A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects

Chemotherapy resistance in acute myeloid leukemia is mediated by A20 suppression of spontaneous necroptosis

Acute myeloid leukemia (AML) is a deadly hematopoietic malignancy. Although many patients achieve complete remission with standard induction therapy, a combination of cytarabine and anthracycline, ~40% of patients have induction failure. These refractory patients pose a treatment challenge, as they do not respond to salvage therapy or allogeneic stem cell transplant. Herein, we show that AML patients who experience induction failure have elevated expression of the NF-κB target gene tumor necrosis factor alpha-induced protein-3 (TNFAIP3/A20) and impaired necroptotic cell death. A20High AML are resistant to anthracyclines, while A20Low AML are sensitive. Loss of A20 in AML restores sensitivity to anthracycline treatment by inducing necroptosis. Moreover, A20 prevents necroptosis in AML by targeting the necroptosis effector RIPK1, and anthracycline-induced necroptosis is abrogated in A20High AML. These findings suggest that NF-κB-driven A20 overexpression plays a role in failed chemotherapy induction and highlights the potential of targeting an alternative cell death pathway in AML.

Performance evaluation and clinical impact of the Oncomine Myeloid Research Assay for gene expression analysis in myeloid haematologic malignancies

AIM

Gene expression analysis facilitates the detection of diagnostic and prognostic biomarkers for myeloid haematological malignancies. The Oncomine Myeloid Research Assay (OMA; Thermo Fisher Scientific, Massachusetts, USA) provides a comprehensive analysis of gene expression of five target genes, along with gene alteration and fusion. Here, we present the performance of the OMA for gene expression analysis.

METHODS

In total, 53 RNA samples from patients diagnosed with acute myeloid leukaemia (AML) or myelodysplastic syndrome were included. Of these 53 samples, 3 were evaluated for reproducibility and 50 were evaluated for comparison with RNA-sequencing (RNA-seq). The prognostic impact of the gene expression profile produced by both OMA and RNA-seq in AML was investigated using follow-up data from 33 patients with AML.

RESULTS

The OMA showed good intrarun and interrun reproducibility. Compared with the RNA-seq results, high correlations were found in BAALC, MECOM and WT1 (all r>0.9), with moderate correlations in MYC (r=0.75, p<0.001) and SMC1A (r=0.42, p=0.002). The agreement between OMA and RNA-seq in classifying the dysregulated expression group was almost perfect, except for SMC1A (κ=0.175). Among these five genes, only BAALC showed a significant clinical impact in patients with AML. Patients with high BAALC expression showed significantly shorter overall survival based on both OMA (p=0.037) and RNA-seq (p=0.003).

CONCLUSIONS

OMA gene expression analysis offers reproducible and accurate gene expression data for most targeted genes and demonstrates the utility of BAALC expression as a prognostic marker in AML.

Distinct associations of NEDD4L expression with genetic abnormalities and prognosis in acute myeloid leukemia

Background

There is mounting evidence that demonstrated the association of aberrant NEDD4L expression with diverse human cancers. However, the expression pattern and clinical implication of NEDD4L in acute myeloid leukemia (AML) remains poorly defined.

Methods

We systemically determined NEDD4L expression with its clinical significance in AML by both public data and our research cohort. Moreover, biological functions of NEDD4L in leukemogenesis were further tested by in vitro experiments.

Results

By the public data, we identified that low NEDD4L expression was correlated with AML among diverse human cancers. Expression of NEDD4L was remarkably decreased in AML compared with controls, and was confirmed by our research cohort. Clinically, low expression of NEDD4L was correlated with greatly lower age, higher white blood cells, and higher bone marrow/peripheral blood blasts. Moreover, NEDD4L underexpression was positively correlated with normal karyotype, FLT3 and NPM1 mutations, but negatively associated with complex karyotype and TP53 mutations. Importantly, the association between NEDD4L expression and survival was also discovered in cytogenetically normal AML patients. Finally, a number of 1024 RNAs and 91 microRNAs were identified to be linked to NEDD4L expression in AML. Among the negatively correlated microRNAs, miR-10a was also discovered as a microRNA that may directly target NEDD4L. Further functional studies revealed that NEDD4L exhibited anti-proliferative and pro-apoptotic effects in leukemic cell line K562.

Conclusions

Our findings indicated that NEDD4L underexpression, as a frequent event in AML, was associated with genetic abnormalities and prognosis in AML. Moreover, NEDD4L expression may be involved in leukemogenesis with potential therapeutic target value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02327-7.

Gene expression signature predicts relapse in adult patients with cytogenetically normal acute myeloid leukemia

Although ∼80% of adult patients with cytogenetically normal acute myeloid leukemia (CN-AML) achieve a complete remission (CR), more than half of them relapse. Better identification of patients who are likely to relapse can help to inform clinical decisions. We performed RNA sequencing on pretreatment samples from 268 adults with de novo CN-AML who were younger than 60 years of age and achieved a CR after induction treatment with standard "7+3" chemotherapy. After filtering for genes whose expressions were associated with gene mutations known to impact outcome (ie, CEBPA, NPM1, and FLT3-internal tandem duplication [FLT3-ITD]), we identified a 10-gene signature that was strongly predictive of patient relapse (area under the receiver operating characteristics curve [AUC], 0.81). The signature consisted of 7 coding genes (GAS6, PSD3, PLCB4, DEXI, JMY, NRP1, C10orf55) and 3 long noncoding RNAs. In multivariable analysis, the 10-gene signature was strongly associated with relapse (P < .001), after adjustment for the FLT3-ITD, CEBPA, and NPM1 mutational status. Validation of the expression signature in an independent patient set from The Cancer Genome Atlas showed the signature's strong predictive value, with AUC = 0.78. Implementation of the 10-gene signature into clinical prognostic stratification could be useful for identifying patients who are likely to relapse.

