C-Myc facilitates prediction of "7+3" induction failure in de novo acute myeloid leukaemia with high-risk cytogenetics
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ABSTRACT:
PROVIDER: PRJNA692419 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13443200_1.fastq.gz | Fastqsanger.gz | |||
| SRR13443200_2.fastq.gz | Fastqsanger.gz | |||
| SRR13443201_1.fastq.gz | Fastqsanger.gz | |||
| SRR13443201_2.fastq.gz | Fastqsanger.gz | |||
| SRR13443202_1.fastq.gz | Fastqsanger.gz |
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Project description:This study aimed to identify whether c-Myc could facilitate prediction of "7+3" induction failure in de novo acute myeloid leukaemia (AML) with high-risk cytogenetics. Seventy-five untreated de novo AML patients who completed the "7+3" induction therapy were enrolled (24 patients from a prospective cohort; 51 patients from a retrospective cohort) and stratified into complete remission (CR) and non-CR groups. Myc-associated molecular signature between the CR and non-CR groups in the prospective cohort was compared after gene set enrichment analysis. c-Myc protein expression was assessed via immunohistochemical staining. A high c-Myc-immunopositivity was defined when ≥ 40% of bone marrow myeloblasts were c-Myc (+). Our results revealed that AML patients who did not achieve CR by the "7+3" induction therapy had a higher Myc gene expression than those that achieved CR (p=0.047). Myc gene expression was positively correlated with c-Myc protein expression (p=0.014). Although the non-CR group did not have more c-Myc protein expression compared to that in the CR group (p=0.151), combination of high c-Myc-immunopositivity and high-risk cytogenetics increased the probability of "7+3" induction failure compared to that in high-risk cytogenetics alone. Induction strategies other than “7+3” might be a solution for de novo patients with high c-Myc-immunopositivity and high-risk cytogenetics.
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Project description:The diagnosis of myelodysplastic syndromes (MDS) remains problematic due to the subjective nature of morphological assessment. The reported high frequency of somatic mutations and increased structural variants by array-based cytogenetics have provided potential objective markers of disease however this has been complicated by reports of similar abnormalities in the healthy population. We aimed to identify distinguishing features between those with early MDS and reported healthy individuals by characterising 69 patients who, following a non-diagnostic marrow, developed progressive dysplasia or acute myeloid leukaemia (AML). Targeted sequencing and array based cytogenetics identified a driver mutation and/or structural variant in 91% (63/69) of pre-diagnostic samples with the mutational spectrum mirroring that in the MDS population. When compared with the reported healthy population the mutations detected had significantly greater median variant allele fraction (40% vs 9-10%) and occurred more commonly with additional mutations (≥2 mutations 64% vs. 8%). Furthermore mutational analysis identified a high-risk group of patients with shorter time to disease progression and poorer overall survival. The mutational features in our cohort are distinct from those seen in the healthy population and, even in the absence of definitive disease, can predict outcome. Early detection may allow consideration of intervention in poor risk patients. We performed array based cytogenetics using HumanCytoSNP-12 (Illumina) on 69 patients diagnosed with acute myeloid leukaemia or myelodysplastic syndrome who had a previously non-diagnostic sample. SNP array analysis was performed on all diagnostic samples. In those with a documented abnormality, SNP-A was performed on the corresponding pre-diagnostic sample (n=32).
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Project description:The t(8;21) Acute Myeloid Leukaemia (AML) Kasumi-1cell line with N822K KIT mutation, is a model system for leukemogenesis. As AML initiating cells reside in the CD34+CD38- fraction, we addressed the refined cytogenomic characterization and miRNA expression of Kasumi-1 cell line and its FACS-sorted subpopulations focussing on this compartment. By conventional cytogenetics, Spectral Karyotyping and array-CGH the cytogenomic profile of Kasumi-1 cells evidenced only subtle regions differentially represented in CD34+CD38- cells. Expression profiling by a miRNA platform showed a set of miRNA differentially expressed in paired subpopulations and the signature of miR-584 and miR-182 upregulation in the CD34+CD38- fraction.
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Project description:Serum miRNA-based risk prediction for stroke
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Project description:BackgroundHeart failure is one of the most common diseases of adults in Europe, with an overall prevalence of 1-2%. Among persons aged 60 and above, its prevalence is above 10% in men and 8% in women. Acute heart failure has a poor prognosis; it is associated with a high rate of rehospitalization and a 1-year mortality of 20-30%.MethodsThis review is based on pertinent literature, including guidelines, retrieved by a selective search in PubMed.ResultsThere are different types of acute heart failure; the basic diagnostic assessment is performed at once and consists of ECG, echocardiography, and the measurement of N-terminal pro-brain natriuretic peptide (NTproBNP) and troponin levels. The most common causes of decompensation are arrhythmia, valvular dysfunction, and acute cardiac ischemia, each of which accounts for 30% of cases. The potential indication for immediate revascularization should be carefully considered in cases where acute heart failure is due to coronary heart disease. The basic treatment of acute heart failure is symptomatic, with the administration of oxygen, diuretics, and vasodilators. Ino-tropic agents, vasopressors, and temporary mechanical support for the circulatory system are only used to treat cardiogenic shock.ConclusionThe treatment of acute heart failure is markedly less evidence-based than that of chronic heart failure. Newer treatment approaches that are intended to improve outcomes still need to be tested in multicenter trials.
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Project description:The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than nonnative structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction method, but for larger and more complex proteins, the native state is virtually never sampled, and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper, we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical "linchpin" features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and, when constrained, dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality.
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Project description:Accurate prediction of transcription factor binding motifs that are enriched in a collection of sequences remains a computational challenge. Here we report on GimmeMotifs, a pipeline that incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing (ChIP-seq) data. Similar redundant motifs are compared using the weighted information content (WIC) similarity score and clustered using an iterative procedure. A comprehensive output report is generated with several different evaluation metrics to compare and evaluate the results. Benchmarks show that the method performs well on human and mouse ChIP-seq datasets. GimmeMotifs consists of a suite of command-line scripts that can be easily implemented in a ChIP-seq analysis pipeline.GimmeMotifs is implemented in Python and runs on Linux. The source code is freely available for download at http://www.ncmls.eu/bioinfo/gimmemotifs/.s.vanheeringen@ncmls.ru.nlSupplementary data are available at Bioinformatics online.
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