1393 results for "se pueden comprar monedas en FC 26 Visité Buyfc26coins.com. ¡Bravo! Ahora puedo disfrutar de FC 26 al máximo..IlP4"

in 10.27 milliseconds.

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - mobile_elements

  Samples from Edwards et al 2015 - doi:10.1186/s12864-015-1685-z

  Dataset EGAD00001001048

• WGS data from both single-cell and bulk samples

  This dataset contains the raw BAM/CRAM files used in the manuscript "Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma" Steemers et al. (2026). This includes: - 332 PTA-based single-cell WGS samples (~15X depth) - 7 bulk tumour samples (~30X depth) with matched germline samples (~30X depth) - 14 bulk tumour samples seqeunced by the Princess Maxima Centre diagnostics department (~105X depth) with matched germline samples (~35X depth)

  Dataset EGAD50000002411

• Liquid Biopsy Versus Tissue Biopsy to Assess Acquired Resistance and Tumor Heterogeneity in Gastrointestinal Cancers

  Tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique molecular resistance alterations in different metastatic lesions in an individual patient, under the selective pressure of therapy1-3. Studies have suggested that liquid biopsy may better capture the heterogeneity of acquired resistance, but systematic, direct comparisons of post-progression liquid vs. standard single-lesion tumor biopsies are lacking. In a prospective cohort of 44 patients with molecularly-defined gastrointestinal cancers and acquired resistance to targeted therapy, direct comparison of post-progression liquid vs. tumor biopsy revealed that liquid biopsy more frequently identified clinically-relevant resistance alterations and multiple resistance mechanisms, detecting resistance alterations not detected in matched tumor biopsy in 78% of cases. Whole-exome sequencing of serial cell-free DNA, tumor biopsies and rapid autopsy specimens elucidated geographic and evolutionary characteristics of heterogeneity. Our data suggest that acquired resistance is frequently characterized by profound tumor heterogeneity and highlight the potential clinical utility of liquid biopsy. Burrell, R. A. & Swanton, C. Tumour heterogeneity and the evolution of polyclonal drug resistance. Molecular oncology 8, 1095-1111, doi:10.1016/j.molonc.2014.06.005 (2014). Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532-536, doi:10.1038/nature11156 (2012). Ahronian, L. G. et al. Clinical Acquired Resistance to RAF Inhibitor Combinations in BRAF-Mutant Colorectal Cancer through MAPK Pathway Alterations. Cancer Disco 5, 358-367, doi:10.1158/2159-8290.Cd-14-1518 (2015).

  Study phs001853

• Replication data for RNA-SEQUENCING: A RELIABLE TOOL TO UNVEIL TRANSCRIPTIONAL LANDSCAPE OF PAEDIATRIC B-OTHER ACUTE LYMPHOBLASTIC LEUKAEMIA

  This dataset contains the raw data of the RNA-seq experiments described in the paper "RNA-Sequencing: a reliable tool to unveil transcriptional landscape of pediatric B-other acute lymphoblastic leukemia" It contains the raw data from 60 pediatric patients diagnosed with B cell progenitor acute lymphoblastic leukemia lacking the main genetic alterations. RNA material was extracted from bone marrow or periphereal blood and RNA libraries were prepared using Illumina® TruSeq™ stranded mRNA kit following manufacturer's instructions and were sequenced in a NextSeq 550 using a v2 HighOutput 150 cycle kit 2x75bp. Funding: Instituto de Salud Carlos III (ISCIII), PI21/00213 

  Dataset EGAD50000000981

• HV31 - Read identifier list for local CCS, CLR, ONT and MGI reads

  This file contains read identifiers for local CCS, CLR, ONT and MGI reads for each of the eight selected genomic regions (HLA, KIR, IGH, IGK, IGL, TRA, TRD, andTRG). We extracted these reads by aligning whole-genome sequencing data to a draft whole-genome de novo assembly, and selecting reads that map to contigs representing each region. These reads were involved in the polishing and validation of the HV31 assembly. Please refer to the relevant manuscript (https://doi.org/10.1101/2021.02.03.429586) for additional details. Read identifiers are stored in JSON format. Along with the full FASTQ files, this file enables convenient re-analysis of the HV31 sequencing data in the eight selected regions.

  Dataset EGAD00001007761

• Unravelling the contribution of HIF pathway and its therapeutic potential in human AML leukemia-initiating cells

  We performed single cell RNA sequencing (scRNA-seq) from bone marrow on 11 pediatric (0-14 years-old) and adolescent and young adult (AYA) (15-39 years-old) de novo AML samples (Dx) (4 inv(16), 3 t(8;21) and 4 rMLL). In addition, for some patients also relapse sample was sequenced (2 inv(16), 2 t(8;21) and 3 rMLL). Cells were sorted into CD34+/CD38- and CD34-/CD38+ and sequenced separately.

  Dataset EGAD00001008514

• Genomic Evolution of Breast Cancer Metastasis and Relapse

  Recurrent breast cancer is almost universally fatal. We characterize 170 patients locally relapsed or distant metastatic cancers using massively parallel sequencing. We identify that the relapse-seeding clone disseminates late from the primary tumor. TP53 and AKT1 appear to be enriched in ER-positive cancers predisposed to relapse. Mutation acquisition continues at relapse as the same mutation signatures continue to operate and new signatures, such as that caused by radiotherapy appear de novo. In 49% of cases we identify drivers mutations private to the relapse and these are sampled from a wider range of cancer genes, including SWI-SNF complex and JAK-STAT signaling.

  Dataset EGAD00001002698

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS)

  Advances in antiretroviral therapies (ART) and patient management have improved survival of people living with HIV (PLWH). However, with exposure to ART and associated side effects and toxicities as well as to the virus itself, PLWH are at an increased risk of several chronic diseases. The Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) research network integrates longitudinal clinical data and treatment management of PLWH and provides research infrastructure to support HIV-associated clinical, social, and behavioral outcomes and comparative effectiveness research using data collected from 8 Centers for AIDS Research clinical sites across United States. For detailed study description, see Kitahata et al. (2008, PMID:18263650).

  Study phs001788

• Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies Risk-Stratifying Subgroups, Proteins Associated with Time-To-Recurrence, and mTOR Effector Molecule EIF4A1 as a Druggable Therapeutic Target

  Using mass spectrometry-based proteomics, we analyzed two independent cohorts comprising 80 and 62 treatment-naive ICC tumors, 67 adjacent non-malignant tissues, and 9 patient-derived xenografts (PDX). In the first cohort, we identifed two subclusters with distinct times-to-recurrence (TTR). A 4-protein classifier trained on our data cohort accurately stratifies these ICC clusters in the Dong et al. dataset (2022) and in our independent second cohort, revealing similar associations between proteomic motifs and clinical outcomes. The Translation regulator EIF4A1 emerges as a therapeutic target, as its inhibition with novel drug eFT226 significantly reduces tumor growth in an ICC PDX model.

  Study EGAS50000001343