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• Pediatric tumor in a single child of three large nuclear families

  We identify three nuclear families in which a pediatric tumor had been diagnosed in a single child and neither parent had been diagnosed with cancer. These families had 4, 5, and 7 children, respectively. Diagnoses included Wilms tumor, low-grade astrocytoma, and Burkitt’s lymphoma, respectively. We used whole-genome sequencing to profile normal cells from each family member and a linked-read technology for genomic phasing, which enabled us to identify compound-heterozygous and de novo variants in addition to simple-heterozygous and homozygous-alternate variants.

  Study EGAS00001005321

• Degradation of Janus kinases in CRLF2-rearranged acute lymphoblastic leukemia

  We evaluated the efficacy of proteolysis-targeting chimeras (PROTACs) directed against Janus kinases. Solving the structure of FDA-approved type I JAK inhibitors ruxolitinib and baricitinib bound to the JAK2 JH1 tyrosine kinase domain enabled the rational design and optimization of Cereblon (CRBN)-directed JAK PROTACs utilizing multiple derivatives of JAK inhibitors, linkers and CRBN-specific molecular glues. The resulting JAK PROTACs were evaluated for target degradation by proteomic approaches, and activity tested in CRLF2-rearranged cell line and xenograft models of ALL.

  Study EGAS00001005180

• Genomics of pediatric myeloid neoplasms

  Genomic profiling of pediatric myeloid tumors (AML, therapy-related myeloid neoplasms, MDS and others) has shown significant differences when compared adult myeloid neoplasms. This includes the recognition of pediatric-enriched entities including UBTF-TD and NUP98 rearranged leukemias. Collectively we have described over 23 unique molecular subtypes of pediatric AML with unique expression signatures, driver mutations, patterns of cooperating mutations and distinct outcomes. Some of the subtypes (KMT2A and NUP98, for example) have remarkable heterogeneity in fusions partners, cellular phenotypes and molecular signatures.

  Study EGAS00001005760

• Whole genome sequencing based on short and long reads from GM09237 cell line with and without folate depletion

  To asess changes in the DNA-sequence occurring as a result of folate deprivation, DNA extracted from the GM09237 cell line grown with and without folate was sequenced using short read and long read sequencing. The DNA was extracted from cells cultured with either normal medium or medium with no folic acid for 5 days, and then sequenced by BGI PE100 platform (100 bp paired-end sequencing 60x coverage; performed by BGI), or PacBio Sequel system (SMARTbell; performed by BGI) respectively.

  Study EGAS00001005345

• Genetics of non-syndromic idiopathic autism spectrum disorders in India

  The study explores the genetic architecture of patients diagnosed with non-syndromic autism spectrum disorders (according to the DSM-V criteria) in India. Patients were previously screened for aneuploidies using chromosomal G-banded karyotyping (at 500 band resolution) and Fragile-X using TP-PCR. Whole exome sequencing was performed on germline DNA from 100 idiopathic patients. Target enrichment was carried out using either Agilent SureSelect V6 or SureSelect Clinical Research Exome V2 kits. Pooled libraries were sequenced on Illumina HiSeq platform at an average coverage of 80-100x in 2x150bp configuration.

  Study EGAS00001006060

• Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma, lung and colon cancer samples

  Discovery of patient-specific tumor antigens usually requires in vitro-expanded autologous tumor infiltrating lymphocytes (TILs), among which tumor antigen-specific T cells are however often rare, thus limiting sensitivity. We designed in vitro culture conditions to improve the identification of rare tumor antigen-specific CD8 TILs. This innovative yet accessible pipeline allows highly-sensitive identification of tumor antigens and cognate T cell receptors (TCRs), greatly improving the selection of candidates for personalized cancer vaccines and TCR-based cellular immunotherapies.

  Study EGAS00001005513

• Genomic History of the Solomon Islands

  The aim of this study was to provide for a better understanding of the genetic relationships among and between populations of Near and Remote Oceania, with a focus on the initial settlement of the Solomon Islands and their role in the first dispersal of people from Near into Remote Oceania. Irina Pugach, Ana T Duggan, D Andrew Merriwether, Françoise R Friedlaender, Jonathan S Friedlaender, Mark Stoneking, "The Gateway from Near into Remote Oceania: New Insights from Genome-Wide Data", Molecular Biology and Evolution, Volume 35, Issue 4, April 2018, Pages 871–886

  Study EGAS00001006116

• Human and rat skeletal muscle multi-omic profiling

  Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples.

