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• Evolution of a FLT3-TKD mutated subclone at meningeal relapse in acute promyelocytic leukemia (H021)

  Here we report the case of an acute promyelocytic leukemia (APL) patient who, though negative for FLT3 mutations at diagnosis, developed isolated FLT3-TKD positive meningeal relapse, which, in retrospect, could be traced back to a minute bone marrow subclone present at first diagnosis. Initially, the 48-year old female diagnosed with high-risk APL had achieved complete molecular remission after standard treatment with all-trans retinoic acid (ATRA) and chemotherapy according to the AIDA protocol. 13 months after start of ATRA maintenance the patient suffered clinically overt meningeal relapse along with minute molecular traces of PML/RARA in the bone marrow. Following treatment with arsenic trioxide (ATO) and ATRA in combination with intrathecal cytarabine and methotrexate, the patient achieved a complete molecular remission in both cerebrospinal fluid (CSF) and bone marrow, which currently lasts for two years after completion of therapy. Whole exome sequencing and subsequent ultradeep targeted re-sequencing revealed a heterozygous FLT3-TKD mutation in CSF leukemic cells (p.D835Y, c.2503G>T, 1000/1961 reads (51%)), which was undetectable in the concurrent bone marrow sample. Interestingly, the FLT3-TKD mutated meningeal clone originated from a small bone marrow subclone present in a variant allele frequency of 0.4% (6/1553 reads) at initial diagnosis. This case highlights the concept of clonal evolution with a subclone harboring an additional mutation being selected as the “fittest” and leading to meningeal relapse. It also further supports earlier suggestions that FLT3 mutations may play a role for migration and clonal expansion in the CSF sanctuary site.H021

  Study EGAS00001001848

• A novel breast cancer cell line derived from a metastatic bone lesion of a breast cancer patient

  Purpose: We sought to generate and characterize a novel cell line from a breast cancer bone metastasis in order to better study the progression of the disease.Methods: The cell line, P7731, was derived from a metastatic bone lesion of a breast cancer patient and assessed for marker expression. P7731 was analyzed for DNA copy number variation, somatic mutations and gene expression and compared to the primary tumor. Results: P7731 cells are negative for estrogen receptor alpha (ERα), progesterone receptor (PR) and HER2 (triple-negative), strongly express vimentin (100% of cells positive) and also express cytokeratins 8/18 and 19 but at lower frequencies. Flow cytometry indicates P7731 cells are predominantly CD44+/CD49f+/EpCAM-, consistent with a primitive, mesenchymal-like phenotype. The cell line is tumorigenic in immunocompromised mice. Exome sequencing identified a total of 45 and 76 somatic mutations in the primary tumor and cell line respectively, of which 32 were identified in both samples and included mutations in known driver genes PIK3CA, TP53 and ARID1A. P7731 retains the DNA copy number alterations present in the matching primary tumor. Homozygous deletions detected in the cell line and in the primary tumor were found in regions containing three known (CDKN2A, CDKN2B and CDKN1B) and 23 putative tumor suppressor genes. Cell line specific gene amplification coupled with mRNA expression analysis revealed genes and pathways with potential pro-metastatic functions.Conclusion: This novel human breast cancer-bone metastasis cell line will be a useful model to study aspects of breast cancer biology, particularly metastasis-related changes from breast to bone.

  Study EGAS00001002840

• Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses

  Personalized cancer therapy based on the somatic mutations identified in patient tumors is becoming an increasingly emphasized approach to improve outcomes of patients with cancer. There are also examples of therapeutic vulnerabilities in cancer that result from changes in gene expression that are a direct or indirect result of tumor specific epigenetic perturbations. These genomic and epigenomic changes are ultimately manifest in the tumor proteome and phosphoproteome. In this study, we integrated genomic, epigenomic and proteomic data for rhabdomyosarcoma (RMS) to identify therapeutic vulnerabilities. RMS was selected for this analysis because RAS pathway mutations in rhabdomyosarcoma (RMS) are the most common potentially actionable lesions across pediatric solid tumors. The epigenomic data was useful for identifying deregulated developmental pathways in RMS including the WNT, HH, BMP, adenyl cyclase, p38/MAPK and PI3K pathways. Perturbations in those 6 myogenic signal transduction pathways were also evident in the proteome and phosphoteome data. In addition, the proteomic/phosphoproteomic data revealed that the cell cycle checkpoint, unfolded protein response and RB/E2F pathways were deregulated in RMS relative to normal muscle. Recent success targeting CDK4/6 and MEK in adult cancers with RAS mutations led us to test the value of these targets in RMS tumors. We also targeted the unfolded protein response pathway and cell cycle checkpoint pathway using molecular targeted therapeutics in orthotopic patient derived xenografts. Taken together, these data demonstrate the value of integrating epigenomic and proteomic data to identify tumor vulnerabilities that extend beyond somatic mutations identified in the genome.

