3796 results for "*oma"

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• Genome and transcriptome sequence data from a solitary fibrous tumors (sarcoma) patient

  Genome and transcriptome sequence data from a solitary fibrous tumors (sarcoma) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003056

• Genome and transcriptome sequence data from a liposarcoma patient

  Genome and transcriptome sequence data from a liposarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003060

• Genome and transcriptome sequence data from an adenocarcinoma of the distal esophagus patient

  Genome and transcriptome sequence data from an adenocarcinoma of the distal esophagus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003061

• Genome and transcriptome sequence data from an extraosseous osteosarcoma patient

  Genome and transcriptome sequence data from an extraosseous osteosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003062

• Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma of the palate patient

  Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma of the palate patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003065

• Genome and transcriptome sequence data from an appendiceal adenocarcinoma patient

  Genome and transcriptome sequence data from an appendiceal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003066

• Genome and transcriptome sequence data from a metastatic gastroesophageal adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic gastroesophageal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003067

• Genome and transcriptome sequence data from a pleural mesothelioma patient

  Genome and transcriptome sequence data from a pleural mesothelioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003071

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the pancreas patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the pancreas patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003073

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003076

• Genome and transcriptome sequence data from a metastatic adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003077

• Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient

  Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003086

• Genome and transcriptome sequence data from a metastatic leiomyosarcoma patient

  Genome and transcriptome sequence data from a metastatic leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003090

• Genome and transcriptome sequence data from a clear cell carcinoma of ovary patient

  Genome and transcriptome sequence data from a clear cell carcinoma of ovary patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003091

• Whole-genome and transcriptome sequencing of NUT midline carcinoma

  In this study, we sequenced three NUT midline carcinoma genomes and their transcriptomes (NMC1, NMC2 and Ty-82), and two paired normal blood samples (for NMC1 and NMC2). Whole-genome sequencing libraries were generated by PCR-free methods, and sequencing run was made in HiSeq X machines. Transcriptome (mRNA) sequencing was performed in HiSeq 2500 machines. PCR duplicates-marked, indel-realigned, and base-recalibrarted BAM files are provided in our dataset.

  Dataset EGAD00001003117

• Whole-genome sequencing (WGS) to characterise five metastatic tumours from a BRAF mutant melanoma patient who presented intrinsic resistance.

  We used WGS (Complete Genomics) to characterise five metastatic tumours from a BRAF mutant melanoma patient who presented intrinsic resistance.

  Dataset EGAD00001003120

• WGS data for medulloblastoma samples (MDT-AP)

  WGS data of medulloblastoma tumor/control pairs.

  Dataset EGAD00001003125

• WGS data for medulloblastoma samples (SJMB)

  WGS data of medulloblastoma tumor/control pairs.

  Dataset EGAD00001003126

• WGS data for medulloblastoma samples (ICGC)

  WGS data of medulloblastoma tumor/control pairs.

  Dataset EGAD00001003127

• Exome sequencing data for medulloblastoma samples

  Exome sequencing data for medulloblastoma tumor/control pairs

  Dataset EGAD00001003128

• Methylation of Ewing sarcoma tumors - RRBS (ICGC)

  RRBS data of 86 Ewing patients (French). Illumina HiSeq 2000/2500 (Fastq files available). Sheffield et al. Nat Med. 2017 Jan 30

  Dataset EGAD00001003133

• The spatial organization of intratumor heterogeneity and evolutionary trajectories of metastasis in hepatocellular carcinoma

  A dataset consisting of Multi-regional Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS) data for 54 samples from 9 patients with hepatocellular carcinoma. The dataset includes 45 tumor samples and 9 normal blood samples. Selected somatic variants were validated by Sequenom. Patients covered are: Patient 1, Patient 2, Patient 3, Patient 4, Patient 5, Patient 6, Patient 7, Patient 8, Patient 9 and Patient 10.

