Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE110590 | GEO | 2018/02/15
REPOSITORIES: GEO
Similar Datasets
Project description:We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel candidate drivers with biological, and possibly therapeutic, importance in cancer. Effects of knockdown of two genes -TBC1D16 and RAB27A - were tested on four cell lines each, each of them with two different hairpins. As a control, we used a hairping targeting GFP. shGFP experiments were done in biological duplicates or more. The second WM1976-shGFP sample was identified as an outlier by a PCA analysis and was excluded from our analysis.
2010-12-01 | E-GEOD-23884 | biostudies-arrayexpress
Project description:We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel candidate drivers with biological, and possibly therapeutic, importance in cancer.
2010-12-01 | GSE23884 | GEO
Project description: Not available
| S-EPMC5873890 | biostudies-literature
Project description:Multiple joint data, including methylome, transcriptomic, BRAF V600E mutation results and clinical features, aiming to identify molecular drivers in papillary thyroid carcinomas.
2017-05-04 | GSE86961 | GEO
Project description:Duplication of the genome in mammalian cells occurs in a defined temporal order referred as its replication-timing program (RT). RT is regulated in units of 400-800 Kb referred as replication domains (RDs) and changes dynamically during development. Changes in RT are generally coordinated with transcriptional competence and changes in sub-nuclear position. We generated genome-wide RT profiles for 29 distinct human cell types including embryonic stem cell (hESC)-derived, primary cells and established cell lines representing intermediate stages of endoderm, mesoderm, ectoderm and neural crest (NC) development. We identified clusters of RDs that replicate at unique times in each stage (RT signatures). Surprisingly, transcriptome data revealed that, despite an overall correlation between early replication and transcriptional activity, most genes that switched RT during differentiation can be expressed when late replicating. Intriguingly, this class of genes was nonetheless induced to high expression levels prior to a late to early RT switch and down-regulated after the switch back to late replication. These results clarify the complex relationship between transcription and RT and identify classes of genes that behave as potential drivers of the RT switch vs. those that may depend upon an RT switch for transcriptional induction. Genome-wide replication timing profiles were constructed from 60 human samples covering 29 distinct cell types including embryonic stem cell (hESC)-derived, primary cells and established cell lines representing intermediate stages of endoderm, mesoderm, ectoderm and neural crest (NC) development.
2015-05-28 | E-GEOD-63428 | biostudies-arrayexpress
Project description:We generated a signature for topsentinol L trisulfate in breast cancer cells to predict drug sensitivity in other available datasets. To process the mRNA sequencing data, we used the TCGA mRNA-seq Pipeline. RNA sequencing reads for the treated and control samples were aligned using MapSplice v12_07, quantified using RSEM, and gene counts were normalized using upper quantile normalization. This was the same methodology used to normalize the PANCAN12 TCGA dataset, which we obtained from TCGA fully processed for use in this analysis. To generate a TLT sensitivity signature, we used the DESeq2 package (version 1.4.5) in the Bioconductor framework (version 2.14.0, version 3.1.0 of R) to identify genes that were significantly deregulated (adjusted p < 0.05) between the treated and control samples. One hundred forty-six genes were found to be significantly deregulated, out of which only 131 were found in the TCGA dataset. To use DEseq2, the reads had to be re-mapped using the Rsubread Bioconductor package. We used this package to map the reads to version hg19 of the human genome and to summarize the data to gene-level values. We predicted TLT sensitivity for the PANCAN12 TCGA dataset using the Bayesian binary regression algorithm version 2.0 (BinReg2.0) used as a MATLAB plug-in. We used default parameters, except that our signature used 131 genes and one metagene. The probability output from the binary regression model was subtracted from one so that probabilities closer to one indicated a higher probability of sensitivity to the drug as previously described [29]. Prior to making the predictions, the data were log2 transformed and DWD normalized to reduce biases that can result from differences in batch processing and platforms.
2020-07-30 | GSE142833 | GEO
Project description:Gene expression profiling of 82 patients with cervical cancer was performed. The expression data were correlated with copy number alterations of the same patients, as assessed with array CGH in a separate study, in order to identify drivers of cervical cancer carcinogenesis. In total, 82 patients with cervical cancer, stage 1b bulky through 4a, were included. Pretreatment biopsies were taken from the tumors and analyzed with Illumina gene expression BeadArrays (Human WG-6 v3).
2013-01-31 | E-GEOD-27469 | biostudies-arrayexpress
Project description:This is a quality control (QC) substudy of GSE48091. The QC substudy comprises gene-expression profiling of re-extracted tumor RNA for a subset of the tumours in the full study. As background, a population-based cohort study of metastatic breast cancer patients was first designed. Thereafter, a case-control study nested in the corresponding population-based cohort of primary breast cancer patients was designed by selecting distant metastasis-free controls to each case. Tumor RNA was extracted in the same order. All RNA was profiled on microarrays in randomized order. For quality control, RNA was also re-extracted (new tumor piece) in a randomized order for randomly selected cases-controls sets and profiled with the rest. Keywords: Expression profiling by array The nested case-control study included 768 study subjects corresponding to 623 primary tumor samples. This QC substudy comprises 97 of the study subjects (all different primary tumor samples). Details concerning case-control status are given in the samples section. This Series includes a re-analysis of 97 samples from GSE48091. The file "full_data_matrix.txt" includes the re-normalized values for the 97 samples from GSE48091 and the normalized values for the 97 new samples from the same patients that were analyzed together.
2016-05-28 | E-GEOD-81954 | biostudies-arrayexpress
Project description:This dataset containing 147 DLBCL patients and 79 healthy controls. The proteomics was detected by our in-depth platform combining DIA-MS and antibody array. The datasets we uploaded were quantile normalized.
2023-06-09 | PXD036842 | iProX
Project description:Metabolic drivers of human keratinocyte differentiation revealed by integrated metabolome and transcriptome analysis
| PRJNA562314 | ENA