Plasma cell neoplasm patient exome sequence data access committee
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ABSTRACT:
PROVIDER: EGAC00001000377 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.
| S-EPMC3617375 | biostudies-literature
Project description:Multiple myeloma is a relatively common B-cell malignancy that is currently incurable. Certain recurrent genetic abnormalities characteristics of different genetic subtypes have been described. Hyperdiploid myeloma characterized by recurrent trisomies is the most common genetic subtypes. However little is know about it's biology. Another common genetic abnormality is chromosome 13 deletion which is also associated with inferior prognosis. This abnormality is already present at the pre-malignant MGUS stage and is clonally selected with disease progression. Although it is biologically and clinically important the molecular consequence of chromosome 13 deletion is unknown. Keywords: disease subtype analysis
2007-04-01 | GSE6477 | GEO
Project description:Plasma cell dyscrasias and myeloproliferative neoplasms (MPN) are hematologic malignancies arising from two distinct hematopoietic cell lineages. They rarely occur concomitantly. Here, we report a case of a patient with a recent diagnosis of a JAK2 V617F positive MPN who presented with a new diagnosis of plasma cell leukemia. The patient had presented to the hospital with a leukocytosis predominantly comprised of plasma cells, followed by work-up involving peripheral blood flow cytometry, FISH analysis, and bone-marrow biopsy. FISH analysis was suggestive of a common progenitor cell for these distinct hematologic malignancies. To our knowledge, this case represents the second reported instance of a concomitant JAK2 positive MPN with primary plasma cell leukemia.
| S-EPMC7482648 | biostudies-literature
Project description:Genome wide DNA methylation profiling of AML patient samples treated with PBS or DAC. The Illumina Infinium 450 Human DNA methylation was used to examine the methylation profile of 8 patient samples and 2 cell lines. Genome wide DNA methylation profiling of AML xenografts treated with either PBS control or with decitacine (DAC) alone, cytarabine (Ara-C) alone, DAC and Ara-C together (D+A), DAC followed by Ara-C (D/A) or with Ara-C followed by DAC (A/D).
2013-03-06 | GSE44830 | GEO
Project description:Genome wide DNA methylation profiling of AML patient samples treated with PBS or DAC. The Illumina Infinium 450 Human DNA methylation was used to examine the methylation profile of 8 patient samples and 2 cell lines. Genome wide DNA methylation profiling of AML xenografts treated with either PBS control or with decitacine (DAC) alone, cytarabine (Ara-C) alone, DAC and Ara-C together (D+A), DAC followed by Ara-C (D/A) or with Ara-C followed by DAC (A/D). DNA was extracted from patient bone marrow samples and xenograft bone marrow samples using Qiagen Allprep kit. Bisulphite converted DNA from all samples were hybridised to the Illumina Infinium 450 Human Methylation arrays and for each analysis the drug treated sample was compared to the corresponding PBS control sample.
2013-03-06 | E-GEOD-44830 | biostudies-arrayexpress
Project description:Data Access Committee EGAC00001002626
| EGAC00001002626 | EGA
Project description:Azacitidine (AZA) and decitabine (DAC) are cytidine azanucleoside analogs with clinical activity in myelodysplastic syndromes (MDS) and potential activity in solid tumors. To better understand the mechanism of action of these drugs, we examined the effects of AZA and DAC in a panel of non-small cell lung cancer (NSCLC) cell lines. Of 5 NSCLC lines tested in a cell viability assay, all were sensitive to AZA (EC50 of 1.8M-bM-^@M-^S10.5 M-BM-5M), while only H1299 cells were equally sensitive to DAC (EC50 of 5.1 M-BM-5M). In the relatively DAC-insensitive cell line A549, both AZA and DAC caused DNA methyltransferase I depletion and DNA hypomethylation; however, only AZA significantly induced markers of DNA damage and apoptosis, suggesting that mechanisms in addition to, or other than, DNA hypomethylation are important for AZA-induced cell death. Cell cycle analysis indicated that AZA induced an accumulation of cells in sub-G1 phase, whereas DAC mainly caused an increase of cells in G2/M. Gene expression analysis of AZA- and DAC-treated cells revealed strikingly different profiles, with many genes distinctly regulated by each drug. In summary, while both AZA and DAC caused DNA hypomethylation, distinct effects were demonstrated on regulation of gene expression, cell cycle, DNA damage, and apoptosis. A549 and H1299 cells were treated with a dose range (0.3M-bM-^@M-^S3.0 M-NM-<M) of AZA or DAC for 48 hours, and effects on gene expression were assessed by microarray analysis.
2011-05-05 | E-GEOD-29077 | biostudies-arrayexpress
Project description:Azacitidine (AZA) and decitabine (DAC) are cytidine azanucleoside analogs with clinical activity in myelodysplastic syndromes (MDS) and potential activity in solid tumors. To better understand the mechanism of action of these drugs, we examined the effects of AZA and DAC in a panel of non-small cell lung cancer (NSCLC) cell lines. Of 5 NSCLC lines tested in a cell viability assay, all were sensitive to AZA (EC50 of 1.8–10.5 µM), while only H1299 cells were equally sensitive to DAC (EC50 of 5.1 µM). In the relatively DAC-insensitive cell line A549, both AZA and DAC caused DNA methyltransferase I depletion and DNA hypomethylation; however, only AZA significantly induced markers of DNA damage and apoptosis, suggesting that mechanisms in addition to, or other than, DNA hypomethylation are important for AZA-induced cell death. Cell cycle analysis indicated that AZA induced an accumulation of cells in sub-G1 phase, whereas DAC mainly caused an increase of cells in G2/M. Gene expression analysis of AZA- and DAC-treated cells revealed strikingly different profiles, with many genes distinctly regulated by each drug. In summary, while both AZA and DAC caused DNA hypomethylation, distinct effects were demonstrated on regulation of gene expression, cell cycle, DNA damage, and apoptosis.
2011-05-05 | GSE29077 | GEO
Project description:BACKGROUND: With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. RESULTS: We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate. CONCLUSIONS: In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1-4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.
| S-EPMC4132917 | biostudies-literature
Project description:The data in this submission relate to whole exome sequencing from murine ovarian cancer cell line ID8. All sequencing was performed by Beckman Coulter Genomics, Grenoble, France in February 2013.
2018-03-31 | E-MTAB-4663 | biostudies-arrayexpress