A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia

Probable Treatment Targets for Diabetic Retinopathy Based on an Integrated Proteomic and Genomic Analysis

Purpose

Using previously approved medications for new indications can expedite the lengthy and expensive drug development process. We describe a bioinformatics pipeline that integrates genomics and proteomics platforms to identify already-approved drugs that might be useful to treat diabetic retinopathy (DR).

Methods

Proteomics analysis of vitreous humor samples from 12 patients undergoing pars plana vitrectomy for DR and a whole genome dataset (UKBiobank TOPMed-imputed) from 1330 individuals with DR and 395,155 controls were analyzed independently to identify biological pathways associated with DR. Common biological pathways shared between both datasets were further analyzed (STRING and REACTOME analyses) to identify target proteins for probable drug modulation. Curated target proteins were subsequently analyzed by the BindingDB database to identify chemical compounds they interact with. Identified chemical compounds were further curated through the Expasy SwissSimilarity database for already-approved drugs that interact with target proteins.

Results

The pathways in each dataset (proteomics and genomics) converged in the upregulation of a previously unknown pathway involved in DR (RUNX2 signaling; constituents MMP-13 and LGALS3), with an emphasis on its role in angiogenesis and blood-retina barrier. Bioinformatics analysis identified U.S. Food and Drug Administration (FDA)-approved medications (raltitrexed, pemetrexed, glyburide, probenecid, clindamycin hydrochloride, and ticagrelor) that, in theory, may modulate this pathway.

Conclusions

The bioinformatics pipeline described here identifies FDA-approved drugs that can be used for new alternative indications. These theoretical candidate drugs should be validated with experimental studies.

Translational Relevance

Our study suggests possible drugs for DR treatment based on an integrated proteomics and genomics pipeline. This approach can potentially expedite the drug discovery process by identifying already-approved drugs that might be used for new indications.

Drug Repositioning for the Treatment of Hematologic Disease: Limits, Challenges and Future Perspectives

Drug repositioning is a strategy to identify new uses for approved or investigational drugs that are used off-label outside the scope of the original medical indication. In this review, we report the most relevant studies about drug repositioning in hematology, reporting the signalling pathways and molecular targets of these drugs, and describing the biological mechanisms which are responsible for their anticancer effects. Although the majority of studies on drug repositioning in hematology concern acute myeloid leukemia and multiple myeloma, numerous studies are present in the literature on the possibility of using these drugs also in other hematological diseases, such as acute lymphoblastic leukemia, chronic myeloid leukemia, and lymphomas. Numerous anti-infectious drugs and chemical entities used for the therapy of neurological or endocrine diseases, oral antidiabetics, statins and medications used to treat high blood pressure and heart failure, bisphosphonate and natural substance such as artemisin and curcumin, have found a place in the treatment of hematological diseases. Moreover, several molecules drastically reversed the resistance of the tumor cells to the chemotherapeutic drugs both in vitro and in vivo.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

RAS-Responsive Element-Binding Protein 1 Blocks the Granulocytic Differentiation of Myeloid Leukemia Cells

RAS-responsive element-binding protein 1 (RREB1) is a transcription factor that is implicated in RAS signaling and multiple tumors. However, the role of RREB1 in acute myeloid leukemia has not been studied. We found that RREB1 is overexpressed in AML patients and myeloid leukemia cell lines (NB4 and HL-60), and RREB1 expression was significantly decreased during granulocytic differentiation of myeloid leukemia cells induced by all-trans retinoic acid (ATRA). Then we performed a RREB1 knockdown assay in NB4 and HL-60 cells; the results showed that knockdown of RREB1 upregulated expression of CD11b, CEBPβ, and microRNA-145 (miR-145), which hinted that knockdown of RREB1 enhanced granulocytic differentiation of myeloid leukemia cells. In addition, inhibitor of miR-145 can offset the enhanced effect on granulocytic differentiation mediated by downregulation of RREB1. These collective findings demonstrated that RREB1 blocks granulocytic differentiation of myeloid leukemia cells by inhibiting the expression of miR-145 and downstream targets of the RAS signal pathway. These may provide a promising therapeutic target for AML patients.

