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• DAC PRECISE Bennstein

  Data access control for datasets submitted by SysMed - PRECISE

  Dac EGAC50000000476

• Hostage_1_phenotype_lab

  Lab values for Hostage1 cohort.

  Dataset EGAD00001007839

• Cancer Alliance Exome

  Exome sequencing for individualized cancer interpretation

  Dataset EGAD00001006236

• Hostage_3_phenotype_basic

  Basic phenotypes for Hostage3 cohort.

  Dataset EGAD00001007842

• BelCovid_2_phenotype_lab

  Lab values for BelCovid2 cohort.

  Dataset EGAD00001007835

• BRACOVID_phenotype_basic

  Basic phenotypes for BRACOVID cohort.

  Dataset EGAD00001007832

• Hostage_1_phenotype_basic

  Basic phenotypes for Hostage1 cohort.

  Dataset EGAD00001007838

• BRACOVID_phenotype_lab

  Lab values for BRACOVID cohort.

  Dataset EGAD00001007833

• Hostage_2_phenotype_lab

  Lab values for Hostage2 cohort.

  Dataset EGAD00001007841

• BelCovid_2_phenotype_basic

  Basic phenotypes for BelCovid2 cohort.

  Dataset EGAD00001007834

• GEN_COVID_phenotype_basic

  Basic phenotypes for GEN_COVID cohort.

  Dataset EGAD00001007836

• GEN_COVID_phenotype_lab

  Lab values for GEN_COVID cohort.

  Dataset EGAD00001007837

• Hostage_2_phenotype_basic

  Basic phenotypes for Hostage2 cohort.

  Dataset EGAD00001007840

• Hostage_3_phenotype_lab

  Lab values for Hostage3 cohort.

  Dataset EGAD00001007843

• Hostage_4_phenotype_basic

  Basic phenotypes for Hostage4 cohort.

  Dataset EGAD00001007844

• INMUNGEN_CoV2_phenotype_basic

  Basic phenotypes for INMUNGEN_CoV2 cohort.

  Dataset EGAD00001007846

• Exome

  Exome Seq for Study EGAS00001001844

  Dataset EGAD00001002159

• Hostage_4_phenotype_lab

  Lab values for Hostage4 cohort.

  Dataset EGAD00001007845

• INMUNGEN_CoV2_phenotype_lab

  Lab values for INMUNGEN_CoV2 cohort.

  Dataset EGAD00001007847

• SPGRX_phenotype_basic

  Basic phenotypes for SPGRX cohort.

  Dataset EGAD00001007848

• Transcriptome sequencing in monozygotic twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome

  Study EGAS00001006370

• A proof-of-concept study of sequential treatment with the HDAC inhibitor vorinostat following BRAF and MEK inhibitors in BRAFV600 mutated melanoma

  "Purpose: Development of resistance limits the clinical benefit of BRAF and MEK inhibitors (BRAFi/MEKi) in BRAFV600 mutated melanoma. It has been shown that short-term treatment (14 days) with vorinostat was able to initiate apoptosis of the resistant tumor cells. We aimed to assess the anti-tumor activity of sequential treatment with vorinostat following BRAFi/MEKi in patients with BRAFV600 melanoma who progressed after initial response to BRAFi/MEKi. Patients and Methods: Patients with BRAFi/MEKi resistant BRAFV600 melanoma were treated with vorinostat 360 mg QD for 14 days followed by BRAFi/MEKi. The primary endpoint was an objective response rate of progressive lesions of at least 30% according to RECIST 1.1. Secondary endpoints included progression-free survival (PFS), overall survival (OS), safety, pharmacokinetics of vorinostat and translational molecular analyses using ctDNA and tumor biopsies. Results: Twenty-six patients with progressive BRAFi/MEKi resistant BRAFV600 mutated melanoma received treatment with vorinostat. Twenty-two patients were evaluable for response. The ORR was 9% (one complete response for 31.2 months and one partial response for 14.9 months. Median PFS and OS were 1.4 and 5.4 months, respectively. Common adverse events were fatigue (23%) and nausea (19%). ctDNA analysis showed emerging secondary mutations in NRAS and MEK in eight patients at time of BRAFi/MEKi resistance. Elimination of these mutations by vorinostat treatment was observed in three patients. Conclusions: Intermittent treatment with vorinostat in patients with resistant BRAFV600 mutated melanoma is well tolerated. Although the primary endpoint of this study was not met, durable anti-tumor responses were observed in a minority of patients (9%). "

