Silencing of TESTIN by dense biallelic promoter methylation is the most common molecular event in childhood acute lymphoblastic leukaemia

<p>Abstract</p> <p>Background</p> <p>Aberrant promoter DNA methylation has been reported in childhood acute lymphoblastic leukaemia (ALL) and has the potential to contribute to its onset and outcome. However, few reports demonstrate consistent, prevalent and dense promoter methylation, associated with tumour-specific gene silencing. By screening candidate genes, we have detected frequent and dense methylation of the <it>TESTIN </it>(<it>TES</it>) promoter.</p> <p>Results</p> <p>Bisulfite sequencing showed that 100% of the ALL samples (n = 20) were methylated at the <it>TES </it>promoter, whereas the matched remission (n = 5), normal bone marrow (n = 6) and normal PBL (n = 5) samples were unmethylated. Expression of <it>TES </it>in hyperdiploid, TEL-AML⁺, BCR-ABL⁺, and E2A-PBX⁺subtypes of B lineage ALL was markedly reduced compared to that in normal bone marrow progenitor cells and in B cells. In addition <it>TES </it>methylation and silencing was demonstrated in nine out of ten independent B ALL propagated as xenografts in NOD/SCID mice.</p> <p>Conclusion</p> <p>In total, 93% of B ALL samples (93 of 100) demonstrated methylation with silencing or reduced expression of the <it>TES </it>gene. Thus, <it>TES </it>is the most frequently methylated and silenced gene yet reported in ALL. <it>TES</it>, a LIM domain-containing tumour suppressor gene and component of the focal adhesion complex, is involved in adhesion, motility, cell-to-cell interactions and cell signalling. Our data implicate <it>TES </it>methylation in ALL and provide additional evidence for the involvement of LIM domain proteins in leukaemogenesis.</p

Transcriptional regulation of the human ALDH1A1 promoter by the oncogenic homeoprotein TLX1/HOX11

The homeoprotein TLX1, which is essential to spleen organogenesis and oncogenic when aberrantly expressed in immature T cells, functions as a bifunctional transcriptional regulator, being capable of activation or repression depending on cell type and/or promoter context. However, the detailed mechanisms by which it regulates the transcription of target genes such as ALDH1A1 remains to be elucidated. We therefore functionally assessed the ability of TLX1 to regulate ALDH1A1 expression in two hematopoietic cell lines, PER-117 T-leukemic cells and human erythroleukemic (HEL) cells, by use of luciferase reporter and mobility shift assays. We showed that TLX1 physically interacts with the general transcription factor TFIIB via its homeodomain, and identified two activities in respect to TLX1-mediated regulation of the CCAAT box-containing ALDH1A1 promoter. The first involved CCAAT-dependent transcriptional repression via perturbation of GATA factor-containing protein complexes assembled at a non-canonical TATA (GATA) box. A structurally intact homeodomain was essential for repression by TLX1 although direct DNA binding was not required. The second activity, which involved CCAAT-independent transcriptional activation did not require an intact homeodomain, indicating that the activation and repression functions of TLX1 are distinct. These findings confirm ALDH1A1 gene regulation by TLX1 and support an indirect model for TLX1 function, in which protein-protein interactions, rather than DNA binding at specific sites, are crucial for its transcriptional activity

Influence of wild-type MLL on glucocorticoid sensitivity and response to DNA-damage in pediatric acute lymphoblastic leukemia

<p>Abstract</p> <p>Background</p> <p>Rearrangement of the mixed-lineage leukemia gene (<it>MLL</it>) is found in 80% of infant acute lymphoblastic leukemia (ALL) and is associated with poor prognosis and resistance to glucocorticoids (GCs). We have recently observed that GC resistance in T-ALL cell lines is associated with a proliferative metabolism and reduced expression of <it>MLL</it>. In this study we have further explored the relationship between <it>MLL </it>status and GC sensitivity.</p> <p>Results</p> <p>Negative correlation of <it>MLL </it>expression with GC resistance in 15 T-ALL cell lines was confirmed by quantitative RT-PCR. The absence of <it>MLL</it>-rearrangements suggested that this relationship represented expression of wild-type <it>MLL</it>. Analysis of <it>MLL </it>expression patterns revealed a negative relationship with cellular metabolism, proliferation and anti-apoptotic transcriptional networks. <it>In silico </it>analysis of published data demonstrated that reduced levels of <it>MLL </it>mRNA are associated with relapse and prednisolone resistance in T-ALL patients and adverse clinical outcome in children with <it>MLL</it>-rearranged ALL. RNAi knockdown of <it>MLL </it>expression in T-ALL cell lines significantly increased resistance to dexamethasone and gamma irradiation indicating an important role for wild-type <it>MLL </it>in the control of cellular apoptosis.</p> <p>Conclusions</p> <p>The data suggests that reduced expression of wild-type <it>MLL </it>can contribute to GC resistance in ALL patients both with and without <it>MLL</it>-translocations.</p

