Abstract

   Acute myeloid leukemia (AML) is an aggressive malignancy, and many
   elderly/unfit patients cannot receive intensive and potentially
   curative therapy. These patients receive low-toxicity
   disease-stabilizing treatment. The combination of all-trans retinoic
   acid (ATRA) and the histone deacetylase inhibitor valproic acid can
   stabilize the disease for a subset of such patients. We performed
   untargeted serum metabolomic profiling for 44 AML patients receiving
   treatment based on ATRA and valproic acid combined with low-dose
   cytotoxic drugs (cytarabine, hydroxyurea, 6-mercaptopurin) which
   identified 886 metabolites. When comparing pretreatment samples from
   responders and non-responders, metabolites mainly belonging to amino
   acid and lipid (i.e., fatty acid) pathways were altered. Furthermore,
   patients with rapidly progressive disease showed an extensively altered
   lipid metabolism. Both ATRA and valproic acid monotherapy also altered
   the amino acid and lipid metabolite profiles; however, these changes
   were only highly significant for valproic acid treatment. Twenty-three
   metabolites were significantly altered by seven-day valproic acid
   treatment (p < 0.05, q < 0.05), where the majority of altered
   metabolites belonged to lipid (especially fatty acid metabolism) and
   amino acid pathways, including several carnitines. These metabolomic
   effects, and especially the effects on lipid metabolism, may be
   important for the antileukemic and epigenetic effects of this
   treatment.

   Keywords: acute myeloid leukemia, all-trans retinoic acid, lipids,
   metabolomics, valproic acid

1. Introduction

   Acute myeloid leukemia (AML) is an aggressive malignancy [[32]1], and
   the only possible cure is intensive therapy possibly including stem
   cell transplantation [[33]2]. The median age at first diagnosis is
   65–70 years [[34]3,[35]4], and the elderly patients above 70–75 years
   of age have an increased frequency of high-risk disease [[36]4] where
   remission is unlikely [[37]5,[38]6]. In addition, these patients are
   often not eligible to receive intensive treatment [[39]7]. Furthermore,
   intensive chemotherapy is not always recommended for patients with
   refractory or relapsed disease either [[40]8]. Such patients may
   instead receive low-toxicity AML-stabilizing treatment, e.g., a histone
   deacetylase (HDAC) inhibitor combined with all-trans retinoic acid
   (ATRA) and low-dose cytotoxic drugs [[41]9].

   HDAC inhibitors increase acetylation of various proteins, including
   histones [[42]10], and this last effect increases gene transcription
   [[43]11], especially in AML cells where HDACs often are overexpressed
   [[44]12]. Valproic acid is a short-chain fatty acid that functions as
   an HDAC inhibitor [[45]13] and has antiproliferative and proapoptotic
   effects in AML cells [[46]14,[47]15,[48]16,[49]17,[50]18]. The toxicity
   of this drug is low, and its antileukemic activity alone or in
   combination with ATRA has been documented in several clinical studies
   [[51]19,[52]20]. ATRA is a derivative of vitamin A that binds to
   retinoid-responsive nuclear receptors [[53]12,[54]21]; it has
   antileukemic activity [[55]22,[56]23,[57]24] and seems to increase the
   antileukemic effects of HDAC inhibitors [[58]25,[59]26]. Thus, the
   combination of ATRA and valproic acid seems to be a low-toxicity
   AML-stabilizing treatment; 30–40% of the patients respond to this
   therapy and responses may last up to one to two years [[60]19,[61]20].
   This treatment can also be used in elderly and unfit patients
   [[62]19,[63]27]. Finally, AML cell metabolism is important for
   chemosensitivity in AML [[64]28,[65]29], and both ATRA and valproic
   acid can modulate cellular and/or systemic metabolism
   [[66]30,[67]31,[68]32,[69]33,[70]34,[71]35,[72]36,[73]37].

   Even though the antileukemic in vivo effects of both ATRA and valproic
   acid are limited, both drugs should still be regarded as candidates for
   the treatment of non-acute promyelocytic leukemia (non-APL) variants of
   AML, especially in combination with other antileukemic agents in
   selected subsets of patients. First, although a large clinical study
   could not find a general improvement of overall survival when ATRA was
   added to intensive chemotherapy, increased survival was observed for
   subsets of patients, especially patients with a favorable prognosis
   (i.e., patients with genetic abnormalities affecting epigenetic
   regulation) [[74]38]. A preliminary report from the randomized DECIDER
   study suggests that ATRA improves survival of AML patients receiving
   hypomethylating agents [[75]39], and recent experimental studies also
   suggest that ATRA induces degradation of the mutated NPM1 protein
   [[76]40], sensitizes AML cells to FLT3 inhibitors [[77]41], and is
   effective in AML cells carrying the metabolism-modulating IDH mutations
   [[78]42]. Second, clinical studies suggest that valproic acid improves
   survival in certain AML subsets (i.e., patients with NPM1 mutations)
   [[79]43], and recent experimental studies suggest that it has
   synergistic effects when combined with autophagy-inhibiting chloroquine
   [[80]44] or inhibitors of DNA methyltransferase [[81]45]. HDAC-induced
   epigenetic reprogramming may also increase the susceptibility to ATRA
   [[82]46]. For future studies, predictive molecular networks may become
   important to identify responders to valproic acid [[83]47].

   Metabolomics is emerging as a powerful approach to study cancer
   metabolism, and a metabolomics study showed that systemic metabolic
   markers (i.e., an altered glucose metabolism signature) was associated
   with prognosis in AML [[84]28]. Such metabolomic signatures probably
   reflect both the characteristics of the AML cells/disease and the
   influence of a severe disease on systemic metabolic regulation. The
   overall systemic metabolomic profiles may also be important for
   leukemic growth and chemosensitivity. AML cells show metabolic
   plasticity; however, leukemia growth and chemosensitivity also seem to
   depend on the environmental context, possibly including the systemic
   supply of nutrients and especially amino acid and fatty acid metabolism
   [[85]48,[86]49]. The systemic metabolomic effects of ATRA and valproic
   acid may thus further contribute to the effects of these agents on AML
   cells. In this context, we have performed systemic metabolomic
   profiling of AML patients treated with the combination of ATRA,
   valproic acid, and low doses of conventional cytotoxic drugs, and we
   also examined the effects of both ATRA and valproic acid monotherapy on
   systemic metabolite profiles of AML patients. In our present study, we
   have focused on metabolite pathways rather than single metabolites. The
   aims of our study were to compare the pretreatment metabolomic profiles
   found in responders and non-responders to the AML-stabilizing
   treatment, and to characterize how these profiles are altered during
   the early period of this antileukemic treatment (two-day or seven-day
   monotherapy with either ATRA or valproic acid, respectively).