  Study EGAS00001002967

• Peripheral blood DNA methylome in adalimumab-treated patients with rheumatoid arthritis

  Background and aims Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade such as anti-TNFα therapy. Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood Methods DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of adalimumab (ADA) was determined using Illumina HumanMethylationEPIC BeadChip Array. After 6 months, treatment response was assessed according to the Alliance of Associations for Rheumatology (EULAR) criteria for disease response. Patients were classified as responders (DAS28<3.2 or decrease of 1.2 points) or as non-responders (DAS28>5.1 or decrease of less than 0.6 points). Machine learning models were built with gradient boosting and stability selection models to predict response prior to ADA treatment with predictor DNA methylation markers. Results We demonstrated a 27-feature panel of DNA methylation markers or CpG classifiers that predict response in RA patients treated with ADA. Forty-nine patients were assigned as responder and 43 patients assigned as non-responders. We differentiate responders from non-responders with a high sensitivity (AUC 0.76). The predictor CpGs annotated to genes involved in immunological- and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology and angiogenesis. Conclusion Our findings indicate that DNA methylation signatures discriminate responders and non-responders to ADA treatment and can serve as a tool for therapy prediction.

  Study EGAS00001007578

• HIV exome pilot, exome data part 2 GRCh37_53

  exome sequence data for 25 HIV elite long term non-progressors and rapid progressors. Partial dataset (overlap with EGAD00001000047) of raw BAMs mapped to GRCh37_53.

  Dataset EGAD00001000087

• Oncotrack_450K_metastatic

  Oncotrack metastatic samples using 450K. The shared AF analysis files oncotrackDNAmAnalysis.R and oncotrackDNAmBetaScores.txt which were applied for both Oncotrack_450K_tumor (EGAD00010001162) and Oncotrack_450K_metastatic (EGAD00010001161) datasets are included on Oncotrack_450K_tumor (EGAD00010001162) dataset.

  Dataset EGAD00010001161

• iPSC Samples for EBiSC

  EBiSC Whole Genome Sequencing raw FASTQ

  Dataset EGAD00001003882

• iPSC alignment file (CRAM) for EBiSC

  EBiSC Whole Genome Sequencing processed CRAM

  Dataset EGAD00001003942

• WTCCC2 bacteraemia susceptibility replication samples

  A WTCCC2 project - replication study for bacteraemia susceptibility in 2518 individuals from Kenya, genotyped on the Illumina Immunochip chip.

  Dataset EGAD00010001509

• GCAT| SNParray coreSpain V1

  4988 samples issued from GCAT cohort, genotyped with MEGAex-Infinium Array, with data for Cr1-22. Plink files with QC but not imputed.

  Dataset EGAD00010001665

• GCAT| SNParray coreSpain V2

  4988 samples issued from GCAT cohort, genotyped with MEGAex-Infinium Array, with data for Cr1-22. Plink files with QC and imputed (SHAPEIT+IMPUTE).

  Dataset EGAD00010001664

• Richter Syndrome Methylation dataset

  Raw idat files for 90 RS + DLBCL + CLL samples.

  Dataset EGAD00010002194

• P50_P76_P763_DAC_DMSO_24h_48h_72h_Illumina_HT12v4_Gene_Expression.xlsx

  Expression dataset for DAC treated CD34 sorted primary CML bone marrow samples

  Dataset EGAD00010002211

• DNA-Methylation data for atypical teratoid/rhabdoid tumoroids study

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00010002400

• Hostage_2_genotype

  Cohort: Raw genotype files for Hostage2 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002171

• Hostage_3_genotype

  Cohort: Raw genotype files for Hostage3 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002173

• Hostage_4_genotype

  Cohort: Raw genotype files for Hostage4 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002178

• Hostage_1_genotype

  Cohort: Raw genotype files for Hostage1 cohort. Genotype Chip: Illumina’s (Illumina Inc., San Diego, U.S.) Global Screening Array-24 Multi Disease (GSA) Version 2.0 B1 genomic build: b37

  Dataset EGAD00010002180

• OncoScan SNP data set for systemic follicular lymphoma (sFL)

  122 unpaired initial tumor samples (122 FFPE samples) of sFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002592

• T and NK cell PBMC metadata

  Cell annotation for single T and NK cells of PBMC collected from 113 patients, including UMAP coordinates and annotation.

  Dataset EGAD50000000140

• WGS and WXS for mismatch repair-deficient human colon organoids

  Whole genome/exome sequencing to detect spontaneous acquired mutations in mismatch repair-deficient human colon organoids.

  Dataset EGAD50000000159

• Genotyping data of 81 patients with multiple sclerosis and other neurological diseases

  This dataset consists of raw unimputed genotype data for 81 individuals with multiple sclerosis (n=33) and other neurological disease (n=48).

  Dataset EGAD50000000183

• GSA reference

  reference whole exome sequence serving as a reference of individuals. Includes the raw GSa files and the called variants in vcf format merge for all individuals (S1-S5).

  Dataset EGAD50000000481

• bfast CohortD OAPL

  ctDNA mutation calls (one mutation per sample per line) for BFAST Cohort D including mutation-level data like coding change, allele frequency, etc.

  Dataset EGAD50000000148

• Dataset for NSCLC samples

  This dataset contains 10x scRNA sequencing data of 16 NSCLC samples. Sequencing was performed on Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD50000000185