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• Field effect of healthy and diseased livers (2019-04-08)

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences). . This dataset contains all the data available for this study on 2019-04-08.

  Dataset EGAD00001004941

• P647 Targeted resequencing project

  In collaboration with Dr Robert Semple we have identified a family harbouring an autosomal dominant variant, which leads to severe insulin resistance (SIR), short stature and facial dysmorphism. This family is unique within the SIR cohort in having normal lipid profiles, preserved adiponectin and normal INSR expression and phosphorylation. DNA is available for 7 affected and 7 unaffected family members across 3 generations. All 14 samples have been genotyped using microsatellites and the Affymetrix 6.0 SNP chip. Linkage analysis identified an 18.8Mb haplotype on chromosome 19 as a possible location of the causative variant. However, Exome sequencing of 3 affected and 1 unaffected family members has not identified the causative variant suggesting the possibility of an intronic or intergenic variant in this region or elsewhere in the genome. We propose to conduct whole genome sequencing of 5 members of the pedigree at a depth of 20X. The chosen samples are two sets of parents plus one member of an unaffected branch of the pedigree who shares the risk haplotype on chromosome 19. Sequencing of the two sets of parents will be used along with the genome-wide SNP data to impute 4 affected children giving an effect sample size of 6 affected individuals.

  Dataset EGAD00001000383

• Evolution and clinical impact of genetic epistasis within EGFR-mutant lung cancers: multi-timepoint exome sequencing of a single patient's disease

  This dataset is a time-series of EGFR-mutant NSCLC clinical specimens from an individual patient profiled using tumor-based whole exome sequencing and the data is in BAM format. DNA was extracted from FFPE for primary tumor and frozen tumor tissue samples and matched non-tumor tissue using the Qiagen Allprep DNA/RNA Mini Kit.  The library preparation protocol was based on the Agilent SureSelect Library Prep and Capture System. DNA was resuspended in a low TE buffer and sheared (Duty Cycle 5%; Intensity 175; Cycles/Burst: 200; Time: 300s, Corvaris S2 Utrasonicator).  Bar-coded exome libraries were prepared using the Agilent Sure Select V5 library kit per manfucaturer’s specifications. The libraries were run on the HiSeq2500. Raw paired end reads (100bp) in FastQ format generated by the Illumina pipeline were aligned to the full hg19 genomic assembly obtained from USCS, gencode 14, using bwa version 0.7.12. Picard tools version 1.117 was used to sort, remove duplicate reads and generate QC statistics. Tumor DNA was sequenced to median depth of 303X (range 114.39-383.41) and the matched germline DNA to average depth of 231.65.

  Dataset EGAD00001003769

• Timing the landmark events in the evolution of clear cell renal cell cancer

  Clear cell renal cancer is characterized by near-universal loss of the short arm of chromosome 3 (3p). This event arises through unknown mechanisms, but critically results in the loss of several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cancer (ccRCC) recruited into the Renal TRACERx study. We find novel hotspots of point mutations in the 5'-UTR of TERT, targeting a MYC-MAX repressor, that result in telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. Using molecular clocks, we estimate this occurs in childhood or adolescence, generally preceding emergence of the most recent common ancestor by years to decades. Similar genomic changes recent common ancestor by years to decades. Similar genomic changes are seen in inherited kidney cancers. Modeling differences in age-incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Targeting essential genes in deleted regions of chromosome 3p could represent a potential preventative strategy for renal cancer.

  Dataset EGAD00001003445

• MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

  This study introduces MorphoITH, a framework for deconvolving intra-tumor heterogeneity (ITH) in clear cell renal cell carcinoma (ccRCC) based on tissue morphology from histopathological slides. The study tested MorphoITH's ability to capture different aspects of morphology and to reflect genetic ITH. The data deposited here is derived from a morphology guided multi-regional sequencing dataset used to study the relationship between morphology and genetic evolution. This dataset consists of whole exome sequencing of tumor from multiple sites and adjacent normal samples which were collected from two patients treated in affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital.

  Study EGAS50000001064

• Somatic mutant selection is altered by prior NOTCH1 mutation in ageing esophageal epithelium

  Authors: Charlotte King1, Emilie Abbie1, Joanna C. Fowler1, Irina Abnizova1, Roshan K. Sood1, Swee Hoe Ong1, Michael W. J. Hall1,2, Faye Lynch-Williams3, Benjamin A. Hall4, Philip H. Jones1,2,5 Abstract: In cancer evolution, genome alterations often occur in a specific order, implying selection depends on the prior clonal genotype 1-3. It is unknown if similar constraints operate in normal epithelia. Here, we mapped mutations in normal mid-esophagus of aged UK subjects. Mutant NOTCH1 clones colonized most of the epithelium by age 60 and above 70 tissue was saturated with mutants under strong competitive selection. Mutant TP53 was more strongly selected in donors over 60 years of age. Samples predominantly mutant for NOTCH1 showed increased selection of NOTCH2 mutants and weaker selection of mutant TP53 compared with samples that were mostly NOTCH1 wild type. In mouse esophagus lacking Notch1 we observed strong selection of mutant Notch2 and other genes not selected in wild type esophagus. In normal ageing esophagus, the first driver mutation may change the trajectory of subsequent somatic evolution by altering mutant selection.

