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• Epigenetics of Cocaine and Nicotine Addiction

  This project characterizes DNA methylation and gene expression changes that occur in the human brain, specifically in neurons from the rostral striatum. Major advances from NIDA funded initiatives for noninvasive neuroimaging studies have made it possible to study neuroanatomical, neurochemical and functional changes in the human brain that contribute to the vulnerability to abuse drugs, together with the neurotoxic consequences of years of drug misuse. Animal models have been developed to explain the fundamental behavioral and biological mechanisms of addiction, including reward, tolerance and dependence. From these studies, we learned that cocaine abuse not only alters the epigenetic status of genes, but also induces particular epigenetic modifications depending on the frequency of the drug's administration. Certain genes are switched on by infrequent (short-term exposure) administration, while others are switched on only after chronic administration (addiction/dependence). Animal studies have also suggested that cocaine-seeking habits modeling chronic cocaine addiction in humans depend upon dopamine-dependent serial connectivity linking the ventral (nucleus accumbens) with the dorsal striatum (caudate nucleus). The primary goal of this study is to identify DNA methylation and gene expression changes that occur in the transition from recreational cocaine use to cocaine addiction. High throughput sequencing studies were designed to investigate unique postmortem human brain specimens from individuals that met criteria for cocaine dependence, as compared to unaffected age-matched controls. Brain biospecimens were available from the University of Miami Brain Endowment BankTM, from a collection of phenotypically well-characterized postmortem tissues sampled from chronic cocaine abusers that came to autopsy. This biobank of postmortem brain specimens and annotated genomic data serve as a research resource to support NIDA's scientific mission.

  Study phs001377

• Genomic Answers for Kids (GA4K)

  Identification of complex genetic variants including defects in gene regulatory circuits and uncharacterized genes present challenges for rare disease diagnosis. In Genomic Answers for Kids program we apply the joint interpretation of patient genome sequence with genomic endophenotypes to expand the clinically actionable genome among pediatric cases of suspected genetic disease. Our objective is to examine the role of novel and under-interpreted sequence variation by combining complete DNA sequences, personal epigenetic variations and massively parallel functional screens. To achieve this we perform augmented whole genome sequence (WGS) interpretation including reanalysis of clinical exomes as well as generating WGS for patients with negative exome results all be subject to family-based semi-automated recall pipeline to discover missed diagnostic variation. We use linked-read and long-read sequencing technologies to focus on putative structural variants missed in short-read genome and exome analysis by the optimized integration of linked and long read technologies that also improve identification of transmission patterns of all variants, as well as resolving genomic regions resistant to standard alignment. We also include tissue DNA sequencing to investigate somatic mosaicism. To further assist with WGS interpretation for uncommon variation we capture snapshots of patient transcriptomes and epigenomes in individual cells using single-cell RNA (scRNA) and sc open chromatin (scATAC) as well as bulk whole genome bisulphite genome sequencing for methylome interpretation. Alternative splicing is functionally assessed in available patient tissues using RNA-seq, including full length cDNA sequences by IsoSeq (PacBio) methodology. Our overarching goal is an increase rate of diagnostic genomic findings up to two-fold among rare disease, resulting in the majority (>50%) of patient cases resolved by the integrated system developed in our program.

  Study phs002206

• Rapid Early Action for Coronary Treatment (REACT-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. Objectives: This multicenter controlled community study developed and evaluated the impact of a community educational intervention program on participant delay time from onset of symptoms of an acute myocardial infarction (AMI) to arrival at a hospital emergency department.Background: Although early reperfusion or thrombolytic therapy can reduce morbidity and mortality following an AMI, delayed access to medical care in participants is relatively common. Mean delay times from symptom onset to hospital arrival range from more than 4 hours to 24 hours, and the largest component of prolonged delay is participant recognition and action.Participants: A total of 20 communities from 5 field centers in the U.S. were pair-matched (10 pairs) according to geographic proximity and demographic characteristics. After initiation of a 4 month baseline surveillance period, one community in each pair was randomly selected to receive the intervention. The baseline surveillance period was followed by an 18 month community intervention and surveillance period. The community surveillance captured a total of 59,944 adults aged 30 years or older presenting to hospital emergency departments with chest pain, of whom 20,364 met study criteria for suspected acute coronary heart disease (CHD) at admission and discharged with a CHD diagnosis.Conclusions: Delay times were decreased in the intervention and reference communities. The results showed that the multicomponent community intervention program did not differentially reduce delay time from onset of AMI symptoms to arrival at a hospital, but did significantly increase the use of Emergency Medical Services by these participants in the intervention communities. (PMID:10872014).

