Menezes, R.X. de

MicroRNA characterize genetic diversity and drug resistance in pediatric acute lymphoblastic leukemia (Article)

2011-05-01T00:00:00Z

Background MicroRNA regulate the activity of protein-coding genes including those involved in hematopoietic cancers. The aim of the current study was to explore which microRNA are unique for seven different subtypes of pediatric acute lymphoblastic leukemia. Design and Methods Expression levels of 397 microRNA (including novel microRNA) were measured by quantitative real-time polymerase chain reaction in 81 cases of pediatric leukemia and 17 normal hematopoietic control cases. Results All major subtypes of acute lymphoblastic leukemia, i.e. T-cell, MLL-rearranged, TEL-AML1- positive, E2A-PBX1-positive and hyperdiploid acute lymphoblastic leukemia, with the exception of BCR-ABL-positive and 'B-other' acute lymphoblastic leukemias (defined as precursor Bcell acute lymphoblastic leukemia not carrying the foregoing cytogenetic aberrations), were found to have unique microRNA-signatures that differed from each other and from those of healthy hematopoietic cells. Strikingly, the microRNA signature of TEL-AML1-positive and hyperdiploid cases partly overlapped, which may suggest a common underlying biology. Moreover, aberrant down-regulation of let-7b (~70-fold) in MLL-rearranged acute lymphoblastic leukemia was linked to up-regulation of oncoprotein c-Myc (PFDR<0.0001). Resistance to vincristine and daunorubicin was characterized by an approximately 20-fold up-regulation of miR- 125b, miR-99a and miR-100 (PFDR≤0.002). No discriminative microRNA were found for prednisolone response and only one microRNA was linked to resistance to L-asparaginase. A combined expression profile based on 14 microRNA that were individually associated with prognosis, was highly predictive of clinical outcome in pediatric acute lymphoblastic leukemia (5- year disease-free survival of 89.4%±7% versus 60.8±12%, P=0.001). Conclusions Genetic subtypes and drug-resistant leukemic cells display characteristic microRNA signatures in pediatric acute lymphoblastic leukemia. Functional studies of discriminative and prognostically important microRNA may provide new insights into the biology of pediatric acute lymphoblastic leukemia.

Evaluation of gene expression signatures predictive of cytogenetic and molecular subtypes of pediatric acute myeloid leukemia (Article)

2011-02-01T00:00:00Z

Background Pediatric acute myeloid leukemia is a heterogeneous disease characterized by non-random genetic aberrations related to outcome. The genetic subtype is currently detected by different diagnostic procedures which differ in success rate and/or specificity. Design and Methods We examined the potential of gene expression profiles to classify pediatric acute myeloid leukemia. Gene expression microarray data of 237 children with acute myeloid leukemia were collected and a double-loop cross validation approach was used to generate a subtype-predictive gene expression profile in the discovery cohort (n=157) which was then tested for its true predictive value in the independent validation cohort (n=80). The classifier consisted of 75 probe sets, representing the top 15 discriminating probe sets for MLL-rearranged, t(8;21)(q22;q22), inv(16)(p13q22), t(15;17)(q21;q22) and t(7;12)(q36;p13)-positive acute myeloid leukemia. Results These cytogenetic subtypes represent approximately 40% of cases of pediatric acute myeloid leukemia and were predicted with 92% and 99% accuracy in the discovery and independent validation cohort, respectively. However, for NPM1, CEBPA, MLL(-PTD), FLT3(-ITD), KIT, PTPN11 and N/K-RAS gene expression signatures had limited predictive value. This may be caused by a limited frequency of these mutations and by underlying cytogenetics. This latter is exemplified by the fact that different gene expression signatures were discovered for FLT3-ITD in patients with normal cytogenetics and in those with t(15;17)(q21;q22)-positive acute myeloid leukemia, which pointed to HOXB-upregulation being specific for FLT3-ITD+ cytogenetically normal acute myeloid leukemia. Conclusions In conclusion, gene expression profiling correctly predicted the most prevalent cytogenetic subtypes of pediatric acute myeloid leukemia with high accuracy. In clinical practice, this gene expression signature may replace multiple diagnostic tests for approximately 40% of pediatric acute myeloid leukemia cases whereas only for the remaining cases (predicted as 'acute myeloid leukemia-other') are additional tests indicated. Moreover, the discriminative genes reveal new insights into the biology of acute myeloid leukemia subtypes that warrants followup as potential targets for new therapies.

