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• Germline alterations of acute myeloid leukemia

  Germline variants may increase the risk of cancer. A systematic analysis was conducted of a consecutive series of primary and secondary adult acute myeloid leukemia (AML) patients. Exome sequencing was performed on leukemic blasts (somatic) and a skin biopsy (germline) followed by analysis on 35 acute myeloid leukemia or cancer predisposition candidate genes. Exome data were available from 68/80 (85%) patients, and full clinical history was available from all patients. The analysis revealed 34 rare single-nucleotide variants or short indels in 16 genes to be functionally adverse in silico.

  Study EGAS00001002760

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771

• NIH RECOVER: A Multi-Site Pathology Study of Post-Acute Sequelae of SARS-CoV-2 Infection

  The RECOVER tissue pathology study is a cross-sectional study designed to define and characterize the epidemiology, natural history, clinical spectrum, and underlying mechanisms of post-acute effects of SARS-CoV-2 infection in a diverse population representative of the general COVID-19 population in the US. The autopsy study will characterize the pathology of PASC in decedents who die more than 60 days after initial onset or after hospital discharge, whether in-hospital or out-of-hospital at time of death, and who meet the working definition of PASC as defined by the recent World Health Organization publication given in Section 5.4. The autopsy study will also explore the pathology of acute SARS-CoV-2 infection in a smaller subset of decedents who died between15-60 days from symptom onset. This protocol defines the common set of clinical data elements, autopsy procedures for tissue collection, core measures, pathology protocols, shared pathology tissues, data elements, and methodology. Each investigator site is expected to perform autopsies on the decedents to address the pathophysiology of the potential long-term effects of SARS-CoV-2 infection on human health. The Consortium analysis plan aims to address research questions by incorporating: 1) tissue obtained from autopsies performed at each Phase II participant's site; and 2) tissue available from other pathology investigators/autopsy sites within the Consortium.

  Study phs003768

• scRNAseq of distal colon biopsies from patients with ulcerative colitis and healthy controls

  scRNAseq dataset of colonic epithelium from distal colon biopsies from 4 patients with ulcerative colitis and 4 healthy individuals. Includes 11 samples split into three conditions: healthy, healthy margin and ulcerated. Dataset includes raw Fastq files and processed csv count matrices. Fastq files are divided into 4 lanes and into index (I1) and read (R1, R2) files. Count matrices contain comma-separated values with cell barcodes as column names and gene names as row names. Cell Ranger (v3.0.1) software from 10x Genomics was used to process the output and align the reads. The refdata-cellranger-GRCh38-3.0.0 reference was downloaded from the 10x Genomics website. First, cellranger mkfastq function was used to demultiplex raw base call files into FASTQ files. Then, the FASTQ files were aligned and filtered with cellranger count function.

  Dataset EGAD00001010167

• WFS1 mutation screening in a large series of Japanese hearing loss patients: Massively parallel DNA sequencing-based analysis

  A heterozygous mutation in the Wolfram syndrome type 1 gene (WFS1) causes autosomal dominant nonsyndromic hereditary hearing loss, DFNA6/14/38, or Wolfram-like syndrome. To date, more than 40 different mutations have been reported to be responsible for DFNA6/14/38. In the present study, WFS1 variants were screened in a large series of Japanese hearing loss (HL) patients to clarify the prevalence and clinical characteristics of DFNA6/14/38 and Wolfram-like syndrome. Massively parallel DNA sequencing of 68 target genes was performed in 2,549 unrelated Japanese HL patients to identify genomic variations responsible for HL. The detailed clinical features in patients with WFS1 variants were collected from medical charts and analyzed. We successfully identified 13 WFS1 variants in 19 probands: eight of the 13 variants were previously reported mutations, including three mutations (p.A684V, p.K836N, and p.E864K) known to cause Wolfram-like syndrome, and five were novel mutations.

  Study JGAS000123

• Low-coverage Whole Genome Sequencing, Colorectal advanced adenomas, NKI-AvL TGO COCOS series

  For this tissue dataset, we applied low-pass whole genome sequencing to 96 advanced adenomas. Advanced adenomas were classified as lesions with low-risk or high-risk of progression, according to the presence of specific DNA copy number changes (Carvalho et al, CancerPrevRes, 2018).