  Dataset EGAD00001003138

• RNA-sequencing of six Pilocytic astrocytoma tumors

  Total stranded TruSeq RNA sequencing by Illumina of six tumor samples from six cases of pediatric Pilocytic astrocytoma. The data is published in the following paper: Tomic TT, Olausson J, Wilzen A, Sabel M, Truve K, Sjogren H, Dosa S, Tisell M, Lannering B, Enlund F, Martinsson T, Aman P, Abel F. A new GTF2I-BRAF fusion mediating MAPK pathway activation in pilocytic astrocytoma. PLoS One. 2017 Apr 27;12(4):e0175638.

  Dataset EGAD00001003143

• GIS-LUNGTCR1-2016_SNP-ARRAY_CASE

  This dataset contains 61 tumors SNP-array dataset from 15 EGFR mutant lung adenocarcinoma patients.

  Dataset EGAD00010001177

• GIS-LUNGTCR1-2016_SNP-ARRAY_CONTROL

  This dataset contains 15 control SNP-array dataset from 15 EGFR mutant lung adenocarcinoma patients.

  Dataset EGAD00010001176

• Leiomyosarcoma Cancer Transcriptome Sequencing

  RNA-Seq data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 75bp paired-end protocol to a minimum mean reads of 50 million.

  Dataset EGAD00001003192

• Constit Genome Sequencing

  WGS blood data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 67 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 100bp paired-end protocol to a minimum mean coverage of 30x.

  Dataset EGAD00001003190

• Leiomyosarcoma Cancer Genome Sequencing

  WGS cancer data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 100bp paired-end protocol to a minimum mean coverage of 50x.

  Dataset EGAD00001003191

• Sequencing files for "Transcriptional Mechanisms of Resistance to Anti-PD-1 Therapy"

  Files from whole exome sequencing of 26 tumors and two matched normals from one melanoma patient. The 26 tumors include the untreated primary, cutaneous metastases and distant metastases to internal organs.

  Dataset EGAD00001003200

• Precursor lesions, clonal architecture and relapse in Wilms nephroblastoma

  Targeted resequencing at high depth (21 genes, 9 chromosomal regions): at least 4 FFPE samples per case and matched germline DNA: * 100 cases with detailed outcome data, including 15 cases with tumour relapse (515 samples) * 40 cases with matched pre-chemotherapy biopsies (240 samples) * 50 nephrogenic rests matched to above cases (50 samples) We expect a proportion (possibly 10%) of cases to be mutationally silent on the above studies, and propose to subsequently carry out integrated whole-genome, methylome and transcriptome studies on matched frozen tissue from these cases

  Dataset EGAD00001003217

• BGISeq-500 sequencing of tumour-normal sample pairs from three mesothelioma patients

  Aligned, merged and deduplicated BAM files from BGISeq-500 sequencing of six samples: matched tumour-normal pairs from three melanoma patients.

  Dataset EGAD00001003221

• HiSeqXTen sequencing of tumour-normal sample pairs from three mesothelioma patients

  Aligned, merged and deduplicated BAM files from HiSeqXTen sequencing of six samples: matched tumour-normal pairs from three melanoma patients.

  Dataset EGAD00001003222

• Poly A transcriptom sequencing of mutifocal hepatocellular carcinoma

  Poly A transcriptome sequence of mutifocal hepatocelular carcinoma

  Dataset EGAD00001003231

• Genetic profiling of mucosal melanoma

  Primary mucosal melanomas (MMs) arise from melanocytes located in mucosal membranes lining the respiratory, gastrointestinal and urogenital tracts. MMs frequently present late and have a poor prognosis; the 5-year survival rate is only 14%. MM makes up only ~1.4% of all melanomas and it is this rarity that makes knowledge of the genetic changes that contribute to its pathogenesis limited to a small number of exome/genome studies and other targeted studies. Thus to investigate the somatic alterations and mutation spectra in MM genomes, we have extracted genomic DNA from formalin-fixed, paraffin-embedded (FFPE) human MMs, and subjected them to whole exome sequencing. Given the propensity of MM to metastasize, we will also be sequencing metastatic MM lesions; primary and metastatic lesions from the same individual represent an excellent opportunity to identify potential drivers of metastasis in MM. Finally we will sequence 'normal' DNA from the same individual, where possible, to exclude germline variations.

