16461 results for "sec+and+unspecified+malignant+neoplasm"

in 44.55 milliseconds.

• Parallel CRISPR Editing of Familial ALS Mutations

  Induced pluripotent stem cells (iPSCs) provide a modeling system for human disease within the critical context of the human genome. CRISPR editing of iPSCs allows the construction of isogenic pairs where the cells have nearly identical genomes except for the desired edited change. Here, we used CRISPR editing on a healthy control line to generate three new isogenic iPSC lines that each contains a different familial amyotrophic lateral sclerosis (fALS) mutation (TARDBP[G298S/+], SOD1[G85R/+], and PFN1[G118V/+]). As controls for each mutation, we retained iPSC lines that went through the editing process but did not have the desired heterozygous mutation. We have also performed editing in a patient cell line with a hexanucleotide repeat in C9ORF72. This edit mutates a non-canonical start codon upstream of the hexanucleotide repeat. These edited lines have been used in three different studies:(1) In “Human sensorimotor organoids derived from healthy and amyotrophic lateral sclerosis stem cells form neuromuscular junctions” (PMID: 34362895), we performed exome sequencing to confirm the absence of off-target CRISPR events. We also performed single-cell sequencing on sensorimotor organoids from a second healthy control iPSC line, which is deposited alongside the exome sequencing data.(2) In “iPSC motor neurons, but not other derived cell types, capture gene expression changes in postmortem sporadic ALS motor neurons (PMID: 37651231), we differentiated these isogenic lines into motor neurons, sensory neurons, cortical neurons, and astrocytes and then performed bulk RNA-sequencing. We then assessed similarities between fALS mutations, differences between cell types, and the capacity of iPSC modeling to capture differentially expressed genes in postmortem sporadic ALS motor neurons.(3) In "Blocking RAN translation without altering RNA foci rescues C9ORF72-related ALS/FTD phenotypes" (Jiang et al. 2025), we mutate a non-canonical start codon upstream of a hexanucleotide repeat in C9ORF72. This mitigates several phenotypes related to neurodegeneration.

  Study phs002440

• Intra-tumor heterogeneity of localized lung adenocarcinomas defined by multi-region sequencing

  Background: Intra-tumor heterogeneity (ITH) may have profound impacts on biopsy strategy, characterization of actionable targets, treatment planning, and drug resistance. ITH of lung cancers has not been well studied. Methods: We performed multi-region whole exome sequencing (average sequencing depth 277x) on 48 tumor regions from 11 resected lung adenocarcinomas (8 stage I, 2 stage II and one stage III tumors) followed by deep sequencing validation (average sequencing depth 863x). Results: All tumors showed clear evidence of spatial ITH. On average, 76% of all mutations and 20/21 known cancer gene mutations were present in all regions of individual tumors (trunk mutations). Copy number analysis did not show substantial difference in large-scale chromosome aberrations. A canonical KRAS p.G12C mutation was detected in only 1 of 4 tumor regions (branch mutations) at exome depth but detected in all four tumor regions (trunk mutations) at increased sequencing depth. In addition, many heterogeneous branch mutations defined by exome sequencing were detectable in all regions of individual tumors with increasing sequencing depth. Significant differences in mutational spectrum were observed in 6 tumors indicating that specific mutational processes were likely operative at different times during development of these tumors. With a median follow up of 21 months post surgery, 3 patients have relapsed and all 3 patients presented with significantly larger fractions of subclonal mutations in their primary tumors. Conclusions: Although multi-region sampling is still required to fully assess ITH complexity, single biopsy analysis at appropriate depth might be sufficient to identify the majority of known cancer gene mutations in localized lung adenocarcinomas. Larger subclonal mutation fractions may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas. Studies on larger cohort, ideally with comprehensive clinical annotation and repeat biopsy at relapse are needed to fully understand the clinical impact of ITH.

  Study EGAS00001000930

• Genetics of Neuropsychiatric and Neurodevelopmental Disorders

  Although schizophrenia is a highly heritable disease, relatively little progress had been made in securely identifying the genetic causes of this disorder, and most instances of schizophrenia in the general population remain unexplained. One avenue of explanation for the genetic basis of schizophrenia, however, has been effectively closed by recent research. Genome-wide association studies (GWAS) have now shown that common variation makes at most a modest contribution to the risk of schizophrenia. At the same time that the role of common variation has been circumscribed by GWAS, however, the analysis of copy number variants that are detectable on a genome-wide scale has revealed and replicated a number of very rare variants that associate with schizophrenia. These rare copy number variants that have been implicated in schizophrenia, however, have one striking feature in common: they are all risk factors for other brain related disorders beyond schizophrenia such as mental retardation, autism and epilepsy. These findings argue that genetic risk factors may confer a highly penetrant vulnerability to neuropsychiatric disorder, which is then further modified by interacting genetic or environmental factors to determine the ultimate manifestation. Most schizophrenia collections that are being studied today, however, have been selected precisely for their homogeneity: including only schizophrenia patients with no comorbidities, or schizophrenia patients with relatives who have schizophrenia but no other neuropsychiatric conditions. These selection criteria are inconsistent with what we now know about the bulk of the genetic differences that have been associated with disease. The central hypothesis of this project is that there are rare genetic variants that strongly elevate the risk of various neuropsychiatric diseases, and that these risk factors can be identified most readily in families segregating multiple neuropsychiatric conditions.

