7790 results for "canc*"

in 29.89 milliseconds.

• Validation for human early embryonic substitutions (2015_09_03)

  PCR and MiSeq validation for early embryonic substitution candidates from 400 Breast cancer patients. This dataset contains all the data available for this study on 2015-09-03.

  Dataset EGAD00001001600

• GBM-ZEB1: RNA sequencing of parental tumors used for cell line generation

  This dataset contains RNA sequencing raw data from four parental tumors that were used for classification of gene expression subtypes (Verhaak, Cancer Cell 2010) using ssGSEA.

  Dataset EGAD00001001627

• Deep sequencing of 151 cancer genes

  Deep sequencing of 151 cancer genes in 6 synchronous CRC of 3 patients

  Dataset EGAD00001001870

• Whole genome sequencing of 64 HER2-Positive Breast Cancer

  Whole genome sequencing of 64 HER2-Positive Breast Cancer

  Dataset EGAD00001001844

• Integrated clinical, whole genome, and transcriptome analysis of multisampled lethal metastatic prostate cancer

  We report the first combined analysis of whole genome sequence, detailed clinical history, and transcriptome sequence of multiple prostate cancer metastases in a single patient (A21). Whole genome and transcriptome sequence was obtained from 9 anatomically separate metastases, and targeted DNA sequencing was performed in cancerous and noncancerous foci within the primary tumor specimen removed 5 years prior to death. Transcriptome analysis revealed increased expression of AR-regulated genes in liver metastases that harbored an AR p.L702H mutation, suggesting a dominant effect by the mutation despite being present in only 1 of an estimated 16 copies per cell. The metastases harbored several alterations to the PI3K/AKT pathway, including a clonal truncal mutation in PIK3CG and present in all metastatic sites studied. The list of truncal genomic alterations shared by all metastases included homozygous deletion of TP53, hemizygous deletion of RB1 and CHD1, and amplification of FGFR1. If the patient were treated today given this knowledge, use of second-generation androgen-directed therapies, cessation of glucocorticoid administration, and therapeutic inhibition of the PI3K/AKT pathway or FGFR1 receptor could provide personalized benefit. Three previously unreported truncal clonal missense mutations (ABCC4 p.R891L, ALDH9A1 p.W89R, and ASNA1 p.P75R) were expressed at the RNA level and assessed as druggable. The truncal status of mutations is critical for actionability, and can only be determined through analysis of multiple sites of metastasis. Our findings suggest that a large set of deeply analyzed cases could serve as powerful guide to more effective prostate cancer basic science and personalized cancer medicine clinical trials.

  Dataset EGAD00001001869

• Fastq data for ChIP-Seq (H3K9me3) assays for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Fastq data for ChIP-Seq (H3K9me3) assays assay for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001408

• Fastq data for ChIP-Seq (H3K4me3) assays for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Fastq data for ChIP-Seq (H3K4me3) assays assay for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001407

• Fastq data for ChIP-Seq (Input) assays for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Fastq data for ChIP-Seq (Input) assays assay for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001409

• ESGI-Exome sequencing in Circulating Tumor Cells to determine therapy related markers

  The objectives of this project are the identification of markers related to cancer therapy resistance in the blood of breast cancer patients and to study the genetic changes in cancer cells during this development of resistance. Whole genome amplified DNA from Circulating Tumor Cells (CTCs), selected during the course of systemic treatment from blood of metastatic breast cancer patients, will be exome sequenced . The patients selected for this study did not respond to therapy.

  Dataset EGAD00001001425

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases (2015-07-02)[MOSAIC]

  Systematic next generation sequencing efforts are beginning to define the genomic landscape across a range of primary tumours, but we know very little of the mutational evolution that contributes to disease progression. We therefore propose to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in a cohort of matched primary and metastatic colorectal cancers, and additionally to explore the extent to which those mutations identified as recurrent in the metastatic setting are able to subvert normal biological processes using both genetically engineered mouse models and established cancer cell lines. This study will enable us to define to what extent primary tumour profiling can capture the biological processes operative in matched metastases as well as the significance of intratumoural heterogeneity. This dataset contains all the data available for this study on 2015-07-02.

  Dataset EGAD00001001426

• GIST SSGXVIII trial targeted gene sequencing

  Targeted cancer gene sequencing of samples enrolled in the SSGXVIII trial from Finland.

