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• Rapid Evolution of a Skin Lightening Allele in Southern African KhoeSan

  The genetic architecture and polygenicity of skin pigmentation is explored in KhoeSan communities, including Nama individuals whose phenotype and genotype data are provided in this dataset. By pairing genotypes and quantitative spectrophotometric skin pigmentation phenotypes, we show that skin pigmentation is highly heritable in KhoeSan, yet known pigmentation loci only explains a small fraction of the phenotypic variance. Using genome-wide association analyses, we identified both canonical and non-canonical skin pigmentation loci. We show that this phenotype is more polygenic and complex than previously characterized. (Martin et al., Cell 2017. PMID: 29195075) Following up on the top associated signal with large effect size in the gene SLC24A5, we demonstrate that the canonical Eurasian nonsynonymous allele was introduced into KhoeSan via a recent migration ~2 kya and was under extremely strong selection. The derived allele was present at high frequency despite controlling for gene flow. With high-throughput sequences in the captured SLC24A5 region, we show that the most common derived haplotype is identical amongst Europeans, eastern African and KhoeSan. Using 4-population demographic simulations with selection, we show that the allele was introduced into the KhoeSan only 2,000 ya via a back-to-Africa migration and then experienced a selective sweep.

  Study phs001753

• NIPT samples for systematic evaluation of NIPT aneuploidy detection software tools

  This dataset contains two sets of samples. The reference sample set consists of a total of 669 samples that had been reported previously to be euploid by the NIPTIFY screening test. The validation sample set is based on a previously published validation study by Zilina et al. (1), consisting of 423 samples, of which 259 were high-risk pregnancies that had undergone diagnostic invasive prenatal analysis (1). All samples were sequenced with Illumina NextSeq 500 platform, producing 85 bp single-end reads with an average per-sample coverage of 0.32× at the University of Tartu, Institute of Genomics Core Facility, according to the manufacturer’s standard protocols, as described previously (1). This study was performed with the approval of the Research Ethics Committee of the University of Tartu (#315/T-13). 1. Zilina O, Rekker K, Kaplinski L, Sauk M, Paluoja P, Teder H, et al. Creating basis for introducing non‐invasive prenatal testing in the Estonian public health setting. Prenat Diagn [Internet]. 2019 Dec 6;39(13):1262-8. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/pd.5578

  Dataset EGAD00001007712

• Mapping_gene_environment_interactions_in_macrophages

  Immune cells sense and respond to external stimuli such as pathogens and initiate an inflammatory response. Genetic variants altering the behaviour and responses of immune cells may inform the results of disease association studies and provide an insight into the regulation of immune response. Studies mapping quantitative trait loci (QTL) are usually performed in quiescent or naïve cells and therefore any variants with a condition-specific manner (e.g. those with an effect only detectable in a specific time-point after cell stimulation) are likely to remain undiscovered. So far, only a handful of studies have attempted to study QTL in multiple states and even on those studies the number of states has remained relatively low (<5). Only very recently Horst and colleagues published the largest study to date investigating the effect of host variable (e.g. age, sex, contraceptive use etc.) on protein levels in macrophages stimulated with 19 different conditions (Horst et al, 2016). This project aims to develop a high-throughput experimental platform to detect QTL in a large number of states using iPSC derived macrophages. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002268

• Genome-Wide Association Study of HIV-1 Host Genetics Among Injection Drug Users

  The overarching goal of this project is to identify and characterize genetic determinants of HIV 1 susceptibility and resistance in samples of African American (AA) and European American (EA) injection drug users (IDUs) by conducting (1) a case/control genome-wide association (GWA) study of HIV 1 infection (positive/negative); (2) a case-only GWA study of viral load among HIV+ IDUs. The study uses existing samples and data from Urban Health Study (UHS) (PI: Alex Kral), which was the longest-running study of street-recruited IDUs in North America, from 1986-2005. UHS was a serial, cross-sectional sero-epidemiological study. Data were collected every 6 months in communities with a high prevalence of injection drug use in the San Francisco Bay Area. It used targeted sampling in neighborhoods at easily accessible community field sites, such as churches, single room occupancy hotels, and community centers. Eligibility criteria for initial entry to the study were (1) injection drug use in past 30 days; (2) ability to provide informed consent; and (3) age 18 or older. The UHS cohort includes over 9,000 African American and European American IDUs whose serum samples have been stored and data are available on HIV antibody status, HIV risk behaviors, drug abuse and demographics. The current study includes 985 HIV+ cases and 5,222 HIV- controls. Approximately six HIV- controls per case were frequency matched on: (1) self-reported ancestry; (2) sex; (3) age; (4) year of ascertainment; and (5) HIV risk class. This GWAS (DA026141) was funded by the National Institute on Drug Abuse (NIDA; PI: Eric O. Johnson). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by NIDA and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease"(HHSN268200782096C).

