Search Results - EGA European Genome-Phenome Archive

Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

Dataset EGAD00001003883

Using genetics to identify cell types and mechanisms underlying susceptibility to primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a T-cell mediated, chronic inflammatory condition of the biliary tree that is strongly associated with inflammatory bowel disease. Genome-wide association studies have identified 22 non-HLA genetic risk variants associated PSC. Identifying the genes impacted by these variants has proven difficult as the majority lie in non-coding regions of the genome. Knowledge of the genes and biological pathways these non-coding variants are perturbing is vital to understanding the disease biology. One means of assessing the impact of non-coding variants within disease associated loci upon genes is via colocalisation with eQTL. Many eQTL are cell-type specific, requiring the analysis of disease relevant cell types to detect colocalisation. We have collected PSC-relevant T-cell-subtypes from the peripheral blood of PSC patients via fluorescence activated cell sorting in preparation for RNA sequencing and mapping of eQTL. Samples were collected at the Norfolk and Norwich University Hopital, for which local ethical approval has been granted. Lysed cell samples will be transferred to WTSI and DNA/RNA will be extracted from lysed cell samples by T143 before genotyping (DNA) and custom library preparation and sequencing (RNA). 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 EGAD00001011815

May 2021 data update (fastq) 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.

May 2021 data update (fastq) 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 EGAD00001008100

Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

Dataset EGAD00001009398

CCOC WGS for the study Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes

For CCOC cohorts, OvCaRe cases were reviewed, including frozen material, by at least two expert gynecopathologists prior to inclusion in the sequencing cohort who provided the confirmation on final selected cohort. Frozen H&E from Tokyo were also used for evaluation along with representative H&E photos and review done at the Jikei School of Medicine. All CCOC tumours are primary tumour samples. Library construction and sequencing Frozen specimens with >50% tumour cellularity (based on initial slide review) were used for cryosectioning and subsequent nucleic acid extraction. Patient tumour and normal blood samples derived from primary, untreated fresh frozen tumour specimens harvested at diagnosis during standard of care debulking surgery. Germline DNA was provided from peripheral blood buffy coat on all specimens except 13 from Tokyo, where non-cancer frozen tissue was used as a germline source. DNA extraction from both matched normal (blood) and tumour samples (frozen tissue) were performed using the QIAamp Blood and Tissue DNA kit (Qiagen) and quantified using a Qbit fluorometer and reagents (high-sensitivity assay). Three lanes of Illumina HiSeq 2500 v4 chemistry for normal samples and five lanes for tumour samples were obtained. The PCR-free protocol was adopted to eliminate the PCR-induced bias and improve coverage across the genome.

Dataset EGAD00001003265

Small-molecule inhibitors in melanoma - Kenski / Kong - WES (2019-04-11)

Small-molecule inhibitors targeting the most commonly activated pathway in melanoma, MAPK pathway (either alone or in combination) are already given to melanoma patients for few years, and initially reduce tumour burden dramatically, eventually melanomas become resistant and tumours progress while on treatment. Resistance to this treatment occurs by acquisition of additional mutations or other alterations that affect the mitogen-activated protein kinase (MAPK) pathway by either direct or indirect signalling. Many resistance mechanisms somehow lead to reactivation of extracellular signal-regulated kinase (ERK), thereby restoring signalling of the oncogenic BRAF/MEK/ERK pathway. In addition, PI3K pathway activation contributes to resistance to BRAF inhibition. Less frequent but equally important to the phenomenon of targeted drug resistance is the observation that B15-20% of BRAF mutant melanoma patients fail to respond to BRAF inhibition already early on treatment, owing to intrinsic resistance. These patients have little therapeutic options, unless immunotherapy can be given. To better understand the resistance mechanisms in MAPK inhibitor-treated melanoma patients and melanoma biology, our lab generated a big panel of MAPK inhibitor resistant melanoma cell lines by continuous drug exposure. The understanding of the genetic landscape and gene expression as well as cross resistance to other treatment regimens, and other aspects of melanoma biology such as phenotype switch, will allow us to better exploit new therapeutic strategies for melanoma patients. . This dataset contains all the data available for this study on 2019-04-11.

Dataset EGAD00001004952

Whole transcriptome RNA sequencing on bone marrow and peripheral blood samples from patients with acute myeloid leukemia at diagnosis or relapse.

