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• Molecular_diagnosis_of_albinism

  Albinism is genetically heterogeneous rare genetic condition affecting 1:17000 in the Western world (but more frequent in Africa) whose main feature is a profound visual impairment, characterised by foveal hypoplasia, abnormal chiasmatic connections, nystagmus and photofobia. All these features result in severly altered visual acuity (<0,1), absent depth perception and poor night vision. People with albinism are primarily visually handicapped. In addition, for some types of albinism, the visual phenotype can be presented with partial or total hypopigmentation, hence resulting in a secondary phenotype which can lead to skin cancer if skin is not adequately protected. Recently a new syndrome has been described, FHONDA, with the same visual abnormalities of albinism but without pigment alteration. The traditional classification differentiates Oculoculatenous albinism (OCA), where hypopigmentation involves hair, skin and eyes versus Ocular Albinism (OA), where hypopigmentation only affects the eyes. These are non-sydrimic types of albinism. Some syndromic forms (Hermansky-Pudlak=HPS, Chediak-Higashi=CHS) affect cells beyond pigment cells, present in the lungs, immune system, platelets and intestines, resulting in more severe phenotypes that can be fatal. Mutations in at least 19 genes are assocaited with the corresponding types of albinism. Most hospitals will only diagnose the most frequent cases using traditional Sanger, MLPA approaches. Some will use CGH arrays. We aim to diagnose all cases of albinism through the Albinochip proposal, which combines a Sequenom first step of known mutations combined with subsequent NGS approaches. In some cases we fail to find a second mutation, these are good candidates for further full exome analyses. 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/

  Study EGAS00001002068

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Lung cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004447

• DSRCT RNA genomic sequencing

  Purpose: To further understand the molecular pathogenesis of desmoplastic small round cell tumor (DSRCT), a rare but often fatal malignancy occurring primarily in young males, we used next-generation RNA sequencing to investigate the gene expression profiles intrinsic to this disease.Experimental Design: RNA from DSRCT tumor samples were obtained from the Cooperative Human Tissue Network biorepository. RNA sequencing was completed using the Illumina HiSeq 2000 system. Presence of the EWSR1-WT1 fusion was verified using both RNA-sequencing and PCR. Validation studies, including WT1 ChIP-seq, RNA sequencing, and EWS-WT1 knockdown experiments, were performed on an established DSRCT cell line, JN-DSRCT-1. Protein expression of clinically relevant genes determined by RNA sequencing was confirmed using immunohistochemistry. A panel of immune signature genes was also evaluated to identify possible immune therapeutic targets. Results: We analyzed the RNA from 14 tumor samples, of which two were confirmed negative for the diagnostic EWSR1-WT1 translocation. Principal component analysis of the sequencing data indicated that fusion-negative and fusion-positive tumors are transcriptionally distinct entities. Overlapping the significant ChIP-seq peaks with genes found to be highly expressed in the RNA sequencing data revealed that IGF2 and FGFR4 were both highly expressed, and targets of the EWS-WT1 fusion gene. This finding has potential clinical relevance for targeted therapeutic intervention. In addition, we identified the immune checkpoints CD200 and CD276 as potentially targetable genes whose expression is independent of the EWS-WT1 fusion gene in vitro.Conclusions: Despite the transcriptional complexity of this disease, we were able to identify IGF2, FGFR4, CD200, and CD276 as potential therapeutic targets for patients suffering from DSRCT.