Survival differences and associated molecular signatures of DNMT3A-mutant acute myeloid leukemia patients

Acute myeloid leukemia (AML) is a very heterogeneous and highly malignant blood cancer. Mutations of the DNA methyltransferase DNMT3A are among the most frequent recurrent genetic lesions in AML. The majority of DNMT3A-mutant AML patients shows fast relapse and poor survival, but also patients with long survival or long-term remission have been reported. Underlying molecular signatures and mechanisms that contribute to these survival differences are only poorly understood and have not been studied in detail so far. We applied hierarchical clustering to somatic gene mutation profiles of 51 DNMT3A-mutant patients from The Cancer Genome Atlas (TCGA) AML cohort revealing two robust patient subgroups with profound differences in survival. We further determined molecular signatures that distinguish both subgroups. Our results suggest that FLT3 and/or NPM1 mutations contribute to survival differences of DNMT3A-mutant patients. We observed an upregulation of genes of the p53, VEGF and DNA replication pathway and a downregulation of genes of the PI3K-Akt pathway in short- compared to long-lived patients. We identified that the majority of measured miRNAs was downregulated in the short-lived group and we found differentially expressed microRNAs between both subgroups that have not been reported for AML so far (miR-153-2, miR-3065, miR-95, miR-6718) suggesting that miRNAs could be important for prognosis. In addition, we learned gene regulatory networks to predict potential major regulators and found several genes and miRNAs with known roles in AML pathogenesis, but also interesting novel candidates involved in the regulation of hematopoiesis, cell cycle, cell differentiation, and immunity that may contribute to the observed survival differences of both subgroups and could therefore be important for prognosis. Moreover, the characteristic gene mutation and expression signatures that distinguished short- from long-lived patients were also predictive for independent DNMT3A-mutant AML patients from other cohorts and could also contribute to further improve the European LeukemiaNet (ELN) prognostic scoring system. Our study represents the first in-depth computational approach to identify molecular factors associated with survival differences of DNMT3A-mutant AML patients and could trigger additional studies to develop robust molecular markers for a better stratification of AML patients with DNMT3A mutations.

Advances in Genomic Profiling and Risk Stratification in Acute Myeloid Leukemia

OBJECTIVE

To review the current state of molecular and genetic profiling of acute myeloid leukemia (AML) and its implications.

DATA SOURCE

Peer-reviewed journal articles.

CONCLUSION

Significant advances in the understanding of the pathology of acute myeloid leukemia have led to refined risk stratification of patients and application of novel targeted therapies based on genetic profiles. Minimal residual disease testing allows for highly sensitive disease surveillance that can be used to predict relapse and assess treatment response.

IMPLICATIONS FOR NURSING PRACTICE

Accurate prognostication and therapeutic decision-making for patients with acute myeloid leukemia is dependent on molecular profiling. Being knowledgeable of the implications of minimal residual disease testing is critical for patient-centered care.

Gene expression profiling of acute myeloid leukemia samples from adult patients with AML-M1 and -M2 through boutique microarrays, real-time PCR and droplet digital PCR

Acute myeloid leukemia (AML) is the most common and severe form of acute leukemia diagnosed in adults. Owing to its heterogeneity, AML is divided into classes associated with different treatment outcomes and specific gene expression profiles. Based on previous studies on AML, in this study, we designed and generated an AML-array containing 900 oligonucleotide probes complementary to human genes implicated in hematopoietic cell differentiation and maturation, proliferation, apoptosis and leukemic transformation. The AML-array was used to hybridize 118 samples from 33 patients with AML of the M1 and M2 subtypes of the French-American-British (FAB) classification and 15 healthy volunteers (HV). Rigorous analysis of the microarray data revealed that 83 genes were differentially expressed between the patients with AML and the HV, including genes not yet discussed in the context of AML pathogenesis. The most overexpressed genes in AML were STMN1, KITLG, CDK6, MCM5, KRAS, CEBPA, MYC, ANGPT1, SRGN, RPLP0, ENO1 and SET, whereas the most underexpressed genes were IFITM1, LTB, FCN1, BIRC3, LYZ, ADD3, S100A9, FCER1G, PTRPE, CD74 and TMSB4X. The overexpression of the CPA3 gene was specific for AML with mutated NPM1 and FLT3. Although the microarray-based method was insufficient to differentiate between any other AML subgroups, quantitative PCR approaches enabled us to identify 3 genes (ANXA3, S100A9 and WT1) whose expression can be used to discriminate between the 2 studied AML FAB subtypes. The expression levels of the ANXA3 and S100A9 genes were increased, whereas those of WT1 were decreased in the AML-M2 compared to the AML-M1 group. We also examined the association between the STMN1, CAT and ABL1 genes, and the FLT3 and NPM1 mutation status. FLT3⁺/NPM1⁻ AML was associated with the highest expression of STMN1, and ABL1 was upregulated in FLT3⁺ AML and CAT in FLT3⁻ AML, irrespectively of the NPM1 mutation status. Moreover, our results indicated that CAT and WT1 gene expression levels correlated with the response to therapy. CAT expression was highest in patients who remained longer under complete remission, whereas WT1 expression increased with treatment resistance. On the whole, this study demonstrates that the AML-array can potentially serve as a first-line screening tool, and may be helpful for the diagnosis of AML, whereas the differentiation between AML subgroups can be more successfully performed with PCR-based analysis of a few marker genes.

Biological and clinical consequences of NPM1 mutations in AML

Acute myeloid leukemia (AML) is characterized by accumulation of myeloid cells in the bone marrow because of impaired differentiation and proliferation, resulting in hematopoietic insufficiency. NPM1 is one of the most commonly mutated genes in AML, present in 20-30% of cases. Mutations in NPM1 represent a distinct entity in the World Health Organization (WHO) classification and commonly indicate a better risk prognosis. In this review, we discuss the many functions of NPM1, the consequence of mutations in NPM1 and possible mechanisms through which mutations lead to leukemogenesis. We also discuss clinical consequences of mutations, associated gene expression patterns and the role of NPM1 mutations in informing prognosis and therapeutic decisions and predicting relapse in AML.

A Ranking Approach for Probe Selection and Classification of Microarray Data with Artificial Neural Networks

Acute leukemia classification into its myeloid and lymphoblastic subtypes is usually accomplished according to the morphology of the tumor. Nevertheless, the subtypes may have similar histopathological appearance, making screening procedures difficult. In addition, approximately one-third of acute myeloid leukemias are characterized by aberrant cytoplasmic localization of nucleophosmin (NPMc(+)), where the majority has a normal karyotype. This work is based on two DNA microarray datasets, available publicly, to differentiate leukemia subtypes. The datasets were split into training and test sets, and feature selection methods were applied. Artificial neural network classifiers were developed to compare the feature selection methods. For the first dataset, 50 genes selected using the best classifier was able to classify all patients in the test set. For the second dataset, five genes yielded 97.5% accuracy in the test set.

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

BACKGROUND

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology.

RESULTS

To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis.

CONCLUSIONS

PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

Acute myeloid leukemia: advancing clinical trials and promising therapeutics

Recent progress in understanding the biology of acute myeloid leukemia (AML) and the identification of targetable driver mutations, leukemia specific antigens and signal transduction pathways has ushered in a new era of therapy. In many circumstances the response rates with such targeted or antibody-based therapies are superior to those achieved with standard therapy and with decreased toxicity. In this review we discuss novel therapies in AML with a focus on two major areas of unmet need: (1) single agent and combination strategies to improve frontline therapy in elderly patients with AML and (2) molecularly targeted therapies in the frontline and salvage setting in all patients with AML.

Secondary mutations as mediators of resistance to targeted therapy in leukemia

The advent of small molecule-based targeted therapy has improved the treatment of both acute and chronic leukemias. Resistance to small molecule inhibitors has emerged as a common theme. The most frequent mode of acquired resistance is the acquisition of point mutations in the kinase domain. FLT3 inhibitors have improved response rates in FLT3-mutated acute myeloid leukemia (AML). The occurrence of the ATP-binding site and activation loop mutations confers varying degrees of resistance to the individual FLT3 inhibitors. Second-generation FLT3 inhibitors such as crenolanib may overcome the resistance of these mutations. Furthermore, nonmutational mechanisms of resistance such as prosurvival pathways and bone marrow signaling may be upregulated in FLT3 inhibitor-resistant AML with secondary kinase domain mutations. More recently, point mutations conferring resistance to the Bruton tyrosine kinase inhibitor ibrutinib in chronic lymphocytic leukemia, arsenic trioxide in acute promyelocytic leukemia, and the BH3-mimetic ABT199 in lymphoma have been identified. In chronic myeloid leukemia, the emergence of tyrosine kinase domain mutations has historically been the dominant mechanism of resistance. The early identification of secondary point mutations and their downstream effects along with the development of second- or third-generation inhibitors and rationally designed small molecule combinations are potential strategies to overcome mutation-mediated resistance.

ELMO1 is upregulated in AML CD34+ stem/progenitor cells, mediates chemotaxis and predicts poor prognosis in normal karyotype AML

Both normal as well leukemic hematopoietic stem cells critically depend on their microenvironment in the bone marrow for processes such as self-renewal, survival and differentiation, although the exact pathways that are involved remain poorly understood. We performed transcriptome analysis on primitive CD34+ acute myeloid leukemia (AML) cells (n = 46), their more differentiated CD34- leukemic progeny, and normal CD34+ bone marrow cells (n = 31) and focused on differentially expressed genes involved in adhesion and migration. Thus, Engulfment and Motility protein 1 (ELMO1) was identified amongst the top 50 most differentially expressed genes. ELMO1 is a crucial link in the signaling cascade that leads to activation of RAC GTPases and cytoskeleton rearrangements. We confirmed increased ELMO1 expression at the mRNA and protein level in a panel of AML samples and showed that high ELMO1 expression is an independent negative prognostic factor in normal karyotype (NK) AML in three large independent patient cohorts. Downmodulation of ELMO1 in human CB CD34+ cells did not significantly alter expansion, progenitor frequency or differentiation in stromal co-cultures, but did result in a decreased frequency of stem cells in LTC-IC assays. In BCR-ABL-transduced human CB CD34+ cells depletion of ELMO1 resulted in a mild decrease in proliferation, but replating capacity of progenitors was severely impaired. Downregulation of ELMO1 in a panel of primary CD34+ AML cells also resulted in reduced long-term growth in stromal co-cultures in two out of three cases. Pharmacological inhibition of the ELMO1 downstream target RAC resulted in a severely impaired proliferation and survival of leukemic cells. Finally, ELMO1 depletion caused a marked decrease in SDF1-induced chemotaxis of leukemic cells. Taken together, these data show that inhibiting the ELMO1-RAC axis might be an alternative way to target leukemic cells.

Rational drug repurposing using sscMap analysis in a HOX-TALE model of leukemia

Drug discovery and development are often hampered by lack of target identification and clinical tractability. Repurposing of approved drugs to life-threatening diseases such as leukemia is emerging as a promising alternative approach. Connectivity mapping systems link approved drugs with disease-related gene signatures. Relevant preclinical models provide essential tools for system validation and proof-of-concept studies. Herein we describe procedures aimed at generating disease-based gene signatures and applying them to established cross-referencing databases of potential candidate drugs. As a proof of principle, we present the identification of Entinostat as a candidate drug for the treatment of HOX-TALE-related leukemia.

A program to identify prognostic and predictive gene signatures

BACKGROUND

The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer. However, analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers.

RESULTS

To overcome this limitation, we developed a freely accessible and user friendly Program to Identify Molecular Signatures (PIMS). Importantly, such signatures allow important insight into cancer biology, as well as provide clinical tools to identify potential biomarkers that might provide means to accurately stratify patients into different risk or treatment groups. We evaluated the performance of PIMS by identifying and testing predictive and prognostic gene signatures for breast cancer, using multiple breast tumor microarray cohorts representing hundreds of patients. Importantly, PIMS identified signatures classified patients into high and low risk groups with at least similar performance to other commonly used gene signature selection techniques.

CONCLUSIONS

Our program is contained entirely within a Microsoft Excel file and therefore requires no installation of any additional programs or training. Hence, PIMS provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research.

Detection of mutations in myeloid malignancies through paired-sample analysis of microdroplet-PCR deep sequencing data

Amplicon-based methods for targeted resequencing of cancer genes have gained traction in the clinic as a strategy for molecular diagnostic testing. An 847-amplicon panel was designed with the RainDance DeepSeq system, covering most exons of 28 genes relevant to acute myeloid leukemia and myeloproliferative neoplasms. We developed a paired-sample analysis pipeline for variant calling and sought to assess its sensitivity and specificity relative to a set of samples with previously identified mutations. Thirty samples with known mutations in JAK2, NPM1, DNMT3A, MPL, IDH1, IDH2, CEBPA, and FLT3, were profiled and sequenced to high depth. Variant calling using an unmatched Hapmap DNA control removed a substantial number of artifactual calls regardless of algorithm used or variant class. The removed calls were nonunique, had lower variant frequencies, and tended to recur in multiple unrelated samples. Analysis of sample replicates revealed that reproducible calls had distinctly higher variant allele depths and frequencies compared to nonreproducible calls. On the basis of these differences, filters on variant frequency were chosen to select for reproducible calls. The analysis pipeline successfully retrieved the associated known variant in all tested samples and uncovered additional mutations in some samples corresponding to well-characterized hotspot mutations in acute myeloid leukemia. We have developed a paired-sample analysis pipeline capable of robust identification of mutations from microdroplet-PCR sequencing data with high sensitivity and specificity.

Two splice-factor mutant leukemia subgroups uncovered at the boundaries of MDS and AML using combined gene expression and DNA-methylation profiling

SF-mutant myeloid malignancies transcend the boundaries between AML and MDS. Integrated analysis of gene expression and DNA-methylation profiles in leukemia uncovers novel subtypes.

Expression profiling of leukemia patients: key lessons and future directions

Gene expression profiling (GEP) is a well-established indispensable tool used to study hematologic malignancies, including leukemias. Here, we summarize the insights into the molecular basis of leukemias obtained by means of GEP, focusing especially on acute myeloid leukemia (AML), one of the first diseases to be extensively studied by GEP. Profiling mRNA and microRNA expression are discussed in view of their applicability to class prediction, class discovery, and comparison, as well as outcome prediction, and special attention is paid to the recent advances in our understanding of the role of alternative RNA splicing in AML. In addition to microarray-based GEP approaches, over the last few years RNA sequencing based on next-generation sequencing technology is gaining wider recognition as an advanced tool for transcriptome profiling. Therefore, the advantages of RNA sequencing-based GEP and its current and potential implications in AML are discussed. Finally, we also highlight recent efforts to integrate already available and newly acquired omics data sets so that a more precise understanding of AML biology and clinical behavior can be achieved, which ultimately will contribute to further refine leukemia management.

Array comparative genomic hybridization in pediatric acute leukemias

Array comparative genomic hybridization has proven to be a very powerful tool in searching for new biomarkers which can find an application in clinical practise. CGH-array technology is satisfying in almost every possible way. It is highly specific, sensitive, simple, and relatively cheap. Thus, this modern method meets the demands of clinical application. An increasing knowledge about molecular pathways and pathologic genome alterations in acute leukemias enable to define unequivocal diagnosis, prognosis and to predict a response to individual compatible therapy. This review shows a various application of CGH-array in pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL).

Identification of interconnected markers for T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a complex disease, resulting from proliferation of differentially arrested immature T cells. The molecular mechanisms and the genes involved in the generation of T-ALL remain largely undefined. In this study, we propose a set of genes to differentiate individuals with T-ALL from the nonleukemia/healthy ones and genes that are not differential themselves but interconnected with highly differentially expressed ones. We provide new suggestions for pathways involved in the cause of T-ALL and show that network-based classification techniques produce fewer genes with more meaningful and successful results than expression-based approaches. We have identified 19 significant subnetworks, containing 102 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Subnetworks contain 6 nondifferentially expressed genes, which could potentially participate in pathogenesis of T-ALL. Although these genes are not differential, they may serve as biomarkers if their loss/gain of function contributes to generation of T-ALL via SNPs. We conclude that transcription factors, zinc-ion-binding proteins, and tyrosine kinases are the important protein families to trigger T-ALL. These potential disease-causing genes in our subnetworks may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease.

The evolving molecular genetic landscape in acute myeloid leukaemia

PURPOSE OF REVIEW

Acute myeloid leukaemia (AML) is a heterogeneous disease with a variable response to therapy. The heterogeneity of AML is evident from variations in morphology, immunophenotype, cytogenetics and molecular abnormalities. The introduction of genome-wide technologies has enabled an even more detailed molecular analysis of AML. As a result, the molecular landscape of AML is rapidly evolving. The purpose of this review is to discuss the recent advances made in this field, with a special focus on risk stratification of AML.

RECENT FINDINGS

Clinical AML has been analysed in great molecular detail by gene expression profiling and more recently through epigenetic profiling and next generation sequencing. This has resulted in the identification of novel biomarkers, some of which appear to have a consistent clinical impact in AML, that is mutations in the genes encoding DNA (cytosine-5)-methyltransferase 3 alpha (DNMT3A), additional sex combs-like 1 (ASXL1), tet methylcytosine dioxygenase 2 (TET2) and Runt-related transcription factor 1 (RUNX1). In addition, massively parallel sequencing has revealed a great mutational heterogeneity as well as temporal clonal evolution in AML.

SUMMARY

The list of acquired mutations with clinical value in AML is growing. Clinical implementation of this multitude of markers will require integrated approaches and selection of markers to facilitate AML risk stratification in the future. The revealed molecular heterogeneity and evolution in AML will have implications for developing targeted therapies.

Genome-wide gene expression profiling, genotyping, and copy number analyses of acute myeloid leukemia using Affymetrix GeneChips

With novel genome-wide technologies it is nowadays possible to perform detailed molecular analyses of normal and malignant tissues. Acute myeloid leukemia (AML) is a heterogeneous group of diseases with variable response to therapy. Gene expression profiling and genome-wide genotyping have recently been successfully applied to unravel the heterogeneity of AML. This chapter gives instructions and recommendations for genome-wide gene expression analyses, genotyping, and copy number analyses, as performed for AML using Affymetrix GeneChips.

MEK/ERK dependent activation of STAT1 mediates dasatinib-induced differentiation of acute myeloid leukemia

Dasatinib (BMS-354825) is a FDA-approved multitargeted kinase inhibitor of BCR/ABL and Src kinases. It is now used in the treatment of chronic myelogenous leukemia (CML) with resistance or intolerance to prior therapies, including imatinib. Here we report a novel effect of dasatinib on inducing the differentiation of acute myeloid leukemia (AML) cells through MEK/ERK-dependent activation of signal transducer and activator of transcription 1 (STAT1). We found that dasatinib could induce the differentiation of AML cells as demonstrated by the expression of differentiation marker CD11b, G0/G1 phase arrest and decreased ratio of nucleus to cytoplasm. Of note, dasatinib induced robust phosphorylation of STAT1 both at Tyr701 and Ser727 as well as the redistribution of STAT1 from the cytoplasm to the nucleus, thus leading to the transcription of STAT1-targeted genes. Knocking down STAT1 expression by shRNA significantly attenuated dasatinib-induced differentiation, indicating an important role of STAT1 in myeloid maturation. We further found that dasatinib-induced activation of STAT1 was regulated by the MEK/ERK kinases. The phosporylation of MEK and ERK occurred rapidly upon dasatinib treatment and increased progressively as differentiation was induced. MEK inhibitors PD98059 and U0216 not only inhibited the phosphorylation of STAT1, but also abrogated dasatinib-induced myeloid differentiation, suggesting that MEK/ERK dependent phosphorylation of STAT1 might be indispensable for the differentiating effect of dasatinib in AML cells. Taken together, our study suggests that STAT1 is an important mediator in dasatinib-induced differentiation of AML cells, whose activation requires the activation of MEK/ERK cascades.

Development of gene expression-based risk score in cytogenetically normal acute myeloid leukemia patients

Patients with normal karyotype represent the single largest cytogenetic group of acute myeloid leukemia (AML), with highly heterogeneous clinical and molecular characteristics. In this study, we sought to determine new prognostic biomarkers in cytogenetically normal (CN)-AML patients. A gene expression (GE)-based risk score was built, summing up the prognostic value of 22 genes whose expression is associated with a bad prognosis in a training cohort of 163 patients. GE-based risk score allowed identifying a high-risk group of patients (53.4%) in two independent cohorts of CN-AML patients. GE-based risk score and EVI1 gene expression remained independent prognostic factors using multivariate Cox analyses. Combining GE-based risk score with EVI1 gene expression allowed the identification of three clinically different groups of patients in two independent cohorts of CN-AML patients. Thus, GE-based risk score is powerful to predict clinical outcome for CN-AML patients and may provide potential therapeutic advances.

Visualising the cross-level relationships between pathological and physiological processes and gene expression: analyses of haematological diseases

The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields.

Melanoma molecular classes and prognosis in the postgenomic era

Gene expression profiling is a powerful method to classify human tumours on the basis of biological aggressiveness, response to therapy, and outcome for the patient, but its application in melanoma lags behind that of other cancers. From more than 100 articles available on the topic, we selected 14 focusing on patients' outcome. We review and briefly discuss salient findings, and list ten reasons why melanoma molecular classes are not yet used in clinical diagnosis and prognosis. The available evidence suggests that we are on the verge of creating a framework for the use of melanoma molecular classes in prognosis, but so far there is little consensus to put together informative diagnostic and prognostic gene sets.

New prognostic markers, determined using gene expression analyses, reveal two distinct subtypes of chronic myelomonocytic leukaemia patients

Chronic myelomonocytic leukaemia (CMML) is a heterogeneous haematopoietic disorder characterized by myeloproliferative or myelodysplastic features. At present, the pathogenesis of this malignancy is not completely understood. In this study, we sought to analyse gene expression profiles of CMML in order to characterize new molecular outcome predictors. A learning set of 32 untreated CMML patients at diagnosis was available for TaqMan low-density array gene expression analysis. From 93 selected genes related to cancer and cell cycle, we built a five-gene prognostic index after multiplicity correction. Using this index, we characterized two categories of patients with distinct overall survival (94% vs. 19% for good and poor overall survival, respectively; P = 0·007) and we successfully validated its strength on an independent cohort of 21 CMML patients with Affymetrix gene expression data. We found no specific patterns of association with traditional prognostic stratification parameters in the learning cohort. However, the poor survival group strongly correlated with high-risk treated patients and transformation to acute myeloid leukaemia. We report here a new multigene prognostic index for CMML, independent of the gene expression measurement method, which could be used as a powerful tool to predict clinical outcome and help physicians to evaluate criteria for treatments.

Retroviral integration mutagenesis in mice and comparative analysis in human AML identify reduced PTP4A3 expression as a prognostic indicator

Acute myeloid leukemia (AML) results from multiple genetic and epigenetic aberrations, many of which remain unidentified. Frequent loss of large chromosomal regions marks haplo-insufficiency as one of the major mechanisms contributing to leukemogenesis. However, which haplo-insufficient genes (HIGs) are involved in leukemogenesis is largely unknown and powerful experimental strategies aimed at their identification are currently lacking. Here, we present a new approach to discover HIGs, using retroviral integration mutagenesis in mice in which methylated viral integration sites and neighbouring genes were identified. In total we mapped 6 genes which are flanked by methylated viral integration sites (mVIS). Three of these, i.e., Lrmp, Hcls1 and Prkrir, were up regulated and one, i.e., Ptp4a3, was down regulated in the affected tumor. Next, we investigated the role of PTP4A3 in human AML and we show that PTP4A3 expression is a negative prognostic indicator, independent of other prognostic parameters. In conclusion, our novel strategy has identified PTP4A3 to potentially have a role in AML, on one hand as a candidate HIG contributing to leukemogenesis in mice and on the other hand as a prognostic indicator in human AML.

High BRE expression predicts favorable outcome in adult acute myeloid leukemia, in particular among MLL-AF9-positive patients

Aberrations in protein ubiquitination have recently been identified in the pathogenesis of acute myeloid leukemia (AML). We studied whether expression changes of more than 1600 ubiquitination related genes correlated with clinical outcome in 525 adult AML patients. High expression of one of these genes, BRE, was observed in 3% of the cases and predicted favorable prognosis independently of known prognostic factors (5-year overall survival: 57%). Remarkably, unsupervised expression profiling showed that 86% of high BRE-expressing patients were confined to a previously unrecognized cluster. High BRE expression was mutually exclusive with FLT3 ITD, CEBPA, IDH1, and IDH2 mutations, EVI1 overexpression, and favorable karyotypes. In contrast, high BRE expression co-occurred strongly with FAB M5 morphology and MLL-AF9 fusions. Within the group of MLL-AF9-positive patients, high BRE expression predicted superior survival, while normal BRE expression predicted extremely poor survival (5-year overall survival of 80% vs 0%, respectively, P = .0002). Both the co-occurrence of high BRE expression with MLL-AF9 and its prognostic impact were confirmed in an independent cohort of 436 AML patients. Thus, high BRE expression defines a novel subtype of adult AML characterized by a favorable prognosis. This work contributes to improved risk stratification in AML, especially among MLL-AF9-positive patients.

Array-based DNA methylation profiling in acute myeloid leukaemia

Methylation in the promoter region of many genes is involved in regulating gene expression patterns. Using the Illumina GoldenGate© methylation assay, we examined the methylation status of 1505 CpG-sites from 807 genes in 32 samples from patients with acute myeloid leukaemia (AML) at diagnosis, nine at relapse and 15 normal controls and performed additional pyrosequencing and semiquantitative methylation specific polymerase chain reaction (MSP) of the GNMT promoter in 113 diagnostic AML samples. We found a gain of overall methylation in AML samples with a further increase at relapse. Regional hypermethylation as assessed by array analysis could be confirmed by both MSP and pyrosequencing. Additionally, large-scale methylation analysis identified interesting candidate genes. Cluster analysis indicated that cytogenetic subgroups seemed to be characterized by additional distinct epigenetic modifications and that basic DNA methylation patterns remain at relapse. Therefore, promoter hypermethylation is a frequent event in AML and is accentuated at relapse. Array-based methylation analysis determined distinct methylation profiles for non-malignant controls and AML samples with specific chromosomal aberrations and can identify target genes for further evaluation.

Gene expression profiling in the leukemic stem cell-enriched CD34+ fraction identifies target genes that predict prognosis in normal karyotype AML

In order to identify acute myeloid leukemia (AML) CD34(+)-specific gene expression profiles, mononuclear cells from AML patients (n=46) were sorted into CD34(+) and CD34(-) subfractions, and genome-wide expression analysis was performed using Illumina BeadChip Arrays. AML CD34(+) and CD34(-) gene expression was compared with a large group of normal CD34(+) bone marrow (BM) cells (n=31). Unsupervised hierarchical clustering analysis showed that CD34(+) AML samples belonged to a distinct cluster compared with normal BM and that in 61% of the cases the AML CD34(+) transcriptome did not cluster together with the paired CD34(-) transcriptome. The top 50 of AML CD34(+)-specific genes was selected by comparing the AML CD34(+) transcriptome with the AML CD34(-) and CD34(+) normal BM transcriptomes. Interestingly, for three of these genes, that is, ankyrin repeat domain 28 (ANKRD28), guanine nucleotide binding protein, alpha 15 (GNA15) and UDP-glucose pyrophosphorylase 2 (UGP2), a high transcript level was associated with a significant poorer overall survival (OS) in two independent cohorts (n=163 and n=218) of normal karyotype AML. Importantly, the prognostic value of the continuous transcript levels of ANKRD28 (OS hazard ratio (HR): 1.32, P=0.008), GNA15 (OS HR: 1.22, P=0.033) and UGP2 (OS HR: 1.86, P=0.009) was shown to be independent from the well-known risk factors FLT3-ITD, NPM1c(+) and CEBPA mutation status.

Gene expression profiling in MDS and AML: potential and future avenues

Today, the classification systems for myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) already incorporate cytogenetic and molecular genetic aberrations in an attempt to better reflect disease biology. However, in many MDS/AML patients no genetic aberrations have been identified yet, and even within some cytogenetically well-defined subclasses there is considerable clinical heterogeneity. Recent advances in genomics technologies such as gene expression profiling (GEP) provide powerful tools to further characterize myeloid malignancies at the molecular level, with the goal to refine the MDS/AML classification system, incorporating as yet unknown molecular genetic and epigenetic pathomechanisms, which are likely reflected by aberrant gene expression patterns. In this study, we provide a comprehensive review on how GEP has contributed to a refined molecular taxonomy of MDS and AML with regard to diagnosis, prediction of clinical outcome, discovery of novel subclasses and identification of novel therapeutic targets and novel drugs. As many challenges remain ahead, we discuss the pitfalls of this technology and its potential including future integrative studies with other genomics technologies, which will continue to improve our understanding of malignant transformation in myeloid malignancies and thereby contribute to individualized risk-adapted treatment strategies for MDS and AML patients.

DNA methylation profiling in acute myeloid leukemia: from recent technological advances to biological and clinical insights

Acute myeloid leukemia represents a heterogeneous malignant hematological disease with a complex underlying biology suggesting multiple patterns of genetic and epigenetic alterations. Recent evidence suggests that epigenetic mechanisms, especially deregulation of DNA methylation, play an important pathogenic role in leukemogenesis and the first epigenetic drugs have entered the clinic. Therefore, an improved understanding of the impact of altered epigenetic patterns on leukemogenesis represents a pre-requisite for improved patient management and outcome. Here, we provide an overview of current advances in deciphering the leukemic epigenome and its clinical relevance. Recent high-throughput analyses and genome-wide studies provide an optimal starting point for future epigenetic and integrative analyses that will further the development and use of predictive and prognostic epigenetic markers in acute myeloid leukemia.

Biology, risk stratification, and therapy of pediatric acute leukemias: an update

PURPOSE

We review recent advances in the biologic understanding and treatment of childhood acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), identify therapeutically challenging subgroups, and suggest future directions of research.

METHODS

A review of English literature on childhood acute leukemias from the past 5 years was performed.

RESULTS

Contemporary treatments have resulted in 5-year event-free survival rates of approximately 80% for childhood ALL and almost 60% for pediatric AML. The advent of high-resolution genome-wide analyses has provided new insights into leukemogenesis and identified many novel subtypes of leukemia. Virtually all ALL and the vast majority of AML cases can be classified according to specific genetic abnormalities. Cooperative mutations involved in cell differentiation, cell cycle regulation, tumor suppression, drug responsiveness, and apoptosis have also been identified in many cases. The development of new formulations of existing drugs, molecularly targeted therapy, and immunotherapies promises to further advance the cure rates and improve quality of life of patients.

CONCLUSION

The application of new high-throughput sequencing techniques to define the complete DNA sequence of leukemia and host normal cells and the development of new agents targeted to leukemogenic pathways promise to further improve outcome in the coming decade.

Expression profiling of cytogenetically normal acute myeloid leukemia identifies microRNAs that target genes involved in monocytic differentiation

MicroRNAs are short ribonucleic acids (RNAs) that play an important role in many aspects of cellular biology such as differentiation and apoptosis, due to their role in the regulation of gene expression. Using microRNA microarrays, we characterized the microRNA gene expression of 27 patients with acute myeloid leukemia (AML) with normal cytogenetics, focusing on the microRNAs differentially expressed between the M1 and M5 French-American-British (FAB) subtypes. An accurate delineation of these two AML entities was observed based on the expression of 12 microRNAs. We hypothesized that these microRNAs may potentially be involved in the differentiation block of M1 blasts and consequently monocytic differentiation. Using publically available mRNA data and microRNA target prediction software, we identified several key myeloid factors that may be targeted by our candidate microRNAs. The expression changes of the candidate microRNAs during monocytic differentiation of AML cell lines treated with Vitamin D and phorbol 12-myristate 13-acetate were examined. All six candidate microRNAs were significantly down-regulated over the time course by quantitative reverse transcriptase polymerase chain reaction suggesting a link between these microRNAs and monocytic differentiation. To further characterize these microRNAs, we confirmed by luciferase assays that these microRNA target several key myeloid factors such as MAFB, IRF8, and KLF4 identifying a possible mechanism for the control of differentiation by these microRNAs.

Untangling the intracellular signalling network in cancer--a strategy for data integration in acute myeloid leukaemia

Protein and gene networks centred on the regulatory tumour suppressor proteins may be of crucial importance both in carcinogenesis and in the response to chemotherapy. Tumour suppressor protein p53 integrates intracellular data in stress responses, receiving signals and translating these into differential gene expression. Interpretation of the data integrated on p53 may therefore reveal the response to therapy in cancer. Proteomics offers more specific data - closer to "the real action" - than the hitherto more frequently used gene expression profiling. Integrated data analysis may reveal pathways disrupted at several regulatory levels. Ultimately, integrated data analysis may also contribute to finding key underlying cancer genes. We here proposes a Partial Least Squares Regression (PLSR)-based data integration strategy, which allows simultaneous analysis of proteomic data, gene expression data and classical clinical parameters. PLSR collapses multidimensional data into fewer relevant dimensions for data interpretation. PLSR can also aid identification of functionally important modules by also performing comparison to databases on known biological interactions. Further, PLSR allows meaningful visualization of complex datasets, aiding interpretation of the underlying biology. Extracting the true biological causal mechanisms from heterogeneous patient populations is the key to discovery of new therapeutic options in cancer.

Imaging and 'omic' methods for the molecular diagnosis of cancer

Molecular imaging methods can noninvasively detect specific biological processes that are aberrant in cancer, including upregulated glycolytic metabolism, increased cellular proliferation and altered receptor expression. PET using the glucose analogue 18F-fluoro-2-deoxyglucose, which detects the increased glucose uptake that is a characteristic of tumor cells, has been widely used in the clinic to detect tumors and their responses to treatment; however, there are many new PET tracers being developed for a wide range of biological targets. Magnetic resonance spectroscopy (MRS), which can be used to detect cellular metabolites, can also provide prognostic information, particularly in brain, breast and prostate cancers. An emerging technique, which by hyperpolarizing 13C-labeled cell substrates dramatically enhances their sensitivity to detection, could further extend the use of MRS in molecular imaging in the clinic. Molecular diagnostics applied to serum samples or tumor samples obtained by biopsy, can measure changes at the individual cell level and the underlying changes in gene or protein expression. DNA microarrays enable high-throughput gene-expression profiling, while mass spectrometry can detect thousands of proteins that may be used in the future as biomarkers of cancer. Probing molecular changes will aid not only cancer diagnosis, but also provide tumor grading, based on gene-expression analysis and imaging measurements of cell proliferation and changes in metabolism; staging, based on imaging of metastatic spread and elevation of protein biomarkers; and the detection of therapeutic response, using serial molecular imaging measurements or monitoring of serum markers. The present article provides a summary of the molecular diagnostic methods that are currently being trialed in the clinic.

MicroRNA expression in acute myeloid leukemia

Acute myeloid leukemia (AML) is a group of diseases that are very heterogeneous with regard to cytogenetic aberrations, gene mutations, and changes in expression of numerous genes. A new class of genes known as microRNAs recently was found to be involved in myeloid leukemogenesis. These genes are transcribed into regulatory, noncoding RNAs that control mRNA and protein expression of target genes. Genome-wide analyses of microRNA expression have revealed signatures associated with selected cytogenetic and molecular subsets of AML and have led to the recognition of previously unreported molecular pathways involved in myeloid leukemogenesis. In cytogenetically normal AML, microRNA-expression profiling has also provided prognostic information in addition to that obtained from cytogenetics and analyses of gene mutations and aberrant gene expression. This article reviews recent studies that were focused on the alterations of microRNA expression in AML and their diagnostic and prognostic significance.

Kit inhibitor APcK110 extends survival in an AML xenograft mouse model

BACKGROUND

Constitutive activation of kit contributes to pathogenesis of acute myeloid leukemia (AML) and targeting Kit may be of therapeutic benefit. APcK110, a novel inhibitor of Kit, has potent proapoptotic and antiproliferative activity in AML cell lines and primary AML samples. Here we extend our studies to the activity of APcK110 in a xenograft mouse model.

METHODS

After sub-lethal whole body radiation, OCI/AML3 cells were injected intravenously in NOD-SCID mice. Ten days later, either APcK110 or phosphate buffered saline (PBS) was injected intraperitoneally every other day. Kaplan-Meier estimates were used to calculate survival.

RESULTS

We show that 1) all mice injected with OCI/AML3 cells developed a clinical and histological picture consistent with myelomonocytic AML; and 2) survival of APcK110-treated mice was significantly longer compared with mice injected with PBS (p = .02).

CONCLUSIONS

APcK110 is a novel kit kinase inhibitor with anti-AML activity in vitro and in vivo. Further evaluation in toxicology and clinical studies is warranted.