Next Article in Journal
Extracellular Vesicles: A Possible Link between HIV and Alzheimer’s Disease-Like Pathology in HIV Subjects?
Next Article in Special Issue
ETV6/RUNX1 Fusion Gene Abrogation Decreases the Oncogenicity of Tumour Cells in a Preclinical Model of Acute Lymphoblastic Leukaemia
Previous Article in Journal
Forkhead Domains of FOXO Transcription Factors Differ in both Overall Conformation and Dynamics
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cells
Volume 8
Issue 9
10.3390/cells8090967
cells-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Targeting Metabolic Reprogramming in Acute Myeloid Leukemia

by
Isabel Castro
1,2,
Belém Sampaio-Marques
1,2 and
Paula Ludovico
1,2,*
1
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal
2
ICVS/3B’s-PT Government Associate Laboratory, 4806-909 Braga/Guimarães, Portugal
*
Author to whom correspondence should be addressed.
Cells 2019, 8(9), 967; https://doi.org/10.3390/cells8090967
Submission received: 16 July 2019 / Revised: 13 August 2019 / Accepted: 22 August 2019 / Published: 24 August 2019
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Acute Leukemia)
Browse Figure
Versions Notes

Abstract

:
The cancer metabolic reprogramming allows the maintenance of tumor proliferation, expansion and survival by altering key bioenergetics, biosynthetic and redox functions to meet the higher demands of tumor cells. In addition, several metabolites are also needed to perform signaling functions that further promote tumor growth and progression. These metabolic alterations have been exploited in different cancers, including acute myeloid leukemia, as novel therapeutic strategies both in preclinical models and clinical trials. Here, we review the complexity of acute myeloid leukemia (AML) metabolism and discuss how therapies targeting different aspects of cellular metabolism have demonstrated efficacy and how they provide a therapeutic window that should be explored to target the metabolic requirements of AML cells.

1. Introduction

Acute myeloid leukemia (AML) is the most frequent acute leukemia in adults, with a median age at diagnosis of around 68 years. It is an aggressive disease, requiring an urgent accurate diagnosis and a multifaceted, timely, and effective therapeutic approach in order to achieve disease remission and even long-term cure [1]. Despite the progress in prognostic markers, the optimization of cytotoxic therapy and advances in supportive care, the long-term outcome of AML is not encouraging, particularly in elderly patients [2]. The intrinsic refractoriness of the disease in the eldest population in association with existing comorbidities contribute towards a high toxicity profile upon application of the standard cytotoxic therapy, yielding an overall survival (OS) lower than 1 year [3]. In fact, the genetic landscape of patients older than 60 years is different from the young ones, namely presenting adverse cytogenetic profile, overexpression of drug resistance genes, and a high frequency of mutations in genes involved in epigenetic regulation [4,5]. Noteworthy, mutations on these epigenetics-related genes are also documented in healthy elderly population in an age dependent manner, increasing the risk of hematologic neoplasms [6]. Thus, it is important for the old/refractory or unfit patients to look for more effective and less toxic therapies and, in such context, metabolic reprogramming occurring in leukemic cells presents as a promising target [7].
The strategies used by malignant cells to escape the mechanisms of homeostasis control are appreciably varied, with metabolic reprogramming playing a key role in the final outcome. Metabolic reprogramming is considered a hallmark of cancer and, basically, consists of providing a rewired metabolism to support high metabolic demands for the elevated rate of growth and proliferation. Indeed, to maintain tumor proliferation, expansion, and survival, a reprogramming of metabolism must be performed to satisfy key bioenergetics, biosynthetic and redox functions of malignant cells. In addition to the altered metabolism, several metabolites have a signaling function, promoting tumor growth and progression [8]. Numerous studies and clinical trials demonstrate both the importance of metabolic adaptation for the survival of AML cells and the therapeutic potential of attacking different metabolic pathways. In the last years, several drugs have been developed to target specific metabolic pathways and enzymes, metabolites, and signaling pathways. Several studies show an acceptable toxicity of some of these metabolism-affecting drugs even when used in combination with standard chemotherapy. In this review, we intend to highlight the most recent data in the field and, whenever possible, discuss the clinical impact.

2. Metabolic Reprogramming in Acute Myeloid Leukemia

Nutrient addiction is considered a hallmark of cancer and this metabolic reprogramming has two fundamental aspects: first, the rapid production of ATP and intermediates for the synthesis of nucleotides, amino acids, lipids, and redox elements (NADPH); second, the reset of the nutrient-sensing signaling pathways operating in physiological conditions. In addition, to support fast cell division, the tumor cells must generate enough energy and escape from cell cycle checkpoints control. The Warburg effect is a well characterized metabolic phenotype of tumor cells, which consists of increased glycolytic flux for the rapid ATP generation per unit time, instead of producing ATP through oxidative phosphorylation (OXPHOS) [9] (Figure 1). Below, a summary of the main metabolic reprogramming findings on AML is presented.

2.1. Glycolysis and Energetic Metabolism

Enhanced glycolysis has been observed in AML cell lines and in human primary AML blasts [10], while both the phosphoinositide 3-kinase (PI3K)/serine-threonine protein kinase B (AKT) and the mammalian target of rapamycin (mTOR) seemingly contribute to this glycolytic metabolism [11,12]. It has also been reported that AML patients have a distinct glucose metabolic signature as well as six serum metabolites involved in the glycolytic pathway which were identified and proposed as new prognostic biomarkers in the subgroup of patients with normal cytogenetics [13].
The high glycolytic profile has been shown to be important for chemotherapy response, having been found to confer greater resistance to apoptosis induced by All-Trans-Retinoic-Acid (ATRA) and arsenic trioxide in primary blasts and AML cell lines, besides having an impact on the clinical prognosis [14]. Increased glycolysis also contributes to the resistance of AML patients to cytarabine [15,16]. The impact on clinical outcome is also reported—AML patients who did not achieve remission when compared to, either, healthy controls, or remission patients, show increased levels in distinct glycolysis elements, such as HIF1a, hexokinase 2 (HK2), glucose transporter 1 (GLUT1), and lactate dehydrogenase (LDH) [15]. Thus, the magnitude of myeloblast glycolysis has been proposed as an effective method to determine the pre-treatment prognosis of AML [10]. Additionally, leukemic stem-cells (LSCs) might have a role in inducing insulin resistance and decreasing insulin secretion, in order to increase the supply of glucose to malignant cells, therefore inducing alterations of systemic physiology by co-opting systemic regulatory mechanisms in their favor [17]. Metabolic plasticity allows AML cells to balance between glycolysis and OXPHOS, however, these metabolic adaptations likely differ between AML sub-types [12] and also within leukemic cells from the same patient [18].

2.2. Amino Acid Metabolism

Metabolic reprogramming in cancer cells occurs far beyond the Warburg Effect [19,20]. The high glycolytic rate with concomitant ATP production requires the availability of precursors for anabolic pathways, such as intermediates of the tricarboxylic acid (TCA) cycle and the pentose phosphate pathway (PPP) (Figure 1). Biosynthetic pathways are also required for the production of nutrients and metabolization of the three major classes of macromolecules (proteins, lipids, nucleic acids), essential for cell growth and proliferation. Commonly, other carbon sources such as amino acids, particularly glutamine, allow malignant cells to rely on high glycolytic rates. Glutaminolysis is an important mitochondrial metabolic pathway catalyzed by glutaminase (GLS) (Figure 1). Glutamine enters cells through specific transporters, is catabolized into ammonia and glutamate, which in turn could be deaminated and have its carbon skeleton converted to alfa-ketoglutarate (alpha-KG) to support anaplerosis of the TCA cycle. Alpha-KG can proceed backwards through the TCA cycle, by reductive carboxylation operated by the enzyme isocitrate dehydrogenase (IDH) producing citrate and further supporting synthesis of acetyl-CoA and lipids (Figure 1). The alpha-KG also works on glutamine metabolism and can be oxidatively metabolized via TCA to lactate. Apparently, this process might support lipogenesis in tumor cells [21].
Glutaminolysis supplies nitrogen for protein and nucleotide synthesis and is necessary to maintain the redox state through the production of NADPH (Figure 1). It is reported that glutamine reactivates the mammalian target of rapamycin complex 1 (mTORC1), specifically through its conversion to glutamate and restoration of non-essential amino acid pool [22]. It is also documented that glutamine levels control OXPHOS in AML cells and glutaminolysis inhibitors activate mitochondrial apoptosis, whereas in normal CD34+ hematopoietic progenitors, glutaminase isoform 1 (GLS1) inhibition does not result in reduced proliferation and/or survival of these cells [23,24]. Therefore, inhibiting the glutamine metabolism in AML has been an appealing strategy against this malignancy [25,26]. Nevertheless, the role of glutamine in cancer metabolism is far more complex. Other amino acids such as asparagine have been shown to cooperate with glutamine in favoring malignant cell metabolism. In cells deprived of glutamine, asparagine can rescue the growth and survival of cells, as it maintains protein synthesis (Figure 1) [27]. Accordingly, the introduction of asparaginase blocks the ability of cells to adhere to glutamine deprivation [27]. Human LSCs have been consistently described as selectively dependent upon amino acid metabolism (e.g., glutamine, glutamate, and proline) for OXPHOS and survival in de novo AML patients, while not depending on amino acid metabolism when isolated from relapsed patients [28,29]. LSCs from relapsed patients can, apparently, metabolically compensate by increasing fatty acid metabolism [29].

2.3. Fatty Acid Metabolism

Lipid metabolism dysregulation has been established as an important metabolic phenotype in malignant cells. It is overly reported that the various processes inherent to lipid metabolism, such as fatty acid synthesis and fatty acid oxidation (FAO) are altered in many cancers [30,31]. In AML a reprogramming of lipid metabolism has also been shown, both in the leukemic hematopoietic precursors and the signaling that these cells induce on the bone marrow microenvironment, namely on bone marrow adipocytes [32]. Bone marrow adipocytes interact with primary myeloid leukemia cells from patients promoting their survival and proliferation [33]. In this adipocyte-leukemic cell interaction, blasts alter metabolism of adipocytes by activating lipolysis thus allowing the transfer of fatty acids from adipocytes to blasts. Curiously, a reservoir of LSCs was found in gonadal adipose tissue, further documenting the dependence of hematopoietic stem-cells (HSCs) and LSCs on lipid metabolism [34].
FAO, which takes place in the mitochondrial matrix, is a process used by leukemic cells to promote their survival and quiescence [35]. The exploration of this metabolic pathway by the malignant cells allows for both the generation of citrate in the TCA cycle and of antioxidant defenses and reduced coenzymes, FADH2 and NADH, which are then oxidized to generate ATP via OXPHOS (Figure 1). Carnitine palmitoyl transferase 1A (CPT1A) and carnitine transporter CT2 (SLC22A16), rate limiting actors of FAO, are overexpressed in AML and constitute novels targets for a subset of AML [36,37] (for a review, see [18]).
The optimization of lipid metabolism is essential for cell proliferation and synthesis of membrane lipid components under stress situations [35]. Of seemingly particular relevance in the global context of metabolism reprogramming is the finding that lipid anabolism is strongly altered in IDH1 mutant AML cells with a crucial dysregulation of fatty acid metabolism and fluxes [32].

