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• Identifying transcriptional programs underlying anti-EGFR small molecule response and resistance with TraCe-seq

  Genetic and non-genetic heterogeneity within cancer cell populations represents a major challenge to anti-cancer therapies. We currently lack robust methods to determine how pre-existing and adaptive features affect cellular responses to therapies. Here, by conducting clonal fitness mapping and transcriptional characterization using expressed barcodes and single-cell RNA-sequencing, we have developed TraCe-seq, a method that captures at clonal resolution the origin, fate, and differential early adaptive transcriptional programs of cells in a complex population in response to distinct treatments. We used TraCe-seq to benchmark how next-generation dual EGFR inhibitors-degraders compare to standard EGFR kinase inhibitors in EGFR-mutant lung cancer cells. To QC the TraCe-seq strategy, single-cell RNA-seq libraries were generated from a variety of human cancer cell lines transduced with the TraCe-seq library to validate the TraCe-seq strategy. Specifically, 5 different cell lines (PC9, MCF-10A, MDA-MB-231, NCI-H358, and NCI-H1373) were each transduced with a unique TraCe-seq barcode. The transduced cells were selected with puromycin only, dissociated to single cell suspensions, and then mixed together. The complex mixture of the 5 cell lines was profiled by 10X scRNA-seq. Furthermore, transduced NCI-H1373 cells were sorted by FACS to enrich for the top 50% of eGFP positive cells, and sorted cells were cultured briefly and used to construct scRNA-seq libraries and profiled by 10x scRNA-seq. To carry out the full TraCe-seq experiment, ~600 PC9 cells carrying unique TraCe-seq barcodes were expanded over 12 doublings to establish the barcoded population. A subset of the barcoded PC9 population was used to generate scRNA-seq libraries and profiled by 10x scRNA-seq prior to treatment to establish a baseline transcription profile for each barcoded clone. The rest of the cells were then treated for four days with 1 µM erlotinib, 1 µM GNE-069, or 1 µM GNE-104 respectively. scRNA-seq libraries were then generated form the treated cells and profiled by 10x scRNA-seq.

  Study EGAS00001005405

• The ERASMUSMC-HEMA Data Access Committee controlling appeals for 3q26-rearranged acute myeloid leukemia datasets from the department of Hematology at Erasmus MC

  Dac EGAC00001000162

• TUM Experimental Neuroimmunology

  This DAC will decide about all data requests to human data deposited at EGA that was primarily produced at the Institute for Experimental Neuroimmunology at the Technical University Munich, Germany.

  Dac EGAC50000000093

• DAC "Genotyping based on SNPs covered by targeted NGS"

  DAC for the dataset from the article "Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples"

  Dac EGAC50000000358

• H3Africa KDRN Phenotype

  The phenotypic data for 6431 samples of the KDRN Study from Ghana and Nigeria.

  Dataset EGAD00001009333

• AmsterdamUMC Data Access Committee for the study "Multi-omic analysis of thyroid dysfunction in Down Syndrome"

  The current data access committee reviews and assesses data requests of researchers that seek to gain access to data acquired as part of the "Multi-omic analysis of thyroid dysfunction in Down Syndrome" study as performed at the AmsterdamUMC, Amsterdam, Netherlands.

  Dac EGAC50000000186

• WGS data subfolder for normal tissue from multifocal ileal NETs study

  This dataset includes linked-read whole-genome sequencing data from the normal ileal of the patient. The normal sample was sequenced using the 10x Genomics linked-read whole-genome sequencing (WGS) approach.

  Dataset EGAD00001008491

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  The extensive primary and secondary drug resistance in many cancer types requires rational approaches to design personalized and selective combinatorial therapies that do not only show synergistic effect in overall cancer cell killing but also result in minimal toxic side effects on non-malignant cells. To address the combinatorial explosion in the number of relevant combinations, we implemented a machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA-sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory AML patient cases, each with a different genetic background, our integrated approach accurately predicted patient-specific combinations that were shown to result not only in synergistic cancer cell co-inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our data-driven approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of non-malignant cells, and highlight the relevance of considering cell heterogeneity for personalized cancer therapy.

  Study EGAS00001004614

• Systematic Analysis of Coding and Non-coding Elements in Developmental Pathways Implicated in Holoprosencephaly Pathogenesis

  We applied targeted capture to examine 153 genes representative of all the major vertebrate developmental pathways among 340 probands and 285 of their unaffected parents to rank their relative significance as causes for holoprosencephaly (HPE).

  Study phs001653

• Germline Genetic Variants in Cancer-Susceptibility Genes in Patients with Osteosarcoma

  We conducted whole-exome sequencing of germline DNA from patients with osteosarcoma to estimate the frequency of pathogenic/likely pathogenic germline genetic variants in known cancer-susceptibility genes in a large population of osteosarcoma patients unselected for family history.

  Study phs002444

• smallRNA-seq of isolated pancreatic islets

  In this study we performed smallRNA-sequencing, a sequencing method designed for small noncoding RNAs, to investigate the miRNA expression profile of human islets isolated from pancreata of donors without diabetes mellitus (n=3) and donors with type 2 diabetes (n=3).

