12214 results for "cancer rna-seq"

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• RNA Editing in breast cancer

  High-throughput sequencing screens suggest that RNA editing, which consists in the substitution of adenosine with inosine by the RNA-specific adenosine deaminase (ADAR) enzyme, occurs at several thousand positions across the human genome. Recent evidences have shown that RNA-editing could promote proliferation and carcinogenesis; however, the general principles of ADAR activity on the transcriptome and how ADAR is controlled in cancers remain to be established. The main aim of this project was to investigate the phenomenon of RNA editing in breast and other cancers. The frequency of A-to-I editing was evaluated in 58 breast cancers equally distributed among the different molecular subtypes and 10 normal breast tissues. The analysis was focused on defining: the relationship between the global amount of editing and ADAR expression; the ability to predict the level of editability of specific sites; the distribution of editing in normal and tumour samples and among different breast cancer subtypes; and the clinical, pathological and genomic factors affecting editing.

  Study EGAS00001000495

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• Enhancer profiling to identify identifies epigenetic markers of endocrine resistance in metastatic castration resistant prostate cancer patients

  ChIP-seq and matched input data of AR, FOXA1 and H3K27ac for mCRPC patient samples taken prior to and after treatment with AR targeted therapy

  Dataset EGAD00001008688

• Integrated Exome-seq analysis of tumor thrombus

  We prospectively enrolled 83 patients with a renal mass and TT scheduled to undergo surgical resection. To accurately integrate genomic and transcriptomic findings and enable histological co-registration, simultaneous isolation of DNA and RNA was performed from individual samples characterized histologically through immediately flanking sections

  Study EGAS00001005511

• Long-read single-cell RNA-seq in COVID-19

  We report the first application of long-read single-cell RNA sequencing on nasopharyngeal swabs from COVID-19 patients and healthy controls to resolve transcript-level changes across cell types. Samples were collected from three patients with moderate COVID-19, six patients with critical COVID-19, and three healthy controls. Single-cell libraries were generated using the 10x Genomics Chromium 3' protocol, producing unfragmented cDNA. Libraries were sequenced on the Oxford Nanopore Technologies PromethION platform. Basecalling was performed with Guppy (v1.6), generating a single FASTQ file per sample.

  Dataset EGAD50000001836

• Transcriptional data of human isogenic iPSCs

  This dataset contains bulk RNA-seq data from 12 autosomally isogenic human iPSCs. Three biological replicas from XXY (sample ID 419295-7), XX (sample ID 419298-419300), XY (sample ID 419302, 419305-6), and XO (sample ID 419308, 419310, 419312) hiPSC. RNA for this dataset was collected using Trizol method, and polyA library type was implemented. The libraries were sequenced using Illumina device. The sample reads are deposited as fastq files. Analysis have been done using reference alignment to GRCh38 reference genome.

  Dataset EGAD50000001361

• RNAseq of human fetal pancreas development

  The dataset comprises RNA-seq information of 4 subpopulations sorted from human fetal pancreas of 3 different donors. Low input libraries were generated using the Smart-seq2 protocol after Ampure XP cleanup of the total RNA extracted from the sorted cells. Libraries were multiplexed and sequenced paired-end over 2 lanes of HiSeq4000 each. Raw data was aligned to the human genome refernence GRCh37 using STAR v2.5.1b with GENCODE v19 as transcriptome reference, and unaligned reads were folded into the final uploaded bams.

  Dataset EGAD00001004210

• Integrated analysis of whole genome and RNA sequencing in 22 HBV-associated HCCs

  RIKEN collection WGS and RNA-seq reads for 66 HBV-associated HCC and matched blood or liver samples from 22 donors.

  Dataset EGAD00001001035

• Bam files from Whole exome sequencing (WES, ~50x mean read depth) of metachronous bladder tumors

  In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors) Data provided here consist of 122 Bam files for WES (83 Tumors and 39 blood)

  Dataset EGAD00001002716

• The effect of anti-HER2/CD3 TDB on transcription in human CD8 T cells (bulk RNA-seq)

  RNA was isolated from purified human CD8 cells that were incubated with anti-HER2/CD3 TDB in the presence of SK-BR-3 cells. Sequencing libraries were generated and submitted for transcriptome profiling by high-throughput sequencing. Experiments were performed in triplicates for anti-HER2/CD3 TDB treatment and control. This Dataset is associated with the following ArrayExpress Experiment: E-MTAB-8211 - The effect of anti-HER2/CD3 TDB on transcription in human CD8 T cells (bulk)

  Dataset EGAD00001005187

• Single nucleus RNA Seq of LUAD patient derived lung samples

  Fastq files of single nucleus RNA Sequencing data from 26 patients including 26 lung adenocarcioma and 12 matched healthy tissue samples for 8 young female never smokers, 8 young female smokers, 7 elderly female never smokers and 3 male never smokers.