  Study EGAS00001002770

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004446

• NHLBI GO-ESP: Early-Onset Myocardial Infarction Exome Chip (Broad EOMI)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. As part of this initiative, the Broad has performed genotyping on several thousand subjects from 4 different cohorts on Illumina's ExomeChip platform as described below: The Duke Study: The Duke study enrolled cases from the Duke University Medical Center with myocardial infarction or coronary artery stenosis > 50%. Controls were individuals who were > 50 years old without coronary stenosis > 30% and without history of myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, or heart transplant. The InterMountain Heart Study: The Intermountain Heart Study is an observational registry of individuals with coronary artery disease and healthy controls who received care at participating Intermountain Healthcare facilities. The Ottawa Heart Study: The Ottawa heart study enrolled cases with angiographically confirmed coronary artery disease (> 1 coronary artery with > 50% stenosis) who did not have type 2 diabetes and were ≤ 50 years old for males and ≤ 50 years old for females. Controls were also enrolled who were asymptomatic males > age 65 and females > age 70. PennCATH: PennCATH is a case-control study that recruited individuals undergoing coronary angiography at the University of Pennsylvania Hospital. Cases had angiographically confirmed coronary artery disease (>1 coronary artery with 50% stenosis) and were ≤ 55 years old if male and ≤ 60 years old if female. Controls were men > 40 years old and women > 45 years old with normal coronary angiography.

  Study phs000936

• Warm_autopsy__mutational_signatures_and_clonal_units

  Aims and objectives 1. To produce a ‘first generation map’ of mutational burdens and mutational processes that operate in normal human tissues. 2. To examine clonal units and their distribution across normal human tissues with the emphasis on these particular areas: a) Tissues with an architecturally defined unit: Are architectural structures populated by a progeny derived from a single stem cell? Are clonal units confined to a given architectural structure? Examples of tissues where such approach will be used include prostate gland (acini and ducts), small bowel (crypts), thyroid gland (follicles) and others. b) Tissues without an architecturally defined unit: What is the average size of clonal populations? This group includes stratified/pseudostratified epithelia with a basal layer within which there are stem cells that give rise to a progeny of cells showing upwards migration and lateral expansion (e.g. urothelium, cervical squamous epithelium, respiratory epithelium). This approach can also be used in tissues with a sheet-like cell arrangement, for example brain and adrenal gland. Methods To answer these questions, we will use a range of normal human tissue samples which have already been collected from a single deceased individual. The samples are currently stored in a -80C freezer. Ethanol fixed frozen sections will be prepared from these biopsies. Putative clonal units will be identified and dissected out using laser capture microscopy. DNA extraction will be performed using the in-house (CGP) protease based method. In terms of sequencing methods, the project will be split into 2 phases (2 different approaches): - Phase 1: This part of the project will utilise a combination of low input library preparation (developed by Peter Ellis) and standard whole genome sequencing (HiSeqX). The phase has already started (December 2016). This approach will be particularly useful in investigation of mitotically active tissues. - Phase 2: This part of the project will utilise a combination of low input library method (Peter Ellis) and Bottle-neck sequencing (BotSeqS). BotSeqS is a type of duplex sequencing method that offers a more accurate variant calling and allows genome interrogation at a single cell level. This method is currently being developed and is expected to be implemented in 3-6 months (March-April 2017). This approach will be particularly useful for post-mitotic tissues and tissues without pre-defined architectural units.

  Study EGAS00001002216

• Analysis of the Patient derived cells that model the intratumoral heterogeneity of hypermutated IDH1 mutant glioma

  Dataset contains paired-end Whole Exome sequencing data from 2 glioma patients (1 oligodendroglioma and 1 astrocytoma) , derived cultured cells, and derived murine xenografts.

  Dataset EGAD00001006340

• The Extracellular RNA Quality Control (exRNAQC) study (phase 1)

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 1.

  Dataset EGAD00001007697

• APL nanopore sequencing data

  APL nanopore sequencing data are deposited into 2 data formats: 1. CRAM files 2. h5 files

  Dataset EGAD00001008146

• Single cell analysis reveals new evolutionary complexity in uveal melanoma

  Uveal melanoma (UM) is a rare eye cancer that is highly aggressive and leads to metastatic death in up to half of patients. Here, we conduct droplet-based single-cell RNA sequencing of 59,915 neoplastic and non-neoplastic cells from 8 primary and 3 metastatic UMs. Single-cell VDJ sequencing of RNA is conducted to determine clonally expanded B/T- cell populations. Single-cell DNA sequencing of 1 primary and 1 metastasis is utilized.

  Study phs001861