4955 results for "*oma"

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• Whole genome bisulfite sequencing on 10 multiple myeloma cases

  Whole genome bisulfite sequencing on 10 multiple myeloma cases. Data quality control and adaptor-trimmed were performed with the Trimomatic tool. Paired-reads were mapped to the hg19 human reference with methylCtools aligner.

  Dataset EGAD00001007821

• Enhanced reduced representation bisulfite sequencing (eRRBS) on 45 multiple myeloma samples and 3 normal plasma cell

  Enhanced reduced representation bisulfite sequencing (eRRBS) on 45 multiple myeloma samples and 3 normal plasma cell. Enhanced reduced representation bisulfite sequencing (eRRBS) on 45 multiple myeloma samples and 3 normal plasma cell. Libraries were sequenced on a HiSeqTM4000 Illumina machine using 75bp paired-end reads

  Dataset EGAD00001007822

• Whole exome sequencing (bam files) of 55 samples of myxofibrosarcoma and 44 matched pairs

  Whole exome sequencing data (bam files) of 55 samples of myxofibrosarcoma and 44 matched pairs.

  Dataset EGAD00001007824

• Targeted-capture sequencing (bam files) of 81 samples of myxofibrosarcoma and 44 matched pairs

  Myxofibrosarcoma (MFS) is a rare subtype of sarcomas in the elderly, whose genetic basis is poorly understood. To elucidate it, the Targeted-capture sequencing was performed.

  Dataset EGAD00001007826

• Double/triple hit lymphoma whole exome and panel sequencing data

  Mutational landscape of high-grade B-cell lymphoma with MYC-, BCL2 and/or BCL6 rearrangements characterized by whole-exome sequencing and panel sequencing.

  Dataset EGAD00001007805

• Whole genome sequencing (bam files) of 5 samples of myxofibrosarcoma and 5 matched pairs

  Myxofibrosarcoma (MFS) is a rare subtype of sarcomas in the elderly, whose genetic basis is poorly understood. To elucidate it, the whole genome sequence was performed.

  Dataset EGAD00001007825

• Neuroblastoma WGS samples used for detection of seismic amplification

  Paired-end WGS data of 27 neuroblastoma patient samples (10 obtained at diagnosis, 6 at relapse and 11 matched blood samples as controls) used for detection of complex "seismic" amplification. Mean coverage is 24-55x per sample. The remaining patient samples of the dataset can be found under accession number EGAS00001001308.

  Dataset EGAD00001007807

• Sequencing data for oesophageal and related samples - Ng, Contino et al (WGS)

  Data supporting "Interplay of processes shapes structural variations undergoing selection in oesophageal adenocarcinoma" Ng, Contino et al. WGS (BAM files) 383 oesophageal adenocarcinoma samples 383 normal samples

  Dataset EGAD00001007808

• Sequencing data for oesophageal and related samples - Ng, Contino et al (RNA)

  Data supporting: "Interplay of processes shapes structural variations undergoing selection in oesophageal adenocarcinoma" Ng, Contino et al. RNAseq (BAM files) 214 oesophageal adenocarcinoma samples

  Dataset EGAD00001007809

• Paired-WGS Sequencing of Primary lymphomas of the central nervous system (PCNSL)

  Paired WGS samples, 24 tumor/control pairs of primary CNS lymphoma, sequenced on HiSeq X Ten using Illumina TruSeq Nano DNA for library preparation.

  Dataset EGAD00001007810

• RBWES

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Whole-Exome-Sequencing.

  Dataset EGAD00001007591

• DNA (exome) sequencing of uveal melanoma metastases

  DNA (exome) sequencing of uveal melanoma metastases.

  Dataset EGAD00001007911

• RNA sequencing of uveal melanoma metastases.

  RNA sequencing of uveal melanoma metastases.

  Dataset EGAD00001007912

• Single-cell RNA and TCR sequencing of PBMC from patients with uveal melanoma

  Single-cell RNA and TCR sequencing of PBMC from patients with uveal melanoma.

  Dataset EGAD00001007913

• RNA-seq data from a variety of pediatric brain tumors

  RNA-seq (n=52) data from a variety of pediatric brain tumors including ependymoma (PFA, PFB, Ste, spinal), medulloblastoma (G3, G4, SHH), high grade glioma (H3K27 and H3-WT), pilocytic astrocytoma, and more. Raw data provided as FASTQ. Data generated on Illumina HiSeq2500.

  Dataset EGAD00001007915

• Hi-C sequencing from a variety of pediatric brain tumors

  Hi-C (n=72) data from a variety of pediatric brain tumors including ependymoma (PFA, PFB, Ste, spinal), medulloblastoma (G3, G4, SHH), high grade glioma (H3K27 and H3-WT), pilocytic astrocytoma, and more. Raw data provided as FASTQ. Data generated on Illumina HiSeq2500.

  Dataset EGAD00001007914

• CM214 - Biomarker Analysis From the Phase 3 CheckMate 214 Trial of Nivolumab Plus Ipilimumab (N+I) or Sunitinib (S) in Advanced Renal Cell Carcinoma (aRCC)

  CM214 - Biomarker Analysis From the Phase 3 CheckMate 214 Trial of Nivolumab Plus Ipilimumab (N+I) or Sunitinib (S) in Advanced Renal Cell Carcinoma (aRCC)

  Dataset EGAD00001007938

• Embryonal Rhabdomyosarcoma sequencing data

  This dataset contains samples from 9 patients with embryonal rhabdomyosarcoma. 9 samples have whole exome tumor data (one has multiple). 7 samples have tumor RNAseq data. 1 sample has matched normal dna sequence data

  Dataset EGAD00001007939

• Cancer and germline exomes consisting of FASTQ reads from melanoma, lung and colon cancer samples

  Cancer and germline exomes consisting of FASTQ reads from 6 individuals (4 melanoma, 1 lung and 1 colon cancer). Exome sequencing was performed on illumina with a depth of 100x to 200x. 2 Melanoma datasets contain reads from 2 different tumor regions 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 1 Melanoma dataset contains reads from 2 healthy tissues Colon and lung datasets contain both 1 matched germline-tumor pair

  Dataset EGAD00001007950

• Cancer RNA-seq consisting of FASTQ paired-end reads from melanoma and lung cancer sample

  Cancer RNA-seq consisting of FASTQ paired-end reads from 6 individuals (4 melanoma, 1 lung cancer). RNASEQ was performed on illumina, Truseq capture kit, 40M-80M clusters. 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 2 Melanoma, 1 Colon and 1 lung datasets contain each reads from a single region.

  Dataset EGAD00001007951

• Inherited MUTYH mutations cause elevated somatic mutation rates and distinctive mutational signatures in normal human cells

  Cellular DNA damage caused by reactive oxygen species is repaired by the base excision repair (BER) pathway which includes the DNA glycosylase MUTYH. Inherited biallelic MUTYH mutations cause predisposition to colorectal adenomas and carcinoma. However, the mechanistic progression from germline MUTYH mutations to MUTYH-Associated Polyposis (MAP) is incompletely understood. Here, we sequenced normal cell DNAs from 10 individuals with MAP and study the somatic mutation burden and mutational signatures.

  Dataset EGAD00001007958

• Immune profiling reveals enrichment of distinct immune signatures in oral epithelial dysplasia

  The dataset contains transcriptomic information of 36 oral potentially malignant disorders (OPMD), 14 fibroepithelial polyps (FEP), and 6 early stage oral squamous cell carcinoma (OSCC) from the Asian population. Total RNA was extracted from formalin-fixed paraffin embedded (FFPE) tissue sections. RNA libraries were prepared using the NEB NextUltra RNA kit with Illumina Ribo-Zero rRNA removal as per manufacturer’s instructions. RNA sequencing was performed on the HiSeq2500 platform to generate paired-end 150 nucleotides reads and with a coverage of 50 million reads per sample. Uploaded bam files have been mapped to the GRCh38 human genome using TopHat2. Clinical and demographic data for these patients are available from the associated publications or by request.

  Dataset EGAD00001007970

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma_CM67-RNASeq

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Dataset EGAD00001006306

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma - CM066-WES

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Dataset EGAD00001006309

• SF11940

  SF11940 snATAC Sequencing.Anaplastic Astrocytoma, IDH-mutant. Tumor location: Left Frontal. Age: 29. Sex: Male .

  Dataset EGAD00001006311