  Study phs000682

• Exome Sequencing for Diseases of the Immune System: X-linked Immunodeficiency with Magnesium Defect, EBV Infection, and Neoplasia (XMEN)

  The etiologies of primary immunodeficiencies often yield novel insights about the immune system. Although a genetic etiology has been suspected for patients with abnormally low CD4+ T cells in the absence of HIV infection or any known causes of lymphopenia, no genetic mutation has been described to date for any case of primary CD4 lymphopenia. In this study, we characterized a non-consanguineous family with two non-HIV infected boys exhibiting an inverted CD4 to CD8 T cell ratio and a history of recurrent chronic viral infections since birth. Consistent with a decreased thymic output of CD4+ T cells, the percentage of CD31+ cells in the CD4+ naive population of these patients was decreased. In addition, the activation of T cells was significantly impaired in the patient upon TCR stimulation. Given the mother's T cells show completely skewed X chromosome inactivation, we suspected that the nature of this disease is X-linked. We performed X-chromosome exon-capture targeted single-end Solexa sequencing on two brothers and the mother and found a 10 base pair deletion at an intron-exon junction of Magnesium Transporter 1 (MAGT1), a Mg2+ specific transporter. We confirmed that this deletion leads to altered splicing, frameshift, early termination of the mRNA, and deficient protein expression in the lymphocytes of the two patients. Moreover, knockdown of this transporter in T cells isolated from healthy donors can recapitulate the observed T cell activation defect while its ectopic expression in the patients' lymphocytes can restore T cell stimulation. Our discovery highlights the significance of this transporter to T cell function.

  Study phs000365

• Genomic and epigenomic characterization of juvenile myelomonocytic leukemia (JMML)

  Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative disorder of early childhood. While some cases show spontaneous remission, allogeneic hematopoietic stem cell transplantation (HSCT) remains the only curative treatment option for the majority of patients, however, the 5-year event-free survival reaches only about 50%. Hyperactive RAS signaling is assumed to be the main driving event in JMML. It is caused by genetic alterations in CBL, KRAS, NF1, NRAS, or PTPN11 in about 90% of patients. So far, there is no clear understanding of how RAS pathway mutations relate to the heterogeneous disease biology and variable clinical outcome seen in JMML patients. As a consequence, established clinical and genetic markers fail to fully represent the observed disease heterogeneity. We hypothesized that DNA methylation profiling, either alone or in combination with genetic alterations, might provide a molecular basis for disease classification. Genome wide DNA methylation analysis using the HumanMethylation450 Bead Chip array was performed in a discovery cohort of 20 patients (19 JMML, 1 Noonan syndrome). The clinical relevance of our findings was assessed in an unselected sample set consisting of 147 consecutive patients with JMML (n=129) or Noonan syndrome (n=18) associated myeloproliferative disorder (n=18) registered in the EWOG-MDS 1998 & 2006 trials. Data integration was performed in a subset of patients with available exome sequencing (n=50) and expression profiling (n=15) data. This integrated approach identified three JMML subgroups characterized by distinct clinical and biological features. This analysis further suggested a molecular mechanism by which additional genetic events, presumably further activating the RAS-RAF-MEK-ERK pathway, mediate DNA hypermethylation via up-regulation of DNMTs in more aggressive JMML cases.

  Study EGAS00001002511

• Intra-tumor heterogeneity and clonal evolution patterns towards platinum-resistant high-grade serous ovarian cancer

  Although many high-grade serous ovarian carcinoma (HGSOC) patients are initially sensitive to the standard-of-care platinum-based therapy, intra-tumor heterogeneity may fuel selection towards platinum-resistant clones over subsequent lines of therapy. We performed whole-exome coupled to ultra-deep re-sequencing and copy number alteration (CNA) profiling on 31 matched diagnosis and relapse tumors from HGSOC patients treated with platinum. Genetic heterogeneity within tumor pairs varied extremely, with some recurrent tumors being identical and others completely different from the primary tumor. All primary tumors showed evidence of homologous recombination (HR)-deficiency, but none of them carried reactivating mutations in the HR pathway at relapse, also not when becoming resistant to subsequent lines of platinum. We did also not identify other mutations, CNAs or pathways that were enriched in platinum-resistant clones. However, a platinum score consisting of 13 CNAs correlated significantly with platinum-free interval (PFI) after first-line therapy in multiple cohorts of >450 primary HGSOCs. A relative increase of this score in the relapsed tumor correlated with a change in PFI during subsequent therapy. Clonal evolution under platinum therapy contributes to genetic heterogeneity within tumor pairs, implicating that genetic tests linked to targeted treatment options for recurrent HGSOC need to be performed on biopsies collected at relapse. Platinum resistance of an initially platinum-sensitive HR-deficient HGSOC at relapse is not caused by reactivation of the HR pathway, suggesting that these tumors are still responsive to PARP inhibitors, instead, several chromosomal aberrations contribute to a score predictive of disease outcome under platinum therapy, both in first and subsequent lines of platinum therapy.