Hypermethylation of specific microRNA genes in MLL-rearranged infant acute lymphoblastic leukemia: major matters at a micro scale (Article)

2010-11-30T00:00:00Z

MLL-rearranged acute lymphoblastic leukemia (ALL) in infants (<1 year) is the most aggressive type of childhood leukemia. To develop more suitable treatment strategies, a firm understanding of the biology underlying this disease is of utmost importance. MLL-rearranged ALL displays a unique gene expression profile, partly explained by erroneous histone modifications. We recently showed that t(4;11)-positive infant ALL is also characterized by pronounced promoter CpG hypermethylation. In this study, we investigated whether this widespread hypermethylation also affected microRNA (miRNA) expression. We identified 11 miRNAs that were downregulated in t(4;11)-positive infant ALL as a consequence of CpG hypermethylation. Seven of these miRNAs were re-activated after exposure to the de-methylating agent Zebularine. Interestingly, five of these miRNAs are associated either with MLL or MLL fusions, and for miR-152 we found both MLL and DNA methyltransferase 1 (DNMT1) as potential targeted genes. Finally, a high degree of methylation of the miR-152 CpG island was strongly correlated with a poor clinical outcome. Our data suggests that inhibitors of methylation have a potential beyond re-expression of hypermethylated protein-coding genes in t(4;11)-positive infant ALL. In this study, we provide additional evidence that they should be tested for their efficacy in MLL-rearranged infant ALL in in vivo models.Leukemia advance online publication, 30 November 2010; doi:10.1038/leu.2010.282.

Filtering, FDR and power (Article)

2010-09-07T00:00:00Z

Background: In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.Results: We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup.Conclusions: Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice.

Gene expression profiling-based dissection of MLL translocated and MLL germline acute lymphoblastic leukemia in infants (Article)

2010-04-08T00:00:00Z

Acute lymphoblastic leukemia (ALL) in infants (< 1 year) is characterized by a poor prognosis and a high incidence of MLL translocations. Several studies demonstrated the unique gene expression profile associated with MLL-rearranged ALL, but generally small cohorts were analyzed as uniform patient groups regardless of the type of MLL translocation, whereas the analysis of translocationnegative infant ALL remained unacknowledged. Here we generated and analyzed primary infant ALL expression profiles (n = 73) typified by translocations t(4;11), t(11;19), and t(9;11), or the absence of MLL translocations. Our data show that MLL germline infant ALL specifies a gene expression pattern that is different from both MLL-rearranged infantALL and pediatric precursor B-ALL. Moreover, we demonstrate that, apart from a fundamental signature shared by all MLL-rearranged infant ALL samples, each type of MLL translocation is associated with a translocation-specific gene expression signature. Finally, we show the existence of 2 distinct subgroups among t(4;11)-positive infant ALL cases characterized by the absence or presence of HOXA expression, and that patients lacking HOXA expression are at extreme high risk of disease relapse. These gene expression profiles should provide important novel insights in the complex biology of MLL-rearranged infant ALL and boost our progress in finding novel therapeutic solutions.