2. Materials and Methods

2.1. Patients

   The study was approved by the Regional Ethics Committee (REK 2017/305
   070417, REK Vest 2015/1410 190615, 215.03 120504, 231.06 150307) and
   included 44 elderly or unfit patients (25 men and 19 women; median age
   76.4 years with range 61–86 years) with high-risk leukemia, i.e., AML
   relapse, secondary AML, complex karyotype and/or TP53 mutations
   ([87]Table S1). Patients with APL were excluded. The patients represent
   an unselected subset of the consecutive patients included in two
   clinical studies [[88]19,[89]27]. Eighteen of the patients were
   classified as responders to the treatment according to the
   myelodysplastic syndrome (MDS) criteria [[90]50]; the others 26
   patients did not respond to the treatment and six of them showed a
   rapidly progressive disease (patients 39–44).

2.2. The Antileukemic Treatment Based on ATRA, Valproic Acid, and Low-Dose
Cytotoxic Drugs

   All serum samples were collected from patients included in two
   previously published clinical studies conducted at Haukeland University
   Hospital in Bergen, based on combined treatment with oral ATRA (22.5
   mg/m^2 twice daily for 14 days every 12th week), continuous valproic
   acid therapy at maximal tolerated doses, and low-dose therapy with
   hydroxyurea, 5-mercaptopurine, and/or cytarabine [[91]19,[92]27].
   Samples were taken prior to treatment, and also after receiving ATRA
   alone (after two days of treatment) or valproic acid alone (after seven
   days of treatment), as described in the following sections (see
   [93]Figure S1).

   Patients included in the first study [[94]27] received oral ATRA as
   described above. On day three of the first cycle the patients received
   valproic acid as an initial intravenous infusion with a loading dose (5
   mg/kg) followed by continuous infusion (28 mg/kg/24 h), and
   theophylline with a loading dose (5 mg/kg) followed by continuous
   infusion (0.65 mg/kg/hour). After five days (day 8), the patients
   received oral valproic acid (serum levels between 300–600 μmol/L) and
   oral theophylline adjusted to reach the therapeutic level of 50–100 μM.
   Patients with (i) peripheral blood blasts exceeding 50 × 10^9/L at
   diagnosis or (ii) later increasing blast counts received cytotoxic
   drugs to control leukocytosis.

   Patients included in the second study [[95]19] received valproic acid
   monotherapy for the first seven days with loading dose and subsequent
   continuous infusion for 24 h before oral valproic acid treatment (serum
   levels 300–600 μmol/L). On days 8–22, the patients received ATRA, and
   on days 15–24, they received subcutaneous cytarabine (10 mg/m^2 once
   daily). ATRA and cytarabine treatment were repeated at 12-week
   intervals. Patients with later increasing peripheral blood blasts (>50
   × 10^9/L) received hydroxyurea or 5-mercaptopurine instead of
   cytarabine.

   There is no general agreement with regard to which response criteria
   should be used for patients receiving low-toxicity AML stabilizing
   treatment [[96]20], and our patients were therefore evaluated both with
   regard to the conventional AML criteria for remission induction
   [[97]51] and the MDS response criteria [[98]52]. Two responders in the
   second study achieved complete hematological remission; the other
   responders fulfilled the criteria for hematological improvement
   according to the MDS criteria and lasting for at least eight weeks
   before progression.

2.3. Preparation and Analysis of Serum Samples

   Venous peripheral blood was collected onto sterile serum-clot activator
   tubes with gel separator (Greiner Bio-One GmbH, Kremsmünster, Austria
   or BD Vacutainer SST, Becton-Dickenson; Franklin Lakes, NJ, USA).
   Samples were left upright for 30–120 min at room temperature before
   being centrifuged (1300× g, 10 min). The serum supernatants were
   immediately allocated in plastic cryotubes (Nunc^TM, Roskilde, Denmark)
   and stored at –80 °C. The patients had no dietary restrictions, and all
   samples were collected from fasting patients, between 7:30 and 8:30 in
   the morning before breakfast.

   The untargeted metabolomic profile analyses were performed in
   collaboration with Metabolon Inc (Durham, NC, USA) as described
   previously [[99]53]. Samples were prepared using the automated MicroLab
   STAR^®® system (Hamilton Company, Bonaduz, Switzerland) and extracted
   using Metabolon‘s standard solvent extraction method [[100]53]. Four
   recovery standards were added prior to the first extraction step,
   DL-2-fluorophenylglycine, tridecanoic acid, d6-cholesterol and
   4-chlorophenylalanine [[101]53]. To remove proteins, dissociate small
   molecules bound to proteins or trapped in the protein matrix, and to
   recover chemically diverse metabolites, proteins were precipitated with
   methanol under vigorous shaking for two minutes (Glen Mills GenoGrinder
   2000) followed by centrifugation. The resulting extract was divided
   into two fractions for analysis by two separate reverse phase
   (RP)/ultra-performance liquid chromatography tandem mass spectrometry
   (UPLC-MS/MS, Waters ACQUITY, Milford, MA, USA) methods with positive
   ion mode electrospray ionization (ESI, Thermo Scientific, Waltham, MA,
   USA) RP/UPLC-MS/MS with negative ion mode ESI, and hydrophilic
   interaction liquid chromatography (HILIC)/UPLC-MS with negative ion
   mode ESI. A last sample was stored for backup. Samples were placed
   briefly on a Zymark TurboVap^®® (McKinley Scientific, Sparta, NJ, USA)
   to remove the organic solvent; they were thereafter stored overnight
   under nitrogen before further preparation. Extracted water samples
   served as process blanks and solvents used during the extraction
   process served as solvent blanks. A pooled matrix sample generated by
   taking a small volume of each experimental sample, and also a large
   pool of human plasma extensively characterized and prepared by
   Metabolon, served as technical replicates throughout the data set.
   Furthermore, a combination of quality control standards that were
   carefully chosen not to interfere with the analyzes, were spiked into
   every analyzed sample to allow monitoring of instrument performance and
   chromatographic alignment as described previously [[102]53].

   Instrument variability was determined for each sample by calculating
   the median relative standard deviation (RSD) prior to injection into
   the mass spectrometers. The overall process variability was determined
   by calculating the median RSD for all endogenous metabolites (i.e.,
   non-instrument standards) present in 100% of the pooled matrix samples
   (serum samples). The experimental samples were randomly spaced across
   the platform and the control samples were evenly spread.

   Metabolon Inc maintains a library based on authentical standards that
   contains the retention time/index (RI), mass to charge ratio (m/z), and
   chromatographic data (including MS/MS spectral data); the metabolites
   were identified by comparison to this library. Peaks were quantified
   using area-under the-curve.

