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• Genomic Profiling Reveals Spatial Intra-tumour Heterogeneity in Follicular Lymphoma

  Follicular lymphoma (FL) is an incurable indolent non-Hodgkin’s lymphoma (NHL) that behaves heterogeneously and is synonymous with the reciprocal translocation t(14;18)(q32;q21), leading to aberrant constitutive over-expression of BCL2 [ref]. High risk groups with poor overall survival include patients that transform to a high grade lymphoma, most commonly diffuse large B cell lymphoma (DLBCL) or progress early (within 2 years) of receiving treatment. Recently several studies have shed light on the diverse genomic landscape of FL, with frequently mutated genes including those involved in chromatin remodelling, (CREBBP, KMT2D, EZH2, EP300), immune modulation (TNFRSF14), JAK-STAT (STAT6, SOCS1), NF-ĸB (CARD11, MYD88, TNFAIP3) and mTORC1 signalling (RRAGC) [ref]. To date, the majority of these studies have either focussed on inter-patient genetic heterogeneity or established the extent of clonal heterogeneity as a patient’s tumour evolves over time. Furthermore, these temporal studies have alluded to the existence of a tumour-repopulating population (referred to as the common progenitor cell (CPC)) that evades treatment and acts as a reservoir, seeding each subsequent relapse and transformation event. Although these previous studies have illuminated the longitudinal clonal dynamics that occur in FL, our understanding of the degree of spatial or intra-tumour heterogeneity (ITH) that exists within an individual patient thus far remains limited. Earlier studies have predominantly focused on differences in cytological grade and immunophenotype between spatially separated lymph nodes and bone marrow, the most common site of extra-nodal involvement in FL, with genomic profiling restricted to examining somatic hypermutation patterns in IgVH genes [ref]. In contrast next generation sequencing has been increasingly utilised in solid organ malignancies to derive comprehensive genomic profiles from spatially separated sites. The seminal study by Gerlinger et al revealed profound differences in the genetic make-up between individual primary and metastatic sites of renal cell carcinoma [ref]. Significant heterogeneity has since been demonstrated both between and within lesions in the same patient in lung, pancreas and breast cancer with presumed driver mutations distributed within the “branches” of the evolutionary phylogenetic tree, suggesting that a single biopsy is incapable of capturing the full genomic heterogeneity of an individual’s malignancy [ref]. This geographical heterogeneity not only adds to the diverse pool of tumour subclones that may contribute to drug resistance mechanisms but importantly, presents a major obstacle for precision-based approaches focussed on targeting specific lesions within a single biopsy. In FL, the exponential increase in clinical trials using novel agents such as EZH2, PI3K and BTK inhibitors reflects this shift in cancer care and along with the development of molecular prognostic scores such as the m7-FLIPI highlights the clinical need to accurately define genomic alterations with clinical relevance. As the majority of FL patients manifest disseminated tumour involvement, we sought to uncover the extent and clinical importance of spatial heterogeneity in FL by comprehensively genetically profiling 22 synchronously removed spatially separated biopsies from 9 patients. Using a combination of whole exome and targeted deep sequencing, our results inferred the complex subclonal architecture within these tumours and the mutational differences between anatomical sites, demonstrating cases with significant intrinsic genetic diversity.

  Study EGAS00001002492

• Exploring high-throughput drug sensitivity testing in neuroblastoma cell lines and patient-derived tumor organoids in the era of precision medicine

