Quizartinib and omacetaxine mepesuccinate combination therapy in FLT3-ITD AML: a phase II trial

Domain: chemistry
Published: 2026-04-06
Editorial outcome: minor_revision

Abstract
FLT3-ITD inhibitors are approved for acute myeloid leukaemia (AML) treatment but relapse is common. In this study, the combined inhibition of FLT3-ITD signal and protein translation by QUIZartinib and Omacetaxine Mepesuccinate (QUIZOM) synergistically suppressed the most critical FLT3-ITD survival signals including mitochondrial respiration and proteostasis, which induced apoptosis and pro-inflammatory response. In a Phase 2 trial (NCT03135054) involving 40 chemo-refractory/unfit FLT3-ITD AML patients, QUIZOM achieved a composite complete remission (CRc) of 83%, a median leukemia-free survival (LFS) of 10 months (Range: 0.7-68.2 months) and a median overall survival (OS) of 12.9 months (Range: 1.8-69.2 months). 13/33 (39%) received allogeneic HSCT after a median of 143 days (Range: 53-367 days). Higher CRc rates were observed in patients with NPM1 mutations, DNMT3A mutations, and wild-type WT1. Single-cell RNA-sequencing of QUIZOM cohort revealed positive correlation between pro-inflammatory response in blasts, CD8+T activation and clinical responsiveness. Further, we identified a leukaemic stem cell (LSC) subpopulation with activated JNK/JUN/HSPA1B axis via PLD1-driven phosphatidylcholine metabolism, which promoted proteostasis and drove QUIZOM resistance. PLD1-inhibitor remodeled phospholipid metabolism, induced ferroptosis and restored QUIZOM response in LSC. Our findings provided the therapeutic and resistant mechanisms of QUIZOM and paved the way for targeted interventions in this AML subtype.

Introduction
Acute myeloid leukemia (AML) is characterized by an abnormal increase in myeloblasts in blood and bone marrow (BM) due to differentiation blockade and increased proliferation. FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) is one of the most common mutations in AML, accounting for up to 30% of cases. In-frame insertion of the duplicated sequence leads to constitutive activation of FLT3, which drives cellular proliferation via activation of oncogenic pathways viz. PI3K, STAT5 and RAS.1 AML carrying FLT3-ITD is associated with a higher risk of relapse and inferior prognosis.2

FLT3 inhibitors are now available for clinical use at relapse. 3-6 Despite an improved outcome compared with standard of care (SOC), the clinical benefit of FLT3 inhibitor monotherapy, notably gilteritinib and quizartinib, is characteristically transient and relapse is uniform unless allogeneic HSCT can be timely arranged.7,8 There is an unmet clinical need to identify the oncogenic vulnerabilities in FLT3-ITD AML and develop mechanism-driven therapeutic strategies at relapse so that more patients can be bridged to HSCT.

In this study, we examined the oncogenic vulnerabilities in FLT3-ITD AML and showed that they were specifically sensitive to perturbation of FLT3-ITD signal and protein translation, which was proven both in the laboratory and in a Phase II clinical trial. Combination treatment with Quizartinib and Omacetaxine mepesuccinate (referred herein QUIZOM) disrupted proteostasis, leading to apoptosis, DNA damage, and activation of inflammatory pathways. Clinical resistance to QUIZOM is associated with increased activity of PLD1-driven phosphatidylcholine metabolism which promoted proteostasis in a leukaemic stem cell (LSC) subpopulation. Inhibition of PLD1 mitigated the resistance in LSC population by remodelling metabolism and inducing ferroptosis. The mechanisms underlying the therapeutic efficacy and subsequent relapses have become important leads for further research that may further improve the outcome in this AML subtype.

Results
Summary of patient recruitment, primary and secondary outcomes in QUIZOM clinical trial
Forty patients, including 35 relapse/refractory (R/R) and 5 newly diagnosed FLT3-ITD AML patients, were recruited and treated with the combination of quizartinib and omacetaxine mepesuccinate (QUIZOM) between November 2017 and September 2020.

The treatment achieved a composite complete remission (CRc) rate of 83% (33/40 patients). For the entire cohort, the median leukemia-free survival (LFS) was 10 months (range: 0.7-68.2 months). Subgroup analysis showed a median LFS of 6.9 months (range: 0.9-15.7 months) in the newly diagnosed group and 10 months (range: 0.7-68.2 months) in the R/R group.

FLT3-ITD AML is vulnerable to the co-inhibition of FLT3-ITD and protein translation
To identify the vulnerability of FLT3-ITD AML that may become target for intervention, we analyzed the DepMap CRISPR-Cas9 knockout database (https://depmap.org/portal/). FLT3-ITD AML dependency involved genes associated with FLT3 signaling, protein translation and respiratory electron transport (Figure 1A, Supplementary Dataset 5 and 6). Functional validation was performed. There was preferential sensitivity of FLT3-ITD AML towards inhibitor of FLT3-ITD (Quizartinib) and protein translation (Omecetaxine Mepesuccinate, OM) (Figure 1B-1C, Supplementary Fig. 1A-1B). Combination treatment (QUIZOM) synergistically reduced protein synthesis and induced apoptosis in human FLT3-ITD AML cell lines (Figure 1D-1F, Supplementary Fig. 1C-1E) and isogenic Ba/F3-FLT3-ITD cell lines (Figure 1G-1I). The synergism was further validated in primary FLT3-ITD AML samples (Figure 1J-1K) and in xenotransplantation model (Figure 1L-1O and Supplementary Fig. 1F), confirming the therapeutic efficacy of QUIZOM in this AML subtype. At the drug doses used, there was no significant toxicity of QUIZOM in animals (Figure 1P).