2.4. Statistical Analyses

   Welch′s two sample t-test was used to identify metabolites that
   differed significantly between different groups, the paired t-test was
   used comparison of paired samples, and ANOVA contrasts used to analyze
   pharmacological effects for responders and non-responders separately.
   Random Forest analysis was used to provide an unbiased estimate of how
   well individuals can be classified into each group based on the
   metabolomic data. Metabolite pathway enrichment analysis was used for
   biological interpretation of metabolite data at a system level using
   the MetaboLync analysis tool (MetaboLync^®® Portal). Differences were
   regarded as statistically significant when p-values were < 0.05.
   Statistical analyses were performed with the programs ArrayStudio, R
   ([103]http://cran.r-project.org/) and the data analyze software program
   JMP (JMP^®®, Statistical Discovery^TM from ©SAS Institute Inc., Lane
   Cove, NSW, Australia).

3. Results

3.1. The Pretherapy Serum Metabolomic Profiles Could Not Predict
Responsiveness to Antileukemic Treatment Based on ATRA, Valproic Acid, and
Low-Dose Chemotherapy

   The serum metabolomic profiles were analyzed in pretreatment serum
   samples derived from 44 patients (18 responders and 26 non-responders).
   Untargeted metabolomic profiling identified 886 metabolites, of which
   approximately 45 significant changes would be expected to occur by
   chance alone when using p < 0.05 as significance level; however, only
   36 metabolites showed a significant difference (p < 0.05, Welch
   two-sample t-test) when comparing pretreatment levels of responders and
   non-responders, and all of these metabolites had high q-values (q >
   0.4). These results suggest that some of the statistically significant
   changes represent false discoveries ([104]Figure S2). First, 10 of the
   36 significantly altered metabolites are classified as peptides
   (gamma-glutamyltyrosine), amino acids (tyrosine, methionine, histidine,
   lysine, threonine, homoarginine), or amino acid metabolites
   (indolepropionate, 2-methylbutyrylcarnitine, 5-hydroxylysinepropionyl)
   ([105]Figure S2). Second, 11 of the 36 significantly altered
   metabolites were classified as lipid metabolites ([106]Figure S2).
   Thirdly, there were also significantly altered levels of five
   metabolites classified as xenobiotics between responders and
   non-responders (i.e., metabolites not naturally produced by the
   organism) and two of these were vitamin A metabolites. Finally, only
   two metabolites from the subclass carbohydrate/energy metabolism
   (mannose, citrate) differed significantly when comparing responders and
   non-responders, and both showed only borderline significance (0.04 > p
   > 0.05).

   As stated above, the significantly altered metabolites between
   responders and non-responders showed high q-values in our analysis
   ([107]Figure S2). Although both fatty acid and amino acid metabolism
   seem important for the growth and chemosensitivity in human AML
   [[108]48], and a distinct pretherapy systemic glucose metabolism
   signature is associated with prognosis (i.e., survival) of younger
   patients receiving intensive and potentially curative AML therapy
   [[109]28], we could not identify any highly significant differences in
   pretreatment systemic metabolite profiles between responder and
   non-responder patients receiving low-intensity AML-stabilizing
   treatment. Finally, a random forest analysis based on the overall
   metabolite profiles showed a predictive accuracy of only 48% for
   responders versus nonresponders (data not shown).

   We also did a metabolite pathway enrichment analysis based on the 36
   metabolites with p < 0.05 ([110]Figure S3). This analysis showed
   enriched pathways associated with amino acid and lipid metabolism,
   although vitamin A metabolism also had a high ranking (two metabolites
   differed between groups). Taken together, these analyses suggest that,
   in contrast to patients receiving intensive and potentially curative
   antileukemic treatment, there is no (or only a minor) prognostic impact
   of the pretherapy metabolomic profiles in our study for patients
   receiving leukemia-stabilizing treatment. Although vitamin A metabolism
   was enriched in the pathway analysis this should be interpreted with
   great care because only two of the five identified metabolites had
   borderline significance between responder and non-responder groups (see
   [111]Figure S2).

3.2. Pretherapy Differences in Fatty Acid Metabolism are Found between
Non-Responders with Rapidly Progressive AML Compared with Other
Non-Responders with Less Aggressive Disease

   Six non-responders had rapidly progressive disease with increasing
   peripheral blood blast counts and survival ≤12 days ([112]Table S1,
   patients 39–44). We compared the metabolomic profile of these six
   patients with the 20 other non-responders because, in clinical
   practice, it will be important to identify such patients and start
   early with alternative therapy. This early cutoff was chosen because a
   clinically relevant response is usually seen after 2–3 weeks of
   treatment with ATRA/valproic acid [[113]19,[114]27]. Eighty of the 886
   metabolites differed significantly between these two groups
   ([115]Figure 1, [116]Table S2). Most of the metabolites had a
   relatively high q-value, but when considering the relatively high
   number of significantly altered metabolites (80 out of 886 metabolites)
   and the overall metabolomic profiles/pathways (instead of individual
   metabolites), the most striking difference between these two groups was
   the high number of lipid metabolites (45 out of 80 metabolites) of
   which patients with rapidly progressive disease showed increased levels
   for 42 out of these 45 lipid metabolites (a more detailed
   classification of metabolites is presented in [117]Table S2). Thus, the
   six non-responders with rapidly progressive disease seem to differ from
   the 20 other non-responders especially with regard to lipid and fatty
   acid metabolites. Finally, a random forest analysis showed a predictive
   accuracy of 77% ([118]Figure 2), suggesting that it might be possible
   to use metabolite profiles to segregate these groups; however, the
   group size of aggressive non-responders is very small, and the results
   therefore need to be confirmed in a larger study. The 30 top-ranked
   metabolites from the random forest analysis included 26 metabolites
   with a p-value < 0.05 (six amino acid metabolites, 16 lipid
   metabolites, and four other metabolites; see [119]Figure 1 and
   [120]Figure 2).

Figure 1.

   [121]Figure 1
   [122]Open in a new tab

   Significantly altered metabolites when comparing the pretreatment
   levels of non-responders with rapidly progressive disease versus
   non-responders with less aggressive disease to antileukemic treatment
   based on all-trans retinoic acid (ATRA) and valproic acid. The
   p-values, q-values and mean fold change values for each metabolite are
   listed to the right in the figure (ranked by p-value), and a fold
   change >1 indicates that the levels were increased in non-responder
   patients with very aggressive disease compared to non-responder
   patients with less aggressive disease. Levels of serum metabolites in
   non-responders with less aggressive disease are shown in grey,
   increased levels found in non-responders with aggressive disease are
   shown in green, while decreased levels in non-responders with
   aggressive disease are shown in yellow. Color codes for classification
   of metabolites are explained at the bottom of the figure. Error bars
   show Standard deviation (SD). * Nicotidamine SD 6.761, ** Spermidine SD
   9.051.