  Study EGAS00001007709

• Germ Cell Tumor Molecular Heterogeneity Revealed through Analysis of Primary and Metastasis Pairs

  Purpose: While primary germ cell tumors (GCTs) have been extensively characterized, molecular analysis of metastatic sites has been limited. We performed whole-exome sequencing and targeted next-generation sequencing on paired primary and metastatic GCT samples in a patient cohort enriched for cisplatin-resistant disease. Experimental Design: Tissue sequencing was performed on 100 tumor specimens from 50 patients with metastatic GCT, and sequencing of plasma cell-free DNA (cfDNA) was performed for a subset of patients. Data is being provided for a subset of 10 patients whose samples were analyzed using whole-exome sequencing. Results: The mutational landscape of primary and metastatic pairs from GCT patients was highly discordant (68% of all somatic mutations were discordant). Whereas genome duplication was common and highly concordant between primary and metastatic samples, only 25% of primary-metastasis pairs had =50% concordance at the level of DNA copy number alterations (CNAs). Evolutionary-based analyses revealed that most mutations arose after CNAs at the respective loci in both primary and metastatic samples, with oncogenic mutations enriched in the set of early occurring mutations versus variants of unknown significance (VUS). TP53 pathway alterations were identified in nine cisplatin-resistant cases and had the highest degree of concordance in primary and metastatic specimens, consistent with their association with this treatment-resistant phenotype. Conclusions: Analysis of paired primary and metastatic GCT specimens revealed significant molecular heterogeneity for both CNAs and somatic mutations. Among loci demonstrating serial genetic evolution, most somatic mutations arose after CNAs but oncogenic mutations were enriched in the set of early occurring mutations as compared to VUS. Alterations in TP53 were clonal when present and shared among primary-metastasis pairs. Reprinted from pending publication, with permission from JCO Precision Oncology.

  Study phs002229

• Genetic Progression of Head and Neck Squamous Cell Carcinoma

  These data are tumor/normal-matched exome sequencing results on 45 participants (median age of 58.6 years) treated for squamous cell carcinoma of the oropharynx at Mass Eye and Ear (MEE; Boston, MA, USA). Some tumors had two portions sequenced separately. All research complied with all relevant ethical regulations. Human subject research on patients at MEE was approved by the MEE Human Studies Committee under protocol HSC 11-024H, with informed consent obtained from participants. Participants were not compensated. Clinical data, including HPV status, were abstracted from patient records. Sequencing and clinical data were combined with TCGA data on 486 cases of head and neck squamous cell carcinoma (HNSCC). The PhylogicNDT suite of genomic analysis tools was used to infer the relative timing of genetic events during tumor progression for major molecularly defined classes of HNSCC: HNSCC negative for human papillomavirus (HPV), HPV-associated HNSCC, and HPV-negative HNSCC involving mutations in CASP8, NSD1, and NOTCH1. The timing of the development of whole-genome somatic events leading to tetraploidy or triploidy and to increased measures of intratumor genetic heterogeneity was determined. Associations of heterogeneity measures with clinical outcomes were assessed. The analysis identified genes with early mutations that likely play roles in HPV-tumor initiation (FBXW7, NOTCH1, CASP8, FAT1, NSD1, HRAS, EP300, CREBBP, KMT2D). Genes with mutations at later median relative timing (PK3CA, NFE2L2, HLA-A, SPEN, and KMT2C) were more likely to be important for progressing to later stages of the disease. Gain of chromosome arm 3q and loss of arm 11q were found to occur more frequent in HPV+ HNSCC cases. A high prevalence of 3p and 17p loss were also observed in HPV+ HNSCC cases.

  Study phs003139

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856