This data was used as an example to illustrate a computational method for assessing statistical significance in microarray experiments
Assessing statistical significance in microarray experiments using the distance between microarrays.
No sample metadata fields
View SamplesWe propose a method to compare the location and variability of gene ex-pression between two groups of microarrays using a permutation test based on the pairwise distance between microarrays. The microarrays could be samples from distinct clinical or biological populations or microarrays prepared at two different levels of an experimental factor. For these tests the entire microarray or some pre-specifed subset of genes, not the individual gene, is the unit of analysis. We apply this method to compare results from two dfferent protocols for preparing labeled targets for microarray hybridization and their subsequent gene expression analysis.
Assessing statistical significance in microarray experiments using the distance between microarrays.
No sample metadata fields
View SamplesMurine healthy tissue samples, DCIS and invasive mammary tumors were analyzed in order to identify marker genes which show enhanced expresssion in DCIS and invasive ductal carcinomas.
Identification of early molecular markers for breast cancer.
Specimen part
View SamplesHuman healthy tissue samples, DCIS and invasive mammary tumors were analyzed in order to identify marker genes which show enhanced expresssion in DCIS and invasive ductal carcinomas.
Identification of early molecular markers for breast cancer.
Specimen part, Disease, Disease stage
View SamplesMajor roadblocks to developing effective progesterone receptor (PR)-targeted therapies in breast cancer include the lack of highly-specific PR modulators, a poor understanding of the pro- or anti-tumorigenic networks for PR isoforms and ligands, and an incomplete understanding of the cross talk between PR and estrogen receptor (ER) signaling. Through genomic analyses of xenografts treated with various clinically-relevant ER and PR-targeting drugs, we describe how the activation or inhibition of PR dictates distinct ER and PR chromatin binding and differentially reprograms estrogen signaling, resulting in the segregation of transcriptomes into separate PR agonist and antagonist-mediated groups. These findings address an ongoing controversy regarding the clinical utility of PR agonists and antagonists, alone or in combination with tamoxifen, for breast cancer management. Genomic analyses of the two PR isoforms, PRA and PRB, indicate that these isoforms bind distinct genomic sites and interact with different sets of co-regulators to differentially modulate gene expression as well as pro- or anti-tumorigenic phenotypes. Of the two isoforms, PRA inhibited gene expression and ER chromatin binding significantly more than PRB. Of note, the two isoforms reprogrammed estrogen activity to be either pro or anti-tumorigenic. In concordance to the in-vitro observations, differential gene expression was observed in PRA and PRB-rich patient tumors and importantly, PRA-rich gene signatures had poorer survival outcomes. In support of antiprogestin responsiveness of PRA-rich tumors, gene signatures associated with PR antagonists, but not PR agonists, predicted better survival outcomes. This differential of better patient survival associated with PR antagonists versus PR agonists treatments was further reflected in the higher anti-tumor activity of combination therapies of tamoxifen with PR antagonists and modulators. Knowledge of various determinants of PR action and their interactions with estrogen signaling to differentially modulate breast cancer biology should serve as a guide to the development of biomarkers for patient selection and translation of PR-targeted therapies to the clinic. Overall design: For in-vitro experiments, cells were grown in steroid-deprived RPMI for 48 hours to 80% confluence, before being treated for with the hormones of interest (vehicle, 10 nM estrogen, 10 nM R5020 or both estrogen +R5020). Cells were then fixed with 1% formaldehyde for 10 minutes and the crosslinking was quenched with 0.125 M glycine for 5 minutes. Fixed cells were suspended in ChIP lysis buffer (1 ml 1M Tris pH 8.0; 200 µl 5M NaCl; 1 ml 0.5M EDTA; 1 ml NP-40; 1 g SDS, 0.5 g deoxycholate) and sheared in the Diagenode Biorupter for 20 minutes (30 second cycles). 100 µl of sheared chromatin was removed as input control. A 1:10 dilution of sheared chromatin in ChIP dilution buffer (1.7 ml 1M Tris pH 8.0; 3.3 ml 5M NaCl; 5 ml 10% NP-40; 200 µl 10% SDS; to 100 ml with H2O), 4 µg antibody and 30 µl magnetic DynaBeads were incubated in a rotator at 4oC overnight. Chromatin was immunoprecipitated overnight using anti-ER (Santa Cruz Biotechnology HC-20), anti-PR (in-house made KD68) or rabbit IgG (Santa Cruz Biotechnology SC-2027). Next, the immunoprecipitated chromatin was washed with ChIP wash buffer I (2 ml 1M Tris pH 8.0; 3 ml 5M NaCl; 400 µl 0.5M EDTA; 10 ml 10% NP-40; 1 ml 10% SDS; to 100 ml with H2O), ChIP wash buffer II (2 ml 1M Tris pH 8.0; 10 ml 5M NaCl; 400 µl 0.5M EDTA; 10 ml 10% NP-40; 1 ml 10% SDS; to 100 ml with H2O), ChIP wash buffer III (1 ml 1M Tris pH 8.0; 5 ml of 5M LiCl; 200 µl 0.5M EDTA; 10 ml 10% NP-40; 10 ml 10% deoxycholate; to 100 ml with H2O) and TE (pH 8.0). Elution was performed twice from beads by incubating them with 100 µl ChIP-elution buffer (1% SDS, 0.1 M NaHCO3) at 65oC for 15 minutes each. The eluted protein-DNA complexes were de-crosslinked overnight at 65oC in 200 µM NaCl. After de-crosslinking, the mixture was treated with proteinase K for 45 minutes followed by incubation with RNase A for 30 minutes. Finally, DNA fragments were purified using Qiagen PCR purification kit and reconstituted in 50 µl nuclear-free water. Real time PCR was performed using SYBR green. For ChIP-seq library preparations, libraries were prepared using KapaBiosystems LTP library preparation kit (#KK8232) according to the manufacturer's protocol.