  Study EGAS00001001244

• Pharmacological and genomic profiling identifies NFκB-targeted treatment strategies for mantle cell lymphoma

  Mantle cell lymphoma (MCL) is an aggressive malignancy characterized by poor prognosis, underscoring the need for novel therapeutic strategies1. In this study, we employed large-scale pharmacological profiling of 16 pathway inhibitors across more than 100 hematological cell line models to identify novel therapeutic approaches. This screen identified a subset of MCL cell lines that were highly sensitive to the B-cell receptor (BCR) signaling inhibitors ibrutinib and sotrastaurin (STN), which are currently in clinical trials2, 3. Sensitive MCL models exhibited chronic activation of the CARD11-BCL10-MALT1 (CBM) complex and dependency on downstream classical NFκB signaling. In contrast, insensitive cell lines displayed activation of the alternative NFκB pathway. Massively parallel transcriptome sequencing identified mutations in negative regulators of the alternative NFκB pathway in ibrutinib-insensitive cell lines. Notably, sequencing of 165 MCL patient samples revealed that components of the alternative NFκB pathway, particularly TRAF2 and BIRC3, are mutated in roughly 15% of MCL patients. Functional analyses of patient-derived BIRC3 mutant alleles indicated that these are loss-of-function mutants that constitutively activate alternative NFκB signaling. While being associated with insensitivity to ibrutinib, genetic lesions in the alternative pathway conferred dependence on NIK, a kinase that mediates signaling downstream of the TRAF2/3-BIRC2/3 complex. Thus, NIK represents a novel therapeutic target for MCL treatment, particularly for tumors refractory to BCR pathway inhibitors. Collectively, these findings reveal a pattern of mutually exclusive activation of the CBM-NFκB or alternative NIK-NFκB pathways in MCL and provide critical insights into patient stratification strategies for NFκB-pathway targeted agents.

  Study EGAS00001000622

• Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in the DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 11 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic 'scarring', indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after 6 lines of therapy (4 platinum-based), whilst another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neo-adjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy.

  Study EGAS00001005395

• Phase II Therapeutic Trial of Mexiletine in Non-Dystrophic Myotonia (IND #77,021)

  Non-dystrophic myotonias are a group of rare muscle disorders caused by abnormalities in different muscle cell membrane proteins. Patients experience delayed muscle relaxation that causes impaired physical activity, stiffness, and pain. Mexiletine has been shown to be beneficial for symptoms of myotonia in case reports of patients with non-dystrophic myotonia, however, current management of these patients primarily depends on individual practitioners' preference. This study is a placebo-controlled, double-blind, cross-over treatment trial using mexiletine in non-dystrophic myotonias. The results, if positive, will permit the identification of mexiletine as an effective therapy for myotonia, making it a first-line therapy for patients with non-dystrophic myotonia. The specific aim of this proposal is to perform a randomized, double-blind, placebo-controlled cross-over study to assess whether mexiletine improves both quantitative and qualitative measures of myotonia in patients with non-dystrophic myotonia. Given the rarity of non-dystrophic myotonia, large randomized controlled treatment trials in these disorders have not been feasible in the past. Primary Outcome Measure: Patient-assessed symptom: stiffness as measured by an Interactive Voice Response Diary (IVR). Secondary Outcome Measures: a) patient-assessed pain, weakness, and fatigue as measured by IVR; b) clinical myotonia assessment; c) quality of life as measured by INQoL, SF36; d) a quantitative measure of grip myotonia; e) measurement of CMAP after short and long exercise; f) grading of myotonia on needle EMG.

  Study phs001311

• Cell-Free, Methylated DNA in Blood Samples Reveals Tissue-Specific, Cellular Damage from Radiation Treatment

  Radiation therapy is an effective cancer treatment, although damage to surrounding healthy tissues can also occur. Cell-type specific DNA methylation patterns can be used to decode the cellular origins of cell-free DNA fragments, allowing for minimally-invasive monitoring of tissue damage. To evaluate whether changes in cell-free DNA methylation can indicate damages to tissues in patients treated with radiation, we collected serum samples from 15 breast cancer patients at three timepoints during their standard-of-care radiation therapy after surgery. A baseline sample was taken for each patient before onset of radiation therapy and a second End-Of-Treatment (EOT) sample was taken 30 minutes after the last treatment fraction. Finally, a recovery sample was taken one month after completion of radiation therapy. From serum samples, cell-free DNA was isolated and bisulfite capture-sequencing DNA methylation data were generated. Bisulfite capture-sequencing DNA methylation data were also generated from buffy coat and tissue-specific endothelial gDNA samples as well as from serum/plasma samples of healthy controls. The analysis of serum samples from breast cancer patients undergoing radiation treatment revealed distinct tissue-specific epithelial and endothelial responses to radiation across multiple organs. In conclusion, as a proof of concept we show that cell-type specific methylation signatures can be applied to detect cellular injury from radiation treatment using minimally invasive cell-free DNA in blood samples.

  Study phs003290

• Noninvasive prenatal molecular karyotyping from maternal plasma

  Fetal DNA is present in the plasma of pregnant women. Massively parallel sequencing of maternal plasma DNA has been used to detect fetal trisomies 21, 18, 13 and selected sex chromosomal aneuploidies noninvasively. Case reports describing the detection of fetal microdeletions from maternal plasma using massively parallel sequencing have been reported. However, these previous reports were either polymorphism-dependent or used statistical analyses which were confined to one or a small number of selected parts of the genome. In this report, we reported a procedure for performing noninvasive prenatal karyotyping at 3 Mb resolution across the whole genome through the massively parallel sequencing of maternal plasma DNA. This method has been used to analyze the plasma obtained from 6 cases. In 5 cases, fetal microduplications or microdeletions have been detected successfully from maternal plasma. The two cases with fetal microduplications represented the first noninvasive prenatal detection of such changes from maternal plasma. In the remaining case, the plasma DNA sequencing result was consistent with the pregnant mother being a carrier of a microduplication. Simulation analyses were performed for determining the number of plasma DNA molecules that would need to be sequenced and aligned for enhancing the diagnostic resolution of noninvasive prenatal karyotyping to 2 Mb and 1 Mb. In conclusion, noninvasive prenatal molecular karyotyping from maternal plasma by massively parallel sequencing is feasible and would enhance the diagnostic spectrum of noninvasive prenatal testing.