2.4. Metabolic Enzymes and Epigenetic Modifiers

In the last decade, mutations in two key metabolic enzymes, cytosolic and mitochondrial isocitrate dehydrogenases (IDH1 and IDH2, respectively), have been identified in myeloid malignancies [38,39]. IDHs catalyze the oxidative decarboxylation of isocitrate producing alpha-KG, NADPH/NADH, and carbon dioxide (Figure 1). These reactions provide the NADPH needed for lipid biogenesis and protection to oxidative damage, facilitating the function of alpha-KG-dependent dioxygenases. Genes encoding isoforms of IDH1 and IDH2 are mutated in approximately 15–20% of AML patients [40,41]. These mutations are acquired early in the leukemogenic process and are kept stable throughout the disease’s evolution, the leukemic IDH mutated clone being able to survive the initial treatment, thus contributing to relapse [42]. IDH mutations induce a neomorphic activity (resulting from a rearrangement of the enzyme’s active site), leading to the production of the oncometabolite 2-hydroxyglutarate (2-HG) (Figure 1), which is a competitive inhibitor of multiple alpha-KG-dependent dioxygenases, including histone demethylases and the TET family of 5-methlycytosine hydroxylases (e.g., TET2). 2-HG promotes DNA and histone hypermethylation, altering the epigenetic landscape of cells. This phenomenon blocks cellular differentiation and promotes cellular transformation and, thus, tumor initiation and progression [38,43]. IDH mutations are both mutually exclusive with each other and with mutations in TET2. In most studies, IDH mutations were found to be heterozygous, resulting in a gain of function phenotype, occurring primarily in three arginine residues critical for isocitrate binding-the R132 codon in IDH1 and the R140 and R172 in IDH2 [44,45]. It has been proposed that the mutant allele burden of IDH can be a biomarker of response to treatment in AML patients [46]. Nonetheless, there is still some controversy about the prognostic value of IDH mutations in AML. A systematic review and a meta-analysis concluded that IDH1 mutations confer worse OS and event-free survival (EFS), especially in patients with normal cytogenetics, and that IDH2 mutation improves OS particularly in patients with intermediate-risk AML [47].

2.5. Signaling Pathways and Autophagy

As mentioned above, multiple signaling pathways control metabolic reprogramming of leukemic cells including PI3K/AKT, mTOR, and the AMP-activated protein kinase (AMPK). This is a complex network hub of signaling proteins and kinases functioning as sensors and regulators of response to environmental stimuli which govern cell growth, proliferation, and survival. These pathways integrate the availability of nutrients, mitogenic growth factors, cellular energy levels, and stress with the biosynthesis of three major classes of macromolecules (proteins, lipids, and nucleic acids). These signaling pathways have been reported to regulate normal hematopoiesis, reviewed in [48], but are frequently overexpressed in AML [49]. While aberrant PI3K/AKT pathway activation is found in 50%–80% of AML cases [50], mTORC1 appears to be active in all reported AML cases [51,52].
mTORC1 is an important anabolic kinase in the regulation of normal hematopoiesis which is inhibited when amino acids and oxygen levels are decreased. Insofar, mTORC1 suppression contributes to HSCs maintenance by repressing mitochondrial biogenesis. Being HSCs in the hypoxic environment of the bone marrow, they are dependent on glycolysis for their energy needs more than of OXPHOS, so mTORC1 suppression is vital for their homeostasis [53,54]. mTORC1 is negatively regulated by AMPK, a key sensor of the cellular energy status, which suppresses anabolic processes and promotes multiple catabolic processes critical in maintaining metabolic homeostasis and survival. In HSCs, AMPK was demonstrated to be fundamental for the maintenance of energy homeostasis [55]. Our results also showed that AMPK is activated in some AML cell lines, which can be related to their metabolic profile [56], suggesting that the use of metabolic inhibitors which activate AMPK in combination with chemotherapy should be explored in the treatment of AML.
Constitutive activation of mTORC1 has been systematically identified in patients’ myeloid blast cells. There are several mechanisms responsible for this activation, namely, activating mutations in the receptor tyrosine kinase FLT3, mutations in the c-KIT gene, increased production of angiogenic factors (vascular endothelial growth factor (VEGF) and angiopoietins) and increased expression of IGF-1 growth factor [57]. Furthermore, activating signals from the microenvironment, including chemokines and adhesion molecules, are also factors that contribute to this signaling pathway activation [58]. mTORC1 is found to be activated even in the absence of an elevated PI3K/AKT signaling [12,59]. In spite of this evidence, the role of AMPK, mTORC1 and/or PI3K/AKT in AML cells is still controversial, having both tumor-suppressor and -promoter functions been assigned to these actors [48,52,56,60,61,62,63,64,65]. Importantly, this signaling network plays a key role in controlling the balance between the promotion of anabolism and the suppression of catabolic pathways such as macroautophagy (hereafter named autophagy). Autophagy is a tightly-regulated catabolic response to numerous cellular stresses such as nutrient starvation, hypoxia, growth factor deprivation, metabolic stress, and most notably, cancer therapy drugs.
Curiously, similar to the signaling pathways controlling this catabolic degradative process, dysregulation of autophagy has been extensively described in AML with both tumor-suppressor and -promoter functions [56,66,67,68]. Autophagy is often found reduced in human AML blasts and the loss of key autophagy genes leads to leukemia initiation and progression in mouse models [69,70,71]. Importantly, different sequencing and in silico studies showed a high frequency of AML patients carrying heterozygous deletions, missense mutations, or copy number variations of autophagy genes, particularly AML patients with complex karyotypes [69,72,73]. These studies are further supported by the correlation between the heterozygous chromosomal loss of 5q, 16q, or 17p and the encoded regions for the autophagy genes ATG10 and ATG12, GABARAPL2 and MAP1LC3B, or GABARAP, respectively [69]. Accordingly, the heterozygous loss of ATG5 increases the progression and aggressiveness of an AML model [69]. A recent study challenges these observations and suggests a tumor-promoting role for autophagy, by showing that knockdown of ATG5 in primary AML cells resulted in impaired engraftment of human cells in immunodeficient NSG mice [74].
We have previously addressed the relationship between signaling pathways and autophagy in the context of AML cell lines [12]. The results showed that preferential OXPHOS metabolism of AML cells is associated with constitutive co-activation of AMPK and mTORC1 and increased autophagy, while a glycolytic profile is mainly associated with AKT/mTORC1 activation and low autophagy flux, highlighting the relevance of signaling pathways and autophagy on metabolic reprograming of AML [12].

3. Modulation of AML Metabolism

New drugs targeting metabolism in AML have been developed and studied in preclinical and clinical trials. These drugs target, among other, glycolysis, amino acids and fatty acid metabolism, signaling pathways, autophagy, metabolic enzymes (e.g., IDH), and epigenetic modifiers. Importantly, a clinical trial, still in the recruitment phase, aims to determine the global metabolic adaptations that occur following exposure to standard chemotherapeutic agents using peripheral blood mononuclear cells from AML patients (NCT02581917). Below, a brief summary of the main approaches targeting metabolism in AML is presented (data from https://clinicaltrials.gov).

3.1. Targeting Glycolysis and Energetic Metabolism

An effective anti-leukemia strategy is to counteract the up-regulated glycolysis, following the example of its proven application in several neoplasms. Glycolysis can be blocked by substrate restriction and/or by the inhibition of glycolytic enzymes. Reducing glucose uptake by attacking one of the two main transporters, such as GLUT1, is a strategy that has also been explored in hematological malignancies, such as acute lymphoid leukemia and multiple myeloma [75,76]. In AML, there is also evidence that high levels of GLUT1 are associated with poor response to chemotherapy and inhibiting glycolysis is a potential anti-tumor strategy [16]. Inhibition of glycolysis with glycolytic inhibitor 2-deoxyglucose (2-DG), which cannot be metabolized by glycolytic enzymes, has been documented to potentiate the cytotoxicity of the chemotherapeutic drug cytarabine [13]. It was also found that 2-DG has anti-leukemic activity in AML cell lines with FLT3 and c-KIT mutations, but this warrants validation in a clinical setting [77]. Another potent inhibitor of glycolysis is 3-bromopyruvate (3-BrPA), which directly inhibits HK2 and triggers apoptosis in a variety of cancers, including the AML cell line HL-60 [16]. It is also reported that AML cell lines resistant to sorafenib (a kinase inhibitor used for FLT3-ITD positive AML) are sensitive to both 2-DG and 3-BrPA [78]. Nevertheless, inhibiting glycolysis requires further investigation to define the clinical applicability of this strategy. Regarding oxidative metabolism, there is an ongoing phase I clinical trial (NCT02882321, Table 1) using IACS-010759, a potent electron transport chain complex 1 inhibitor. This drug has shown cytotoxic efficacy by inducing AMPK-dependent apoptosis in OXPHOS-reliant AML, likely owing to a combination of energy depletion and reduced aspartate production that leads to impaired nucleotide biosynthesis [79].

3.2. Targeting Amino Acid Metabolism

Glutamine dependence is a common metabolic strategy in many neoplasms, therefore constituting a potential metabolic target to be explored [83]. Recently, it was reported that depleting glutamine in culture media or decreasing its catabolism trough GLS1 inhibition induced significant growth suppression and cell death in AML cell lines [84]. Furthermore, the knockdown of the GLS1 and its inhibition with CB-839 reduces OXPHOS, inhibits leukemic cell proliferation and induces apoptosis without affecting normal CD34+ hematopoietic cells [84]. It was also shown that glutaminolysis inhibition triggers mitochondria-dependent cell death and sensitizes AML cells to the BCL-2 inhibitor, ABT-199 [80]. This combination is an example of synthetic lethality that has been explored in many tumors by associating effective and less toxic drugs [24,85]. Another example is the combination of glutaminase inhibitor CB-839 with the tyrosine kinase inhibitor (AC220-Quizartinib), which causes major loss of viability through apoptotic cell death in FLT3-mutated AML cell lines and improves the survival in a patient-derived xenograft AML mouse model [86]. A clinical trial that is now completed used a combination of CB-839 with azacitidine, a DNA methyltransferase (DNMT) inhibitor, which has in vitro and in vivo demethylating effects (NCT02071927, Table 1). Although no results were posted so far, this same combination has been proven effective in up to 60% of patients with myelodysplastic syndromes in a Phase III randomized controlled trial [87].
As mentioned above, asparagine is also a key player in the metabolism of malignant cells and can be explored in therapeutic approaches. Since asparaginase is already approved for treating acute lymphoid leukemia in children, it has been included in several clinical trials in AML patients integrating strategies that contemplate the complex metabolism of asparagine and glutamine, or in combination with low doses of cytarabine (NCT02283190 and NCT01810705, Table 1) [88].
Preclinical studies have demonstrated that AML blasts are dependent on arginine to proliferate and survive since, at diagnosis, most leukemic cells have deficiencies in the arginine-recycling pathway enzymes [89]. BCT-100, a pegylated recombinant arginase, causes a depletion of intra- and extra-cellular arginine, resulting in halted proliferation of blasts and a reduction of engraftment in vivo [89]. Depletion of arginine is, therefore, an important strategy of ongoing clinical trials (NCT02899286 and NCT01910012, Table 1). Overall, there is an emerging interest in targeting amino acid metabolism, given its crucial role in sustaining the growth and proliferation of malignant cells, including AML ones.

