Search Results - EGA European Genome-Phenome Archive

The Role of CDX2 in Controlling ABCB1 Expression and Chemosensitivity in Human Colon Cancer

This study reports the results of RNA sequencing (RNA-seq) experiments performed on two human colon cancer cell lines (HT-29, DLD-1) that were genetically engineered to achieve either the constitutive expression (in HT-29 cells) or the selective inactivation (in DLD-1 cells) of the transcription factor CDX2, a master regulator of embryonic gut development and a key regulator of multi-lineage differentiation in adult colon epithelial cells. Our previous studies had identified lack of CDX2 expression as a prognostic biomarker of poor prognosis and a predictive biomarker of benefit from adjuvant chemotherapy in Stage-II and Stage-III colon cancer patients (Dalerba et al., NEJM, 374:211-222, 2016; PMID: 26789870). The molecular basis of these associations, however, remained poorly understood. The present study was designed to identify which genes are under the transcriptional control of CDX2, in order to clarify their role in shaping colon cancer resistance to chemotherapy. Of the two lines included in this study, the first (HT-29) is CDX2-negative at baseline, and was infected with a lentivirus vector encoding for the cDNA of the human CDX2 gene under the control of a constitutive promoter (CMV). The second line (DLD-1) is CDX2-positive at baseline, and was infected with a series of lentivirus vectors encoding for CRISPR/Cas9 constructs designed to selectively inactivate the CDX2 gene. In both cases, infected cells were purified by fluorescence-activated cell sorting (FACS), based on the differential expression of a green fluorescent reporter (EGFP) that was also encoded by the lentivirus vectors, and transcribed in tandem with either the CDX2 cDNA (HT-29) or CRISPR/Cas9 gRNAs targeting CDX2 (DLD-1). The experiments were designed to include 3 experimental replicates per condition, and to compare the transcriptional profile of genetically engineered cells with that of autologous controls (i.e., HT-29 cells infected with an "empty" version of the same lentivirus vector used to over-express CDX2, DLD-1 cells engineered with a "non-targeting" CRISPR/Cas9 gRNA). In the specific case of DLD-1 cells engineered with CRIPSR-Cas9 constructs targeting CDX2, infected cells were sorted based on the differential expression of EGFP and then plated as single-cells, in order to sub-culture three isogenic clones, all of which were genetically sequenced to confirm bi-allelic knock-out of the CDX2 gene, and confirmed to lack CDX2 protein expression by Western blot. The results of our experiments demonstrate that, in human colon epithelial cells, CDX2 controls the expression of the ABCB1 efflux pump, by positively regulating the transcription of the corresponding gene.

Study phs002903

Genomic Analysis of Metastatic Brain Cancer

This study contains targeted exome and transcriptome sequencing of metastatic brain tumors from 68 patients. Sequencing data includes i) targeted Tempus xT exome sequencing from matched tumor/normal from 68 patients, and ii) whole-transcriptome RNA sequencing from a subset of 65 patients.

Study phs002639

The Dynamic Landscape of Open Chromatin During Human Cortical Neurogenesis

Non-coding regions comprise most of the human genome and harbor a significant fraction of risk alleles for neuropsychiatric diseases, yet their functions remain poorly defined. We created a high-resolution map of non-coding elements involved in human cortical neurogenesis by contrasting chromatin accessibility and gene expression in the germinal zone and cortical plate of the developing cerebral cortex. To obtain a high resolution depiction of chromatin structure and gene expression in developing human fetal cortex, we dissected the post-conception week (PCW) 15-17 human neocortex into two major anatomical divisions to distinguish between proliferating neural progenitors and post mitotic neurons: (1) GZ: the neural progenitor-enriched region encompassing the ventricular zone (VZ), subventricular zone (SVZ), and intermediate zone (IZ) and (2) CP: the neuron-enriched region containing the subplate (SP), cortical plate (CP), and marginal zone (MZ). Tissues were obtained from three independent donors and three to four technical replicates from each tissue were processed for ATAC-seq to define the landscape of accessible chromatin and RNA-seq for genome-wide gene expression profiling.

Study phs001438

Upper cortical layer-driven network impairment in schizophrenia

Schizophrenia is one of the most wide-spread and complex mental disorders. To characterize the impact of schizophrenia, we performed single nucleus RNA sequencing (snRNA-seq) of >220,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls. Additionally, >115,000 neurons were analyzed topographically by immunohistochemistry. Compositional analysis of snRNA-seq data revealed a reduction in abundance of GABAergic neurons and a concomitant increase in principal neurons, most pronounced for upper cortical layer subtypes, which was substantiated by histological analysis. Many neuronal subtypes showed extensive transcriptomic changes, the most dramatic - in upper layer GABAergic neurons, including downregulation in energy metabolism and upregulation in neurotransmission. Transcription factor network analysis demonstrated a developmental origin of transcriptomic changes. Finally, Visium spatial transcriptomics further corroborated upper layer neuron vulnerability in schizophrenia. Overall, our results point towards general network impairment within upper cortical layers as a core substrate associated with schizophrenia symptomatology. Study is available on bioRxiv (https://doi.org/10.1101/2020.11.17.386458) and in upcoming publication.

Study EGAS00001006495

Cold Ischemia Study

This study aimed to evaluate the impact of cold ischemia time on the expression of cancer-related genes, particularly those involved in hypoxia, apoptosis, and epithelial-to-mesenchymal transition (EMT). We performed RNA sequencing on 54 normal colon mucosa samples from nine patients undergoing colorectal cancer surgeries, freezing samples at predefined intervals ranging from 0 to 60 minutes. A total of 44 differentially expressed genes (DEGs) (p < 0.05) were identified when comparing samples frozen immediately (T0) with those frozen after 60 minutes (T5).

Study EGAS00001008233

RNA sequencing data of 66 matched primary and recurrent high grade serous ovarian cancer

Gene expression profiles of matched primary and recurrent fresh frozen tumor tissues from 66 HGSOC patients were obtained by RNA sequencing. RNA libraries were created using the Illumina TruSeq RNA sample preparation kit V2 according to the manufacturer’s instructions and sequenced on a HiSeq2000 (Illumina, California, USA) using a V3 flowcell generating 1 x 50 bp reads. Raw sequencing reads were mapped to the transcriptome and the human reference genome (NCBI37/hg19) using TopHat 2.0 and Bowtie 2.0.

Study EGAS00001002660

Pseudogene RNAseq

RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

Dataset EGAD00001000732

SCNA-Seq of plasma DNA samples

Low coverage whole genome sequencing for the identification of somatic copy number alterations (SCNA) and focal amplification mapping in plasma DNA of prostate cancer patients

Dataset EGAD00001002149

Integrative Single-Cell Analysis of Transcriptome, Epigenome, and Lineage in HIV Latency and Activation

With the development of triple combination antiretroviral therapy, routine HIV treatment eliminates nearly all actively infected cells. Nevertheless, the small reservoir of latently infected cells, which can remain dormant for long periods of time before becoming active and producing new virus particles, represents a crucial barrier to completely curing the disease. Identifying markers that identify latently infected cells or the biochemical factors that control latency activation could enable the effective use of a “shock and kill” strategy, where specific targeting or activation of latently infected cells eliminates the viral reservoir. Our recent work suggests that the global transcriptomic and epigenomic changes during hematopoietic differentiation affect viral latency and activation. Additionally, we recently found that global inhibition of histone deacetylase activity increases viral activation in these cells, further implicating epigenomic changes during activation. These results raise fundamental questions: What are the markers of latently infected cells? How do the transcriptomic and epigenomic states of a cell affect latency and activation? How does the differentiation state relate to viral latency? Here, we leverage our experimental platform for identifying latently and actively infected cells, single-cell transcriptome and epigenome sequencing, and our recently developed computational integration methods to investigate these questions. Our interdisciplinary team combines expertise in HIV basic science, HIV clinical treatment, and bioinformatics to develop an experimental and computational framework for integrated gene expression, chromatin accessibility, and lineage into a single picture of viral latency and activation. Specifically, this project will (1) use single-cell RNA-seq and single-cell ATAC-seq to map the diversity of infected cells, (2) investigate the relationship between hematopoietic differentiation state and viral activation, (3) determine viral integration sites through single-cell RNA-seq, (4) computationally integrate single-cell transcriptome and epigenome profiles, and (5) computationally infer cell lineage relationships among viral genomes and infected cells. To accomplish these goals, we will carry out the following aims: (1) Characterize lineage, transcriptomic and epigenomic diversity of single latently and actively infected primary cells. (2) Investigate latency and activation during in vitro differentiation. (3) Survey single cell diversity of re-activated and in vitro infected cells from cART-suppressed patients. Together, these aims will produce a comprehensive, integrated transcriptomic and epigenomic atlas of the HIV reservoir, identify DNA and RNA biomarkers of latency, and characterize clonal expansion patterns. Our work also develops a broadly applicable experimental and computational framework, laying a foundation for the discovery of novel insights into HIV latency and activation.

Study phs002915

How to use the EGA search box

Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

Documentation discovery/metadata/search-box

Region-specific Transcriptome Analysis of the Human Retina and Retinal Pigment Epithelium (RPE)/Choroid

Proper spatial differentiation of retinal cell types is necessary for normal human vision. Many retinal diseases, such as Best disease and male germ cell associated kinase (MAK)-associated retinitis pigmentosa, preferentially affect distinct topographic regions of the retina. While much is known about the distribution of cell types in the retina, the distribution of molecular components across the posterior pole of the eye has not been well-studied. To investigate regional difference in molecular composition of ocular tissues, we assessed differential gene expression across the temporal, macular, and nasal retina and retinal pigment epithelium (RPE)/choroid of human eyes using RNA-Seq. RNA from temporal, macular, and nasal retina and RPE/choroid from four human donor eyes was extracted, poly-A selected, fragmented, and sequenced as 100 bp read pairs. Digital read files were mapped to the human genome and analyzed for differential expression using the Tuxedo software suite. Retina and RPE/choroid samples were clearly distinguishable at the transcriptome level. Numerous transcription factors were differentially expressed between regions of the retina and RPE/choroid. Photoreceptor-specific genes were enriched in the peripheral samples, while ganglion cell and amacrine cell genes were enriched in the macula. Within the RPE/choroid, RPE-specific genes were upregulated at the periphery while endothelium associated genes were upregulated in the macula. Consistent with previous studies, BEST1 expression was lower in macular than extramacular regions. The MAK gene was expressed at lower levels in macula than in extramacular regions, but did not exhibit a significant difference between nasal and temporal retina. The regional molecular distinction is greatest between macula and periphery and decreases between different peripheral regions within a tissue. Datasets such as these can be used to prioritize candidate genes for possible involvement in retinal diseases with regional phenotypes. Reprinted from Whitmore, S.S., Wagner, A.H., DeLuca, A.P., Drack, A.V., Stone, E.M., Tucker, B.A., Zeng, S., Braun, T.A., Mullins, R.F., Scheetz, T.E., 2014. Transcriptomic analysis across nasal, temporal, and macular regions of human neural retina and RPE/choroid by RNA-Seq. Experimental Eye Research. Used under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license. Additional RNA sequencing was performed on temporal, macular, nasal, inferior, and superior retina from a fifth subject and is included in this dataset. See original publication for details.

Study phs001151

Proteogenomic Characterization Unveils Biomarkers Associated With Chemoresistance in Muscle Invasive Bladder Cancer

Cisplatin-based neoadjuvant chemotherapy (NAC) combined with radical cystectomy has been used as a standard of care for patients with clinically non-metastatic muscle invasive urothelial bladder cancer, but 40-50% patients will benefit from the treatment. Molecular subtypes of muscle invasive bladder cancer (MIBC) provide a possible biomarker roadmap that may help identify patients who may or may not respond to NAC. Recently, by integrating Mass Spectrometry-based proteomics, proteogenomic studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have demonstrated the potential of this approach in prioritizing candidate driver genes and facilitating understanding of the chemotherapy resistance mechanisms. To demonstrate the proteogenomic workflow for investigating the mechanisms underlying chemotherapy resistance in MIBC, we conducted proteogenomic characterization of 46 pre- and 14 post- treatment tumors, including whole exome sequencing (WES), mRNA sequencing, Tandem Mass Tag (TMT)-proteomics, or TMT-phosphoproteomic. These samples were selected from an initial cohort of 143 MIBC tissues and filtered by tumor purity, clinical history, pathological response, and proteomic quality control metrics. The final cohort comprised samples from 53 patients, including 45 with both genomic and proteomic data and 8 with genomic data only.Our multi-omics-based clustering reveals subtypes associated with chemotherapy sensitivity. Protein abundance of a short isoform of ATAD1 and RAF family proteins were identified as biomarkers of chemosensitivity which highlights the importance of proteomics data. Integration of proteomic and phosphoproteomic data reveals Wnt signaling via GSK3B-S9 phosphorylation and the JAK/STAT pathway as potential targets to overcome chemoresistance. Correlations between PD-L1 and TROP-2/NECTIN-4 indicate an additive benefit of combination therapy targeting these proteins. Our data analysis clearly demonstrates that proteogenomic plays an important role in uncovering potential resistance mechanisms that operate only at protein and Post-Translational Modification (PTM) levels. Proteomics data can be accessed through PRIDE with ID PXD060290. RNA-seq and whole-exome sequencing (WES) FASTQ files are archived in the dbGaP database.

Study phs004049

Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

The dataset for the study “Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer” includes 173 bam files from hybrid capture targeted error-correction next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 32 individuals with esophageal/gastroesophageal junction cancer, who received immunotherapy-containing regimens.

Dataset EGAD00001011822

The characterization of gene expression pattern of anticancer agent-resistant cancer stem cells using RNA sequencing analysis.

Slow cycling cancer stem cells persist and contribute to tumor regrowth after chemotherapy.To investigate this mechanism, we developed stemness- and cell cycle-visualized colon cancer organoids from patient tissues, and perfomed comparative transcriptome analysis using sorted rapid cycling stem cells (LGR5+p27-) and slow cycling stem cells (LGR5+p27+). In addition, we analyzed the changes in the gene expression patterns in the slow cycling stem cells and all of tumor cells after chemotherapy (camptothecin) treatment. Gene ontology analysis and gene set enrichment analysis using these transcriptome data revealed cell adhesion signal and YAP siganling pathway were important to maintain the dormancy and resume propagation after chemotherapy. In each experiments, total RNA was extracted from organoids and sequenced with HiSeq X Ten (Illumina).

Study JGAS000350

RNA sequencing

RNAseq of 45 high-grade serous ovarian cancer tumour samples. Libraries were generated using the NEB Ultra II Directional RNA library Prep kit with polyA enrichment. Libraries were sequenced as paired-end 50 or 100bp on an Illumina NextSeq or NovaSeq.

Dataset EGAD00001009844

11936 results for "cancer rna-seq"

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