15037 results for "cancer studies using rna-seq"

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• 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

• Single_cell_resolution_of_human_CNV_body_map

  12 tissues from the warm autopsy are selected for this project. Using 10X Chromium technology we will generate ~1000 single cell/nulei genomic libraries per tissue. Each tissue will be whole genome sequenced (~2 lanes per 1000 cells) on hiseq X10. per single cell we will generate CNV profile and we investigate the level of genomic heterogenity with in tissue and across different tissues.

  Study EGAS00001003162

• 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

• GEnomics and Transcriptomics of Human INsulinoma (GETHIN)

  The Genomics and Transcriptomics of Human Insulinoma (GETHIN) The common forms of diabetes - Types 1 and 2 - ultimately result from a deficiency of insulin-producing pancreatic beta cells. The Genomics and Transcriptomics of Human Insulinoma (GETHIN) study was performed in order to identify novel approaches to inducing human pancreatic beta cells to replicate and regenerate. As a corollary, developing drugs that are able to expand human beta cell mass in people with diabetes should reverse diabetes. Unfortunately, identifying druggable pathways that can enhance human beta cell replication has been a major challenge. In 2017, there is only one class of drugs - the harmine analogues - that can induce human beta cells to replicate, and in this case, higher replication rates are desirable. Thus, identifying additional drugs and druggable pathways is a priority in diabetes research. Insulinomas are rare, benign adenomas of the pancreatic beta cell that cause excess insulin production and hypoglycemia: exactly the opposite of Types 1 and 2 diabetes. Beta cell proliferation rates in insulinomas are abnormally high. Thus, the premise for The Genomics and Transcriptomics of Human Insulinoma study is that benign human insulinomas hold the genomic and transcriptomic "recipe", and the repertoire of druggable pathways, that can be exploited to induce regeneration or replication of human beta cells in diabetes. Because, insulinomas are so rare, are almost always benign (non-malignant), and are easily resected by laparoscopic surgery, little attention has been paid to understanding the genomics or transcriptomics of insulinoma. There are at present only three published studies employing next-gen sequencing in insulinoma (PMID:24326773; PMID:25787250; and PMID:25763608). These studies contained 10, 7 and 8 insulinomas, respectively, and highlighted likely mutations in YY1 and MEN1. Our goal was to markedly expand the database and to add RNAseq to these earlier studies. The GETHIN study reports next-gen sequencing on 38 insulinomas, by far the largest series of human insulinomas subjected to next-gen sequencing (see the Selected publications section for reference). This includes paired (genomic plus tumor) whole exome sequencing on 26 human insulinomas (22 sequenced at Mount Sinai, 4 downloaded from Cao et al, PMID:24326773), and 25 sets of RNAseq from insulinomas, some of which also had paired whole exome sequencing, and some of which did not. The insulinoma RNAseq was compared to RNAseq from 22 sets of FACS-sorted normal human beta cells. Since insulinomas are so rare, the 38 insulinomas were collected by several investigators at several institutions over several decades, but most (22 whole exome sets, and all RNAseq) were sequenced at the Icahn School of Medicine at Mount Sinai in New York. The current dataset contains whole exome sequencing and RNAseq on the 11 insulinomas harvested at Mount Sinai. The four from Cao et al can be retrieved from Cao et al PMID:24326773. Fastq files from the remaining 23 insulinomas will be added as the local IRBs and Institutional Certifications are acquired. Complete patient data are provided in our Nature Communications report. Going forward, our intention is to expand this series, with the goal of sequencing 100 human insulinomas. These will be added to dbGaP as they accrue. Paired-end whole exome sequencing (mean usable sequencing depth 79X and 105X for blood and insulinoma, respectively) was performed using an Illumina HiSeq 2500. Insulinoma and sorted normal beta cell RNAseq was performed on Ribozero and polyA paired end libraries using the Illumina HiSeq 2500. Complete sequencing and bioinformatic details are provided in our Nature Communications report. The principal findings from the study are that although each insulinoma has a different set of presumptive driver mutations, the majority converge on genes that are members of the Polycomb Complex, Trithorax Complex and other epigenetic modifying enzymes. In addition, 20% of insulinomas have copy number loss or loss of heterozygosity of all or most of chromosome 11, and the majority display abnormalities in CpG methylation and imprinting control on the imprinted Chr 11 p15.5-15.4 region that contains INS, IGF2, CDKN1C, KCNQ1, and other genes involved in beta cell specification and proliferation.

  Study phs001422

• Whole Genome and Exon Capture Sequencing of Bladder Cancers

  One primary bladder cancer and paired peripheral blood sample were subjected to whole genome sequencing on an Illumina HiSeq 2000 platform. This technology was utilized to investigate the genetic basis of a durable remission of metastatic bladder cancer in a patient treated with everolimus, a drug that inhibits the mTOR (mammalian target of rapamycin) signaling pathway. Among the somatic mutations found was a loss-of-function mutation in TSC1 (Tuberous Sclerosis Complex 1), a regulator of mTOR pathway activation. Targeted sequencing using an exon capture and sequencing assay was performed on 13 tumors derived from patients on the same everolimus trial as the index patient and the sequencing data from these tumors is included. TSC1 mutation status was correlated with response to everolimus. The index patient responder tumor and peripheral blood DNA were also subjected to exon capture and sequencing.

  Study phs000535

• Genomic Alterations and Transcriptional Phenotypes in Circulating Tumor DNA and Matched Metastatic Tumor

  We analyzed a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent small cell lung cancer. We conducted cfDNA low-pass whole genome sequencing (0.1X coverage) and exome sequencing (130X coverage) in comparison with time-point matched tumor characterized using whole-exome (130X) and transcriptome sequencing. A direct comparison of cfDNA and tumor biopsy revealed that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor, but also identifies clinically relevant resistance mechanisms and cancer driver alterations not detected in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Sequencing coverage of plasma DNA fragments around transcription start sites showed distinct treatment-related changes and captured the expression of key transcription factors n the corresponding SCLC tumors.

  Study phs003689

• Pharmacogenomic landscape of patient-derived cells informs precision oncology therapy

  Outcomes of anticancer therapy vary dramatically among patients, which may be caused by the specific molecular alterations in each patient’s tumor. Precision oncology aims to apply optimal therapies for each tumor based on its molecular characteristics. We have established a resource reporting the genomic and transcriptomic profiles of 462 patient tumor-derived cells (PDCs) across 14 cancer types, together with responses to 60 targeted agents. Compared with long-term cultured cancer cell lines, PDCs better recapitulate the molecular profiles of the original tumors. Among other unreported associations, we identify molecular factors inducing resistance to EGFR inhibitors in glioblastoma, and we suggest repurposing ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, using a retrospective clinical study, we find that PDC-derived sensitivities can be used to predict patient responses.

  Study EGAS00001002515

• Spatial atlas of clonal copy number alterations in co-existing benign and malignant tissue

  Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

  Study EGAS00001006124