Figure 2.

   [123]Figure 2
   [124]Open in a new tab

   Random forest analysis based on pretreatment serum metabolites when
   comparing non-responders with rapidly progressive disease versus
   non-responders with less aggressive disease to antileukemic treatment
   based on ATRA and valproic acid. The figure shows the 30 top-ranked
   metabolites from this analysis, which can segregate the two patient
   groups with a predictive accuracy of 77%. Amino acid and lipid
   metabolites constituted the majority of the top-ranked metabolites.
   Color codes for classification of metabolites are shown to the lower
   right. This analysis included 26 metabolites with a p-value < 0.05 (see
   [125]Figure 1; six amino acid metabolites, 16 lipid metabolites, and
   four other metabolites). Abbreviations; NR-Agg, non-responders
   aggressive disease (i.e., rapidly progressive); NR-NAgg, non-responders
   non-aggressive disease.

   We did a pathway enrichment analysis ([126]Figure 3) based on
   metabolites with p-value below 0.05, and mostly fatty acid pathways
   were enriched between non-responders with rapidly progressive AML and
   non-responders with less aggressive disease. The metabolites important
   for the tricarboxylic acid cycle (five metabolites, see [127]Figure 1)
   all reflect transition of fatty acid metabolites to the energy
   metabolism/mitochondria. Thus, our results suggest that rapidly
   progressive AML is associated with alterations especially in lipid
   (i.e., fatty acid) metabolism.

Figure 3.

   [128]Figure 3
   [129]Open in a new tab

   Pathway enrichment analysis based on altered metabolites between
   non-responder patients with rapidly progressive disease (i.e.,
   aggressive disease) versus non-responders with less aggressive disease.
   This analysis was based on significantly altered metabolites p < 0.05
   (see [130]Figure 1). Only pathways with an enrichment value greater
   than two and at least two metabolites within each pathway are shown in
   the figure.

3.3. Effects of ATRA Monotherapy on Serum Metabolite Profiles

   We compared the serum metabolite profiles before and after two days of
   ATRA monotherapy for ten patients from the first clinical study
   ([131]Table S1, the first five responders and five non-responders)
   [[132]27]. A total of 54 metabolites were then significantly altered
   after ATRA monotherapy (paired t-test, p < 0.05, [133]Figure 4), though
   all metabolites had high q-values (q > 0.1, [134]Figure 4). However, of
   the 54 altered metabolites, a large majority of 50 metabolites were
   increased after ATRA treatment.

Figure 4.

   [135]Figure 4
   [136]Open in a new tab

   The effect of ATRA monotherapy on the serum metabolomic profiles of
   patients after two days of treatment. Fifty-four metabolites were
   significantly altered after ATRA treatment (p < 0.05). The p-values,
   q-values, and mean fold change values for each metabolite are listed to
   the right in the figure (ranked by p-value), and a fold change >1
   indicates that the levels were increased in responders compared with
   non-responders. Pretherapy levels of serum metabolites for the ten
   patients are presented in grey, increased levels during ATRA treatment
   are presented in green and decreased levels presented in yellow. Color
   codes for classification of metabolites are explained at the bottom of
   the figure. Error bars show Standard deviation (SD). * Androstenediol
   (3 beta, 17 beta) pre-ATRA SD 3.862, ** Androstenediol (3 beta,17 beta)
   post-ATRA SD 2.542.

   A large fraction of these 54 metabolites were amino acid metabolites
   (24/54), including isoleucine/leucine and tryptophan together with
   several of their metabolites as well as histidine, lysine,
   methionine/cysteine/taurine arginine, lysine, and valine metabolites
   ([137]Figure 4). Nineteen lipid metabolites were also altered by ATRA;
   especially sphingolipid/sphingomyelin but also plasmalogen metabolites
   were increased. The largest subgroup of altered lipid metabolites was
   sphingolipids that are largely synthesized from serine and
   palmitoyl-CoA and are important as cell membrane components and
   regulators of cell proliferation and survival
   [[138]54,[139]55,[140]56]. Although several sphingomyelins were
   increased, their second messenger ceramide was not altered. Finally,
   several one-carbon/methylated metabolites were increased after ATRA
   therapy together with the methylation reaction product
   S-adenosylhomocysteine.

   We performed a pathway enrichment analysis ([141]Figure S4) based on
   the significantly altered metabolites observed after ATRA treatment.
   This analysis also showed that especially lipid metabolites but also
   amino acid metabolites before and during ATRA therapy. In addition, we
   finally also investigated the effects of ATRA monotherapy for the five
   responders and the five non-responders separately. A similar profile
   was then observed where significantly altered metabolites (p < 0.05,
   but q > 0.05) consisted mainly of amino acid and lipid metabolites in
   both groups (data not shown).

   To conclude, our present metabolomic results suggest that ATRA mainly
   increases levels of metabolites involved in amino acid and lipid
   metabolism, but these observations must be interpreted with great care
   because the effects were relatively weak and showed high q-values.
   However, previous animal studies have also shown that ATRA can alter
   the systemic levels of amino acid and fatty acid metabolites [[142]37];
   these results are thus consistent with our present observations in AML
   patients.

3.4. Valproic Acid Monotherapy Alters the Systemic Amino Acid and Lipid
Metabolite Profiles in Both Responders and Non-Responders to the Antileukemic
Treatment

   We compared the systemic metabolomic profiles before and after seven
   days of valproic acid monotherapy for ten patients included in the
   second clinical study, including the first five responders and
   non-responders ([143]Table S1) [[144]19]. A total of 109 metabolites
   were significantly altered in patients treated with valproic acid
   compared to pretreatment levels; 55 metabolites were increased, and 54
   were decreased (p < 0.05; see [145]Figure S5). Among these, 36
   metabolites had both a p-value < 0.05 and a q-value < 0.1 ([146]Figure
   5), and 23 of these metabolites also had a q-value below 0.05. Four of
   these 36 metabolites were valproic acid metabolites, whereas the rest
   included 13 amino acid metabolites and 12 lipid metabolites. Only one
   of these metabolites reflected energy/carbohydrate metabolism.

Figure 5.

   [147]Figure 5
   [148]Open in a new tab

   Identification and classification of serum metabolites that differed
   significantly when comparing samples taken prior to treatment and after
   seven days of valproic acid (VPA) therapy. Thirty-six metabolites
   differed significantly between untreated and VPA-treated samples (p <
   0.05, Welch′s two sample t-test), with q-value < 0.1 (the upper 23
   metabolites with q < 0.05). The p-values, q-values, and mean fold
   change for each metabolite are listed to the right in the figure
   (ranked by p and q-value), and a fold change >1 indicates that the
   levels were increased after valproic acid therapy. Metabolite levels
   found in pretreatment samples are shown in grey, while increased levels
   during treatment are shown in green (22/36 increased) and decreased
   levels during treatment are shown in orange (14/36 decreased). Color
   codes for classification of metabolites are explained at the bottom of
   the figure. Error bars show Standard deviation (SD). *Adipoylcranitine
   (C6-DC) SD 6.928 **Suberoylcarnitine (C8-DC) SD 8.50.