3.3. Targeting Fatty Acid Metabolism

Both increased lipogenesis and FAO allow malignant cells to survive periods of metabolic stress [30]. Some studies assessed the therapeutic potential of counteracting alterations of lipid metabolism in AML [90]. For example, the drug ST-1326, an inhibitor of CPT1A crucial for the acyl-CoA shuttling to mitochondria, slows the proliferation of leukemic cells and results in their apoptosis, sensitizing these malignant cells to the cytotoxic effect of cytarabine [91]. Interestingly, a small molecule with broad anti-tumor activity (SR9243) was described as simultaneously reducing the intracellular concentration of glycolytic metabolites and preferentially suppressing lipogenesis in malignant cells [92]. Conversely, FAO antagonizes the oligomerization of the pro-apoptotic components BAX and BAK, thus compromising cell death and raising the threshold of response to pro-apoptotic stimuli [93]. Furthermore, the FAO inhibitor Avocatin B was found to decrease NADPH levels and cause reactive oxygen species (ROS)-dependent leukemic cell death in AML [94]. FAO is, therefore, important for AML and its inhibition could become another potential target of cancer therapy. However, given the complexity of lipid metabolism and its signaling network in cancer cells, it is still difficult to select the best lipid dysregulation-targeting therapy.

3.4. Targeting Metabolic Enzymes and Epigenetic Modifiers

There are several clinical trials featuring drugs that inhibit mutated IDHs (Table 1) with promising results and low toxicity profiles in both refractory/resistant leukemia patients and in untreated patients [95]. Enasidenib (AG-221) is a covalent inhibitor of R140Q- and R172K-mutated IDH2 which does not affect the activity of wild-type IDH2 [96]. In preclinical studies, enasidenib inhibited the aberrant IDH2 protein, decreased total serum 2-HG levels in more than 90%, reduced abnormal histone hypermethylation, and restored myeloid differentiation [97]. Previous clinical trials with enasidenib in relapsed or refractory AML patients provided positive results [98]. Currently, enasidenib is being tested in a multicenter randomized phase III study (NCT02577406, Table 1). This drug is well tolerated, but almost 12% of the patients experienced a differentiation syndrome similar to the one seen with ATRA therapy in acute promyelocytic leukemia patients [99].
Ivosidenib (AG-120), IDH305, AG-881, BAY-1436032, and FT-2102 are some of the IDH1 inhibitors under investigation (Table 1). Ivosidenib is being used in different ongoing clinical trials, and both the patient response rates and differentiation syndrome (associated with the terminal differentiation of the leukemic clone) are similar to enasidenib’s [100]. A small number of patients with complete remission had clearance of the mutated clone and remained in the trial for over a year [95]. In a recent study of ivosidenib monotherapy in advanced IDH1 mutated relapsed or refractory AML, similar results were obtained with respect to the duration of remission, molecular remission, and therapeutic-related adverse events [101]. In conclusion, these drugs are profiled as optimal agents for use in combination strategies, since they act against specific targets and exhibit low extra-medullary toxicity.
Mutations in IDH1/2 and in the epigenetic modifier enzymes—DNMT3A and TET2—were detected in more than 70% of AML patients [45]. All of these mutations are associated with epigenetic aberrations in the pathogenesis of myeloblastic leukemia, so it is necessary to point out, albeit briefly, the clinical experience acquired with drugs that act on DNA methylation. Decitabine and 5-azacitidine—hypomethylating agents—are standard drugs used in myelodysplastic neoplasms and some AML subtypes with proven clinical efficacy and acceptable toxicity (NCT01757535, Table 1). Several multicenter and randomized trials showed that they increase OS when compared to conventional options, including low-dose-cytosine arabinoside and intensive chemotherapy [102,103]. It was also found that decitabine was effective even in patients with high-risk karyotype and/or TP53 mutations, constituting a possible therapeutic option in cases with poor prognoses [104]. In fact, these drugs altered the treatment algorithm of elderly patients, since they have the capacity to prolong patient survival, keeping the disease stable and/or improving cytopenia, even if remission is not achieved [105].
Guadecitabine is a new-generation hypomethylating agent with improved uptake of decitabine, the active metabolite, into the DNA of rapidly dividing cancer cells [106]. Decitabine restores expression of silenced tumor suppressor genes and tumor-associated antigens and may sensitize tumor cells to other anticancer agents, including immunotherapeutic drugs. Guadecitabine is being used in clinical trials (NCT02920008, Table 1) yielding good therapeutic responses, a pattern that overlapped with other hypomethylating agents [107]. However, in hematologic malignancies, such agents have a double effect on the immune system: they increase anti-tumor immune response and, conversely, dampen the immune response by increasing the expression of immune-checkpoint molecules. Clinical trials exploring the combination of hypomethylating agents (e.g., azacitidine) with immune-checkpoint inhibitor drugs (e.g., nivolumab) are underway, with promising results (NCT02397720, Table 1) [82].
An important therapeutic strategy in AML is the combination of venetoclax (a BH3-mimetic that blocks the anti-apoptotic BCL-2, frequently overexpressed in AML, reviewed in [108]) with low-doses of cytarabine or decitabine or azacitidine in previously untreated patients older than 65 years [109]. Recently, it was shown that LSCs from patients undergoing treatment with venetoclax and azacitidine displayed decreased levels of alpha-KG and increased levels of succinate, pointing at an alteration of carbon flux in the TCA cycle, thus suggesting an efficient and targeted suppression of OXPHOS [110]. It was also found that IDH1- and IDH2-mutant primary human AML cells were more sensitive than IDH1/2 wild-type cells to venetoclax, which was due to mitochondrial electron transport chain alterations that lowered the threshold of apoptosis induced by this BCL-2 antagonist [111]. In view of these promising assays, biomarkers are being studied that predict the efficacy of venetoclax in each patient [112].

3.5. Targeting Signaling Pathways and Autophagy

Due to the relevance of PI3K/AKT, mTOR, and AMPK on AML pathogenesis, these signaling pathways present appealing therapeutic targets. In a murine model in which primary leukemic cells were co-cultured with stroma, PP242, a mTOR inhibitor, simultaneously showed inhibitory activity against mTORC1 and mTORC2 as well as an antagonism against the stromal-induced survival signal, by suppressing expression of CXC chemokine receptor type 4 (CXCR4) [113]. This effect on the medullary microenvironment should be emphasized, since this signaling affects the stemness of leukemic precursors and contributes to chemoresistance of leukemic cells. Although the first mTOR inhibitors (rapamycin and analogues) had a cytostatic effect and a low inhibitory growth effect, other inhibitors subsequently demonstrated synergistic action with either standard chemotherapy, or with other mTOR inhibitors [65,114]. In preclinical models, several dual mTORC1/2 inhibitors and dual PI3K/mTOR inhibitors showed greater anti-leukemia activity [115,116]. The interactions between mTORC1 and PI3K/AKT are complex, with mTORC1 exerting a negative control of PI3K/AKT that is alleviated upon mTORC1 inhibition by sirolimus [117].
A potential mechanism for PI3K activation in AML might implicate increased VEGF levels [48]. Enhanced VEGF signaling has been proposed as a cause of reduced apoptosis susceptibility upon AML treatment [118]. Recently, it was shown that blocking VEGF receptor 2 (VEGFR2) signaling sensitized chemoAML (human AML cells from terminally ill mice treated with chemotherapy) to chemotherapy by inducing a transcriptional program that promotes mitochondria biogenesis associated with increased oxidative stress, further suggesting mitochondria function is a vulnerable target for chemotherapy in AML [119].
AMPK pathway has also been implicated in hematological malignancies, mainly as a tumor suppressor axis [63,120,121], by providing metabolic stress resistance to leukemia-initiating cells. The anticancer properties of metformin were first associated with a complex mechanism of AMPK activation, but recently different studies have demonstrated that antitumor effects of metformin— and, globally biguanides—are AMPK-independent, reviewed in [59]. Notwithstanding this controversy in the context of hematologic malignancies, metformin was reported to activate AMPK and inhibit growth of AML cell lines and primary AML cells, while sparing normal hematopoiesis ex vivo [63]. However, additional studies in AML cell lines showed that metformin mediated effects on apoptosis, and proliferation is preserved even in cells with siRNA-downregulated AMPK, thereby suggesting AMPK-independent effects [122]. Among the direct activators of AMPK, GSK621 was reported to selectively kill AML cell lines and primary cells without affecting normal hematopoietic progenitors [62]. Nevertheless, our previous results have shown that AMPK is constitutively activated in some AML cell lines and that standard chemotherapy targets AMPK for degradation [12,56]. These somehow contradictory results suggest that further studies should be performed to clarify the value of AMPK as a therapeutic target.
Although several studies have demonstrated beneficial effects of drugs targeting signaling pathways in preclinical models of AML, results of clinical trials are either modest, or disappointing (reviewed in [115,123,124]). Table 1 illustrates some of the clinical trials targeting mTOR (NCT01154439 and NCT01869114) or PI3K/AKT (NCT01396499 and NCT02392572) signaling pathways.
Decreased mTOR activity as well as nutrient or growth factor deprivation increase autophagy, therefore constituting an essential survival pathway [125]. Autophagy is critical for the maintenance of HSCs, but plays context-dependent roles in leukemia initiation, progression, and drug resistance [126]. Autophagy is needed for the maintenance of murine HSCs and its impairment diverts normal HSCs to a pre-leukemic state [67]. These findings are supported by evidence showing that hematopoietic-specific deletions of ATG5, ATG7, or the ULK1-interacting partner Fip200 diminished normal HSCs activities, promoted a pre-leukemic phenotype and, consequently, impaired survival of these mice [66,69,127]. Other studies showed that ATG5 or ATG7 are required for the efficient initiation of AML associated with MLL (mixed lineage leukemia)-AF9 fusion gene, while autophagy is no longer required for the maintenance of established AML or LSCs functions in secondary xenotransplantation experiments [128,129,130,131]. In contrast, knockout of ATG5 or ATG7 in a murine MLL-ENL (eleven nineteen leukemia) AML model decreased the number of functional LSCs, increased activation of mitochondria and ROS levels in these cells, and extended survival of leukemic mice [132]. These aspects were elegantly reviewed in [126].
Recently, it was also shown that autophagy suppression mediated by receptor tyrosine kinase effectors-mTORC1 signaling stabilize mutant FLT3 in AML, while an increase in autophagy was achieved through receptor tyrosine kinase inhibition and led to FLT3 depletion [71]. In contrast, a different study showed that FLT3-ITD increases autophagy in AML cell lines and AML patient blasts, and that inhibition of autophagy abolishes FLT3 inhibitor resistance [133]. Recent studies also revealed inconsistent levels of basal autophagic flux in various leukemic cell lines [12,56,74] and purified CD34+ cells from AML patients [74]. Particularly high autophagic levels were found in poor risk AML, which are frequently associated with TP53 mutations [74].
Altogether, these contradictory data suggest a highly complex, context-dependent role for autophagy in leukemic transformation and LSCs properties in AML [126]. To further complicate this scenario, autophagy is also established as one of the resistance mechanisms of leukemic cells to chemotherapy [56,67]. However, although autophagy is generally considered a critical target in the therapeutic approach to myeloblastic leukemia, it is documented that the effectiveness of this proteolytic system will depend on the subtype of AML [12,134]. Clinical trials with hydroxychloroquine, an inhibitor of autophagy, showed increased cytotoxicity of conventional chemotherapy in leukemic cells (NCT02631252, Table 1) [135,136]. Although there are still diverging findings on this subject, it is accepted that autophagy has a versatile role which depends, simultaneously, on both the progenitor and driver engaged in leukemia’s transformation process, as well as the state of leukemic expansion.

