Current status of gene expression profiling in the diagnosis and management of acute leukaemia

Current status and trends in the diagnostics of AML and MDS

Diagnostics of acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) have recently been experiencing extensive modifications regarding the incorporation of next-generation sequencing (NGS) strategies into established diagnostic algorithms, classification and risk stratification systems, and minimal residual disease (MRD) detection. Considering the increasing arsenal of targeted therapies (e.g. FLT3 or IDH1/IDH2 inhibitors) for AML, timely and comprehensive molecular mutation screening has arrived in daily practice. Next-generation flow strategies allow for immunophenotypic minimal residual disease (MRD) monitoring with very high sensitivity. At the same time, standard diagnostic tools such as cytomorphology or conventional cytogenetics remain cornerstones for the diagnostic workup of myeloid malignancies. Herein, we summarize the most recent advances and new trends for the diagnostics of AML and MDS, discuss the difficulties, which accompany the integration of these new methods and their results into daily routine, and aim to define the role hemato-oncologists may play in this new diagnostic era.

A 4-gene expression prognostic signature might guide post-remission therapy in patients with intermediate-risk cytogenetic acute myeloid leukemia

In intermediate-risk cytogenetic acute myeloid leukemia (IRC-AML) patients, novel biomarkers to guide post-remission therapy are needed. We analyzed with high-density arrays 40 IRC-AML patients who received a non-allogeneic hematopoietic stem-cell transplantation-based post-remission therapy, and identified a signature that correlated with early relapse. Subsequently, we analyzed selected 187 genes in 49 additional IRC-AML patients by RT-PCR. BAALC, MN1, SPARC and HOPX overexpression correlated to refractoriness. BAALC or ALDH2 overexpression correlated to shorter overall survival (OS) (5-year OS: 33 ± 8.6% vs. 73.7 ± 10.1%, p = .006; 32 ± 9.3% vs. 66.4 ± 9.7%, p = .016), whereas GPR44 or TP53INP1 overexpression correlated to longer survival (5-year OS: 66.7 ± 10.3% vs. 35.4 ± 9.1%, p = .04; 58.3 ± 8.2% vs. 23.1 ± 11.7%, p = .029). A risk-score combining these four genes expression distinguished low-risk and high-risk patients (5-year OS: 79 ± 9% vs. 30 ± 8%, respectively; p = .001) in our cohort and in an independent set of patients from a public repository. Our 4-gene signature may add prognostic information and guide post-remission treatment in IRC-AML patients.

Prognostic stratification improvement by integrating ID1/ID3/IGJ gene expression signature and immunophenotypic profile in adult patients with B-ALL

Background

Survival of adults with B-Acute Lymphoblastic Leukemia requires accurate risk stratification of patients in order to provide the appropriate therapy. Contemporary techniques, using clinical and cytogenetic variables are incomplete for prognosis prediction.

Methods

To improve the classification of adult patients diagnosed with B-ALL into prognosis groups, two strategies were examined and combined: the expression of the ID1/ID3/IGJ gene signature by RT-PCR and the immunophenotypic profile of 19 markers proposed in the EuroFlow protocol by Flow Cytometry in bone marrow samples.

Results

Both techniques were correlated to stratify patients into prognostic groups. An inverse relationship between survival and expression of the three-genes signature was observed and an immunophenotypic profile associated with clinical outcome was identified. Markers CD10 and CD20 were correlated with simultaneous overexpression of ID1, ID3 and IGJ. Patients with simultaneous expression of the poor prognosis gene signature and overexpression of CD10 or CD20, had worse Event Free Survival and Overall Survival than patients who had either the poor prognosis gene expression signature or only CD20 or CD10 overexpressed.

Conclusion

By utilizing the combined evaluation of these two immunophenotypic markers along with the poor prognosis gene expression signature, the risk stratification can be significantly strengthened. Further studies including a large number of patients are needed to confirm these findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-017-0506-4) contains supplementary material, which is available to authorized users.

Introduction: the impact of molecular pathology on the practice of pathology

Molecular pathology is affecting and influencing the entire clinical laboratory. Furthermore, the union of pathology and molecular medicine continues to mature into an amalgam that will both define and serve the emerging field of personalized medicine. Advances in the understanding of pathobiology, high throughput automation, cost containment, and refined methodology will avail greater diagnostic and prognostic prowess and provide more efficient and appropriate therapeutic selection as well guide effective patient monitoring with respect to disease responses.

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.

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.

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.

