, do the multiple determinants act in fundamentally similar or different ways?Both intrinsic and extrinsic determinants influence phenotypes through their effects on gene expression. For instance, intraspecific behavioral differences between individuals are associated with changes in the expression of hundreds to thousands of genes in the brain (2). Also, manipulations of genetic, environmental, and hormonal determinants of some of these behavioral differences can induce similar changes in gene expression (3). This manipulation leads to the hypothesis that multiple intrinsic and extrinsic determinants that influence the same behavior exert shared effects on shared transcriptional regulatory mechanisms, for instance, relying on the activities of the same sequence-specific transcription factors (TFs).Testing this hypothesis poses serious challenges: transcriptional mechanisms linking genes to behavior are not well-understood, and although it is increasingly common to obtain transcriptomic profiles for a variety of determinants of a complex phenotype, there is no existing tool that can directly test if specific cis-elements are activated in multiple transcriptomic states in a statistically significant manner. Some existing tools (4-7) have been widely used to search for associations in specifi...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior

Ament

Blatti²,

Alaux

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…This list represents the ER specific expression (ERSE) set (Table S2). Many of the ERSE genes have previously been identified as differentially expressed in breast tumors in an ER status-dependent manner, or to be direct ER target genes (24)(25)(26).…”

Section: Detection Of Ct-x Antigen Expression In Massively Parallel Smentioning

confidence: 99%

CT-X antigen expression in human breast cancer

Grigoriadis

Caballero

Hoek

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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Cancer/testis (CT) genes are predominantly expressed in human germ line cells, but not somatic tissues, and frequently become activated in different cancer types. Several CT antigens have already proved to be useful biomarkers and are promising targets for therapeutic cancer vaccines. The aim of the present study was to investigate the expression of CT antigens in breast cancer. Using previously generated massively parallel signature sequencing (MPSS) data, together with 9 publicly available gene expression datasets, the expression pattern of CT antigens located on the X chromosome (CT-X) was interrogated. Whereas a minority of unselected breast cancers was found to contain CT-X transcripts, a significantly higher expression frequency was detected in estrogen and progesterone receptor (ER) negative breast cancer cell lines and primary breast carcinomas. A coordinated pattern of CT-X antigen expression was observed, with MAGEA and NY-ESO-1/ CTAG1B being the most prevalent antigens. Immunohistochemical staining confirmed the correlation of CT-X antigen expression and ER negativity in breast tumors and demonstrated a trend for their coexpression with basal cell markers. Because of the limited therapeutic options for ER-negative breast cancers, vaccines based on CT-X antigens might prove to be useful.cancer/testis antigens ͉ estrogen receptor ͉ therapy

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“…Gene expression analysis has become an established clinical predictive tools to identify tumor subtypes, to identify patients showing poor/good prognosis and to identify patients likely to have disease recurrence [15]. We validated the data from our itive and the ER-negative group (supplementary table 4).…”

Section: Introductionmentioning

confidence: 81%

Breast Cancer Proteomics – Differences in Protein Expression between Estrogen Receptor-Positive and -Negative Tumors Identified by Tandem Mass Tag Technology

Ruckhäberle

Karn²,

Hanker³

et al. 2010

Breast Care

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Background: Proteomic analysis has become an effective tool in breast cancer research. In this study, we applied the new gel-free tandem mass tag (TMT) reference method for the first time in breast cancer. Materials and Methods: Proteomic analysis was used to compare 10 estrogen receptor (ER)-positive and 10 ER-negative samples. The results of the proteomic approach were validated by Western blot, immunohistochemistry and gene expression analysis. Results: 17 proteins with significant differences in expression were identified. 13 proteins were overexpressed in ER-negative tumors and 4 were overexpressed in ER-positive samples. All these proteins were characterized by relatively high cellular abundance. Conclusions: Our results demonstrate that the gel-free TMT approach allows the quantification of differences in protein expression levels. Further improvement of the sensitivity by subfractionation of the tissue should allow also the identification of low-abundance proteins and might lead to the use of this method in breast cancer research.

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Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation

Cited by 42 publications

References 75 publications

New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior

New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior

CT-X antigen expression in human breast cancer

Breast Cancer Proteomics – Differences in Protein Expression between Estrogen Receptor-Positive and -Negative Tumors Identified by Tandem Mass Tag Technology

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