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Applications of microarray technology to Acute Myelogenous Leukemia.

Goswami RS, Sukhai MA, Thomas M, Reis PP, Kamel-Reid S - Cancer Inform (2008)

Bottom Line: Arrays have also been used extensively in elucidating the mechanism of and predicting therapeutic response in AML, as well as to further define the mechanism of AML pathogenesis.In this review, we discuss the major paradigms of gene expression array analysis, and provide insights into the use of software tools to annotate the array dataset and elucidate deregulated pathways and gene interaction networks.Finally, we discuss several new and emerging array technologies, and how they can be further utilized to improve our understanding of AML.

View Article: PubMed Central - PubMed

Affiliation: Division of Applied Molecular Oncology, Princess Margaret Hospital/Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.

ABSTRACT
Microarray technology is a powerful tool, which has been applied to further the understanding of gene expression changes in disease. Array technology has been applied to the diagnosis and prognosis of Acute Myelogenous Leukemia (AML). Arrays have also been used extensively in elucidating the mechanism of and predicting therapeutic response in AML, as well as to further define the mechanism of AML pathogenesis. In this review, we discuss the major paradigms of gene expression array analysis, and provide insights into the use of software tools to annotate the array dataset and elucidate deregulated pathways and gene interaction networks. We present the application of gene expression array technology to questions in acute myelogenous leukemia; specifically, disease diagnosis, treatment and prognosis, and disease pathogenesis. Finally, we discuss several new and emerging array technologies, and how they can be further utilized to improve our understanding of AML.

No MeSH data available.


Related in: MedlinePlus

Oligonucleotide microarraysA) cDNA synthesis, labeling and hybridization to oligonucleotide array slides. B) Correlation coefficient analysis of gene expression data, showing, in red, probes with fluorescent intensities above the threshold of detection, and in yellow, absent fluorescence. C) Scatter plot analysis of gene expression data, showing the correlation between two of the samples that clustered together, where most probes have similar expression levels, with some probes differentially expressed between these samples. D) Hierarchical clustering of microarray data; in this analysis, samples with similar gene expression profiles are grouped together, cluster of genes is shown on the Y-axis and dendogram or cluster of samples is seen in the X-axis.
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f1-cin-07-13: Oligonucleotide microarraysA) cDNA synthesis, labeling and hybridization to oligonucleotide array slides. B) Correlation coefficient analysis of gene expression data, showing, in red, probes with fluorescent intensities above the threshold of detection, and in yellow, absent fluorescence. C) Scatter plot analysis of gene expression data, showing the correlation between two of the samples that clustered together, where most probes have similar expression levels, with some probes differentially expressed between these samples. D) Hierarchical clustering of microarray data; in this analysis, samples with similar gene expression profiles are grouped together, cluster of genes is shown on the Y-axis and dendogram or cluster of samples is seen in the X-axis.

Mentions: A schematic of a typical gene expression array analysis experiment is shown in Figure 1. Different approaches may be used for the interpretation of gene expression array data. One approach relies on the identification of statistically significant deregulated genes between two or more groups of samples. Current analysis of microarray data involves applying both statistical and machine learning techniques, such as hierarchical clustering (Lee et al. 2002), self-organizing maps (Alevizos et al. 2001), or K-means clustering (Wang et al. 2000) to organize genes and samples into meaningful groups (Eisen et al. 1998; Vesanto, 1999; Friedman et al. 2000; Sultan et al. 2002; Table 2). These methods have been extensively used in microarray studies (Baugh et al. 2001; Hu et al. 2002; Iscove et al. 2002; Makrigiorgos et al. 2002). There are numerous methods for statistical analysis of microarray data. Most existing tools have been developed for relational types of data, which typically have a large number of instances but low complexity. Thus, high complexity causes many existing tools to fail or provides outcomes with limited usefulness. New tools must be flexible enough to support the diverse tasks associated with clinically relevant genomic research.