  Study EGAS00001005730

• Placebo-only arm transcriptomic analysis of the phase 3 PROTECT clinical trial

  It is poorly understood how the tumor immune microenvironment influences disease recurrence in localized clear cell renal cell carcinoma (ccRCC). Here we perform whole-transcriptomic profiling of 236 tumors from patients assigned to the placebo-only arm of a randomized, adjuvant clinical trial for high-risk localized ccRCC. Unbiased pathway analysis identifies myeloid-derived interleukin-6 (IL-6) as a key mediator. Furthermore, a novel myeloid gene signature strongly correlates with disease recurrence and overall survival on uni- and multivariate analysis and is linked to TP53 inactivation across multiple datasets.

  Study EGAS00001006344

• RNA-seq analysis of metastatic prostate cancer solid tumor biopsies

  The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)–targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival.

  Study EGAS00001006369

• Chromatin accessibility changes in hiPSC-derived neurons after BDNF and KCl stimulations

  Brain-Derived Neurotrophic Factor (BDNF) is crucial for neuronal survival, differentiation, synaptic plasticity, memory formation, and neurocognitive health. Molecular mechanisms of BDNF promoting cellular survival and synaptic plasticity have been intensely studied, yet its role in genome regulation is obscure. Using human induced pluripotent stem cell (hiPSC)-derived neurons via lentiviral delivery of the neuronal transcription factor Ngn2, we performed a temporal profiling (1h, 6h and 10h) of chromatin accessibility upon BDNF treatment or depolarization (KCl) to identify BDNF-specific chromatin-to-gene expression programs.

  Study EGAS00001006394

• Multi-institutional collaboration to characterize 5hmC in prostate cancer, both tumor biopsies and cfDNA.

  This study used pull-down sequencing to measure 5-hydroxymethylcytosine (5hmC) levels in tissue biopsy specimens from patients with localized and metastatic castration-resistant (mCRPC) prostate cancer. For a subset of the cohort, the same technique was used to sequence cell-free DNA (cfDNA) isolated from patient plasma. To assess 5hmC levels in cfDNA from a larger number of specimens, plasma samples from another mCRPC cohort were also assayed with this method. The larger cohort of cfDNA samples was further anlayzed with a targeted sequencing gene panel.

  Study EGAS00001004942

• cfDNA in Hereditary And High-Risk Malignancies (CHARM)

  Our goal is to improve early cancer detection by developing a blood test to predict cancer development in carriers using circulating DNA. As carriers develop cancer, they may have pre-cancerous cells which release this DNA into the blood (cell-free DNA; cfDNA), which can be detected with novel genetic technologies. We propose to use three complementary cfDNA assays:shallow whole genome sequencing (sWGS), Liquid-Biopsy Sequencing (LB-Seq) and Cell-free Methylated DNA Immunoprecipitation and High-throughput Sequencing (cfMeDip-Seq) for early cancer detection.

  Study EGAS00001006539

• Splicing signature analysis of RNU4-2, RNU5A-1 and RNU5B-1

  Pathogenic variants in the snRNA RNU4-2 were recently described as a frequent cause of neurodevelopmental disorders. In our study we further delineate the phenotype associated with RNU4-2 and we also identify pathogenic variants in RNU5A-A and RNU5B-1. Capture-based RNA-Seq were performed to compare splicing pattern between affected and control individuals. A strong signature based on alternative 5' splice site usage was found for RNU4-2 patients, and we suggest an either 5' or 3' signature depending on the variant's localiztion in U5 genes.

  Study EGAS50000000889

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - dcc_release_24_dataset

  WGS sequencing for cases from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are all available in ICGC release 24

  Dataset EGAD00001003292

• A practical guide for mutational signature analysis in hematological malignancies

  Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

  Dataset EGAD00001005028

• Genomic variants in 121 genes associated with ovarian cancer in cancer tissue and derived organoids

  High-grade serous ovarian cancer (HGSOC) likely originates from the fallopian tube (FT) epithelium, but advanced stages are mostly found outside the FT. We used ex-vivo cultures of HGSOC and knock-out of tumor suppressors in FT organoids to study changes in epithelial cells and niche requirements for normal and transformed FT cells. We found that transformed cells require BMP signaling and are growth arrested in Wnt rich medium. A SureSelectXT Automation Custom Capture Library (Agilent) target enrichment panel was designed. The enrichment panel comprised all coding exons of 121 genes associated with ovarian cancer. Capture was performed according to the manufacturer’s instructions using an NGS Workstation Option B (Agilent) for automated library preparation starting with 3 μg DNA per sample. Sequencing was performed on a Illumina Hiseq 2500 system gnerating 2x100bp paired end reads and a target coverage of >200 per sample. Sequence reads were mapped to the haploid human reference genome (hg19) using BWA. Variants where called with FreeBayes v1.1.