  Study phs003885

• Genomic Landscape of Multiple Myeloma and of its Precursor Conditions, and its Clinical Implications

  Reliable strategies to capture patients at risk of progression from precursor stages of multiple myeloma (MM) to overt disease are still missing. In this study, we generated whole-genome sequencing (WGS) data from matched tumor and normal samples from 98 individuals from the PCROWD study (Precursor Crowd, NCT02269592). These individuals have either a precursor condition for MM, which can be a monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM), or an active multiple myeloma (MM). To do so, we enriched tumor plasma cells from the bone marrow by magnetic bead selection (CD138+), sequenced the DNA of these samples to a target depth of 60X (tumor), and analyzed them against normal white blood cells (peripheral blood mononuclear cells) from the same individuals sequenced to a target depth of 30X (normal). In our first publication [Alberge, Dutta, Poletti, et al., 2025], we integrated and homogeneously analyzed these data together with data from mostly publicly available datasets (phs003084 and phs000748) to reach 1,030 total samples. We used these data to identify recurrent coding and non-coding candidate drivers, as well as significant hotspots of structural variation, and used those drivers to define and validate a simple “MM-like” score which we could use to place patients' tumors on a gradual axis of progression toward active disease. Our MM precursor genomic map provides new insights on the time of initiation and cell of origin of the disease, on the order of acquisition of genomic alterations, and on mutational processes found across stages of the transformation. Taken together, we highlight here the potential of genome sequencing to better inform risk assessment and monitoring of MM precursor conditions.

  Study phs003846

• Genetic Risk for Subsequent Neoplasms among Long-term Survivors of Childhood Cancer in the St. Jude Lifetime Cohort

  While childhood cancer survivors are known to be at increased risk of subsequent neoplasms (SNs), the contribution of germline mutations in cancer predisposition genes to the risk of SN is largely unknown. Whole-genome sequencing (30X) was performed on 3,006 5+ year survivors of childhood cancer (median age, 35.8 [range: 7.1-69.8] years) from the St. Jude Lifetime Cohort. Survivors underwent a comprehensive clinical assessment. Germline mutations in 156 cancer predisposition genes (SJCPG156), including a subset of 60 (SJCPG60) autosomal dominant genes with moderate to high penetrance, were classified for their pathogenicity. Piecewise exponential regression, stratified by radiation exposure, was used to evaluate the relative rate (RR) and 95% confidence interval (95% CI) of SN occurrence by mutation status. Pathogenic or likely pathogenic mutations in SJCPG156 and SJCPG60 were identified in 11.7% and 5.8% of survivors, respectively. The most frequently mutated SJCPG60 genes included RB1 (n=43), NF1 (n=22), BRCA2 (n=14), BRCA1 (n=12) and TP53 (n=10). Mutations in SJCPG60 were associated with the rate of subsequent sarcoma (RR, 10.3; 95% CI, 4.2 to 25.6) and breast cancer (RR, 13.3; 95% CI, 5.8 to 30.5) among irradiated survivors, and the rate of developing any SN (RR, 4.8; 95% CI, 2.4 to 9.4) and breast cancer (RR, 7.8; 95% CI, 2.5 to 24.0) among non-irradiated survivors. The rate of developing ≥2 histologically distinct SNs was increased 17.8-fold (95% CI, 3.5 to 89.8) among SJCPG60 mutation carriers treated without radiotherapy. Similar but attenuated associations were observed for mutations in SJCPG156. Survivors who develop a SN without prior radiotherapy or who are diagnosed with a subsequent sarcoma or breast cancer within a site of prior radiotherapy should be referred to genetic counseling.