  Dataset EGAD00001004078

• BLUEPRINT release January 2015, DNase-Hypersensitivity for monocyte

  DNase-Hypersensitivity data for 2 monocyte sample(s). 4 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001001185

• BLUEPRINT September 2016, ATAC-seq for bone marrow, on Genome GRCh38

  ATAC-seq data for 4 sample(s) from bone marrow, on Genome GRCh38. 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002710

• BLUEPRINT September 2016, ATAC-seq Mantle Cell Lymphoma from venous blood, on Genome GRCh38

  ATAC-seq data for 5 sample(s) Mantle Cell Lymphoma from venous blood, on Genome GRCh38. 5 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002918

• BLUEPRINT September 2016, ATAC-seq Chronic Lymphocytic Leukemia from venous blood, on Genome GRCh38

  ATAC-seq data for 106 sample(s) Chronic Lymphocytic Leukemia from venous blood, on Genome GRCh38. 111 run(s), 109 experiment(s), 109 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002916

• BLUEPRINT release January 2015, DNase-Hypersensitivity for Acute myeloid leukemia

  DNase-Hypersensitivity data for 1 Acute myeloid leukemia sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001001190

• BLUEPRINT September 2016, ATAC-seq for tonsil, on Genome GRCh38

  ATAC-seq data for 7 sample(s) from tonsil, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002708

• Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC)

  In 2016 we established the Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC) funded by the Ice Bucket Challenge Grant administered by the Motor Neurone Disease Research Institute of Australia. The goals of the SALSA-SGC are to collect biological samples from clinics across Australia with matched in depth clinical and self-report phenotypes and to generate multiple levels of genetic and genomic data. In this first data generation exercise of the SALSA-SGC the majority of the samples were collected prior to the formal establishment of SALSA-SGC from clinics across Australia.Briefly, the cohort includes the University of Sydney’s Australian Motor Neuron Disease DNA Bank (MND Bank) cohort recruited April 2000 to June 2011), with study protocol approved by the Sydney South West Area Health Service Human Research Ethics Committee (HREC). Cases were recruited from around Australia via state-based MND associations with diagnosis verified by a neurologist. The remainder of the cases were recruited from clinics across Australia between 2015 and 2017 under HREC approvals from Royal Brisbane and Women’s Hospital, Macquarie University Multidisciplinary Motor Neurone Disease Clinic,  Calvary Health Care Bethlehem in Melbourne , Fiona Stanley Hospital in Perth, and from 2016 under HREC approvals at each site for the sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC). The ALS cases were diagnosed with definite or probable ALS according to the revised El Escorial criteria. Some controls were recruited as either partners or friends of patients, healthy individuals free of neuromuscular diseases. We are providing GWAS and MWAS data in this dataset. Individual level GWAS data were generated using Illumina Infinium CoreExome-24 version 1.1 chips for N= 846 cases and N=665 controls. Individual MWAS data was generated using the Illumina Human methylation 450K array for N=782 cases and N=613 controls. There 1315 individuals where GWAS and MWAS data has been generated and is available. Further information on these data sets can be found: Paper 1: Restuadi, R, Garton, FC, Benyamin, B, Lin, T, et al. Amyotrophic Lateral Sclerosis Genetic Correlation with Cognitive Performance, educational attainment and schizophrenia: evidence from polygenic risk score analysis. (submitted) Paper 2: Nabais, MF, Lin, T, Benyamin, B et al. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. 2020. NPJ genomic medicine. 5(10). Files provided in this submission include: GWAS: This folder contains QCed genotype for the Australian ALS case-control cohort. Contains PLINK files for genotyping data (not imputed yet). The individuals selected here have: good consistency on phenotype data ethics approval registered as part of sporadic ALS studies unrelated by GRM cut-off 0.05 No ancestry QC yet MWAS: This folder contains the IDAT and post-QC normalized DNAm (beta) for the Australian ALS case-control cohort. 2019_AUS_ALS_PCTG_DNAm.tar.gz - IDATS for 1315 individuals analyzed in the MWAS study normalized_beta_values - Binary files (created with the OSCA software) containing information on the individuals, probes and the DNAm (beta) values obtained after QC phenotype_file - contains all the covariates analysed in the MWAS including: case-control status, coded 0 = Control and 1 = ALS, predicted age, predicted cell-type proportions, predicted smoking scores, slide and chip position and sex Important Notes: The DNAm data were normalized together with samples that were not part of this ALS case/control study and thus, the normalization procedure may not be 100% reproducible using only the IDAT files uploaded here. Summary data has been made publicly available and can be accessed directly: Data collection and sample processing were performed at several clinics across Australia. Genotyping and DNA methylation arrays were performed by the Human Studies Unit, at the Institute for Molecular Bioscience (University of Queensland). Quality control of the genotypic, phenotypic and DNA methylation data was done by the Program of Complex Traits Genomics, at the Institute for Molecular Bioscience (University of Queensland).