Progesterone receptor isoforms, agonists and antagonists differentially reprogram estrogen signaling.
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View SamplesPhysiological, anatomical, and clinical laboratory analytic scoring systems (APACHE, Injury Severity Score (ISS)) have been utilized, with limited success, to predict outcome following injury. We hypothesized that a peripheral blood leukocyte gene expression score could predict outcome, including multiple organ failure, following severe blunt trauma.
A genomic score prognostic of outcome in trauma patients.
Sex, Age
View Samplesexpression files supporting: Application of genome-wide expression analysis to human health and disease. PNAS
Application of genome-wide expression analysis to human health and disease.
No sample metadata fields
View SamplesRecent trials with MAPK inhibitors have shown promising results in many patients with metastatic melanoma; however, nearly all responding patients experience disease relapse. We describe here how melanoma cells respond to MAPK inhibition in a phenotype-specific manner, suggesting that slow cycling invasive phenotype cells provide a treatment-resistant pool from which disease relapse may be derived. The implication is that while MAPK inhibition may successfully treat proliferating cells, another cell population needs to be addressed at the same time.
A proliferative melanoma cell phenotype is responsive to RAF/MEK inhibition independent of BRAF mutation status.
Cell line
View SamplesThe goal of this study is to define genes that are differentially expressed in Down syndrome leukemic blasts after treatment with valproic acid (VPA)
Histone deacetylase inhibitors induce apoptosis in myeloid leukemia by suppressing autophagy.
Specimen part, Treatment
View SamplesThe analysis of capped RNAs by massively parallel sequencing has identified a large number of previously unknown transcripts, some of which are small RNAs and others are 5 truncated forms of RefSeq genes. The latter may be generated by endonuclease cleavage or by stalling of Xrn1 at defined sites. With the exception of promoter-proximal transcripts the caps on all of these are added post-transcriptionally by a cytoplasmic capping enzyme complex that includes capping enzyme and a kinase that converts 5-monophosphate ends to a diphosphate capping substrate. We previously described a modified form of capping enzyme with dominant negative activity against cytoplasmic capping (DN-cCE). A tet-inducible form of this was used to identify substrates for cytoplasmic capping by treating cytoplasmic RNA from control and induced cells with and without Xrn1. Surviving RNA was analyzed on Affymetrix Human Exon 1.0 arrays and scored for changes in probe intensity as a function of its position on each RefSeq gene to derive a factor (alpha) that could be compared between sets. Notably, transcriptome-wide changes were not evident unless RNA was treated with Xrn1. This analysis identified 2,666 uncapped mRNAs in uninduced cells, 672 mRNAs that appeared in the uncapped pool in cells expressing DN-cCE, and 835 mRNAs that were in both populations. Changes in cap status of 10 re-capping targets and 5 controls were assessed by 3 independent measures; susceptibility to Xrn1, recovery with a biotin-tagged DNA primer after ligating a complementary RNA oligonucleotide to uncapped 5 ends, and binding or exclusion from a high affinity cap-binding matrix comprised of immobilized eIF4E and the corresponding binding domain of eIF4G.
Identification of cytoplasmic capping targets reveals a role for cap homeostasis in translation and mRNA stability.
Cell line
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