  Dataset EGAD00001001427

• Anaplastic Meningioma V3 cancer gene panel

  Investigation into causal genes underlying anaplastic meningioma

  Dataset EGAD00001001430

• Aligned whole genome bisulfite sequencing data for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  Aligned whole genome bisulfite sequencing data for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001001435

• Colorectal organoids and tumour tissue

  Whole genome sequencing of single cell derived organoids from normal colon tissue and colorectal cancer.

  Dataset EGAD00001001447

• MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells

  Despite the established role of the transcription factor MYC in cancer, little is known about the impact of a new class of transcriptional regulators, the long non-coding RNAs (lncRNAs), on the way MYC is able to influence cellular transcriptome. To this aim we have intersected RNA-sequencing data from two MYC-inducible cell lines and from a cohort of 91 mature B-cell lymphomas carrying, or not carrying, genetic variants resulting in MYC over-expression. By this approach, we identified 13 lncRNAs differentially expressed in IG-MYC-positive Burkitt lymphoma and regulated in the same direction by MYC in the model cell lines. Among them we focused on a lncRNA that we named MINCR, for MYC-Induced long Non-Coding RNA, showing a strong correlation with MYC expression in MYC-positive lymphomas and also in pancreatic ductal adenocarcinomas. To understand its cellular role we performed RNA interference (RNAi) experiments and found that MINCR knock-down is associated with a reduction in cellular viability, due to an impairment in cell cycle progression. Differential gene expression analysis following RNAi showed a strongly significant enrichment of cell cycle genes among the genes down-regulate following MINCR knock-down. Interestingly these genes are enriched in MYC binding sites in their promoters, suggesting that MINCR acts as a modulator of MYC transcriptional program. Accordingly, following MINCR knock-down, we observed a reduction in the binding of MYC to the promoters of selected cell cycle genes. Finally we provide evidences that down-regulation of AURKA, AURKB and CTD1 may explain the reduction in cellular proliferation observed upon MINCR knock-down. We therefore suggest that MINCR is a newly identified player in the MYC transcriptional network able to control the expression of cell cycle genes.

  Dataset EGAD00001001441

• Chugai colorectal organoid sequencing

  Genomic and transcriptomic characterization of drug-resistant colon cancer stem cell lines.

  Dataset EGAD00001001446

• Feasibility of targeted capture sequencing in FFPE cancer specimens 2

  Testing the feasibility of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using targeted pulldown capture prior to Illumina sequencing.

  Dataset EGAD00001001448

• ChIP sequencing in Cancer Cell Lines

  The project is to evaluate the genomic binding sites of the histone demethylase JARID1C. This gene was recently identified in CGP as a novel recessive cancer gene in human renal cell carcinoma.

  Dataset EGAD00001001453

• RNAseq - Colorectal organoids and tumoroids (2015-08-05)

  Transcriptome sequencing of tumour tissue, adjacent normal tissue and derived organoids/tumoroids from colorectal cancer. This dataset contains all the data available for this study on 2015-08-05.

  Dataset EGAD00001001459

• Transcriptome profiling for Korean Early Onset Gastric Cancer

  To identify transcriptome profile in this unique subset of gastric cancers, RNA-seq analyses were performed using frozen cancer tissue. Adjacent normal tissue of the same patients were used in differently expressed gene selection and fusion gene prediction.

  Dataset EGAD00001002187

• Barcelona kids with melanoma

  This set of samples is composed of eight young people (7-16 years old) that have developed melanoma with first-degree relatives that have also developed cancer, which suggests a genetic component to their disease. Here we want to sequence these samples in order to find the causative mutations. As these samples do not carry any of the high-penetrance mutations known to date, finding the genes(s) responsible will offer new insights into the genetic mechanisms underlying predisposition to melanoma.

  Dataset EGAD00001002198

• NSCCG CRC WES

  1006 Familial early onset gemrline CRC patients sequenced by the Molecular and Population Genetics group of the Institute of Cancer Research

  Dataset EGAD00001002204

• Low coverage WGS from plasma DNA

  Low coverage whole genome sequencing plasma DNA from 50 male, 54 female non-cancer donors. For the analysis of nucleosomal positioning all data from the non-cancer controls were merged. Furthermore, two patients with metastasized breast cancer were sequenced on a NextSeq with higher depth.

  Dataset EGAD00001002215

• RNA-Seq of tumors

  RNA-Seq on an Ion Torrent Proton of corresponding tumor material of two metastasized breast cancer patients (Breast7, Breast13).