  Study EGAS00001000439

• Neoadjuvant combination PD-L1 plus CTLA-4 blockade in patients with cisplatin-ineligible operable urothelial carcinoma

  Immune checkpoint therapy (ICT) is being tested in the neoadjuvant setting for patients with localized urothelial carcinoma (UC), with one study reporting data in cisplatin-ineligible patients who received anti PD-L1 monotherapy. The study reported that patients with bulky tumors, a known high-risk feature defined as greater than clinical T2 disease, had fewer responses, with pathologic complete response (pCR) rate of 17%. Here, we report on the first pilot combination neoadjuvant trial (NCT02812420) with anti-PD-L1 (durvalumab) plus anti-CTLA-4 (tremelimumab) in cisplatin-ineligible patients, with all tumors identified as having high risk features (N=28). Primary endpoint was safety and we observed 6 of 28 patients (21%) with grade ≥3 immune-related adverse events, consisting of asymptomatic laboratory abnormalities (N=4), hepatitis and colitis (N=2). We also observed pCR of 37.5% and downstaging to pT1 or less in 58% of patients who completed surgery (N=24). In summary, we provide initial safety, efficacy and biomarker data with neoadjuvant combination anti-PD-L1 plus anti-CTLA-4, which warrants further development for patients with localized UC, especially cisplatin-ineligible patients with high-risk features who do not currently have an established standard-of-care neoadjuvant treatment. Publication: Gao et al. Neoadjuvant PD-L1 plus CTLA-4 blockade in patients with cisplatin-ineligible operable high-risk urothelial carcinoma. Nature Medicine volume 26, pages1845–1851(2020)

  Study EGAS00001004074

• Assessing mitochondrial bioenergetics in coronary artery disease: A translational left ventricular tissue study in humans (The AMBITION study).

  Severe or recurrent myocardial ischemia can lead to long-term left ventricular (LV) dysfunction and heart failure in patients with coronary artery disease (CAD). In this study, we assessed for chronic metabolomic and transcriptomic adaptations within LV myocardium of patients with evidence of preoperative inducible ischemia undergoing coronary artery bypass grafting (CABG). During surgery, paired transmural LV biopsies were acquired on the beating heart from a region of inducible ischemia (determined by stress perfusion cardiovascular magnetic resonance), and a remote LV segment. From 33 patients, 63 LV biopsies were acquired on the beating heart during CABG, comparing to analysis of LV samples from 11 donor hearts. The global myocardial ATP/ADP ratio was reduced in CAD patients as compared to donor LV tissue with increased expression of oxidative phosphorylation (OXPHOS) genes encoding the electron transport chain complexes across multiple cell types. Paired analyses of biopsies obtained during CABG from LV segments with or without inducible ischemia revealed no significant difference in the ATP/ADP ratio, broader metabolic profile or expression of ventricular cardiomyocyte genes implicated in OXPHOS. Finally, differential metabolite analysis suggested dysregulation of several intermediates in patients with impaired LVEF; this notably included the tricyclic acid cycle metabolite, succinate. Overall, our results suggest that viable human myocardium in patients with stable CAD has global alterations in bioenergetic and transcriptional profile without large regional differences between areas with or without inducible ischemia.

  Study EGAS00001007351

• Efficacy of JAK/STAT pathway inhibition in murine xenograft models of early T-cell precursor (ETP) acute lymphoblastic leukemia

  Early T-cell precursor (ETP) acute lymphoblastic leukemia (ALL) is a recently described subtype of T-ALL characterized by a unique immunophenotype and genomic profile as well as a high rate of induction failure. Frequent mutations in cytokine receptor and JAK/STAT signaling pathways led us to hypothesize that ETP-ALL is dependent on JAK/STAT signaling. Here we demonstrate aberrant activation of the JAK/STAT pathway in ETP-ALL blasts relative to non-ETP T-ALL. Moreover, ETP-ALL showed hyperactivation of STAT5 in response to IL7, an effect that was abrogated by the JAK1/2 inhibitor ruxolitinib. In vivo, ruxolitinib displayed activity in 6/6 patient-derived murine xenograft models of ETP-ALL, with profound single-agent efficacy in 5 models. Ruxolitinib treatment decreased peripheral blast counts relative to pre-treatment levels and compared to control (P<0.01) in 5/6 ETP-ALL xenografts, with marked reduction in mean splenic blast counts (P<0.01) in 6/6 samples. Surprisingly, both JAK/STAT pathway activation and ruxolitinib efficacy were independent of the presence of JAK/STAT pathway mutations, raising the possibility that the therapeutic potential of ruxolitinib in ETP-ALL extends beyond those cases with JAK mutations. These findings establish the preclinical in vivo efficacy of ruxolitinib in ETP-ALL, a biologically distinct subtype for which novel therapies are needed.

