15174 results for "cancer studies using rna-seq"

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• Capture-based Methods for Transcriptomic Profiling of FFPE material

  This dataset includes all FASTQ files for 11 samples where different capture-based methods for transcriptome profiling have been tested. Specifically, we have the 'traditional' RNA-seq experiment with fresh frozen (FF) material, and 3 different capture methods for the matching formalin-fixed paraffin-embedded samples: Agilent (AGI), IDT, and Twist Biosciences (TBS). In total, there are 43 samples with paired-end FASTQ files (1 sample did not have sufficient material to test all methods). Samples are identified by the R01-R11 IDs with a suffix that indicates the capture method used (or FF for fresh frozen).

  Dataset EGAD00001007655

• Sequencing data associated with Smith et al, Acta Neuropathologica, 2020 (PMID: 32519082)

  The dataset includes 144 BAM files of WGS, WES, and RNA-seq data from primary and PDOX samples analyzed in Smith et al, Acta Neuropathologica, 2020 (PMID: 32519082).

  Dataset EGAD00001007980

• Phylogenetic analysis of treatment-naive metastases using whole exome and genome sequencing data

  Metastases are responsible for the majority of cancer related deaths and are often difficult to treat successfully. Even for metastases that occur subsequent to treatment, we do not know whether such relapsing metastases were originally heterogeneous or if the observed heterogeneity is a consequence of therapy. Given that treatment can influence evolutionary dynamics by selecting resistant clones, imposing bottlenecks on cancer cell populations, and even inducing novel somatic mutations, characterizing the prior standing variation among metastases remains an important goal to predict initial therapeutic response. To quantify the heterogeneity at clinical presentation of advanced disease, we surveyed the literature for patients in which at least two treatment-naïve metastases underwent genome/exome-wide sequencing. Across all cancer types surveyed, only 18 subjects were found to fulfill this requirement. Including previously unpublished data from two subjects, we analyzed data of 74 untreated metastases and inferred cancer phylogenies. Putative driver gene mutations were acquired at a 2-fold higher rate on the trunk of all metastases than on branches. Mutations in driver genes along the trunk were strongly enriched for predicted functional consequences. Using a stochastic mathematical model, we find that driver gene heterogeneity among metastases mostly occurs in slowly growing cancers for highly advantageous driver gene mutations. Seeding efficiency only weakly affects the heterogeneity among metastases. These results explain why functional driver gene heterogeneity is uncommon in advanced disease prior to treatment, thus providing optimism for future therapies that depend on mutations across metastases.

  Study EGAS00001002777

• CD4+ cell transcriptional profiling by RNA sequencing

  We use next generation sequencing to investigate the different transcriptomes of closely related CD4+ T-cells from healthy human donors to elucidate the genetic programs that underlie their specialized immune functions. Six cell types were included: Regulatory T-cells (CD25hiCD127low/neg with >95% FOXP3+ purity), regulatory T-cells activated using PMA/ionomycin, CD25-CD45RA+ ('naive' helper T-cells), CD25-CD45RO+ ('memory' helper T-cells), activated Th17 cells (>98% IL17A+ purity) and activated IL17-CD4+ T-cells (called 'ThPI'). Poly-T capture beads were used to isolate mRNA from total RNA, and fragment sizes of ~200 were sequenced from both ends on Illumina's genome analyzer. We confirm many of the canonical signature genes of T-cell populations, but also discover new genes whose expression is limited to specific CD4 T-cell lineages, including long non-coding RNAs. Additionally, we find that genes encoded at loci linked to multiple human autoimmune diseases are enriched for preferential expression upon T-cell activation, suggesting that an aberrant response to T-cell activation is fundamental to pathogenesis.

  Study phs000626

• NGS sequencing data of archival FFPE tumor tissue

  The aim of the study was to identify mutations in key ovarian cancer genes (BRCA1, BRCA2,TP53, PTEN, ATM, ATR, NF1) in archival fresh-frozen paraffin embedded (FFPE) tumor tissue from platinum-resistant ovarian cancer patients enrolled in the phase I/II GANNET53 clinical trial. From the enrolled 133 patients, archival FFPE tissue was available and DNA was extracted thereof. The DNA samples passing quality control (n=118) were NGS sequenced using the SureSeq™ Ovarian Cancer Panel and the NGS Library Preparation kit (Oxford Gene Technology), which covers all exons of 7 key ovarian cancer genes for single nucleotide variants (SNV) and indels, and the Illumina MiSeq platform. For the paired-end runs, one Read 1 (R1) and one Read 2 (R2) FASTQ file were created for each sample. FASTQ files were compressed and created with the extension \.fastq.gz.