Specific promoter methylation identifies different subgroups of MLL-rearranged infant acute lymphoblastic leukemia, influences clinical outcome, and provides therapeutic options (Article)

2009-12-24T00:00:00Z

MLL-rearranged infant acute lymphoblastic leukemia (ALL) remains the most aggressive type of childhood leukemia, displaying a unique gene expression profile. Here we hypothesized that this characteristic gene expression signature may have been established by potentially reversible epigenetic modifications. To test this hypothesis, we used differential methylation hybridization to explore the DNA methylation patterns underlying MLL-rearranged ALL in infants. The obtained results were correlated with gene expression data to confirm gene silencing as a result of promoter hypermethylation. Distinct promoter CpG island methylation patterns separated different genetic subtypes of MLL-rearranged ALL in infants. MLL translocations t(4;11) and t(11;19) characterized extensively hypermethylated leukemias, whereas t(9;11)-positive infant ALL and infant ALL carrying wild-type MLL genes epigenetically resembled normal bone marrow. Furthermore, the degree of promoter hypermethylation among infant ALL patients carrying t(4; 11) or t(11;19) appeared to influence relapse-free survival, with patients displaying accentuated methylation being at high relapse risk. Finally, we show that the demethylating agent zebularine reverses aberrant DNA methylation and effectively induces apoptosis in MLL-rearranged ALL cells. Collectively these data suggest that aberrant DNA methylation occurs in the majority of MLL-rearranged infant ALL cases and guides clinical outcome. Therefore, inhibition of aberrant DNA methylation may be an important novel therapeutic strategy for MLL-rearranged ALL in infants.

Relative power and sample size analysis on gene expression profiling data (Article)

2009-09-17T00:00:00Z

Background: With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental. Results: Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPARα agonists were administered to wild-type and PPARα-null mice. Our analysis confirms the hierarchy of PPARα-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal δC-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms. Conclusion: Power and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA http://bioconductor.org/packages/release/bioc/html/SSPA.html.

Integrated analysis of DNA copy number and gene expression microarray data using gene sets (Article)

2009-06-29T00:00:00Z

Background: Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes. Results: We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes. Conclusion: We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation.

A subtype of childhood acute lymphoblastic leukaemia with poor treatment outcome: a genome-wide classification study (Article)

2009-02-01T00:00:00Z

Background: Genetic subtypes of acute lymphoblastic leukaemia (ALL) are used to determine risk and treatment in children. 25% of precursor B-ALL cases are genetically unclassified and have intermediate prognosis. We aimed to use a genome-wide study to improve prognostic classification of ALL in children. Methods: We constructed a classifier based on gene expression in 190 children with newly diagnosed ALL (German Cooperative ALL [COALL] discovery cohort) by use of double-loop cross-validation and validated this in an independent cohort of 107 newly diagnosed patients (Dutch Childhood Oncology Group [DCOG] independent validation cohort). Hierarchical cluster analysis with classifying gene-probe sets revealed a new ALL subtype, the underlying genetic abnormalities of which were characterised by comparative genomic hybridisation-arrays and molecular cytogenetics. Findings: Our classifier predicted ALL subtype with a median accuracy of 90·0% (IQR 88·3-91·7) in the discovery cohort and correctly identified 94 of 107 patients (accuracy 87·9%) in the independent validation cohort. Without our classifier, 44 children in the COALL cohort and 33 children in the DCOG cohort would have been classified as B-other. However, hierarchical clustering showed that many of these genetically unclassified cases clustered with BCR-ABL1-positive cases: 30 (19%) of 154 children with precursor B-ALL in the COALL cohort and 14 (15%) of 92 children with precursor B-ALL in the DCOG cohort had this BCR-ABL1-like disease. In the COALL cohort, these patients had unfavourable outcome (5-year disease-free survival 59·5%, 95% CI 37·1-81·9) compared with patients with other precursor B-ALL (84·4%, 76·8-92·1%; p=0·012), a prognosis similar to that of patients with BCR-ABL1-positive ALL (51·9%, 23·1-80·6%). In the DCOG cohort, the prognosis of BCR-ABL1-like disease (57·1%, 31·2-83·1%) was worse than that of other precursor B-ALL (79·2%, 70·2-88·3%; p=0.026), and similar to that of BCR-ABL1-positive ALL (32·5%, 2·3-62·7%). 36 (82%) of the patients with BCR-ABL1-like disease had deletions in genes involved in B-cell development, including IKZF1, TCF3, EBF1, PAX5, and VPREB1; only nine (36%) of 25 patients with B-other ALL had deletions in these genes (p=0·0002). Compared with other precursor B-ALL cells, BCR-ABL1-like cells were 73 times more resistant to L-asparaginase (p=0·001) and 1·6 times more resistant to daunorubicin (p=0·017), but toxicity of prednisolone and vincristine did not differ. Interpretation: New treatment strategies are needed to improve outcome for this newly identified high-risk subtype of ALL. Funding: Dutch Cancer Society, Sophia Foundation for Medical Research, Paediatric Oncology Foundation Rotterdam, Centre of Medical Systems Biology of the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research, American National Institute of Health, American National Cancer Institute, and American Lebanese Syrian Associated Charities.

