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• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• Clinical cancer panel sequencing (UCSF500) analysis of TERT promoter duplication in GBM.

  Dataset contains paired-end clinical cancer panel sequencing (UCSF500) data from 2 samples of an initial tumor and one sample of a recurrence from one GBM patient and one sample from a second GBM patient.

  Dataset EGAD00001009286

• IL-17B uses IL-17RA and IL-17RB to induce Type-2 inflammation from human lymphocytes

  IL-17 family cytokines are critical to host defense responses at cutaneous and mucosal surfaces. Whereas IL-17A, IL-17F, and IL-17C induce overlapping inflammatory cascades to promote neutrophil-mediated immunity, IL-17E/IL-25 drives type 2 immune pathways and eosinophil activity. Genetic and pharmacological studies reveal the significant contribution these cytokines play in antimicrobial and autoimmune mechanisms. However, little is known about the related family member, IL-17B, with contrasting reports of both pro- and anti-inflammatory function in rodents. We demonstrate that in the human immune system, IL-17B is functionally similar to IL-25 and elicits type 2 cytokine secretion from innate type 2 lymphocytes, NKT, and CD4+ CRTH2+ Th2 cells. Like IL-25, this activity is dependent on the IL-17RA and IL-17RB receptor subunits. Furthermore, IL-17B can augment IL-33–driven type 2 responses. These data position IL-17B as a novel component in the regulation of human type 2 immunity.

  Study EGAS50000000579

• The Ashkenazi Genome Consortium: Phase I

  Whole genome, high coverage, sequencing of 128 Ashkenazi Jewish controls

  Dataset EGAD00001000781

• Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination

  The study aimed to explore immune responses during a time course of BNT162b2 mRNA vaccination. To better characterize these responses, we utilize a suite of multi-modal single-cell sequencing technologies to identify and characterize antigen-specific CD8+ T cells. We leverage an integrative analysis strategy to characterize five molecular modalities in these cells. Our dataset enables the discovery of new subpopulations, molecular signatures, and developmental regulators of these cells. Moreover, we identify these cells in SARS-CoV-2 infected patients and demonstrate their positive predictive value for subsequent clinical recovery. The sequence data of CITE-seq, ASAP-seq and ECCITE-seq experiments are deposited in dbGaP. Peripheral blood mononuclear cell (PBMC) samples for CITE-seq and ASAP-seq were collected from six healthy donors at four time points: immediately before (Day 0) vaccination, after primary vaccination (Day 2, Day 10), and seven days after boost vaccination (Day 28). PBMC samples for ECCITE-seq were collected from eight healthy donors on Day 28.

  Study phs003322

• Recurrent adeno-associated virus 2-related insertional mutagenesis in human hepatocellular carcinomas

  Hepatocellular carcinomas (HCC) are liver tumors related to various etiologies including alcohol intake, hepatitis B (HBV) or C (HCV) virus infection. Additional risk factors remain to be identified, particularly in patients who develop HCC without cirrhosis. We identified clonal integration of adeno-associated virus type 2 (AAV2) in 11 out of 193 HCC. These AAV2 integrations occurred within known cancer driver genes, namely, CCNA2 (Cyclin A2, 4 cases), TERT (Telomerase Reverse-Transcriptase, 1 case), CCNE1 (Cyclin E1, 3 cases), TNFSF10 (Tumor Necrosis Factor member 10, 2 cases) or KMT2B (Lysine (K)-Specific Methyltransferase 2B, 1 case) leading to over-expression of the target genes. Tumors with viral integration mainly developed in non-cirrhotic liver (9 out of 11 cases) and without known risk factors (6 out of 11) suggesting a pathogenic role of AAV2 in these patients. In conclusion, AAV2 is a DNA virus associated with oncogenic insertional mutagenesis in human HCC.

