scholarly journals Identification of hub genes and its correlation with the prognosis of acute myeloid leukemia based on high‐throughput data analysis

2020 ◽  
Vol 4 (2) ◽  
pp. 49-56
Author(s):  
Wei Fu ◽  
Guo‐bin Cheng ◽  
Yao Ding ◽  
Ya‐jie Deng ◽  
Peng‐xiang Guo
Keyword(s):  
Acute Myeloid Leukemia ◽  
Data Analysis ◽  
High Throughput ◽  
Myeloid Leukemia ◽  
Hub Genes ◽  
High Throughput Data ◽  
High Throughput Data Analysis ◽  
Acute Myeloid

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

2020 ◽  
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis

High-Throughput Data Analysis of Proteomic Mass Spectra on the SwissBioGrid

2011 ◽  
pp. 228-244
Author(s):  
Andreas Quandt ◽  
Sergio Maffioletti ◽  
Cesare Pautasso ◽  
Heinz Stockinger ◽  
Frederique Lisacek
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Throughput ◽  
Protein Function ◽  
Mass Spectra ◽  
High Throughput Data ◽  
Analysis Process ◽  
Speed Up ◽  
Grid Infrastructures ◽  
High Throughput Data Analysis

Proteomics is currently one of the most promising fields in bioinformatics as it provides important insights into the protein function of organisms. Mass spectrometry is one of the techniques to study the proteome, and several software tools exist for this purpose. The authors provide an extendable software platform called swissPIT that combines different existing tools and exploits Grid infrastructures to speed up the data analysis process for the proteomics pipeline.

scholarly journals Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States

2015 ◽  
Vol 94 (7) ◽  
pp. 1127-1138 ◽  
Author(s):  
Bruno C. Medeiros ◽  
Sacha Satram-Hoang ◽  
Deborah Hurst ◽  
Khang Q. Hoang ◽  
Faiyaz Momin ◽  
...  
Keyword(s):  
United States ◽  
Acute Myeloid Leukemia ◽  
Big Data ◽  
Data Analysis ◽  
Myeloid Leukemia ◽  
The United States ◽  
Big Data Analysis ◽  
Treatment Patterns ◽  
Acute Myeloid

High-Throughput Drug Screening Identifies Pyrimethamine As a Potent and Selective Inhibitor of Acute Myeloid Leukemia

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2304-2304
Author(s):  
Amit Sharma ◽  
Nidhi Jyotsana ◽  
Courteney K. Lai ◽  
Anuhar Chaturvedi ◽  
Kerstin Görlich ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Acute Myeloid Leukemia ◽  
High Throughput ◽  
Dihydrofolate Reductase ◽  
Drug Screening ◽  
Validation Studies ◽  
Myeloid Leukemia ◽  
Leukemic Cell ◽  
High Throughput Drug Screening ◽  
Acute Myeloid

Abstract Introduction: Hematopoietic stem/progenitor cell differentiation is blocked in acute myeloid leukemia (AML) resulting in cytopenias and high risk of death. Most patients with AML become resistant to treatment due to lack of effective cytotoxic and differentiation fostering compounds. High expression of MN1 confers poor prognosis to AML patients and induces resistance to cytarabine and all-trans-retinoic acid (ATRA) induced differentiation. We thus set out to identify compounds which could potentially overcome the differentiation block in AML. Methods: Based on the above concepts and in an effort to identify novel compounds which are potent inducers of differentiation and apoptosis in AML, high-throughput drug screening was employed in the MN1 leukemic model. A total of 3580 bioactive compounds were tested in duplicate at a concentration of 2.5 µM using alamar blue fluorescence as readout. As MN1 cells are resistant to ATRA (at 1µM and even 10µM ATRA), the drug screening was performed in the presence of a clinically relevant dose of ATRA (1 µM) to identify compounds that concurrently act with the cytotoxic and/or differentiating effects of ATRA. To determine whether a compound was effective as monotherapy or if it synergized with ATRA, we also performed a validation phase study in which the IC50 of each candidate compound was tested alone and in combination with ATRA. Fifty-four inhibitors were chosen from the primary screen for further validation based on presumed mechanism of action and novelty. The shortlisted compound pyrimethamine (PMT) was validated for its differentiation and apoptosis promoting effects in various murine and human AML models. Results: Our high-throughput drug screening identified 117 compounds, which reduced MN1 leukemic cell proliferation by more than 80% above the ATRA-treated control in both replicates (inhibitors), 8 borderline inhibitors (one replicate with more than 80% inhibition and one with 74 to 80% inhibition), and 35 outliers, which inhibited cell proliferation by 80% or more in only one replicate. The biologic processes most frequently targeted by the 117 inhibitors were DNA replication (n=26), microtubule assembly (n=12), NF-kB pathway (n=8), dihydrofolate reductase (DHFR, n=3) and heat shock protein 90 (HSP90). Dihydrofolate reductase inhibitors, pyrimethamine and amethopterin/methotrexate emerged as top hits from the screening and preliminary validation studies. Validation studies identified the antifolate pyrimethamine (PMT) that potently induced apoptosis and differentiation in several murine and human leukemic cell lines when administered as a single agent. The cytotoxic effects of pyrimethamine were reversed by addition of an excess of folic acid whereas induction of myeloid differentiation at higher concentrations of pyrimethamine was not mediated through DHFR inhibition. We further evaluated the effect of pyrimethamine in an in vivo xenograft mouse model by subcutaneously inducing tumors with HL60 and THP1 cell lines. Oral pyrimethamine treatment significantly reduced tumor volumes after 14, 19 and 24 days post-transplantation and at death compared to solvent treated mice (P<0.01). The effect of pyrimethamine was further assessed in primary human AML cells and normal CD34+ cells by CFC assays. Colony numbers from primary AML cells, but not normal CD34+ bone marrow cells, were significantly reduced by pyrimethamine as compared to solvent control. Thus, our study identifies pyrimethamine as a candidate drug that is a potent and specific inducer of apoptosis and differentiation with the property of specifically targeting leukemic cells. Conclusion: Our high-throughput drug screening identified pyrimethamine as a potent and specific antileukemic compound and reinforces targeting of folate metabolism as a treatment strategy in acute myeloid leukemia. Disclosures No relevant conflicts of interest to declare.

High-throughput mutation analysis in acute myeloid leukemia (AML).

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 6529-6529
Author(s):  
J. Dunlap ◽  
C. L. Corless ◽  
W. H. Fleming ◽  
R. Braziel ◽  
N. Leeborg ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Mutation Analysis ◽  
High Throughput ◽  
Myeloid Leukemia ◽  
Acute Myeloid

Proteomic Data Analysis Workflow for Discovery of Candidate Biomarker Peaks Predictive of Clinical Outcome for Patients with Acute Myeloid Leukemia†

2008 ◽  
Vol 7 (6) ◽  
pp. 2332-2341 ◽  
Author(s):  
Jenny Forshed ◽  
Maria Pernemalm ◽  
Chuen Seng Tan ◽  
Marita Lindberg ◽  
Lena Kanter ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Data Analysis ◽  
Clinical Outcome ◽  
Myeloid Leukemia ◽  
Proteomic Data ◽  
Analysis Workflow ◽  
Acute Myeloid

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

Gene ◽  
2021 ◽  
pp. 146111
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas W. Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis
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