  Dataset EGAD00001002216

• Paired-end sequenced plasma DNA samples

  Merged file of low-coverage WGS from 179 plasma DNA samples from non-cancer controls and cancer patients for assessment of size distribution of plasma nuclear DNA fragments.

  Dataset EGAD00001002217

• Identification of drug resistance genes in cancer cell lines by insertional mutagenesis

  Our aim is to identify genes involved in resistance to anti-cancer therapies. In order to do this we have taken advantage of a lentiviral vector (LV)-based insertional mutagen to mutagenize cancer cell lines. LV-transduced cell lines were then treated with anti-cancer therapies and the emergence of resistant clones scored. DNA from pools of resistant clones was collected, subjected to custom capture by baits designed against the LV sequence, and then sequenced to identify the LV-genomic junction. We hope that the identification of recurrently targeted genes in resistant cell population will allow us to identify genes that mediate drug resistance.

  Dataset EGAD00001002207

• PSCP_mutation analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shred by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002231

• The genetic evolution of precursor lesions in pancreatic cancer

  Mapping genetic evolution of pancreatic cancer precursor lesions such as IPMNs and PanINs.

  Dataset EGAD00001002232

• Direct transcriptional consequences of somatic mutation in breast cancer

  The disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription; co-ordinated secondary alterations in transcriptional pathways; and increased transcriptional noise. To catalogue the rules governing how somatic mutation Overall, 59% of 6980 exonic substitutions were expressed. Compared to other classes, nonsense mutations showed lower expression levels than expected with patterns characteristic of nonsense-mediated decay. 14% of 4234 genomic rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusion transcripts and premature poly-adenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes may drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data therefore reveals the rules by which transcriptional machinery interprets somatic mutation.

  Dataset EGAD00001002236

• Direct transcriptional consequences of somatic mutation in breast cancer

  The disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription; co-ordinated secondary alterations in transcriptional pathways; and increased transcriptional noise. To catalogue the rules governing how somatic mutation Overall, 59% of 6980 exonic substitutions were expressed. Compared to other classes, nonsense mutations showed lower expression levels than expected with patterns characteristic of nonsense-mediated decay. 14% of 4234 genomic rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusion transcripts and premature poly-adenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes may drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data therefore reveals the rules by which transcriptional machinery interprets somatic mutation.

  Dataset EGAD00001002237

• PSCP_bisulphite analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. 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 EGAD00001002235

• ChIP-Seq (H3K4me3, H3K4me1, H3K9me3, H3K27ac, H3K27me3, H3K36me3, Input) data for HL60 cell line generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency.

  ChIP-Seq (H3K4me3, H3K4me1, H3K9me3, H3K27ac, H3K27me3, H3K36me3, Input) data for HL60 cell line generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency.

  Dataset EGAD00001002238

• June 2016 data update for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  June 2016 data update (bam/fastq for CEMT0062, CEMT0068, CEMT0072, CEMT0086, CEMT0087 ChIP-Seq and RNA-Seq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001002239

• A case of colorectal cancer with ERBB2 c.2264T>C (p.Leu755Ser) mutation

  This data set contains next generation sequencing (NGS) data of two serial tumor samples (primary and a metastasis) from a patient with colorectal cancer showing an ERBB2 c.2264T>C (p.Leu755Ser). NGS was performed using the Illumina TruSeq Amplicon Cancer Panel (TSACP, Illumina) covering 212 amplicons in 48 cancer associated genes on the Illumina MiSeq sequencing platform. The dataset contains two BAM files.

  Dataset EGAD00001002252

• Trimmed bam-files from whole genome sequencing data from plasma DNA

  Single-end sequencing data (trimmed to 60bp) of 104 plasma samples from donors without tumors (male=50; female=54) were merged and used to establish coverage profiles around the TSS and to establish a gene expression prediction algorithm. Dataset includes merged alignements of low coverage whole genome sequencing from plasma DNA from 50 male, 54 female non-cancer donors. Furthermore, 2 patients with metastasized breast cancer were sequenced on a NextSeq with higher depth.

  Dataset EGAD00001002254

• Breast Cancer -Very young women with ER+ tumor

  Corresponding data set is composed of whole exome sequencing of Korean ER positive breast cancer under 35. This set provides 100 alignment files from normal-tumor paired whole exome sequencing of 50 patients. This is a part of total project data set.