  Dataset EGAD00001005279

• Lenalidomide Resistance in del(5q) Myelodysplastic Syndrome Follows Loss of RUNX1/TP53-mediated Megakaryocytic Differentiation

  Interstitial deletion of the long arm of chromosome 5 (del(5q)) is the commonest structural genomic variant in myelodysplastic syndromes (MDS). Lenalidomide (LEN) is the treatment of choice for patients with del(5q) MDS, but half of the responding patients become resistant within two years. TP53 mutations are detected in ~20% of patients who become resistant to LEN. Our data show that patients who become resistant to LEN harbor either TP53 or RUNX1 mutations or loss of RUNX1 expression. Here we show that LEN-induced degradation of IKZF1 permits a RUNX1/GATA2 complex to drive megakaryocytic differentiation and consequent del(5q) MDS progenitor cell death via CRBN-mediated CSNK1A1 degradation. Overexpression of GATA2 is able to restore LEN sensitivity in the context of RUNX1 or TP53 mutations by enhancing LEN-induced megakaryocytic differentiation. Screening for TP53 and RUNX1 mutations or downregulation should identify patients resistant to LEN, and strategies to activate GATA2 may resensitize del(5q) MDS cells to LEN.

  Dataset EGAD00001005769

• MutWP5: CRUK Mutographs of Cancer: Breast: BRCA Carriers (Targeted) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastectomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Targeted data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005920

• MutWP5: CRUK Mutographs of Cancer: Breast: BRCA Carriers (Exome) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastectomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005921

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (Targeted) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 30 women who mastectomies due to Breast Cancer. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Targeted data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with germline BRCA 1/2 mutations. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005923

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (Exome) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 30 women who underwent mastectomies due to a breast cancer diagnosis. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with germline BRCA 1/2 mutations. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005924

• DRIP-seq data for Molecular Characterization of ETMRs

  This dataset contains DRIP-seq data from 2 patients. DNA–RNA hybrids were extracted from tissue derived from ETMR patient-derived xenograft (PDX) models (BT183) that were treated using topotecan or saline as described previously27. Tumours were subsequently frozen and pelleted using ultracentrifugation. DNA–RNA hybrids were extracted as described previously using the same protocol that is applied for cultured cells21. DNA was extracted using proteinase K followed by phenol–chloroform extraction and ethanol precipitation. Subsequently the DNA was fragmented using the restriction enzymes HindIII, EcoRI, BsrGI, XbaI and SspI (New England Biolabs). Digested DNA was subsequently incubated with the anti-DNA–RNA hybrid anti- body S9.6 (Merck, MABE1095) and immunoprecipitated using agarose beads. Bound DNA–RNA hybrids were eluted and incubated with pro- teinase K and cleaned with an additional phenol–chloroform–ethanol extraction. The DNA was subsequently sonicated and sequenced using a Hiseq 2000 machine with a 50-bp single-read protocol. Each treat- ment condition was performed in duplicate and both RNase H and the input was included as negative controls resulting in 10 Fastq files.

  Dataset EGAD00001006219

• WES, sWGS and RNA-seq of Asian breast cancer

  The dataset contains a full genomics characterization of 527 Asian breast tumours. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, shallow-whole genome sequencing at 0.1X for copy number analyses, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a HiSEQ4000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a HiSEQ4000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001006399

• MutWP5: CRUK Mutographs of Cancer: Lung: PD37920 (Targeted) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006929

• Oesophageal adenocarcinoma RNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains bulk transcriptomes from the inoperable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19). Transcriptomes were prepared from pre-treatment oesophageal tumour biopsies using a ribodepletion approach in order to assess both previously reported (e.g. PD-L1 expression) and novel predictive expression-based biomarkers for immunochemotherapy treatment in this setting. On-treatment and post-treatment biopsies were generated as well to characterize response to therapy, and samples were also prepared from paired normal GI tissues for a subset of patients. scRNA-seq based deconvolution was also applied to bulk transcriptomes in this study to estimate the cell composition of tumour biopsies and assess the link between the presence of specific cell types with immunochemotherapy outcomes.

  Dataset EGAD00001009399

• 10x Genomics Single Cell Gene Expression for SA1035X4XB02879

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 4

  Dataset EGAD00001009147

• 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 SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA535X6XB03101

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009149

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03211

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009150

• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 10x Genomics Single Cell Gene Expression for SA1035X5XB03021

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 5

  Dataset EGAD00001009153

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA535X9XB03617

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 9

  Dataset EGAD00001009157

• 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

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• 10x Genomics Single Cell Gene Expression for SA604X7XB02089

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 7

  Dataset EGAD00001009119

• 10x Genomics Single Cell Gene Expression for SA1188

  10x Genomics Single Cell Gene Expression for Telomerase immortalized breast epithelium cell line 184-hTert L9 116.66

  Dataset EGAD00001009114

• 10x Genomics Single Cell Gene Expression for SA609X6XB01899

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA609 passage 6

  Dataset EGAD00001009120

• 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 SA535X6XB03099

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009116