15020 results for "cancer studies using rna-seq"

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• Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses

  Personalized cancer therapy based on the somatic mutations identified in patient tumors is becoming an increasingly emphasized approach to improve outcomes of patients with cancer. There are also examples of therapeutic vulnerabilities in cancer that result from changes in gene expression that are a direct or indirect result of tumor specific epigenetic perturbations. These genomic and epigenomic changes are ultimately manifest in the tumor proteome and phosphoproteome. In this study, we integrated genomic, epigenomic and proteomic data for rhabdomyosarcoma (RMS) to identify therapeutic vulnerabilities. RMS was selected for this analysis because RAS pathway mutations in rhabdomyosarcoma (RMS) are the most common potentially actionable lesions across pediatric solid tumors. The epigenomic data was useful for identifying deregulated developmental pathways in RMS including the WNT, HH, BMP, adenyl cyclase, p38/MAPK and PI3K pathways. Perturbations in those 6 myogenic signal transduction pathways were also evident in the proteome and phosphoteome data. In addition, the proteomic/phosphoproteomic data revealed that the cell cycle checkpoint, unfolded protein response and RB/E2F pathways were deregulated in RMS relative to normal muscle. Recent success targeting CDK4/6 and MEK in adult cancers with RAS mutations led us to test the value of these targets in RMS tumors. We also targeted the unfolded protein response pathway and cell cycle checkpoint pathway using molecular targeted therapeutics in orthotopic patient derived xenografts. Taken together, these data demonstrate the value of integrating epigenomic and proteomic data to identify tumor vulnerabilities that extend beyond somatic mutations identified in the genome.

  Study EGAS00001002967

• Single cell RNA sequencing of human cord Blood CD34 Cells

  We performed single cell RNA sequencing and ATAC e valuation of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage, our target cells contain CD34-positive cord blood. That population is evaluated by single cell RNA sequencing and single cell ATAC.

  Study JGAS000528

• SEARCH for Diabetes in Youth Study - Genetic Risk Score

  Genetic risk scores (GRS) quantify polygenic disease risk into a single measure and can aid in disease classification. GRS studies have focused primarily on adult populations of recent European ancestry. We aimed to assess the utility of GRS in classification of diabetes type among racially/ethnically diverse youth in the United States. We used data from SEARCH baseline visits and follow up visits for which key data elements were available. 2260 participants are included. Phenotypic Data to be submitted here include: Self-reported sex, race and ethnicity Diabetes type determined by provider Etiologic diabetes type T1D (antibody positive, or antibody negative/missing and insulin sensitive) T2D (antibody negative and insulin resistant) Diabetes duration (years) Age at diagnosis (years) Measures from baseline, SEARCH 3 (follow-up) and SEARCH 4 (follow-up): Age Height Weight Waist circumference HbA1c Fasting/non-fasting glucose C-peptide Triglycerides HDL LDL Systolic Blood Pressure Diastolic Blood Pressure Measures from SEARCH 3 and 4:Arterial stiffness as characterized by PWVRetinopathy Measures from SEARCH 4 only: LV structure and diastolic function measurements from echocardiogram GAD autoantibody IAD autoantibody ZnT8 autoantibody We performed genotyping using the Illumina Multi-Ethnic Global Array (MEGA) array with 1,697,069 genotyped variants including 748,291 with minor allele frequency <0.01. Genotyping and preliminary quality control checks were performed at the Colorado Center for Personalized Medicine. After additional quality control, 2,238 samples and 900,743 variants remained for analyses. Samples genotyped on the MEGA array were categorized using SEARCH etiologic type and consisted of predominantly type 1 diabetes cases (n=2,051) but also those with other diabetes (n=133 type 2 diabetes, n=52 other diabetes including monogenic diabetes; genetic confirmation with either a genetic clinical test or test performed as an ancillary study to SEARCH). The median reported age at DNA collection was 11.2 years (interquartile range 7.6 – 14.1) with a minimum age of 1.9 years and maximum of 21.9 years. We used additional data genotyped on the Affymetrix 500K imputation scaffold chip with 239,279 genotyped variants. This cohort consisted of predominantly type 2 diabetes cases (n=417) but also those with type 1 (n=104), and other diabetes types (n=16). After additional quality control, 537 samples and 235,967 variants remained. The median reported age at collection was 11.2 years (quartile range 8.1 – 14.2) with a minimum of 2.0 years and a maximum of 21.1 years. About 100 type 2 diabetes cases were also genotyped using the MEGA chip to ensure concordance between the data sets and facilitate strand alignment between the two chips before genotypic imputation. The data sets had n=230,228 genotyped variants in common and concordance between the genotypes was high (mean correlation r2 for SNPs used in GRS=0.95). We used the 1000 Genomes reference panel to impute each data set separately, resulting in a total of 34.5M and 27.8M well-imputed variants (r2>0.8) for the MEGA and Affymetrix data sets respectively. We combined high quality imputed variants for analysis.

  Study phs002703

• CCA ChIP-seq data (63 CCA, 8 normal, 19 cell-line)

  Fastq files for H3K27ac ChIP-seq (with matched input-DNA control) for 63 CCAs, 8 normal bile duct tissues, 16 CCA cell-lines (including replicates), and 3 normal cholangiocyte cell-lines (including replicates). Library prep was performed with the NEBNext ChIP-seq library preparation kit. Each library (including matching input DNA) was sequenced to an average depth of 20 to 30 million raw reads on Illumina HiSeq4000 sequencer, with paired-end sequencing (except for 3 normal bile tissues done with single-end sequencing).

  Dataset EGAD00001012103

• scRNA-seq and scATAC-seq data of bone marrow

  17 scRNA-seq and 16 scATAC-seq datasets on bone marrows derived from 16 patients. The sequencing dataset consists of 5 samples without bone marrow infiltration, defined as controls (C1, C2, C3, C4, and C5) and 11 neuroblastoma infiltrated bone marrow samples from patients with MYCN amplification (M1, M2, M3, M4), ATRX mutations (A1, A2), and cases lacking either alteration (S1, S2, S3, S4, S5).

  Dataset EGAD00001008635

• scRNA-seq of placental immune cells

  scRNA-seq analysis of 384 placental immune cells

  Dataset EGAD00001010040

• STAG1-ChIP-Seq of STAG2-mutated and cohesin wildtype adult AMLs

  ChIPseq targeting the cohesin subunit STAG1 in STAG2-mutated AMLs or cohesin wildtype AMLs

  Dataset EGAD00001011206

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• Reference epigenome ADMSC07 mRNA-Seq data generated from KEP study

  ADMSC07 mRNA-Seq paired end data

  Dataset EGAD00001007155

• Reference epigenome ADMSC06 mRNA-Seq data generated from KEP study

  ADMSC06 mRNA-Seq paired end data

  Dataset EGAD00001007154

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153

• Reference epigenome ADMSC08 mRNA-Seq data generated from KEP study

  ADMSC08 mRNA-Seq paired end data

  Dataset EGAD00001007156

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170