   A random forest analysis was performed after excluding the four
   valproic acid metabolites, and then 14 lipid and seven amino acid
   metabolites were among the 30 top-ranked metabolites of this analysis,
   with a predictive accuracy of 90% ([149]Figure 6). Twenty-two of these
   30 metabolites were significantly altered by the valproic acid
   treatment (p < 0.05, see [150]Figure S5), including six of the highly
   significant carnitine metabolites ([151]Figure 6). In addition, another
   random forest analysis was performed including all identified
   metabolites (not excluding valproic acid metabolites) and the results
   showed that the four top-ranked metabolites were all valproic acid
   metabolites as expected ([152]Figure S6; predictive accuracy of 100%).
   Hence, responsiveness to the combination of ATRA and valproic acid is
   probably not determined by a difference in valproic acid metabolism.

Figure 6.

   [153]Figure 6
   [154]Open in a new tab

   The effect of 7-day valproic acid (VPA) monotherapy on the serum
   metabolomic profiles for ten patients (five responders and five
   non-responders). The random forest analysis was based on the
   identification of 886 metabolites in pretherapy samples and samples
   collected after seven days of valproic acid monotherapy. The analysis
   showed a predictive accuracy of 90% (see the insert table) after
   exclusion of the four valproic acid metabolites. The top-30 most
   important metabolites for separation of the two groups are shown in
   ranking order. Color codes indicate the classification of individual
   metabolites at the lower right part of the figure.

   A pathway enrichment analysis for differential metabolites after
   valproic acid treatment revealed enrichment of pathways relating
   especially to lipid metabolism as well as amino acid metabolism
   ([155]Figure 7). As discussed above, several lipid and fatty acid
   metabolites were altered after valproic acid therapy, including
   increased levels of several fatty acid-carnitines ([156]Figure 5,
   [157]Figure S5). This is consistent with impaired β-oxidation, and the
   increased levels of five out of nine significantly altered dicarboxyl
   fatty acids suggests that ω-oxidation is used as an alternative
   mechanism in the liver to compensate for impaired β oxidation
   [[158]57]. Furthermore, a total of 33 amino acid metabolites were
   significantly altered by valproic acid, in particular metabolites
   reflecting tryptophan (six metabolites) and valine/leucine/isoleucine
   metabolism (eight metabolites). Seven amino acid metabolites were also
   included among the top-ranked metabolites in the random forest analysis
   ([159]Figure 6). In our analysis, the 109 metabolites with significant
   p-values < 0.05 (but high q-value) had a similar overall profile as
   that found for metabolites with p < 0.05 and q < 0.05 ([160]Figure 5),
   mainly consisting of metabolites belonging to amino acid (see
   [161]Figure S7) and lipid pathways (see [162]Figure S8), whereas
   metabolites reflecting the carbohydrate and energy metabolism were
   scarce.

Figure 7.

   [163]Figure 7
   [164]Open in a new tab

   Pathway enrichment analysis based on metabolites altered after seven
   days of valproic acid treatment compared to pretherapy levels. This
   analysis was based on significant altered metabolites p < 0.05 (see
   [165]Figure S5), and only pathways with an enrichment value greater
   than two and at least two metabolites within each pathway are shown.
   The most significant pathway is shown in red and less significant
   pathways in light yellow.

   We finally analyzed and compared the effects of valproic acid
   monotherapy for the five patients classified as responders and five
   non-responders separately ([166]Table 1). Then, 78 metabolites were
   significantly altered after valproic acid treatment for the responders,
   including divergent effects for 17 amino acid and 32 lipid metabolites,
   while 105 metabolites were significantly altered after valproic acid
   treatment for the non-responders, including divergent effects for 21
   amino acid and 49 lipid metabolites ([167]Table 1, [168]Tables S3 and
   S4). Thus, a similar pattern was observed for both responders and
   non-responders as described above; valproic acid mainly alters amino
   acid and lipid metabolism. The metabolites showing both p- and q-value
   < 0.05 are listed in [169]Table 1. These metabolites included the
   valproic acid metabolites and also several carnitines, which also
   reflect differences in lipid and amino acid metabolism.

Table 1.

   An overview of significantly altered serum metabolites (p < 0.05 and q
   < 0.05) after seven-day valproic acid therapy.
   Biochemical Name Classification During VPA Therapy/Pretherapy
   p-Value q-Value Fold Change
   RESPONDERS
   *Valproate Drug concentration 0.0000 0.0000
   *2-propyl-2-pentenoate (2-ene-valproate) Valproic acid metabolite
   0.0000 0.0000 55.81
   *5-hydroxyvalproate Valproic acid metabolite 0.0000 0.0000 13.83
   *3-hydroxyvalproate Valproic acid metabolite 0.0000 0.0000 25.77
   *Glucuronide of C8H16O2 (1)* Partially characterized 0.0000 0.0000
   28.34
   *Suberoylcarnitine (C8-DC) Fatty acid metabolism, acyl carnitine 0.0000
   0.0000 30.82
   Phenylacetylcarnitine Acetylated peptide 0.0000 0.0000 17.30
   *Adipoylcarnitine (C6-DC) Fatty acid metabolism 0.0000 0.0000 17.06
   *3-methylglutarylcarnitine (2) Leucine/isoleucin/valine metabolism
   0.0000 0.0000 0.18
   *Adipate (C6-DC) Fatty acid, dicarboxylate 0.0000 0.0027 3.41
   10-undecenoate (11:1n1) Medium chain fatty acid 0.0001 0.0043 0.49
   *Isobutyrylca′rnitine (C4) Leucine/isoleucin/valine metabolism 0.0001
   0.0093 3.06
   Glucuronide of C14H22O4 (2) Partially characterized 0.0003 0.0182 0.22
   4-hydroxycinnamate sulfate Tyrosine metabolism 0.0004 0.0259 3.00
   Isoeugenol sulfate Food component, plant 0.0005 0.0264 0.04
   NON-RESPONDERS
   *Valproate Drug concentration 0.0000 0.0000
   *2-propyl-2-pentenoate (2-ene-valproate) Valproic acic metabolite
   0.0000 0.0000 48.32
   *5-hydroxyvalproate Valproic acic metabolite 0.0000 0.0000 31.58
   *3-hydroxyvalproate Valproic acic metabolite 0.0000 0.0000 93.38
   *Glucuronide of C8H16O2 (1) Partially characterized 0.0000 0.0000 16.16
   *Suberoylcarnitine (C8-DC) Fatty acid, dicarboxylate 0.0000 0.0000
   12.96
   *Adipoylcarnitine (C6-DC) Fatty acid metabolism 0.0000 0.0000 7.34
   *Adipate (C6-DC) Fatty acid, dicarboxylate 0.0000 0.0001 4.33
   *3-methylglutarylcarnitine (2) Leucine/isoleucin/valine metabolism
   0.0000 0.0001 0.25
   Suberate (C8-DC) Fatty acid, dicarboxylate 0.0000 0.0022 3.43
   3-carboxy-4-methyl-5-pentyl-2-Furanpropionate (3-Cmpfp) Fatty acid
   metabolism, dicarboxylate 0.0001 0.0054 0.44
   Hexanoylglycine Fatty acid metabolism, acyl glycine 0.0001 0.0077 3.03
   3-hydroxyhexanoate Fatty acid metabolism, monohydroxy 0.0001 0.0100
   2.03
   Androstenediol (3beta,17beta) disulfate (1) Androgenic steroid 0.0003
   0.0197 1.45
   *Isobutyrylcarnitine (C4) Leucine/isoleucin/valine metabolism 0.0004
   0.0208 3.15
   Gamma-CEHC Cofactors/vitamins 0.0004 0.0234 1.56
   Isoursodeoxycholate Secondary bile acid metabolism 0.0005 0.0278 0.06
   Indoleacetylglutamine Tryptophane metabolism 0.0006 0.0298 0.09
   4-hydroxyphenylacetylglutamine Acetylated peptide 0.0008 0.0352 0.28
   1-palmitoyl-2-linoleoyl-GPI (16:0/18:2) Phosphatidylinositole 0.0008
   0.0352 1.51
   5-bromotryptophan Tryptophane metabolism 0.0010 0.0387 0.49
   4-allylphenol sulfate Food component, plant 0.0010 0.0387 0.29
   N-acetyltyrosine Tyrosine metabolism 0.0011 0.0413 2.42
   Trans-4-hydroxyproline Urea cycle. Proline metabolism 0.0011 0.0411
   1.73
   Galactonate Carbohydrate metabolism 0.0012 0.0430 0.38
   3-aminoisobutyrate Pyrimidine metabolism, thymine 0.0013 0.0433 1.86
   [170]Open in a new tab

   The metabolites are ranked according to their p- and q-values, and the
   metabolites that were altered in responders and non-responders to the
   antileukemic AML-stabilizing therapy are listed separately. Overlapping
   metabolites between these two groups are marked with *. VPA, valproic
   acid.

4. Discussion

   Epigenetic targeting based on combination therapy with ATRA and
   valproic acid can stabilize the disease for a subset of AML patients.
   Furthermore, both drugs can also modulate the cellular and/or systemic
   metabolism [[171]32,[172]33,[173]34,[174]35,[175]36,[176]37]. Such
   pharmacological effects may influence the metabolic regulation of the
   leukemia cells through altered levels of nutrients in their
   microenvironment [[177]48,[178]49], and these effects may even
   contribute to the epigenetic effects of the drugs [[179]58,[180]59]. We
   have therefore performed metabolomic profiling of serum samples
   collected from AML patients before receiving disease-stabilizing
   treatment to compare responders and non-responders to treatment, and we
   also compared systemic metabolite profiles before and after drug
   monotherapy. Our study suggests that differences in pretreatment amino
   acid and lipid metabolite profiles are associated with disease
   aggressiveness, and both ATRA and valproic acid cause further
   modulation of the systemic metabolic signature (i.e., amino acid and
   lipid metabolism). This is different from patients receiving intensive
   and potentially curative chemotherapy where a glucose metabolism
   signature has been shown to be associated with chemoresistance and
   survival after chemotherapy [[181]28].

   Untargeted metabolomics generates large amounts of data that can be
   complex and challenging to analyze. A p-value of 0.05 is the false
   positive rate when one test is performed, but for a large number of
   tests on the data one has to account for false positives. There are
   different methods to correct for multiple testing. One strategy is to
   use the family-wise error rate adjustment (e.g., Bonferroni
   correction), but this is regarded as very conservative when one has a
   large number of tests; the use of false discovery rates (i.e., the
   q-values) is therefore more common although it allows for a small
   number of false discoveries [[182]60]. A relatively high q-value (e.g.,
   q > 0.10) indicates diminished confidence but it does not necessarily
   rule out the biological significance of a result. In our opinion other
   evidence should also be considered, for example (i) significance in
   another dimension of the study, (ii) inclusion in a common pathway
   together with highly significant compounds, and (iii) residing in a
   similar metabolic family with other statistically significant
   metabolites. Throughout the present study we have therefore for each
   biological comparison listed all metabolites with the corresponding
   p-value < 0.05, we have presented p-values together with the
   corresponding q-values and fold change values for all these
   metabolites, and when we discuss the importance of single metabolites
   we focus on the q-values (i.e., the studies of the valproic acid
   effects). We also performed pathway enrichment analysis based on
   significantly altered metabolites (p < 0.05) which revealed enriched
   pathways instead of focusing on single metabolites. In our opinion such
   an identification of a metabolic pathway can be justified because it is
   based on several metabolites and not a single metabolite. However, we
   would like to emphasize that all background information (p- and
   q-values and fold change values) are provided in the text or in
   figures/tables for all metabolites included in our analyses, so the
   level of confidence in the results can be further evaluated by the
   reader.

   Our pretreatment profiles were probably determined by the metabolic
   characteristics of a large leukemia cell burden together with the
   effects of this burden on systemic metabolic regulation. The lipid and
   amino acid profiles were further modulated by metabolic effects of ATRA
   but especially of valproic acid. The drugs have direct (i.e.,
   epigenetic) effects on the AML cells, and metabolic modulation may then
   represent an additional indirect effect on the leukemia cells by
   modulation of metabolite/nutrient levels in their bone marrow
   microenvironment. Thus, altered metabolomic profiles may contribute for
   the prognostic evaluation for such patients, but they may also
   influence AML cell chemosensitivity and/or contribute to the
   antileukemic effects of the treatment.

   The clinically relevant responses to this AML-stabilizing treatment are
   usually seen after 14–20 days of treatment [[183]19,[184]27]. Thus, in
   this study, the effects of ATRA and valproic acid on metabolomic
   profiles were evaluated early before improvement of peripheral blood
   cell counts could be expected. Despite the late clinical
   responsiveness, alterations in metabolomic signatures were found during
   the first days of treatment and these effects differed between patients
   whom about two weeks later became responders or non-responders to
   treatment.

