4116 results for "*oma"

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• Evolutionary trajectories of IDH-mutant astrocytoma identify molecular grading markers related to cell cycling

  To study the evolutionary processes that drive malignant transformation in IDH-mutant astrocytomas, we performed multi-omics on a large cohort of matched initial and recurrent tumor samples. We find that genome-wide DNA-methylation levels decreased over time, mainly in patients with high-grade recurrences. DNA-demethylation was lifted from specific loci associated with DNA replication, and was associated with upregulation of cell cycling associated genes. Upregulation of cell cycling genes occurred predominantly in proliferating tumor cells, and was associated with tumor grade. Temporal DNA-demethylation and upregulation of cell cycling were hardly affected by chemo- and/or radiotherapy, indicating treatment does not affect the course of disease. Further, we defined a DNA-methylation based signature associated with tumor grade and poor prognostic genetic events, with a prognostic power stronger than CDKN2A/B deletion. We validated its prognostic power on primary tumor samples of patients included in the CATNON randomized phase 3 clinical trial. Therefore this model can serve as a molecular grading marker for IDH-mutant astrocytomas.

  Study EGAS00001007527

• Epigenomic dataset of Human Hepatocellular Carcinoma for EpiHK

  This dataset is generated for EpiHK. It contains Histone PTM ChIP-seq, WGBS, RNA-seq of Human Hepatocellular Carcinoma and tumor adjacent normal tissue sample.

  Study EGAS50000000039

• Epigenomic dataset of Human Hepatocellular Carcinoma for EpiHK

  This dataset is generated for EpiHK. It contains Histone PTM ChIP-seq, WGBS, RNA-seq of 7 Human Hepatocellular Carcinoma and 7 tumor adjacent normal tissue sample.

  Dataset EGAD50000000058

• UAMS - Myeloma Institute Data Access Committee

  Dac EGAC00001000845

• Cell-Free DNA Genomic and Fragmentomic Features for Early Outcome Prediction in Diffuse Large B-Cell Lymphoma

  Purpose: Diffuse large B-cell lymphoma (DLBCL) patients need an accurate and early risk stratification strategy as prompt therapy escalation may improve outcome. Patients and methods: We evaluated cell free DNA (cfDNA) genomic and fragmentomic features in 190 patients with large B-cell lymphoma from more than 40 hospitals in the Benelux using Whole Genome Sequencing (WGS). We defined a new metric called ACT score (Aberrations, Contribution of short fragments, Terminal motif analyses) by combining four cfDNA signals and evaluated its performance to predict end-of-treatment (EOT) response and survival. Results: Individual cfDNA features and ACT score were altered per EOT response after one cycle of treatment (T1). The ACT score at T1 outperformed all individual cfDNA features in predicting EOT response (AUC = 0.74). Patients with a positive ACT score had an inferior outcome compared to ACT score negative patients [progression-free survival (PFS): HR 5.0 (95% CI 3.1-8.2), log-rank test, p < 0.0001) and overall survival (OS): HR 6.5 (95% CI 3.5-12.2), log-rank test, p < 0.0001]. The 2-year PFS in ACT score positive and negative patients were 29.8% and 78.1%, respectively. The prognostic value of the ACT score is independent of the International Prognostic Index and interim imaging. Conclusions: ACT score computed from a single plasma sample collected after one cycle of treatment can predict clinical outcome. This low-cost and easy-to-interpret test does not require tissue biopsies or a priori knowledge of mutations and has the potential to aid patient selection in interventional clinical trials and risk-adapted treatment strategies.

  Study EGAS50000000412

• The evolution of ovarian high grade serous carcinoma from STIC lesions

  We performed shallow whole genome sequencing of normal fallopian tube, stroma, p53 signatures, STIL/STIC, invasive and metastasis carcinoma from 5 high grade serous carcinoma cases.

  Study EGAS50000000360

• The evolution of ovarian high grade serous carcinoma from STIC lesions

  We performed Ampliseq on 11 samples on a targeted panel of 17 genes.

  Study EGAS50000000361

• DAC of the STIC project

  Ovarian high-grade serous carcinoma (HGSC) originates in the fallopian tube, with secretory cells carrying a TP53 mutation, known as ‘p53 signatures’, identified as potential precursors. p53 signatures evolve into serous tubal intraepithelial carcinomas (STIC) lesions, which, in turn, progress into invasive HGSC that readily spread to the ovary and disseminate around the peritoneal cavity. We recently investigated the genomic landscape of early- and late-stage HGSC and found higher ploidy in late-stage (median 3.1) than early-stage (median 2.0) samples. Here, to explore whether the high ploidy and possible whole genome duplication observed in late-stage disease are determined early in the evolution of HGSC, we analysed archival formalin-fixed paraffin-embedded samples (FFPE) from five HGSC patients. p53 signatures and STIC lesions were laser-capture microdissected and sequenced using shallow whole genome sequencing (sWGS), while invasive ovarian/fallopian tube and metastatic carcinoma samples underwent macrodissection and were profiled using both sWGS and targeted next generation sequencing. Results showed highly similar patterns of global copy number change between STIC lesions and invasive carcinoma samples within each patient. Ploidy changes were evident in STIC lesions, but not p53 signatures, and there was strong correlation between ploidy in STIC lesions and invasive ovarian/fallopian tube and metastatic samples in each patient. The reconstruction of sample phylogeny for each patient from relative copy number indicated that high ploidy, when present, occurred early in the evolution of HGSC, which was further validated by copy number signatures in ovarian and metastatic tumours. These findings suggest that aberrant ploidy, suggestive of whole genome duplication, arises early in HGSC, and is detected in STIC lesions, implying that the trajectory of HGSC may be determined at the earliest stages of tumour development.

  Dac EGAC50000000227

• H3K27ac ChIP-seq in Insulinomas

  H3K27ac ChIP-seq datasets in human insulinoma samples

  Dataset EGAD50000000461

• RNA-seq in insulinomas

  RNA-seq datasets in human insulinoma samples

  Dataset EGAD50000000462