Gene-based outcome prediction in multiple cohorts of pediatric T-cell acute lymphoblastic leukemia: a Children's Oncology Group study

<p>Abstract</p> <p>Background</p> <p>Continuous complete clinical remission in T-cell acute lymphoblastic leukemia (T-ALL) is now approaching 80% due to the implementation of aggressive chemotherapy protocols but patients that relapse continue to have a poor prognosis. Such patients could benefit from augmented therapy if their clinical outcome could be more accurately predicted at the time of diagnosis. Gene expression profiling offers the potential to identify additional prognostic markers but has had limited success in generating robust signatures that predict outcome across multiple patient cohorts. This study aimed to identify robust gene classifiers that could be used for the accurate prediction of relapse in independent cohorts and across different experimental platforms.</p> <p>Results</p> <p>Using HG-U133Plus2 microarrays we modeled a five-gene classifier (5-GC) that accurately predicted clinical outcome in a cohort of 50 T-ALL patients. The 5-GC was further tested against three independent cohorts of T-ALL patients, using either qRT-PCR or microarray gene expression, and could predict patients with significantly adverse clinical outcome in each. The 5-GC featured the interleukin-7 receptor (<it>IL-7R</it>), low-expression of which was independently predictive of relapse in T-ALL patients. In T-ALL cell lines, low <it>IL-7R </it>expression was correlated with diminished growth response to IL-7 and enhanced glucocorticoid resistance. Analysis of biological pathways identified the NF-κB and Wnt pathways, and the cell adhesion receptor family (particularly integrins) as being predictive of relapse. Outcome modeling using genes from these pathways identified patients with significantly worse relapse-free survival in each T-ALL cohort.</p> <p>Conclusions</p> <p>We have used two different approaches to identify, for the first time, robust gene signatures that can successfully discriminate relapse and CCR patients at the time of diagnosis across multiple patient cohorts and platforms. Such genes and pathways represent markers for improved patient risk stratification and potential targets for novel T-ALL therapies.</p

Validation of a mouse xenograft model system for gene expression analysis of human acute lymphoblastic leukaemia

<p>Abstract</p> <p>Background</p> <p>Pre-clinical models that effectively recapitulate human disease are critical for expanding our knowledge of cancer biology and drug resistance mechanisms. For haematological malignancies, the non-obese diabetic/severe combined immunodeficient (NOD/SCID) mouse is one of the most successful models to study paediatric acute lymphoblastic leukaemia (ALL). However, for this model to be effective for studying engraftment and therapy responses at the whole genome level, careful molecular characterisation is essential.</p> <p>Results</p> <p>Here, we sought to validate species-specific gene expression profiling in the high engraftment continuous ALL NOD/SCID xenograft. Using the human Affymetrix whole transcript platform we analysed transcriptional profiles from engrafted tissues without prior cell separation of mouse cells and found it to return highly reproducible profiles in xenografts from individual mice. The model was further tested with experimental mixtures of human and mouse cells, demonstrating that the presence of mouse cells does not significantly skew expression profiles when xenografts contain 90% or more human cells. In addition, we present a novel <it>in silico </it>and experimental masking approach to identify probes and transcript clusters susceptible to cross-species hybridisation.</p> <p>Conclusions</p> <p>We demonstrate species-specific transcriptional profiles can be obtained from xenografts when high levels of engraftment are achieved or with the application of transcript cluster masks. Importantly, this masking approach can be applied and adapted to other xenograft models where human tissue infiltration is lower. This model provides a powerful platform for identifying genes and pathways associated with ALL disease progression and response to therapy <it>in vivo</it>.</p

Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR – how well do they correlate?