  Study EGAS00001002499

• Targeted sequencing of genes recurrently mutated in AML

  Background Massively parallel sequencing technology has transformed cancer genomics. It is now feasible, in a clinically relevant time-frame, for a clinically manageable cost, to screen DNA from patient tumours for mutations essentially genome-wide. The challenge for personalised medicine will be to increase the sample size to thousands or tens of thousands of well-characterised cases in order to attain sufficient statistical power to stratify patients accurately across the complexity and genomic heterogeneity expected for most of the common tumour types. Currently, whole genome sequencing on this scale is not feasible, and targeted sequencing of relevant portions of the genome will be required. Pilot data We have developed protocols for large-scale, multiplexed sequencing of 100-200 genes in thousands of samples. Essentially, using robotic technology, genomic DNA from the cancer specimen is processed into sequencing libraries with unique DNA barcodes, thereby allowing sequencing reads to be attributed to the sample they derive from. Currently, these sequencing libraries can be generated in a 96-well format using fully automated protocols, and we are exploring methods to expand this to a 384-well format. The sequencing libraries are pooled and hybridized to custom sets of RNA baits representing the genomic regions of interest. Sequencing of the pulled-down libraries is done in pools of 48-96 samples per lane of an Illumina Hi-Seq. This protocol is already implemented at the Sanger Institute. We have published proof that somatic mutations in novel cancer genes can be identified from exome-wide sequencing. In unpublished pilot data, we have established the feasibility of robotic library production, custom pull-down, and multiplexed sequencing of barcoded libraries for 100 known myeloid cancer genes across 760 myelodysplasia samples. Highlights of the data thus far analysed reveal that the coverage is remarkably even between samples; when 96 samples are run, average coverage per lane of sequencing is ~250, with 90-95% of targeted exons covered by >25 reads; known mutations can be discovered in the data set; and the protocol is amenable to whole genome amplified DNA. The bioinformatic algorithms for identification of substitutions and indels in pull-down data are well-established; we have pilot data proving that copy number changes, LOH and genomic rearrangements in specific regions of interest can also be identified by tiling of baits across the relevant loci. Proposal We propose to apply this methodology to 10000 samples from patients with AML enrolled in clinical trials over the last 10-20 years. Oncogenic point mutations and potentially genomic rearrangements will be identified, and linked to clinical outcome data, with a view to undertaking the following sorts of analyses: ? Identification of co-occurrence, mutual exclusivity and clusters of driver mutations. ? Correlation of prognosis with driver mutations and potentially gene-gene interactions ? Exploration of genomic markers of drug response Ultimately, we would like to be in a position to release the mutation data together with matched clinical outcome data to genuine medical researchers via a controlled access approach, possibly within the COSMIC framework (www.sanger.ac.uk/genetics/CGP/cosmic/). The vision here is to generate a portal whereby a clinician faced with an AML patient and his / her mutational profile can obtain a ?personalised? prediction of outcome, together with a fair assessment of the uncertainty of the estimate. With a sufficient sample size, there would also be the potential to develop decision support algorithms for therapeutic choices based on such data.

