15016 results for "cancer studies using rna-seq"

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• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

  Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitors. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Here we evaluate bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples. In CheckMate 026, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne CDx™ assay. TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. Parameter variation can therefore impact TMB calculations, highlighting the need for standardization. Encouragingly, WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90) and differences could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms and centers is achievable.

  Study EGAS00001003661

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis using bronchial specimens

  Non-diagnostic findings in Transbronchial Lung Biopsy (TBLB) and Endobronchial Ultrasound-guided Transbronchial Lung Biopsy (EBUS-TBLB) are not uncommon. A challenge is to improve lung cancer detection by TBLB/EBUS-TBLB. In order to improve the diagnostic yield of bronchoscopy, we used 850K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules.We found that the combination of HOXA7, SHOX2,and SCT methylation analysis has the best diagnostic yield in 123 bronchial washing (sensitivity: 74·1%; AUC: 0·851) and 172 brushing samples (sensitivity: 86·1%; AUC: 0·915). We developed a kit comprising these three genes and validated the kit in 329 unique bronchial washing samples, 397 unique brushing samples, and 179 unique patients with both washing and brushing samples. The diagnostic accuracy of the panel alone in lung cancer diagnosis was 86·9%, 91·2% and 95% in bronchial washing, brushing, and washing+brushing samples, respectively. The panel's sensitivity in combination with cytology, rapid on-site evaluation (ROSE), and histology in lung cancer diagnosis was 90·8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing+brushing samples. Quantitative analysis of the three-gene panel improves lung cancer diagnosis by bronchoscopy.

  Study EGAS00001007089

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  Dataset EGAD00001000355

• ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  Dataset EGAD00001000648

• EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  Dataset EGAD50000001975

• Reference epigenome Pancreas-Islet07 input ChIP-Seq data generated from KEP study

  Pancreas-Islet07 input ChIP-Seq paired end data

  Dataset EGAD00001007463

• Reference epigenome Kidney-Podocyte01 h3k27me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007229

• Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

  A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003868

• Reference epigenome Pancreas-Islet07 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007462

• Reference epigenome Pancreas-Islet08 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007466

• Reference epigenome Pancreas-Islet02 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007198

• Reference epigenome Pancreas-Islet02 input ChIP-Seq data generated from KEP study

  Pancreas-Islet02 input ChIP-Seq paired end data

  Dataset EGAD00001007199

• Reference epigenome IPS-ENeuron02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007302

• Reference epigenome Pancreas-Islet07 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007461

• Reference epigenome Fat-adipocyte04 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007483

• Reference epigenome Kidney-Podocyte03 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007237

• Reference epigenome Pancreas-Islet04 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007211

• Reference epigenome ADMSC03_smRNA-Seq data generated from KEP study

  A ADMSC03_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003869

• Reference epigenome Pancreas-Islet07 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007457

• Reference epigenome IPS-NPC01 input ChIP-Seq data generated from KEP study

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome Pancreas-Islet08 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007467

• Reference epigenome Fat-adipocyte03 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007472