2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

in 20.81 milliseconds.

• Identification of hypermutation and defective mismatch repair in ctDNA from metastatic prostate cancer

  DNA mismatch repair defects (MMRd) and tumor hypermutation are rare and under-characterized in metastatic prostate cancer. Furthermore, since hypermutated MMRd prostate cancers can respond to immune checkpoint inhibitors, there is an urgent need for practical detection tools. We analyzed cell-free DNA targeted sequencing data from 434 metastatic prostate cancer patients with circulating tumor DNA (ctDNA) purity above 2%. Samples with somatic hypermutation were subjected to 150x whole exome sequencing. These data allowed us to interrogate the salient genomic properties of this rare prostate cancer subtype and correlate findings with patient clinical outcomes.

  Study EGAS00001003899

• Clinical utility of combined low-pass whole genome and targeted sequencing in liquid biopsies for diagnosis and monitoring of pediatric solid tumors

  We designed a liquid biopsy (LB) platform employing Low-Pass Whole Genome Sequencing (LP-WGS) and targeted sequencing of cell-free (cf) DNA isolated from plasma for detecting genome-wide copy number alterations and targeted gene fusions in pediatric solid tumors. A total of 143 plasma samples were analyzed from 19 controls (patients with non-oncologic diagnoses) and 73 patients, including 44 with bone or soft tissue sarcomas, 12 with renal tumors, 10 with germ cell tumors, 5 with hepatic tumors, and 2 with thyroid tumors.

  Study EGAS00001006913

• Transcriptomic profiles studies CAREs group - IDIBGI

  The purpose of this DAC document is to outline the principles and procedures governing access to the transcriptomic data generated from endothelial progenitor cells, or any biological origin from patients. Data Access Policy: 1) Access to this dataset is restricted and subject to approval by the DAC to ensure ethical and legal compliance. 2) The DAC reserves the right to evaluate all applications for access based on: 2.1) The scientific validity and merit of the proposed research. 2.2) The ethical considerations and adherence to data protection regulations, including GDPR. 2.3) The qualifications and affiliations of the applicants. 3) Applicants must submit a detailed research proposal, including objectives, methods, and anticipated outcomes. 4) Data usage is limited to the specified research project approved by the DAC. Any secondary use of the data requires additional approval. Confidentiality: All applicants must agree to maintain the confidentiality of the dataset. Personal identifiers have been pseudonymized to protect patient privacy. Application Process: 1) Researchers must complete the application form provided by the DAC. 2) Submit proof of ethical approval and relevant certifications for their research project. 3) Provide a signed agreement affirming adherence to the DAC's terms and conditions.

  Dac EGAC50000000607

• Genetic landscape of pediatric Infant Acute Lymphoblastic leukemia

  We performed whole genome sequencing of tumor and normal DNA samples obtained from 22 infant ALL cases with MLL rearrangements. In addition, we sequenced 2 paired diagnostic-relapse samples. Using complementary pipelines, somatically acquired genetic changes were analyzed, including single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes. In addition, exome sequencing was performed on paired diagnostic and normal DNA samples obtained from 20 cases of non-infant MLL rearranged leukemias and somatic mutations in the coding regions were identified.

  Study EGAS00001000246

• COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis

  To investigate the immune response and mechanisms associated with severe COVID-19, we performed single-cell RNA-seq on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from 5 healthy controls. We identified airway epithelial cell types and states vulnerable to SARS-CoV-2 infection. In COVID-19 patients, epithelial cells showed an average threefold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared with moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand–receptor expression profiles, and activated immune cells , including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF . The transcriptional differences in critical cases compared with moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways may suppress immune hyperactivation in critical COVID-19.

  Dataset EGAD00001006339

• DNA Methylation Analysis of Prostate Cancer Cell Lines and Tissues Using Next Generation Sequencing

  Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. Overall Design: We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.

  Study phs000597

• Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute

  Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and the only leading cause of death that is steadily increasing in frequency. This project will establish a racially diverse cohort that is sufficiently large and appropriately designed for genome-wide association analysis of COPD. A total of 10,000 subjects will be recruited, including control smokers, definite COPD cases (GOLD Stage 2 to 4), and subjects not included in either group (GOLD 1 or GOLD-Unclassified). This cohort will be used for cross-sectional analysis, although long-term longitudinal follow-up will be a future goal. The primary focus of the study will be genome-wide association analysis to identify the genetic risk factors that determine susceptibility for COPD and COPD-related phenotypes. Detailed phenotyping of both cases and controls, including chest CT scan assessment of emphysema and airway disease, will allow identification of genetic determinants for the heterogeneous components of the COPD syndrome. The hypotheses to be studied are: 1) Precise phenotypic characterization of COPD subjects using computed tomography, as well as clinical and physiological measures, will provide data that will enable the broad COPD syndrome to be decomposed into clinically significant subtypes. 2) Genome-wide association studies will identify genetic determinants for COPD susceptibility that will provide insight into clinically relevant COPD subtypes. 3) Distinct genetic determinants influence the development of emphysema and airway disease. The initial phase of genome-wide association analysis included 500 COPD cases and 500 control subjects (all non-Hispanic White) genotyped with the Illumina Omni-1 chip. The second phase genotyped the entire study cohort using the Illumina Omni-Express chip. Unique aspects of the study include: 1) Inclusion of large numbers of African American subjects (approximately 1/3 of the cohort); 2) Obtaining chest CT scans (including inspiratory and expiratory images); and 3) Inclusion of the full range of disease severity. The COPDGene_v6 Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000179 COPDGene_v6 Cohort. phs000296 ESP LungGO COPDGene phs000765 COPDGene_Geno

