Global approach to the diagnosis of leukemia using gene expression profiling

Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives

Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Cancer is considered one of the deadliest diseases across all age categories. Diagnosing such a deadly disease at the initial stage is essential to cure the disease. Hematologists and pathologists rely on microscopic evaluation of blood or bone marrow smear images to diagnose blood-related ailments. The abundance of overlapping cells, cells of varying densities among platelets, non-illumination levels, and the amount of red and white blood cells make it more difficult to diagnose illness using blood cell images. Pathologists are required to put more effort into the traditional, time-consuming system. Nowadays, it becomes possible with machine learning and deep learning techniques, to automate the diagnostic processes, categorize microscopic blood cells, and improve the accuracy of the procedure and its speed as the models developed using these methods may guide an assisting tool. In this article, we have acquired, analyzed, scrutinized, and finally selected around 57 research papers from various machine learning and deep learning methodologies that have been employed in the diagnosis of leukemia and its classification over the past 20 years, which have been published between the years 2003 and 2023 by PubMed, IEEE, Science Direct, Google Scholar and other pertinent sources. Our primary emphasis is on evaluating the advantages and limitations of analogous research endeavors to provide a concise and valuable research directive that can be of significant utility to fellow researchers in the field.

Management of ALL in adults: 2024 ELN recommendations from a European expert panel

ABSTRACT

Experts from the European Leukemia Net (ELN) working group for adult acute lymphoblastic leukemia have identified an unmet need for guidance regarding management of adult acute lymphoblastic leukemia (ALL) from diagnosis to aftercare. The group has previously summarized their recommendations regarding diagnostic approaches, prognostic factors, and assessment of ALL. The current recommendation summarizes clinical management. It covers treatment approaches, including the use of new immunotherapies, application of minimal residual disease for treatment decisions, management of specific subgroups, and challenging treatment situations as well as late effects and supportive care. The recommendation provides guidance for physicians caring for adult patients with ALL which has to be complemented by regional expertise preferably provided by national academic study groups.

Definition and Prognostic Value of Ph-like and IKZF1plus Status in Children With Down Syndrome and B-cell Precursor Acute Lymphoblastic Leukemia

Children with Down syndrome have an augmented risk for B-cell acute lymphoblastic leukemia (DS-ALL), which is associated with lower survival than in non-DS-ALL. It is known that cytogenetic abnormalities common in childhood ALL are less frequent in DS-ALL, while other genetic aberrancies (ie, CRLF2 overexpression and IKZF1 deletions) are increased. A possible cause for the lower survival of DS-ALL that we herewith evaluated for the first time was the incidence and prognostic value of the Philadelphia-like (Ph-like) profile and the IKZF1plus pattern. These features have been associated with poor outcome in non-DS ALL and therefore introduced in current therapeutic protocols. Forty-six out of 70 DS-ALL patients treated in Italy from 2000 to 2014 displayed Ph-like signature, mostly characterized by CRLF2 (n = 33) and IKZF1 (n = 16) alterations; only 2 cases were positive for ABL-class or PAX5-fusion genes. Moreover, in an Italian and German joint cohort of 134 DS-ALL patients, we observed 18% patients positive for IKZF1plus feature. Ph-like signature and IKZF1 deletion were associated with poor outcome (cumulative incidence of relapse: 27.7 ± 6.8% versus 13 ± 7%; P = 0.04 and 35.2 ± 8.6% versus 17 ± 3.9%; P = 0.007, respectively), which further worsens when IKZF1 deletion was co-occurring with P2RY8::CRLF2, qualifying for the IKZF1plus definition (13/15 patients had an event of relapse or treatment-related death). Notably, ex vivo drug screening revealed sensitivity of IKZF1plus blasts for drugs active against Ph-like ALL such as Birinapant and histone deacetylase inhibitors. We provided data in a large setting of a rare condition (DS-ALL) supporting that these patients, not associated with other high-risk features, need tailored therapeutic strategies.

Philadelphia-like acute lymphoblastic leukemia: the journey from molecular background to the role of bone marrow transplant-review article

Philadelphia chromosome-like (Ph-like) ALL is a recent subtype of acute lymphoblastic leukemia. Although it does not express the BCR-ABL fusion gene, it has a behavior like true BCR/ABL1-positive cases. This subtype harbors different molecular alterations most commonly CRLF2 rearrangements. Most cases of Ph-like ALL are associated with high white blood cell count, high minimal residual disease level after induction therapy, and high relapse rate. Efforts should be encouraged for early recognition of Ph-like ALL to enhance therapeutic strategies. Recently, many trials are investigating the possibility of adding the tyrosine kinase inhibitor (TKI) to chemotherapy to improve clinical outcomes. The role and best timing of allogeneic bone marrow transplant in those cases are still unclear. Precision medicine should be implemented in the treatment of such cases. Here in this review, we summarize the available data on Ph-like ALL.

Verification of prognostic expression biomarkers is improved by examining enriched leukemic blasts rather than mononuclear cells from acute myeloid leukemia patients

BACKGROUND

Studies have not systematically compared the ability to verify performance of prognostic transcripts in paired bulk mononuclear cells versus viable CD34-expressing leukemic blasts from patients with acute myeloid leukemia. We hypothesized that examining the homogenous leukemic blasts will yield different biological information and may improve prognostic performance of expression biomarkers.

METHODS

To assess the impact of cellular heterogeneity on expression biomarkers in acute myeloid leukemia, we systematically examined paired mononuclear cells and viable CD34-expressing leukemic blasts from SWOG diagnostic specimens. After enrichment, patients were assigned into discovery and validation cohorts based on availability of extracted RNA. Analyses of RNA sequencing data examined how enrichment impacted differentially expressed genes associated with pre-analytic variables, patient characteristics, and clinical outcomes.