  Dataset EGAD00001002256

• Whole exome sequencing of Finnish hereditary breast cancer families

  Dataset contains Whole Exome Sequencing(WES) data from 37 individuals as aligned bam-files. The reads have been aligned using bowtie2 to human genome hg19 build.

  Dataset EGAD00001002133

• A somatic reference standard for cancer genome sequencing.

  Paired PCR-free whole genome sequencing data of a matched metastatic melanoma cell line (COLO829) and normal across three lineages and across separate institutions, with independent library preparations, sequencing, and analysis. The data was generated with mean mapped coverages of 99X for COLO829 and 103X for the paired normal across three institutions. Overall, common events include >35,000 point mutations, 446 small insertion/deletions, and >6,000 genes affected by copy number changes. We present this reference to the community as an initial standard for enabling quantitative evaluation of somatic mutation pipelines across institutions.

  Dataset EGAD00001002142

• SCNA-Seq of plasma DNA samples

  Low coverage whole genome sequencing for the identification of somatic copy number alterations (SCNA) and focal amplification mapping in plasma DNA of prostate cancer patients

  Dataset EGAD00001002149

• Illumina_RNA_MET

  RNAseq on Illumina HiSeq2000/2500 of colorectal cancer metastasis sample

  Dataset EGAD00001002072

• Illumina_RNA_MET-CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002073

• Illumina_RNA_MET-CELL-GFP

  RNAseq on Illumina HiSeq2000/2500 of WNT reporter of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002074

• Illumina_RNA_MET-CELL-XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002075

• Illumina_RNA_MET-XEN

  RNAseq of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002076

• Illumina_RNA_T

  RNAseq on Illumina HiSeq2000/2500 of colorectal cancer primary tumor sample

  Dataset EGAD00001002077

• Illumina_RNA_T-CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002078

• Illumina_RNA_T-CELL-XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002079

• Illumina_RNA_T-XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002080

• Illumina_RNA_T-XEN-CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002081

• Illumina_WGS_MET

  Whole-genome sequencing on Illumina HiSeq2000/2500 of colorectal cancer metastasis sample

  Dataset EGAD00001002084

• Illumina_WGS_MET-CELL

  Whole-genome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002085

• Illumina_WGS_MET-CELL-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002086

• Illumina_WGS_MET-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002087

• Illumina_WGS_T

  Whole-genome sequencing on Illumina HiSeq2000/2500 of colorectal cancer primary tumor sample

  Dataset EGAD00001002088

• Illumina_WGS_T-CELL

  Whole-genome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002089

• Illumina_WGS_T-CELL-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002090

• Illumina_WGS_T-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002091

• Illumina_WGS_T-XEN-CELL

  Whole-genome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002092

• Illumina_WXS_MET

  Whole-exome sequencing on Illumina HiSeq2000/2500 of colorectal cancer metastasis sample

  Dataset EGAD00001002095

• Illumina_WXS_MET-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002096

• Illumina_WXS_MET-CELL-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002097

• Illumina_WXS_MET-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002098

• Illumina_WXS_T

  Whole-exome sequencing on Illumina HiSeq2000/2500 of colorectal cancer primary tumor sample

  Dataset EGAD00001002099

• Illumina_WXS_T-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002100

• Illumina_WXS_T-CELL-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002101

• Illumina_WXS_T-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002102

• Illumina_WXS_T-XEN-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002103

• Solid_WXS_MET

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer metastasis sample

  Dataset EGAD00001002105

• Solid_WXS_MET-XEN

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002106

• Solid_WXS_T

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer primary tumor sample

  Dataset EGAD00001002107

• Sequencing data for Murtaza et al. Nature Communications 2015 (doi:10.1038/ncomms9760)

  Exome and targeted amplicon sequencing data for tumor, germline and plasma samples from a patient with metastatic breast cancer.

  Dataset EGAD00001002108

• Chromatin accessibility maps of chronic lymphocytic leukemia identify subtype-specific epigenome signatures and transcription regulatory networks

  Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research.