  Study phs000179

• Immunotesting cohort with RNA-seq data of melanoma samples

  This dataset contains RNA-seq raw data in fastq format from 9 melanoma samples. The samples are formalin-fixed paraffin-embedded (FFPE) treated. For target enrichment SureSelect XT Human All Exon V6 was used. The libraries were sequenced in paired-end mode (2 x 50 nt) on a NovaSeq6000 S2 flow cell.

  Dataset EGAD00001006783

• H3Africa CAfGEN Exome

  Whole Exome Sequencing data from a retrospective paediatric HIV-disease progression cohort defined on the World Health Organization's criteria for paediatric HIV progression. Data comprises of BAM and VCF files for 314 participants from 2 countries: Botswana and Uganda. DNA and RNA samples are linked in bio-repository.

  Dataset EGAD00001006224

• MPM patients

  - RNA-sequencing data: 5 normal pleurae and 40 malignant pleural mesotheliomas - Targeted DNA-sequencing of the 165 genes included in the “Solid and Haematological tumors” panel (BRIGHTCore, Brussels, Belgium): 6 MPM samples. 2 FASTQ files for each sample (paired).

  Dataset EGAD00001008740

• single-cell RNA-Seq samples of CRC patients

  The dataset contains samples of 11 CRC patients (2 samples for each patient, tumor and normal adjacent tissue site, 22 samples in total). Dataset is composed by fastq file (paired end) type from 10x single-cell RNA-Seq.

  Dataset EGAD00001009634

• CHROMATIN ACTIVATION PROFILING OF STEREOTYPED CHRONIC LYMPHOCYTIC LEUKEMIAS

  We used chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) with an antibody for the H3K27ac (a bona fide histone mark for regulatory element activation) in sorted CLL cells from 15 CLL, including cases from stereotyped subsets #1, #2, #4, and #8. The samples were sequenced by Illumina HiSeq 2500.

  Dataset EGAD00001009082

• 4_eCLIP_NOVA1_NOVA2_RBFOX2

  eCLIP of NOVA1, NOVA2 and RBFOX2 from iPSC-derived motor neurons in 2 control lines. Per line and RNA-binding protein input and IP samples and analysis including bigWig files and peak files.

  Dataset EGAD00001008428

• FFPE CRC sequence data and somatic variants

  We included 3 BAM files of the genome sequencing data: 2 of 3 are from tumour samples, namely 1 repaired-FFPE and 1 unrepaired FFPE; the third BAM file is from normal tissue of FFPE block. There is also a VCF file containing all somatic mutations in the dataset.

  Dataset EGAD00001007723

• Transcriptome/Exome for glioblastoma intra-tumoral heterogeneity

  Total of 49 tumor specimens from 20 patients were subjected for whole-exome and/or whole-transcriptome sequencing including matched normal/blood. Tumor samples are acquired based on 4 categories; 1) locally adjacent tumors, 2) multifocal/multicentric tumors, 3) 5-ALA (+/-) tumors and 4) Longitudinal tumors.

  Dataset EGAD00001002248

• Nimblegen

  The aim of this project is to identify rare variants in the 1q region associated with type 2 diabetes. To this end 651 case samples and 651 control samples from six populations have been pooled (pool sizes range from 27-33 individuals), and are being sequenced. The hybridization solution being used captures the exons and UTRs of genes in the 1q region.

  Dataset EGAD00001000424

• NGS-ProToCol prostate cancer RNA-seq data.

  NGS-ProToCol RNA-seq dataset contains 41x normal adjacent prostate and 51x prostate cancer samples taken from fresh frozen radical prostatectomies, sequenced using random-hexamer priming. RNA-seq was performed on the Illumina HiSeq 2500 platform, 2 x 126 bp stranded paired-end reads at a depth of 70 mln reads.

  Dataset EGAD00001004215

• RRBS sequencing data of ovarian cancer, breast cancer, control tissues, and white blood cell DNA.

  The dataset comprises 8 breast cancer, 11 ovarian cancer, 1 benign tumour, 18 normal tissue, 2 endometrium, and 23 white blood cell samples. Genome wide methylation analysis was performed by Reduced Representation Bisulfite Sequencing (RRBS) on Illumina HiSeq 2500. Data is provided as FASTQ files

  Dataset EGAD00001003822

• WES of probands in KLB project

  These are the vcf files of exome sequencing of the two probands who were found to harbor mutations in KLB. Sample: EGAN00001564799 is the proband 1; Sample: EGAN00001564800 is the proband 11 in the KLB paper. Exome capture was performed using the SureSelect All Exon capture (Agilent Technologies, Santa Clara, CA USA) and sequenced on the HiSeq2500 (Illumina, San Diego CA USA).