  Dataset EGAD00001003237

• Melanoma C32 ENU Resistance to Single Agent Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Dataset EGAD00001003239

• Identification of drug resistance genes in melanoma by mRNA gene expression

  We aim to sequence the mRNA transcriptome of 22 human melanoma cell lines in biological triplicate in order to define the gene expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003244

• Identification of drug resistance genes in melanoma by small RNAs expression analysis

  We aim to sequence the small RNAs of 22 human melanoma cell lines in biological triplicate in order to define the microRNAs expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003245

• Genetic Basis of Hepatosplenic T Cell Lymphoma (HSTL)

  Whole exome sequencing of hepatosplenic T cell lymphoma (HSTL) tumors, paired normals, and cell lines, including (1) 68 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL tumor samples, (2) 20 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL paired normal samples, and (3) 2 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL cell lines.

  Dataset EGAD00001003246

• Clonal expansion of mutated cell population in bladder urothelium

  1cm biospies of from patients undergoing bladder cystectomy will be collected. The underlying muscle and stroma will be removed and the remaining epithelia dissected into small sequential areas which will be sent for ultra-deep exome sequencing using a panel of known cancer and viral genes. Sequence analysis using similar methods to Martincorena I et al (Science 2015, 348:880) will provide an idea of the somatic mutational landscape in these patient samples. Individual patient muscle samples will also be sequenced as a reference.

  Dataset EGAD00001003250

• Identification of drug resistance genes in melanoma by Exome Sequencing

  The cell lines in this study are a combination of internally sequenced (cosmic) and externally sequenced cell lines known to be “double-wild-type” (lacking BRAF and NRAS somatic mutations). These sequences were realigned in this data set for consistency.

  Dataset EGAD00001003260

• Whole-genome sequencing (WGS) data of Gastric adenocarcinoma and adjacent normal tissues

  High-coverage WGS sequencing of DNA samples from 90pairs GCs was performed on the Illumina HiSeq X Ten System.

  Dataset EGAD00001003269

• Pseudomyxoma peritonei exome sequencing vcf-files

  Dataset EGAD00001003297

• RNASeq on HiSeq platform of 95 pancreatic adenocarcinoma cases

  BAM outputs from RSEM (https://deweylab.github.io/RSEM/) analysis of RNASeq sequencing on HiSeq platform of tumour samples from 95 pancreatic adenocarcinoma cases.

  Dataset EGAD00001003298

• WGS of T-cell and NK-cell lymphoma for ICGC (NKTL-SG)

  WGS of T-cell and NK-cell lymphoma The tumor samples were sequenced with Illumina HiSeq 2500 platform and the resulting FASTq files have been uploaded.

  Dataset EGAD00001003271

• Melanoma_SNP6

  Melanoma cell lines CNV by SNP6

  Dataset EGAD00010001216

• Whole Exome and Target Sequencing Data in 75 Samples from 5 Hepatocellular Carcinoma Patients.

  Whole Exome and Target Sequencing Data in 75 Samples from 5 Hepatocellular Carcinoma Patients. The sequencing was performed by Illumina HiSeq 4000. Background and aims: Intratumoral heterogeneity (ITH) challenges identifying mutations with target therapy potential whereas circulating cell-free DNAs (cfDNAs) could reflect nearly the entire mutation spectrum in given tumors. We investigated how to minimize the limit of ITH for profiling hepatocellular carcinoma (HCC).Methods: Thirty-two multi-regional HCC samples from five patients were subjected to whole exome sequencing (WES) and targeted deep sequencing (TDS). ITH extent was measured by the average percentage of non-ubiquitous mutations (present in parts of tumor regions). Matched cfDNAs were also analyzed by WES and TDS. Profiling efficiencies of single tumor specimen and cfDNA were compared and the one better depicted mutational landscape was selected to screen therapeutic targets.Results: We found variable extents of ITH in HCCs and observed branched and parallel evolution patterns. ITH level decreased at higher sequencing depth of TDS than that measured by WES (28.1% vs 34.9%, P < 0.01) but it remained unchanged upon additional samples analyzed. TDS of single tumor specimen detected an average of 70% the total mutations in HCC. Although more mutations were detected in cfDNA under TDS than WES, an average of 47.2% total HCC mutations uncovered by cfDNA suggested tissue outperform cfDNA and the latter may serve as alternative in profiling HCC genome. Consequently, TDS of single tumor tissue in 66 patients and cfDNAs in four unresectable HCCs identified 38.6% (26/66 and 1/4) patients bearing therapeutic targets.Conclusions: TDS of single tumor specimen could largely circumvent ITH to uncover mutations indicative of target therapy in HCC.

