Identification of genes with abnormal expression changes in acute myeloid leukemia

Differential fucosyltransferase IV expression in squamous carcinoma cells is regulated by promoter methylation

Enhanced fucosyltransferase IV (FUT4) expression correlates with increased tumor malignancy in many carcinomas. However, little is known about the regulation of FUT4 expression, and whether FUT4 expression is influenced by the methylation status of the FUT4 promoter is unclear. In this study, we demonstrated that FUT4 expression is negatively correlated with the methylation degree of a CpG island in the FUT4 promoter, suggesting that the methylation status of FUT4 promoter regulates the expression of FUT4. The results indicate that manipulating the methylation status of the FUT4 promoter to regulate FUT4 expression may be a novel approach in the treatment of malignant tumors.

Empirical evidence of the applicability of functional clustering through gene expression classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.

Cyclin-A1 represents a new immunogenic targetable antigen expressed in acute myeloid leukemia stem cells with characteristics of a cancer-testis antigen

Targeted T-cell therapy is a potentially less toxic strategy than allogeneic stem cell transplantation for providing a cytotoxic antileukemic response to eliminate leukemic stem cells (LSCs) in acute myeloid leukemia (AML). However, this strategy requires identification of leukemia-associated antigens that are immunogenic and exhibit selective high expression in AML LSCs. Using microarray expression analysis of LSCs, hematopoietic cell subpopulations, and peripheral tissues to screen for candidate antigens, cyclin-A1 was identified as a candidate gene. Cyclin-A1 promotes cell proliferation and survival, has been shown to be leukemogenic in mice, is detected in LSCs of more than 50% of AML patients, and is minimally expressed in normal tissues with exception of testis. Using dendritic cells pulsed with a cyclin-A1 peptide library, we generated T cells against several cyclin-A1 oligopeptides. Two HLA A*0201-restricted epitopes were further characterized, and specific CD8 T-cell clones recognized both peptide-pulsed target cells and the HLA A*0201-positive AML line THP-1, which expresses cyclin-A1. Furthermore, cyclin-A1-specific CD8 T cells lysed primary AML cells. Thus, cyclin-A1 is the first prototypic leukemia-testis-antigen to be expressed in AML LSCs. The pro-oncogenic activity, high expression levels, and multitude of immunogenic epitopes make it a viable target for pursuing T cell-based therapy approaches.

A topology-based score for pathway enrichment

Investigators require intuitive tools to rationalize complex datasets generated by transcriptional profiling experiments. Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways to facilitate assessment of relative enrichment, lead investigators more effectively to biologically testable hypotheses. However, once a set of differentially expressed genes is isolated, pathway analysis approaches tend to ignore rich gene expression information and, moreover, do not exploit relationships between transcripts. In this article, we report the development of a new method in which both pathway topology and the magnitude of gene expression changes inform the scoring system, thereby providing a powerful filter in the enrichment of biologically relevant information. When four sample datasets were evaluated with this method, literature mining confirmed that those pathways germane to the physiological process under investigation were highlighted by our method relative to z-score overrepresentation calculations. Moreover, non-relevant processes were downgraded using the method described herein. The inclusion of expression and topological data in the calculation of a pathway regulation score (PRS) facilitated discrimination of key processes in real biological datasets. Specifically, by combining fold-change data for those transcripts exceeding a significance threshold, and by taking into account the potential for altered gene expression to impact upon downstream transcription, one may readily identify those pathways most relevant to pathophysiological processes.

p53-Independent, normal stem cell sparing epigenetic differentiation therapy for myeloid and other malignancies

Cytotoxic chemotherapy for acute myeloid leukemia (AML) usually produces only temporary remissions, at the cost of significant toxicity and risk for death. One fundamental reason for treatment failure is that it is designed to activate apoptosis genes (eg, TP53) that may be unavailable because of mutation or deletion. Unlike deletion of apoptosis genes, genes that mediate cell cycle exit by differentiation are present in myelodysplastic syndrome (MDS) and AML cells but are epigenetically repressed: MDS/AML cells express high levels of key lineage-specifying transcription factors. Mutations in these transcription factors (eg, CEBPA) or their cofactors (eg., RUNX1) affect transactivation function and produce epigenetic repression of late-differentiation genes that antagonize MYC. Importantly, this aberrant epigenetic repression can be redressed clinically by depleting DNA methyltransferase 1 (DNMT1, a central component of the epigenetic network that mediates transcription repression) using the deoxycytidine analogue decitabine at non-cytotoxic concentrations. The DNMT1 depletion is sufficient to trigger upregulation of late-differentiation genes and irreversible cell cycle exit by p53-independent differentiation mechanisms. Fortuitously, the same treatment maintains or increases self-renewal of normal hematopoietic stem cells, which do not express high levels of lineage-specifying transcription factors. The biological rationale for this approach to therapy appears to apply to cancers other than MDS/AML also. Decitabine or 5-azacytidine dose and schedule can be rationalized to emphasize this mechanism of action, as an alternative or complement to conventional apoptosis-based oncotherapy.

Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data

MOTIVATION

Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome.

RESULTS

We developed a method that integrates gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging. Applying our integrated method to CML, we identified small sets of signature genes that are highly predictive of disease phases and that are more robust and stable than using expression data alone. The accuracy of our algorithm was evaluated using cross-validation on the gene expression data. We then tested the hypothesis that gene sets associated with advanced phase CML would predict relapse after allogeneic transplantation in 176 independent CP CML cases. Our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes.

A MULTI-STRATEGY APPROACH TO INFORMATIVE GENE IDENTIFICATION FROM GENE EXPRESSION DATA

An unsupervised multi-strategy approach has been developed to identify informative genes from high throughput genomic data. Several statistical methods have been used in the field to identify differentially expressed genes. Since different methods generate different lists of genes, it is very challenging to determine the most reliable gene list and the appropriate method. This paper presents a multi-strategy method, in which a combination of several data analysis techniques are applied to a given dataset and a confidence measure is established to select genes from the gene lists generated by these techniques to form the core of our final selection. The remainder of the genes that form the peripheral region are subject to exclusion or inclusion into the final selection. This paper demonstrates this methodology through its application to an in-house cancer genomics dataset and a public dataset. The results indicate that our method provides more reliable list of genes, which are validated using biological knowledge, biological experiments, and literature search. We further evaluated our multi-strategy method by consolidating two pairs of independent datasets, each pair is for the same disease, but generated by different labs using different platforms. The results showed that our method has produced far better results.

Myc roles in hematopoiesis and leukemia

Hematopoiesis is a process capable of generating millions of cells every second, as distributed in many cell types. The process is regulated by a number of transcription factors that regulate the differentiation along the distinct lineages and dictate the genetic program that defines each mature phenotype. Myc was first discovered as the oncogene of avian leukemogenic retroviruses; it was later found translocated in human lymphoma. From then on, evidence accumulated showing that c-Myc is one of the transcription factors playing a major role in hematopoiesis. The study of genetically modified mice with overexpression or deletion of Myc has shown that c-Myc is required for the correct balance between self-renewal and differentiation of hematopoietic stem cells (HSCs). Enforced Myc expression in mice leads to reduced HSC pools owing to loss of self-renewal activity at the expense of increased proliferation of progenitor cells and differentiation. c-Myc deficiency consistently results in the accumulation of HSCs. Other models with conditional Myc deletion have demonstrated that different lineages of hematopoietic cells differ in their requirement for c-Myc to regulate their proliferation and differentiation. When transgenic mice overexpress c-Myc or N-Myc in mature cells from the lymphoid or myeloid lineages, the result is lymphoma or leukemia. In agreement, enforced expression of c-Myc blocks the differentiation in several leukemia-derived cell lines capable of differentiating in culture. Not surprising, MYC deregulation is recurrently found in many types of human lymphoma and leukemia. Whereas MYC is deregulated by translocation in Burkitt lymphoma and, less frequently, other types of lymphoma, MYC is frequently overexpressed in acute lymphoblastic and myeloid leukemia, through mechanisms unrelated to chromosomal translocation, and is often associated with disease progression.

CpG methylation patterns and decitabine treatment response in acute myeloid leukemia cells and normal hematopoietic precursors