4. Conclusions

AML is a very heterogeneous and complex group of diseases and, despite advances in therapeutic strategies, there is still a high rate of resistance to the standard treatment. Accordingly, it is necessary to find innovative ways of approaching and treating patients with AML. Age, performance status, white blood cell count, secondary disease, cytogenetic risk, and the mutational state are incorporated in the predictive models of resistance [137]. However, other variants such as the co-occurrence of mutations, the minimal residual disease, and the emergence of sub-clones with a different sensitivity to chemotherapy are also key players in the search for a more effective therapy [5,138]. In this context, it is tempting to propose that the patient’s metabolic profile, as well as the metabolic phenotype of malignant cells, may be considered prognostic factors in AML. The modulation of metabolic reprogramming and the use of directed drugs open new promising possibilities in fighting AML. Nevertheless, results supporting this hypothesis are mainly based on preclinical and in vitro studies—not on data from clinical trials, which are still scarce. Another aspect to consider on metabolic targeting is that these approaches will likely have a differential effect on normal hematopoiesis [139].
The results reviewed above show that energy metabolism is a fundamental process that should be considered in AML therapy. Depending on the balance between glycolysis and OXPHOS, the amino acid requirements of AML cells also change, and this could be useful for metabolic targeting. Importantly, lipid metabolism is emerging with great therapeutic potential given the alterations found in AML cells. Nevertheless, the knowledge of the complexities of lipid metabolism is still scarce, and probably this is one of the reasons why no current clinical trials are found which feature drugs targeting this axis of metabolic reprogramming. Summing up, more preclinical and clinical studies have to be performed in order to establish the best metabolic target (or combination of targets) that is better suited to affect LSCs sparing HSCs. Hopefully, the current preclinical and clinical ongoing studies will shed light on the yet unknown aspects of metabolic reprogramming in AML.
In this review, we intended to provide an updated look at AML therapy; this new perspective will, eventually, allow age constraints that current therapies still have to be overcome and, in addition, facilitate the prediction of better results in refractory cases in the near future.

Author Contributions

I.C.; writing—original draft preparation, B.S.-M. and P.L.; writing—review and editing.

Funding

This research was funded by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, by the European Regional Development Fund (FEDER), through the Competitiveness Factors Operational Programme (COMPETE), grant number NORTE-01-0145-FEDER-000013 and by the projects POCI-01-0145-FEDER-028159 and POCI-01-0145-FEDER-030782 by FEDER, through the COMPETE, and by National funds, through the Foundation for Science and Technology (FCT).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Acute Myeloid Leukemia, Version 1.2019, NCCN Clinical Practice Guidelines in Oncology. Available online: https://www.nccn.org/professionals/physician_gls/default.aspx (accessed on 24 April 2019).
  2. Estey, E.H. Acute myeloid leukemia: 2019 update on risk-stratification and management. Am. J. Hematol. 2018, 93, 1267–1291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Klepin, H.D. Myelodysplastic Syndromes and Acute Myeloid Leukemia in the Elderly. Clin. Geriatr. Med. 2016, 32, 155–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Jaiswal, S.; Fontanillas, P.; Flannick, J.; Manning, A.; Grauman, P.V.; Mar, B.G.; Lindsley, R.C.; Mermel, C.H.; Burtt, N.; Chavez, A.; et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 2014, 371, 2488–2498. [Google Scholar] [CrossRef] [PubMed]
  5. Papaemmanuil, E.; Gerstung, M.; Bullinger, L.; Gaidzik, V.I.; Paschka, P.; Roberts, N.D.; Potter, N.E.; Heuser, M.; Thol, F.; Bolli, N.; et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N. Engl. J. Med. 2016, 374, 2209–2221. [Google Scholar] [CrossRef] [PubMed]
  6. Klepin, H.D.; Rao, A.V.; Pardee, T.S. Acute myeloid leukemia and myelodysplastic syndromes in older adults. J. Clin. Oncol. 2014, 32, 2541–2552. [Google Scholar] [CrossRef] [PubMed]
  7. Bose, P.; Vachhani, P.; Cortes, J.E. Treatment of Relapsed/Refractory Acute Myeloid Leukemia. Curr. Treat. Options Oncol. 2017. [Google Scholar] [CrossRef] [PubMed]
  8. Danhier, P.; Banski, P.; Payen, V.L.; Grasso, D.; Ippolito, L.; Sonveaux, P.; Porporato, P.E. Cancer metabolism in space and time: Beyond the Warburg effect. Biochim. Biophys. Acta Bioenerg. 2017, 1858, 556–572. [Google Scholar] [CrossRef]
  9. Koppenol, W.H.; Bounds, P.L.; Dang, C.V. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat. Rev. Cancer 2011, 11, 325–337. [Google Scholar] [CrossRef]
  10. Herst, P.M.; Howman, R.A.; Neeson, P.J.; Berridge, M.V.; Ritchie, D.S. The level of glycolytic metabolism in acute myeloid leukemia blasts at diagnosis is prognostic for clinical outcome. J. Leukoc. Biol. 2011, 89, 51–55. [Google Scholar] [CrossRef]
  11. Poulain, L.; Sujobert, P.; Zylbersztejn, F.; Barreau, S.; Stuani, L.; Lambert, M.; Palama, T.L.; Chesnais, V.; Birsen, R.; Vergez, F.; et al. High mTORC1 activity drives glycolysis addiction and sensitivity to G6PD inhibition in acute myeloid leukemia cells. Leukemia 2017, 31, 2326–2335. [Google Scholar] [CrossRef]
  12. Pereira, O.; Teixeira, A.; Sampaio-Marques, B.; Castro, I.; Girao, H.; Ludovico, P. Signalling mechanisms that regulate metabolic profile and autophagy of acute myeloid leukaemia cells. J. Cell Mol. Med. 2018, 22, 4807–4817. [Google Scholar] [CrossRef]
  13. Chen, W.L.; Wang, J.H.; Zhao, A.H.; Xu, X.; Wang, Y.H.; Chen, T.L.; Li, J.M.; Mi, J.Q.; Zhu, Y.M.; Liu, Y.F.; et al. A distinct glucose metabolism signature of acute myeloid leukemia with prognostic value. Blood 2014, 124, 1645–1654. [Google Scholar] [CrossRef]
  14. Herst, P.M.; Hesketh, E.L.; Ritchie, D.S.; Berridge, M.V. Glycolytic metabolism confers resistance to combined all-trans retinoic acid and arsenic trioxide-induced apoptosis in HL60rho0 cells. Leuk. Res. 2008, 32, 327–333. [Google Scholar] [CrossRef]
  15. Song, K.; Li, M.; Xu, X.J.; Xuan, L.; Huang, G.N.; Song, X.L.; Liu, Q.F. HIF-1alpha and GLUT1 gene expression is associated with chemoresistance of acute myeloid leukemia. Asian Pac. J. Cancer Prev. 2014, 15, 1823–1829. [Google Scholar] [CrossRef]
  16. Song, K.; Li, M.; Xu, X.; Xuan, L.I.; Huang, G.; Liu, Q. Resistance to chemotherapy is associated with altered glucose metabolism in acute myeloid leukemia. Oncol Lett 2016, 12, 334–342. [Google Scholar] [CrossRef]
  17. Ye, H.; Adane, B.; Khan, N.; Alexeev, E.; Nusbacher, N.; Minhajuddin, M.; Stevens, B.M.; Winters, A.C.; Lin, X.; Ashton, J.M.; et al. Subversion of Systemic Glucose Metabolism as a Mechanism to Support the Growth of Leukemia Cells. Cancer Cell 2018, 34, 659–673. [Google Scholar] [CrossRef]
  18. Chapuis, N.; Poulain, L.; Birsen, R.; Tamburini, J.; Bouscary, D. Rationale for Targeting Deregulated Metabolic Pathways as a Therapeutic Strategy in Acute Myeloid Leukemia. Front. Oncol. 2019. [Google Scholar] [CrossRef]
  19. Hsu, P.P.; Sabatini, D.M. Cancer cell metabolism: Warburg and beyond. Cell 2008, 134, 703–707. [Google Scholar] [CrossRef]
  20. Liberti, M.V.; Locasale, J.W. The Warburg Effect: How Does it Benefit Cancer Cells? Trends Biochem. Sci. 2016, 41, 211–218. [Google Scholar] [CrossRef] [Green Version]
  21. DeBerardinis, R.J.; Chandel, N.S. Fundamentals of cancer metabolism. Sci Adv. 2016. [Google Scholar] [CrossRef]
  22. Tan, H.W.S.; Sim, A.Y.L.; Long, Y.C. Glutamine metabolism regulates autophagy-dependent mTORC1 reactivation during amino acid starvation. Nat. Commun. 2017. [Google Scholar] [CrossRef]
  23. Nicklin, P.; Bergman, P.; Zhang, B.; Triantafellow, E.; Wang, H.; Nyfeler, B.; Yang, H.; Hild, M.; Kung, C.; Wilson, C.; et al. Bidirectional transport of amino acids regulates mTOR and autophagy. Cell 2009, 136, 521–534. [Google Scholar] [CrossRef]
  24. Altman, B.J.; Stine, Z.E.; Dang, C.V. From Krebs to clinic: Glutamine metabolism to cancer therapy. Nat. Rev. Cancer 2016, 16, 619–634. [Google Scholar] [CrossRef]
  25. Kalyanaraman, B. Teaching the basics of cancer metabolism: Developing antitumor strategies by exploiting the differences between normal and cancer cell metabolism. Redox Biol. 2017, 12, 833–842. [Google Scholar] [CrossRef]
  26. Willems, L.; Jacque, N.; Jacquel, A.; Neveux, N.; Maciel, T.T.; Lambert, M.; Schmitt, A.; Poulain, L.; Green, A.S.; Uzunov, M.; et al. Inhibiting glutamine uptake represents an attractive new strategy for treating acute myeloid leukemia. Blood 2013, 122, 3521–3532. [Google Scholar] [CrossRef] [Green Version]
  27. Pavlova, N.N.; Hui, S.; Ghergurovich, J.M.; Fan, J.; Intlekofer, A.M.; White, R.M.; Rabinowitz, J.D.; Thompson, C.B.; Zhang, J. As Extracellular Glutamine Levels Decline, Asparagine Becomes an Essential Amino Acid. Cell Metab. 2018, 27, 428–438. [Google Scholar] [CrossRef]
  28. Jones, C.L.; Stevens, B.M.; D’Alessandro, A.; Reisz, J.A.; Culp-Hill, R.; Nemkov, T.; Pei, S.; Khan, N.; Adane, B.; Ye, H.; et al. Inhibition of Amino Acid Metabolism Selectively Targets Human Leukemia Stem Cells. Cancer Cell 2018, 34, 724–740. [Google Scholar] [CrossRef]
  29. Ho, T.C.; LaMere, M.; Stevens, B.M.; Ashton, J.M.; Myers, J.R.; O’Dwyer, K.M.; Liesveld, J.L.; Mendler, J.H.; Guzman, M.; Morrissette, J.D.; et al. Evolution of acute myelogenous leukemia stem cell properties after treatment and progression. Blood 2016, 128, 1671–1678. [Google Scholar] [CrossRef] [Green Version]
  30. Currie, E.; Schulze, A.; Zechner, R.; Walther, T.C.; Farese, R.V., Jr. Cellular fatty acid metabolism and cancer. Cell Metab 2013, 18, 153–161. [Google Scholar] [CrossRef]
  31. Rohrig, F.; Schulze, A. The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 2016, 16, 732–749. [Google Scholar] [CrossRef]
  32. Stuani, L.; Riols, F.; Millard, P.; Sabatier, M.; Batut, A.; Saland, E.; Viars, F.; Tonini, L.; Zaghdoudi, S.; Linares, L.K.; et al. Stable Isotope Labeling Highlights Enhanced Fatty Acid and Lipid Metabolism in Human Acute Myeloid Leukemia. Int J. Mol. Sci. 2018, 19, 3325. [Google Scholar] [CrossRef]
  33. Shafat, M.S.; Oellerich, T.; Mohr, S.; Robinson, S.D.; Edwards, D.R.; Marlein, C.R.; Piddock, R.E.; Fenech, M.; Zaitseva, L.; Abdul-Aziz, A.; et al. Leukemic blasts program bone marrow adipocytes to generate a protumoral microenvironment. Blood 2017, 129, 1320–1332. [Google Scholar] [CrossRef]
  34. Ye, H.; Adane, B.; Khan, N.; Sullivan, T.; Minhajuddin, M.; Gasparetto, M.; Stevens, B.; Pei, S.; Balys, M.; Ashton, J.M.; et al. Leukemic Stem Cells Evade Chemotherapy by Metabolic Adaptation to an Adipose Tissue Niche. Cell Stem Cell 2016, 19, 23–37. [Google Scholar] [CrossRef] [Green Version]
  35. Carracedo, A.; Cantley, L.C.; Pandolfi, P.P. Cancer metabolism: Fatty acid oxidation in the limelight. Nat. Rev. Cancer 2013, 13, 227–232. [Google Scholar] [CrossRef]
  36. Shi, J.; Fu, H.; Jia, Z.; He, K.; Fu, L.; Wang, W. High Expression of CPT1A Predicts Adverse Outcomes: A Potential Therapeutic Target for Acute Myeloid Leukemia. EBioMedicine 2016, 14, 55–64. [Google Scholar] [CrossRef] [Green Version]
  37. Wu, Y.; Hurren, R.; MacLean, N.; Gronda, M.; Jitkova, Y.; Sukhai, M.A.; Minden, M.D.; Schimmer, A.D. Carnitine transporter CT2 (SLC22A16) is over-expressed in acute myeloid leukemia (AML) and target knockdown reduces growth and viability of AML cells. Apoptosis 2015, 20, 1099–1108. [Google Scholar] [CrossRef]
  38. Dang, L.; White, D.W.; Gross, S.; Bennett, B.D.; Bittinger, M.A.; Driggers, E.M.; Fantin, V.R.; Jang, H.G.; Jin, S.; Keenan, M.C.; et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 2009, 462, 739–744. [Google Scholar] [CrossRef] [Green Version]
  39. Mardis, E.R.; Wilson, R.K. Cancer genome sequencing: A review. Hum. Mol. Genet. 2009. [Google Scholar] [CrossRef]
  40. Paschka, P.; Schlenk, R.F.; Gaidzik, V.I.; Habdank, M.; Kronke, J.; Bullinger, L.; Spath, D.; Kayser, S.; Zucknick, M.; Gotze, K.; et al. IDH1 and IDH2 mutations are frequent genetic alterations in acute myeloid leukemia and confer adverse prognosis in cytogenetically normal acute myeloid leukemia with NPM1 mutation without FLT3 internal tandem duplication. J. Clin. Oncol. 2010, 28, 3636–3643. [Google Scholar] [CrossRef]
  41. Marcucci, G.; Maharry, K.; Wu, Y.Z.; Radmacher, M.D.; Mrozek, K.; Margeson, D.; Holland, K.B.; Whitman, S.P.; Becker, H.; Schwind, S.; et al. IDH1 and IDH2 gene mutations identify novel molecular subsets within de novo cytogenetically normal acute myeloid leukemia: A Cancer and Leukemia Group B study. J. Clin. Oncol. 2010, 28, 2348–2355. [Google Scholar] [CrossRef]
  42. Patel, J.P.; Gonen, M.; Figueroa, M.E.; Fernandez, H.; Sun, Z.; Racevskis, J.; Van Vlierberghe, P.; Dolgalev, I.; Thomas, S.; Aminova, O.; et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N. Engl. J. Med. 2012, 366, 1079–1089. [Google Scholar] [CrossRef]
  43. Figueroa, M.E.; Abdel-Wahab, O.; Lu, C.; Ward, P.S.; Patel, J.; Shih, A.; Li, Y.; Bhagwat, N.; Vasanthakumar, A.; Fernandez, H.F.; et al. Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell 2010, 18, 553–567. [Google Scholar] [CrossRef]
