3738 results for "*oma"

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• Melanoma post mortem analysis

  We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem. Herein we show the findings for the first such patient.

  Dataset EGAD00001005072

• RNA-seq of multiple myeloma patient samples

  RNA from CD138+ plasma cells from patients with a plasma cell dyscrasia were sequenced using the TruSeq Stranded Total RNA Ribo-zero Gold kit (Illumina). Paired-end 75bp reads were generated on a NextSeq500 or HiSeq4000 (Illumina). The dataset consists of 1 MGUS sample, 5 SMM samples, 69 newly diagnosed myeloma samples, 1 relapsed myeloma sample, 1 previously treated myeloma samples, and 4 PCL samples. Matching whole genome sequencing data are available for these samples under study EGAS00001003164.

  Dataset EGAD00001004543

• Paired-end RNA-seq analysis of the Hypermutation and Malignant Progression in Low-grade Glioma Patients

  44 DNA samples were derived from the tumors of the low grade glioma patients and sequenced using Illumina RNAseq paired-end technology. Dataset contains 44 BAM files aligned to hg19 using Tophat 2.

  Dataset EGAD00001005387

• Paired-end Whole Exome-seq analysis of the Hypermutation and Malignant Progression in Low-grade Glioma Patients

  113 DNA samples were derived from the tumors of the low grade glioma patients and sequenced using Illumina WES (exome seq) paired-end technology. Dataset contains 113 BAM files aligned to hg19 using BWA v.0.5.9. After mapping duplicated reads were removed, reads were re-aligned around InDels and read base quality score was re-calibrated.

  Dataset EGAD00001005386

• Melanoma post mortem analysis

  These are 21 metastatic melanoma exomes matched with 7 germlines from 7 multisite metastatic melanoma cases.

  Dataset EGAD00001005421

• Melanoma multi site metastases

  These are caveman, pindel, battenberg and brass calls for index patients' metastatic melanoma genomes within this study.

  Dataset EGAD00001005483

• Melanoma multi site metastases

  These are caveman, pindel and sequenza calls for the metastatic melanoma exomes within this study.

  Dataset EGAD00001005487

• MBD4 targeted sequencing in pool experiment

  Targeted sequencing of MBD4 of either tumor and germline DNA from Uveal Melanoma assembled by pool (germline pool or tumor pool).

  Dataset EGAD00001006989

• 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

• Neuroblastoma sequencing data

  This dataset contains tumor and normal whole exome DNA sequence data for a patient with neuroblastoma

  Dataset EGAD00001008120

• MBD4 targeted sequencing

  We sequenced a gene of interest, MBD4, in both germline (n=1099) and tumor (n=192) sample from Uveal Melanoma patients.

  Study EGAS00001005012

• 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.

  Study EGAS00001004346

• Neuroblastoma sequencing data

  This study shares tumor and normal DNA for a patient with neuroblastoma

  Study EGAS00001005602

• Melanoma post mortem analysis

  This is the affymetrix gene expression data of the metastatic tumours related to this study.

  Dataset EGAD00010001717

• Integrated genomic, transcriptional and epigenomic analyses in germinal center-cell lymphomas link the mutation landscape with differential DNA methylation in Burkitt lymphoma

  Biologically and clinically diverse B-cell neoplasms, including Burkitt, follicular and diffuse large B-cell lymphoma, show features of germinal center (GC) B-cells. Here we present a comprehensive analysis of the epigenetic landscape of GC-B-cell lymphomas using whole genome bisulfite sequencing paired with genome and transcriptome sequencing from 29 primary tumor samples and four normal GC-B-cell samples. All lymphomas studied showed extensive genome-wide methylation losses as well as intragenic regions where DNA methylation levels were strongly correlated with expression of associated genes. Pathway analysis revealed that these intragenic regions were enriched in signaling networks differentially activated in Burkitt and follicular lymphomas and affected regulatory mechanisms deregulated by recurrent mutations in a large fraction of Burkitt lymphomas (including the ID3/TCF3 complex, the SWI/SNF remodeling complex and the inhibitory GI signaling pathway). Taken together, these results provide a global picture of Burkitt lymphoma pathomechanism and demonstrate a tight connection between epigenetic regulation and other levels of regulatory control in these tumors.

  Study EGAS00001001067

• HYPERMUTATION AND MALIGNANT PROGRESSION IN LOW-GRADE GLIOMA PATIENTS

  While patients with primary glioblastoma (GBM) have benefited from increased overall survival due to treatment with the alkylating chemotherapy temozolomide (TMZ), the benefit ofTMZ for patients with diffuse low-grade gliomas (LGG) remains unknown. To further explore the relationships among TMZ treatment, hypermutation and malignant progression, we studied tumor evolution in an expanded cohort of untreated and TMZ-treated patients by exome-sequencing of paired IDH1/2-mutant LGGs and their post-TMZ recurrences. Together, these findings further our understanding of the molecular features and clinical behavior of hypermutated clones.

  Study EGAS00001002368

• Whole genome sequencing of multiple myeloma identifies novel structural and non-coding mutations.

  The coding region of the myeloma genome is well understood with key mutations having been identified. However, the non-coding regions of the myeloma genome are yet to be analyzed. Here we have performed whole genome sequencing of myeloma patient samples to identify structural abnormalities including chromothripsis and chromoplexy, as well as gene dysregulation through super-enhancer juxtaposition with key oncogenes. We have also identified non-coding region hotspots of mutation, especially in promoter regions, that affect gene expression.

  Study EGAS00001003164

• RNA sequencing of multiple myeloma identifies genes dysregulated by structural variants.

  Multiple myeloma is a disease of structural changes brought about by copy number abnormalities and translocations. Many of these changes result in super-enhancers being placed next to key oncogenes. Here, we have investigated which genes are commonly dysregulated by these changes.

  Study EGAS00001003411

• noninvasive lung cancer subtyping

  Accurate diagnosis of lung cancer is important for treatment decision-making. Currently, the gold standard for diagnosing histological subtypes of lung cancer relies on tumor biopsies. Recently, liquid biopsy, particularly cell-free DNA (cfDNA), has shown promising results in cancer detection and classification. In this study, we investigated the potential of cfDNA methylome for the noninvasive classification of lung cancer histological subtypes. Specifically, we focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcription activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in the cross-validation and an AUC of 0.747 in the independent validation. Additionally, tumor copy number variations inferred from cfDNA methylome analysis revealed potential implications for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasively lung cancer subtyping, offering insights for cancer monitoring and early detection.

  Study EGAS00001007717

• Oesophageal adenocarcinoma RNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains bulk transcriptomes from the inoperable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19). Transcriptomes were prepared from pre-treatment oesophageal tumour biopsies using a ribodepletion approach in order to assess both previously reported (e.g. PD-L1 expression) and novel predictive expression-based biomarkers for immunochemotherapy treatment in this setting. On-treatment and post-treatment biopsies were generated as well to characterize response to therapy, and samples were also prepared from paired normal GI tissues for a subset of patients. scRNA-seq based deconvolution was also applied to bulk transcriptomes in this study to estimate the cell composition of tumour biopsies and assess the link between the presence of specific cell types with immunochemotherapy outcomes.

  Dataset EGAD00001009399

• Oesophageal adenocarcinoma WGS from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains whole genome sequencing (WGS) generated from the inoperable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19). WGS data were generated with the aim to assess previously reported (e.g. tumour mutational burden) and novel predictive genomic markers for immunochemotherapy treatment in this setting. Using these data, mutation and copy number profiles were generated for the LUD2015-005 study, which were assessed for correlation with patient outcomes from this trial.

  Dataset EGAD00001009400

• A Risk Score Incorporating Low Pass Whole Genome Sequencing of Cell Free DNA from Patients Receiving CD19 CAR T-Cell Therapy for Large B-Cell Lymphoma

  Study EGAS00001006308

• Low Pass Whole Genome Sequencing of Cell Free DNA from Patients Receiving CD19 CAR T-Cell Therapy for Large B-Cell Lymphoma

  Low Pass Whole Genome Sequencing of Cell Free DNA from Patients Receiving CD19 CAR T-Cell Therapy for Large B-Cell Lymphoma consisting of 123 samples with FASTQs with hg19 aligned BAM/BAI files.

  Dataset EGAD00001009175

• MD Anderson Lymphoma & Myeloma Data Access Committee

  Dac EGAC00001002706

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007545