Differential gene expression in acute lymphoblastic leukemia cells surviving allogeneic transplant

The effectiveness of allogeneic graft-versus-leukemia (GVL) activity in control of acute lymphoblastic leukemia is generally regarded as poor. One possible factor is dynamic adaptation of the leukemia cell to the allogeneic environment. This work tested the hypothesis that the pattern of gene expression in acute lymphoblastic leukemia cells in an allogeneic environment would differ from that in a non-allogeneic environment. Expression microarray studies were performed in murine B lineage acute lymphoblastic leukemia cells recovered from mice that had undergone allogeneic MHC-matched but minor histocompatibility antigen mismatched transplants. A limited number of genes were found to be differentially expressed in ALL cells surviving in the allogeneic environment. Functional analysis demonstrated that genes related to immune processes, antigen presentation, ubiquitination and GTPase function were significantly enriched. Several genes with known immune activities potentially relevant to leukemia survival (Ly6a/Sca-1, TRAIL and H2-T23) were examined in independent validation experiments. Increased expression in vivo in allogeneic hosts was observed, and could be mimicked in vitro with soluble supernatants of mixed lymphocyte reactions or interferon-gamma. The changes in gene expression were reversible when the leukemia cells were removed from the allogeneic environment. These findings suggest that acute lymphoblastic leukemia cells respond to cytokines present after allogeneic transplantation and that these changes may reduce the effectiveness of GVL activity.

Dynamic single-cell network profiles in acute myelogenous leukemia are associated with patient response to standard induction therapy

PURPOSE

Complete response to induction chemotherapy is observed in approximately 60% of patients with newly diagnosed non-M3 acute myelogenous leukemia (AML). However, no methods exist to predict with high accuracy at the individual patient level the response to standard AML induction therapy.

EXPERIMENTAL DESIGN

We applied single-cell network profiling (SCNP) using flow cytometry, a tool that allows a comprehensive functional assessment of intracellular signaling pathways in heterogeneous tissues, to two training cohorts of AML samples (n = 34 and 88) to predict the likelihood of response to induction chemotherapy.

RESULTS

In the first study, univariate analysis identified multiple signaling "nodes" (readouts of modulated intracellular signaling proteins) that correlated with response (i.e., AUC(ROC) > or = 0.66; P < or = 0.05) at a level greater than age. After accounting for age, similar findings were observed in the second study. For patients <60 years old, complete response was associated with the presence of intact apoptotic pathways. In patients > or =60 years old, nonresponse was associated with FLT3 ligand-mediated increase in phosphorylated Akt and phosphorylated extracellular signal-regulated kinase. Results were independent of cytogenetics, FLT3 mutational status, and diagnosis of secondary AML.

CONCLUSIONS

These data emphasize the value of performing quantitative SCNP under modulated conditions as a basis for the development of tests highly predictive for response to induction chemotherapy. SCNP provides information distinct from other known prognostic factors such as age, secondary AML, cytogenetics, and molecular alterations and is potentially combinable with the latter to improve clinical decision making. Independent validation studies are warranted.

Genetic tests to evaluate prognosis and predict therapeutic response in acute myeloid leukemia

Management of patients with acute myeloid leukemia relies on genetic tests that inform diagnosis and prognosis, predict response to therapy, and measure minimal residual disease. The value of genetics is reinforced in the revised 2008 World Health Organization acute myeloid leukemia classification scheme. The various analytic procedures-karyotype, fluorescence in situ hybridization, reverse transcription polymerase chain reaction, DNA sequencing, and microarray technology-each have advantages in certain clinical settings, and understanding their relative merits assists in specimen allocation and in effective utilization of health care resources. Karyotype and array technology represent genome-wide screens, whereas the other methods target specific prognostic features such as t(15;17) PML-RARA, t(8;21) RUNX1-RUNX1T1, inv(16) CBFB-MYH11, 11q23 MLL rearrangement, FLT3 internal tandem duplication, or NPM1 mutation. New biomarkers and pharmacogenetic tests are emerging. The pathologist's expertise is critical in 1) consulting with clinicians about test selection as well as specimen collection and handling; 2) allocating tissue for immediate testing and preserving the remaining specimen for any downstream testing that is indicated once morphology and other pertinent test results are known; 3) performing tests that maximize outcome based on the strengths and limitations of each assay in each available specimen type; and 4) interpreting and conveying results to the rest of the health care team in a format that facilitates clinical management. Acute myeloid leukemia leads the way for modern molecular medicine.

affyPara-a Bioconductor Package for Parallelized Preprocessing Algorithms of Affymetrix Microarray Data

Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule’s prediction quality honestly.

This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.

affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.