  Dataset EGAD00001000606

• Submitter Portal API

  Submitter Portal API The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. For those that want a more flexible and automated approach, we also provide a programmatic approach using the Submitter Portal AP in addition to the user interface of the Submitter Portal. With the help of our API, you can quickly include the submission of metadata into your own workflow for a more effective and individualised experience. Previous steps Create your EGA account To submit data you first need to create your EGA user. Then, once your account has been verified, you will have to request a submitter role and sign the EGA Data Processing Agreement (DPA). When the DPA is signed, you must send it to EGA Helpdesk for further validation. Please, note that if you already have an EGA account, or you have an ega-box (submission account), you can skip this step.  Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find the relevant information in the DAC Portal Guide. Programmatic submission All the calls to the API need to be Authenticated. We use the OpenID Connect protocol. API Usage Flow: Click here to check the API usage flow schema Obtain Access and Refresh Token: The first step to using the API is to obtain an access and refresh token. These tokens are required for authentication and authorisation of API requests. To obtain these tokens, you need to log in using your EGA credentials. Example: curl "https://idp.ega-archive.org/realms/EGA/protocol/openid-connect/token" \ -d "grant_type=password" \ -d "client_id=sp-api" \ -d "username=your-username" \ --data-urlencode "password=your-password" Use Access Token in API Calls: Once you have obtained the access token, you must use it in all the calls to the API. The access token is valid for a limited time period. When it expires, you will need to use the refresh token to obtain a new access token. Create Submission Object: After authentication, you can start creating a submission object. This object will be used to store all the metadata objects and files related to the submission. Example: curl "https://submission.ega-archive.org/api/submissions" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" \ -d "{ \"title\":\"My submission title\", \"description\":\"My submission description\" }" Create EGA Metadata Objects: Within the submission object, you can create other metadata objects required for the submission. These objects may include information about the submitter, study, sample, experiment, and run. Example of study creation: curl "https://submission.ega-archive.org/api/submissions/{submisison-id}/studies" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" \ -d "{ \"title\":\"My study title\", \"description\":\"My study description\", \"study_type\":\"Metagenomics\" }" If you would like to reuse already registered objects in new submissions, bear in mind that they must have an accession which you must use to reference them. You cannot reuse objects that have not been finalised and accepted yet.For example, when creating a new run which is reusing a previously submitted experiment, instead of using experiment_provisional_id, you must use experiment_accession_id. Enums There are some fields that only accept specific values, i.e. study_type. The API provides several enumerations to list these values. You can list all the enumerations, or a specific one, for example, study_types. Obtain IDs of files You can list the files available in your inbox and filter by a prefix by adding the prefix param, i.e. &prefix=/my_folder/abc Example: curl "https://submission.ega-archive.org/api/files?status=inbox&prefix=/my_folder/abc" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" To improve performance and manageability, we have limited the number of files that the Submitter Portal API can return in a response. If a given call exceeds this limit, user will be asked to narrow down the search by using the query parameters already available in this endpoint. Finalise Submission: Once all the metadata objects are created and linked to the submission, you can finalise the submission. Finalising the submission sends it to the Helpdesk team for review. Please, note that all files linked to the submission must be ingested, and all objects created in a submission must be linked before you can finalise the submission. API Reference: For a full API reference, please check our specification documentation.

  Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal-api

• Durvalumab Plus Tremelimumab Alone or in Combination with Low-Dose or Hypofractionated Radiotherapy in Metastatic Non-Small-Cell Lung Cancer Refractory to Previous PD(L)-1 Therapy: an Open-Label, Multicentre, Randomised, Phase 2 Trial