  Dataset EGAD00001003463

• Tam-seq of tumor samples for HGSOC copy-number signatures study

  This dataset contains targeted amplicon sequencing of DNA extracted from 300 samples of 142 patients (158 methanol-fixed relapse biopsies and 142 FFPE archival diagnostic tissues). Samples were sequenced on Illumina HiSeq 2500 and were aligned to human genome assembly GRCh37 (hg19)to produce 600 bam files (2 technical replicates per sample).

  Dataset EGAD00001004173

• Primary breast cancers and paired brain metastases

  Targeted next-generation-sequencing of 494 cancer-associated genes was done in a series of 14 frozen pairs of matched primary breast cancers and brain metastases (28 samples). DNA libraries of all coding exons were prepared using the Haloplex Target Enrichment System. Sequencing was done using the 2*150bp paired-end technology on the Illumina NextSeq500 platform.

  Dataset EGAD00001004309

• RRBS data of 48 individuals of the Dutch Hunger Winter Families Study

  The dataset entails 48 RRBS libraries of 24 siblings. 24 individuals are conceived during the Dutch Famine, a severe 6 month famine at the end of World War 2. A same sex sibling was added as a control, allowing partial matching for (early) familial environment and genetics.

  Dataset EGAD00001000733

• Whole exome sequencing for HELIC

  The HELIC study has been whole genome sequencing individuals from 2 Greek isolatedpopulations at 1x depth. The genotype calling process crucially involves a VQSR stepfollowed by imputation-based refinement. We have been investigating optimal ways toincrease calling accuracy. To aid us in setting appropriate parameters for VQSR and otherQC steps, we have carried out whole exome sequencing of a small number ofHELIC samples.

  Dataset EGAD00001001638

• Advanced iPSC-CMs maturation by integrating maturation medium, nanopatterning, and electrical stimulation

  RNA seq of iPSC-derived cardiomyocytes cultured in lipid-enriched maturation medium (MM), in MM on nanopattern-surfaces (NP) and under the influence of MM, NP and electrical Stimulation (ES, 2Hz, 14 days). 9 Samples from 3 Independent batches/differentiations of cardiomyocytes from 2 different iPSC-lines (iWTD2.1 and isWT7.22).

  Dataset EGAD00001011289

• Bulk and single-cell RNA sequencing of LCP1-mutated patients

  This dataset contains bulk RNA-seq and single-cell RNA-seq (scRNA-seq) of bone marrow obtained from 2 patients diagnosed with severe congenital neutropenia (SCN) attributed to LCP1 mutations. This is the first dataset of its kind. Moreover, the dataset also contains scRNA-seq of bone marrow derived from a healthy control.

  Dataset EGAD00001015698

• Congenital anosmia 2

  Congenital anosmias can be complete (the lack of a sense of smell) or specific (the inability to detect specific smells). Here we obtained genomic DNA from families with multiple individuals with anosmia, suggesting they are congenital. These include those inherited in a manner consistent with dominant and recessive alleles. We have sequenced the exomes of both affected and unaffected family members on the Illumina platform.

  Dataset EGAD00001002228

• Predictor_ChemoNEAR_TNBC (2019-08-14)

  In this study a collection of core biopsies from breast cancer patients receiving neoadjuvant chemotherapy as part of the ChemoNEAR trial will be investigated. The study is intended to detect early acquired resistance in women with diagnosed breast carcinoma. Samples will undergo whole genome sequencing and analysis, including use of HR Predict. . This dataset contains all the data available for this study on 2019-08-14.

  Dataset EGAD00001005255

• Sequencing data for personalized therapy design and endotype identification

  Whole exome sequencing of 15 DNA samples, and whole genome sequencing of 2 matched DNA samples. Whole exome sequencing is of RMS samples (both alveolar and embryonal) and from cell lines as well as patient samples. Patient samples are of pediatric RMS patients.

  Dataset EGAD00001005458

• Long read sequencing of 5 Intellectual Disability (ID) trios with PacBio Sequel. Dataset of samples: T2P, T2F and T2M

  5 trios were whole genome sequenced with PacBio Sequel to a depth of 15X (Trios 1-4) or 40X (Trio 5). For each trio the child was affected with severe ID, and the parents were unaffected. Dataset consists of Trio 2 samples: T2P, T2F and T2M

  Dataset EGAD00001006050

• Whole genome sequencing of HSPC and SI clones of disomy- and trisomy 21 fetuses samples (Novaseq 6000 samples)

  Whole genome sequencing of HSPC and SI clones of 3 disomy- and 3 trisomy 21 fetuses samples and 2 TMD samples (Novaseq 6000 samples). 22 disomy clones, 20 trisomy clones were included in this experiment. 11 bulk samples were also included.

  Dataset EGAD00001006343

• National Eye Institute (NEI) Age-Related Eye Disease Study 2 (AREDS2)

  The Age-Related Eye Disease Study 2 (AREDS2) was a multi-center, randomized Phase III clinical trial designed to assess the effects of oral supplementation of high doses of lutein, zeaxanthin, and omega-3 fatty acids (docosahexaenoic acid [DHA] and eicosapentaenoic acid [EPA]) in reducing the risk of progression to late age-related macular degeneration (AMD), progression to cataract surgery, and/or progression to moderate vision loss in high-risk participants. The effect of the study supplements on cardiovascular outcomes and cognitive function were also examined. Other study goals included evaluation of eliminating beta-carotene and/or reducing zinc in the original AREDS formulation on the progression and development of late AMD. AREDS2 sought to validate the AREDS AMD scale developed from the photographic data obtained from AREDS, (Study Accession: phs000001).Eighty-two clinical sites across the United States enrolled 4,203 participants aged 50 to 85 (mean age 74 years) between October 2006 and September 2008. Enrollment was restricted to people at high risk of progression to late AMD with either bilateral large drusen or large drusen in 1 eye and late AMD in the fellow eye. Follow-up assessments in the clinic occurred on an annual basis for an average of 5 years, with telephone calls every six months during each year. Blood or saliva samples were also collected from 2,000+ AREDS2 participants for genetic research. Of these, samples from 1,800+ AREDS2 participants were included in the International Age-Related Macular Degeneration Genomics Consortium - Exome Chip Experiment, and have exome chip data available in the dbGaP (Study Accession: phs001039). In addition, samples from 1300+ AREDS2 participants were included in the International Age-Related Macular Degeneration Genomics Consortium - Whole Genome Sequencing Study in the dbGaP (Study Accession: TBD). Of 1363 AREDS2 participants with whole genome sequencing, 488 previously participated in AREDS.Please see "The Age-Related Eye Disease Study 2 (AREDS2) Research Group" document for a full list of AREDS2 study contributors.

  Study phs002015

• First genome-wide association study in an Australian Aboriginal population provides insights into genetic risk factors for Body Mass Index and Type 2 Diabetes

  A body mass index (BMI) >22kg/m2 is a risk factor for type 2 diabetes (T2D) in Aboriginal Australians. To identify loci associated with BMI and T2D we undertook a genome-wide association study using 1,355,133 quality-controlled single nucleotide polymorphisms (SNPs) genotyped (Illumina 2.5M Duo Beadchip) in 402 individuals in extended pedigrees from a Western Australian Aboriginal community. Imputation using the thousand genomes (1000G) reference panel extended the analysis to 6,724,284 post quality-control autosomal SNPs. No associations achieved genome-wide significance, commonly accepted as P<5x10-8. Nevertheless, genes/pathways in common with other ethnicities were identified despite the arrival of Aboriginal people in Australia >45,000 years ago. The top hit (rs10868204 Pgenotyped=1.50x10-6; rs11140653 Pimputed_1000G=2.90x10-7) for BMI lies 5’ of NTRK2, the type 2 neurotrophic tyrosine kinase receptor for brain-derived neurotrophic factor (BDNF) that regulates energy balance downstream of melanocortin-4 receptor (MC4R). PIK3C2G (rs12816270 Pgenotyped=8.06x10-6; rs10841048 Pimputed_1000G=6.28x10-7) was associated with BMI, but not with T2D as reported elsewhere. BMI also associated with CNTNAP2 (rs6960319 Pgenotyped=4.65x10-5; rs13225016 Pimputed_1000G=6.57x10-5), previously identified as the strongest gene-by-environment interaction for BMI in African-Americans. The top hit (rs11240074 Pgenotyped=5.59x10-6, Pimputed_1000G=5.73x10-6) for T2D lies 5’ of BCL9 that, along with TCFL2, promotes beta-catenin’s transcriptional activity in the WNT signaling pathway. Additional hits occurred in genes affecting pancreatic (KCNJ6, KCNA1) and/or GABA (GABRR1, KCNA1) functions. Notable associations observed for genes previously identified at genome-wide significance in other populations included MC4R (Pgenotyped=4.49x10-4) for BMI and IGF2BP2 Pimputed_1000G=2.55x10-6) for T2D. Our results may provide novel functional leads in understanding disease pathogenesis in this Australian Aboriginal population.

  Study EGAS00001001004

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study — under the direction of National Heart, Lung, and Blood Institute; NHLBI — began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group — 5,124 of the original participants' children and the spouses of these children — to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects' consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The 'shareid' will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed 'triplet_sim' file or for larger families' relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

  Study phs000128

• Genetics of Disorders Affecting Tooth Structure, Number, Morphology and Eruption

  The long-term objective of this research project is to discover the complete spectrum of genes that contribute to the etiologies of genetic disorders of dentition. Inherited tooth defects can be isolated (the only symptom) or syndromic (having additional symptoms), and can be caused by mutations in many different genes. Clinically, it can be difficult to distinguish among the diverse forms. Making an accurate diagnosis would be greatly improved if reliable tests could identify the specific genetic defect that causes the disease in each case. Current technology can identify the numerous sequence variations throughout a patient's exome, but the disease-causing variation among them cannot be identified unless it is already on the list of genes that potentially cause the disease. A diagnosis based upon the genetic defect that causes the disorder distinguishes between isolated and syndromic tooth defects. A precise diagnosis permits formulation of a more accurate prognosis, which informs treatment decisions. The benefits of accomplishing our aims are to 1) enable genetic testing to achieve a precise and accurate diagnosis, and thereby optimize treatment, and 2) improve our understanding of normal and pathological tooth formation, leading to future improvements in treatment and eventually cures. Our short-term objectives are to 1) recruit kindreds with inherited disorders affecting their dentition, characterize their dental phenotype (and non-dental phenotypes when applicable) and the distribution of the disorder throughout the family, and 2) determine the genetic etiology of their disorders.

  Study phs001491

• RNA-Sequencing of B-Lymphoblastic Leukemia with Glucocorticoids and PI3K Delta Inhibition

  Mononuclear cells were isolated from bone marrow or peripheral blood of 7 pediatric subjects with B-lymphoblastic leukemia at the time of diagnosis. To determine the effect of PI3K delta inhibition on glucocorticoid-induced gene regulation, B-lymphoblastic leukemia cells were treated with glucocorticoids +/- the PI3K delta inhibitor, idelalisib, for 24 hours prior to RNA extraction and sequencing. Biological replicates were performed for 4 of the 7 subjects; 2 of these subjects had specimens demonstrating an additive response to the combination of glucocorticoids and idelalisib in viability testing in vitro, and 2 subjects' specimens demonstrated a synergistic response. All 7 subjects were analyzed together and the 4 specimens with biological replicates were also evaluated in an additional analysis. An average of 40 million reads per specimen and treatment condition were obtained. We compared gene expression in each treatment condition to vehicle-treated cells and expression in glucocorticoid or idelalisib only treated cells to combination treated cells. Using a high dose of dexamethasone (25 or 50 nM) and an equivalent dose of prednisolone, we found that the addition of idelalisib (500 nM) enhanced prednisolone-induced gene regulation but not dexamethasone-induced gene regulation. Surprisingly, the specimens with additive response to combination therapy demonstrated enhancement of prednisolone-induced gene regulation with the addition of idelalisib, while the specimens with synergistic responses did not. Raw sequencing data (FASTQ) and count tables will be available through dbGaP.

  Study phs003085

• Use of Whole Genome Sequencing for Diagnosis and Discovery in the Cancer Genetics Clinic

  Despite the potential of whole-genome sequencing (WGS) to improve patient diagnosis and care, the empirical value of WGS in the cancer genetics clinic is unknown. We performed WGS on members of two cohorts of cancer genetics patients: those with BRCA1/2 mutations (n = 176) and those without (n = 82). Initial analysis of potentially pathogenic variants (PPVs, defined as nonsynonymous variants with allele frequency < 1% in ESP6500) in 163 clinically-relevant genes suggested that WGS will provide useful clinical results. This is despite the fact that a majority of PPVs were novel missense variants likely to be classified as variants of unknown significance (VUS). Furthermore, previously reported pathogenic missense variants did not always associate with their predicted diseases in our patients. This suggests that the clinical use of WGS will require large-scale efforts to consolidate WGS and patient data to improve accuracy of interpretation of rare variants. While loss-of-function (LoF) variants represented only a small fraction of PPVs, WGS identified additional cancer risk LoF PPVs in patients with known BRCA1/2 mutations and led to cancer risk diagnoses in 21% of non-BRCA cancer genetics patients after expanding our analysis to 3209 ClinVar genes. These data illustrate how WGS can be used to improve our ability to discover patients' cancer genetic risks. "Reprinted from doi:10.1016/j.ebiom.2014.12.003, with permission from EBioMedicine."

  Study phs000942

• Neoadjuvant immune checkpoint blockade in women with mismatch repair deficient endometrial cancer

  Neoadjuvant immune checkpoint blockade (ICB) has shown unprecedented activity in mismatch repair deficient (MMRd) colorectal cancers, but its effectiveness in MMRd endometrial cancer (EC) remains unknown. In this investigator-driven, phase I, feasibility study (NCT04262089), 10 women with MMRd EC of any grade, planned for primary surgery, received two cycles of neoadjuvant pembrolizumab (200 mg IV) every three weeks. A pathologic response (primary objective) was observed in 5/10 patients, with 2 patients showing a major pathologic response. No patient achieved a complete pathologic response. A partial radiologic response (secondary objective) was observed in 3/10 patients, 5/10 patients had stable disease and 2/10 patients were non-evaluable on Magnetic Resonance Imaging (MRI). All patients completed treatment without severe toxicity (exploratory objective). At median duration of follow-up of 22.5 months, two non-responders experienced disease recurrence. In-depth analysis of the loco-regional and systemic immune response (predefined exploratory objective) showed that monoclonal T cell expansion significantly correlated with treatment response. Tumour-draining lymph nodes displayed clonal overlap with intra-tumoural T cell expansion. All pre-specified endpoints, efficacy in terms of pathologic response as primary endpoint, radiologic response as secondary outcome and safety and tolerability as exploratory endpoint, were reached. Neoadjuvant ICB with pembrolizumab proved safe and induced pathologic, radiologic, and immunologic responses in MMRd EC, warranting further exploration of extended neoadjuvant treatment.

  Study EGAS50000000483

• National Cancer Institute Multi-Ancestry Genome-Wide Association Study of Kidney Cancer (NCI-3)

  The National Cancer Institute (NCI) multi-ancestral genome-wide association study (GWAS) of kidney cancer ("NCI-3") was performed by NCI to further investigate common genetic variants associated with risk of kidney cancer and its two major subtypes across individuals of different ancestries (European, African, East Asian, Latin American). The NCI-3 GWAS includes 12,315 cases and 73,091 controls from 25 centers in North America, South America and Europe, genotyped using the Illumina Global Screening Array v2.0. SNP-level results from this GWAS were combined by meta-analysis with results from two earlier NCI European-ancestry GWAS (RCC; phs000351: 1,311 cases and 3,424 controls and phs001736: 2,417 cases and 4,391 controls), a NCI GWAS of African Americans (phs000863; 255 cases and 375 controls), GWAS conducted at other institutions [International Agency for Research on Cancer (scan 1: 2,438 cases and 5,071 controls; scan 2: 2,781 cases and 2,940 controls), The University of Texas MD Anderson Cancer Center (scan 1: 893 cases and 556 controls, scan 2: 1,241 cases and 1,712 controls) and the Institute for Cancer Research, UK (944 cases and 4,024 controls)] and GWAS analyses of data from three large biobanks (Biobank Japan: 523 cases and 86,306 controls; FinnGen: 2,095 cases and 304,956 controls; UK Biobank: 1,807 cases and 348,824 controls). Only individual-level data from NCI-3 and the two MD Anderson Cancer Center scans are included in this dbGaP submission. All study-specific and meta-analysis summary statistics are included.

  Study phs003505

• Pseudodiastrophic dysplasia expands the known phenotypic spectrum of defects in proteoglycan biosynthesis

  Pseudodiastrophic dysplasia (PDD) is a severe skeletal dysplasia associated with prenatal manifestation and early lethality. Clinically, PDD is classified as a ‘dysplasia with multiple joint dislocations’ however, the molecular aetiology of the disorder is currently unknown. In order to identify the genetic defects underlying PDD, we performed whole exome sequencing (WES) on 3 patients from 2 unrelated families, clinically diagnosed with PDD. WES resulted in the identification of bi-allelic variants in the established skeletal dysplasia genes, B3GAT3 (Family 1) and CANT1 (Family 2). Mutations in these genes have previously been reported to cause ‘multiple joint dislocations, short stature, and craniofacial dysmorphism with or without congenital heart defects’ (B3GAT3) and Desbuquois dysplasia 1 (CANT1); disorders in the same nosological group as PDD. Follow-up of the B3GAT3 variants demonstrated significantly reduced B3GAT3/GlcAT-I expression. Downstream in vitro functional analysis revealed abolished biosynthesis of glycosaminoglycan side chains on proteoglycans. Functional evaluation of the CANT1 variant showed impaired nucleotidase activity, which results in inhibition of glycosaminoglycan synthesis through accumulation of uridine diphosphate. For the families described in this study, the PDD phenotype was caused by mutations in the known skeletal dysplasia genes B3GAT3 and CANT1, demonstrating the advantage of genomic analyses in delineating the molecular diagnosis of skeletal dysplasias. This finding expands the phenotypic spectrum of B3GAT3- and CANT1- related skeletal dysplasias to include PDD, and highlights the significant phenotypic overlap of conditions within the proteoglycan biosynthesis pathway.

  Study EGAS00001004145

• Plasma-Seq of patients with metastatic prostate cancer

  Study 1 In this study we analysed patients with metastatic prostate cancer to scan their tumor genomes noninvasively in plasma DNA. We wanted to make whole-genome analysis from plasma DNA amenable to clinical routine applications and developed an approach based on a benchtop high-throughput platform, i.e. Illuminas MiSeq instrument. We performed whole-genome sequencing from plasma at a shallow sequencing depth to establish a genome-wide copy number profile of the tumor at low costs within 2 days. The genome-wide profiling in the plasma of our patients revealed multiple copy number aberrations including those previously reported in prostate tumors, such as losses in 8p and gains in 8q. High-level copy number gains in the AR locus were observed in patients with CRPC but not with CSPC disease. We identified the TMPRSS2-ERG rearrangement associated 3-Mbp deletion on chromosome 21 and found corresponding fusion plasma fragments in these cases. In an index case multiregional sequencing of the primary tumor identified different copy number changes in each sector, suggesting multifocal disease. Our plasma analyses of this index case, performed 13 years after resection of the primary tumor, revealed novel chromosomal rearrangements, which were stable in serial plasma analyses over a 9 months period, which is consistent with the presence of one metastatic clone. Our approach provides specific genomic signatures within 2 days which may therefore serve as “liquid biopsy”.

  Study EGAS00001000451

• How to upload Crypt4GH files

  Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs)  for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username.  If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days.  Specific to using keys to authenticate Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation submission/data/uploading-files/inbox

• CD8-targeted IL-2 unleashes tumor-specific immunity in human cancer tissue by reviving the dysfunctional T cell pool

  Tumor-specific CD8+ T cells are key effectors of antitumor immunity but are often rendered dysfunctional in the tumor microenvironment. Immune checkpoint blockade can restore antitumor T cell function in some patients, however most do not respond to this therapy, often despite T cell infiltration in their tumors. We here explored a CD8-targeted IL-2 fusion molecule (CD8-IL2) to selectively reactivate intratumoral CD8+ T cells in patient-derived tumor fragments. Treatment with CD8-IL2 broadly armed intratumoral CD8+ T cells with enhanced effector capacity, thereby specifically enabling reinvigoration of the dysfunctional T cell pool to elicit potent immune activity. Notably, the revival of dysfunctional T cells to mediate effector activity by CD8-IL2 depended on simultaneous antigen recognition and was quantitatively and qualitatively superior to that achieved by PD-1 blockade. Finally, CD8-IL2 was able to functionally reinvigorate T cells in tumors resistant to anti-PD-1, underscoring its potential as a novel treatment strategy for cancer patients.