The DNA hypomethylating drug decitabine maintains normal hematopoietic stem cell (HSC) self-renewal but induces terminal differentiation in acute myeloid leukemia (AML) cells. The basis for these contrasting cell-fates, and for selective CpG hypomethylation by decitabine, is poorly understood. Promoter CpGs, with methylation measured by microarray, were classified by the direction of methylation change with normal myeloid maturation. In AML cells, the methylation pattern at maturation-responsive CpG suggested at least partial maturation. Consistent with partial maturation, in gene expression analyses, AML cells expressed high levels of the key lineage-specifying factor CEBPA, but relatively low levels of the key late-differentiation driver CEBPE. In methylation analysis by mass-spectrometry, CEBPE promoter CpG that are usually hypomethylated during granulocyte maturation were significantly hypermethylated in AML cells. Decitabine treatment induced cellular differentiation of AML cells, and the largest methylation decreases were at CpG that are hypomethylated with myeloid maturation, including CEBPE promoter CpG. In contrast, decitabine-treated normal HSC retained immature morphology, and methylation significantly decreased at CpG that are less methylated in immature cells. High expression of lineage-specifying factor and aberrant epigenetic repression of some key late-differentiation genes distinguishes AML cells from normal HSC and could explain the contrasting differentiation and methylation responses to decitabine.

Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data

Background

Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.

Methods

AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database).

Results

Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (SAMD4A) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation.

Conclusions

The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.

Southwest Oncology Group Study S0530: a phase 2 trial of clofarabine and cytarabine for relapsed or refractory acute lymphocytic leukaemia

Clofarabine and cytarabine target different steps in DNA synthesis and replication, are synergistic in vivo, and have non-overlapping toxicities, making this combination a potentially promising treatment for acute lymphocytic leukaemia. Thirty-seven patients were treated. The median age was 41 years, 44% of patients were either in ≥2nd relapse or had refractory disease and 59% of patients had poor risk cytogenetics. Six out of 36 patients (17%) achieved a complete remission with or without complete count recovery; median overall survival was 3 months. Nucleoside transporter expression did not predict outcome. This regimen lacked sufficient activity to warrant further testing.

Regulation patterns in signaling networks of cancer

Background

Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample.

Results

We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells.

Conclusion

Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed.

Identification of radiation-induced expression changes in nonimmortalized human T cells

In the event of a radiation accident or attack, it will be imperative to quickly assess the amount of radiation exposure to accurately triage victims for appropriate care. RNA-based radiation dosimetry assays offer the potential to rapidly screen thousands of individuals in an efficient and cost-effective manner. However, prior to the development of these assays, it will be critical to identify those genes that will be most useful to delineate different radiation doses. Using global expression profiling, we examined expression changes in nonimmortalized T cells across a wide range of doses (0.15-12 Gy). Because many radiation responses are highly dependent on time, expression changes were examined at three different times (3, 8, and 24 h). Analyses identified 61, 512 and 1310 genes with significant linear dose-dependent expression changes at 3, 8 and 24 h, respectively. Using a stepwise regression procedure, a model was developed to estimate in vitro radiation exposures using the expression of three genes (CDKN1A, PSRC1 and TNFSF4) and validated in an independent test set with 86% accuracy. These findings suggest that RNA-based expression assays for a small subset of genes can be employed to develop clinical biodosimetry assays to be used in assessments of radiation exposure and toxicity.

Proteomic cell surface phenotyping of differentiating acute myeloid leukemia cells

Immunophenotyping by flow cytometry or immunohistochemistry is a clinical standard procedure for diagnosis, classification, and monitoring of hematologic malignancies. Antibody-based cell surface phenotyping is commonly limited to cell surface proteins for which specific antibodies are available and the number of parallel measurements is limited. The resulting limited knowledge about cell surface protein markers hampers early clinical diagnosis and subclassification of hematologic malignancies. Here, we describe the mass spectrometry based phenotyping of 2 all-trans retinoic acid treated acute myeloid leukemia model systems at an unprecedented level to a depth of more than 500 membrane proteins, including 137 bona fide cell surface exposed CD proteins. This extensive view of the leukemia surface proteome was achieved by developing and applying new implementations of the Cell Surface Capturing (CSC) technology. Bioinformatic and hierarchical cluster analysis showed that the applied strategy reliably revealed known differentiation-induced abundance changes of cell surface proteins in HL60 and NB4 cells and it also identified cell surface proteins with very little prior information. The extensive and quantitative analysis of the cell surface protein landscape from a systems biology perspective will be most useful in the clinic for the improved subclassification of hematologic malignancies and the identification of new drug targets.

Analysis of microRNA transcriptome by deep sequencing of small RNA libraries of peripheral blood

Background

MicroRNAs are a class of small non-coding RNAs that regulate mRNA expression at the post - transcriptional level and thereby many fundamental biological processes. A number of methods, such as multiplex polymerase chain reaction, microarrays have been developed for profiling levels of known miRNAs. These methods lack the ability to identify novel miRNAs and accurately determine expression at a range of concentrations. Deep or massively parallel sequencing methods are providing suitable platforms for genome wide transcriptome analysis and have the ability to identify novel transcripts.

Results

The results of analysis of small RNA sequences obtained by Solexa technology of normal peripheral blood mononuclear cells, tumor cell lines K562 and HL60 are presented. In general K562 cells displayed overall low level of miRNA population and also low levels of DICER. Some of the highly expressed miRNAs in the leukocytes include several members of the let-7 family, miR-21, 103, 185, 191 and 320a. Comparison of the miRNA profiles of normal versus K562 or HL60 cells revealed a specific set of differentially expressed molecules. Correlation of the miRNA with that of mRNA expression profiles, obtained by microarray, revealed a set of target genes showing inverse correlation with miRNA levels. Relative expression levels of individual miRNAs belonging to a cluster were found to be highly variable. Our computational pipeline also predicted a number of novel miRNAs. Some of the predictions were validated by Real-time RT-PCR and or RNase protection assay. Organization of some of the novel miRNAs in human genome suggests that these may also be part of existing clusters or form new clusters.

Conclusions

We conclude that about 904 miRNAs are expressed in human leukocytes. Out of these 370 are novel miRNAs. We have identified miRNAs that are differentially regulated in normal PBMC with respect to cancer cells, K562 and HL60. Our results suggest that post - transcriptional processes may play a significant role in regulating levels of miRNAs in tumor cells. The study also provides a customized automated computation pipeline for miRNA profiling and identification of novel miRNAs; even those that are missed out by other existing pipelines. The Computational Pipeline is available at the website: http://mirna.jnu.ac.in/deep_sequencing/deep_sequencing.html

A differentiation-based phylogeny of cancer subtypes

Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors.

Gene expression profiling of malignancies is often held to demonstrate genes that are “up-regulated” or “down-regulated”, but the appropriate frame of reference against which observations should be compared has not been determined. Fully differentiated somatic cells arise from stem cells, with changes in gene expression that can be experimentally determined. If cancers arise as the result of an abruption of the differentiation process, then poorly differentiated cancers would have a gene expression more similar to stem cells than to normal differentiated tissue, and well differentiated cancers would have a gene expression more similar to fully differentiated cells than to stem cells. In this paper, we describe a novel computational algorithm that allows orientation of cancer gene expression between the poles of the gene expression of stem cells and of fully differentiated tissue. Our methodology allows the construction of a multi-branched phylogeny of human malignancies and can be used to identify genes related to differentiation as well as novel therapeutic targets.

Correlation between array-comparative genomic hybridization-defined genomic gains and losses and survival: identification of 1p31-32 deletion as a prognostic factor in myeloma

In this study, we correlated array-comparative genomic hybridization-defined abnormalities with survival in two different cohorts of patients treated with therapy based on high-dose melphalan with autologous stem-cell transplantation (64 from the Mayo Clinic and 67 from the University of Arkansas Medical School) and identified that several regions of genomic gains and losses were significantly associated with poorer survival. Three noncontiguous survival relevant regions covering 1p31-33 and two noncontiguous regions covering 20p12.3-12.1 were common between the two datasets. The prognostic relevance of these hotspots was validated in an independent cohort using fluorescent in situ hybridization, which showed that 1p31-32 loss is significantly associated with shorter survival (24.5 months versus 40 months, log-rank P-value=0.01), whereas 20p12 loss has a trend toward shorter survival (26.3 months versus 40 months, log-rank P-value=0.06). On multivariate analysis, 1p31-32 loss is an independent prognostic factor. On further analysis, the prognostic impact of 1p31-32 loss is due to shortening of post-relapse survival as there is no impact on complete response rates and progression-free survival.

Integrative meta-analysis of differential gene expression in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a heterogeneous disease with an overall poor prognosis. Gene expression profiling studies of patients with AML has provided key insights into disease pathogenesis while exposing potential diagnostic and prognostic markers and therapeutic targets. A systematic comparison of the large body of gene expression profiling studies in AML has the potential to test the extensibility of conclusions based on single studies and provide further insights into AML.

Methodology/Principal Findings

In this study, we systematically compared 25 published reports of gene expression profiling in AML. There were a total of 4,918 reported genes of which one third were reported in more than one study. We found that only a minority of reported prognostically-associated genes (9.6%) were replicated in at least one other study. In a combined analysis, we comprehensively identified both gene sets and functional gene categories and pathways that exhibited significant differential regulation in distinct prognostic categories, including many previously unreported associations.

