6048 results for "canc*"

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• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

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

  Dataset EGAD00001001311

• Fastq data for whole genome bisulfite sequencing 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 whole genome bisulfite sequencing assays 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 EGAD00001001312

• Deep Sequencing of somatic cancer mutations

  We enriched a panel of cancer associated genes using the Custom Sure Select Target Enrichment Kit. Identified mutations were validated with deep sequencing in order to assess mutated allele frequencies more accurately.

  Dataset EGAD00001001313

• mFAST-SeqS of plasma-DNA

  Sequence data from L1-amplicon libraries prepared from plasma-DNA from a set of 24 female controls and 18 male controls without malignant disease and samples from patients breast (n= 28) and prostate cancer patients (n=61).

  Dataset EGAD00001001314

• ICGC Korean Breast Cancer Project

  A comprehensive characterisation and analysis of human breast cancers through whole-genome sequencing.

  Dataset EGAD00001001322

• CPC-GENE Prostate Cancer Heterogeneity Study

  Aligned Sequence (bam format), Duplicates removed

  Dataset EGAD00001001329

• Her2 BC WGS dataset

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001334

• Triple Negative BC WGS Dataset

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001335

• Xenograft BC WGS Dataset

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001336

• BASIS BC WGS Dataset

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001337

• BC WGS Dataset

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001338

• Triple Negative BC RNA Sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001339

• Breast RNA Sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001340

• BASIS RNA Sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001341

• WGS on patients 1-4, study of metastatic prostate cancer

  Data from the study of subclonal metastatic expansion in prostate cancer. Whole genome shotgun sequencing of fifteen samples, tumour and whole blood, from the four initial patients.

  Dataset EGAD00001001343

• RNA-seq on patients 1-4, study of metastatic prostate cancer

  Data from the study of subclonal metastatic expansion in prostate cancer. RNA-seq of twelve samples, tumour and benign tissue, from the four initial patients.

  Dataset EGAD00001001345

• Cell Line Dataset

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001349

• BC WGS Dataset 1

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001350

• BC WGS Dataset 3

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001351

• BC WGS Dataset 4

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001353

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• CGP CORE CELL LINES - RNA-seq

  Genomic characterisation of a large series of cancer cell lines.

  Dataset EGAD00001001357

• Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Dataset EGAD00001001421

• The transcription factor GABP selectively binds and activates the mutant TERT promoter in cancer

  Dataset contains Exome-seq and RNA-seq from 2 GBM patients, as well as RNA-seq from the derived cultured cells (GNS).

  Dataset EGAD00001001359

• Raw sequencing data - Intra-tumor heterogeneity and clonal evolution patterns towards platinum-resistant high-grade serous ovarian cancer

  This dataset includes 69 sampels of whole-exome sequencing data of high-grade serous ovarian carcinoma (HGSOC). We included patients with advanced (International Federation of Gynecology and bstetrics [FIGO] stage IIIeIV) HGSOC for which biopsies were obtained during debulking surgery, the first at initial diagnosis and the second at disease relapse. Where possible, matched normal DNA from each participating patient was obtained from a whole-blood sample. Written informed consent was obtained from all patients and approved by the local ethics committee.

  Dataset EGAD00001001381