  Study phs002068

• BLUEPRINT September 2016, ATAC-seq for osteoclast from venous blood, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for osteoclast from venous blood, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002907

• BLUEPRINT September 2016, ATAC-seq Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38

  ATAC-seq data for 4 sample(s) Multiple Myeloma for plasma cell from bone marrow, on Genome GRCh38. 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002920

• BLUEPRINT September 2016, ATAC-seq for plasma cell from tonsil, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for plasma cell from tonsil, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002912

• BLUEPRINT September 2016, ATAC-seq for naive B cell from tonsil, on Genome GRCh38

  ATAC-seq data for 3 sample(s) for naive B cell from tonsil, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002903

• BLUEPRINT release August 2014, DNase-Hypersensitivity for macrophage

  DNase-Hypersensitivity data for 1 macrophage sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001000931

• BLUEPRINT release January 2015, RNA-Seq for monocyte

  RNA-Seq data for 8 monocyte sample(s). 8 run(s), 8 experiment(s), 8 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001191

• BLUEPRINT release January 2015, DNase-Hypersensitivity for macrophage

  DNase-Hypersensitivity data for 14 macrophage sample(s). 18 run(s), 14 experiment(s), 14 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001001198

• BLUEPRINT release August 2015, DNase-Hypersensitivity for monocyte, on genome GRCh38

  DNase-Hypersensitivity data for 2 monocyte sample(s). 4 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001560

• MNM - 16S rDNA amplicon dataset of 20 dense timeseries of fecal samples from Belgian women

  Fastq files for the 16S rDNA amplicon library of 714 fecal samples of 20 time series (as described in Vandeputte et al. 2021, Nature Communications)

  Dataset EGAD00001008275

• RNA-sequencing data from vitamin C experiment

  Fasq-files from 3 unaffected TET2 mutation carriers, 2 mutation carriers diagnosed with lymphoma and 3 family members without TET2 mutation. Time series data collected from 0, 6 and 12 months after daily dose of 1g vitamin C.

  Dataset EGAD00001010009

• SCI-MAP: Single Cell Microgel embedded iPS-cells to map molecular variability of cell differentiation using a systems biology approach.

  In the SCI-MAP project we will map cell states encountered across a previously established 49 day in vitro chondrogenic differentiation protocol. We do this to optimise the differentiation process for therapeutic and in vitro modelling ends. We collect epigenetic open chromatin and phenotypic transcriptome data to determine cell states and so performed single nucleus sequencing in two modalities: RNA- and ATAC-sequencing. Data was collected in a time series experiment at 7 points: Days 0, 3, 6, 8, 14, 21, and 49, starting with the first pilot data from day 21 (D21).

  Study EGAS50000001041

• Primary breast cancers and paired brain metastases sequencing study

  Improving management of brain metastases (BM) from primary breast cancer (PBC) is a challenge in which targeted therapies play a promising role. However this approach is impaired by miss-knowledge of molecular patterns of the metastatic and drug-resistance processes.We performed a differential analysis of copy-number-alterations (CNAs) and mutations profiles by using pan-genomic array-CGH and targeted next-generation-sequencing of 494 cancer-associated genes, in a series of 14 pairs of matched PBC/BM and assessed if the observed genomic alterations may be used as a druggable agent in a clinical trial.

  Study EGAS00001003173