  44. Chou, W.C.; Hou, H.A.; Chen, C.Y.; Tang, J.L.; Yao, M.; Tsay, W.; Ko, B.S.; Wu, S.J.; Huang, S.Y.; Hsu, S.C.; et al. Distinct clinical and biologic characteristics in adult acute myeloid leukemia bearing the isocitrate dehydrogenase 1 mutation. Blood 2010, 115, 2749–2754. [Google Scholar] [CrossRef] [Green Version]
  45. Chan, S.M.; Majeti, R. Role of DNMT3A, TET2, and IDH1/2 mutations in pre-leukemic stem cells in acute myeloid leukemia. Int. J. Hematol. 2013, 98, 648–657. [Google Scholar] [CrossRef]
  46. Ferret, Y.; Boissel, N.; Helevaut, N.; Madic, J.; Nibourel, O.; Marceau-Renaut, A.; Bucci, M.; Geffroy, S.; Celli-Lebras, K.; Castaigne, S.; et al. Clinical relevance of IDH1/2 mutant allele burden during follow-up in acute myeloid leukemia. A study by the French ALFA group. Haematologica 2018, 103, 822–829. [Google Scholar] [CrossRef]
  47. Xu, Q.; Li, Y.; Lv, N.; Jing, Y.; Xu, Y.; Li, Y.; Li, W.; Yao, Z.; Chen, X.; Huang, S.; et al. Correlation Between Isocitrate Dehydrogenase Gene Aberrations and Prognosis of Patients with Acute Myeloid Leukemia: A Systematic Review and Meta-Analysis. Clin. Cancer Res. 2017, 23, 4511–4522. [Google Scholar] [CrossRef] [Green Version]
  48. Park, S.; Chapuis, N.; Tamburini, J.; Bardet, V.; Cornillet-Lefebvre, P.; Willems, L.; Green, A.; Mayeux, P.; Lacombe, C.; Bouscary, D. Role of the PI3K/AKT and mTOR signaling pathways in acute myeloid leukemia. Haematologica 2010, 95, 819–828. [Google Scholar] [CrossRef]
  49. Luengo, A.; Gui, D.Y.; Vander Heiden, M.G. Targeting Metabolism for Cancer Therapy. Cell Chem. Biol. 2017, 24, 1161–1180. [Google Scholar] [CrossRef] [Green Version]
  50. Horne, G.A.; Kinstrie, R.; Copland, M. Novel drug therapies in myeloid leukemia. Pharm Pat. Anal. 2015, 4, 187–205. [Google Scholar] [CrossRef]
  51. Tamburini, J.; Elie, C.; Bardet, V.; Chapuis, N.; Park, S.; Broet, P.; Cornillet-Lefebvre, P.; Lioure, B.; Ugo, V.; Blanchet, O.; et al. Constitutive phosphoinositide 3-kinase/Akt activation represents a favorable prognostic factor in de novo acute myelogenous leukemia patients. Blood 2007, 110, 1025–1028. [Google Scholar] [CrossRef] [Green Version]
  52. Xu, Q.; Simpson, S.E.; Scialla, T.J.; Bagg, A.; Carroll, M. Survival of acute myeloid leukemia cells requires PI3 kinase activation. Blood 2003, 102, 972–980. [Google Scholar] [CrossRef] [Green Version]
  53. Dibble, C.C.; Manning, B.D. Signal integration by mTORC1 coordinates nutrient input with biosynthetic output. Nat. Cell Biol. 2013, 15, 555–564. [Google Scholar] [CrossRef] [Green Version]
  54. Laplante, M.; Sabatini, D.M. mTOR signaling in growth control and disease. Cell 2012, 149, 274–293. [Google Scholar] [CrossRef]
  55. Gurumurthy, S.; Xie, S.Z.; Alagesan, B.; Kim, J.; Yusuf, R.Z.; Saez, B.; Tzatsos, A.; Ozsolak, F.; Milos, P.; Ferrari, F.; et al. The Lkb1 metabolic sensor maintains haematopoietic stem cell survival. Nature 2010, 468, 659–663. [Google Scholar] [CrossRef] [Green Version]
  56. Fernandes, A.; Azevedo, M.M.; Pereira, O.; Sampaio-Marques, B.; Paiva, A.; Correia-Neves, M.; Castro, I.; Ludovico, P. Proteolytic systems and AMP-activated protein kinase are critical targets of acute myeloid leukemia therapeutic approaches. Oncotarget 2015, 6, 31428–31440. [Google Scholar] [CrossRef]
  57. Hirao, A.; Hoshii, T. Mechanistic / mammalian target protein of rapamycin signaling in hematopoietic stem cells and leukemia. Cancer Sci. 2013, 104, 977–982. [Google Scholar] [CrossRef] [Green Version]
  58. Huang, K.; Fingar, D.C. Growing knowledge of the mTOR signaling network. Semin. Cell Dev. Biol. 2014, 36, 79–90. [Google Scholar] [CrossRef] [Green Version]
  59. Visnjic, D.; Dembitz, V.; Lalic, H. The role of AMPK/mTOR modulators in therapy of acute myeloid leukemia. Curr Med. Chem 2018. [Google Scholar] [CrossRef]
  60. Saito, Y.; Chapple, R.H.; Lin, A.; Kitano, A.; Nakada, D. AMPK Protects Leukemia-Initiating Cells in Myeloid Leukemias from Metabolic Stress in the Bone Marrow. Cell Stem Cell 2015, 17, 585–596. [Google Scholar] [CrossRef] [Green Version]
  61. Sujobert, P.; Poulain, L.; Paubelle, E.; Zylbersztejn, F.; Grenier, A.; Lambert, M.; Townsend, E.C.; Brusq, J.M.; Nicodeme, E.; Decrooqc, J.; et al. Co-activation of AMPK and mTORC1 Induces Cytotoxicity in Acute Myeloid Leukemia. Cell Rep. 2015, 11, 1446–1457. [Google Scholar] [CrossRef]
  62. Sujobert, P.; Tamburini, J. Co-activation of AMPK and mTORC1 as a new therapeutic option for acute myeloid leukemia. Mol. Cell Oncol 2016. [Google Scholar] [CrossRef]
  63. Green, A.S.; Chapuis, N.; Maciel, T.T.; Willems, L.; Lambert, M.; Arnoult, C.; Boyer, O.; Bardet, V.; Park, S.; Foretz, M.; et al. The LKB1/AMPK signaling pathway has tumor suppressor activity in acute myeloid leukemia through the repression of mTOR-dependent oncogenic mRNA translation. Blood 2010, 116, 4262–4273. [Google Scholar] [CrossRef] [Green Version]
  64. Willems, L.; Chapuis, N.; Puissant, A.; Maciel, T.T.; Green, A.S.; Jacque, N.; Vignon, C.; Park, S.; Guichard, S.; Herault, O.; et al. The dual mTORC1 and mTORC2 inhibitor AZD8055 has anti-tumor activity in acute myeloid leukemia. Leukemia 2012, 26, 1195–1202. [Google Scholar] [CrossRef]
  65. Recher, C.; Beyne-Rauzy, O.; Demur, C.; Chicanne, G.; Dos Santos, C.; Mas, V.M.; Benzaquen, D.; Laurent, G.; Huguet, F.; Payrastre, B. Antileukemic activity of rapamycin in acute myeloid leukemia. Blood 2005, 105, 2527–2534. [Google Scholar] [CrossRef] [Green Version]
  66. Mortensen, M.; Soilleux, E.J.; Djordjevic, G.; Tripp, R.; Lutteropp, M.; Sadighi-Akha, E.; Stranks, A.J.; Glanville, J.; Knight, S.; Jacobsen, S.E.; et al. The autophagy protein Atg7 is essential for hematopoietic stem cell maintenance. J. Exp. Med. 2011, 208, 455–467. [Google Scholar] [CrossRef]
  67. Auberger, P.; Puissant, A. Autophagy, a key mechanism of oncogenesis and resistance in leukemia. Blood 2017, 129, 547–552. [Google Scholar] [CrossRef]
  68. Kung, C.P.; Budina, A.; Balaburski, G.; Bergenstock, M.K.; Murphy, M. Autophagy in tumor suppression and cancer therapy. Crit Rev. Eukaryot. Gene Expr. 2011, 21, 71–100. [Google Scholar] [CrossRef]
  69. Watson, A.S.; Riffelmacher, T.; Stranks, A.; Williams, O.; De Boer, J.; Cain, K.; MacFarlane, M.; McGouran, J.; Kessler, B.; Khandwala, S.; et al. Autophagy limits proliferation and glycolytic metabolism in acute myeloid leukemia. Cell Death Discov. 2015, 1. [Google Scholar] [CrossRef]
  70. Jin, J.; Britschgi, A.; Schlafli, A.M.; Humbert, M.; Shan-Krauer, D.; Batliner, J.; Federzoni, E.A.; Ernst, M.; Torbett, B.E.; Yousefi, S.; et al. Low Autophagy (ATG) Gene Expression Is Associated with an Immature AML Blast Cell Phenotype and Can Be Restored during AML Differentiation Therapy. Oxid Med. Cell Longev. 2018. [Google Scholar] [CrossRef]
  71. Rudat, S.; Pfaus, A.; Cheng, Y.Y.; Holtmann, J.; Ellegast, J.M.; Buhler, C.; Marcantonio, D.D.; Martinez, E.; Gollner, S.; Wickenhauser, C.; et al. RET-mediated autophagy suppression as targetable co-dependence in acute myeloid leukemia. Leukemia 2018, 32, 2189–2202. [Google Scholar] [CrossRef]
  72. Visconte, V.; Przychodzen, B.; Han, Y.; Nawrocki, S.T.; Thota, S.; Kelly, K.R.; Patel, B.J.; Hirsch, C.; Advani, A.S.; Carraway, H.E.; et al. Complete mutational spectrum of the autophagy interactome: A novel class of tumor suppressor genes in myeloid neoplasms. Leukemia 2017, 31, 505–510. [Google Scholar] [CrossRef]
  73. Walter, M.J.; Payton, J.E.; Ries, R.E.; Shannon, W.D.; Deshmukh, H.; Zhao, Y.; Baty, J.; Heath, S.; Westervelt, P.; Watson, M.A.; et al. Acquired copy number alterations in adult acute myeloid leukemia genomes. Proc. Natl Acad Sci U S A 2009, 106, 12950–12955. [Google Scholar] [CrossRef] [Green Version]
  74. Folkerts, H.; Hilgendorf, S.; Wierenga, A.T.J.; Jaques, J.; Mulder, A.B.; Coffer, P.J.; Schuringa, J.J.; Vellenga, E. Inhibition of autophagy as a treatment strategy for p53 wild-type acute myeloid leukemia. Cell Death Dis 2017. [Google Scholar] [CrossRef]
  75. Liu, T.; Kishton, R.J.; Macintyre, A.N.; Gerriets, V.A.; Xiang, H.; Liu, X.; Abel, E.D.; Rizzieri, D.; Locasale, J.W.; Rathmell, J.C. Glucose transporter 1-mediated glucose uptake is limiting for B-cell acute lymphoblastic leukemia anabolic metabolism and resistance to apoptosis. Cell Death Dis 2014. [Google Scholar] [CrossRef]
  76. Dalva-Aydemir, S.; Bajpai, R.; Martinez, M.; Adekola, K.U.; Kandela, I.; Wei, C.; Singhal, S.; Koblinski, J.E.; Raje, N.S.; Rosen, S.T.; et al. Targeting the metabolic plasticity of multiple myeloma with FDA-approved ritonavir and metformin. Clin. Cancer Res. 2015, 21, 1161–1171. [Google Scholar] [CrossRef]
  77. Larrue, C.; Saland, E.; Vergez, F.; Serhan, N.; Delabesse, E.; Mansat-De Mas, V.; Hospital, M.A.; Tamburini, J.; Manenti, S.; Sarry, J.E.; et al. Antileukemic Activity of 2-Deoxy-d-Glucose through Inhibition of N-Linked Glycosylation in Acute Myeloid Leukemia with FLT3-ITD or c-KIT Mutations. Mol. Cancer Ther. 2015, 14, 2364–2373. [Google Scholar] [CrossRef] [Green Version]
  78. Huang, A.; Ju, H.Q.; Liu, K.; Zhan, G.; Liu, D.; Wen, S.; Garcia-Manero, G.; Huang, P.; Hu, Y. Metabolic alterations and drug sensitivity of tyrosine kinase inhibitor resistant leukemia cells with a FLT3/ITD mutation. Cancer Lett. 2016, 377, 149–157. [Google Scholar] [CrossRef]
  79. Yang, H.; Tabe, Y.; Sekihara, K.; Saito, K.; Ma, H.; Ruvolo, V.; Imoto, J.; Ikeo, K.; Mogushi, K.; Hosoya, M.; et al. Novel Oxidative Phosphorylation Inhibitor IACS-010759 Induces AMPK-Dependent Apoptosis of AML Cells. Blood 2017, 130, 1245. [Google Scholar]
  80. Jacque, N.; Ronchetti, A.M.; Larrue, C.; Meunier, G.; Birsen, R.; Willems, L.; Saland, E.; Decroocq, J.; Maciel, T.T.; Lambert, M.; et al. Targeting glutaminolysis has antileukemic activity in acute myeloid leukemia and synergizes with BCL-2 inhibition. Blood 2015, 126, 1346–1356. [Google Scholar] [CrossRef] [Green Version]
  81. Cai, T.; Lorenzi, P.L.; Rakheja, D.; Pontikos, M.A.; Lodi, A.; Han, L.; Zhang, Q.; Ma, H.; Rahmani, M.; Bhagat, T.D.; et al. Gls Inhibitor CB-839 Modulates Cellular Metabolism in AML and Potently Suppresses AML Cell Growth When Combined with 5-Azacitidine. Blood 2016, 128, 4064. [Google Scholar] [CrossRef]
  82. Daver, N.; Garcia-Manero, G.; Basu, S.; Boddu, P.C.; Alfayez, M.; Cortes, J.E.; Konopleva, M.; Ravandi-Kashani, F.; Jabbour, E.; Kadia, T.; et al. Efficacy, Safety, and Biomarkers of Response to Azacitidine and Nivolumab in Relapsed/Refractory Acute Myeloid Leukemia: A Nonrandomized, Open-Label, Phase II Study. Cancer Discov 2019, 9, 370–383. [Google Scholar] [CrossRef]
  83. Choi, Y.K.; Park, K.G. Targeting Glutamine Metabolism for Cancer Treatment. Biomol. Ther. 2018, 26, 19–28. [Google Scholar] [CrossRef] [Green Version]
  84. Matre, P.; Velez, J.; Jacamo, R.; Qi, Y.; Su, X.; Cai, T.; Chan, S.M.; Lodi, A.; Sweeney, S.R.; Ma, H.; et al. Inhibiting glutaminase in acute myeloid leukemia: Metabolic dependency of selected AML subtypes. Oncotarget 2016, 7, 79722–79735. [Google Scholar] [CrossRef]