  Study phs000454

• Integrative analysis reveals a macrophage-predominant, immunosuppressive immune microenvironment and subtype-specific therapeutic vulnerabilities in advanced salivary gland cancer

  Background Salivary gland cancers (SGC) are rare and heterogeneous malignant tumors. Advanced SGC lack established treatment options and show poor response to immunotherapy. Here, an integrative multi-omics analysis in a large cohort of advanced SGC revealed insights into the tumor immune microenvironment (TIM) and distinct mechanisms of immune evasion. Methods A total of 104 patients with recurrent/metastatic SGC from the DKTK MASTER program were included in this study. Whole-exome or whole-genome sequencing and RNA-sequencing was performed on fresh frozen tumor tissue. The tumor immune microenvironment was analyzed using CIBERSORT deconvolution analysis and immune gene expression scores in bulk RNA-sequencing data. Single-nuclei sequencing and immunohistochemistry analyses were performed in selected samples. Results were validated in bulk RNA-sequencing data of a previously published independent dataset. Results Bulk transcriptome analysis revealed an immune-deserted TIM in the majority of advanced SGC samples. Immune exclusion was most prominent in adenoid cystic carcinoma (ACC) subgroup 1 exhibiting a downregulation of the antigen processing machinery. Only a small subset of advanced SGC, including few adenoid cystic carcinoma, exhibited T-cell inflammation, which was correlated with tumor mutational burden in Non-ACC samples. Subtype specific expression of immune checkpoints as well as cancer testis antigens were identified with prominent expression of VTCN1 in luminal cells within ACC. Single-cell RNA-sequencing and bulk RNA-seq deconvolution analysis validated immune cell exclusion and revealed a TIM that was dominated by M2 macrophages across SGC subtypes. Among evaluable patients treated with immune checkpoint inhibitors, a high T-cell to macrophage ratio was associated with clinical benefit. Conclusions These data support biomarker-based development of immune-checkpoint inhibition and the development of novel immune-checkpoint inhibitors and cellular therapies in SGC.

  Study EGAS50000000809

• Single-Gene vs. Panel Sequencing in Advanced HR+/HER2− Breast Cancer

  Background: Hormone receptor-positive (HR+)/HER2-negative (HER2−) breast cancer is the most common subtype of breast cancer, with biomarker-driven therapies improving outcomes. Liquid biopsies, which analyze circulating tumor DNA (ctDNA), are increasingly essential in clinical management, offering a minimally invasive approach to assess somatic alterations and guide therapy decisions. However, the choice of assay directly affects clinical utility and global access to validated testing platforms remains inconsistent. To address these challenges, this study aimed to compare the performance of single-gene and panel-based sequencing for detecting PIK3CA mutations in HR+/HER2− breast cancer and evaluate the added value of broader genomic profiling in guiding treatment decisions. Patients and methods: We conducted a prospective, multicenter study comparing our previously established SiMSen-Seq (SSS) assay, a single-gene method targeting PIK3CA hotspot mutations, with the AVENIO ctDNA Expanded assay, enriching for 77 clinically relevant cancer genes. Additionally, tumor fractions were estimated with mFAST-SeqS by assessing chromosomal aneuploidy. We analyzed 161 plasma samples collected from 146 patients with HR+/HER2− advanced breast cancer before starting a new line of palliative therapy. Results: High concordance (92.6%) was observed between SSS and AVENIO for PIK3CA mutation detection, with accurate variant allele frequency (VAF) quantification confirmed by Bland-Altman analysis and linear mixed-effects modeling. AVENIO identified additional PIK3CA mutations in 19% of samples as well as other actionable alterations, including ESR1 mutations (17.5%) and PI3K pathway alterations (40.6%). Panel sequencing improved the interpretation of negative liquid biopsy results by integrating tumor fraction and VAF metrics. Conclusions: Panel sequencing assays such as AVENIO provide broader genomic coverage, enhance the detection of actionable mutations and refine the interpretation of negative liquid biopsy findings. These findings support the integration of comprehensive ctDNA profiling into routine clinical practice for HR+/HER2− advanced breast cancer while highlighting the need for improved assay accessibility and expanded genomic targets.

