12068 results for "cancer rna-seq"

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• Single Cell DNA Methylation Analysis for Forensic Epigenetics

  As part of the Epigenetic CHaracterization and Observation (ECHO) program, the Genomic Analysis Laboratory at the Salk Institute generated the DNA methylation profiles of certain PBMC cell types to determine the change in methylation levels before, during or after exposure to certain biological or chemical agents. Samples from individuals who either handled anthrax in controlled environments or who tested positive to MRSA, MSSA, HIV, COVID-19 or exposed to Flu virus or organophosphates, or control samples were used in single-nucleus methylation (snmC-seq) assays to quantify DNA methylation levels of cytosine residues of the DNA. Around 5-10% cytosine sites were sampled in the assay. Using computational analyses, regions in the genome differentially methylated in response to the exposure were identified. This information can be used to develop diagnostic tools to predict the type of infection or time since exposure.

  Study phs003204

• Multimodal epigenetic sequencing analysis of cell-free DNA identifies biomarkers for ALS diagnosis and progression

  The role of the circulating epigenome in ALS remains understudied. We aim to develop diagnostic and prognostic blood-based biomarkers for Amyotrophic Lateral Sclerosis (ALS) by profiling methylome signatures in circulating cell-free DNA (cfDNA) in 20 sporadic ALS cases, 10 C9orf72-associated ALS cases, 10 asymptomatic C9orf72 expansion carriers, and 21 healthy controls using targeted enzymatic methyl-sequencing (EM-seq) of ~4 million CpG loci. We detected numerous differentially methylated genes, including several implicated in ALS risk and pathobiology, and integrated multiple epigenetic features with our Multimodal Epigenetic Sequencing Analysis (MESA) algorithm, achieving an ROC AUC of 0.91 and detecting ~70% of ALS cases at ~100% specificity. Methylation at a subset of loci also correlated with clinical progression and cerebrospinal-fluid neurofilament levels, supporting the potential of cfDNA-based biomarkers for ALS diagnosis and monitoring.

  Study EGAS50000001267

• H3K27ac ChIP-seq in primary inflammatory macrophages from 2 minor allele homozygotes and 2 major allele homozygotes at rs2836882

  This dataset consists of H3K27ac ChIP-sequencing in inflammatory (TPP) macrophages from 2 minor allele homozygotes and 2 major allele homozygotes at rs2836882. Monocytes were positively selected from PBMC using CD14 Microbeads and inflammatory macrophage differentiation performed using conditions that model chronic inflammation (TPP): 3 days GM-CSF (50ng/mL) followed by 3 days GM-CSF, TNFa (50ng/mL), PGE2 (1mg/mL), and Pam3CSK4 (1mg/mL). After harvesting, cells were cross-linked, quenched, lysed, and sheared. Immunoprecipitation of histone-DNA complexes was performed overnight at 4C with rotation using an anti-H3K27ac antibody and the SimpleChIP Plus Sonication ChIP kit (Cell Signaling Technology). Following reverse cross-linking, 50ng of immunoprecipitated DNA or input DNA were used to prepare sequencing libraries using the iDeal Library Preparation kit (Diagenode), according to manufacturer instructions. 10 PCR cycles were used for the amplification step and size selection was not performed. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 (Agilent) and the libraries were sequenced in pools of 8, with each pool being sequenced in 2 lanes of an Illumina HiSeq2500 high output flow-cell (50bp, single-end reads). Raw data are provided as raw and aligned single-end sequencing reads from H3K27ac-bound DNA and the input chromatin. Raw reads were trimmed using Trim Galore and aligned to the reference human genome (hg19) using Burrows-Wheeler Aligner (v0.7.12) with default parameters. Aligned reads were converted to BAM files, sorted, and technical duplicates merged before indexing – all using SAMtools (v1.4). PCR duplicates were identified using Picard tools (v2.18.1) and removed together with unmapped reads using SAMtools (v1.4). The resulting BAM files were re-sorted and indexed after filtering.

  Dataset EGAD00001011351

• 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

• Germline elongator mutations in sonic hedgehog medulloblastoma

  Cancer genomics has illuminated a wide spectrum of genes and core molecular processes contributing to human malignancy. Still, the genetic and molecular basis of many cancers remains only partially explained. Genetic predisposition accounts for 5-10% of cancer diagnoses and genetic events cooperating with known somatic driver events are poorly understood. Analyzing established cancer predisposition genes in medulloblastoma (MB), a malignant childhood brain tumor, we recently identified pathogenic germline variants that account for 5% of all MB patients. Here, by extending our previous analysis to include all protein-coding genes, we discovered and replicated rare germline loss-of-function (LoF) variants across Elongator Complex Protein 1 (ELP1) on 9q31.3 in 15% of pediatric MBSHH cases, thus implicating ELP1 as the most common MB predisposition gene and increasing genetic predisposition to 40% for pediatric MBSHH. Inheritance was verified based on parent-offspring and pedigree analysis, which identified two families with a history of pediatric MB. ELP1-associated MBs were restricted to the molecular SHHa subtype and were characterized by universal biallelic inactivation of ELP1 due to somatic loss of chromosome 9q. The majority of ELP1-associated MBs exhibited co-occurring somatic PTCH1 (9q22.32) alterations, suggesting that ELP1-deficiency predisposes to tumor development in combination with constitutive activation of SHH signaling. ELP1 is an essential subunit of the evolutionary conserved Elongator complex, whose primary function is to enable efficient translational elongation through tRNAs modifications at the wobble (U34) position. Biochemical, transcriptional, and proteomic analyses revealed that ELP1-associated MBSHH are characterized by a destabilized core Elongator complex, loss of Elongator-dependent tRNA modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response (UPR), consistent with deregulation of protein homeostasis due to Elongator-deficiency in model systems. Our findings suggest that genetic predisposition to proteome instability is a previously underappreciated determinant in the pathogenesis of pediatric brain cancer. These results provide strong rationale for further investigating the role of protein homeostasis in other pediatric and adult cancer types and potential opportunities for novel therapeutic interference.

  Study EGAS00001004126

• Integration of intra-sample contextual error modeling for improved detection of somatic mutations

  Study EGAS00001003806

• IRCCS Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" Italian Cancer Center Data Access Committee

  Dac EGAC00001003223

• IRCCS Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" Italian Cancer Center Data Access Committee

  Dac EGAC00001003227

• St. Jude Children’s Research Hospital – Washington University Pediatric Cancer Genome Project Steering Committee

  Dac EGAC00001000045

• Data access policy for PDTX Breast Cancer data from Bruna et al (2016), Cell.

  Dac EGAC00001000540