  Despite druggable events in 80% of neuroblastoma patients within the Princess Máxima Center precision medicine program 'iTHER', clinical uptake of treatment recommendations has been low, and the clinical impact for individual patients remains hard to predict. This stresses the need for a method integrating genomics and transcriptomics with functional approaches into therapeutic decision making. We aimed to launch an online repository integrating genomics and transcriptomics with high-throughput drug screening (HTS) of nineteen commonly used neuroblastoma cell lines and fourteen generated neuroblastoma patient-derived organoids (NBL-PDOs) to improve identification of molecularly matched therapies and support clinical uptake. Cell lines, NBL-PDOs and their parental tumors were characterized utilizing (lc)WGS, WES and RNAseq. Cells were exposed to a ~200 compound library, and viability was assessed using CellTiterGlo or MTT. Molecular and HTS results were transferred to the R2 platform (http://r2platform.com/pmc_nb_drugs/), in which a comprehensive suite of visualizations and analysis options were implemented. As a result, a powerful reference set of cell lines is available, reflecting distinct known pharmacologic vulnerabilities, including resistance of TP53-inactivated lines to MDM2 inhibitor idasanutlin; ALK-inhibitor sensitivity in ALK-activated lines, and sensitivity of an ATM-deleted cell line to PARP- and CHEK1-inhibitors. Novel established NBL-PDOs retained molecular features of their parental tumor and again reflected established drug sensitivities. However, HTS also identified additional therapeutic vulnerabilities in vitro, such as a striking correlation between a positive mesenchymal signature and sensitivity to BCL2-inhibitor venetoclax. Finally, we explored personalized drug sensitivities within iTHER, demonstrating HTS can support genomic and transcriptomic results, thereby strengthening the rationale for clinical uptake. Furthermore, in vitro insensitivity may avoid ineffective treatments. Lastly, novel treatment options may be identified, as indicated by sensitivity to ponatinib correlating with overexpression of RET or KIT. In conclusion, we established a dynamic publicly available dataset with detailed genomic, transcriptomic, and pharmacological annotation of classical neuroblastoma cell lines as well as NBL-PDOs, representing the heterogeneous landscape of neuroblastoma. We anticipate that in vitro drug screening will be complementary to genomic-guided precision medicine by supporting clinical decision making, thereby improving prognosis for all neuroblastoma patients in the future.

  Study EGAS00001007160

• Purified vs unpurified stem cell-islets

  A scRNAseq dataset consisting of stem cell-derived islets treated with gradient separation (HUES8_Pure_Islets) and unpurified control (HUES8_control_islets). scRNAseq was done on cryopreserved stage 7 stem cell (HUES8 cell line)-derived islets. Details about stem cell differentiation and sequencing can be found on the publication by Rajaei et al. (2025, Science Translational Medicine). This dataset correspond to Fig 4 of the publication.

  Dataset EGAD50000001396

• 10x genomics RNAseq of an isogenic human iPSC model of SMA

  10x genomics single-cell RNAseq of an isogenic human iPSC model for SMA and control. The transcriptomic analysis was performed at 3 timepoints, day 4, day 20 and day 40. The analysis of this dataset was reported in the manuscript "An isogenic human iPSC model unravels neurodevelopmental abnormalities in SMA" from Grass et al.:

  Dataset EGAD00001011259

• Metagenome for FINRISK2002

  The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012). The files are in FASTQ format.

  Dataset EGAD00001007035

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - barretts_1

  Samples from Ross Innes et. al 2015 - doi:10.1038/ng.3357

  Dataset EGAD00001001394

• Capture Hi-C on Hodgkin lymphoma

  In situ promoter capture Hi-C on Hodgkin lymphoma cell line L-428 in experimental triplicates. Hi-C libraries were prepared as previously described (Orlando et al., 2018, https://currentprotocols.onlinelibrary.wiley.com/doi/pdf/10.1002/cphg.63). Promoter capture was based on 32,313 biotinylated 120-mer RNA baits (Agilent). Hi-C libraries were sequenced using Illumina HiSeq 2000 technology. The files are in FASTQ format.