  Study EGAS00001008200

• Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

  Background: Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

  Study EGAS00001003564

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• Separation of chronic myeloid leukemia stem cells from normal hematopoietic stem cells at single-cell resolution

  Chronic myeloid leukemia (CML) is a clonal disease, initiated and sustained by a subpopulation of CML stem cells (CML-SC) containing the BCR::ABL1 oncogene. Genetic markers that enrich for CML-SC have been identified, including CD26 being most specific. Genome sequencing analysis remain yet to be performed at single cell level in order to determine differences and heterogeneity in the genetic profile of CML-SC versus normal hematopoietic stem cells (HSC). Here we show that CD26 and CD33 separate CML-SC from normal HSC in freshly isolated bone marrow samples from newly diagnosed CML patients. DNA sequencing confirms that single CML-SC derived colonies are positive for BCR::ABL1 and harbor a recurrent set of patient-specific clonal somatic mutations across different CML-SC derived colonies. In contrast, marker-negative (CD26-CD33-) HSC from the same patients are negative for the BCR::ABL1 oncogene and do not carry recurrent, clonal mutation across different colonies. These results support the concept that CD26/CD33 expression separates genetically defined, clonal mutant CML-SC from normal HSC that carry individual sets of random mutations in CML patients. We provide whole exome DNA sequencing data for 23 clinical samples of CML-SC plus two buccal swipes derived normal samples. CML samples are comprised of 4 to 8 replicates from two patients, at diagnosis and after treatment. Single CML stem cells before treatment and single non-transformed HSC at remission were selected from bone marrow samples by FACS, according to genetic markers CD33+CD26+ at diagnosis and CD33+CD26-/CD33-CD26- at remission. WES libraries of colony forming assays derived CML-SC and HSC populations were prepared using Agilent SureSelect Human All Exon V6 kit and sequenced running 150 cycles (2x 75bp paired-end) on an Illumina NextSeq 500 platform. Following GATK best practices for somatic mutation calling, novel CML variants and random mutation events during remission phase were obtained.

  Study EGAS00001006904

• Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

  We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

  Study EGAS50000000005

• SUM-seq data for spontaneously differentiated iPSCs to EBs

  Clonal CRISPRi/a hiPS cell lines were plated to induce spontaneous differentiation in KSR medium (KSR medium: knockout DMEM/F12 (Gibco, 12660012), 2 mM GlutaMAX, 1× NEAAs, 20% knockout serum replacement (Gibco, 10828010) and 0.1 mM β-mercaptoethanol (Merck-Millipore)). non-TC plates and dishes were used. At the day of plating, KSR medium was supplemented with Y-27632 (10 µM). The next day, medium was replaced with fresh KSR medium. Medium changes were performed every other day. EBs were transferred to 15 ml conical tubes and allowed to sediment at room temperature for 5 min. The supernatant was replaced with fresh KSR medium and cells were transferred onto non-TC treated plates and maintained at 37 °C, 5% CO2. On day 12 of differentiation, the KSR medium was replaced with Essential 6 medium (Gibco, A1516401). EBs were collected and subjected to SUM-seq library preparation as described in Lobato-Moreno et al. 2025 Nature Methods.

  Dataset EGAD50000001589

• Single-cell RNA sequencing of control and Notch inhibitor treated HCC PDX model

  We generated single-cell RNA-seq data for anti-Notch2 (n=3), anti-JAG1 (n=1) and anti-gD (n=4) treated LIV78 tumors. NCR nude mice bearing similarly sized LIV78 tumors (volumes between 300-900mm3) were injected intravenously with 30mg/kg body weight of either anti-Notch2, anti-Jag1 or anti-gD control antibody 72hrs prior to tumor harvest and tumor cell isolation. Following the depletion of CD45+ and Ter119+ cells, the samples were processed for single-cell RNA-seq (scRNAseq) as described previously (Long et al., 2019) using the Chromium Single Cell 3’ Library and Gel bead kit v2, following the manufacturer’s manual. cDNAs and libraries were prepared following the manufacturer’s manual (10X Genomics). Libraries were profiled by Bioanalyzer High Sensitivity DNA kit (Agilent Technologies) and quantified using Kapa Library Quantification Kit (Kapa Biosystems, Wilmington, MA). Each library was sequenced in one lane of HiSeq 2500 (Illumina) following the manufacturer’s sequencing specification (10X Genomics) to produce the resulting FASTQ files.

  Dataset EGAD50000000738

• Mechanisms of patient response to Dabrafenib in Melanoma

  Samples will be from the BRF113683 (BREAK-3) study which is a Phase III Randomized, Open-label Study Comparing GSK2118436 to Dacarbazine (DTIC) in Previously Untreated Subjects With BRAF Mutation Positive Advanced (Stage III) or Metastatic (Stage IV) Melanoma (n=250 enrolled)*NGS [Agilent capture (Sanger V2 panel): 360 genes and 20 gene fusions; Illumina HiSEQ Sequencing]*CNV: [via NGS or Affy SNP 6.0 or Illumina Omni (TBD)]Bioinformatics: Analysis will be performed using core Sanger informatics pipelines similar to those previously described (Papaemmanuil E et al. (2013) Blood. 22:3616 -3627). Briefly, copy number analysis will be performed using the ASCAT algorithm, and base substitutions, small insertions and deletions using the CAVEMAN and Pindel algorithms, respectively. Statistical approaches including generalized linear models will be used to predict clinical variables such as maximum clinical response and duration of response using genetic data. Sanger and EBI to conduct analysis; Raw data and correlation with clinical endpoints to be analyzed by both EBI/Sanger and GSK (unique pipeline analyses to increase call confidence)

  Dataset EGAD00001001375

• Tagged-amplicon deep sequencing

  This dataset contains a collection of tumour samples from high grade serous ovarian carcinoma patients with recurrent disease collected near the point of diagnosis as well as tumour samples collected after patient relapse upon or some time after study entry. Whole blood samples were also collected from patients at study entry for the purpose of germline variant detection. The majority of diagnosis samples were preserved in neutral buffered formalin whereas the majority of post-relapse samples were preserved in universal molecular fixative (UMFIX, Sakura Finetek USA, Inc). DNA was extracted and the tagged-amplicon deep sequencing assay was applied (TAm-Seq, Forshew et al. 2002, Sci Transl Med) with the aid of fluidigm access array technology. Targeted loci were sequenced at very high depths (typically >100X) for the detection of both somatic and germline variants. Sequenced reads were aligned to the GRCh37 reference genome (release hs37d5).

  Dataset EGAD00001011058

• Targeted Myeloid DNA-Panelsequencing, DKFZ

  Targeted myeloid DNA-Panelsequencing from purified bone marrow blasts of 104 treatment naive AML patients from the discovery cohort. For more details, we refere to Jayavelu, Wolf, Buettner et al. Libraries were prepared from 40 ng DNA using the QIASeq Human Myeloid Neoplasms Panel (Qiagen) according to the manufacturer’s protocol. Samples were tagged with the QIAseq 96-Unique Dual Index Set A for Illumina platforms (Qiagen) to yield unique combinations of i5 and i7 barcodes for each sample. Sample fragment size distribution and concentration was estimated using the Agilent High Sensitivity DNA kit on a 2100 Bioanalyzer (Agilent). Samples were pooled in an equimolar fashion, denatured, and diluted to 1.5 pM according to Illumina’s recommendations. The diluted library was sequenced on a NextSeq 500 benchtop sequencer (Illumina) using NextSeq High Output cartridges. Demultiplexing was performed using the BaseSpace cloud platform (Illumina).

  Dataset EGAD00001008501

• The effect of freezing delay of cell-type specific transcriptome responses in human brain via snRNA-seq

  In other analysis in the current manuscript, we find a similar gene signature (to dissociation based artifacts in mouse and human tissue) is present in post-mortem microglia and astrocytes, across all snRNA-seq datasets analyzed, although it is highly variable between subjects. ​ Using acutely-resected neurosurgical tissue, we performed single-nucleus RNA-seq and reveal that a similar signature can be detected in microglia following prolonged exposure to room temperature. Tissue handling and methods details, as well as sequencing and analysis details) can be found in the methods section of related manuscript (Marsh et al., 2022). Together, these results suggest that the presence of this signature in post-mortem brain samples may be the result of a combination of acute pre-mortem (agonal state, cause of death, comorbidities, etc.) and post-mortem (post-mortem interval (PMI), storage time, RNA quality, etc.) variables and may not represent normally present cell state.

  Dataset EGAD00001008541

• RNA-Seq of Whole Blood from Patients with Intracranial Aneurysms

  Rupture of intracranial aneurysms (IAs) causes intracranial hemorrhaging that leads to high rates of neurological deficits and death. Although rupture rates are low, clinicians must decide whether to treat or monitor these potentially dangerous lesions. In the current clinical practice, the most common metric to measure risk of rupture is aneurysm size. However, clinical data show that small aneurysms can also rupture. As a result, alternative clinical stratification scores have been proposed, including the PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, and Site) score based on patient demographics and IA characteristics to stratify ruptured and unruptured IAs, and the Rupture Resemblance Score (RRS) that stratifies ruptured and unruptured IAs based on hemodynamic and morphological properties. However, all metrics require imaging on digital subtraction angiography (DSA), which is invasive, expensive, requires the use of X-rays, and is associated with transient or permanent neurological and non-neurological complications. We hypothesize that individuals with dangerous IAs have detectable gene expression differences in their blood that could be used as biomarkers to determine rupture risk. We propose to use whole blood transcriptomes to develop a "one-stop" diagnostic test that can detect the presence of IAs and determine the risk of rupture based on circulating RNA expression biomarkers using our prototype AneuScreenTM platform. We aim to develop and validate biomarkers to predict risk, as calculated by the currently-used metric of aneurysm size, the clinical PHASES score, and the RRS. Here, we collected blood samples from consented individuals who were receiving cerebral imaging at the Gates Vascular Institute (Buffalo, NY) for intracranial aneurysm. RNA extracted from blood samples (n=43) was subjected to RNA-sequencing and added to our existing database (n=44). When combined with our previous data, we had transcriptome data from 68 aneurysms (after additional sample filtering). This dataset was stratified into low- and high-risk according to IA size, PHASES score, and RRS score. Differentially expressed genes were identified and used to construct predictive models. Fastq files and basic demographic information for the 43 newly-sequenced intracranial aneurysm samples on this project will be available through dbGaP.

  Study phs003072

• Longitudinal Studies of Patients with Familial Platelet Disorder with Associated Myeloid Malignancy (FPDMM)

  FPDMM (OMIM #601399) is a rare autosomal dominant disease characterized by defective megakaryocytic development, thrombocytopenia, platelet functional defects, and a life-long risk of developing hematological malignancies. FPDMM is caused by germline mutations in the RUNX1 gene, which encodes a master regulator of hematopoiesis. The overall lifetime risk of hematological malignancies in FPDMM patients is 35 - 45%. It is hypothesized that mutations in other genes cooperate with the germline RUNX1 mutations for leukemia development. However, it is not clear which genes need to be mutated for leukemia to develop and which biomarkers or clinical tests can be used to predict which patients will progress to leukemia. Likewise, there are no treatments and/or preventative measures that can reduce or stop disease progression except for stem cell transplantation, which is not available for all patients and may be associated with significant complications. To address these questions, we launched a longitudinal natural history study of patients associated with RUNX1 germline mutations at the NIH Clinical Center in early 2019 (Trial Registration ID: NCT03854318). The goals of this study are to: (1) perform comprehensive longitudinal phenotyping of FPDMM patients to document the full spectrum of both hematopoietic and non-hematopoietic manifestations of the disease; and (2) perform extensive genomic analyses of FPDMM patient samples to identify cooperating genomic variants, which may confer growth advantage that leads to clonal expansion of affected hematopoietic cells that may serve as the precursor to malignancies. The data available through dbGaP include exome sequencing and RNA-seq data from these patients. Diverse germline mutation types were identified in the RUNX1 gene, including splice site mutations and large deletions, in addition to nucleotide substitutions and small indels. Somatic mutations, especially in genes associated with clonal hematopoiesis of indeterminate potential (CHIP), were found in a substantial proportion of FPDMM patients who were leukemia-free. The findings suggest that the genomes in these patients are prone to somatic mutations and monitoring the dynamic changes of these somatic mutations prospectively may provide a way to predict progression to malignancies.

  Study phs003075

• NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (Ischemic Stroke Genetic Study, ISGS)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The Ischemic Stroke Genetics Study (ISGS) is a study of newly onset cases (~600) with ischemic stroke (a stroke due to sudden interruption of blood flow to a part of the brain) compared with sex- and age-matched non-stroke participants. The study was conducted to determine the genes and their variants that contribute to an individual's risk of developing an ischemic stroke. The coordination of the recruitment and flow of the samples occurred at the Mayo Clinic, Jacksonville, FL, under the direction of James F. Meschia, MD. The University of Virginia (Stephen S. Rich, PhD) served as the analytic site for the genetic data. All GWAS data on ISGS participants have been deposited into dbGaP. As part of the NHLBI Exome Sequencing Project, DNA from a subset of ISGS participants will undergo exome sequencing. For the NHLBI ESP, a subset of 92 individuals with lacunar (small vessel) or atherosclerotic (large vessel) TOAST subtypes were selected from among all ISGS participants, excluding those individuals with TOAST subtypes of stroke of other etiology or of stroke with undetermined etiology. All 92 samples pass initial quality control metrics and 89 samples completed exome sequencing. A total of 75 participants with appropriate consent and variant calls had their genetic and phenotypic data deposited into dbGaP.

  Study phs000546

• Longitudinal Studies of Brain Structure and Function in MPS Disorders

  Mucopolysaccharidosis (MPS) types I, II, IV, VI and VII, are inherited diseases that result from the bodies inability to break down large sugar molecules that are by-products of metabolism. MPS affects most organs of the body and causes abnormalities in the liver, spleen, bones, and brain. We are studying the central nervous system (primarily the brain) so we can better understand the nature of the problems individuals with MPS I, II, IV, VI and VII have so we can find ways of better treating these problems. We would like to find out about the changes in the brain of individuals with MPS I, II, IV, VI and VII using data from MRI and neuropsychological tests. This is a longitudinal study so patients who enroll will need to be seen 3 times over 5 years. The longitudinal nature of the study allows us to make conclusions about how changes in some structures of the brain and changes in cognitive ability are related. The research objectives are: To identify abnormalities of brain (and central nervous system) structure and function in patients with MPS I, II, IV, VI and VII, regardless of how they are being treated or have been treated in the past; and to track disease progression over time. To examine the degree to which independent variables (e.g., age at first treatment, severity of disease, types of medical abnormalities, mutation, medical events, and sensory abnormalities) have an impact on both functional and structural outcomes as well as on quality-of-life. To identify, through longitudinal study, those measures that most accurately reflect the current disease state. This is a longitudinal study of 75-100 individuals with either MPS I, II, IV, VI and VII. Those participating in the study will be evaluated 3 times over 5 years. The primary site for this study is the University of Minnesota but there are an additional 6 centers in the United States and Canada that are also participating and will provide data for analysis. You will need to be able to travel to Minnesota or one of the participating centers in order to be a part of this study.

  Study phs001328

• Parallel Genomic Alterations of Antigen and Payload Targets Mediate Polyclonal Acquired Clinical Resistance to the Antibody Drug Conjugate Sacituzumab Govitecan

  Sacituzumab Govitecan (SG), a novel antibody drug conjugate (ADC) incorporating the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 inhibitor (SN-38) payload, is the first ADC to be approved for advanced triple negative breast cancer (TNBC). However, mechanisms governing therapeutic resistance to SG are not known. We sought to identify mechanisms of de novo and acquired resistance to SG through unbiased whole-exome sequencing (WES) and RNA sequencing analysis of pre-treatment and multi-site post-progression (autopsy) tumor specimens. We examined three metastatic TNBC cases exhibiting (1) de novo progression, (2) stable disease, and (3) a deep response followed by progression, then mapped the temporal and spatial genomic evolution of acquired resistance in the responding patient. We then conducted additional pre-clinical experiments to validate the observed resistance mechanisms. TROP2 RNA and gene copy number were associated with de novo resistance, as case (1) was found to have absent TROP2 expression in all specimens, case (2) expressed TROP2, while case (3) exhibited both expression and focal genomic amplification of the TACSTD2/TROP2 locus. The genomic phylogeny tree inferred from case (3) post-progression specimens revealed one branch harboring an acquired canonical E418K resistance mutation in TOP1 (encoding topoisomerase 1) and a subsequent sub-clonal TOP1 inactivating frameshift mutation, while a distinct branch exhibited acquisition of a novel T256R missense mutation in TACSTD2 (encoding TROP2). Both the TOP1- and TACSTD2-mutant clones seeded multiple distinct metastatic sites. Through reconstitution experiments in TROP2 negative cells we found that TROP2 T256R is a stable protein with defective cell membrane localization and reduced cell surface binding by RS7 compared to wild-type TROP2. Collectively, these findings underscore TROP2 as a determinant of initial response to SG, and they reveal parallel and mutually exclusive polyclonal molecular mechanisms of acquired resistance involving the direct antibody target and drug payload target in distinct metastatic subclones of a single patient. While further research is needed to extend these novel findings, this study highlights the specificity of SG and illustrates how identifying such mechanisms will inform rational therapeutic strategies to overcome ADC resistance.  