  85. Li, J.; Song, P.; Zhu, L.; Aziz, N.; Zhou, Q.; Zhang, Y.; Xu, W.; Feng, L.; Chen, D.; Wang, X.; et al. Synthetic lethality of glutaminolysis inhibition, autophagy inactivation and asparagine depletion in colon cancer. Oncotarget 2017, 8, 42664–42672. [Google Scholar] [CrossRef]
  86. Gregory, M.A.; Nemkov, T.; Reisz, J.A.; Zaberezhnyy, V.; Hansen, K.C.; D’Alessandro, A.; DeGregori, J. Glutaminase inhibition improves FLT3 inhibitor therapy for acute myeloid leukemia. Exp. Hematol. 2018, 58, 52–58. [Google Scholar] [CrossRef]
  87. Raj, K.; Mufti, G.J. Azacytidine (Vidaza(R)) in the treatment of myelodysplastic syndromes. Ther. Clin. Risk Manag. 2006, 2, 377–388. [Google Scholar] [CrossRef]
  88. Luo, M.; Brooks, M.; Wicha, M.S. Asparagine and Glutamine: Co-conspirators Fueling Metastasis. Cell Metab. 2018, 27, 947–949. [Google Scholar] [CrossRef] [Green Version]
  89. Mussai, F.; Egan, S.; Higginbotham-Jones, J.; Perry, T.; Beggs, A.; Odintsova, E.; Loke, J.; Pratt, G.; U, K.P.; Lo, A.; et al. Arginine dependence of acute myeloid leukemia blast proliferation: A novel therapeutic target. Blood 2015, 125, 2386–2396. [Google Scholar] [CrossRef]
  90. Southam, A.D.; Khanim, F.L.; Hayden, R.E.; Constantinou, J.K.; Koczula, K.M.; Michell, R.H.; Viant, M.R.; Drayson, M.T.; Bunce, C.M. Drug Redeployment to Kill Leukemia and Lymphoma Cells by Disrupting SCD1-Mediated Synthesis of Monounsaturated Fatty Acids. Cancer Res. 2015, 75, 2530–2540. [Google Scholar] [CrossRef] [Green Version]
  91. Ricciardi, M.R.; Mirabilii, S.; Allegretti, M.; Licchetta, R.; Calarco, A.; Torrisi, M.R.; Foa, R.; Nicolai, R.; Peluso, G.; Tafuri, A. Targeting the leukemia cell metabolism by the CPT1a inhibition: Functional preclinical effects in leukemias. Blood 2015, 126, 1925–1929. [Google Scholar] [CrossRef]
  92. Flaveny, C.A.; Griffett, K.; El-Gendy Bel, D.; Kazantzis, M.; Sengupta, M.; Amelio, A.L.; Chatterjee, A.; Walker, J.; Solt, L.A.; Kamenecka, T.M.; et al. Broad Anti-tumor Activity of a Small Molecule that Selectively Targets the Warburg Effect and Lipogenesis. Cancer Cell 2015, 28, 42–56. [Google Scholar] [CrossRef] [Green Version]
  93. Samudio, I.; Konopleva, M. Targeting leukemia’s "fatty tooth". Blood 2015, 126, 1874–1875. [Google Scholar] [CrossRef]
  94. Lee, E.A.; Angka, L.; Rota, S.G.; Hanlon, T.; Mitchell, A.; Hurren, R.; Wang, X.M.; Gronda, M.; Boyaci, E.; Bojko, B.; et al. Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death. Cancer Res. 2015, 75, 2478–2488. [Google Scholar] [CrossRef]
  95. Dang, L.; Yen, K.; Attar, E.C. IDH mutations in cancer and progress toward development of targeted therapeutics. Ann. Oncol. 2016, 27, 599–608. [Google Scholar] [CrossRef] [Green Version]
  96. Kats, L.M.; Reschke, M.; Taulli, R.; Pozdnyakova, O.; Burgess, K.; Bhargava, P.; Straley, K.; Karnik, R.; Meissner, A.; Small, D.; et al. Proto-oncogenic role of mutant IDH2 in leukemia initiation and maintenance. Cell Stem Cell 2014, 14, 329–341. [Google Scholar] [CrossRef]
  97. Yen, K.; Travins, J.; Wang, F.; David, M.D.; Artin, E.; Straley, K.; Padyana, A.; Gross, S.; DeLaBarre, B.; Tobin, E.; et al. AG-221, a First-in-Class Therapy Targeting Acute Myeloid Leukemia Harboring Oncogenic IDH2 Mutations. Cancer Discov. 2017, 7, 478–493. [Google Scholar] [CrossRef]
  98. Stein, E.M.; DiNardo, C.D.; Pollyea, D.A.; Fathi, A.T.; Roboz, G.J.; Altman, J.K.; Stone, R.M.; DeAngelo, D.J.; Levine, R.L.; Flinn, I.W.; et al. Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood 2017, 130, 722–731. [Google Scholar] [CrossRef]
  99. Fathi, A.T.; DiNardo, C.D.; Kline, I.; Kenvin, L.; Gupta, I.; Attar, E.C.; Stein, E.M.; de Botton, S.; Investigators, A.C.S. Differentiation Syndrome Associated With Enasidenib, a Selective Inhibitor of Mutant Isocitrate Dehydrogenase 2: Analysis of a Phase 1/2 Study. JAMA Oncol. 2018, 4, 1106–1110. [Google Scholar] [CrossRef]
  100. Norsworthy, K.J.; Mulkey, F.; Ward, A.F.; Przepiorka, D.; Deisseroth, A.B.; Farrell, A.T.; Pazdur, R. Incidence of Differentiation Syndrome with Ivosidenib (IVO) and Enasidenib (ENA) for Treatment of Patients with Relapsed or Refractory (R/R) Isocitrate Dehydrogenase (IDH)1- or IDH2-Mutated Acute Myeloid Leukemia (AML): A Systematic Analysis By the U.S. Food and Drug Administration (FDA). Blood 2018, 132. [Google Scholar] [CrossRef]
  101. DiNardo, C.D.; Stein, E.M.; de Botton, S.; Roboz, G.J.; Altman, J.K.; Mims, A.S.; Swords, R.; Collins, R.H.; Mannis, G.N.; Pollyea, D.A.; et al. Durable Remissions with Ivosidenib in IDH1-Mutated Relapsed or Refractory AML. N. Engl. J. Med. 2018, 378, 2386–2398. [Google Scholar] [CrossRef]
  102. Huls, G. Azacitidine in AML: A treatment option? Blood 2015, 126, 283–284. [Google Scholar] [CrossRef]
  103. Dombret, H.; Seymour, J.F.; Butrym, A.; Wierzbowska, A.; Selleslag, D.; Jang, J.H.; Kumar, R.; Cavenagh, J.; Schuh, A.C.; Candoni, A.; et al. International phase 3 study of azacitidine vs conventional care regimens in older patients with newly diagnosed AML with >30% blasts. Blood 2015, 126, 291–299. [Google Scholar] [CrossRef]
  104. Welch, J.S.; Petti, A.A.; Miller, C.A.; Fronick, C.C.; O’Laughlin, M.; Fulton, R.S.; Wilson, R.K.; Baty, J.D.; Duncavage, E.J.; Tandon, B.; et al. TP53 and Decitabine in Acute Myeloid Leukemia and Myelodysplastic Syndromes. N. Engl. J. Med. 2016, 375, 2023–2036. [Google Scholar] [CrossRef]
  105. Wouters, B.J.; Delwel, R. Epigenetics and approaches to targeted epigenetic therapy in acute myeloid leukemia. Blood 2016, 127, 42–52. [Google Scholar] [CrossRef] [Green Version]
  106. Issa, J.J.; Roboz, G.; Rizzieri, D.; Jabbour, E.; Stock, W.; O’Connell, C.; Yee, K.; Tibes, R.; Griffiths, E.A.; Walsh, K.; et al. Safety and tolerability of guadecitabine (SGI-110) in patients with myelodysplastic syndrome and acute myeloid leukaemia: A multicentre, randomised, dose-escalation phase 1 study. Lancet Oncol 2015, 16, 1099–1110. [Google Scholar] [CrossRef]
  107. Daver, N.; Kantarjian, H.; Ravandi, F.; Estey, E.; Wang, X.; Garcia-Manero, G.; Jabbour, E.; Konopleva, M.; O’Brien, S.; Verstovsek, S.; et al. A phase II study of decitabine and gemtuzumab ozogamicin in newly diagnosed and relapsed acute myeloid leukemia and high-risk myelodysplastic syndrome. Leukemia 2016, 30, 268–273. [Google Scholar] [CrossRef]
  108. Yogarajah, M.; Stone, R.M. A concise review of BCL-2 inhibition in acute myeloid leukemia. Expert Rev. Hematol 2018, 11, 145–154. [Google Scholar] [CrossRef]
  109. Wei, A.; Strickland, S.A.; Roboz, G.J.; Hou, J.-Z.; Fiedler, W.; Lin, T.L.; Martinelli, G.; Walter, R.B.; Enjeti, A.; Fakouhi, K.; et al. Safety and Efficacy of Venetoclax Plus Low-Dose Cytarabine in Treatment-Naive Patients Aged ≥65 Years with Acute Myeloid Leukemia. Blood 2016, 128, 102–103. [Google Scholar]
  110. Pollyea, D.A.; Stevens, B.M.; Jones, C.L.; Winters, A.; Pei, S.; Minhajuddin, M.; D’Alessandro, A.; Culp-Hill, R.; Riemondy, K.A.; Gillen, A.E.; et al. Venetoclax with azacitidine disrupts energy metabolism and targets leukemia stem cells in patients with acute myeloid leukemia. Nat. Med. 2018, 24, 1859–1866. [Google Scholar] [CrossRef]
  111. Chan, S.M.; Thomas, D.; Corces-Zimmerman, M.R.; Xavy, S.; Rastogi, S.; Hong, W.J.; Zhao, F.; Medeiros, B.C.; Tyvoll, D.A.; Majeti, R. Isocitrate dehydrogenase 1 and 2 mutations induce BCL-2 dependence in acute myeloid leukemia. Nat. Med. 2015, 21, 178–184. [Google Scholar] [CrossRef] [Green Version]
  112. Kontro, M.; Kumar, A.; Majumder, M.M.; Eldfors, S.; Parsons, A.; Pemovska, T.; Saarela, J.; Yadav, B.; Malani, D.; Floisand, Y.; et al. HOX gene expression predicts response to BCL-2 inhibition in acute myeloid leukemia. Leukemia 2017, 31, 301–309. [Google Scholar] [CrossRef]
  113. Zeng, Z.; Shi, Y.X.; Tsao, T.; Qiu, Y.; Kornblau, S.M.; Baggerly, K.A.; Liu, W.; Jessen, K.; Liu, Y.; Kantarjian, H.; et al. Targeting of mTORC1/2 by the mTOR kinase inhibitor PP242 induces apoptosis in AML cells under conditions mimicking the bone marrow microenvironment. Blood 2012, 120, 2679–2689. [Google Scholar] [CrossRef]
  114. Rizzieri, D.A.; Feldman, E.; Dipersio, J.F.; Gabrail, N.; Stock, W.; Strair, R.; Rivera, V.M.; Albitar, M.; Bedrosian, C.L.; Giles, F.J. A phase 2 clinical trial of deforolimus (AP23573, MK-8669), a novel mammalian target of rapamycin inhibitor, in patients with relapsed or refractory hematologic malignancies. Clin. Cancer Res. 2008, 14, 2756–2762. [Google Scholar] [CrossRef]
  115. Tabe, Y.; Tafuri, A.; Sekihara, K.; Yang, H.; Konopleva, M. Inhibition of mTOR kinase as a therapeutic target for acute myeloid leukemia. Expert Opin. Ther. Targets 2017, 21, 705–714. [Google Scholar] [CrossRef]
  116. Jewell, J.L.; Russell, R.C.; Guan, K.L. Amino acid signalling upstream of mTOR. Nat. Rev. Mol. Cell Biol. 2013, 14, 133–139. [Google Scholar] [CrossRef] [Green Version]
  117. Harrington, L.S.; Findlay, G.M.; Gray, A.; Tolkacheva, T.; Wigfield, S.; Rebholz, H.; Barnett, J.; Leslie, N.R.; Cheng, S.; Shepherd, P.R.; et al. The TSC1-2 tumor suppressor controls insulin-PI3K signaling via regulation of IRS proteins. J. Cell Biol. 2004, 166, 213–223. [Google Scholar] [CrossRef]
  118. Wang, L.; Zhang, W.; Ding, Y.; Xiu, B.; Li, P.; Dong, Y.; Zhu, Q.; Liang, A. Up-regulation of VEGF and its receptor in refractory leukemia cells. Int J. Clin. Exp. Pathol. 2015, 8, 5282–5290. [Google Scholar]
  119. Nobrega-Pereira, S.; Caiado, F.; Carvalho, T.; Matias, I.; Graca, G.; Goncalves, L.G.; Silva-Santos, B.; Norell, H.; Dias, S. VEGFR2-Mediated Reprogramming of Mitochondrial Metabolism Regulates the Sensitivity of Acute Myeloid Leukemia to Chemotherapy. Cancer Res. 2018, 78, 731–741. [Google Scholar] [CrossRef]
  120. Martelli, A.M.; Chiarini, F.; Evangelisti, C.; Ognibene, A.; Bressanin, D.; Billi, A.M.; Manzoli, L.; Cappellini, A.; McCubrey, J.A. Targeting the liver kinase B1/AMP-activated protein kinase pathway as a therapeutic strategy for hematological malignancies. Expert Opin. Ther. Targets 2012, 16, 729–742. [Google Scholar] [CrossRef]
  121. Shi, W.Y.; Xiao, D.; Wang, L.; Dong, L.H.; Yan, Z.X.; Shen, Z.X.; Chen, S.J.; Chen, Y.; Zhao, W.L. Therapeutic metformin/AMPK activation blocked lymphoma cell growth via inhibition of mTOR pathway and induction of autophagy. Cell Death Dis. 2012, 3, e275. [Google Scholar] [CrossRef]
  122. Scotland, S.; Saland, E.; Skuli, N.; de Toni, F.; Boutzen, H.; Micklow, E.; Senegas, I.; Peyraud, R.; Peyriga, L.; Theodoro, F.; et al. Mitochondrial energetic and AKT status mediate metabolic effects and apoptosis of metformin in human leukemic cells. Leukemia 2013, 27, 2129–2138. [Google Scholar] [CrossRef]
  123. Dinner, S.; Platanias, L.C. Targeting the mTOR Pathway in Leukemia. J. Cell Biochem. 2016, 117, 1745–1752. [Google Scholar] [CrossRef]
  124. Herschbein, L.; Liesveld, J.L. Dueling for dual inhibition: Means to enhance effectiveness of PI3K/Akt/mTOR inhibitors in AML. Blood Rev. 2018, 32, 235–248. [Google Scholar] [CrossRef]