  Dataset EGAD00001004321

• Genomic Advances in Sepsis (GAinS): RNA-seq

  Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 34 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected on the first, third and/or fifth day following ICU admission. RNA was extracted from leukocytes isolated at the bedside using LeukoLOCK kits. We have previously identified sepsis response signatures (SRSs), transcriptomic endotypes that are associated with differential early mortality (Davenport et al, Lancet Respir Med, 2016; Burnham et al, AJRCCM, 2017) and response to treatment in a clinical trial (Antcliffe et al, AJRCCM, 2018). We generated RNA sequencing data on 903 samples, including 134 samples repeated from our previously released microarray data. Libraries were prepared using NEB Ultra II Library Prep kits (Illumina) and sequenced on a NovaSeq 6000. Reads were aligned to the reference genome (GRCh38) using STAR and gene counts quantified using featureCounts (annotation Ensembl v99). Counts were TMM-normalised and log-transformed. Following QC, processed data were available on 864 samples from 667 unique patients.

  Dataset EGAD00001008730

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• Genes and Blood Clotting Study (GABC)

  Objectives: Use genome-wide approaches to identify genetic variants that influence common thrombosis and hemostasis factors, as well as selected common human traits. Design/Methods: The GABC study was a prospective sibling cohort design. Siblings were recruited by targeted email to the undergraduate and graduate student email lists at the University of Michigan. Healthy persons between 14 and 35 years old who had healthy siblings within the same age restriction were able to participate. Study participants agreed to an online informed consent and subsequently completed a 52-question online survey describing their specific bleeding traits as well as many common human traits. Fifty milliliters of blood was collected into a citrate-dextrose solution (ACD) from each participant. An aliquot of whole blood was used for an automated complete blood count analysis and the remainder was processed into platelet poor plasma and buffy coat portions. Plasma and buffy coat aliquots were snap frozen and stored in liquid nitrogen for future studies. 1189 individuals representing 507 sibships were collected between 06/26/2006 and 01/30/2009. Phenotyping Survey Details: To characterize individual bruising and bleeding history, the online survey recorded answers to questions based on a modified von Willebrand Disease (VWD) screening questionnaire. To characterize a collection of participant's common human traits, the survey recorded answers to questions about height, weight, presence of skin tags, history of acne, eye color, hair color, hair line characteristics, skin sunburn sensitivity, skin tanning ability, natural skin color, freckling, cheek dimpling, earlobe shape, shoe size, foot arch characteristics, hand fifth digit morphology, history of dyslexia, history of migraine headaches, history of seasonal allergies, history of apthous ulcers, tendency to sneeze while walking into a bright sunny place, history of dental caries, need for corrective eye lenses, handedness and like or dislike of strongly flavored foods. Biochemical phenotyping: Assays for plasma Von Willebrand Factor (VWF) antigen were performed using ELISA and "Alphalisa" techniques. Automated complete blood count analysis was performed on a Bayer Advia 120 on all participants (including WBC differential, RBC indices, and platelet count.) For the dbGaP v2 update, new biochemical phenotypes have been submitted and include von Willebrand Factor, von Willebrand Factor propeptide, plasminogen, gamma prime fibrinogen, ADAMTS 13, antithrombin III, protein C, and protein S. All new phenotypes were obtained using "Alphalisa" techniques. Genotyping Details: SNP genotyping was performed using genomic DNA extracted from peripheral blood at the Broad Institute, (MIT/Harvard). Genotyping was performed on the Illumina Omni-1 quad chip at the Broad Institute. For the dbGaP v2 update, genotyping data from the Illumina Human Exome was deposited. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to blood clotting through large-scale genome-wide association studies of siblings. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization was performed by the primary investigators at the University of Michigan, Ann Arbor, and at the GEI-funded GENEVA Coordinating Center at the University of Washington. This study serves as a resource for investigators who are interested in the genetic determinants of specific plasma proteins in a healthy population. The sibling cohort design allows for linkage analysis in addition to association studies. Analysis of thrombosis and hemostasis related traits should help elucidate specific biochemical and genetic networks that maintain hemostasis. We hope to identify specific genetic determinants of VWF levels in order to better understand the factors that influence the development of VWD.

  Study phs000304