  Study EGAS50000000865

• scD&D Multiome of GATA1 regulatory network dynamics during erythropoiesis

  Ex vivo differentiation experiments of human CD34+ hematopoietic stem and progenitor cells and profiled cells along the erythropoietic trajectory at multiple timepoints (differentiation days 0, 5, 8, 18, and 24), for simultaneous assessment of transcriptome, chromatin accessibility and GATA1 binding

  Study EGAS50000001606

• Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma

  Investigating the spatial architecture and microenvironment of EMD lesions in Multiple Myeloma patients using spatial whole transcriptome sequencing (10X Visium), tomography for spatially resolved transcriptomics (TOMO-seq), and single-cell RNA sequencing (scRNAseq)

  Study EGAS50000000227

• 10X snMultiome (ATAC+GEX) for the study of "SPEN loss drives extra-follicular diffuse large B cell lymphoma with female-specific lethality and TLR pathway therapeutic vulnerabilities"

  10X snMultiome (ATAC+GEX) sequencing of 1 human reactive tonsil sample and 1 DLBCL sample for the study of "SPEN loss drives extra-follicular diffuse large B cell lymphoma with female-specific lethality and TLR pathway therapeutic vulnerabilities"

  Dataset EGAD50000002277

• RNA fastq files and mutation annotation files

  This dataset contains the results of both RNA sequencing and DNA sequencing. For RNA sequencing, it includes data from 12 samples derived from 9 patients, provided as raw sequencing files in FASTQ format. For DNA sequencing, it contains mutation information obtained from targeted panel DNA sequencing, including data from 35 individuals in the mutation annotation file (MAF) format.

  Dataset EGAD50000001768

• Emirati Phased Diploid Trio-Assemblies (27 Individuals, 54 Assemblies)

  This dataset comprises 27 phased diploid genome assemblies (i.e., 54 haplotypes) from Emirati family trios, provided in FASTA format. Sequencing used PacBio HiFi reads generated on the Revio platform at ≥25× coverage for each parent and offspring. Assemblies were produced with hifiasm (trio mode), then scaffolded with NTLink and reference-guided merging via RagTag against CHM13v2. The collection offers haplotype-resolved references suitable for downstream variant discovery and pangenome analyses.

  Dataset EGAD50000001755

• Large deletion predisposes to familial melanoma

  Mutations in CDKN2A and CDK4 account for ~40% of multi-case melanoma families, with another ~10% attributable to BAP1 and genes associated with telomere maintenance. Thus, a substantial proportion of the genetic risk underlying familial melanoma remains unexplained.

  Study EGAS50000001496

• Evolution of four ER+ breast cancers

  The evolution of four breast cancers was analyzed using longitudinal samples collected over 2-15 years. Whole-genome sequencing and single-cell RNA-Seq were used to analyze evolution. We have deposited VCF files for SNV, indel, and structural variant calls from WGS data, and a text file showing transcripts per million (TPM) expression for the single-cell RNA-Seq data.

  Dataset EGAD00001003303

• Sequencing data for oesophageal and related samples - Alex Frankell et al (RNA)

  Data supporting: "The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic." Frankell et al. RNAseq (BAM files) 116 tumours

  Dataset EGAD00001004423

• Mutational_burden_in_skin_following_UV_treatment_Nanoseq

  We wish to assess the changes in mutational burden in patients under going UV treatment for psoriasis. Using duplex sequencing we will assess mutational burden in traditionally sun exposed (arm) and non-sun exposed (buttock) skin epidermis both pre and post UV treatment.

  Study EGAS00001007681

• Mutational_burden_in_skin_following_UV_treatment_WGS

  We wish to assess the changes in mutational burden in patients under going UV treatment for psoriasis. Using duplex sequencing we will assess mutational burden in traditionally sun exposed (arm) and non-sun exposed (buttock) skin epidermis both pre and post UV treatment.

  Study EGAS00001007682

• Transcriptomic response of miRNAs of monocytes to bacterial and viral stimuli assessed by RNA-seq in Africans and Europeans

  The dataset includes raw RNA-seq data (fastq files) for miRNAs of monocytes before and after 6-hour exposure to four different immune stimuli, measured in 200 African- and European-descent healthy donors from Belgium. The stimuli include ligands for TLR4 (LPS), TLR1/2 (Pam3CSK4) and TLR7/8 (R848) and to a human seasonal influenza A virus (IAV).

  Dataset EGAD00001006181

• Improved Sezary cell detection and novel insights into immunophenotypic and molecular heterogeneity in Sézary syndrome

  We performed a prospective investigation in Sézary syndrome by the application of a standardized multiparameter flow cytometry, FACS-cell sorting, and RNA-sequencing for an in-depth immunophenotypic and transcriptional profiling of Sézary cells.

  Study EGAS00001005229

• Set_of_human_mesenchymal_CSA

  51 DNA methylation arrays of human samples initially diagnosed as mesenchymal chondrosarcoma. Microdissection of the cartilage and/or the small round cell component from the same sample may have occurred. As mentioned in the sample descriptions, the diagnoses of four samples have been revised. Additional molecular investigations were conducted for a subset of samples as described in the related publication.

  Dataset EGAD00010002515

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226