  Brief SummaryThis randomized phase II trial studies the side effects of durvalumab and tremelimumab and to see how well they work with or without high or low-dose radiation therapy in treating patients with colorectal or non-small cell lung cancer that has spread to other parts of the body (metastatic). Immunotherapy with durvalumab and tremelimumab, may induce changes in body's immune system and may interfere with the ability of tumor cells to grow and spread. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving durvalumab and tremelimumab with radiation therapy may work better in treating patients with colorectal or non-small cell lung cancer.Detailed DescriptionPRIMARY OBJECTIVES:I. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 (durvalumab) and tremelimumab alone or with high or low-dose radiation in non-small cell lung cancer (NSCLC). (NSCLC Cohort) II. To compare the overall response (excluding the irradiated lesion[s]) between combined checkpoint blockade with MEDI4736 and tremelimumab alone or combined checkpoint blockade with low or high dose radiation. (NSCLC Cohort) III. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 and tremelimumab with high or low-dose radiation. (Colorectal Cohort) IV. To determine the overall response rate (excluding the irradiated lesion[s]) with combined checkpoint blockade with MEDI4736 and tremelimumab with either low or high dose radiation. (Colorectal Cohort)SECONDARY OBJECTIVES:I. To estimate median progression-free survival and overall survival. (NSCLC Cohort) II. To determine local control within the irradiated field(s) and abscopal response rates. (NSCLC Cohort) III. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells and overall response. (NSCLC Cohort) IV. To explore changes in PD-L1 expression, circulating T-cell populations, T-cell infiltration, ribonucleic acid (RNA) expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (NSCLC Cohort) V. To estimate median progression-free survival and overall survival. (Colorectal Cohort) VI. To determine local control within the irradiated field and abscopal response rates. (Colorectal Cohort) VII. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+ CD8+ T-cells and overall response. (Colorectal Cohort) VIII. To evaluate changes between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells induced by targeted low or high dose radiation. (Colorectal Cohort) IX. To explore changes in circulating T-cell populations, T-cell infiltration, RNA expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (Colorectal Cohort)OUTLINE: Patients are assigned to 1 of 2 cohorts. NOTE: This Study is only for Cohort 1. Please see phs003294 for Cohort 2COHORT 1: Patients with NSCLC are randomized to 1 of 3 arms.ARM A: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.ARM C: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses.COHORT 2: Patients with CRC are randomized to 1 of 2 arms.ARM A: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.After completion of study treatment, patients are followed for up to 12 weeks.Official TitleA Phase 2 Study of MEDI4736 (Durvalumab) and Tremelimumab Alone or in Combination With High or Low-Dose Radiation in Metastatic Colorectal and NSCLC Conditions Metastatic Colorectal CarcinomaMetastatic Lung Non-Small Cell CarcinomaStage IV Colorectal Cancer AJCC v7Stage IV Lung Non-Small Cell Cancer AJCC v7Stage IVA Colorectal Cancer AJCC v7Stage IVB Colorectal Cancer AJCC v7 Intervention/Treatment Biological: DurvalumabRadiation: Radiation TherapyBiological: Tremelimumab Other Study ID Numbers NCI-2016-01325NCI-2016-01325 ( Registry Identifier ) (REGISTRY: CTRP (Clinical Trial Reporting Program))17-71910021 ( Other Identifier ) (OTHER: Dana-Farber - Harvard Cancer Center LAO)10021 ( Other Identifier ) (OTHER: CTEP) Molecular Technologies / AssaysCyTOFIHCmIFOlinkRNAseqWES

  Study phs003295

• Genome_Diversity_in_Africa_Project__Uganda

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region. The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group). Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics

  Study EGAS00001002523

• Whole exome and RNA sequencing of organoid samples derived from TRACERx patients

  Immune checkpoint blockade in non-small cell lung cancer (NSCLC) rarely leads to complete tumour remission, suggesting that tumours harbour subpopulations of cancer cells that can escape immune predation. However, while tumours consist of multiple genetically distinct clones, the extent to which separate tumour subclones differ intrinsically in their functional capacity for immune evasion remains largely unexplored. This has been challenging to address due to an inability to isolate and propagate individual tumour subclones from human cancers. Here, we leverage the multi-region TRACERx lung cancer evolution study to generate a patient-derived platform that allows the functional analysis of immune escape at single clone resolution. We generated libraries of >20 separate NSCLC organoid lines per patient by isolating individual (clonal) organoids established from multiple spatially separated tumour regions. Each organoid subline was co-cultured with autologous tumour infiltrating lymphocytes (TIL) to evaluate their capacity to elicit a T cell response. Organoid lines derived from separate tumour regions, or from single clones within individual regions, differ vastly in their capacity to elicit a T cell response, showing the coexistence of intrinsically immune evading (‘hot’) and non-evading (‘cold’) clones in the same primary tumour. Organoid lines escaping T cell recognition represent genetically and transcriptionally unique subclones with a distinct evolutionary history, showing that evolutionary sculpting of subclonal diversity can have functional consequences for anti-tumour immunity. Notably, the identification of immune-evasive subclones would not have been possible using computational approaches alone, highlighting the unique value of functional co-culture platforms. This patient-derived functional co-culture platform reveals that tumour evolution can give rise to distinct cancer clones with intrinsic differences in immune evasion capacity and provides an approach to prospectively identify, isolate and characterise immune evading subclones from patients with cancer.

  Study EGAS00001008092