   There is no general agreement on how responses to AML-stabilizing
   treatment should be classified. We used the generally accepted
   definition of complete hematological remission for AML patients
   [[185]51] and the MDS criteria for stable disease that require
   improvement/stabilization of normal peripheral blood cell counts for at
   least two months [[186]61]. However, the median survival for elderly
   patients not receiving AML-directed therapy is only two-three months
   [[187]7,[188]62,[189]63] and we therefore regarded such disease
   stabilization as unexpected and classified this as a response.

   Amino acid profiles in healthy individuals and cancer patients have
   been compared in several previous studies [[190]64]. The plasma amino
   acid profiles for patients with non-small-cell lung cancer or
   colorectal cancer is considerably different from healthy controls, and
   altered amino acid profiles have been detected also in early stage
   breast cancer [[191]64]. Our present study found altered amino acid
   profiles in AML after both ATRA and valproic acid treatment, and we
   observed a considerable heterogeneity in pretherapy metabolite levels
   among AML patients when comparing (i) responders versus non-responders
   and (ii) non-responders with rapidly progressive disease versus other
   non-responders. The mechanisms behind altered amino acid profiles in
   cancer patients may be related to the risk of cancer [[192]65] or
   reflect nutritional or microbiotic alterations, progression of the
   disease, cachexia or weight loss [[193]66,[194]67,[195]68]. In our
   opinion, it is most likely that the mechanism behind the differences in
   amino acid metabolism between various AML patient groups is also
   multifactorial.

   To the best of our knowledge, the present study is the first to
   investigate metabolomic profiles in patients receiving AML-stabilizing
   treatment. We investigated elderly and unfit patients that were
   included in two previous clinical studies; in both clinical studies the
   patients received the same doses of ATRA, guidelines for adjustment of
   valproic acid treatment were similar, and we used low-dose (i.e.,
   low-toxicity) cytarabine/mercaptopurin/hydroxyurea. Our results showed
   that responders and non-responders showed relatively small differences
   in pretherapy metabolic profiles, but non-responders with rapidly
   progressive disease seemed to have more extensive differences compared
   with the other non-responders. Fatty acid oxidation may be important
   for AML growth [[196]69], and increased levels of lipids/fatty acids in
   non-responders could then be a strategy to provide energy substrates
   for the growing leukemia cells. In addition, the long chain fatty acid
   oleoyl ethanolamid is suggested to contribute to drug resistance in
   other patients with hematological malignancies [[197]70]. The increased
   oleoyl ethanolamid levels may also reflect cachexia that is associated
   with poor prognosis in other malignancies [[198]68]. Our responders and
   non-responders differed in their pretherapy levels of the three
   endocannabinoids oleoyl ethanolamide, linoleoyl ethanolamide (both
   increased in non-responders) and N-palmitoylserine (decreased in
   non-responders). Oleoyl ethanolamide activates the nuclear peroxisome
   proliferator-activated receptor alpha (PPAR-alpha) that induces
   lipolysis and fat utilization [[199]71]. Consistent with increased
   lipolysis and energy utilization, non-responders also showed
   significantly altered serum levels of four free fatty acids and one
   fatty acid metabolite, suggesting release of fatty acids. Oleoyl
   ethanolamide may therefore represent a link between endocannabinoid and
   fatty acid metabolism in different patient groups. In addition,
   responders and non-responders also differed in xenobiotic metabolites
   including gut-bacteria derived metabolites. Taken together, these
   observations suggest that differences in nutritional status or gut
   microbiome contribute to the metabolomic heterogeneity of elderly
   patients with newly diagnosed AML.

   Our analyses showed that the non-responders are not only heterogeneous
   with regard to survival but also with regard to pretherapy metabolomic
   profiles. Patients with rapidly progressive disease differed
   considerably from other non-responders especially in lipid metabolism
   but also amino acid metabolism. AML is a very aggressive disease and if
   the first treatment fails the patients may become unfit for further
   treatment. Our present observations have to be interpreted with great
   care because few patients with very aggressive disease were studied and
   further studies are needed. Still, they suggest that pretreatment
   metabolomic profiling may be helpful to select patients that should not
   receive treatment based on ATRA plus valproic acid, especially patients
   that have rapidly progressive disease because it takes 15–20 days
   before clinical responses can be detected.

   Neither our comparisons of pretherapy metabolomic profiles of
   responders and non-responders nor our studies of metabolite profiles
   during monotherapy detected any major differences in carbohydrate or
   energy metabolism (glycolysis, citric acid cycle), even though such
   differences have a prognostic impact for AML patients receiving
   intensive therapy [[200]28]. However, clinically relevant responses to
   our disease-stabilizing therapy can be seen even for patients with
   high-risk AML (e.g., high-risk karyotype, relapse; [201]Table S1).
   Thus, the conventional high-risk criteria, and also glucose metabolism,
   seem less important for patients receiving treatment based on ATRA and
   valproic acid. Instead, differences in or altered lipid and amino acid
   metabolism seem to be important for patients receiving this
   disease-stabilizing treatment, and effects on systemic levels of
   metabolites induced by valproic acid generally seem to be stronger than
   the ATRA-induced effects. Previous studies have also described an
   effect of ATRA on one-carbon metabolism [[202]72], and similar effects
   were also seen in our AML patients although they seemed to be
   relatively weak.

   We also examined the effects of ATRA and valproic acid on metabolomic
   profiles for ten patients, i.e., five responders and five
   non-responders analyzed together, after relatively short duration of
   treatment (two and seven days, respectively); whereas clinically
   relevant responses are usually detected after two-three weeks
   [[203]19,[204]27]. Thus, the observed alteration of metabolite profiles
   should be regarded as early pharmacological effects that are common for
   responders and non-responders.

   The most striking effects by the two drugs in our studies were altered
   lipid and amino acid metabolism. ATRA increased serum levels of
   sphingolipids and sphingomyelins in non-responders; a hypothesis is
   therefore that ATRA contributes to chemoresistance in the
   non-responders through a growth-enhancing and antiapoptotic effect of
   these metabolites as described for other malignancies
   [[205]54,[206]55,[207]56]. Several sphingomyelins were altered by ATRA,
   but their second messenger ceramide derived from hydrolysis of
   sphingomyelin was not altered. Altered ceramide synthesis has been
   observed in other malignancies [[208]73], and a subset of AML patients
   may even have mutations in the sphingomyelin/ceramide pathway
   [[209]74]. Taken together, these observations suggest that ATRA also
   has complex effects on this pathway. Moreover, the levels of several
   plasmalogens were also altered by ATRA. These glycerophospholipid
   derivatives are thought to be protective against reactive oxygen
   species [[210]75], their levels are high in inflammatory cells
   [[211]76], and their levels increase during differentiation-induction
   in the HL60 human AML cell line [[212]77]. Thus, increased plasmalogens
   may reflect ATRA-induced effects on AML cells that contribute to
   chemoresistance at least for certain patients.

