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• Cerebrospinal fluid cfDNA sequencing for classification of central nervous system glioma

  Primary central nervous system (CNS) gliomas can be classified by characteristic genetic alterations. In addition to solid tissue obtained by surgery or biopsy, cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) is an alternative source of material for genomic analyses. Experimental design: We performed targeted next-generation sequencing (NGS) of CSF cfDNA in a representative cohort of 85 patients presenting with with suspicion of primary or recurrent glioma at two neurooncological centers. Copy-number variation (CNV) profiles, single nucleotide variants (SNVs), and small insertions/ deletions (indels) were combined into a molecular-guided tumor classification. Comparison with the solid tumor was performed for 38 cases with matching solid tissue available. Results: Cases were stratified into four groups: glioblastoma (n = 32), other glioma (n = 19), non-malignant (n = 17) and no confirmed diagnosis available (n = 17). We introduce a molecular-guided tumor classification, enabling identification of tumor entities and/ or cancer specific alterations in 75.0 % (n = 24) of glioblastoma and 52.6 % (n = 10) of other glioma cases. The overlap between CSF and matching solid tissue was highest for CNVs (26-48 %) and SNVs at pre-defined gene loci (44 %), followed by SNVs/indels identified via uninformed variant calling (8-14 %). A molecular-guided tumor classification was made 23.5 % (n = 4) of cases with no confirmed diagnosis available. Conclusions: We developed a workflow for targeted sequencing of CSF cfDNA as well as a strategy for interpretation and reporting of sequencing results based on a molecular-guided tumor classification in glioma.

  Study EGAS50000000060

• Target sequencing of 27 cancer-predisposing genes and 13 renal cell carcinoma-related genes in Japanese renal cell carcinoma patients

  Identifying causative genes via genetic testing is useful for the screening, prevention, and treatment of cancer. Several hereditary syndromes are known to occur in renal cell carcinoma (RCC). However, these evidences came from the European population; it remains unclear how the RCC-related genes and other cancer-predisposing genes contribute to the development RCC in the Japanese population. A case-control study of 14 RCC-related genes and 26 cancer-predisposing genes was performed using 1,563 Japanese patients with RCC and 6,016 controls. Patients were divided into two major histological subtypes of clear cell RCC (ccRCC) or non-clear cell RCC (nccRCC). Gene-based analysis of germline pathogenic variants in patients with each subtype and cancer-free subjects was performed. Following quality control, 1,532 patients with RCC and 5,996 controls were further analyzed. For ccRCC, 52 of 1,283 (4.05%) patients carried pathogenic variants mainly in the cancer-predisposing genes such as TP53 (P = 1.73 �� 10-4; OR, 5.8; 95% CI, 2.2���15.7). Approximately 80% of patients with pathogenic variants in TP53 had p.Ala189Val that was specific in East Asian population. For nccRCC, 14 of 249 (5.62%) patients carried pathogenic variants mainly in the RCC-related genes such as BAP1 (P = 6.27 �� 10-5; OR, Inf; 95% CI, 10.0���Inf), and FH (P = 6.27 �� 10-5; OR, Inf; 95% CI, 10.0���Inf). Our study showed different and population-specific contributions of risk genes between ccRCC and nccRCC in Japanese for better-personalized medicine.

  Study JGAS000414

• Young Boost Trial for Breast Cancer patients

  The Young Boost Trial (YBT, YOUNG BOOST / BOOG 2004-01 (Borstkanker) | Kanker.nl investigated the optimal radiation dose for breast cancer patients aged 50 years or younger, treated with breast-conserving therapy (BCT). In this randomized study, participants were assigned to receive either a 26 Gy or a 16 Gy radiation boost, with the primary endpoint being local recurrence. To explore potential predictive biomarkers of treatment response in this patient group, whole exome sequencing (WES) was performed on tumor samples to identify genetic factors that could guide personalized treatment strategies and improve clinical outcomes for these young breast cancer patients.

  Study EGAS50000000797

• H3Africa H3AChipDesign Phenotype

  Basic phenotypic data (country, ethnicity and sex) for 348 samples of the H3Africa Chip Design Study. Divided into 8 datasets of 41 samples from Zambia, 24 samples from Cameroon, 50 samples from Mali, 26 samples from Cameroon, 49 samples from Nigeria, 48 samples from Botswana, 50 samples from Benin, 60 samples from Burkina Faso and Ghana.

  Dataset EGAD00001005310

• Whole-exome/genome sequencing of childhood acute leukemia in Iraq

  This dataset contains 26 whole-genome sequencing (13 paired tumor and normal), 106 whole-exome sequencing (53 paired tumor and normal), and 43 targeted sequencing data. Sequencing was performed using an Illumina platform. The data are BAM files aligned to the hg19 reference genome.