  Dataset EGAD00001003278

• RNA data for medulloblastoma samples (ICGC)

  RNA sequencing data for 170 medulloblastoma tumor samples

  Dataset EGAD00001003279

• Genomics of enteropathy-associated T cell lymphoma (EATL)

  Whole exome sequencing of enteropathy-associated T cell lymphoma (EATL) tumors and paired normals, as well as RNA-sequencing of EATL tumors: including (1) 69 exome capture, paired-end Illumina Hiseq sequencing, BAM files from EATL tumor samples, (2) 36 exome capture, paired-end Illumina Hiseq sequencing, BAM files from EATL paired normal samples, and (3) 32 RNAseq, paired-end Illumina Hiseq sequencing, BAM files from EATL tumor samples.

  Dataset EGAD00001003284

• Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma

  Dataset EGAD00001003324

• RNA-Seq and WXS from 6 glioblastoma patients

  RNA-Seq and WXS from 6 glioblastoma patients

  Dataset EGAD00001003293

• Targeted resequencing of coding regions of ribosomal protein genes in multiple myeloma samples

  Dataset EGAD00001003302

• Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue

  Diffuse Intrinsic Pontine Glioma (DIPG) is a fatal brain cancer that arises in the brainstem of children with no effective treatment. To understand what drives DIPGs we integrated whole-genome-sequencing with methylation, expression and copy-number profiling.

  Dataset EGAD00001003305

• Glioblastoma CRISPR Screen (2017-04-27)

  This is an in vitro genome-wide CRISPR/cas9 screen in human glioblastoma stem cells, screening for genes essential for survival of these cells. These cells express cas9 and have been transfected with a guide RNA library causing gene knockouts. We will analyse the sequencing data for depletion of guide RNAs. This dataset contains all the data available for this study on 2017-04-27.

  Dataset EGAD00001003308

• Multiple Myeloma Diagnosis to Relapse study samples (2017-04-27)

  The study will investigate serial samples from the same patient taken at the time of MGUS or SMM diagnosis, and later at the time of evolution towards MM. Samples will be sequenced by whole genome along with a matched normal to obtain the highest possible amount of information toinvestigate genomic changes at disease evolution. This dataset contains all the data available for this study on 2017-04-27.

  Dataset EGAD00001003309

• RNA-sequencing of SYSCOL colorectal adenoma-carcinoma samples

  RNA-sequencing alignment for SYSCOL colorectal adenoma-carcinoma samples

  Dataset EGAD00001003318

• 25 metastatic cutaneous squamous cell carcinoma WGS VCF

  whole genome sequencing on lymph node metastases and blood DNA from 25 cSCC patients with regional metastases of the head and neck. We designed a multifaceted computational analysis at the whole genome level to provide a more comprehensive perspective of the genomic landscape of metastatic cSCC. This study contains the majority of 15 samples which are previously submitted in EGAC00001001100.

  Dataset EGAD00001009004

• Detection of cancer cell transcriptomes

  Single cell transcriptomes, generated using chromium 10X 3' sequencing, for two tumour types (AT/RT, and Ewing's sarcoma). For each individual, tumour and normal whole genome sequencing was also obtained using Illumina short read sequencing to an average depth of 30X. These data were used to validate the accuracy of a method for identifying cancer cell transcriptomes based on the allelic shift produced by copy number changes.