Conclusions/Significance

We developed a novel approach for granular, cross-study analysis of gene-by-gene data and their relationships with established prognostic features and patient outcome. We identified many robust novel prognostic molecular features in AML that were undetected in prior studies, and which provide insights into AML pathogenesis with potential diagnostic, prognostic, and therapeutic implications. Our database and integrative analysis are available online (http://gat.stamlab.org).

Tribbles-1 and -2 are tumour suppressors, down-regulated in human acute myeloid leukaemia

Constitutive MAPK signalling is observed in approximately 50% of acute myeloid leukaemia (AML) cases. JNK activation in particular is associated with treatment failure in AML. Tribbles proteins (trb-1, trb-2 and trb-3) are potent negative regulators of MAPK pathways influencing apoptosis, differentiation and cell-cycle progression. Here we aimed to examine tribbles gene expression in AML and to characterise their role in leukaemic cells. A microarray dataset was interrogated for tribbles expression levels in AML cases and healthy controls. Myeloid cell proliferation and apoptosis were assayed in response to trb-1/trb-2 gene knockdown and overexpression, as well as a physical and functional interaction between trb and C/EBPalpha. Trb-2 expression was reduced in AML compared to healthy controls (correlating with nucleophosmin (NPM1) mutations), while low trb-1 expression was associated with inactive C/EBPalpha. In vitro assays indicated that trb-1/trb-2 are growth restrictive and pro-apoptotic in Me-1 cells, each capable of inhibiting JNK activation. JNK inactivation was itself associated with reduced Bcl-2 Ser70 phosphorylation, a residue which, when phosphorylated, maintains the anti-apoptotic activity of Bcl-2. Consistent with this, tribbles-mediated dephosphorylation of Bcl-2 Ser70 was associated with subsequent apoptosis. Trb-1/trb-2 transcription appeared to be moderately C/EBPalpha-responsive, and physical interaction between C/EBPalpha and trb-1/trb-2 was observed, suggesting a potential for auto-regulation of trb-1 and trb-2 transcription. In conclusion, we propose that trb-1 and trb-2 tumour suppressor activity may be abrogated in a proportion of AML patients. This may lead to enhanced cell survival, and therefore contribute to pathogenesis of the disease. Trb-1/trb-2 may, therefore, represent useful therapeutic targets for the treatment of AML in patients with dys-regulated trb activity.

Gene expression changes in normal haematopoietic cells

The complexity of the healthy haematopoietic system is immense, and as such, one must understand the biology driving normal haematopoietic expression profiles when designing experiments and interpreting expression data that involve normal cells. This article seeks to present an organised approach to the use and interpretation of gene profiling in normal haematopoiesis and broadly illustrates the challenges of selecting appropriate controls for high-throughput expression studies.

Dysregulated gene expression networks in human acute myelogenous leukemia stem cells

We performed the first genome-wide expression analysis directly comparing the expression profile of highly enriched normal human hematopoietic stem cells (HSC) and leukemic stem cells (LSC) from patients with acute myeloid leukemia (AML). Comparing the expression signature of normal HSC to that of LSC, we identified 3,005 differentially expressed genes. Using 2 independent analyses, we identified multiple pathways that are aberrantly regulated in leukemic stem cells compared with normal HSC. Several pathways, including Wnt signaling, MAP Kinase signaling, and Adherens Junction, are well known for their role in cancer development and stem cell biology. Other pathways have not been previously implicated in the regulation of cancer stem cell functions, including Ribosome and T Cell Receptor Signaling pathway. This study demonstrates that combining global gene expression analysis with detailed annotated pathway resources applied to highly enriched normal and malignant stem cell populations, can yield an understanding of the critical pathways regulating cancer stem cells.

The role of the Wilms' tumour-suppressor protein WT1 in apoptosis

The Wilms' tumour-suppressor gene (WT1), encodes a zinc-finger transcription factor that is critical for the development of several organs, including the kidneys, gonads and spleen. Despite its identification as a tumour suppressor that plays a crucial role in the formation of a paediatric malignancy of the kidneys (Wilms' tumour), it has also emerged as an oncogenic factor influencing proliferation and apoptosis in a large variety of adult cancers. This review focuses on new insights into WT1's role in early development and its potential oncogenic role in adult cancer.