  Study EGAS00001001284

• Rare Genetic Steroid Disease Consortium (GSD) Apparent Mineralocorticoid Excess (AME) Syndrome Natural History Clinical Protocol

  The objective of this project is to observe the natural history of apparent mineralocorticoid excess (AME). AME is a rare monogenic hypertensive disease first described by the Principal Investigator, Dr. Maria I. New, in 1977 [1], for which the first mutation in the causative HSD11B2 gene was reported by her group in 1995. [2] Little is known about the progression of this rare hypertensive disorder, which is defined by the presence of hypertension in conjunction with low renin, low to absent levels of the mineralocorticoid aldosterone, elevated urinary cortisol/cortisone metabolite ratio 5β-tetrahydrocortisol (THF) + allo-THF/ tetrahydrocortisone (THE) (i.e., THF+αTHF/THE) and the presence of mutations in the HSD11B2 gene on both alleles. The range of symptoms reflecting progressive end-organ damage is wide in the small number of affected individuals studied to date. It is important to develop accurate information on the longitudinal pattern of progression (natural history) of AME not only because it may improve the care of affected individuals, but also because it may serve as a platform for understanding the role of mineralocorticoid excess in other forms of low-renin hypertension, which constitute 40% of all forms of hypertension. In this study, we will recruit as widely as possible through the Rare Diseases Clinical Research Network (RDCRN) to identify individuals with the triad of low renin, low aldosterone, and elevated urinary THF+αTHF/THE and confirm their disorder as AME by molecular genetic testing of the HSD11B2 gene. We will gather clinical and biochemical data yearly for the duration of the grant. We will describe phenotype and genotype at diagnosis and follow the progression or regression of symptoms over time under standard treatment, with rigorous review of end organs potentially damaged by chronic hypertension. Finally, we will study the natural history of the carriers of HSD11B2 mutations, i.e., the heterozygote relatives of the probands, in order to ascertain evidence of existing or developing hypertension and resulting end-organ damage. This will be the first rigorous study of AME heterozygotes. The participant accrual for AME-affected patients and the carriers of HSD11B2 mutations will be at least two years, but as long as seven years.

  Study phs000603

• Single-cell RNA-seq of peripheral blood mononuclear cells in classic Hodgkin lymphoma

  Abstract: Classic Hodgkin lymphoma (cHL) is a largely MHC class I-negative tumor with recurrent 9p24.1/ PD-L1/ PD-L2 copy gains and the highest reported response rates to PD-1 blockade. We utilized scRNA sequencing to characterize the peripheral immune response to PD-1 blockade and more broadly define non-CD8+ dependent mechanisms of immune evasion in cHL. Peripheral blood mononuclear cells were obtained from 20 patients with relapsed/refractory (R/R) cHL treated with PD-1 blockade (nivolumab) on the CheckMate 205 clinical trial (paired samples from cycle 1 day 1 [C1D1] and cycle 4 day 1 [C4D1]), 11 patients with newly diagnosed, previously untreated cHL and 13 healthy donors.

  Dataset EGAD00001011360

• Genome-wide identification of distinct miRNA-mRNA target regulation pairs in Non-Hodgkin lymphomas: a report from the ICGC MMML-Seq consortium

  We obtained miRNA profiles by miRnome sequencing from ICGC MMML-Seq patients diagnosed with Burkitt lymphoma, diffuse large B-cell lymphoma and follicular lymphoma and provide evidence of subtype-specific miRNA expression differences. We describe differentially expressed, mutated, edited and not previously annotated miRNAs. Addditionally, by performing argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) experiments, we obtained a set of biochemically-validated miRNA binding sites and identified miRNA-mRNA interaction pairs with a negative correlation in patient RNASeq data.

  Study EGAS00001001394

• Cancer sequencing for somatic variant calling

  This dataset includes WES and RNAseq for 11 patients with metastatic melanoma, lung, kidney, and stomach cancers, enrolled in phase I clinical trials of TIL ACT (NCT03475134 & NCT04643574). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 2500/4000 and Illumina NextSeq 550 systems.

  Study EGAS00001007101