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• UK10K NEURO GURLING

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. This sample set consists of DNA from multiply affected schizophrenia families. The families have been diagnosed using the SADS-L clinical instrument which gives diagnoses at the probable level of the research diagnostic criteria (RDC). In addition all diagnoses are available using DSMIIIR criteria. These criteria are widely accepted as being valid and reliable for the diagnosis of schizophrenia. All families have been collected to ensure that they are uni-lineal for transmission of schizophrenia, i.e. they have only one affected parent with schizophrenia, or a relative of only one transmitting or obligate carrier parent with schizophrenia. Families with bi-lineal transmission of schizophrenia (i.e. with both parents being affected) were not sampled for this study. All families have multiple cases of schizophrenia and related disorders. The families have been selected to ensure there are no cases of bipolar disorder within them and that they do not contain bipolar disorder in any relatives on either side of the family.For further information on this cohort please contact Hugh Gurling (h.gurling@ucl.ac.uk).

  Study EGAS00001000225

• Genomic analysis of patient-derived xenograft models reveals intratumor-heterogeneity in endometrial cancer and can predict tumor growth inhibition with talazoparib

  Background: Endometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. Methods: Here, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the copy number high/p53-mutant and mismatch-repair deficient molecular subtypes of EC. Results: PDX models were successfully generated with similar histomorphology from all four molecular subtypes of EC and uterine carcinosarcomas. We also observed a wide range of inter-tumor and intra-tumor heterogeneity across different PDX lineages, however one arm of most PDX models recapitulated the molecular landscape of the primary tumor without major genomic drift. An in vivo response to talazoparib was detected in four copy number high models. Two models (carcinosarcomas) showed a response consistent with stable disease and two models (one copy number high serous EC and another carcinosarcoma) showed significant tumor growth inhibition, albeit one consistent with progressive disease, however all lacked the HR deficiency genomic signature. Conclusions: EC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the copy number high molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.

  Study EGAS00001004666

• RNA-seq and WGS data of MB-0152

  Identification of fusion transcripts by RNA-sequencing and Whole genome sequencing of a breast cancer patient sample (METABRIC ID MB-0152)

  Dataset EGAD00001003342

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• RCRF release 2

  RNA sequencing of a Chromophobe renal cell carcinoma, Non-clear cell chromophobe renal cell carcinoma, Neuroendocrine PDAC and clear cell sarcoma

  Dataset EGAD50000000872

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• WES from tFL with PMBL GE signature

  Exome sequencing data from transformed Follicular Lymphoma samples that express a PMBL-like gene expression signature

  Dataset EGAD00001008498

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169