  Dataset EGAD00001002110

• Targeted Sequencing of 173 genes

  Targeted sequencing of 173 genes in 2433 primary breast tumours. Data includes 2433 tumour samples, 523 adjacent normal (breast) samples and 127 blood samples. Libraries were prepared with Illumina's Nextera custom enrichment kit targetting all the exons of the most frequently mutated breast cancer genes. Libraries were multiplexed (48 libraries per lane) and sequenced on Illumina HiSeq 2000 (100bp paired-end reads). Somatic mutations were calling with a custom pipeline. We identified 40 mutation-driver (Mut-driver) genes, and determined associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assessed the clonal states of Mut-driver mutations, and estimated levels of intra-tumour heterogeneity using mutant-allele fractions. The results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies. Referece: Pereira et al. (2016) The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nature Communications

  Dataset EGAD00001002115

• Whole-genome sequencing data for inflammatory breast cancer patients

  This study reports the whole-genome sequencing data for 20 inflammatory breast cancer patients, each of whom has one normal blood sample and one breast tumor sample. Overall, there are 40 files included in this study, in the format of BAM.

  Dataset EGAD00001005749

• Colon Cancer Organoid Cultures and Tumors RNASeq Data

  Four micrograms of total RNA was used for cDNA library construction using the KAPA Stranded mRNA-Seq Kit (KR0960-v3.15), following manufacturer's protocol. The adaptor-ligated libraries were enriched by 6 cycles of polymerase chain reaction (PCR). Libraries were sequenced using the Novaseq 6000 with paired end 151bp reads.

  Dataset EGAD00001005753

• Colon Cancer Organoid Cultures and Tumors Whole Exome Sequencing Data

  Five hundred fifty nanograms of genomic DNA were input for library preparation after fragmentation by Covaris S2, following the KAPA Hyper Prep Kit (KR0961-V1.14) protocols, with selection for a library size range of 250-450 bp. Three hundred nanograms per library DNA each from 12 samples were normalized and combined into a single pool for exome capture using the xGen Lockdown Probes and Reagents based on their standard protocols

  Dataset EGAD00001005754

• Colon Cancer Organoid Cultures and Tumors Whole Genome Sequencing Data

  Five hundred nanograms of genomic DNA was fragmented by Covaris S2, the fragmented DNAs were performed end-repair, A-tailing at the 3 prime end, adaptors ligation with an IDT dual-indexed UMI adaptor system at the terminal ends. The adapter ligated library with size range 300-750bp were selected by dual-SPRI method. Twenty percent of the size selected PCR-free libraries were enriched by 5 PCR cycles prior to library size assessment by Bioanalyzer Fragment Analyzer. The PCR-free libraries were quantified by qPCR.The PCR-free libraries were denatured and diluted to optimal concentration. Illumina NovaSeq 6000 was used for Pair-End 151bp sequencing.

  Dataset EGAD00001005759

• Hyperpolarized 13C MRI in breast cancer

  Transcriptomics for samples obtained from six patients (MBR01, MBR03, MBR05, MBR07, MBR10, MBR11)

  Dataset EGAD00001005760

• cfDNA analysis reveals relation of POLR1D amplification to bevacizumab resistance in colorectal cancer patients

  Bevacizumab is an approved anti-angiogenic drug for patients with metastasized colorectal cancer (mCRC) targeting VEGF. The survival benefit of anti-VEGF therapy in mCRC patients is limited to a few months and acquired resistance mechanisms are greatly unknown. Using plasma DNA, we studied the evolution of tumor genomes in a cohort of patients with mCRC (n=150) and observed a recurrent focal amplification (8.7% of cases) on chromosome 13q12.2. Analysis of TCGA data (n=619) suggested an association with later stages, which we confirmed by longitudinal plasma analyses. We defined the minimally amplified region and studied the mechanistic consequences of copy number gain of the involved genes. The amplification of one gene, POLR1D, impacted cell proliferation, resulting in upregulation of VEGFA, an important regulator of angiogenesis which has been implicated in the resistance to bevacizumab. In several patients, we observed the emergence of this 13q12.2 amplicon under bevacizumab treatment, which was invariably associated with evolution of therapy resistance. Hence, we describe a novel resistance mechanism against a widely applied treatment in mCRC patients which will impact clinical management .

  Dataset EGAD00001005761

• Genome and transcriptome sequence data from a pancreatic cancer patient

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

  Dataset EGAD00001005762

• Exome and Transcriptome for gastric cancer gene-drug analysis

  Newly generated 52 gastric tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing

  Dataset EGAD00001005766

• Multi-region sequencing of a RET fusion positive cancer patient

  Deep WGS sequencing (160x) of 2 different sites of disease of a patient with a RET fusion positive cancer. Amplicon sequencing of 19 other sites of the same patients for the RET fusion.

  Dataset EGAD00001005776

• Sequencing files for "The Evolutionary Origins of Recurrent Pancreatic Cancer."