One hundred cryopreserved bone marrow and peripheral blood samples from patients with acute myeloid leukemia (AML) with 10-90% blasts were selected from the biobank of the Department of Hematology of Leiden University Medical Center (LUMC). The AML cases cover all subtypes, and specifically include known subtype-defining balanced chromosomal translocations according to the WHO classification. The samples were obtained from 96 patients and include three pairs of de novo and relapsed AML and one pair of de novo and presumed therapy-related AML (tAML). Total RNA was isolated from mononuclear cells without prior enrichment for leukemic blasts. The quality and integrity of total RNA was checked and RNA libraries were prepared using the TruSeq RNA library preparation kit v2 (Illumina, San Diego, CA) in an ISO/IEC 17025-accredited protocol. This workflow started with enrichment of messenger RNA by oligo dT magnetic beads. After fragmentation, cDNA synthesis was performed, followed by adaptor ligation and PCR amplification. Paired-end sequencing with a read length of 126 bp was performed on an Illumina HiSeq 2500 v4 sequencer to at least 12.5 Gbp per sample. Image analysis, base calling, and quality check was performed with Illumina data analysis pipeline RTA v1.18.64 and Bcl2fastq v1.8.4. RNAseq reads are provided in compressed Sanger FASTQ format.

Dataset EGAD00001004187

GoNL aligned sequence data in BAM format.

We mapped the data to the UCSC human reference genome build 37 using BWA 0.5.9-r16. We first mapped each read pair separately using bwa aln. Then we used bwa sampe to map the paired reads together to a BAM9 file. The BAM file was then sorted by genomic position and indexed using PicardTools-1.32 SortSam. To prevent PCR artifacts from influencing the downstream analysis of our data, we used Picard to mark the duplicate reads, which were ignored in downstream analysis. We used GATK IndelRealigner on our data around known indels (from 1KG Pilot). The IndelRealigner creates all possible read alignments using the source and computes the likelihood of the data containing the indel based on the read pileup. Whenever the maximum likelihood contains an indel, the reads are realigned accordingly. Each base is associated with a phred-scaled base quality score. Calibration of Phred scores is crucial as they are used in some of the downstream analysis models. We used GATK to recalibrate the base qualities with respect to (i) the base cycle, (ii) original quality score, and (iii) dinucleotide context. To minimize issues stemming from mapping problems around indels, we decided to undergo a second round of indel realignment using the GATK IndelRealigner by family rather than by individual. For this second round, we considered two sources of possible indels: 1KG Phase 1 indels and indels aligned by BWA in the GoNL data.

Dataset EGAD00001001038

Prognostic relevance of microenvironmental factors CD163 and CD8 combined with EZH2 and chromosome 18 gain in a validation cohort of follicular lymphoma patients of the Lunenburg Lymphoma Biomarker Consortium

Background: In follicular lymphoma (FL), studies addressing the prognostic value of microenvironment-related immunohistochemical (IHC) markers and tumor cell-related genetic markers have yielded conflicting results, precluding implementation in practice. Therefore, the Lunenburg Lymphoma Biomarker Consortium (LLBC) performed a validation study for published markers. Methods: To maximize sensitivity, an end-of-spectrum design was applied for 122 uniformly immunochemotherapy-treated FL patients retrieved from international trials and registries; early failure (EF): progression or lymphoma-related death <2 years versus long remission: response duration of >5 years. IHC staining for T-cells and macrophages was performed on tissue microarrays from initial biopsy and scored with a validated computer-assisted protocol. Shallow whole-genome and deep targeted sequencing was performed on the same samples. Results: 96/122 cases with complete molecular and immunohistochemical data were included in the analysis. EZH2 wild-type (p=0.006), gain of chromosome 18 (p=0.002), low percentages of CD8+ cells (p=0.011) and CD163+ areas (p=0.038) were associated with EF. No significant differences in other markers were observed, thereby refuting previous claims on their prognostic significance. Conclusion: Using an optimized study design, this LLBC study validates wild-type EZH2 status, gain of chromosome 18, low percentages of CD8+ cells and CD163+ area as predictors of EF to immunochemotherapy in FL.

Dataset EGAD00001002738

MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-04-03.

Dataset EGAD00001004893

22984 results for "ega"

in 27.66 milliseconds.