  125. Saxton, R.A.; Sabatini, D.M. mTOR Signaling in Growth, Metabolism, and Disease. Cell 2017, 169, 361–371. [Google Scholar] [CrossRef]
  126. Rothe, K.; Porter, V.; Jiang, X. Current Outlook on Autophagy in Human Leukemia: Foe in Cancer Stem Cells and Drug Resistance, Friend in New Therapeutic Interventions. Int J. Mol. Sci. 2019, 20, 461. [Google Scholar] [CrossRef]
  127. Liu, F.; Lee, J.Y.; Wei, H.; Tanabe, O.; Engel, J.D.; Morrison, S.J.; Guan, J.L. FIP200 is required for the cell-autonomous maintenance of fetal hematopoietic stem cells. Blood 2010, 116, 4806–4814. [Google Scholar] [CrossRef] [Green Version]
  128. Piya, S.; Kornblau, S.M.; Ruvolo, V.R.; Mu, H.; Ruvolo, P.P.; McQueen, T.; Davis, R.E.; Hail, N., Jr.; Kantarjian, H.; Andreeff, M.; et al. Atg7 suppression enhances chemotherapeutic agent sensitivity and overcomes stroma-mediated chemoresistance in acute myeloid leukemia. Blood 2016, 128, 1260–1269. [Google Scholar] [CrossRef] [Green Version]
  129. Liu, Q.; Chen, L.; Atkinson, J.M.; Claxton, D.F.; Wang, H.G. Atg5-dependent autophagy contributes to the development of acute myeloid leukemia in an MLL-AF9-driven mouse model. Cell Death Dis. 2016. [Google Scholar] [CrossRef]
  130. Porter, A.H.; Leveque-El Mouttie, L.; Vu, T.; Bruedigam, C.; Sutton, J.; Jacquelin, S.; Hill, G.R.; MacDonald, K.P.A.; Lane, S.W. Acute myeloid leukemia stem cell function is preserved in the absence of autophagy. Haematologica 2017. [Google Scholar] [CrossRef]
  131. Chen, X.; Clark, J.; Wunderlich, M.; Fan, C.; Davis, A.; Chen, S.; Guan, J.L.; Mulloy, J.C.; Kumar, A.; Zheng, Y. Autophagy is dispensable for Kmt2a/Mll-Mllt3/Af9 AML maintenance and anti-leukemic effect of chloroquine. Autophagy 2017, 13, 955–966. [Google Scholar] [CrossRef]
  132. Sumitomo, Y.; Koya, J.; Nakazaki, K.; Kataoka, K.; Tsuruta-Kishino, T.; Morita, K.; Sato, T.; Kurokawa, M. Cytoprotective autophagy maintains leukemia-initiating cells in murine myeloid leukemia. Blood 2016, 128, 1614–1624. [Google Scholar] [CrossRef] [Green Version]
  133. Heydt, Q.; Larrue, C.; Saland, E.; Bertoli, S.; Sarry, J.E.; Besson, A.; Manenti, S.; Joffre, C.; Mansat-De Mas, V. Oncogenic FLT3-ITD supports autophagy via ATF4 in acute myeloid leukemia. Oncogene 2018, 37, 787–797. [Google Scholar] [CrossRef]
  134. White, E.; DiPaola, R.S. The double-edged sword of autophagy modulation in cancer. Clin. Cancer Res. 2009, 15, 5308–5316. [Google Scholar] [CrossRef]
  135. Bosnjak, M.; Ristic, B.; Arsikin, K.; Mircic, A.; Suzin-Zivkovic, V.; Perovic, V.; Bogdanovic, A.; Paunovic, V.; Markovic, I.; Bumbasirevic, V.; et al. Inhibition of mTOR-dependent autophagy sensitizes leukemic cells to cytarabine-induced apoptotic death. PLoS ONE 2014. [Google Scholar] [CrossRef]
  136. Piya, S.; Andreeff, M.; Borthakur, G. Targeting autophagy to overcome chemoresistance in acute myleogenous leukemia. Autophagy 2017, 13, 214–215. [Google Scholar] [CrossRef]
  137. Walter, R.B.; Othus, M.; Burnett, A.K.; Lowenberg, B.; Kantarjian, H.M.; Ossenkoppele, G.J.; Hills, R.K.; Ravandi, F.; Pabst, T.; Evans, A.; et al. Resistance prediction in AML: Analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center. Leukemia 2015, 29, 312–320. [Google Scholar] [CrossRef]
  138. Schuurhuis, G.J.; Heuser, M.; Freeman, S.; Bene, M.C.; Buccisano, F.; Cloos, J.; Grimwade, D.; Haferlach, T.; Hills, R.K.; Hourigan, C.S.; et al. Minimal/measurable residual disease in AML: A consensus document from the European LeukemiaNet MRD Working Party. Blood 2018, 131, 1275–1291. [Google Scholar] [CrossRef]
  139. Hauge, M.; Bruserud, O.; Hatfield, K.J. Targeting of cell metabolism in human acute myeloid leukemia--more than targeting of isocitrate dehydrogenase mutations and PI3K/AKT/mTOR signaling? Eur. J. Haematol. 2016, 96, 211–221. [Google Scholar] [CrossRef]
Cells 08 00967 g001 550
Figure 1. Overview of an acute myeloid leukemia cell’s metabolism. Metabolic reprogramming provides ATP and intermediates for the synthesis of nucleotides, amino acids, lipids and redox elements (NADPH) needed to sustain high proliferation rates. Further details are found in the main body of text. PPP—pentose phosphate pathway; IDH1,2/3—Isocitrate dehydrogenase 1,2/3; GLS—glutaminase; GLUD—glutamate dehydrogenase; GLUTs—glucose transporters; MCTs—monocarboxylate transporters; OXPHOS—Oxidative Phosphorylation.
Figure 1. Overview of an acute myeloid leukemia cell’s metabolism. Metabolic reprogramming provides ATP and intermediates for the synthesis of nucleotides, amino acids, lipids and redox elements (NADPH) needed to sustain high proliferation rates. Further details are found in the main body of text. PPP—pentose phosphate pathway; IDH1,2/3—Isocitrate dehydrogenase 1,2/3; GLS—glutaminase; GLUD—glutamate dehydrogenase; GLUTs—glucose transporters; MCTs—monocarboxylate transporters; OXPHOS—Oxidative Phosphorylation.
Cells 08 00967 g001
Table
Table 1. Modulators of metabolism and clinical trials on acute myeloid leukemia.
Table 1. Modulators of metabolism and clinical trials on acute myeloid leukemia.
DrugsDrugs in CombinationClinical TrialStatus
Targeting Oxidative Phosphorylation (OXPHOS)
IACS-010759 NCT02882321
Phase I study to dose escalation and dose expansion of the IACS-010759.		Active, recruiting [79]
Targeting amino acid metabolism
CB-839AzacitidineNCT02071927
Phase I/II study to evaluate the dependence of AML and acute promyelocytic leukemia on glutamine.		Completed, no results posted [80,81]
Erwinase
(ASPARAGINASE)		 NCT02283190
Phase I study to determine and analyze the effects of Erwinaze/Asparaginase on reducing serum glutamine levels.		Completed, no results posted
GRASPA (L-asparaginase encapsulated in red blood cells)Low-dose cytarabineNCT01810705
Phase II study to evaluate the efficacy and tolerability of GRASPA plus low-dose cytarabine in treatment of AML patients over 65 years old, unfit for intensive chemotherapy.		Completed, no results posted
PEG-BCT-100
(PEGylated recombinant human arginase)		 NCT02899286
Phase II, non-randomized, open-label study to evaluate the efficacy of single agent PEG-BCT-100 on arginine depletion in adult patients with relapsed/refractory AML.		Active, not recruiting
ADI-PEG 20
(arginine deiminase formulated with polyethlene glycol)		 NCT01910012
Phase II study to evaluate the response rate of arginine depletion by arginine deiminase.		Active, not recruiting
Targeting metabolic enzymes and epigenetic modifiers
Enasidenib (AG-221) NCT02577406
Phase III study comparing the efficacy and safety of AG-221 versus conventional care regimens in subjects 60 years or older with AML refractory to or relapsed after second- or third-line AML therapy and positive for IDH2 mutation.		Active, recruiting
Ivosidenib (AG-120)AzacitidineNCT03173248
Phase III study to evaluate the efficacy and safety of AG-120 plus azacitidine in adult subjects with previously untreated IDH1m AML who are considered appropriate candidates for non-intensive therapy.		Active, recruiting
IDH305 NCT02381886
Phase I study to estimate the maximum tolerated dose/recommended dose for expansion of IDH305 in patients with advanced malignancies that harbor IDH1 R132 mutations.		Active, not recruiting
BAY-1436032 NCT03127735
Phase II study to determine the maximum tolerated and/or recommended dose of BAY1436032, a mIDH1 inhibitor in patients with mIDH1-R132X advanced AML.		Completed, no results posted
AG-881 NCT02492737
Phase I study to evaluate the safety, pharmacokinetics, pharmacodynamics, and clinical activity of AG-881 in advanced hematologic malignancies that harbor an IDH1 and/or IDH2 mutation.		Completed, no results posted
FT-2102 Azacitidine or cytarabineNCT02719574
Phase I/II study to evaluate the safety efficacy, pharmacokinetics and pharmacodynamics of FT-2102 as a single agent or in combination with azacitidine or cytarabine.		Active, recruiting
Guadecitabine NCT02920008
Phase III study of the efficacy and safety of guadecitabine in adults with previously treated AML.		Active, not recruiting
Oral azacitidineBest-supportive careNCT01757535
Phase III study to compare efficacy and safety of oral azacitidine plus best-supportive care versus best supportive care as maintenance therapy in subjects with AML in complete remission.		Active, not recruiting
AzacitidineNivolumabNCT02397720
Phase II study to determine the side effects and the best dose of nivolumab and azacitidine with or without ipilimumab for treating patients with AML that have not responded to previous treatment or have returned after a period of improvement or are newly diagnosed.		Recruiting [82]
Signaling pathways and autophagy inhibition
BKM120 NCT01396499
Phase I study to determine the highest tolerable dose and the safety of BKM120, a PI3K kinase inhibitor that can be given to patients with relapsed or refractory leukemia.		Completed, no results posted
ONC201Low-dose cytarabineNCT02392572
Phase I/II study to find the highest tolerable dose and safety of ONC201, an inhibitor of the serine/threonine protein kinase AKT and extracellular signal-regulated kinase (ERK), alone or in combination with low dose cytarabine in patients with relapsed or refractory AML, acute promyelocytic leukemia, or myelodysplastic syndromes.		Active, recruiting
EverolimusMICE-regimen followed by consolidation therapy with idarubicin, cytarabine, and etoposideNCT01154439
Phase I study to determine the maximum-tolerated dose of everolimus, a derivative of rapamycin that inhibits mTOR, in combination with standard remission-induction therapy comprising mitoxantrone hydrochloride, cytarabine, and etoposide (MICE-regimen) followed by consolidation therapy comprising idarubicin, cytarabine, and etoposide in older patients with newly diagnosed AML.		Active, not recruiting
SirolimusAzacitidineNCT01869114
Phase II trial studies how well sirolimus and azacitidine works in treating patients with high-risk myelodysplastic syndrome or recurrent AML.		Recruiting
Hydroxychloroquine (HCQ)Mitoxantrone and EtoposideNCT02631252
Phase I study to find the safest and most effective dose of hydroxychloroquine, an autophagy inhibitor, when combined with the usual medication for AML, mitoxantrone, and etoposide.		Terminated, no results posted

Share and Cite

MDPI and ACS Style

Castro, I.; Sampaio-Marques, B.; Ludovico, P. Targeting Metabolic Reprogramming in Acute Myeloid Leukemia. Cells 2019, 8, 967. https://doi.org/10.3390/cells8090967

AMA Style

Castro I, Sampaio-Marques B, Ludovico P. Targeting Metabolic Reprogramming in Acute Myeloid Leukemia. Cells. 2019; 8(9):967. https://doi.org/10.3390/cells8090967

Chicago/Turabian Style

Castro, Isabel, Belém Sampaio-Marques, and Paula Ludovico. 2019. "Targeting Metabolic Reprogramming in Acute Myeloid Leukemia" Cells 8, no. 9: 967. https://doi.org/10.3390/cells8090967

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop