1021 results for "中国疾控中心2024年12月传染病死亡病例统计数据"

in 7.62 milliseconds.

• Chronic Renal Insufficiency Cohort (CRIC) - GWAS

  The Chronic Renal Insufficiency Cohort (CRIC study) was established in 2001 by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) to improve the understanding of the relationship between chronic kidney disease and cardiovascular disease. The goals of the CRIC Study are to examine risk factors for progression of chronic kidney disease and cardiovascular disease among patients with chronic kidney disease and to develop predictive models to identify high-risk subgroups, informing future treatment trials and increasing application of available preventive therapies. Data available at dbGaP include genotype data for 3527 participants released in version 1. Plasma metabolomics and plasma proteomics data will be available at dbGaP in "Chronic Renal Insufficiency Cohort (CRIC) Study Metabolomics and Proteomics". Additional phenotypic, clinical, and outcomes data are available at the NIDDK Central Repository and can be linked to data available at dbGaP. More than 330 manuscripts have been published, reporting on the effects of a broad range of risk factors (from molecular biomarkers of disease pathways to clinical, demographic, and behavioral characteristics) on the progression of CKD and other health consequences. The full CRIC bibliography can be found at http://cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/bibliography-2024-2023.

  Study phs000524

• Targeted capture ctDNA Library CRCQV34Run015-12

  Targeted capture ctDNA Library CRCQV34Run015-12 from patient PBC0002108, plasma 12 month sample

  Dataset EGAD00001010378

• Targeted capture ctDNA Library CRCQV42Run036-12

  Targeted capture ctDNA Library CRCQV42Run036-12 from patient PBC0003587, plasma 12 month sample

  Dataset EGAD00001010594

• RNA sequencing of 12 NSCLC samples from 4 patients at MDACC

  Multi-region RNA sequencing of 12 NSCLC samples from 4 patients at MDACC

  Study EGAS00001005951

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• Whole genome sequencing, SNP array and RNA-seq of uveal melanomas

  We conducted whole genome sequencing and DNA SNP array of 12 uveal melanoma genomes and their matched DNA from blood. We also conducted RNA-seq of the 12 tumour samples.

  Study EGAS00001000472

• Mammary cell samples from donors 28/32/33

  Mammary cell samples from donors 28/32/33. Contains 12 MiSeq sequencefiles and 12 alignment files derived from HiSeq runs.

  Dataset EGAD00001001439

• Whole exome sequencing of 12 NSCLC samples from 4 patients at MDACC

  Multi-region WES from 4 NSCLC patients, totaling 12 tumor samples and 4 matched control samples.

  Study EGAS00001005829

• Colorectal-Cancer-Tumor-Spheres-H012-Microarray_Expression

  Gene expression of 12 colon cancer TSCs (sensitive or resistant) after 12h treatment with 3µM NCT02 or DMSO (control) was analyzed using Illumina microarrays (HumanHT-12 v4 BeadChip).

  Dataset EGAD00010001936

• Target Capture Sequencing of 12 patients

  We perfomed target capture DNA sequencing of 12 patients to assess the detailed biological mechanisms of resistance to immune checkpoint inhibitors.

  Study JGAS000589