Can subtle changes in gene expression be consistently detected with different microarray platforms? (Article)

2008-03-10T00:00:00Z

Background: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Results: Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. Conclusion: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.

Variation of CNV distribution in five different ethnic populations (Article)

2007-09-01T00:00:00Z

Recent studies have revealed a new type of variation in the human genome encompassing relatively large genomic segments (∼100 kb-2.5 Mb), commonly referred to as copy number variation (CNV). The full nature and extent of CNV and its frequency in different ethnic populations is still largely unknown. In this study we surveyed a set of 12 CNVs previously detected by array-CGH. More than 300 individuals from five different ethnic populations, including three distinct European, one Asian and one African population, were tested for the occurrence of CNV using multiplex ligation-dependent probe amplification (MLPA). Seven of these loci indeed showed CNV, i.e., showed copy numbers that deviated from the population median. More precise estimations of the actual genomic copy numbers for (part of) the NSF gene locus, revealed copy numbers ranging from two to at least seven. Additionally, significant inter-population differences in the distribution of these copy numbers were observed. These data suggest that insight into absolute DNA copy numbers for loci exhibiting CNV is required to determine their potential contribution to normal phenotypic variation and, in addition, disease susceptibility. Copyright

Genomewide identification of prednisolone-responsive genes in acute lymphoblastic leukemia cells (Article)

2007-05-01T00:00:00Z

Glucocorticoids are keystone drugs in the treatment of childhood acute lymphoblastic leukemia (ALL). To get more insight in signal transduction pathways involved in glucocorticoid-induced apoptosis, Affymetrix U133A GeneChips were used to identify transcriptionally regulated genes on 3 and 8 hours of prednisolone exposure in leukemic cells of 13 children as compared with nonexposed cells. Following 3 hours of exposure no significant changes in gene expression could be identified. Following 8 hours of exposure, 51 genes were differentially expressed (P < .001 and false discovery rate < 10%) with 39 genes being up-regulated (median, 2.4-fold) and 12 genes were downregulated (median, 1.7-fold). Twenty-one of those genes have not been identified before to be transcriptionally regulated by prednisolone. Two of the 3 most highly up-regulated genes were tumor suppressor genes, that is, thioredoxin-interacting protein (TXNIP; 3.7-fold) and zinc finger and BTB domain containing 16 (ZBTB16; 8.8-fold). About 50% of the differentially expressed genes were functionally categorized in 3 major routes, namely MAPK pathways (9 genes), NF-κB signaling (11 genes), and carbohydrate metabolism (5 genes). Biologic characterization of these genes and pathways might elucidate the action of glucocorticoids in ALL cells, possibly suggesting causes of glucocorticoid resistance and new potential targets for therapy.

Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH. (Article)

2004-01-01T00:00:00Z

Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, phi29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells.