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• Asian Immune Diversity Atlas (AIDA) sQTL

  Current efforts within the Human Cell Atlas (HCA) are largely focused on defining reference human cell types using a relatively small number of samples of predominantly European origin. However, the next phase of the HCA is likely to address the topic of human diversity by comprehensively characterizing variation in cell states across age, sex, population groups, diseases and environments. To spur this next phase, and also expand the geographical scope of the HCA, we propose an HCA-Asia seed network to generate an Asian Immune Diversity Atlas (AIDA). AIDA will generate transcriptome variation datasets from 5 major Asian population groups (Chinese, Japanese, Korean, Indian, Malay), define an atlas of Asian cell types and states, and characterize their variation associated with ethnicity, age and sex. We will also formalize a study design based on common controls that can in principle reduce technical artifacts in a broad range of comparative single cell studies. This study design will be complemented with novel algorithms designed to normalize datasets of interest against the common controls, to increase the robustness of biological inferences. In addition to providing new tools and resources to the HCA, this seed network will serve as a template for future comparative studies in the single cell field.The AIDA dataset consists of scRNA-seq (10x 5′ v2 scRNA-seq) and genotype (Illumina Infinium Global Screening Array v.3) data. In the AIDA splicing quantitative trait loci (sQTL) project, we identified 11,577 independent cis-sQTLs, 607 trans-sQTLs, and 107 dynamic sQTLs. The post-imputation genotype for the Japanese and Korean cohort will be available through dbGaP. The Singaporean (ethnically Chinese, Indian and Malay) genotypes require a data access application to the HELIOS Data Access Committee (helios_science@ntu.edu.sg). European samples are used as control.

  Study phs003848

• National Emphysema Treatment Trial (NETT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data: The current release of the NETT study dataset includes follow-up data through May of 2013.Objectives: To compare lung-volume-reduction surgery with medical therapy for severe emphysema, and to identify participant selection criteria for lung volume reduction surgery.Background: Lung-volume-reduction surgery has been proposed as a palliative treatment for severe emphysema. Effects on mortality, the magnitude and durability of benefits, and criteria for the selection of participants have not been established.Participants: A total of 1,218 participants with severe emphysema underwent pulmonary rehabilitation and were randomly assigned at 17 centers to undergo lung-volume-reduction surgery (bilateral stapled wedge resection) or to receive continued medical treatment. Participants were randomized after a 6-10 week pulmonary rehabilitation period and participants with a forced expiratory volume in one second (FEV1) that was 20 percent or less of predicted and a homogeneous distribution of emphysema or carbon monoxide diffusing capacity 20 percent or less of predicted were not eligible for randomization due to poor post-surgery prognosis for death or functional improvement.Conclusions: Overall, lung-volume-reduction surgery increases the chance of improved exercise capacity but does not confer a survival advantage over medical therapy. It does yield a survival advantage for participants with both predominantly upper-lobe emphysema and low base-line exercise capacity. Participants previously reported to be at high risk and those with non-upper-lobe emphysema and high base-line exercise capacity are poor candidates for lung-volume-reduction surgery, because of increased mortality and negligible functional gain. (NEJM 2003;348:2059-2073).

  Study phs004077

• Cellular Profiling Identifies Targetable T Cell Phenotypes in Lymphocytic Variant Hypereosinophilic Syndrome

  Lymphocytic variant hypereosinophilic syndrome (LHES) is driven by aberrant Th2‑skewed lymphocyte clones that promote tissue eosinophilia. We performed multimodal single‑cell RNA‑sequencing (scRNA‑seq), cell‑surface protein (ADT) profiling, and paired αβ‑TCR sequencing on peripheral‑blood mononuclear cells (PBMCs) from two clinically characterized LHES patients and one idiopathic HES control. One LHES patient was sequenced at two timepoints, pre-JAK inhibitor treatment and post-JAK inhibitor treatment. Single‑cell regulatory‑network inference (SCENIC), CellChat ligand–receptor modelling, and scRNA variant calling were integrated with flow‑cytometric validation and 5,000× amplicon sequencing. Two clonally expanded CD3‑CD4⁺ central‑/effector‑memory T‑cell clusters were observed exclusively in LHES. These cells showed Th2‑associated regulons (GATA3, BATF, MAF), high expression of CCR4, CD52, PTGDR2 and IL13, and down‑regulation of CD247 (CD3ζ), a mechanism experimentally confirmed to abolish surface CD3/TCR. Autocrine IL‑7 signaling, predicted by CellChat, provides an antigen‑independent proliferative cue. Therapeutically actionable targets (CCR4, CD52, IL‑5, IL‑7, TNF) were prioritized; one refractory patient achieved complete clinical and cytologic remission after mogamulizumab (anti‑CCR4) therapy. Longitudinal sequencing revealed clonal evolution with acquisition of a STAT3 p.G618R mutation under JAK‑inhibition, highlighting a resistance mechanism. Raw scRNA‑seq (5′ cDNA, VDJ, ADT) data, and sample‑level metadata will be distributed through dbGaP. These resources provide the first high‑resolution immune atlas of LHES and a framework for rational, precision immunotherapy in eosinophilic disorders.

