3796 results for "*oma"

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• Exome reads

  This dataset contains WES data (.bam files) and associated phenotype information from 10 patients included in our microbiome study who went on to anti PD-1 immunotherapy for the treatment of metastatic melanoma at the University of Texas MD Anderson Cancer Center. Both tumor and matching germ line normal were sequenced on each patient using Illumina HiSeq 2500. The average coverage was 283X in tumors and 135X in germline (tumor+germline overall:209, Range: 0-1552).

  Dataset EGAD00001003797

• Delineate genomic and epigenomic changes in esophageal squamous cell carcinoma following radiotherapy

  We performed whole-exome sequencing and whole epigenome sequencing (RRBS) of samples collected from different time points during radiotherapy from thirty-four ESCC patients. We compared the genetic and epigenetic features of the different time biopsy samples to reveal the changes in ESCC received radiotherapy.

  Dataset EGAD00001003799

• Somatic mutations in 106 small intestine adenocarcinoma

  106 FFPE tumor samples from small bowel were sequenced with Illumina HiSeq 4000. Exome capture was performed with NimbleGen SeqCap EZ Exome Library v3 Kit. Reads were aligned with BWA–MEM v.0.7.12 to GRCh37 reference genome. Variant calls were produced with GATK HaplotypeCaller. Variant calls were filtered against all data from gnomAD database using allele frequency threshold 0.0001 in order to remove germline variation.

  Dataset EGAD00001003802

• Variant calling analysis of cfDNA whole exome sequencing in neuroblastoma

  This dataset contains VCF files from a variant calling analysis of 19 neuroblastoma patients. WES or WGS data of the primary tumor were compared to WES cfDNA analysis at the time of diagnosis and at a 2nd timepoint (complete remission, partial remission, disease progression or relapse). For 4 patients, WGS of germline, tumor at diagnosis and tumor at relapse DNA was performed on Illumina HiSeq2500, with 100-bp paired-end reads. For the other patients, WES was performed using either an AgilentSureSelect Human All Exon v5 or a Roche Nimblegen SeqCap EZ Exome V3 kit on Illumina HiSeq2000, with 100-bp paired-end reads. SNVs observed in any of the primary tumors or cfDNA samples studied by WES were targeted using a capture sequencing panel at all intermediate time points.

  Dataset EGAD00001003803

• Whole genome sequence of hepatocellular carcinoma (BCM)

  Exome fastq files of 98 hepatocellular carcinoma and matched nomral (BCM, HCC-JP)

  Dataset EGAD00001003804

• Exome sequencing data for LMS tumor and control samples

  The data set contains paired end fastq files for whole exome sequencing data for Leiomyosarcoma tumor and control samples

  Dataset EGAD00001003829

• BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle

  BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle (2 paired tumor-normal samples and 11 tumor-only samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from 13 patients with chordoid glioma of the third ventricle using the QIAamp DNA FFPE Tissue Kit (Qiagen). Genomic DNA was also extracted from leukocytes in a peripheral blood sample from one of the patients and a non-neoplastic gastric biopsy specimen from one of the patients. Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of approximately 500 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001003818

• Whole exome sequencing data from two patients with isolated myelosarcoma followed by acute myeloid leukemia

  Dataset EGAD00001003831

• Cholangiocarcinoma whole genome sequencing data 12 samples

  This dataset contains whole genome sequencing FASTQ data for 12 cholangiocarcinoma tumor samples, and their matched normal samples. These 12 samples are in addition to 59 samples available in dataset EGAD00001001988, and consist of patients from Thailand, Romania, and Singapore. Paired-end sequencing data was generated by Illumina Hiseq 2000 and 2500, with insert sizes of 170 and 350.

  Dataset EGAD00001003834

• Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

  Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

  Dataset EGAD00001003883