  Study phs002555

• European Genome-phenome Archive 15th Anniversary Celebration

  2023 marks the 15th Anniversary of the EGA, jointly managed by the European Bioinformatics Institute (EMBL-EBI) and the Centre for Genomic Regulation (CRG). To mark the occasion, a simultaneous celebration was held in both institutions on the 13th of December 2023. The teams gathered online to play a quiz game and celebrate all the achievements and milestones with two wonderful anniversary cakes. In 2008, the European Genome-phenome Archive was created at the EBI-EMBL in Cambridge to guarantee that human genome and phenome data were available to the international scientific community while data privacy was preserved. The six-person staff at the beginning of the project is now far behind us, with a team that reaches the number of 35 members. Since 2013, the European Bioinformatics Institute and the Centre for Genomic Regulation share responsibility for The European Genome-phenome Archive (EGA). At that time, the EGA had data for about 0,5 petabytes. Currently, the Archive contains more than 12 petabytes. 2023 comes to an end with several good news. The EGA has been renewed as an ELIXIR Core Data Resource. This was announced during the GA4GH 11th Plenary held in San Francisco last September when ELIXIR-Beacon was also confirmed to be a GA4GH Driver Project. Thus, the Beacon API has maintained this title since 2018. What’s more, the first Federated EGA dataset is now live on our website. This year we also launched new services for EGA users in September. By the numbers, in 2023 the EGA counts 2.5 PB archived, 371 studies published, 208 new submitters and 19 active projects in which the team is participating, among others. We look forward to continuing to support your research in 2024!

  Blog 15-anniversary

• Comprehensive cancer predisposition gene testing in an adult multiple primary tumor series shows a broad range of deleterious variants and atypical tumor phenotypes

  Multiple primary tumors (MPT) affect a substantial proportion of cancer survivors and may result from various causes including inherited predisposition. Currently, germline genetic testing of MPT cases for cancer predisposition gene (CPG) variants is mostly targeted by tumor type. We ascertained pre-assessed MPT cases from genetics centers (defined as ≥2 primaries by age 60 years or ≥3 by 70) and performed whole genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment/molecular investigations, pathogenic variants in moderate and high-risk CPGs were detected in 67/440 (15.2%) of probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies including structural variants at low frequency (6/440 (1.4%) of probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant) the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice site CPG variants and at least one discordant tumor type was significantly higher than a control population (χ2=43.642 P=<0.0001). 2/67 (3%) of probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Summing together variant detection rates from a similarly ascertained previous MPT case series, the present results suggest that first-line comprehensive CPG analysis in a clinical genetics referral-based MPT cohort would detect a deleterious variant in about a third of cases.

  Dataset EGAD00001004088

• PREDICT-HD Huntington Disease Study

  The purpose of this project is to make clinical measurements from the PREDICT-HD consortium available through the dbGaP mechanism. The phenotype data will first be converted into a community open standard and subsequently exported to dbGaP for archival and open access distribution of the results of the studies. This will permit members of the scientific community to utilize a permanent resource for investigating the interactions of phenotypes upon an international cohort of early Huntington Disease. In version 2 cut of the data we provided HD CAG repeat lengths for both alleles as well as enrollment age of all participants. We have also generated unique identifiers prospectively compatible with the larger initiative GWAS in Huntington's Disease project (also on DbGaP). As such, the version 1 cut of the data was mainly proof of concept and should be deprecated. Going forward, all updates will add-on to version 2 cut of the data. In version 3 cut of the data, we provided baseline or the first usable MRI T1-weighted imaging analysis subcortical and cortical segmentations and cortical parcellations based on a customized Freesurfer 5.2 pipeline developed at The University of Iowa. The customizations to the standard pipeline were mainly to improve bias field correction and image normalization such that segmentation of gray, white, internal csf, dura and surface CSF are optimized for the Freesurfer pipeline. There are 1111 subjects with results in this data release. In version 4 cut of the data, we provided all longitudinal clinical measurements for all subjects (total of 1476) assessmented or enrolled through the end of 2013. Additionally, we are providing measurements on 39 baseline FDG PET images spatially normalized by SPM5 into MNI space, relative regional metabolic values computed in 120 volumes of interest (VOI) defined in the Automated Anatomical Labeling (AAL) Atlas (Tzourio-Mazoyer et al. 2002), and global metabolic values calculated by SPM standard mean voxel value (within per image fullmean/8 mask). This project is a funded ancillary study of PREDICT-HD. In version 5 cut of the data, we provided the first of many forthcoming results from ancilliary studies of PREDICT-HD. In this data cut, we provide individual subject results derived from structural MRI data. The earliest MRI session for each subject was used. The results summarized represent source based morphometry loading coefficients for 23 components (see: "Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome", Ciarochi et al., 2016, Front Neurol. 2016; 7: 147, Published online 2016 Sep 21. doi: 10.3389/fneur.2016.00147]. In this version 6 cut of the data, we provide a full set of derived data, more than 10,000 raw MRI images, and ancillary study data sets. For sample information please link to: PREDICT-HD Biospecimen Resources

  Study phs000222

• The African American Breast Cancer Epidemiology and Risk (AMBER) Consortium Study

  The AMBER Consortium Study was formed to pool interview data, questionnaire data, and biological samples from epidemiological studies of breast cancer in African-American women to discover the potential causes of early-onset and aggressive breast cancer in African-American women. AMBER is funded through a Program Project grant from the National Cancer Institute. Genetic data submitted to dbGaP come from participants in the Carolina Breast Cancer Study, Women's Circle of Health Study, and Black Women's Health Study. The P01 consists of four scientific projects; the aims include follow-up on previous GWAS findings for breast cancer susceptibility in AA women as well as investigation of SNPs in candidate genes in biologically plausible pathways. These SNPs were genotyped using DNA from 3130 African-American women with breast cancer and 3700 controls. Descriptions of the original studies that provided the data and samples for this collaborative study are given below. The Carolina Breast Cancer Study (CBCS): a North Carolina population-based case-control study of breast cancer, conducted in three phases. The current study phase, phase 3 (years 2008-2014), includes women residents in 44 counties. CBCS phases 1 and 2 were conducted in 24 counties. Breast cancer cases are identified using Rapid Case Ascertainment in cooperation with the NC Central Cancer Registry. Controls were identified for phases 1 and 2 only (1993-1996 and 1996-2001), using Division of Motor Vehicles lists for women under age 65 and Health Care Financing Administration lists for women 65 and older. Randomized recruitment was used to oversample AA women and women under age 50. In-depth interviews are conducted by study nurses in participants' homes to obtain information on potential risk factors for breast cancer. DNA samples have been obtained from most participants. Overall response rates for Phases 1 and 2 were 74% for AA cases and 54% for AA controls. Phase 3, conducted in 44 counties from 2008-2014, includes cases only. The response rate for AA cases in Phase 3 was 70.5%. The Women's Circle of Health Study (WCHS): a multi-site case-control study in New York City (NYC) and New Jersey (NJ) aimed at evaluating risk factors for early and aggressive breast cancer in women of AA and EA ancestry. Recruitment in NYC took place between January 2002 and December 2008 and involved hospital-based ascertainment of cases, while controls were identified through random digit dialing (RDD). Recruitment at the NJ site started in March 2006 and is ongoing. Phase I of the study ended in April 2012 and covered seven counties in NJ. WCHS2 includes two additional counties. Cases in NJ were identified from 2006 to 2012 by the NJ State Cancer Registry using rapid case ascertainment. Controls were initially recruited though RDD (2006 to 2010) and later through community-based efforts (2009-2012). In-person interviews ascertained data on established and suspected risk factors for breast cancer. DNA samples were obtained. Among eligible AA women, 75% in NY and 54% in NJ completed an interview and provided a biologic specimen. Black Women's Health Study (BWHS): an ongoing prospective cohort study of health and illness among U.S. black women, with a focus on cancer. The study began in 1995 when 59,000 AA women 21-69 years of age from across the United States completed a 14-page postal health questionnaire. The median age at entry was 38, and participants were residents of 17 states in mainland U.S.: Northeast, 28%; South, 30%; Midwest, 23%; West, 19%. The baseline questionnaire elicited information on a wide range of variables, including demographic factors, use of medical care, family history of breast cancer, reproductive and medical history, cigarette and alcohol use, weight, height, waist and hip circumference, medication use, diet, and exercise. Biennial follow-up questionnaires ascertain new cases of breast cancer and other illnesses and update covariate information. Medical record and cancer registry data are sought for all participants who report a diagnosis of breast cancer. As of 2014, approximately 80% of the baseline cohort have completed follow-up. DNA samples were obtained from about 50% of participants. BWHS data for the AMBER consortium were prepared as a nested case-control study, with controls frequency-matched to cases on year of birth, geographic region, and most recent questionnaire completed prior to the end of the at-risk period.

  Study phs000669