  Study phs004041

• IDH-wildtype untreated human glioblastoma samples (GB-UK cohort), published in Noorani & Haughey et al 2025.

  Extrachromosomal DNA (ecDNA) oncogene amplification is associated with treatment resistance and shorter survival in cancer. Currently, the spatial dynamics of ecDNA, and their evolutionary impact, are poorly understood. Here, we investigate the spatiotemporal evolution of ecDNA by integrating computational modeling with samples from 94 treatment-naive human IDH wild-type glioblastoma patients. Random ecDNA segregation combined with ecDNA-conferred fitness advantages induce distinct spatial ecDNA copy number patterns which depend on ecDNA oncogenic makeup. EGFR-ecDNAs often reach high copy numbers and confer strong fitness advantages. In contrast, PDGFRA-ecDNAs reach lower copy numbers and confer weaker fitness advantages. EGFR structural variants occur exclusively on ecDNA, arise from and are intermixed with wild-type EGFR-ecDNAs. Computational modeling suggests wild-type and variant EGFR-ecDNAs often accumulate before clonal expansion. Using a genetically engineered mouse model, we confirm that the accumulation of oncogenic ecDNA prior to clonal expansion under strong positive selection is observable both in vivo and in vitro in mouse neural stem cells. Intermixed wild-type and variant ecDNAs remain prevalent at diagnosis, as less fit wild-type ecDNAs hitchhike on strongly selected variant EGFR-ecDNAs. A dN/dS analysis in the Genomics England cohort further suggests positive selection of variants on EGFR-ecDNAs but not PDGFRA-ecDNAs in glioblastoma. Overall, our results suggest ecDNA oncogenic makeup determines unique evolutionary trajectories in glioblastoma. New concepts like ecDNA tumor clonality, ecDNA heteroplasmy and multi-species ecDNAs require a refined evolutionary interpretation of genomic data in a large subset of glioblastoma patients and likely other solid cancers.

  Study EGAS00001008126

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Accurate mutation detection in leukemia by re-sequencing a cancer gene set

  With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using the Roche/454 technology coupled with Nimblegen sequence capture. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely BWA-SW and SSAHA2, coupled with the variant calling algorithm Atlas-SNP2 yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.

  Study EGAS00001000268

• Evolutionary dynamics of residual disease in human glioblastoma

  Glioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery and chemoradiation provide limited benefit. Even when a good treatment response is obtained, recurrence inevitably occurs either locally (c.80%) or distally (c.20%), driven by cancer clones that are often genomically distinct from those in the primary tumour. Glioblastoma cells display a characteristic infiltrative phenotype, invading the surrounding tissue and often spreading across the whole brain. We hypothesised that cancer cells responsible for relapse reside in two compartments of residual disease that are left behind after treatment: the infiltrated normal brain parenchyma, and the sub-ventricular zone (SVZ). In this model, residual disease subclones diverge early during glioblastoma evolution, may remain dormant in the normal parenchyma and are responsible for recurrence. To test our hypothesis, we performed whole-exome sequencing of 69 multi- region samples collected using fluorescence-guided resection from 11 patients, including the infiltrating tumour margin (M) and the SVZ for each patient, as well as matched blood. We used a phylogenomic approach to dissect the spatio-temporal evolution of each tumour and unveil the relation between residual disease and the main tumour mass. We also analysed two patients with paired primary-recurrence samples with matched residual disease. Our results suggest that the infiltrative phenotype is an early evolutionary trait of glioblastoma, giving rise to the tumour mass detected at surgery. After treatment, the same infiltrative clone may seed the growth of a recurrent tumour, thus representing the ‘missing link’ between the primary tumour and recurrent disease. These results are consistent with recognised clinical phenotypic behaviour and suggest that more specific therapeutic targeting of cells in the infiltrated brain parenchyma may improve patient’s outcome.