  Study EGAS00001001146

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Study EGAS00001004538

• Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

  Whole-genome sequencing of primary breast tumors enabled the identification of cancer driver genes and non-coding cancer driver plexuses from somatic mutations. However, differentiating between driver and passenger events among non-coding genetic variants remains a challenge to understand the etiology of cancer and inform the delivery of personalized cancer medicine. Herein, we reveal enrichment of non-coding mutations in cis-regulatory elements that cover a subset of transcription factors linked to tumor progression in luminal breast cancers. Using a cohort of 26 primary luminal ER+PR+ breast tumors, we compiled a catalogue of ~100,000 unique cis-regulatory elements from ATAC-seq data. Integrating this catalogue with somatic mutations from 350 publicly available breast tumor whole genomes, we identified four recurrently mutated individual cis-regulatory elements. By then partitioning the non-coding genome into cistromes, defined as the sum of binding sites for a transcription factor, we uncovered cancer driver cistromes for ten transcription factors, namely CTCF, ELF1, ESR1, FOSL2, FOXA1, FOXM1 GATA3, JUND, TFAP2A, and TFAP2C in luminal breast cancer. Nine of these ten transcription factors were shown to be essential for growth in breast cancer, with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of non-coding mutations over cis-regulatory elements concatenated into a functional unit drawn from an accessible chromatin catalogue derived from primary cancer tissues.

  Study EGAS00001005235

• Bibliography Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here we expose in a yearly and cumulative manner, the publications, citations, publishers journals and impact factor of EGA related studies. Publications citing data stored at the EGA The studies that use EGA datasets are requested to cite the EGA into their bibliography. This fact allows for a faster spreading of the role and purpose of the EGA, being the availability of their studies for testability and reusability purposes. In order to track the studies we use the unique study accession, provided by the EGA when submitting a new study, and comprised of the keyword EGAS followed by 11 digits such as EGAS00000000001. We use the Europe PMC RESTful Web Service in order to query, and index studies where the EGAS accession was used. Below, you can find a chart showing the journal publishers of the studies citing data stored in the EGA being cited: Publications Citing Data Stored in the EGA pieChart('publishers-citations', 'https://stats.ega-archive.org/publishers/citations', 'Publishers') Number of publications citing data stored in the EGA by year Here, you can find a chart showing the number of publications citing data stored in the EGA by year. These publications could have deposit, or re-used the data. The study acession can be found in the publication. The default values are non-cumulative. You can see cumulative values by clicking "Cumulative". Number of Publications Citing Data Stored in the EGA by Year barChart('publications-by-year', 'https://stats.ega-archive.org/publications/year', ['Number of Publications by Year', 'Publications by Year']) Journal Impact Factor of Publications Citing Data Stored in the EGA Here, you can find a chart showing the journal impact factor of publications citing data stored in the EGA. We have used Scimago Journal Rank for this purpose. Low impact treshold was defined between 0.0 and 5.0 excluding; Medium impact treshold was defined between 5.0 and 10.0 excluding; High impact range was established from values equal to or higher than 10.0; Journal Impact Factor of Publications Citing Data Stored in the EGA pieChart('citations-publication-impact-factor', 'https://stats.ega-archive.org/citations/publication/impact/factor', 'Publication Impact Factor') Publications citing a publication containing a EGA study accession The publications, stating a EGA study accession in the bibliography, can be cited for testibility or reusability purposes. Below, you can find a chart showing the publishers of the publications being cited: Publications Citing a Publication Containing a EGA Study Accession pieChart('publications-citations', 'https://stats.ega-archive.org/publications/citations', 'Publishers') Number of publications citing a publication containing a EGA study accession by year Here, you can find a chart showing the number of publications citing a publication containing a EGA study accession by year by year. The values are non-cumulative: Number of published citations by year barChart('citations-year', 'https://stats.ega-archive.org/citations/year', ['Number of Publications', 'Citations by Year'])

  Documentation about/statistics/bibliography

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• WTCCC case-control study for Hypertension - Combined Controls

  WTCCC genome-wide case-control association study for Hypertension (HT) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000010

• SCLC

  The aim of this study is to use sequence data generated from 500 bp paired end reads using the Illumina platform to characterise the breakpoints of chromosomal translocations, amplifications and deletions at high resolution (to the bp).

  Study EGAS00001000009

• Renal Cancer Exome Sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 25 renal cancers and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting 50 exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Dataset EGAD00001000014

• Renal Cancer Exome Sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 25 renal cancers and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting 50 exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Dataset EGAD00001000287

• genotyped_bacterial_meningitis

  This study includes 1146 samples of host genotyping data (genotyped) from Illumina Omni arrays. Samples were collected from adults (&gt;16 yrs) patients with CSF confirmed bacterial meningitis in the Netherlands between 2006 and 2015. Metadata includes patient outcome, species of bacteria, and for 467 samples a link to an ENA run with the associated bacterial genome (S. pneumoniae only).

  Dataset EGAD00010002328

• A Collaborative Search for New Genes for Non-Syndromic Deafness

  We seek to identify the DNA variants underlying sensorineural hearing loss. We obtain the DNA sequence of affected individuals and their family members. DNA variants are filtered to determine their segregation in affected families.

  Study phs003701

• ABCB1 expression in HCC biopsies

  We analyzed the ABCB1 expression in RNA-seq data of 134 HCCs (120 of these were previously published), including the biopsies from which the 3 doxorubicin-sensitive and 3 doxorubicin-resistant HCCOs were derived.