BACKGROUND: The use of microarray technology to assess gene expression levels is now widespread in biology. The validation of microarray results using independent mRNA quantitation techniques remains a desirable element of any microarray experiment. To facilitate the comparison of microarray expression data between laboratories it is essential that validation methodologies be critically examined. We have assessed the correlation between expression scores obtained for 48 human genes using oligonucleotide microarrays and the expression levels for the same genes measured by quantitative real-time RT-PCR (qRT-PCR). RESULTS: Correlations with qRT-PCR data were obtained using microarray data that were processed using robust multi-array analysis (RMA) and the MAS 5.0 algorithm. Our results indicate that when identical transcripts are targeted by the two methods, correlations between qRT-PCR and microarray data are generally strong (r = 0.89). However, we observed poor correlations between qRT-PCR and RMA or MAS 5.0 normalized microarray data for 13% or 16% of genes, respectively. CONCLUSION: These results highlight the complementarity of oligonucleotide microarray and qRT-PCR technologies for validation of gene expression measurements, while emphasizing the continuing requirement for caution in interpreting gene expression data

Aberrant over-expression of a forkhead family member, FOXO1A, in a brain tumor cell line

<p>Abstract</p> <p>Background</p> <p>The mammalian FOXO (forkhead box, O subclass) proteins are a family of pleiotropic transcription factors involved in the regulation of a broad range of cellular processes critical for survival. Despite the essential and diverse roles of the FOXO family members in human cells and their involvement in tumor pathogenesis, the regulation of <it>FOXO </it>expression remains poorly understood. We have addressed the mechanisms underlying the high level of expression of the <it>FOXO1A </it>gene in a cell line, PER-453, derived from a primitive neuroectodermal tumor of the central nervous system (CNS-PNET).</p> <p>Methods</p> <p>The status of the <it>FOXO1A </it>locus in the PER-453 CNS-PNET cell line was investigated by Southern blotting and DNA sequence analysis of the proximal promoter, 5'-UTR, open reading frame and 3'-UTR. FOXO1A expression was assessed by conventional and quantitative RT-PCR, Northern and Western blotting.</p> <p>Results</p> <p>Quantitative real-time RT-PCR (qRT-PCR) data indicated that after normalization to <it>ACTB </it>mRNA levels, canonical <it>FOXO1A </it>mRNA expression in the PER-453 cell line was 124-fold higher than the average level of five other CNS-PNET cell lines tested, 24-fold higher than the level in whole fetal brain, and 3.5-fold higher than the level in fetal brain germinal matrix cells. No mutations within the <it>FOXO1A </it>open reading frame or gross rearrangements of the <it>FOXO1A </it>locus were detected. However, a single nucleotide change within the proximal promoter and several nucleotide changes within the 3'-UTR were identified. In addition, two novel <it>FOXO1A </it>transcripts were isolated that differ from the canonical transcript by alternative splicing within the 3'-UTR.</p> <p>Conclusion</p> <p>The CNS-PNET cell line, PER-453, expresses <it>FOXO1A </it>at very high levels relative to most normal and cancer cells from a broad range of tissues. The <it>FOXO1A </it>open reading frame is wild type in the PER-453 cell line and the abnormally high <it>FOXO1A </it>mRNA expression is not due to mutations affecting the 5'-UTR or proximal promoter. Over expression of <it>FOXO1A </it>may be the result of PER-453 specific epimutations or imbalances in regulatory factors acting at the promoter and/or 3'-UTR.</p

Translating microarray data for diagnostic testing in childhood leukaemia

BACKGROUND: Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF). METHODS: We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort. RESULTS: We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups. CONCLUSION: Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team

FusionFinder: A Software Tool to Identify Expressed Gene Fusion Candidates from RNA-Seq Data

The hallmarks of many haematological malignancies and solid tumours are chromosomal translocations, which may lead to gene fusions. Recently, next-generation sequencing techniques at the transcriptome level (RNA-Seq) have been used to verify known and discover novel transcribed gene fusions. We present FusionFinder, a Perl-based software designed to automate the discovery of candidate gene fusion partners from single-end (SE) or paired-end (PE) RNA-Seq read data. FusionFinder was applied to data from a previously published analysis of the K562 chronic myeloid leukaemia (CML) cell line. Using FusionFinder we successfully replicated the findings of this study and detected additional previously unreported fusion genes in their dataset, which were confirmed experimentally. These included two isoforms of a fusion involving the genes BRK1 and VHL, whose co-deletion has previously been associated with the prevalence and severity of renal-cell carcinoma. FusionFinder is made freely available for non-commercial use and can be downloaded from the project website (http://bioinformatics.childhealthresearch.org.au/software/fusionfinder/)