Applications of microarray technology to Acute Myelogenous Leukemia.

Goswami RS, Sukhai MA, Thomas M, Reis PP, Kamel-Reid S - Cancer Inform (2008)

Oligonucleotide microarraysA) cDNA synthesis, labeling and hybridization to oligonucleotide array slides. B) Correlation coefficient analysis of gene expression data, showing, in red, probes with fluorescent intensities above the threshold of detection, and in yellow, absent fluorescence. C) Scatter plot analysis of gene expression data, showing the correlation between two of the samples that clustered together, where most probes have similar expression levels, with some probes differentially expressed between these samples. D) Hierarchical clustering of microarray data; in this analysis, samples with similar gene expression profiles are grouped together, cluster of genes is shown on the Y-axis and dendogram or cluster of samples is seen in the X-axis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2664704&req=5

f1-cin-07-13: Oligonucleotide microarraysA) cDNA synthesis, labeling and hybridization to oligonucleotide array slides. B) Correlation coefficient analysis of gene expression data, showing, in red, probes with fluorescent intensities above the threshold of detection, and in yellow, absent fluorescence. C) Scatter plot analysis of gene expression data, showing the correlation between two of the samples that clustered together, where most probes have similar expression levels, with some probes differentially expressed between these samples. D) Hierarchical clustering of microarray data; in this analysis, samples with similar gene expression profiles are grouped together, cluster of genes is shown on the Y-axis and dendogram or cluster of samples is seen in the X-axis.
Mentions: A schematic of a typical gene expression array analysis experiment is shown in Figure 1. Different approaches may be used for the interpretation of gene expression array data. One approach relies on the identification of statistically significant deregulated genes between two or more groups of samples. Current analysis of microarray data involves applying both statistical and machine learning techniques, such as hierarchical clustering (Lee et al. 2002), self-organizing maps (Alevizos et al. 2001), or K-means clustering (Wang et al. 2000) to organize genes and samples into meaningful groups (Eisen et al. 1998; Vesanto, 1999; Friedman et al. 2000; Sultan et al. 2002; Table 2). These methods have been extensively used in microarray studies (Baugh et al. 2001; Hu et al. 2002; Iscove et al. 2002; Makrigiorgos et al. 2002). There are numerous methods for statistical analysis of microarray data. Most existing tools have been developed for relational types of data, which typically have a large number of instances but low complexity. Thus, high complexity causes many existing tools to fail or provides outcomes with limited usefulness. New tools must be flexible enough to support the diverse tasks associated with clinically relevant genomic research.

Bottom Line: Arrays have also been used extensively in elucidating the mechanism of and predicting therapeutic response in AML, as well as to further define the mechanism of AML pathogenesis.In this review, we discuss the major paradigms of gene expression array analysis, and provide insights into the use of software tools to annotate the array dataset and elucidate deregulated pathways and gene interaction networks.Finally, we discuss several new and emerging array technologies, and how they can be further utilized to improve our understanding of AML.

View Article: PubMed Central - PubMed

Affiliation: Division of Applied Molecular Oncology, Princess Margaret Hospital/Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.

ABSTRACT
Microarray technology is a powerful tool, which has been applied to further the understanding of gene expression changes in disease. Array technology has been applied to the diagnosis and prognosis of Acute Myelogenous Leukemia (AML). Arrays have also been used extensively in elucidating the mechanism of and predicting therapeutic response in AML, as well as to further define the mechanism of AML pathogenesis. In this review, we discuss the major paradigms of gene expression array analysis, and provide insights into the use of software tools to annotate the array dataset and elucidate deregulated pathways and gene interaction networks. We present the application of gene expression array technology to questions in acute myelogenous leukemia; specifically, disease diagnosis, treatment and prognosis, and disease pathogenesis. Finally, we discuss several new and emerging array technologies, and how they can be further utilized to improve our understanding of AML.

No MeSH data available.


Related in: MedlinePlus