  Study EGAS50000000041

• PARK7/DJ-1 deficiency modulates microglial activation in response to LPS-induced inflammation

  Starting point is one patient from which iPSC cells are created, some received a genome correction to be called iCtrl from those two batches (DJ1 and iCtrl). 3 independent differentiation to Microglia cells were done, named R1, R2 and R3. On top, growing conditions were either untreated or LPS, leading to the 12 samples.

  Dataset EGAD50000000291

• Whole-genome sequencing of tumour, germline and plasma samples from a BRCA1-mutant breast cancer patient

  This data set contains the fastq files from whole-genome sequencing of temporally matched tumour (fresh frozen biopsies), blood germline and plasma samples collected from a BRCA1-mutant breast cancer patient to directly compare mutation signature analysis using gold-standard tumour-germline paired variant calling with a novel ctDNA-based method (MisMatchFinder).

  Dataset EGAD50000000811

• Multidimensional Proteomics analysis of intractable cancers with prospective observational cohort for precision medicine

  Multi-omics project of Non small cell Lung Cancer. Exome and RNAseq datasets were generated from normal-tumor pairs of Non small cell Lung Cancer patients from the retrospective cohort.

  Study EGAS50000000592

• Single-cell RNA sequencing of 16 NPM1 mutated AML patients

  This dataset contains paired-end fastq files for single-cell RNA-seq of 16 NPM1 mutated AML patients. 19 fastq pairs are included, with two samples split into two lanes and a second (technical) replicate for one patient. Libraries were constructed using 10x Genomics Chromium Next GEM Single cell 3’ v3.1 and sequencing was performed using Illumina NovaSeq6000.

  Dataset EGAD50000000478

• 10X Genomics single-nuclei RNA-sequencing of endometrium from women with and without PCOS

  Chromium Next GEM Single Cell 3’ Gel Bead Kit (Dual Index) v3.1 (10X Genomics) of endometrium from 27 women including 5 without PCOS and 12 with PCOS. Following baseline biopsies, PCOS participants underwent a 16-week randomized controlled trial with metformin (n=7) or lifestyle intervention (n=3). 25,000 raw reads per nucleus

  Dataset EGAD50000001017

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  Single cell mRNAseq of T-ALL samples including presentation samples for 13 with refractory disease and 8 with responsive disease, and Day 28 samples for 8 patients with refractory disease. Also includes bulk mRNA sequencing on 6 of the patients with responsive disease.

  Dataset EGAD50000001128

• H3Africa AWIGEN Pilot MetaboChip

  Pilot study on the interplay between genetic, epigenetic, and environmental risk factors for obesity and related cardiometabolic diseases with 973 samples from South Africa genotyped on Illumina Human MetaboChip array.

  Dataset EGAD00010001258

• 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

• Whole genome sequencing data of relapsed paediatric KMT2A-rearranged acute lymphoblastic leukemia

  Whole genome sequencing data of relapsed KMT2A-rearranged leukemia. Dataset includes bam-file of released case. Diagnostic (ALLT-375D) and remission (ALLT-375R) samples can be found with EGA ID: EGAD50000001568.

  Study EGAS50000001100

• Targeted sequencing data of cfDNA, archival tissue, and WBC from 226 patients with mUC

  Targeted tissue (n=210), cfDNA (n=402), and WBC (n=245) Illumina sequencing of metastatic urothelial carcinoma patients. All cfDNA is collected in the metastatic setting, while archival tissue is from across the disease course. White blood cell (WBC) samples are used as patient matched germline samples. All files are in paired-end fastq format.

  Dataset EGAD50000001571

• RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia

  Total RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia. Libraries were prepared using Illumina TruSeq Stranded Total RNA with Ribo-Zero Gold kits. The samples were paired-end sequenced using the NovaSeq 6000 platform at a depth of 60 million reads per sample with a read length of 100 base pairs.

  Dataset EGAD50000001818

• Characterizing Immune Profiles in Extrapulmonary Tuberculosis via RNA Sequencing

  This dataset provides insight into the immune response in extrapulmonary tuberculosis (EPTB), using bulk RNA-seq data to analyze different disease severities. Hierarchical clustering identified three distinct levels of severity, revealing disease progression driven by interferon and IL-1β-mediated signaling. Additionally, the dataset helped develop a diagnostic gene expression signature for both EPTB and pulmonary TB.

  Dataset EGAD50000000943

• Spatial transcriptomics

  This dataset contains raw sequencing and processed data of spatially resolved transcriptomes. The data were generated with 10x Visium probe-based assay on FFPE tissues of 30 ST-EPN tumour resections. Raw data contain two-lane fastq files of the sequencing. Processed data contain aligned and filtered .h5 counts matrices as well as positional files as generated by 10x SpaceRanger.

  Dataset EGAD50000001876

• WGS

  A genome-wide approach based on next generation sequencing (NGS) is performed on trios ( Patient+ Father + Mother) with SAID in order to identify known and new gene variants associated with certain forms of the disease. WGSwas performed on frozen PBMCs. DNA was extracted using the QIAamp DNA Blood Midi Kit and quantified with Nanodrop. Sequencing was done on a NovaSeq 6000 with S4 flow cells, targeting 30× coverage.

  Dataset EGAD50000002024

• Comprehensive genomic analysis for AYA with acute lymphoblastic leukemia

  AYA with B-ALL showed extremely inferior outcomes when compared to children. Genetic basis undelying AYA B-ALL is largely unknown. We performed integrated analysis of RNA-seq (hum0045���JGAS000047) and target DNA-seq.