  Files from DNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues. Sequencing files include those derived from whole exome sequencing as well as MSK-IMPACT sequencing.

  Dataset EGAD00001005779

• RNA-seq from PDAC samples

  Bam and fastq files from RNA-seq of PDAC samples described in Transcription phenotypes of pancreatic cancer are driven by genomic events events during tumour evolution

  Dataset EGAD00001005799

• The identification of genetic vulnerabilities in head and neck cancers for the development of novel therapies (2020-01-15)

  CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005784

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786

• Somatic mutation and clonal evolution normal breast tissue TGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005787

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas_LCM (2020-01-15)

  Samples prepared by LCM - 5 cases for pilot study. Bulk DNA not available. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005789

• Anaplastic Thyroid Cancer aligned whole exome sequence data

  Whole exome sequence data in fastq format was aligned to the GRCH38 reference genome. Aligned sequence was preprocessed with GATK for Indel Realignment and Base Quality Score Recalibration. Duplicates were marked with Picard Mark Duplicates. Aligned sequence is in bam format. Details of the alignment can be found in he bam header. Tumour samples were classified as Anaplastic Thyroid, Poorly-differentiated or well-differentiated cancers.

  Dataset EGAD00001005791

• Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

  The dataset for Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer includes 106 bam files from whole exome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include matched tumor/normal samples from non-small cell lung cancer patients treated with immunotherapy.

  Dataset EGAD00001005796

• Metastases of a cancer of unknown primary (CUP)

  RNA seq analysis of 6 CUP metastases (each in triplicate), analysed by paired sequencing with NextSeq 500. Whole exome sequencing of 15 CUP metastases, analysed by paired sequencing with NextSeq 500.

  Dataset EGAD00001005963

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• single-cell ATAC-seq

  Single-cell ATAC-seq data for 5 CLL samples (2 controls, 3 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005965

• T-WGBS for Naive B Cell

  Tagged-WGBS for 3 Naive B Cell samples of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005966

• WGBS for T-Cell and B-Cell, control and tumor

  WGBS data for 75 paired fastq, spread over 31 samples (4 healthy T-cell, 7 healthy B-cell, 20 B-cell CLL tumors) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005970

• combined ChIPseq (H3, modifications and TF)

  ChIPseq data for 31 CLL samples (12 controls, 19 tumor) of the CancerEpiSys-PRECiSe project; containing histone H3, histone modifications and transcription factor binding sites (CTCF, EBF1).

  Dataset EGAD00001005969

• long RNA data

  long RNA data for 27 CLL samples (8 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005968

• Single MII oocyte mRNA expression from women

  This dataset contains 14 paired-end FASTQ sequences from mRNA-Seq on single human M-II stage oocytes that were collected from ovarian tissue from unstimulated patients undergoing fertility preservation treatments due to cancer diagnoses, which did not influence ovarian function. Cumulus-oocyte-complexes were matured in vitro according Gruhn et al (Science 365: 1466-1469) and short term flash frozen prior to lysis, RNA extraction, full length cDNA preparation and amplification using the Ultra-low-input SMART-Seq2 v4 kit from Takara Clonetech. Further, these cDNA were used to prepare libraries for sequencing according the Nextera XT DNA library preparation kit from Illumina

  Dataset EGAD00001005971

• CYPTAM - PacBio SMRT sequencing

  Dataset contains CYP2D6 sequencing data of 566 individuals who used tamoxifen as adjuvant breast cancer therapy. Phenotype data consists of the ratio between the metabolites endoxifen and desmethyltamoxifen (Metabolic ratio (MR)) as a proxy for CYP2D6 enzyme activity. Each sample is linked to one bam-file containing the CYP2D6 sequence.

  Dataset EGAD00001005972

• Long-read sequencing of prostate adenocarcinoma metastatic to left axillary lymph node. Data used to support Figure 6 in Pubmed ID 32025007 "Pan-Cancer Analysis of Whole Genomes Consortium." Nature 2020 578:8293.

  Oxford Nanopore long-read sequencing of A17-LAxillaryLN2Met-23312 PELICAN sample, identified as D051965 un Pan-Cancer Analysis of Whole Genomes study, and identified as PD13412a by prior Gundem et al whole genome sequencing study (PMID 25830880). Data used to support Figure 6 in Pubmed ID 32025007 "Pan-Cancer Analysis of Whole Genomes Consortium." Nature 2020 578:8293.

  Dataset EGAD00001005974