Responses of Transgenic Melatonin-Enriched Goats on LPS Stimulation and the Proteogenomic Profiles of Their PBMCs

The anti-inflammatory activity of melatonin (MT) has been well documented; however, little is known regarding endogenously occurring MT in this respect, especially for large animals. In the current study, we created a MT-enriched animal model (goats) overexpressing the MT synthetase gene Aanat. The responses of these animals to lipopolysaccharide (LPS) stimulation were systematically studied. It was found that LPS treatment exacerbated the inflammatory response in wild-type (WT) goats and increased their temperature to 40 °C. In addition, their granulocyte counts were also significantly elevated. In contrast, these symptoms were not observed in transgenic goats with LPS treatment. The rescue study with MT injection into WT goats who were treated with LPS confirmed that the protective effects in transgenic goats against LPS were attributed to a high level of endogenously produced MT. The proteomic analysis in the peripheral blood mononuclear cells (PBMCs) isolated from the transgenic animals uncovered several potential mechanisms. MT suppressed the lysosome formation as well as its function by downregulation of the lysosome-associated genes Lysosome-associated membrane protein 2 (LAMP2), Insulin-like growth factor 2 receptor (IGF2R), and Arylsulfatase B (ARSB). A high level of MT enhanced the antioxidant capacity of these cells to reduce the cell apoptosis induced by the LPS. In addition, the results also uncovered previously unknown information that showed that MT may have protective effects on some human diseases, including tuberculosis, bladder cancer, and rheumatoid arthritis, by downregulation of these disease-associated genes. All these observations warranted further investigations.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

LG-362B targets PML-RARα and blocks ATRA resistance of acute promyelocytic leukemia

Acute promyelocytic leukemia (APL) is a M3 subtype of acute myeloid leukemia (AML). Promyelocytic leukemia (PML)-retinoic acid receptor α (RARα) translocation generally occurs in APL patients and makes APL unique both for diagnosis and treatment. However, some conventional drugs like all-transretinoic acid (ATRA) and arsenic trioxide (ATO), as the preferred ones for APL therapy, induce irreversible resistance and responsible for clinical failure of complete remission. Herein, we screened a library of novel chemical compounds with structural diversity and discovered a novel synthetic small compound, named LG-362B, specifically inhibited the proliferation of APL and induced apoptosis. Notably, the differentiation arrest was also relieved by LG-362B in cultured APL cells and APL mouse models. Moreover, LG-362B overcame the ATRA resistance on cellular differentiation and transplantable APL mice. These positive effects were driven by caspases-mediated degradation of PML-RARα when treated with LG-362B, making it specific to APL and reasonable for ATRA resistance relief. We propose that LG-362B would be a potential candidate agent for the treatment of the relapsed APL with ATRA resistance in the future.

Inhibition of hypoxia inducible factors combined with all-trans retinoic acid treatment enhances glial transdifferentiation of neuroblastoma cells

Neuroblastoma (NBL) is a heterogeneous tumor characterized by a wide range of clinical manifestations. A high tumor cell differentiation grade correlates to a favorable stage and positive outcome. Expression of the hypoxia inducible factors HIF1-α (HIF1A gene) and HIF2-α (EPAS1 gene) and/or hypoxia-regulated pathways has been shown to promote the undifferentiated phenotype of NBL cells. Our hypothesis is that HIF1A and EPAS1 expression represent one of the mechanisms responsible for the lack of responsiveness of NBL to differentiation therapy. Clinically, high levels of HIF1A and EPAS1 expression were associated with inferior survival in two NBL microarray datasets, and patient subgroups with lower expression of HIF1A and EPAS1 showed significant enrichment of pathways related to neuronal differentiation. In NBL cell lines, the combination of all-trans retinoic acid (ATRA) with HIF1A or EPAS1 silencing led to an acquired glial-cell phenotype and enhanced expression of glial-cell differentiation markers. Furthermore, HIF1A or EPAS1 silencing might promote cell senescence independent of ATRA treatment. Taken together, our data suggest that HIF inhibition coupled with ATRA treatment promotes differentiation into a more benign phenotype and cell senescence in vitro. These findings open the way for additional lines of attack in the treatment of NBL minimal residue disease.

HIF factors cooperate with PML-RARα to promote acute promyelocytic leukemia progression and relapse

Acute promyelocytic leukemia (APL) is epitomized by the chromosomal translocation t(15;17) and the resulting oncogenic fusion protein PML-RARα. Although acting primarily as a transcriptional repressor, PML-RARα can also exert functions of transcriptional co-activation. Here, we find that PML-RARα stimulates transcription driven by HIF factors, which are critical regulators of adaptive responses to hypoxia and stem cell maintenance. Consistently, HIF-related gene signatures are upregulated in leukemic promyelocytes from APL patients compared to normal promyelocytes. Through in vitro and in vivo studies, we find that PML-RARα exploits a number of HIF-1α-regulated pro-leukemogenic functions that include cell migration, bone marrow (BM) neo-angiogenesis and self-renewal of APL blasts. Furthermore, HIF-1α levels increase upon treatment of APL cells with all-trans retinoic acid (ATRA). As a consequence, inhibiting HIF-1α in APL mouse models delays leukemia progression and exquisitely synergizes with ATRA to eliminate leukemia-initiating cells (LICs).

Identification of target genes of transcription factor CEBPB in acute promyelocytic leukemia cells induced by all-trans retinoic acid

OBJECTIVE

To identify target genes of transcription factor CCAAT enhancer-binding protein β (CEBPB) in acute promyelocytic leukemia cells induced by all-trans retinoic acid.

METHODS

A new strategy for high-throughput identification of direct target genes was established by combining chromatin immunoprecipitation (ChIP) with in vitro selection. Then, 106 potential CEBPB binding fragments from the genome of the all-trans retinoic acid (ATRA)-treated NB4 cells were identified.

RESULTS

Of them, 82 were mapped in proximity to known or previously predicted genes; 7 were randomly picked up for further confirmation by ChIP-PCR and 3 genes (GALM, ITPR2 and ORM2) were found to be specifically up-regulated in the ATRA-treated NB4 cells, indicating that they might be the down-stream target genes of ATRA.

CONCLUSIONS

Our results provided new insight into the mechanisms of ATRA-induced granulocytic differentiation.

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients

This study describes a method for the comparison of gene expression data of any type of cancer cells with their corresponding normal cells. Our analyses reveal novel disease entities, identify common deregulated transcriptional networks, and predict survival.

HemaExplorer: a database of mRNA expression profiles in normal and malignant haematopoiesis

The HemaExplorer (http://servers.binf.ku.dk/hemaexplorer) is a curated database of processed mRNA Gene expression profiles (GEPs) that provides an easy display of gene expression in haematopoietic cells. HemaExplorer contains GEPs derived from mouse/human haematopoietic stem and progenitor cells as well as from more differentiated cell types. Moreover, data from distinct subtypes of human acute myeloid leukemia is included in the database allowing researchers to directly compare gene expression of leukemic cells with those of their closest normal counterpart. Normalization and batch correction lead to full integrity of the data in the database. The HemaExplorer has comprehensive visualization interface that can make it useful as a daily tool for biologists and cancer researchers to assess the expression patterns of genes encountered in research or literature. HemaExplorer is relevant for all research within the fields of leukemia, immunology, cell differentiation and the biology of the haematopoietic system.

Molecular diagnosis and prognosis with DNA microarrays

Microarray analysis makes it possible to determine thousands of gene expression values simultaneously. Changes in gene expression, as a response to diseases, can be detected allowing a better understanding and differentiation of diseases at a molecular level. By comparing different kinds of tissue, for example healthy tissue and cancer tissue, the microarray analysis indicates induced gene activity, repressed gene activity or when there is no change in the gene activity level. Fundamental patterns in gene expression are extracted by several clustering and machine learning algorithms. Certain kinds of cancer can be divided into subtypes, with different clinical outcomes, by their specific gene expression patterns. This enables a better diagnosis and tailoring of individual patient treatments.

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.