  Dataset EGAD00001008955

• RNA-seq bulk

  This dataset gather all RNA-sequencing data in MM.1S cell line in control and Dex condition; both in 3 biological replicates

  Dataset EGAD00001011137

• scWGS profile, paediatric acute lymphoblastic leukemia ALL40

  Single cell sequencing using the direct nuclear tagmentation and RNA sequencing (DNTR-seq) method was performed on the diagnostic bone marrow sample (ALL40). This dataset consists of the DNA (scWGS) profile from 384 cells. Two fastq files are associated with each cell identifier.

  Dataset EGAD50000001774

• The function of LAMP5 in multiple myeloma

  This dataset includes Fastq files from bulk RNA seq from myeloma cell lines (MM.1S and NCI-H929) expressing a DOX-inducible LAMP5 shRNA, S1 (CACTTCAAAGACGCAGTCAGT) or S5 (GCACACAGAATACAACCTCAT), or a scramble control treated with DOX during 48h.

  Dataset EGAD50000001178

• Whole Transcriptome Sequencing Data of prDLBCL

  We used sequenced 30 prDLBCL samples using RNA-seq to evaluate possible fusion events and to get expression profiles.

  Dataset EGAD50000000592

• Long-read RNA-seq of doxycycline-inducible DUX4 human myoblast cell lines

  The dataset includes the PacBio full-length isoform sequencing data of DUX4i myoblasts (Clone-5) with and without 16-hour DOX-treatment (referred to as DUX4- and DUX4+; n=1 in each condition).

  Dataset EGAD50000000718

• cHCC-ICC RNAseq

  The RNA-seq data of 97 tumor samples from 77 cHCC-ICC cases, data of two other HCC tumor samples were also included.

  Dataset EGAD00001005182

• Gene expression in LPS-stimulated human monocyte-derived macrophages

  This dataset consists of RNA-seq data from human monocyte-derived macrophages that were subjected to siRNA treatment targeting RAD21 and either left untreated, or stimulated with LPS. In total, it includes 24 samples.

  Dataset EGAD00001007952

• Gene expression in human monocyte differentiation

  This dataset consists of RNA-seq data data from human monocytes, monocyte-derived dendritic cells or monocyte-derived macrophages as well as monocyte-derived cells that were subjected to siRNA treatment targeting TET2, IRF4 and EGR2. In total, it includes 43 samples.

  Dataset EGAD00001006604

• Log2 normalized TPM matrix for POPLAR (GO28753)

  This dataset contains log2(TPM + 1) for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008390

• Log2 normalized TPM matrix for OAK (GO28915)

  This dataset contains log2(TPM + 1) for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008391

• Immune single-cell profiling of human chronic inflammatory skin disease

  This dataset includes 10X Genomics 3' single cell-RNA-seq profiles from 24 human rashes and 7 healthy controls. BAM files are provided for each sample.

  Dataset EGAD00001008624

• Atypical teratoid/rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities - RNA-Seq

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00001009794

• Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer

  Current understanding of acquired resistance to osimertinib in EGFR-mutant lung cancer is largely based on the use of targeted sequencing platforms and cell-free DNA. In this study we examined the clonal evolution and heterogeneity of acquired resistance mechanisms to osimertinib through whole exome and RNA-sequencing of prospectively collected pre-and post-osimertinib resistant tumors and metastases at rapid autopsy. We enrolled 34 patients with histologically confirmed EGFR mutant advanced lung adenocarcinoma in a prospective Phase II clinical trial to evaluate osimertinib treatment and the use of local ablative therapy (LAT) for oligoprogressive disease (NCT02759835) in EGFR mutant NSCLC from April 2016 until data cutoff in September 2019. Patients were not selected based on oligometastatic status, rather 32/34 patients had multiple organ involvement, including lung, liver, adrenal, brain and bone. Biopsies or surgeries were performed pre-osimertinib treatment and at first progression on osimertinib. A subset of patients also underwent biopsies at second progression on osimertinib, and following end of life in-patient hospice and rapid autopsy upon death. 63% (n=15/24) of first-line osimertinib and 50% (n=5/10) of second-line osimertinib treated patients had RECIST defined partial response. Overall, 21 patients had RECIST defined first progression on osimertinib. Two patients on this trial discontinued treatment and were lost to follow up. Of the 21 patients who had RECIST defined progression, 14 patients underwent LAT (8 first-line and 6 second-line). Twelve patients had paired pre- and post-osimertinib resistant tumors and three had post-osimertinib resistant tumors available for analysis. Four patients (LAT002, LAT006, LAT014 and LAT021) consented for a rapid autopsy protocol and underwent rapid autopsy after deatH. To elucidate acquired osimertinib resistance mechanisms, we performed whole-exome (WES), targeted sequencing and RNA-seq on 66 tumors and matched germline DNA from tumor tissue obtained from a total of 15 patients.

  Study phs002001

• RNA sequencing data of high-grade serous ovarian cancer samples

  RNA sequencing data of over 200 HGSOC samples at diagnosis, after chemotherapy and during progression.

  Dataset EGAD00001006456