  Study JGAS000276

• Mitochondrial DNA Mutations at Low-level Heteroplasmy in Chronic Kidney Disease

  To explore for relationship between somatic or heteroplasmic mutations of mtDNA and kidney function, we here performed ultra-deep sequencing based on long-range PCR from DNAs of 78 CKD kidney tissues.

  Study JGAS000611

• Cell-free DNA sequencing using newly developed single-strand DNA library preparation

  We performed whole genome sequencing of cell-free DNA that was collected from human plasma using an improved cfDNA purification protocol and DNA library prepared with newly developped protocol of our own.

  Study JGAS000257

• Targeted sequencing data of cfDNA, archival tissue, and WBC from 208 patients with mUC

  Targeted tissue (n=81), cfDNA (n=319), and WBC (n=208) Illumina sequencing of metastatic urothelial carcinoma patients. All cfDNA is collected in the metastatic setting, while archival tissue is from across the disease course. White blood cell (WBC) samples are used as patient matched germline samples. All files are in paired-end fastq format.

  Dataset EGAD50000002089

• CIAO Clinical Trial

  Whole exome and RNA sequencing for samples from CIAO (Checkpoint Inhibitors Assessment in Oropharynx cancer) (NCT03144778) clinical trial testing neoadjuvant anti-PD-L1 durvalumab with or without the anti-CTLA-4 tremelimumab in oropharynx cancer

  Study EGAS50000001174

• The dataset of Detection and characterization of lung cancer using cell-free DNA fragmentomes on NovaSeq 6000 at 1-2x coverage

  The dataset contains 364 CRAM files from whole-genome next-generation sequencing on the Illumina NovaSeq 6000 at 1- 2x coverage. These are plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD50000001961

• Using Exome-sequencing to characterize the resistance to lirafugratinib

  We characterized resistance to lirafugratinib with whole exome sequencing in patients with FGFR2-driven cancers, treated in the phase 1/2 ReFocus trial (NCT04526106) and enrolled in the UNLOCK program at Gustave Roussy

  Study EGAS50000001371

• TenK10K Phase 1: scRNA-seq AnnData objects

  Single cell RNA-sequencing data for PBMCs from the Tenk10k Phase 1 cohort (1925 individuals post-QC). Libraries were prepared using the 10x Genomics 3’ Chromium Next GEM Single Cell HT V3.1 kit and sequenced on the NovaSeq 6000 platform. Reads were mapped to the GRCh38 reference genome with Cellranger, and count matrices were preprocessed using Scanpy.

  Dataset EGAD50000002379

• Whole exome sequencing variant data for Mendelian disorders cohort

  This dataset contains whole exome sequencing variant data (VCF files) from individuals with suspected Mendelian disorders, including sickle cell disease, muscular dystrophy, haemophilia, and Fanconi anemia. Variant calling was performed using the Illumina DRAGEN pipeline, with downstream variant prioritisation conducted using the Zi-Mendelian bioinformatics pipeline. The dataset supports research into rare genetic diseases and genomic variation in African populations.

  Dataset EGAD50000002453

• Biomodal duet +modC sequencing of endometrial cancer plasma cfDNA samples

  This dataset contains biomodal sequencing data generated using the duet + modC assay from plasma-derived cell-free DNA of endometrial cancer patients and healthy donors. Sequencing was performed on an Illumina NovaSeq 6000. Dataset includes raw sequencing FASTQ files. Duet +modC output enables whole-genome profiling of both genetic variation and DNA methylation.

  Dataset EGAD50000002261

• TEST_STUDY for submitter testing

  This is a test study for the purpose of submitters wishing to test the EGA submission procedure. All objects, such as DAC and datasets, linked to this study can also be considered to be for testing purposes.

  Study EGAS00001000889

• Molecular analysis of cancer genomes in children with Lynch syndrome: exploring causal associations

  Molecular characterization of 20 tumors of 18 children with Lynch syndrome to investigate causality. This characterization includes the investigation of second hits, tumor mutational load, mutational signatures and MMR protein expression.

  Study EGAS00001006992

• Breast_Cancer_Matched_Pair_Cell_Line_Whole_Genomes

  We propose to definitively characterise the somatic genetics of matched pair breast cancer cell lines through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000166

• Characterization of Arabian Peninsula whole exomes

  We conducted whole exome sequencing (using the SureSelect Human All Exon V5 + UTRs target enrichment kit) of 90 individuals from AP (23 from Saudi Arabia, 24 from Yemen, 24 from Oman and 19 from UAE).

  Study EGAS00001006487

• Triple_Negative_Breast_Cancer_Whole_Genomes

  We propose to definitively characterise the somatic genetics of Triple negative breast cancer through generation of comprehensive catalogues of somatic mutations in 500 cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000092

• Recurrent loss of heterozygosity correlates with clinical outcome in pancreatic neuroendocrine cancer

  Pancreatic neuroendocrine tumors (pNETs) are uncommon cancers arising from pancreatic islet cells. Here we report analysis of gene mutation, copy number and RNA expression of 57 sporadic well-differentiated pNETs.

  Study EGAS00001003038

• Genetics_and_Networks_of_Congenital_Heart_Defects

  Exome sequencing of families with Congenital Heart Defects of diverse sub-phenotypes. Comprises both parent-offspring trios for sporadic cases and multiplex families. Collaboration with David Brook, University of Nottingham. Funded by the British Heart Foundation.

  Study EGAS00001000762

• Genomic Rearrangements in Pediatric Cancer

  This study contains a collection of samples used to determine the role and impact of genomic rearrangements in a pan cancer pediatric cohort.

  Study EGAS00001005312

• SNP array study in Autism Spectrum Disorder patients

  The aim of this study was to investigate two ancestral inversions (inv17q21.31 and inv8p23.1) in Autism Spectrum Disorder (ASD) patients. We have performed SNP array in order to genotype these inversions using the package scoreInvHap.

  Study EGAS00001005606

• Multi-region exome sequencing of lung adenocarcinoma precursors -1

  To characterize the genomic landscape of invasive lung adenocarcinoma and its precursors; to depict the genomic evolution from preneoplasia of lung to invasive lung adenocarcinoma; to decipher mechanism to drive the tumor progression.

  Study EGAS00001003439

• LifeLines-NEXT pilot study

  The pilot study from the LifeLines-NEXT (NEXT) cohort consisting of 30 mother-infant pairs with longitudinal sampling from pregnancy till 3 months after birth. Shotgun data is available for virome and microbiome annotation.

  Study EGAS00001005969

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  Development of a novel machine learning guided ctDNA detection platform for use in liquid biopsy detection and therapeutic monitoring of solid tumors in several clinical contexts. Included are WGS alignments from our study.

  Study EGAS00001007306

• germline variants in children with hematological cancer

  Trio parent-child whole exome sequencing has been used to identify cancer predisposing syndrome (CPS) in our pediatric patients with hematological cancer, and it was identified in 3.8% of the total number of patients(131).

  Study EGAS00001006907

• A complex chromosomal rearrangement (CCR) was resolved at the nucleotide level by whole genome long read sequencing using PacBio sequencing platform.

  A complex chromosomal rearrangement (CCR) involving 7 chromosomes and 11 breakpoints was resolved at the nucleotide level by whole genome long read sequencing using PacBio sequencing platform.

  Study EGAS00001008133

• Genome and transcriptome sequence data from a retroperitoneal leiomyosarcoma patient

  Genome and transcriptome sequence data from a retroperitoneal leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002583

• Genome and transcriptome sequence data from a endometrial carcinoma patient

  Genome and transcriptome sequence data from a endometrial carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001008921

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010951

• Genome and transcriptome sequence data from an uveal melanoma patient

  Genome and transcriptome sequence data from an uveal melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005824

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005841

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005849

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005855

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005858

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005867

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005907

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002532

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002537

• Genome and transcriptome sequence data from an oligodendroglioma patient

  Genome and transcriptome sequence data from an oligodendroglioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002539

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002540

• Genome and transcriptome sequence data from a Ewing sarcoma patient

  Genome and transcriptome sequence data from a Ewing sarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005894

• Genome and transcriptome sequence data from a melanoma patient

  Genome and transcriptome sequence data from a melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002546

• Exome and transcriptome sequence data from a breast cancer patient

  Exome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002547

• Genome and transcriptome sequence data from a cervical cancer patient

  Genome and transcriptome sequence data from a cervical cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002560

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002576

• Genome and transcriptome sequence data from a testicular cancer patient

  Genome and transcriptome sequence data from a testicular cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002632

• Genome and transcriptome sequence data from a porocarcinoma patient

  Genome and transcriptome sequence data from a porocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002596

• Genome and transcriptome sequence data from a multiple myeloma patient

  Genome and transcriptome sequence data from a multiple myeloma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001008947

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002635

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005893

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001008933

• Genome and transcriptome sequence data from a metastatic melanoma patient

  Genome and transcriptome sequence data from a metastatic melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004931

• Whole Exome Sequencing of Mixed Histology Lung Cancer

  We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histologic patterns including 6 LUAD, 6 LCNEC, 3 SCLC, 3 LUSC, and one poorly differentiated NSCLC-NOS.

  Dataset EGAD00001007076

• Genome and transcriptome sequence data from an osteosarcoma patient

  Genome and transcriptome sequence data from an osteosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005870

• BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 Full Summary Statistics

  BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 full summary statistics data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each full summary statistics file contains the associations for all tested variants for each phenotype ID.

  Dataset EGAD00001005199

• Genome and transcriptome sequence data from an ovarian cancer patient

  Genome and transcriptome sequence data from an ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002533

• Genome and transcriptome sequence data from a leiomyosarcoma patient

  Genome and transcriptome sequence data from a leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002891

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005863

• Genome and transcriptome sequence data from a colorectal adenocarcinoma patient

  Genome and transcriptome sequence data from a colorectal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005881

• Genome and transcriptome sequence data from a metastatic melanoma patient

  Genome and transcriptome sequence data from a metastatic melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005852

• Single Cell Targeted DNA sequencing for Variant Calling in T-ALL

  This dataset contains the raw fastq-files and the VCF files of single cell targeted DNA sequening with the MissionBio Tapestri platform. This was performed on 8 male pediatric T-ALL cases: X09-XB37-XB47-XD83-XF91-XF97-XF100-XF121. For some patients we have timepoints during treatment: XF100 and XG121. XD83 is a patient that relapsed twice.

  Dataset EGAD00001006167

• Genome and transcriptome sequence data from a lung carcinoma patient

  Genome and transcriptome sequence data from a lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005853

• RNA-Seq data for Academic and For-Profit researchers.

  RNA-Seq data for both Academic and For-profit use

  Dataset EGAD00001009676