  Study EGAS00001007712

• Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci

  Human genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D). However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS). We characterised patterns of chromatin accessibility (ATAC-seq, n=17) and DNA methylation (whole-genome bisulphite sequencing, n=10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks. We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant. At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants. The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.

  Study EGAS00001002592

• Unrelated Donor Reduced Intensity Bone Marrow Transplant for Children with Severe Sickle Cell Disease (BMT CTN-0601-BioLINCC)

  ObjectivesThe primary objective is to determine event-free survival (EFS) at 1 year after unrelated donor (URD) hematopoietic stem cell transplantation (HCT) using bone marrow (BM) in patients with sickle cell disease (SCD).BackgroundSickle cell disease (SCD), also known as sickle cell anemia, is an inherited blood disease that can cause organ damage, stroke, and intense pain episodes. Children with sickle cell disease experience organ damage, impaired quality of life, and premature mortality. A blood stem cell transplant is a treatment option for someone with a severe form of the disease. Prior to undergoing a transplant, people typically receive a conditioning regimen of high doses of chemotherapy and other medications to prepare the body to accept the transplant. This type of conditioning regimen is known as a myeloablative conditioning regimen, but it can result in toxicities and sterility. A conditioning regimen that uses lower doses of chemotherapy and medications may be safer for transplant recipients. This type of regimen is known as reduced intensity conditioning (RIC) regimen. RIC has a more favorable toxicity profile but is associated with higher rates of graft rejection (GR), especially with graft sources such as umbilical cord blood This study evaluated the safety and effectiveness of blood stem cell transplants, using bone marrow from unrelated donors, in children with severe SCD who receive a RIC regimen prior to the transplant.SubjectsPatients 3.0-19.75 years old with symptomatic SCD AND one or more of the following complications: (1)-(i) a clinically significant neurologic event (stroke) or any neurologic defect lasting > 24 hours and accompanied by an infarct on cerebral magnetic resonance imaging (MRI); OR, (ii) patients who have a Transcranial Doppler (TCD) velocity that exceeds 200 cm/sec by the non-imaging technique (or TCD measurement of >185 cm/sec by the imaging technique) measured at a minimum of 2 separate occasions one month or more apart; OR, (2) Minimum of two episodes of acute chest syndrome within the preceding 2-year period defined as new pulmonary alveolar consolidation involving at least one complete lung segment (associated with acute symptoms including fever, chest pain, tachypnea, wheezing, rales, or cough that is not attributed to asthma or bronchiolitis) despite adequate supportive care measures; OR, (3) History of 3 or more severe pain events (defined as new onset of pain that lasts for at least 2 hours for which there is no other explanation) per year in the 2 years prior to enrollment despite adequate supportive care measures (if patients are receiving hydroxyurea and compliant with therapy, being symptomatic is an indication for transplantation; however, if patients decline hydroxyurea or non-compliant with this therapy, they would still remain eligible for study if pain criteria as described above are met). Lansky/Karnofsky performance score must be &#8805; 40. Hemoglobin S must be &#8804; 45%. Patients must have an unrelated adult bone marrow donor who is HLA-matched at 8 of 8 HLA-A, -B, -C and -DRB1 at high resolution using DNA-based typing. Patients with bridging fibrosis or cirrhosis of the liver, with uncontrolled bacterial, viral, or fungal infection in the past month, or seropositivity for HIV are excluded. Patients with HLA-matched family donors, or who have received prior HCT, and females who are pregnant or breast feeding are excluded. Thirty patients were enrolled on this study and of these, 29 patients met the criteria and proceeded to the study transplant.DesignParticipants attended a study visit prior to the transplant to undergo a blood collection, neurocognitive testing to measure learning and brain function, magnetic resonance angiogram (MRA) and magnetic resonance imaging (MRI) scans. Questionnaires to assess quality of life were also completed. All patients received erythrocyte transfusions before transplant. Twenty-two days (-22) before the transplant, participants began receiving a reduced intensity conditioning regimen of chemotherapy and medications. On days -21, -20, and -19 participants weighing 10 kg or more received 10 mg, 15 mg, and then 20 mg of Alemtuzumab intravenously (IV) followed by 30 mg/m2/day IV on days -8 through -4 of Fludarabine. Eight days (-8) before the transplant, participants were admitted to the hospital to continue the conditioning regimen which included 140 mg/m2 IV of Melphalan on day -3. Participants received the bone marrow transplant on day 0. Prophylaxis for GVHD consisted of a calcineurin inhibitor (tacrolimus or cyclosporine) administered from day -3 through day 100 after graft infusion, with subsequent taper through day 180; methotrexate 7.5 mg/m2 on days 1, 3, and 6; and methylprednisolone 1 mg/kg per day from days 7 through 28, with subsequent taper by 20% per week. One week after the transplant continuing until the WBC is normal, participants received granulocyte-colony-stimulating factor (G-CSF). After leaving the hospital, participants attended study visits weekly during weeks 1 to 8, at day 60, weekly during weeks 9 to 14, at Day 100, at month 6, and at years 1 and 2. At all study visits, a blood collection, medical history review, and physical exam occurred. In addition, at day 100, month 6, and years 1 and 2, questionnaires to assess quality of life were completed. At select visits the following procedures were conducted: lung function testing, heart function testing, MRA and MRI scans, and neurocognitive testing.The primary outcome was 1-year EFS. Death, disease recurrence or graft rejection by 1 year were considered events for this endpoint.ConclusionsThe trial met its prespecified 1-year EFS, and significantly improved HRQL was reported posttransplant. However, although the Reduced-intensity conditioning (RIC) provided successful engraftment in most patients, the regimen cannot be considered safe for widespread adoption without modification due to the regimen-related toxicity (RRT) and high rate of chronic GVHD, which was the predominant cause of mortality.