  Study EGAS00001003043

• Genomic diversity of the African-descent Makranis of Pakistan

  From the eighth century onward, the Indian Ocean was the scene of extensive trade of sub-Saharan African slaves, via sea routes controlled by Muslim Arab and Swahili traders. Several populations in present-day Pakistan and India are thought to be the descendants of such slaves, yet their genetic ancestries and history of admixture and natural selection remain largely undefined. Here we studied the genome-wide diversity of the African-descent Makranis, residing on the Arabian Sea coast of Pakistan, together with that of four neighboring Pakistani populations, to investigate the genetic legacy, population dynamics and tempo of the Indian Ocean slave trade. We show that the Makranis result from an admixture event between local Baluch tribes and Bantu-speaking populations from eastern/southeastern Africa, which we dated to ~300 years ago during the Omani Empire domination. Levels of inbreeding, measured through cumulative runs of homozygosity, were found to be similar across Pakistani populations, suggesting that the admixed Makranis rapidly adopted the traditional practice of endogamous marriages. Finally, we searched for signatures of post-admixture selection at several traits evolving under positive selection, including skin color, lactase persistence and resistance to malaria. We demonstrate that the African-specific Duffy-null blood group — believed to confer resistance against Plasmodium vivax infection — was recently introduced to Pakistan through the slave trade and evolved under positive selection in this vivax malaria-endemic region. Collectively, our study reconstructs the genetic and adaptive history of a neglected episode of the African Diaspora, and illustrates the impact of recent admixture on the diffusion of adaptive traits across human populations.

  Study EGAS00001002558

• Integrative genomic analysis reveals cancer-associated mutations at diagnosis of CML in patients with high risk disease

  Genomic events associated with poor outcome in chronic myeloid leukemia (CML) are poorly understood. We performed whole exome sequencing, copy number variation and/or RNA-Seq for 65 patients to discover mutations at diagnosis and blast crisis (BC). Forty-six chronic phase patients with the extremes of outcome were studied at diagnosis. Cancer gene variants were detected in 15/27 patients (56%) with subsequent BC or poor outcome and in 3/19 optimal responders (16%), P=.007. Frequently mutated genes at diagnosis were ASXL1, IKZF1 and RUNX1. The methyltransferase SETD1B was a novel recurrently mutated gene. A novel class of variant associated with the Philadelphia translocation was detected at diagnosis in 11/46 patients (24%) comprising fusions and/or rearrangement of genes on the translocated chromosomes, with evidence of fragmentation, inversion and imperfect sequence reassembly. These were more frequent at diagnosis in patients with poor outcome: 9/27 (33%) versus 2/19 optimal responders (11%), P=.07. Thirty-nine patients were tested at BC and all had cancer gene variants, including ABL1 kinase domain mutations in 58%. However, ABL1 mutations co-occurred with other mutated cancer genes in 89% of cases, and these predated ABL1 mutations in 62% of evaluable patients. Gene fusions not associated with the Philadelphia translocation occurred in 42% of patients at BC and commonly involved fusion partners that were known cancer genes (78%). Genomic analysis revealed numerous relevant variants at diagnosis in patients with poor outcome and all patients at BC. Future refined biomarker testing of specific variants will likely provide prognostic information to facilitate a risk-adapted therapeutic approach.

  Study EGAS00001003071

• Matching of actionable mutations with therapies in cancer patients: comparison of three commercial decision support platforms

  Purpose: In oncology, precision medicine centers on tailoring the most appropriate therapy to a patient according to their tumor molecular profile. We addressed the question whether there is a consensus in the classification of actionability of somatic variants and their alignment to treatment recommendations. To this end, we evaluated three commercial clinical decision support tools to assess their variant classification and drug matching strategies.Methods: In this two centers study 48 patients with metastatic breast (n=12), colorectal (n=17) or non-small cell lung cancer (n=19) were recruited based on circulating tumor DNA levels in peripheral blood. In individual patients’ plasma samples, we established somatic copy number alterations and somatic mutations across a 77 cancer-associated gene panel and exposed the obtained molecular profiles to the decision support packages NAVIFY Mutation Profiler (NMP; Roche), Qiagen Clinical Insight (QCI) Interpret (Qiagen) and CureMatch Bionov (CureMatch).Results: In the plasma samples, altogether 492 somatic alterations were assessed. Each decision support platform varied in their format of data input, mode of variant classification and strategies for identifying druggable targets and clinical trials, which resulted in discrepancies in tier classification as well as designations of actionability. The frequency of concordant actionable events for tier I-A or tier I-B classifications, i.e. those with the strongest clinical evidence, was only 4.3%, 9.5% and 28.4% when comparing NMP with QCI, NMP with CureMatch, and CureMatch with QCI, respectively. As a consequence, the obtained treatment recommendations differed drastically, and alignment of treatment recommendations was limited to established predictive markers.Conclusions: The lack of standards for the extraction of treatment recommendations from comprehensive tumor molecular profiling data challenge the promising concepts of precision oncology.

  Study EGAS00001004383