  Dataset EGAD00001009005

• RNA-seq from melanoma biopsies

  RNA-seq data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009059

• RRBS melanoma biopsies

  RRBS data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009060

• WES of germline, nevi, primary and metastatic Cutaneous Melanoma from patient 009 in CASVAC-0401 trial

  Samples: primary cutaneous melanoma (CM) non-associated or distal nevus (A); adjacent or CM-associated nevus (B); Primary-CM (C); and Lymph-Node Metastasis (LN-mts) (D). Whole-exome sequencing (WES) was performed in DNA extracted from the different samples (A-D) paired with the germline reference (G), processed with the Agilent SureSelect All Exon Human V5 Library in an Illumina Hiseq 4000 PE101 platform.

  Dataset EGAD00001009083

• in silico drug target prediction for melanoma

  31 samples transcriptomics to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance.

  Dataset EGAD00001009089

• BAM-file spanning the breakpoint region +/- 100kb of Ewing sarcoma

  Extracted regions from WGS of Ewing sarcoma spanning fusion breakpoints +/- 100kb for ctDNA tracking in plasma

  Dataset EGAD00001009110

• 10x Genomics Single Cell Gene Expression for SA1049AX1XB01417

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009121

• 10x Genomics Single Cell Gene Expression for SA1052DX1XB01519

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009122

• 10x Genomics Single Cell Gene Expression for SA1053EX1XB01608

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009123

• 10x Genomics Single Cell Gene Expression for SA1051AX1XB01452

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009124

• 10x Genomics Single Cell Gene Expression for SA1050FX1XB01446

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009125

• 10x Genomics Single Cell Gene Expression for SA1050DX1XB01437

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009126

• 10x Genomics Single Cell Gene Expression for SA1052BX1XB01516

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009127

• 10x Genomics Single Cell Gene Expression for SA1053FX1XB01611

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009128

• 10x Genomics Single Cell Gene Expression for SA1093CX1XB01917

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1093 passage 1

  Dataset EGAD00001009129

• 10x Genomics Single Cell Gene Expression for SA1091AX1XB01790

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1091 passage 1

  Dataset EGAD00001009130

• 10x Genomics Single Cell Gene Expression for SA1053BX1XB01603

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009131

• 10x Genomics Single Cell Gene Expression for SA1096AX1XB02034

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

  Dataset EGAD00001009132

• 10x Genomics Single Cell Gene Expression for SA1051DX1XB01482

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009133

• 10x Genomics Single Cell Gene Expression for SA1052JX1XB01535

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009134

• 10x Genomics Single Cell Gene Expression for SA1181AX1XB02700

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1181 passage 1

  Dataset EGAD00001009135

• 10x Genomics Single Cell Gene Expression for SA1096CX1XB02039

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

  Dataset EGAD00001009136

• 10x Genomics Single Cell Gene Expression for SA1096BX1XB02037

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

  Dataset EGAD00001009137

• 10x Genomics Single Cell Gene Expression for SA1162BX1XB03004

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1162 passage 1

  Dataset EGAD00001009138

• 10x Genomics Single Cell Gene Expression for SA1096A

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1096

  Dataset EGAD00001009139

• 10x Genomics Single Cell Gene Expression for SA1049CX1XB01422

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009140

• 10x Genomics Single Cell Gene Expression for SA1050EX1XB01442

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009141

• 10x Genomics Single Cell Gene Expression for OV2295

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295

  Dataset EGAD00001009143

• 10x Genomics Single Cell Gene Expression for OV2295(R2)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295(R2)

  Dataset EGAD00001009144

• 10x Genomics Single Cell Gene Expression for SA1184SA

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1184

  Dataset EGAD00001009145

• 10x Genomics Single Cell Gene Expression for SA1047BX1XB01022

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1047 passage 1

  Dataset EGAD00001009146

• 10x Genomics Single Cell Gene Expression for TOV2295(R)

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line TOV2295(R)

  Dataset EGAD00001009158

• Exome-sequencing of H3-K27M glioma.

  Fastq or bam files are deposited for 28 patient H3-K27M diffuse midline gliomas. UMPEDD65 was profiled by targeted exome-sequencing using the TSO500 Illumina assay, while all other samples were sequenced by whole-exome sequencing.