  Study phs003470

• Maintenance of Brain Tumor Profile on Organotypic Brain Slice Culture (OBSC)

  We used uncultured tumor resection tissue from an NF2 mutation-driven grade II meningioma (MG-II) to investigate whether tumors engrafted onto organotypic brain slice cultures (OBSCs) maintained the genetic profile of the parent tumor. We prepared three groups of samples for whole exome sequencing (WES) from the same pool of dissociated MG-II tissue. Group HT is the original uncultured human tumor tissue (n = 3). Group CL is the parent tumor tissue expanded in standard in vitro cell culture until the minimum number of cells required for WES had grown. Because of initial cell loss and subsequent clonal expansion, this required six passages over the span of approximately one month (n = 3). Group BSHT is the uncultured tumor resection tissue engrafted onto OBSCs and subsequently dissected from the OBSCs at the conclusion of our standardized assay length (4 days; n = 4). WES analysis showed that tumor tissue engrafted onto OBSCs maintained a significant genetic resemblance to the parent tumor, while tumor tissue expanded in vitro displayed a distinctly different profile. Furthermore, the mutational profiles of all four BSHT biological replicates were markedly similar, indicating that each OBSC-engrafted tumor indeed contained a representative sample of the original patient tumor. A closer look at the hallmark NF2 mutations existing within all samples revealed that while all samples from the original tumor (HT1-3) and the OBSC-engrafted tumor (BSHT1-4) maintain the frame shift deletion at V24, this mutation was lost in all samples expanded in vitro (CL1-3) and replaced by mutations in other areas. Together, this data suggests that the rapid assay design and tumor-accommodating niche of our OBSC platform enables effective maintenance of the original patient tumor profile. The BBsplit algorithm from the BBtools suite was run on all samples to eliminate rat DNA contamination in the BSHT samples as well as to account for any biases that may result as a part of that process. Only the reads that were binned to the human reference were used for subsequent analysis. Reads were then mapped to the GRCh38 version of the human genome with BWA v0.7.17 and realigned together with ABRA2 v.23. Quality control was implemented using the GATK/Picard v4.1.7.0 toolkit. Somatic variants were called for each sample using the MuTect2 algorithm v4.1.7.0. Variants were merged into a single cohort variant call file and then converted to MAF via vcf2maf v1.6.21 tool. Variants were annotated using VEP v87. To identify mutations with potentially high biological impact, multiple filtering steps were applied to somatic mutation calling. First, we selected only the somatic variants that passed all filters from the MuTect2 FilterMutectCalls algorithm and second, only high/moderate impact (change coding) variants as defined by the VEP annotation were further analyzed. Over 1900 single nucleotide variants (SNVs) were detected across all samples. Figures that summarized the results were generated using maftools.

  Study phs003268

• InterPregGen-GWAS-UZB-2

  GWAS genotype data for maternal and fetal (baby) cases of preeclampsia and controls from Uzbekistan. This dataset is a component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium members in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology

  Dataset EGAD00010001917

• Saliva Microbiota of Finnish children from the PANIC study

  Saliva microbiota of 407 participants of PANIC study, is profiled with 16S rRNA gene sequencing (regions V3-V4). Amplification was performed using the TruSeq-switched tail amplicons. The sequencing was performed using the 2 × 301 base-pair on the Illumina MiSeq PE300 platform. Data includes information on sex, age, and caries status of participants. File format is fastq.gz.

  Dataset EGAD50000000989

• scRNAseq and scTCRseq data from tumor-infiltrating lymphocytes

  SCRNAseq and TCRseq are performed using 10X genomics technology on lymphocytes from tumor of 8 individuals (4 different conditions and 2 replicates per condition): - ISO : isotype sample_1 - ISO_dup :isotype sample_2 aPD1 : sample anti-PD1_1 aPD1_dup :sample anti-PD1_2 aCTLA4: sample anti-CTLA4_1 aCTLA4_dup :sample anti-CTLA4_2 combo : sample_anti-PD1_anti-CTLA4_1 combo_dup : sample_anti-PD1_anti-CTLA4_2

  Dataset EGAD50000001214

• 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

• WGS data of paediatric TCF3::PBX1 acute lymphoblastic leukemia (set2)

  The dataset includes FASTQ and BAM files from diagnostic and matched remission (control) samples of one TCF3::PBX1 ALL patient (ALLT-376). Sequencing library was made using PCR free-based WGS library preparation (PCR-), using NEB Next® Ultra™ DNA Library Prep Kit. Both diagnostic (90x) and remission (30x) samples were sequenced using HiSeq X.

  Dataset EGAD50000001795