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• Single-cell genome-wide concurrent haplotyping and copy-number profiling through genotyping-by-sequencing

  scGBS is a single-cell sequencing-based methodology to haplotype and copy-number profile single cells. Genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing of the restriction fragments. scGBS data serves as the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping (Zamani Esteki et al., 2015). We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells with the use of a HapMap cell line pedigree (7 single cells). A clinical validation of the methodology with a total of 14 single blastomeres and 3 trophectoderm samples biopsies from human preimplantation embryos for 6 PGT-M families were processed with scGBS and were previously haploptyped via SNP array.

  Dataset EGAD00001007794

• WGS dataset of Mutational Mechanisms in Multiply Relapsed Pediatric Acute Lymphoblastic Leukemia

  Pediatric acute lymphoblastic leukemia (ALL) is marked by low mutational load at initial diagnosis, which increases at relapse. The elevated mutational load at relapse can partly be explained by at least two therapy-related effects and a combination of therapy and underlying mismatch repair deficiency. However, our understanding of the type and timing of mutational mechanisms in relapsed ALL is limited, and it is unclear to what extent mutational processes contribute to disease progression. We collected a cohort of 29 Dutch ALL patients across multiple treatment protocols who had multiple relapses. Using whole genome sequencing of the sequential tumor samples of each patient we were able to distinguish the mutational processes active in relapsed ALL and could track the activity of mutational processes over time. This allowed us to investigate whether subtype-specific mutational processes at diagnosis can continue in relapse or emerge at relapse if absent in initial diagnosis. Furthermore, we assessed whether the activity of mutational processes contributed to disease development and relapse.

  Dataset EGAD00001015401

• A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

  Abstract: Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cancer cell trajectory regionalizes tumours into distinct tissue niches and manifests in a conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to jointly map cancer cell states and clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

  Dataset EGAD00001015516

• Tumor Profiler DAC

  The Tumor Profiler DAC is responsible for the access to data generated in the Tumor Profiler Study.

  Dac EGAC50000000199

• Dataset that contains analyses of submission 6

  This is the description of analyses dataset of submission 6

  Dataset EGAD50000001107

• Melanoma post mortem analysis

  This is the affymetrix gene expression data of the metastatic tumours related to this study.

  Dataset EGAD00010001717

• RBtargetedSeq

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Targeted Sequencing.

  Dataset EGAD00001008004

• The molecular basis of T-PLL is an actionable perturbation of TCL1/ATM- and epigenetically instructed damage responses

  T-cell prolymphocytic leukemia (T-PLL) is a rare and poor-prognostic mature T-cell malignancy. To address its incomplete molecular concept, we integrated large-scale profiling data of alterations in gene expression, allelic copy number (CN), and nucleotide sequences in 111 well-characterized patients. Besides prominent signatures of T-cell activation and prevalent clonal variants, we also identified novel hot-spots for CN variability, fusion molecules, alternative transcripts, and progression-associated dynamics. The overall lesional spectrum of T-PLL is mainly annotated to axes of DNA damage responses, T-cell receptor/cytokine signaling, and histone modulation. We formulate a multi-dimensional model of T-PLL pathogenesis centered around a unique combination of TCL1 overexpression with damaging ATM aberrations as initiating core lesions. The effects imposed by TCL1 cooperate with compromised ATM towards a leukemogenic phenotype of impaired DNA damage processing. Dysfunctional ATM appears inefficient in alleviating elevated redox burdens and telomere attrition and in evoking a p53-dependent apoptotic response to genotoxic insults. As non-genotoxic strategies, synergistic combinations of p53 reactivators and deacetylase inhibitors reinstate such cell death execution.

  Study EGAS00001002744

• DAC Johns Hopkins University - AML scRNAseq study

  This is the Data Access Committee (DAC) from Johns Hopkins University for the study scRNAseq of acute myeloid leukemia

  Dac EGAC50000000242

• CNCD Recall by Genotypes

  This is a DAC to handle recall by genotype studies by the conducted by the Center for Non-Communicable Disease Karachi, Pakistan.

  Dac EGAC50000000937