  Dataset EGAD00001009269

• Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: WES and RNA sequencing of biopsies from a Phase I Trial

  This dataset contains whole-exome and RNA sequencing of biopsies obtained from patients enrolled in a phase I clinical trial investigating the UV1 vaccine in combination with pembrolizumab in patients with advanced melanoma.

  Dataset EGAD00001011074

• Quantitative Exploratory Proteomics Analysis of Glioblastoma in Initial and Recurrent Tumors

  Shotgun Proteomics; Glioblastoma samples from 11 patients were obtained at initial and recurrent tumor stages. Proteins were extracted, identified and quantified via tandem mass spectrometry based on a TMT isobaric labelling approach. Quatitative proteomics reveals 146 differentially abundant proteins using a patient-matched statistical modelling. Analysis of proteolytic processing reveals differential proteolytic patterns in recurrent tumors. Proteogenomics reveals the presense of 30 single-amino acid variants present in glioblastoma tumor and 1 of those as increased in recurrent tumor.

  Dataset EGAD00010002350

• Neuroblastoma WGS samples used for analysis of telomeric sequences

  Paired-end WGS data of 10 neuroblastoma patient samples (5 obtained at diagnosis and 5 matched blood samples as controls) used for analysis of telomeric content and sequence composition. Mean coverage is 11-65x per sample. The remaining patient samples of the dataset can be found under accession numbers EGAS00001001308 and EGAS00001005424 and mappings of the patients IDs in the supplementary material of the publication.

  Dataset EGAD00001009289

• SPECIAL: scRNA-seq

  Tumour biopsies were collected from twenty-three patients (46 samples) (of which 20 were matched) with locally advanced or metastatic melanoma (stage IIIB – stage IV). Library construction was done either with the Chromium Single Cell 3ʹ GEM, Library & Gel Bead Kit v3 (n = 16; 10x genomics, Cat#1000092) or the Chromium Single Cell A Chip Kit and 5’ Library & Gel Bead Kit (10x genomics, Cat#1000014). All libraries were sequenced on Illumina NextSeq, HiSeq4000 or NovaSeq6000 until sufficient saturation was reached (60% on average). The reference genome used in this study was GRCh38.

  Dataset EGAD00001009291

• WTS and WES of Renal Cell Carcinoma Tumors From a Phase III Anti-Angiogenic Adjuvant Therapy Trial

  Archival de-identified formalin-fixed paraffin-embedded RCC tumor tissue blocks from nephrectomy or tumor biopsy were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (ACE version 3; Illumina NovaSeq), samples were profiled using Personalis ACE Cancer Exome (Personalis, Inc, Menlo Park, CA) Whole-transcriptome profiles were generated by RNA-seq (Accuracy and Content Enhanced (ACE) version 3; Illumina NovaSeq) using Personalis ACE Cancer Transcriptome (Personalis, Inc, Menlo Park, CA ) Of the 615 patients in the intent-to-treat population in S-TRAC trial, 193 individual specimens were available for molecular profiling, of which 171 (27.8%) (sunitinib, n = 91; placebo, n = 80) returned results for the WES analysis, and 133 (21.6%) (sunitinib, n = 72; placebo, n = 61) returned results for the GES analysis. Of the 138 WTS samples with data, replicates for two patients were summarized by median expression, and three samples were excluded from the final analysis due to low counts.

  Dataset EGAD00001009294

• Single Cell Genome Sequence for DLP+ library A95650A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1052J passage 1 on DLP+ library A95650A

  Dataset EGAD00001009325

• extended cohort of single cell RNAseq data of lung adenocarcinoma

  extended cohort of single cell RNAseq data of lung adenocarcinoma

  Dataset EGAD00001009300

• Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA-sequencing the pros and cons

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Dataset EGAD00001009301

• Single Cell Genome Sequence for DLP+ library A95652B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049A passage 1 on DLP+ library A95652B

  Dataset EGAD00001009327

• Single Cell Genome Sequence for DLP+ library A95629B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1051D passage 1 on DLP+ library A95629B

  Dataset EGAD00001009320

• Single Cell Genome Sequence for DLP+ library A95634A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1091A passage 1 on DLP+ library A95634A

  Dataset EGAD00001009323