  Dataset EGAD00001015261

• Sequential single-cell transcriptional and protein marker profiling reveals TIGIT as a marker of CD19 CAR-T cell dysfunction in patients with non-Hodgkin’s lymphoma

  Employed single cell RNA sequencing and protein surface marker profiling of serialCAR-T cell samples from patients with non-Hodgkin’s lymphoma (NHL) to reveal CAR-T cell evolution, identify biomarkers of response, and test for evidence of exhaustion inCAR-T cells of poor responders. At the transcriptional and protein levels, we note the evolution of CAR-T cells toward a non-proliferative, highly-differentiated, andexhausted state that is enriched in CAR-T cells of patients with poor response.Furthermore, we identified the checkpoint receptor TIGIT as a novel prognosticbiomarker and potential driver of CAR-T cell exhaustion.

  Study EGAS00001005356

• Mutation analysis in human iPS cells

  PCR products were obtained from each target loci using genomic DNA from human iPS cells. Subsequently, PCR products are pooled and subjected to Illumina library preparation. The library will be sequenced by MiSeq. 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 EGAD00001000357

• DNA sequening

  The dataset consist of DNA and RNA sequencing results and metadata of the samples. All sample numbers starting with 6716 are tumor samples which has been sequenced using WES (see BAM files). It concerns biopsies of metastatic lesions from patients with BRAFV600 mutated melanoma, obtained before, during and after the study treatment (see samples metadata) and in some cases blood for germline mutation analysis. Sequencing is performed using the Illumina Novaseq 6000 system.

  Dataset EGAD50000000382

• Exome sequencing of samples for Corvin_Dublin_BipEx

  This research project was a collaboration between Trinity College Dublin, Ireland and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 191 Bipolar case/control samples from collaborators in Ireland. Genomic DNA from each sample was sequenced to a mean depth of 20x. The project used Illumina WXS sequencing of DNA and the file type is cram.

  Dataset EGAD50000000392

• Adipose tissue macrophage dysfunction is associated with a breach of vascular integrity in NASH

  Our study describes for the first time the myeloid cell landscape in human visceral adipose tissue at single-cell level within a cohort of well-characterized patients with non-alcoholic fatty liver disease.

  Study EGAS50000000424

• SIGi001-A-13 / SAMEA104386250 WGS data

  The iPSC line SIGi001-A-13 / SAMEA104386250 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line SIGi001-A-13 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001031

• BIONi010-C-8 / SAMEA4454011 WGS data

  The iPSC line BIONi010-C-8 / SAMEA4454011 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-8 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001028

• BIONi010-C-3 / SAMEA4342740 WGS data

  The iPSC line BIONi010-C-3 / SAMEA4342740 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-3 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001034

• BIONi010-C-7 / SAMEA4454010 WGS data

  The iPSC line BIONi010-C-7 / SAMEA4454010 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-7 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001035

• BIONi010-C-6 / SAMEA4454009 WGS data

  The iPSC line BIONi010-C-6 / SAMEA4454009 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-6 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001037

• BIONi010-C-9 / SAMEA4454012 WGS data

  The iPSC line BIONi010-C-9 / SAMEA4454012 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-9 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001040

• SIGi001-A-3 / SAMEA4448571 WGS data

  The iPSC line SIGi001-A-3 / SAMEA4448571 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line SIGi001-A-3 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001041

• BIONi010-C-4 / SAMEA4452060 WGS data

  The iPSC line BIONi010-C-4 / SAMEA4452060 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-4 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001043

• EDi014-A / SAMEA4459369 WGS data

  The iPSC line EDi014-A / SAMEA4459369 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi014-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001051

• EDi014-B / SAMEA4459371 WGS data

  The iPSC line EDi014-B / SAMEA4459371 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi014-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001053

• SIGi001-A-7 / SAMEA4448730 WGS data

  The iPSC line SIGi001-A-7 / SAMEA4448730 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line SIGi001-A-7 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001060

• BIONi010-C-5 / SAMEA4452061 WGS data

  The iPSC line BIONi010-C-5 / SAMEA4452061 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-5 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001062

• BIONi010-C-2 / SAMEA4342705 WGS data

  The iPSC line BIONi010-C-2 / SAMEA4342705 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C-2 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001068

• SIGi001-A-1 / SAMEA4451096 WGS data

  The iPSC line SIGi001-A-1 / SAMEA4451096 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line SIGi001-A-1 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001073