11745 results for "cancer rna-seq"

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• The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  Acute lymphoblastic leukaemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1 and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard of care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard of care diagnostics. KMT2A-rearrangements (MLL), whilst usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard of care testing in nine patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the commonest IKZF1 deletion from RNA-seq data and validated this using a RQ-PCR assay. We applied an expression classifier to identify Ph-like B-ALL patients. T-ALL proved a rich source of novel gene fusions which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

  Study EGAS00001004212

• Glioblastoma stem cell lines RNA-seq data

  RNA-seq data for 54 Glioblastoma stem cell (GSC) lines. Fastq files of the strand-specific paired-end RNA-seq data are available.

  Dataset EGAD00001006195

• SF12090 snRNA-Seq IDHR132H Wild-type Primary GBM Male

  Single Nuclei RNA-Seq Primary IDHR132H Wild-type GBM. Male, 61. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005412

• SF4297 snRNA-Seq Primary GBM

  Single Nuclei RNA-Seq Primary High-grade Glioma. Gender male age 39. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference

  Dataset EGAD00001005415

• SF10022 snRNA-Seq Primary High-grade Glioma

  SF10022 single nuclei RNA-Seq Primary High-grade glioma. gender Male Age 65. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference

  Dataset EGAD00001005409

• SF4400 snRNA-Seq Primary GBM

  Single Nuclei RNA-Seq Primary High-grade Glioma. Gender Female Age 51. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference

  Dataset EGAD00001005399

• SF9259R snRNA-Seq Primary GBM

  Single Nuclei RNA-Seq Primary High-grade Glioma. Gender Male Age 73. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005401

• SF6996 snRNA-Seq Primary GBM

  Single Nuclei RNA-Seq Primary High-grade Glioma. Gender Female age 40. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005402

• Inference_of_B_cell_clonality_and_function_from_single_cell_RNA_seq_data

  cDNA libraries for RNA-seq will be prepared for sequencing. cDNA will be created following the Smart-Seq2 protocol, and a part of the cDNA from each cell will be used for preparation of RNA-seq libraries using the Nextera XT kit. The other part of the cDNA will be used as template for verification of the computationally recovered immunoglobulin sequences from the RNA-seq dataset by using a targeted PCR approach to generate a multiplexed sequencing library

  Study EGAS00001002963

• RNA-seq, ChIP-seq, ATAC-seq files for PCGP SJERG

  RNA-seq, ChIP-seq, and ATAC-seq files for PCGP SJERG paper titled "Deregulation of DUX4 and ERG in acute lymphoblastic leukemia"

  Dataset EGAD00001002681

• Single-Cell RNA-Sequencing of Human Prostatectomy Tissue

  The tissue microenvironment in prostate cancer is profoundly altered. How prostate cancer cells and their precursors mediate those changes is unclear, in part due to the inability to longitudinally study the disease evolution in human tissues. To overcome this limitation, we performed extensive single-cell RNA-sequencing (scRNA-seq) to assess the transcriptional profiles of the tissue microenvironment in prostate tissues from prostatectomies from men diagnosed with prostate cancer. For each subject, benign-enriched and tumor-enriched tissues were collected from the peripheral zone of the prostate. Our studies of human tissues revealed that cancer cell-intrinsic activation of MYC signaling was the top up-regulated pathway in human cancers, representing a common denominator across the well-known molecular and pathological heterogeneity of human prostate cancer. Numerous non-malignant cell states in the tumor microenvironment (TME), including non-cancerous epithelial, immune, and fibroblast cell compartments, were conserved across individuals, suggesting that these cell types may be a sequelae of the convergent MYC activation in the cancer cells.

  Study phs003480

• BELOB: Ribo-minus RNA-seq data corresponding to 96x GBM samples from the BELOB trial.

  Ribo-minus RNA-seq data corresponding to 96x GBM samples from the BELOB trial.

  Study EGAS00001004570

• Bulk and single cell RNA-seq analysis of keratinocytes and human skin

  106 bulk RNA-seq samples of primary human keratinocytes 8 single cell RNA-seq samples of human epidermis

  Dataset EGAD00001005998

• A Personalized Neoantigen Vaccine Generates Anti-Tumor Immunity in High-Risk Renal Cell Carcinoma

  Personalized cancer vaccines (PCVs) can generate circulating immune responses against predicted neoantigens. However, whether such responses can target cancer driver mutations, lead to immune recognition of a patient's tumor, and result in clinical activity are largely unknown, particularly in patients with lower mutation burden tumors. We conducted a phase I trial testing a neoantigen-targeting PCV in patients with high-risk, fully resected clear cell renal cell carcinoma (RCC; stage III or IV), with or without ipilimumab administered adjacent to the vaccine. At a median follow-up of 40.2 months from surgery, none of the 9 initial participants experienced a recurrence of RCC. No dose-limiting toxicities were observed. All patients generated T cell immune responses against PCV antigens, including RCC driver mutations in VHL, PBRM1, BAP1, KDM5C, and PIK3CA. Following vaccination, there was a durable expansion of peripheral T cell clones, and T cell reactivity against autologous tumor was detected in 7 of the 9 patients. Our results demonstrate that neoantigen-targeting PCVs in high-risk RCC are highly immunogenic, capable of targeting key driver mutations, and induce anti-tumor immunity. These observations, in conjunction with the absence of recurrence in all 9 vaccinated patients, highlights the promise of PCV as effective adjuvant therapy in RCC. We are submitting WES data from whole blood samples and tumor FFPE samples, and RNA-Seq data from tumor FFPE samples. We are also submitting RNA-Seq data from purified tumor samples. scRNA-seq data will be submitted for skin samples (vaccine site biopsies at Weeks 0, 4, and 13), tumor samples (including resected metastatic tumor samples for two patients), and normal kidney samples. Bulk TCR-seq data will be submitted for blood samples.

  Study phs003710

• RNAseq from 6 organotypic co-cultures (OC cells bulk/OCSC with or without TME)

  The dataset contains samples of 6 organotypic co-cultures, assembled with patient-derived material from ovarian cancer (OC) patients. Tumor cells, both as bulk and as cancer stem cells-enriched (OCSC) populations, are cultured or not with in vitro peritoneal TME (for details see Battistini C et al, Tumor microenvironment-induced FOXM1 regulates ovarian cancer stemness, CDDis 2024). Dataset is composed by fastq file (paired end) type from bulk RNA-Seq.

  Dataset EGAD50000000523

• single cell RNA-seq of small cell lung cancer tumors

  This study investigated the transcriptomic profile of small cell lung cancer from 10 patients. Tumors were rapidly dissociated after the surgical procedure. Libraries were constructed using the VDJ NextGEM v1.1 10x Genomics Chromium kit. Samples were sequenced on a NextSeq 550 sequencer (Illumina).

  Study EGAS50000001400

• Lung Adenocarcinoma Promotion by Air Pollutants

  A complete understanding of how environmental carcinogenic exposures promote cancer formation is lacking. Over 70 years ago, tumour formation was proposed to occur in a two step process: an initiating step which induces mutations in normal tissue, followed by a promoter step which triggers cancer development. Recent evidence has revealed healthy human tissue contains a patchwork of clones harbouring oncogenic mutations.This led us to hypothesise that environmental particulate matter measuring PM2.5, known to be associated with lung cancer risk, might promote lung cancer by acting on pre-existing cells harbouring oncogenic mutations in normal lung tissue. Here we use a combination of WGS and RNA-seq of mouse tumours from pollution-exposed mice to examine the impact of particulate matter on mutagenesis and gene expression respectively.

  Study EGAS00001006951

• SF11979 scRNA-Seq Primary GBM IDHR132H Wild-type Female

  Single Cell-RNA Seq IDHR132H Wild-type Primary GBM Female, 76. Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through cellranger count (10xGenomics.)

  Dataset EGAD00001005390

• SF11964 scRNA-Seq Low Grade Glioma IDHR132H mutant Male

  Single cell RNA-Seq Low Grade Astrocytoma IDHR132H mutant. Male, 64. Single Cell RNA seq from primary astrocytoma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005396

• SF11977 scRNA-Seq Primary GBM IDHR132H Wildtype Female

  SF11977 single cell RNA-seq IDHR132H Wild-type GBM Female, 61 Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005391

• WES, sWGS and RNA-seq of Asian breast cancer

  The dataset contains a full genomics characterization of 527 Asian breast tumours. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, shallow-whole genome sequencing at 0.1X for copy number analyses, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a HiSEQ4000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a HiSEQ4000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001006399

• glioblastoma and glioblastoma stem cells (GSCs), RNA-seq and WGS

  Characterization of glioblastoma tumors and glioblastoma stem cells (GSCs) lines via RNA-seq and/or WGS profiling.

  Study EGAS00001004395

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. FInally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Study EGAS00001004064

• RNU2-2 splicing signature RNA-Seq

  This dataset is composed of RNA-Seq samples: 49 controls which are probands without snRNA rare variants and 19 affected probands with either monoallelic or biallelic variants in RNU2-2. RNA was extracted from short-term lymphocytes cultures. RNA-Seq libraries were obtained by hybrid capture of fragmented cDNA with RNA probes from exome v8 (Agilent)

  Dataset EGAD50000002045

• RNA-Seq-Dataset

  RNA-Seq-Dataset

  Dataset EGAD00001000043