RESULTS

Blast enrichment yielded significantly different expression profiles and biological pathways associated with clinical characteristics (e.g., cytogenetics). Although numerous differentially expressed genes were associated with clinical outcomes, most lost their prognostic significance in the mononuclear cells and blasts after adjusting for age and ELN risk, with only 11 genes remaining significant for overall survival in both cell populations (CEP70, COMMD7, DNMT3B, ECE1, LNX2, NEGR1, PIK3C2B, SEMA4D, SMAD2, TAF8, ZNF444). To examine the impact of enrichment on biomarker verification, these 11 candidate biomarkers were examined by quantitative RT/PCR in the validation cohort. After adjusting for ELN risk and age, expression of 4 genes (CEP70, DNMT3B, ECE1, and PIK3CB) remained significantly associated with overall survival in the blasts, while none met statistical significance in mononuclear cells.

CONCLUSIONS

This study provides insights into biological information gained/lost by examining viable CD34-expressing leukemic blasts versus mononuclear cells from the same patient and shows an improved verification rate for expression biomarkers in blasts.

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling

FAT atypical cadherin 1 (FAT1), a transmembrane protein, is frequently mutated in various cancer types and has been described as context-dependent tumor suppressor or oncogene. The FAT1 gene is mutated in 12-16% of T-cell acute leukemia (T-ALL) and aberrantly expressed in about 54% of T-ALL cases contrasted with absent expression in normal T-cells. Here, we characterized FAT1 expression and profiled the methylation status from T-ALL patients. In our T-ALL cohort, 53% of patient samples were FAT1 positive (FAT1pos) compared to only 16% FAT1 positivity in early T-ALL patient samples. Aberrant expression of FAT1 was strongly associated with FAT1 promotor hypomethylation, yet a subset, mainly consisting of TLX1-driven T-ALL patient samples showed methylation-independent high FAT1 expression. Genes correlating with FAT1 expression revealed enrichment in WNT signaling genes representing the most enriched single pathway. FAT1 knockdown or knockout led to impaired proliferation and downregulation of WNT pathway target genes (CCND1, MYC, LEF1), while FAT1 overexpressing conveyed a proliferative advantage. To conclude, we characterized a subtype pattern of FAT1 gene expression in adult T-ALL patients correlating with promotor methylation status. FAT1 dependent proliferation and WNT signaling discloses an impact on deeper understanding of T-ALL leukemogenesis as a fundament for prospective therapeutic strategies.

A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia

One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community. The dimensionality of the microarray data is too high, and so selection of suitable genes is quite an important step for the improvement of data classification. Therefore, for the prediction and diagnosis of cancer, there is an utmost necessity to select the most informative genes. In this work, Minimum Redundancy Maximum Relevance (MRMR), Signal to Noise Ratio (SNR), Multivariate Error Weight Uncorrelated Shrunken Centroid (EWUSC), and multivariate correlation-based feature selection (CFS) are chosen as initial feature selection techniques. Then, to select the most informative genes, five different kinds of evolutionary optimization techniques too are incorporated here such as African Buffalo Optimization (ABO), Artificial Bee Colony Optimization (ABCO), Cockroach Swarm Optimization (CSO), Imperialist Competitive Optimization (ICO), and Social Spider Optimization (SSO). Finally, the optimized values are fed through classification process and the best results are obtained when multivariate CFS with SSO is utilized and classified with Probabilistic Neural Network (PNN), and a high classification accuracy of 95.70% is obtained.

Emerging tyrosine kinase inhibitors for the treatment of adult acute lymphoblastic leukemia

Introduction: The broadening of targeted and immunotherapeutic strategies markedly impacted on the management of acute lymphoblastic leukemia (ALL). The advent of tyrosine kinase inhibitors (TKIs) changed the history of Philadelphia-chromosome positive (Ph+) ALL. Nowadays, almost all Ph+ ALL patients treated with TKIs achieve a complete hematologic response, and most become minimal residual disease negative. In Ph- ALL, genomic profiling studies have identified a subtype associated with a high relapse risk and a transcriptional profile similar to that of Ph+ ALL, the so-called Ph-like ALL. Given the high prevalence of kinase-activating lesions in this subset, there is compelling evidence from experimental models and clinical observations favoring TKI administration.Areas covered: We discuss the main findings exploring the efficacy of TKIs in ALL.Expert opinion: The use of more potent TKIs will further enhance the inhibitory activity on leukemia cells and increase the possibility of eradicating the disease at a molecular level. In the future, 'combined' approaches of different inhibitors may be considered to prevent/avoid resistance and/or mutations. A rapid identification of Ph-like ALL patients is needed to propose early TKI-based intervention. Several questions remain open, including the initial TKI choice in Ph+ ALL and whether Ph-like ALL patients might benefit from immunotherapy.

Clinical diagnostics and treatment strategies for Philadelphia chromosome-like acute lymphoblastic leukemia

Philadelphia chromosome-like B-cell acute lymphoblastic leukemia (Ph-like ALL) accounts for 15% to 30% of B-cell acute lymphoblastic leukemia in older children, adolescents, and adults and is associated with high rates of conventional treatment failure and relapse. Current clinical trials are assessing the efficacy of the addition of tyrosine kinase inhibitors (TKIs) to chemotherapy for children and adults with Ph-like ALL harboring ABL class translocations or CRLF2 rearrangements and other JAK pathway alterations. However, real-time diagnosis of patients can be quite challenging given the genetic heterogeneity of this disease and the often cytogenetically cryptic nature of Ph-like ALL-associated alterations. In this review, we discuss the complex biologic and clinical features of Ph-like ALL across the age spectrum, available diagnostic testing modalities, and current clinical treatment strategies for these high-risk patients. We further propose a practical and step-wise approach to Ph-like ALL genetic testing to facilitate the identification and allocation of patients to appropriate clinical trials of TKI-based therapies or commercially available drugs. Although the majority of patients with Ph-like ALL can be successfully identified via current clinical assays by the end of induction chemotherapy, increasing diagnostic efficiency and sensitivity and decreasing time to test resulting will facilitate earlier therapeutic intervention and may improve clinical outcomes for these high-risk patients.

A three-gene signature might predict prognosis in patients with acute myeloid leukemia

The identification of effective signatures is crucial to predict the prognosis of acute myeloid leukemia (AML). The investigation aimed to identify a new signature for AML prognostic prediction by using the three-gene expression (octamer-binding transcription factor 4 (OCT4), POU domain type 5 transcription factor 1B (POU5F1B) and B-cell-specific Moloney murine leukemia virus integration site-1 pseudogene 1 (BMI1P1). The expressions of genes were obtained from our previous study. Only the specimens in which three genes were all expressed were included in this research. A three-gene signature was constructed by the multivariate Cox regression analyses to divide patients into high-risk and low-risk groups. Receiver operating characteristic (ROC) analysis of the three-gene signature (area under ROC curve (AUC) = 0.901, 95% CI: 0.821–0.981, P<0.001) indicated that it was a more valuable signature for distinguishing between patients and controls than any of the three genes. Moreover, white blood cells (WBCs, P=0.004), platelets (PLTs, P=0.017), percentage of blasts in bone marrow (BM) (P=0.011) and complete remission (CR, P=0.027) had significant differences between two groups. Furthermore, high-risk group had shorter leukemia-free survival (LFS) and overall survival (OS) than low-risk group (P=0.026; P=0.006), and the three-gene signature was a prognostic factor. Our three-gene signature for prognosis prediction in AML may serve as a prognostic biomarker.

Not Only Mutations Matter: Molecular Picture of Acute Myeloid Leukemia Emerging from Transcriptome Studies

The last two decades of genome-scale research revealed a complex molecular picture of acute myeloid leukemia (AML). On the one hand, a number of mutations were discovered and associated with AML diagnosis and prognosis; some of them were introduced into diagnostic tests. On the other hand, transcriptome studies, which preceded AML exome and genome sequencing, remained poorly translated into clinics. Nevertheless, gene expression studies significantly contributed to the elucidation of AML pathogenesis and indicated potential therapeutic directions. The power of transcriptomic approach lies in its comprehensiveness; we can observe how genome manifests its function in a particular type of cells and follow many genes in one test. Moreover, gene expression measurement can be combined with mutation detection, as high-impact mutations are often present in transcripts. This review sums up 20 years of transcriptome research devoted to AML. Gene expression profiling (GEP) revealed signatures distinctive for selected AML subtypes and uncovered the additional within-subtype heterogeneity. The results were particularly valuable in the case of AML with normal karyotype which concerns up to 50% of AML cases. With the use of GEP, new classes of the disease were identified and prognostic predictors were proposed. A plenty of genes were detected as overexpressed in AML when compared to healthy control, including KIT, BAALC, ERG, MN1, CDX2, WT1, PRAME, and HOX genes. High expression of these genes constitutes usually an unfavorable prognostic factor. Upregulation of FLT3 and NPM1 genes, independent on their mutation status, was also reported in AML and correlated with poor outcome. However, transcriptome is not limited to the protein-coding genes; other types of RNA molecules exist in a cell and regulate genome function. It was shown that microRNA (miRNA) profiles differentiated AML groups and predicted outcome not worse than protein-coding gene profiles. For example, upregulation of miR-10a, miR-10b, and miR-196b and downregulation of miR-192 were found as typical of AML with NPM1 mutation whereas overexpression of miR-155 was associated with FLT3-internal tandem duplication (FLT3-ITD). Development of high-throughput technologies and microarray replacement by next generation sequencing (RNA-seq) enabled uncovering a real variety of leukemic cell transcriptomes, reflected by gene fusions, chimeric RNAs, alternatively spliced transcripts, miRNAs, piRNAs, long noncoding RNAs (lncRNAs), and their special type, circular RNAs. Many of them can be considered as AML biomarkers and potential therapeutic targets. The relations between particular RNA puzzles and other components of leukemic cells and their microenvironment, such as exosomes, are now under investigation. Hopefully, the results of this research will shed the light on these aspects of AML pathogenesis which are still not completely understood.

The WNT Pathway Is Relevant for the BCR-ABL1-Independent Resistance in Chronic Myeloid Leukemia

Notwithstanding the introduction of Tyrosine Kinase Inhibitors (TKIs) revolutionized the outcome of Chronic Myeloid Leukemia (CML), one third of patients still suspends treatment for failure response. Recent research demonstrated that several BCR/ABL1-independent mechanisms can sustain resistance, but the relationship between these mechanisms and the outcome has not yet been fully understood. This study was designed to evaluate in a “real-life” setting if a change of expression of several genes involved in the WNT/BETA-CATENIN, JAK-STAT, and POLYCOMB pathways might condition the outcome of CML patients receiving TKIs. Thus, the expression of 255 genes, related to the aforementioned pathways, was measured by quantitative PCR after 6 months of therapy and compared with levels observed at diagnosis in 11 CML patients, in order to find possible correlations with quality of response to treatment and event-free-survival (EFS). These results were then re-analyzed by the principal component method (PCA) for tempting to better cluster resistant cases. After 12 months of therapy, 6 patients achieved an optimal response and 5 were “resistant;” after application of both statistical methods, it was evident that in all pathways a significant overall up-regulation occurred, and that WNT was the pathway mostly responsible for the TKIs resistance. Indeed, 100% of patients with a “low” up-regulation of this pathway achieved an optimal response vs. 33% of those who showed a “high” gene over-expression (p = 0.016). Analogously, the 24-months EFS resulted significantly influenced by the degree of up-regulation of the WNT signaling: all patients with a “low” up-regulation were event-free vs. 33% of those who presented a “high” gene expression (p = 0.05). In particular, the PCA analysis confirmed the role of WNT pathway and showed that the most significantly up-regulated genes with negative prognostic value were DKK, WNT6, WISP1, and FZD8. In conclusion, our results sustain the need of a wide and multitasking approach in order to understand the resistance mechanisms in CML.

BCR/ABL1-like acute lymphoblastic leukemia: How to diagnose and treat?

BCR/ABL1-like acute lymphoblastic leukemia (ALL) accounts for 15% to 30% of B-lineage ALL, with a peak of incidence occurring in adolescence. This subgroup of patients is characterized by a peculiar transcriptional profile that resembles that of true BCR/ABL1-positive cases, and have a heterogeneous genetic background and a poor outcome. Next-generation sequencing studies have demonstrated that the majority of patients carry rearrangements of tyrosine kinases or cytokine receptors and mutations of janus kinase (JAK)/signal transducer and activator of transcription (STAT), thus opening the way to the possible use of targeted therapeutic approaches. However, several issues remain unresolved at both the diagnostic and therapeutic level, such as the definition of a standardized method to identify BCR/ABL1-like ALL and the design of ad hoc clinical trials examining tyrosine kinase inhibitors or other tailored treatments. These aspects are discussed in this review.

Effects of endoplasmic reticulum stress on autophagy and apoptosis of human leukemia cells via inhibition of the PI3K/AKT/mTOR signaling pathway

The present study aimed to explore the regulatory effects of endoplasmic reticulum stress (ERS) on the phosphoinositide 3‑kinase (PI3K)/AKT serine/threonine kinase 1 (AKT)/mammalian target of rapamycin (mTOR) signaling pathway, and its subsequent effects on autophagy and apoptosis of human leukemia cells. Human leukemia cells were cultured and treated with various concentrations of tunicamycin for 0, 24, 48, 72 and 90 h. Subsequently, human leukemia cells were assigned into the ER activation group, which was treated with 100 ng/ml tunicamycin, the ER activation + TO901317 (PI3K inhibitor) group, and the control group. An MTT assay was conducted to detect cell proliferation. In addition, a monodansylcadaverine (MDC) assay was used to detect the formation of autophagosomes and Annexin V‑fluorescein isothiocyanate/propidium iodide double staining was used to examine cell apoptosis. Western blotting was performed to detect the expression levels of 78‑kDa glucose‑regulated protein (GRP78), phosphorylated (p)‑protein kinase R‑like endoplasmic reticulum kinase (PERK), p‑α subunit of eukaryotic initiation factor 2 (eIF2α), microtubule‑associated protein 1A/1B‑light chain 3 (LC3), caspase‑3, CCAAT‑enhancer‑binding protein homologous protein (CHOP), PI3K, AKT and mTOR. Treatment with 100 ng/ml tunicamycin for 72 h was considered the optimal condition for further experiments. Compared with in cells prior to treatment, human leukemia cells treated with tunicamycin exhibited increased expression of p‑PERK, p‑eIF2α and GRP78 after 72 h (P<0.05). In addition, the expression levels of mTOR, AKT and PI3K were decreased in the ER activation group compared with in the control and ER activation + TO901317 groups (P<0.05). Compared with in the control group, cell proliferation was inhibited and MDC fluorescence intensity was increased in the ER activation group (P<0.05). Furthermore, compared with in the control and ER activation + TO901317 groups, western blotting indicated that the expression levels of LC3‑II were increased in the ER activation group (P<0.05). The apoptotic rate was also higher in the ER activation group compared with in the control group (P<0.05), and caspase‑3 and CHOP expression was elevated in the ER activation group (P<0.05). These findings indicated that ERS may induce autophagy and apoptosis of human leukemia cells via inhibiting the PI3K/AKT/mTOR signaling pathway.

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.

High-Dimensional Profiling for Computational Diagnosis

New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study.

Silencing of GATA3 defines a novel stem cell-like subgroup of ETP-ALL

Background

GATA3 is pivotal for the development of T lymphocytes. While its effects in later stages of T cell differentiation are well recognized, the role of GATA3 in the generation of early T cell precursors (ETP) has only recently been explored. As aberrant GATA3 mRNA expression has been linked to cancerogenesis, we investigated the role of GATA3 in early T cell precursor acute lymphoblastic leukemia (ETP-ALL).

Methods

We analyzed GATA3 mRNA expression by RT-PCR (n = 182) in adult patients with T-ALL. Of these, we identified 70 of 182 patients with ETP-ALL by immunophenotyping. DNA methylation was assessed genome wide (Illumina Infinium® HumanMethylation450 BeadChip platform) in 12 patients and GATA3-specifically by pyrosequencing in 70 patients with ETP-ALL. The mutational landscape of ETP-ALL with respect to GATA3 expression was investigated in 18 patients and validated by Sanger sequencing in 65 patients with ETP-ALL. Gene expression profiles (Affymetrix Human genome U133 Plus 2.0) of an independent cohort of adult T-ALL (n = 83) were used to identify ETP-ALL and investigate GATA3^low and GATA3^high expressing T-ALL patients. In addition, the ETP-ALL cell line PER-117 was investigated for cytotoxicity, apoptosis, GATA3 mRNA expression, DNA methylation, and global gene expression before and after treatment with decitabine.

Results

In our cohort of 70 ETP-ALL patients, 33 % (23/70) lacked GATA3 expression and were thus defined as GATA3^low. DNA methylation analysis revealed a high degree of GATA3 CpG island methylation in GATA3^low compared with GATA3^high ETP-ALL patients (mean 46 vs. 21 %, p < 0.0001). Genome-wide expression profiling of GATA3^low ETP-ALL exhibited enrichment of myeloid/lymphoid progenitor (MLP) and granulocyte/monocyte progenitor (GMP) genes, while T cell-specific signatures were downregulated compared to GATA3^high ETP-ALL. Among others, FLT3 expression was upregulated and mutational analyses demonstrated a high rate (79 %) of FLT3 mutations. Hypomethylating agents induced reversal of GATA3 silencing, and gene expression profiling revealed downregulation of hematopoietic stem cell genes and upregulation of T cell differentiation.

Conclusions

We propose GATA3^low ETP-ALL as a novel stem cell-like leukemia with implications for the use of myeloid-derived therapies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-016-0324-8) contains supplementary material, which is available to authorized users.

Adults with Philadelphia chromosome-like acute lymphoblastic leukemia frequently have IGH-CRLF2 and JAK2 mutations, persistence of minimal residual disease and poor prognosis

Philadelphia-like B-cell precursor acute lymphoblastic leukemia (Ph-like ALL) is characterized by distinct genetic alterations and inferior prognosis in children and younger adults. The purpose of this study was a genetic and clinical characterization of Ph-like ALL in adults. Twenty-six (13%) of 207 adult patients (median age: 42 years) with B-cell precursor ALL (BCP-ALL) were classified as having Ph-like ALL using gene expression profiling. The frequency of Ph-like ALL was 27% among 95 BCP-ALL patients negative for BCR-ABL1 and KMT2A-rearrangements. IGH-CRLF2 rearrangements (6/16; P=0.002) and mutations in JAK2 (7/16; P<0.001) were found exclusively in the Ph-like ALL subgroup. Clinical and outcome analyses were restricted to patients treated in German Multicenter Study Group for Adult ALL (GMALL) trials 06/99 and 07/03 (n=107). The complete remission rate was 100% among both Ph-like ALL patients (n=19) and the "remaining BCP-ALL" cases (n=40), i.e. patients negative for BCR-ABL1 and KMT2A-rearrangements and the Ph-like subtype. Significantly fewer Ph-like ALL patients reached molecular complete remission (33% versus 79%; P=0.02) and had a lower probability of continuous complete remission (26% versus 60%; P=0.03) and overall survival (22% versus 64%; P=0.006) at 5 years compared to the remaining BCP-ALL patients. The profile of genetic lesions in adults with Ph-like ALL, including older adults, resembles that of pediatric Ph-like ALL and differs from the profile in the remaining BCP-ALL. Our study is the first to demonstrate that Ph-like ALL is associated with inferior outcomes in intensively treated older adult patients. Ph-like adult ALL should be recognized as a distinct, high-risk entity and further research on improved diagnostic and therapeutic approaches is needed. (NCT00199056, NCT00198991).

Diagnosis and subclassification of acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a disseminated malignancy of B- or T-lymphoblasts which imposes a rapid and accurate diagnostic process to support an optimal risk-oriented therapy and thus increase the curability rate. The need for a precise diagnostic algorithm is underlined by the awareness that both ALL therapy and related success rates may vary greatly between ALL subsets, from standard chemotherapy in patients with standard-risk ALL, to allotransplantation (SCT) and targeted therapy in high-risk patients and cases expressing suitable biological targets, respectively. This review summarizes how best to identify ALL and the most relevant ALL subsets.

Genomic characterization of acute leukemias

Over the past two decades, hematologic malignancies have been extensively evaluated due to the introduction of powerful technologies, such as conventional karyotyping, FISH analysis, gene and microRNA expression profiling, array comparative genomic hybridization and SNP arrays, and next-generation sequencing (including whole-exome sequencing and RNA-seq). These analyses have allowed for the refinement of the mechanisms underlying the leukemic transformation in several oncohematologic disorders and, more importantly, they have permitted the definition of novel prognostic algorithms aimed at stratifying patients at the onset of disease and, consequently, treating them in the most appropriate manner. Furthermore, the identification of specific molecular markers is opening the door to targeted and personalized medicine. The most important findings on novel acquisitions in the context of acute lymphoblastic leukemia of both B and T lineage and de novo acute myeloid leukemia are described in this review.

The evolving landscape in the therapy of acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous clonal disorder of myeloid precursors arrested in their maturation, creating a diverse disease entity with a wide range of responses to historically standard treatment approaches. While significant progress has been made in characterizing and individualizing the disease at diagnosis to optimally inform those affected, progress in treatment to reduce relapse and induce remission has been limited thus far. In addition to a brief summary of the factors that shape prognostication at diagnosis, this review attempts to expand on the current therapies under investigation that have shown promise in treating AML, including hypomethylating agents, gemtuzumab ozogamicin, FLT3 tyrosine kinase inhibitors, antisense oligonucleotides, and other novel therapies, including aurora kinases, mTOR and PI3 kinase inhibitors, PIM kinase inhibitors, HDAC inhibitors, and IDH targeted therapies. With these, and undoubtedly many others in the future, it is the hope that by combining more accurate prognostication with more effective therapies, patients will begin to have a different, and more complete, outlook on their disease that allows for safer and more successful treatment strategies.

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.

Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure

DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.

Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study

PURPOSE

To identify a robust prognostic gene expression signature as an independent predictor of survival of patients with acute myeloid leukemia (AML) and use it to improve established risk classification.

PATIENTS AND METHODS

Four independent sets totaling 499 patients with AML carrying various cytogenetic and molecular abnormalities were used as training sets. Two independent patient sets composed of 825 patients were used as validation sets. Notably, patients from different sets were treated with different protocols, and their gene expression profiles were derived using different microarray platforms. Cox regression and Kaplan-Meier methods were used for survival analyses.

RESULTS

A prognostic signature composed of 24 genes was derived from a meta-analysis of Cox regression values of each gene across the four training sets. In multivariable models, a higher sum value of the 24-gene signature was an independent predictor of shorter overall (OS) and event-free survival (EFS) in both training and validation sets (P < .01). Moreover, this signature could substantially improve the European LeukemiaNet (ELN) risk classification of AML, and patients in three new risk groups classified by the integrated risk classification showed significantly (P < .001) distinct OS and EFS.

CONCLUSION

Despite different treatment protocols applied to patients and use of different microarray platforms for expression profiling, a common prognostic gene signature was identified as an independent predictor of survival of patients with AML. The integrated risk classification incorporating this gene signature provides a better framework for risk stratification and outcome prediction than the ELN classification.

High expression of lymphoid enhancer-binding factor-1 (LEF1) is a novel favorable prognostic factor in cytogenetically normal acute myeloid leukemia

Lymphoid enhancer-binding factor-1 (LEF1) is a key transcription factor of Wnt signaling. We recently showed that aberrant LEF1 expression induces acute myeloid leukemia (AML) in mice, and found high LEF1 expression in a subset of cytogenetically normal AML (CN-AML) patients. Whether LEF1 expression associates with clinical and molecular patient characteristics and treatment outcomes remained unknown. We therefore studied LEF1 expression in 210 adults with CN-AML treated on German AML Cooperative Group trials using microarrays. High LEF1 expression (LEF1high) associated with significantly better relapse-free survival (RFS; P < .001), overall survival (OS; P < .001), and event-free survival (EFS; P < .001). In multivariable analyses adjusting for established prognosticators, LEF1high status remained associated with prolonged RFS (P = .007), OS (P = .01), and EFS (P = .003). In an independent validation cohort of 196 CN-AML patients provided by the German-Austrian AML Study Group, LEF1high patients had significantly longer OS (P = .02) and EFS (P = .04). We validated the prognostic relevance of LEF1 expression by quantitative PCR, thereby providing a clinically applicable platform to incorporate this marker into future risk-stratification systems for CN-AML. Gene-expression profiling and immunophenotyping revealed up-regulation of lymphopoiesis-related genes and lymphoid cell-surface antigens in LEF1high patients. In summary, we provide evidence that high LEF1 expression is a novel favorable prognostic marker in CN-AML.

Imatinib therapy of chronic myeloid leukemia restores the expression levels of key genes for DNA damage and cell-cycle progression

BACKGROUND

Chronic myeloid leukemia (CML) is a malignant clonal disorder of the hematopoietic system caused by the expression of the BCR/ABL fusion oncogene. It is well known that CML cells are genetically unstable. However, the mechanisms by which these cells acquire genetic alterations are poorly understood. Imatinib mesylate is the standard therapy for newly diagnosed CML patients. Imatinib mesylate targets the oncogenic kinase activity of BCR-ABL.

OBJECTIVE

To study the gene expression profile of bone marrow hematopoietic cells in the same patients with CML before and 1 month after imatinib therapy.

METHODS

Samples from patients with CML were analyzed using Affymetrix GeneChip Expression Arrays.

RESULTS

A total of 594 differentially expressed genes, most of which (393 genes) were downregulated, as a result of imatinib therapy were observed.

CONCLUSION

The blockade of oncoprotein Bcr-Abl by imatinib could cause a decrease in the expression of key DNA repair genes and substantially modify the expression profile of the bone marrow cells in the first days of therapy.

Comparative proteome analysis of acute myeloid leukemia with and without maturation

Acute myeloid leukemia (AML) is a severe, rapidly progressing disease triggered by blocking granulocyte or monocyte differentiation and maturation. Because of its heterogeneity, AML is divided into a number of subtypes. Unfortunately, so far very few correlations have been found between AML classification and its clinical course or patient response to treatment. In addition, as yet only a few subtype-specific AML biomarkers have been discovered. To solve these problems here, we focused on two AML subtypes M1 and M2 that are especially difficult to differentiate. Using 2D electrophoresis and mass spectrometry, we analyzed the protein profiles of peripheral blood (PB) and/or bone marrow (BM) samples collected from 38 AML-M1/M2 patients and 17 healthy volunteers. Comparative analysis of AML-M1/M2 and control PB/BM cells revealed 25 proteins that accumulated differentially. Hierarchical clustering of proteomic results clearly divided the AML samples into 2 groups (M1 and M2). Annexin III, L-plastin and 6-phosphogluconate dehydrogenase were found only in the M2 group. We also observed that the levels of annexin I and actin gamma 1 were correlated with resistance to treatment and the time of relapse. It appears that these five proteins can serve as potential AML biomarkers.

RUNX1 mutations in cytogenetically normal acute myeloid leukemia are associated with a poor prognosis and up-regulation of lymphoid genes

BACKGROUND

The RUNX1 (AML1) gene is a frequent mutational target in myelodysplastic syndromes and acute myeloid leukemia. Previous studies suggested that RUNX1 mutations may have pathological and prognostic implications.

DESIGN AND METHODS

We screened 93 patients with cytogenetically normal acute myeloid leukemia for RUNX1 mutations by capillary sequencing of genomic DNA. Mutation status was then correlated with clinical data and gene expression profiles.

RESULTS

We found that 15 out of 93 (16.1%) patients with cytogenetically normal acute myeloid leukemia had RUNX1 mutations. Seventy-three patients were enrolled in the AMLCG-99 trial and carried ten RUNX1 mutations (13.7%). Among these 73 patients RUNX1 mutations were significantly associated with older age, male sex, absence of NPM1 mutations and presence of MLL-partial tandem duplications. Moreover, RUNX1-mutated patients had a lower complete remission rate (30% versus 73% P=0.01), lower relapse-free survival rate (3-year relapse-free survival 0% versus 30.4%; P=0.002) and lower overall survival rate (3-year overall survival 0% versus 34.4%; P<0.001) than patients with wild-type RUNX1. RUNX1 mutations remained associated with shorter overall survival in a multivariate model including age and the European Leukemia Net acute myeloid leukemia genetic classification as covariates. Patients with RUNX1 mutations showed a unique gene expression pattern with differential expression of 85 genes. The most prominently up-regulated genes in patients with RUNX1-mutated cytogenetically normal acute myeloid leukemia include lymphoid regulators such as HOP homeobox (HOPX), deoxynucleotidyltransferase (DNTT, terminal) and B-cell linker (BLNK), indicating lineage infidelity.

CONCLUSIONS

Our findings firmly establish that RUNX1 mutations are a marker of poor prognosis and provide insights into the pathogenesis of RUNX1 mutation-positive acute myeloid leukemia.

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples

Background

Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified.

Methods

Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n = 101) and/or poor quality control criteria (n = 10) (test set).

Results

With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load.

Conclusion

Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.

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.

Molecular genetics of adult acute myeloid leukemia: prognostic and therapeutic implications

Molecular analyses of leukemic blasts from patients with acute myeloid leukemia (AML) have revealed a striking heterogeneity with regard to the presence of acquired gene mutations and changes in gene and microRNA expression. Multiple submicroscopic genetic alterations with prognostic significance have been discovered. Application of gene- and microRNA profiling has identified genome-wide expression signatures that separate cytogenetic and molecular subsets of patients with AML into previously unrecognized biologic and/or prognostic subgroups. These and similar future findings are likely to have a major impact on the clinical management of AML because many of the identified genetic alterations not only represent independent prognosticators, but also may constitute targets for specific therapeutic intervention. In this report, we review genetic findings in AML and discuss their clinical implications.

Acute myeloid leukemia diagnosis in the 21st century

CONTEXT

Rapid advances in understanding the molecular biology of acute myeloid leukemia are transforming the approach to diagnosis, prognostication, and treatment of these cases.

OBJECTIVE

To briefly review the current state of AML classification with a particular emphasis on the role of molecular studies and their impact on the management of acute myeloid leukemia and other malignancies.

DATA SOURCES

Current literature and experience of the authors.

CONCLUSIONS

While morphology, immunophenotyping, cytogenetics, and clinical history continue to play an important role, an increasing number of molecular tests are now required to properly classify these cases.

Information technology solutions for integration of biomolecular and clinical data in the identification of new cancer biomarkers and targets for therapy

The quest for new cancer biomarkers and targets for therapy requires not only the aggregation and analysis of heterogeneous biomolecular data but also integration of clinical data. In this review we highlight information technology solutions for the integration of biomolecular and clinical data and focus on a solution at the departmental level, i.e., decentralized and medium-scale solution for groups of labs working on a specific topic. Both, hardware and software requirements are described as well as bioinformatics methods and tools for the data analysis. The highlighted IT solutions include storage architecture, high-performance computing, and application servers. Additionally, following computational approaches for data integration are reviewed: data aggregation, integrative data analysis including methodological aspects as well as examples, biomolecular pathways and network reconstruction, and mathematical modelling. Finally, a case study in cancer immunology including the used computational methods is shown, demonstrating how IT solutions for integrating biomolecular and clinical data can help to identify new cancer biomarkers for improving diagnosis and predicting clinical outcome.

In vitro transformation of primary human CD34+ cells by AML fusion oncogenes: early gene expression profiling reveals possible drug target in AML

Different fusion oncogenes in acute myeloid leukemia (AML) have distinct clinical and laboratory features suggesting different modes of malignant transformation. Here we compare the in vitro effects of representatives of 4 major groups of AML fusion oncogenes on primary human CD34+ cells. As expected from their clinical similarities, MLL-AF9 and NUP98-HOXA9 had very similar effects in vitro. They both caused erythroid hyperplasia and a clear block in erythroid and myeloid maturation. On the other hand, AML1-ETO and PML-RARA had only modest effects on myeloid and erythroid differentiation. All oncogenes except PML-RARA caused a dramatic increase in long-term proliferation and self-renewal. Gene expression profiling revealed two distinct temporal patterns of gene deregulation. Gene deregulation by MLL-AF9 and NUP98-HOXA9 peaked 3 days after transduction. In contrast, the vast majority of gene deregulation by AML1-ETO and PML-RARA occurred within 6 hours, followed by a dramatic drop in the numbers of deregulated genes. Interestingly, the p53 inhibitor MDM2 was upregulated by AML1-ETO at 6 hours. Nutlin-3, an inhibitor of the interaction between MDM2 and p53, specifically inhibited the proliferation and self-renewal of primary human CD34+ cells transduced with AML1-ETO, suggesting that MDM2 upregulation plays a role in cell transformation by AML1-ETO. These data show that differences among AML fusion oncogenes can be recapitulated in vitro using primary human CD34+ cells and that early gene expression profiling in these cells can reveal potential drug targets in AML.

Multilineage dysplasia has no impact on biologic, clinicopathologic, and prognostic features of AML with mutated nucleophosmin (NPM1)

NPM1-mutated acute myeloid leukemia (AML) is a provisional entity in the 2008 World Health Organization (WHO) classification of myeloid neoplasms. The significance of multilineage dysplasia (MLD) in NPM1-mutated AML is unclear. Thus, in the 2008 WHO classification, NPM1-mutated AML with MLD is classified as AML with myelodysplasia (MD)–related changes (MRCs). We evaluated morphologically 318 NPM1-mutated AML patients and found MLD in 23.3%. Except for a male predominance and a lower fms-related tyrosine kinase 3–internal tandem duplication (FLT3-ITD) incidence in the MLD+ group, no differences were observed in age, sex, cytogenetics, and FLT3-–tyrosine kinase domain between NPM1-mutated AML with and without MLD. NPM1-mutated AML with and without MLD showed overlapping immunophenotype (CD34 negativity) and gene expression profile (CD34 down-regulation, HOX genes up-regulation). Moreover, overall and event-free survival did not differ among NPM1-mutated AML patients independently of whether they were MLD+ or MLD−, the NPM1-mutated/FLT3-ITD negative genotype showing the better prognosis. Lack of MLD impact on survival was confirmed by multivariate analysis that highlighted FLT3-ITD as the only significant prognostic parameter in NPM1-mutated AML. Our findings indicate that NPM1 mutations rather than MLD dictate the distinctive features of NPM1-mutated AML. Thus, irrespective of MLD, NPM1-mutated AML represents one disease entity clearly distinct from AML with MRCs.

Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group

PURPOSE

The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies.

METHODS

The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling-based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients.

RESULTS

On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods.

CONCLUSION

Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias.

Gene expression-based classification as an independent predictor of clinical outcome in juvenile myelomonocytic leukemia

PURPOSE Juvenile myelomonocytic leukemia (JMML) is a rare early childhood myelodysplastic/myeloproliferative disorder characterized by an aggressive clinical course. Age and hemoglobin F percentage at diagnosis have been reported to predict both survival and outcome after hematopoietic stem cell transplantation (HSCT). However, no genetic markers with prognostic relevance have been identified so far. We applied gene expression-based classification to JMML samples in order to identify prognostic categories related to clinical outcome. PATIENTS AND METHODS Samples of 44 patients with JMML were available for microarray gene expression analysis. A diagnostic classification (DC) model developed for leukemia and myelodysplastic syndrome classification was used to classify the specimens and identify prognostically relevant categories. Statistical analysis was performed to determine the prognostic value of the classification and the genes identifying prognostic categories were further analyzed through R software. RESULTS The samples could be divided into two major groups: 20 specimens were classified as acute myeloid leukemia (AML) -like and 20 samples as nonAML-like. Four patients could not be assigned to a unique class. The 10-year probability of survival after diagnosis of AML-like and nonAML-like patients was significantly different (7% v 74%; P = .0005). Similarly, the 10-year event-free survival after HSCT was 6% for AML-like and 63% for nonAML-like patients (P = .0010). CONCLUSION Gene expression-based classification identifies two groups of patients with JMML with distinct prognosis outperforming all known clinical parameters in terms of prognostic relevance. Gene expression-based classification could thus be prospectively used to guide clinical/therapeutic decisions.

CD11b is a therapy resistance- and minimal residual disease-specific marker in precursor B-cell acute lymphoblastic leukemia

A consistently increased mRNA expression of the adhesion receptor CD11b is a hallmark of the reported genomewide gene expression changes in precursor B-cell acute lymphoblastic leukemia (PBC-ALL) after 1 week of induction therapy. To investigate its clinical relevance, CD11b protein expression in leukemic blasts has been prospectively measured at diagnosis (159 patients) and during therapy (53 patients). The initially heterogeneous expression of CD11b inversely correlated with cytoreduction rates measured at clinically significant time points of induction therapy in the ALL-Berlin-Frankfurt-Münster 2000 protocol. CD11b positivity conferred a 5-fold increased risk of minimal residual disease (MRD) after induction therapy (day 33) and of high-risk group assignment after consolidation therapy (day 78). In the multivariate analysis CD11b expression was an independent prognostic factor compared with other clinically relevant parameters at diagnosis. During therapy, CD11b expression increased early in most ALL cases and remained consistently increased during induction/consolidation therapy. In more than 30% of MRD-positive cases, the CD11b expression on blast cells exceeded that of mature memory B cells and improved the discrimination of residual leukemic cells from regenerating bone marrow. Taken together, CD11b expression has considerable implications for prognosis, treatment response monitoring, and MRD detection in childhood PBC-ALL.

Genes predictive of outcome and novel molecular classification schemes in adult acute myeloid leukemia

The pretreatment karyotype of leukemic blasts is currently the key determinant in therapy decision making in acute myeloid leukemia (AML). The World Health Organization (WHO) has recognized this important information by including, besides clinical, cytological, cytochemical, and immunophenotypical features, recurrent cytogenetic abnormalities in its classification (Table 1). However, although the WHO defines important biologically and clinically relevant entities, the prognostic value of some of the well-defined cytogenetic subgroups is partially masked in the WHO classification. Moreover, in the recent past a number of novel molecular aberrations with marked prognostic value, which are not yet incorporated in the WHO classifications have been identified. These molecular abnormalities include mutations (e.g., in FLT3, c-KIT, and NPM1), partial duplications (e.g., of MLL and FLT3), and abnormal expression of pathogenetic genes (e.g., EVI1, WT1, BCL2, MDR1, BAALC, and ERG). In addition, novel molecular approaches in genomics, like monitoring the expression levels of thousands of genes in parallel using DNA microarray technology, open possibilities for further refinement of prognostication of AML. Gene expression profiling in AML is already well established and has proven to be valuable to recognize various cytogenetic subtypes, discover novel AML subclasses, and predict clinical outcome. The current advances made in molecular understanding of AML will ultimately lead to a further refinement of prognostics of AML.

Quantitative DNA methylation predicts survival in adult acute myeloid leukemia

Acute myeloid leukemia (AML) is characterized by molecular heterogeneity that is not fully reflected in the current classification system. Recent insights point toward a significant role of aberrant DNA methylation in leukemogenesis. Therefore, we investigated the prognostic impact of DNA methylation in AML. To screen for promoter methylation in AML we applied a combination of base-specific cleavage biochemistry and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a powerful methodology allowing for quantitatively investigating DNA methylation status in a large series of both promoter regions and leukemia samples. We analyzed 92 genomic regions in 182 patient samples, correlated findings with clinical and molecular data, and validated the results in an independent cohort of 74 AML samples. Using this approach, we were able to identify novel leukemia subgroups based on distinct DNA methylation patterns. Furthermore, we defined a methylation-based outcome predictor for patient survival (P < .01) that in multivariable analysis provided independent prognostic information (hazard ratio, 1.52; 95% CI, 1.06-2.16). Here, we report the first large-scale methylation-based outcome predictor in AML, and thereby our findings support the use of genomic methylation markers for improved molecular classification and prognostication in adult AML.

Pharmacogenomics in acute myeloid leukemia

Acute myeloid leukemia (AML) in adults is a heterogeneous malignant pathology with a globally unfavorable prognosis. The classification of AML allows identification of subgroups with favorable prognosis. However, besides these specific subgroups, most patients will have an intermediate or unfavorable prognosis often resulting in induction failure, probably due to drug resistance of the leukemic blasts, and more frequently resulting in early relapse after achieving complete remission. This unfavorable situation leads to a strong need to develop new diagnostic and therapeutic options. However, development of these therapies and their efficient use requires a better understanding of the biology and the molecular pathogenesis of AML. Pharmacogenomics focuses on the genetic variation of drug-metabolizing enzymes, targets and transporters, and how these genetic variations interact to produce specific drug-related phenotypes. Potential genetic markers may serve to functionally subclassify patients by their disease and therefore influence the nature and intensity of treatment. This review summarizes important aspects of and recent advances in the field of pharmacogenomics in AML.