  Dataset EGAD00001007873

• Validation_of_a_Haloplex_platform_for_targeted_re_sequencing_of_the_exons_of_25_genes

  We recently used the Agilent SureSelect platform to re-sequence a set of genes known to bemutated in human AML. The results from 10 AML DNA samples were very satisfactory, butthe effort required was significant.Thus, we decided to re-sequence the same genes using the Haloplax system for targetenrichment in 48 AML samples. We planned to do this using MiSeq and have data from apilot of 3 samples. The data is promising but coverage appears pathcy so far.However, in order to get a better understanding of the data we will need deeper sequencing. Wewill need two lanes of HiSeq to get the same degree coverage as Sureselect.his 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 EGAS00001000285

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 17)

  The dataset contains whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients sequenced with Novoseq 6000. The 86 samples are either fresh frozen tumour samples or blood samples. The files provided are paired fastq files.

  Dataset EGAD50000002409

• Genetics of Glucose Regulation in Gestation and Growth (Gen3G) Cohort - Placenta Transcriptomics RNA Sequencing

  The overarching goals of Genetics of Glucose regulation in Gestation and Growth (Gen3G) are to increase our understanding of biological, environmental, and genetic determinants of glucose regulation during pregnancy and their impact on fetal/child development. Gen3G is a prospective cohort study: we initially recruited 1,024 pregnant women between 2010-2013 at Blood Sampling in Pregnancy Clinic during the first trimester of pregnancy (median 9 weeks of gestation) in Sherbrooke, Québec, Canada. We assessed 898 pregnant women at second trimester (median 26 weeks of gestation), when participants completed a 75g oral glucose tolerance test (OGTT) as clinically indicated for screening of gestational diabetes mellitus (GDM). We collected data for 854 mother-child dyads at delivery (from medical records), in addition to placenta and/or cord blood samples in majority of newborns.The overall goal of the grant that supported data that is included in this dbGaP submission was to discover novel placental factors that regulate glucose metabolism in pregnancy and predict GDM by conducting genome-wide transcriptomics (RNA and miRNA) in carefully collected placenta samples from Gen3G.

  Study phs003151

• Blood-based monitoring of relapsed/refractory cHL patients predict responses to PD-1 blockade treatment

  Hodgkin lymphoma (cHL) patients are uniquely susceptible to treatment with programmed cell death-1 (PD-1) treatment. However, monitoring with FDG-PET/CT, the current response detection standard, is challenging. Here, we address the potential applicability of tissue biomarkers and blood-based biomarkers in PD-1 treated cHL patients. We evaluated 9p24.1/PD-L1/PD-L2 alterations, and expression of PD-L1 and antigen presentation molecules (HLA class I/II) in 9 tissue biopsies of 4 patients. In addition, we assessed circulating tumor DNA (ctDNA), extracellular vesicle associated miRNAs (EV-miRNAs), soluble PD-L1 and serum TARC (sTARC) in 26 longitudinal blood samples of these patients during PD-1 treatment. We found that the predictive power of tissue biomarkers is dependent on the timing of the biopsy . The dynamics of blood-based biomarkers: sTARC, EV-miRNAs and ctDNA corresponded well immunotherapy treatment response. Therefore, blood-based biomarkers are promising monitoring strategies in PD-1 treated cHL patients and should be further explored in clinical trials or observational studies.

  Study EGAS00001005894

• Single-cell RNA-seq of celiac disease-specific plasma cells

  Disease-specific plasma cells (PCs) reactive with transglutaminase 2 (TG2) or deamidated gluten peptides (DGP) are abundant in celiac disease (CeD) gut lesions. Their contribution toward CeD pathogenesis is unclear. We assessed expression of markers associated with PC longevity in 15 untreated and 26 treated CeD patients in addition to 13 non-CeD controls, and performed RNA-sequencing with clonal inference and transcriptomic analysis of 3251 single PCs. We observed antigen-dependent V-gene selection and stereotypic antibodies. Generation of recombinant DGP-specific antibodies revealed a key role of a heavy-chain residue that displays polymorphism, suggesting that immunoglobulin gene polymorphisms may influence CeD-specific antibody responses. We identified transcriptional differences between CeD-specific vs non-disease-specific PCs and between short-lived vs long-lived PCs. The short-lived CD19+CD45+ phenotype dominated in untreated and short-term-treated CeD, in particular among disease-specific PCs but also in the general PC population. Thus, the disease lesion of untreated CeD is characterized by massive accumulation of short-lived PCs that are not only directed against disease-specific antigens.

  Study EGAS00001004623