Quantitative-Morphological and Cytological Analyses in Leukemia

Leukemia, a blood cancer originating in the bone marrow, presents as a heterogeneous disease with highly variable survival rates. Leukemia is classified into major types based on the rate of cancerous cell growth and cell lineage: chronic or acute and myeloid or lymphoid leukemia. Histological and cytological analysis of the peripheral blood and the bone marrow can classify these major leukemia categories. However, histological analyses of patient biopsies and cytological microscopic assessment of blood and bone marrow smears are insufficient to diagnose leukemia subtypes and to direct therapy. Hence, more expensive and time-consuming diagnostic tools routinely complement histological-cytological analysis during a patient’s diagnosis. To extract more accurate and detailed information from patient tissue samples, digital pathology is emerging as a powerful tool to enhance biopsy- and smear-based decisions. Furthermore, digital pathology methods integrated with advances in machine learning enable new diagnostic features from leukemia patients’ histological and cytological slides and optimize patient classification, thus providing a cheaper, more robust, and faster diagnostic tool than current standards. This review summarizes emerging approaches to automatically diagnose leukemia from morphological and cytological-histological analyses

Modeling Protein Expression and Protein Signaling Pathways

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant sub-pathways in relation to the unfolding of the biological process under study

Proteoforms in Acute Leukemia: Evaluation of Age- and Disease-Specific Proteoform Patterns

Acute leukemia are a heterogeneous group of malignant diseases of the bone marrow that occur at all ages. Acute lymphoid leukemia (ALL) accounts for about 80% of all pediatric leukemia patients, whereas acute myeloid leukemia (AML) is more common in adults compared to pediatric patients. Despite similar patterns in the pathogenesis of acute leukemia in children and adults, clinical outcome in response to therapy differs substantially. Studying proteoforms in acute leukemia in children and adults, might identify similarities and differences in crucial signaling pathways that play a key role in the development or progression of the disease. In this chapter we will discuss how the study of proteoforms in acute leukemia could potentially contribute to a better understanding of the leukemogenesis, can help to identify effective targets for specific targeted treatment approaches in different subgroups of age and disease, and could aid the development of reliable biomarkers for prognostic stratification

Targeted therapy in acute myeloid leukemia:current status and new insights from a proteomic perspective

Introduction: The biological heterogeneity of acute myeloid leukemia (AML) complicates personalized medicine. Individual prognosis is typically based on the presence of chromosomal and genetic lesions. Nevertheless, these classifications often lack a priori information about response to therapy. Since the protein expression landscape reflects the functional activity state of cells, we hypothesize that analyzing this can be used for the identification of protein activity markers to provide better risk stratification as well as may provide targeted therapeutic guidance in AML. Areas covered: Herein, we review recently new adopted drugs in the treatment for AML and discuss how quantitative proteomic techniques may contribute to better therapeutic selection in AML. Expert commentary: The net functional state of the cell is defined by the activity of protein within all the pathways that are active in the cell. Recognition of the proteomic profile of the leukemic blast could, therefore, complement current classification systems by providing a better a priori description of what pathways are important within a cell as a guide to the selection of therapy for the patient

Evolution‐informed modeling improves outcome prediction for cancers

Despite wide applications of high‐throughput biotechnologies in cancer research, many biomarkers discovered by exploring large‐scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature’s test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution‐informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene‐specific, position‐specific, or allele‐specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous “omics” data to accelerate biomarker discoveries.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135247/1/eva12417_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135247/2/eva12417.pd

Epidermal growth factor receptor is expressed and active in a subset of acute myeloid leukemia

The epidermal growth factor receptor (EGFR) inhibitor erlotinib has been shown to induce complete remission of acute myeloid leukemia (AML) in two patients with concurrent lung cancer and raised attention for a role of EGFR in AML whereas a recent phase II clinical study with gefitinib in AML demonstrated a negative result on the outcome. However, from several studies, EGFR expression in AML is poorly defined and the role of EGFR in AML remains unclear. Herein, we report the results of EGFR expression in AML of large cohorts of adult and pediatric AML patients with the data of total protein and phosphorylation levels of EGFR. Our data conclude that there is the expression of EGFR at the protein level in a subset of AML, which was identified to be functionally active in similar to 15 % of AML patients. This suggests that future studies need to be conducted with a subset of AML patients characterized by high EGFR expression

Synergistic induction of apoptosis by simultaneous disruption of the Bcl-2 and MEK/MAPK pathways in acute myelogenous leukemia

: Recent studies suggest that the Bcl-2 and mitogen-activated protein kinase (MAPK) pathways together confer an aggressive, apoptosis-resistant phenotype on acute myelogenous leukemia (AML) cells. In this study, we analyzed the effects of simultaneous inhibition of these 2 pathways. In AML cell lines with constitutively activated MAPK, MAPK kinase (MEK) blockade by PD184352 strikingly potentiated the apoptosis induced by the small-molecule Bcl-2 inhibitor HA14-1 or by Bcl-2 antisense oligonucleotides. Isobologram analysis confirmed the synergistic nature of this interaction. Moreover, MEK blockade overcame Bcl-2 overexpression-mediated resistance to the proapoptotic effects of HA14-1. Most importantly, simultaneous exposure to PD184352 significantly (P =.01) potentiated HA14-1-mediated inhibition of clonogenic growth in all primary AML samples tested. These findings show that the Bcl-2 and MAPK pathways are relevant molecular targets in AML and that their concurrent inhibition could be developed into a new therapeutic strategy for this disease

Metabolic gatekeeper function of B-lymphoid transcription factors.

B-lymphoid transcription factors, such as PAX5 and IKZF1, are critical for early B-cell development, yet lesions of the genes encoding these transcription factors occur in over 80% of cases of pre-B-cell acute lymphoblastic leukaemia (ALL). The importance of these lesions in ALL has, until now, remained unclear. Here, by combining studies using chromatin immunoprecipitation with sequencing and RNA sequencing, we identify a novel B-lymphoid program for transcriptional repression of glucose and energy supply. Our metabolic analyses revealed that PAX5 and IKZF1 enforce a state of chronic energy deprivation, resulting in constitutive activation of the energy-stress sensor AMPK. Dominant-negative mutants of PAX5 and IKZF1, however, relieved this glucose and energy restriction. In a transgenic pre-B ALL mouse model, the heterozygous deletion of Pax5 increased glucose uptake and ATP levels by more than 25-fold. Reconstitution of PAX5 and IKZF1 in samples from patients with pre-B ALL restored a non-permissive state and induced energy crisis and cell death. A CRISPR/Cas9-based screen of PAX5 and IKZF1 transcriptional targets identified the products of NR3C1 (encoding the glucocorticoid receptor), TXNIP (encoding a glucose-feedback sensor) and CNR2 (encoding a cannabinoid receptor) as central effectors of B-lymphoid restriction of glucose and energy supply. Notably, transport-independent lipophilic methyl-conjugates of pyruvate and tricarboxylic acid cycle metabolites bypassed the gatekeeper function of PAX5 and IKZF1 and readily enabled leukaemic transformation. Conversely, pharmacological TXNIP and CNR2 agonists and a small-molecule AMPK inhibitor strongly synergized with glucocorticoids, identifying TXNIP, CNR2 and AMPK as potential therapeutic targets. Furthermore, our results provide a mechanistic explanation for the empirical finding that glucocorticoids are effective in the treatment of B-lymphoid but not myeloid malignancies. Thus, B-lymphoid transcription factors function as metabolic gatekeepers by limiting the amount of cellular ATP to levels that are insufficient for malignant transformation

Peptide microarray of pediatric acute myeloid leukemia is related to relapse and reveals involvement of DNA damage response and repair

The majority of acute myeloid leukemia (AML) patients suffer from relapse and the exact etiology of AML remains unclear. The aim of this study was to gain comprehensive insights into the activity of signaling pathways in AML. In this study, using a high-throughput PepChip™ Kinomics microarray system, pediatric AML samples were analyzed to gain insights of active signal transduction pathway. Unsupervised hierarchical cluster analysis separated the AML blast profiles into two clusters. These two clusters were independent of patient characteristics, whereas the cumulative incidence of relapse (CIR) was significantly higher in the patients belonging to cluster-2. In addition, cluster-2 samples showed to be significantly less sensitive to various chemotherapeutic drugs. The activated peptides in cluster-1 and cluster-2 reflected the activity of cell cycle regulation, cell proliferation, cell differentiation, apoptosis, PI3K/AKT, MAPK, metabolism regulation, transcription factors and GPCRs signaling pathways. The difference between two clusters might be explained by the higher cell cycle arrest response in cluster-1 patients and higher DNA repair mechanism in cluster-2 patients. In conclusion, our study identifies different signaling profiles in pediatric AML in relation with CIR involving DNA damage response and repair