   Furthermore, several methylated metabolites were increased after ATRA
   treatment, including 3-methylhistidine, N6,N6,N6-trimethylhistidine,
   S-methycysteine, 2′-O-methylcytidine,
   N1-methyl-4-pyridone-3-carboxamide, as well as the methylation reaction
   product S-adenosylhomocysteine. ATRA has been shown to increase both
   glycine N-methyltransferase that regulates the methyl group supply for
   S-adenosylmethionine-dependent transmethylation reactions [[213]77] as
   well as the activation of histone methyltransferase SUV39H2 that is
   important for epigenetic regulation of gene transcription [[214]78].
   ATRA-induced alteration of DNA methylation has also been described in
   the HL60 AML cell line [[215]79]. Thus, these observations suggest that
   ATRA influences the general methylation potential in AML cells.

   Lipid and amino acid metabolism were both altered during valproic acid
   therapy, and especially altered levels of fatty acid metabolites were
   observed. There seem to be several links between epigenetic regulation
   and fatty acid metabolism in AML [[216]58], including expression of
   fatty acid binding protein 4 (FABP4) in AML cells that is important
   both for fatty acid uptake and epigenetic regulation [[217]80].
   Epigenetic regulation is also important for the expression of
   acetyl-CoA carboxylase 2, a key driver of fatty acid β-oxidation in AML
   cells [[218]81] and a regulator of the general lipid metabolism
   [[219]82]. Repression of this gene by histone deacetylation allows for
   simultaneous β-oxidation and fatty acid synthesis to take place
   [[220]81]. Finally, valproic acid has been shown to alter lipid
   metabolism and fatty acid oxidation [[221]33,[222]35]. Our overall
   results thereby support the hypothesis that valproic acid alters
   epigenetic regulation both through direct effects on histone
   acetylation and indirectly through modulation of fatty acid metabolism.

   We compared the effects of ATRA and valproic acid for five responders
   and five non-responders; these results have to be interpreted with
   great care due to the low number of samples. However, non-responders
   showed more extensive effects on amino acid and lipid metabolism after
   two days of ATRA monotherapy but especially after seven days of
   valproic acid monotherapy. In our opinion, these more extensive effects
   in non-responders may partly reflect pharmacological effects, but may
   also be influenced by disease progression
   [[223]30,[224]31,[225]32,[226]33,[227]34,[228]35,[229]36].

5. Conclusions

   Both ATRA and valproic acid have been used in the treatment of AML, and
   this is the first study to investigate systemic metabolomic profiles in
   AML patients receiving leukemia-stabilizing treatment based on ATRA
   plus valproic acid. Our study shows that especially amino acid and
   lipid metabolism varies between patients and during treatment. The
   effect of valproic acid on the regulation of lipid and amino acid
   metabolism is particularly strong and may contribute to the
   antileukemic and/or epigenetic effects of this drug, whereas the
   effects of ATRA and the differences between patient subsets are weaker.
   Metabolites related to carbohydrate or energy metabolism showed only
   minor variations in our study. Systemic metabolomics should be further
   investigated to identify biomarkers for pretreatment evaluation of
   susceptibility to AML stabilizing treatment, though larger studies are
   needed. Finally, changes in metabolomic profiles may influence the bone
   marrow microenvironment and thereby modulate AML cell metabolism as
   well as epigenetic regulation and contribute to therapy resistance.

Supplementary Materials

   The following are available online at
   [230]https://www.mdpi.com/2073-4409/8/10/1229/s1, Figure S1: Timeline
   of treatment schedule for patients included in two clinical studies,
   Figure S2: Identification and classification of metabolites in
   pretherapy serum samples that differed significantly between responders
   and non-responders to the antileukemic treatment of ATRA plus valproic
   acid, Figure S3: Comparison of pretreatment metabolomics profiles for
   responders and non-responders to antileukemic treatment based on ATRA
   and valproic acid; a metabolite pathway enrichment analysis, Figure S4:
   Comparison of metabolomics profiles before and during ATRA treatment
   for responders and non-responders to antileukemic treatment based on
   ATRA and valproic acid; a metabolite pathway enrichment analysis,
   Figure S5: The effect of seven-day valproic acid (VPA) monotherapy on
   the serum metabolomic profiles of AML patients, Figure S6: The effect
   of valproic acid monotherapy for seven days on the serum metabolomics
   profiles for 10 patients (five responders and five non-responders;
   patients included in the study described in PMID 23915396, valproic
   acid metabolites included in the study), Figure S7: The effect of seven
   days of valproic acid monotherapy on serum metabolomic profiles;
   significantly altered amino acid and peptide metabolites when comparing
   samples derived from 10 patients (five responders and five
   non-responders; all patients included in the study by Fredly et al.
   PMID 23915396), Figure S8: The effect of seven days of valproic acid
   monotherapy on serum metabolomic profiles; significantly altered lipid
   metabolites when comparing samples derived from 10 patients (five
   responders and five non-responders; all patients included in the study
   by Fredly et al. PMID 23915396), Table S1: Clinical and biological
   characteristics of the included patients, Table S2: Significantly
   altered serum metabolites between subsets of non-responders to
   antileukemic treatment based on ATRA and valproic acid; a comparison of
   non-responders with very aggressive (i.e., rapidly progressive) and
   less aggressive disease, Table S3: Significantly altered metabolites
   after seven days of valproic acid monotherapy; a comparison of
   pretreatment samples versus samples collected during treatment for
   patients classified as responders to antileukemic therapy based on ATRA
   and valproic acid, Table S4: Significantly altered metabolites after
   seven days of valproic acid therapy; a comparison of pretreatment
   samples versus samples collected during treatment for patients
   classified as non-responders to antileukemic treatment based on ATRA
   and valproic acid.
   [231]Click here for additional data file.^ (3.5MB, pdf)

Author Contributions

   Conceptualization, Ø.B., H.K.F., and B.T.G.; formal analysis, I.S.G.,
   Ø.B., and K.J.H.; review of clinical work, Ø.B., H.K.F., and B.T.G.;
   writing—review and editing, Ø.B., K.J.H., and I.S.G.; visualization,
   I.S.G., Ø.B., and K.J.H.; supervision, Ø.B. and K.J.H.

Funding

   This research was funded by Helse Vest (grant number 911788), Norwegian
   Cancer Society (182609, 188802, 18902), and the Trond Mohn Foundation.

Conflicts of Interest

   The authors declare no conflict of interest.

References