9338 results for "cancer rna-seq"

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• Prostate cancer datasets WGS

  Low pass WGS: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (Next Seq )

  Dataset EGAD00001004466

• Integrated Analysis of Multimodal Single-Cell Data

  The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce 'weighted nearest neighbor' analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. All CITE-seq and ECCITE-seq raw matrices are available in GEO database under the accession number GEO: GSE164378.

  Study phs002315

• Single-cell RNA sequencing data using the BDRhapsody platform of human B-cell lymphoma. The data comprises both immune and stromal cells of 19 individuals (6 healthy control and 13 cancer patients)

  Study EGAS00001006986

• Whole Genome, RNA, and ChIP Sequencing of matched brain tumor-normal pairs (ICGC)

  Whole Genome Seq: Illumina HiSeq sequence data (with >30x coverage) were aligned to the hg19 human reference genome assembly using BWA (Li and Durbin, 2009);duplicate reads were removed from the final BAM file. No realignment or recalibration was performed. Paired-end RNA sequencing reads were mapped to the hg19 assembly of the human reference genome using BWA.Each ChIP-seq library was sequenced with two complete lanes on the Illumina HiSeq 2500 in the 101-bases paired-end rapid mode and aligned to hg19 using bwa.This resulted in the following coverage values (genome-wide, after deduplication, including all uniquely mapping reads):GBM103 macroH2A1: 17x H3K36me3: 20xMB59 macroH2A1: 11x H3K36me3: 11x

  Dataset EGAD00001000664

• Single-cell RNA-seq of human kidney tumors

  Fresh nephrectomy samples were collected from a total of 5 untreated ccRCC patients. Out of these 5 patients, 7 samples were obtained. Two samples consisted of fresh versus frozen single cells from the primary tumor site of one patient. Two other samples consisted of matched primary and distant thrombus sites (the vena cava) of a second patient. The three remaining samples came from the primary tumor sites of three distinct ccRCC patients. Single-cells were captured into 10x barcoded gel beads and RNA-sequencing library preparation was done using Chromium Single Cell 3' v2 chemistry. Sequencing was performed on a Illumina HiSeq 4000 sequencer.

  Dataset EGAD00001009306

• Unraveling the transcriptomic profile of utero-tubal lavage fluid of ovarian cancer patients

  mRNA capture seq of uterotubal lavage samples

  Dataset EGAD00001007933

• HGSOC-celllines-SmartSeq3

  Smart-seq3 scRNA-seq of cells from primary (OV2295) and metastatic (OV2295R2) high-grade serous ovarian cancer cell-line

  Dataset EGAD00001009798

• 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

• Transcriptomic Profiling of Oropharyngeal Squamous Cell Carcinoma

  The study is a retrospective transcriptomic profiling of oropharyngeal squamous cell carcinoma. The pathologic database was queried for squamous cell carcinoma tumors originating from the anatomic oropharynx (tonsil or tongue base) with available samples from surgical archives. This retrospective RNA sequencing cohort included cases dating prior to the routine implementation of p16 immunohistochemistry (IHC) testing at our institution, therefore, p16 status was not required for inclusion in this study. Formalin-fixed paraffin-embedded (FFPE) tissue samples were sent to the UNC Lineberger Comprehensive Cancer Center (LCCC) Translational Genomics Lab (TGL) for RNA isolation using the Maxwell 16 MDx Instrument (Promega AS3000) and the Maxwell 16 LEV RNA FFPE Kit (Promega AS1260) following the manufacturer's protocol (Promega 9FB167). After a pathology review of a hematoxylin and eosin (H&E) stained slides to identify tumor area, RNA was extracted from unstained slides using macrodissection. Total RNA quality was measured using a NanoDrop spectrophotometer (Thermo Scientific ND-2000C) and a TapeStation 4200 (Agilent G2991AA). Total RNA concentration was quantified using a Qubit 3.0 fluorometer (Life Technologies Q33216). Libraries were prepared with Illumina TruSeq Stranded Total RNA with Ribo-Zero protocol. Libraries were sequenced on an Illumina HiSeq2500 sequencer. Paired end read data, with read lengths of 75 were collected.

  Study phs002935

• NCCS-NSCLC-ITH2-WES

  NCCS-NSCLC-ITH2 dataset of 185 sectors from 48 patients with early-stage non-small cell lung cancer diagnosed in National Cancer Centre Singapore; these are paired-end, whole-exome and bulk RNA sequencing data, sequenced by Illumina HiSeq 4000/2000.

  Dataset EGAD00001010158