Originally, oligonucleotide arrays have been developed for high-throughput DNA and even peptide research (39-41). Although expression profi ling is widely applied, DNA profi ling is still an emerging fi eld at an early stage of development. Further development of the early arrays for targeting DNA sequences have now resulted in the production of microarrays for probing single nucleotide polymorphisms (SNP), so-called SNP-arrays. Such arrays target 10.000 to 500.000 common SNP’s, which are more or less equally divided over the genome. Not only genomic polymorphisms are established, but also DNA copy numbers. This technique can thus be used to get information on chromosomal gains or losses. The genotype calls provide data of loss of heterozygosity. An alternative approach to DNA profi ling is array-based comparative genomic hybridization (array-CGH). As dual-channel microarrays, array CGH uses DNA from a test and a reference sample to assess copy numbers, but this technique cannot by applied to assess genotypes (42). Current resolutions are comparable to those of SNP-chips. Interestingly, SNP-array analyses of AML have revealed the presence of uniparental disomy, probably as a result of mitotic recombination, in about 20% of AML patients (43, 44).
Losses or gains of chromosomal materials detected with DNA-profi ling may have effects on mRNA transcript levels and prognostically relevant chromosomal abnormalities could therefore also be identifi ed through expression profi ling. However, genome-wide SNP-array analyses in AML may facilitate the identifi cation of common abnormalities underlying distinct expression clusters.
Several other high-throughput techniques are currently in development or have recently been applied. Chromatin immunoprecipitation (ChIP) is a technique to study protein-DNA interactions (45) to assess e.g. the binding of transcription factors to particular DNA regions or the epigenetic status of a gene and its regulatory regions. . To facilitate large-scale analyses, ChIP has been combined with microarray technology (46). This allows the identifi cation of target sites for a certain protein on a genome-wide level. In AML, target genes of disrupted transcription factors such as the core binding factor complex (involved in inv(16) and t(8;21)) or the PML-RARα fusion protein (involved in t(15;17)) could as such be identifi ed. ChIP- on-Chip profi ling in combination with gene expression profi ling could be useful fo elucidating regulatory networks involved in leukemogenesis (47).
Methylation of CpG-islands in the genome is frequently seen and is known to play a role in regulating gene expression (48). Methylation profi ling of cancer
may reveal specifi c patterns of CpG island methylation resulting from clonal selection of cells with growth advantages, e.g. due to silencing of associated tumor suppressor genes (49). For instance, methylation of genes associated with CEBPα mutations, possibly through the upregulation of methyl transferase enzymes such as DNMT3B, has been demonstrated (30). It is thought that this epigenetic event plays a role in leukemogenesis. Therefore, methylation profi ling of AML might be of use in identifying critical regulatory genes that are silenced as a result of molecular abnormalities.
Alternative splicing of primary RNA transcripts results in different mRNA transcripts of the same gene and plays an important role in cell homeostasis (50-52). For instance, different splice forms of EVI1 with different oncogenic characteristics have been shown to be present in AML (53). To identify the infl uence of alternative splicing, exon arrays have been developed targeting over 300.000 different Ensembl transcripts (54). Arrays targeting different splice variants can be used to identify particular active splice forms and specifi c combinations of co-occurring abnormalities that are involved in AML pathogenesis.
MicroRNA’s are short length RNAs (22 bp) that represent a class of mRNA translation regulators (55). They have been postulated to have a role in the control of cell development, growth, maturation and other cellular processes. A single microRNA can have up to hundreds of target mRNAs, which are subject to mRNA degradation and translation repression. A variety of microRNAs have been identifi ed in recent years. Profi ling tumors for expression of a series of microRNAs has been shown to characterize and to distinguish different sorts of cancer (56). Profi ling of microRNAs will in the future be applied to distinguish microRNAs that are associated with different subtypes of AML. Some of these microRNAs may be involved in pathogenesis of leukemia (57).
On the protein level, mass spectrometry is a promising technique for assessing protein content and protein levels in cells (58). However, this technique has to be further developed to reliably identify the proteome present in a biological sample. Currently, mass spectrometry reliably identifi es only one to fi ve percent of most abundant proteins present. Protein levels may correlate with mRNA levels but it is clear that there is no direct relationship between mRNA and protein levels, e.g. due to variations of cellular processing of mRNA (59). Mass spectrometry furnishes a more specifi c way of identifying the active components that play a role in the cellular processes in leukemia cells.
A key challenge in current high-throughput research is the identifi cation of the most relevant targets for further investigation. Due to the large amounts of data Figure 2 (facing page). Screenshot of the JAVA heatmap explorer, which is part of MADEx. The right upper half of the (mirror image) heatmap is left out and several histograms, indicating different sample characteristics, have been added. In this example, the fi rst bar indicates FAB status ; the second bar indicates karyotype. Different colors indicate different patient class. The third and fourth bar indicate presence of AML specifi c acquired mutations in the genes FLT3 and NPM1. Green indicates absence of a mutation while red indicates presence of a mutation. The fi fth bar displays relative expression levels of the CD34 gene. All data is retrieved dynamically from the MADEx database.
involved, the number of false-positive and false-negative results will be signifi cant even at low rates. It is therefore necessary that results are confi rmed using other techniques. Combining data from different high-throughput sources, like SNP- arrays, methylation profi ling and gene expression analyses, allows a technique- independent validation of results (Figure 3). As an example of the potential use of integrative genomic analyses, a prognostically relevant mutation in Microphthalmia- associated transcription factor was identifi ed by comparing expression data with DNA copy number changes (60). From this example, we can learn that the complementary use of different techniques for complete characterization of patient samples can add to the understanding of tumorigenic and pathogenetic processes.