  Dataset EGAD50000001371

• OV04 chemo prediction

  Predicting resistance to chemotherapy using chromosomal instability signatures Joe Sneath Thompson1,2,*, Laura Madrid2,*, Barbara Hernando1,*, Carolin M. Sauer3, Maria Vias3, Maria Escobar-Rey1,2, Wing-Kit Leung2,3, Diego Garcia-Lopez2, Jamie Huckstep3, Magdalena Sekowska3, Karen Hosking4,5, Mercedes Jimenez-Linan5,6, Marika A. V. Reinius3,5,6, Abhipsa Roy2, Omar Abdulle2, Justina Pangonyte3, Harry Dobson2, Amy Cullen2,3, Dilrini De Silva2, David Gómez-Sánchez1,7, Marina Torres1, Ángel Fernández-Sanromán1, Deborah Sanders3, Filipe Correia Martins3,5,6, Ionut-Gabriel Funingana3,4,5, Giovanni Codacci-Pisanelli3,4,8, Miguel Quintela-Fandino1, Florian Markowetz2,3,4, Jason Yip2, James D. Brenton2,3,4,5,6, Anna M. Piskorz#,2,3, Geoff Macintyre#,1,2 1 Spanish National Cancer Research Centre (CNIO), Madrid, Spain 2 Tailor Bio Ltd, Cambridge, UK 3 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK 4 Department of Oncology, University of Cambridge, Cambridge, UK 5 Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK 6 Cancer Research UK Major Centre - Cambridge, University of Cambridge, Cambridge, UK 7 H12O-CNIO Lung Cancer Clinical Research Unit, Health Research Institute Hospital 12 de Octubre (imas12), Madrid, Spain 8 University of Rome "la Sapienza", Rome, Italy

  Dac EGAC50000000612

• National Cancer Institute (NCI) Waldenstrom Macroglobulinemia Genome-wide Association Study

  A discovery GWAS is performed, consisting of genotyping 221 Waldenstrom macroglobulinemia (WM) cases on the Illumina Omni Express 12v1.1 platform and analyzing the genotypes together with those of 3798 previously genotyped controls in a model taking into account gender, age, and principal components of ancestry (i.e., significant eigenvectors). Directly genotyped data are supplemented by imputation using the Haplotype Reference Consortium.

  Study phs001284

• Clonal hematopoiesis in rheumatoid arthritis

  To evaluate the clinical impact of CHIP on RA phenotypes and outcomes, we collected DNA samples from four distinct RA patient cohorts of newly diagnosed, previously untreated, RA patients without hematologic malignancies. A total of 573 RA patients and 163 healthy controls underwent targeted NGS sequencing using a sequencing panel consisting of 65 genes recurrently mutated in myeloid malignancies.

  Study EGAS50000000890

• TSO500 sequencing of ovarian tumour samples

  This dataset consists of 8 ovarian cancer DNA sample libraries prepared using the Illumina TruSight Oncology 500 Kit (Illumina, cat no. 20076480) following the manufacturer's protocol. Library quality and quantity were assessed with High Sensitivity/D5000 Screentape Assay (Agilent Technologies, cat no. 5067-5592) on 4150/4200 Tapestation Quant-IT/Qubit dsDNA HS (Qiagen, cat no. Q32851) assay according to the supplier’s recommendations. Libraries were then pooled together in equal ratios and sequenced using PE-150bp mode on NovaSeq S1 flow cell 300 cycles kit (Illumina, 20028317) or S4 flow cell 300 cycles kit (Illumina, 20028312) aiming for 100 million reads per sample. The dataset contains sequence reads in fastq file format.

  Dataset EGAD50000001451

• Adenoma to Carcinoma transition in Colorectal Cancer

  Although colorectal carcinoma development is well accepted as a step-wise and sequential process, it is commonly investigated using non-sequential adenoma and carcinoma samples. Here we compared molecular pathologies of the earliest carcinoma stages with those in their adenomatous precursors to unravel common and divergent genetic traits between matched neoplasms with a shared evolutionary history but different contemporary biology.

  Study EGAS00001003468