QUIZOM synergistically remodeled the translatome in FLT3-ITD AML
To study the mechanism underlying the vulnerability to QUIZOM in FLT3-ITD AML, we examined the effects on newly synthesized proteins (NSPs) (see also Supplementary Note 2) (Figure 2A) and identified 5917 NSPs that showed ≥1 unique peptide in all samples (Supplementary Fig. 2A and Supplementary Dataset 7-10). Substantial decrease in NSPs associated with various biological processes, such as protein folding, cell cycle and RNA metabolism, were observed upon drug treatment (Supplementary Fig. 2B-2D). A total of 165 NSPs (Figure 2B) were selected for subsequent analyses based on pre-determined criteria: log2 fold-change <-0.5 (downregulated) and synergistic score >0.5 (non-antagonistic QUIZOM v.s. monotherapy). Among them, MCL1 (a reported OM target)9 and the 2 most downregulated NSPs (HSP70 and UBL3) was selected for validation by Western blot (Figure 2C). Reportedly, the anti-leukaemic effect of OM might be driven by MCL1 downregulation.9 However, forced expression of MCL1 only partially mitigated the QUIZOM-induced apoptosis suggesting that alternative mechanisms might account for QUIZOM-induced cytotoxicity (Figure 2D and Supplementary Fig. 2E). Gene Ontology analysis of the 165 NSPs showed that they were solely enriched with mitochondrial respiration and chaperone-mediated protein folding functions (Supplementary Dataset 11, Figure 2E), suggesting these processes might account for QUIZOM cytotoxicity.

To investigate the molecular events involved in the QUIZOM-induced translatome remodelling, RNA-seq was performed. QUIZOM significantly upregulated 1689 and 1327 genes and downregulated 1160 and 1577 genes in MV4-11 and MOLM-13 respectively (Supplementary Fig. 2F). Pathway analysis and Western blot confirmed the suppression of FLT3-ITD downstream signal, mTOR/S6K/4E-BP1 axis10, that might account for the synergistic inhibition of protein translation by QUIZOM (Supplementary Fig. 2G-2I). In addition, there was negative enrichment of pathways pertaining to mitochondrial metabolism and chaperone-mediated protein folding (Supplementary Fig. 2J) based on GSEA, consistent with the translatome findings.

The observations of DepMap dependency, translatome and transcriptome aforementioned underscored a key therapeutic effect of QUIZOM via perturbation of mitochondrial function. The latter was further investigated. QUIZOM decreased mitochondrial mass and membrane potential in FLT3-ITD AML (Figure 2F-2G). There was ultrastructural evidence of mitochondria rupture (Figure 2H-2I).

Functionally, QUIZOM significantly reduced both glycolysis (glucose uptake and extracellular acidification rate (ECAR)), mitochondrial respiration and ATP level (Supplementary Fig. 2K-2P), underscoring a significant reduction of functional mitochondria. An untargeted metabolomics was performed and principal component analysis (PCA) confirmed a unique spectrum of metabolic remodeling by QUIZOM, which was distinct from those of single drug treatment (Supplementary Fig. 2Q). In particular, QUIZOM significantly reduced glucose, amino acid and nucleotide metabolism which were reported to be essential in supporting the survival of FLT3-ITD AML (Figure 2K).11-13 However, pathway analysis of metabolites with significant changes upon QUIZOM treatment showed that lipid metabolism was not affected indicating the dispensable role of lipid metabolism in mitochondrial respiration in parental FLT3-ITD AML (Supplementary Fig. 2R). Nicotinamide riboside (NR), a precursor of NAD+ cofactor, could ameliorate the growth inhibitory effect of QUIZOM (Figure 2L-M). NR has been shown to promote mitochondrial biogenesis and mitochondrial respiration14,15. In this study, NR alleviated the adverse effects of QUIZOM on mitochondria and ATP level (Figure 2N-Q). Untargeted metabolomics showed that NR induced increase in sugar and glycerophospholipid metabolism in FLT3-ITD AML upon treatment with QUIZOM (Supplementary Fig. 2S and 2T). Similar effects of QUIZOM and NR rescue were seen in primary FLT3-ITD AML samples (Figure 2R-T).

QUIZOM abrogated mitochondrial-assisted protein refolding and led to apoptosis
Potential effects of QUIZOM on chaperone-mediated protein folding were also examined. QUIZOM was shown to reduce the amount of chaperone protein HSP70 (Figure 2C) and induce the formation of protein aggregates in multiple independent assay platforms (Figure 3A-3B, Supplementary Fig. 3A-3C). Pharmacologic inhibition of HSP70 similarly enhanced the formation of protein aggregates (Figure 3C-3D) and induced apoptosis (Figure 3E-3F) in AML cell lines and primary samples. Bortezomib, a proteasomal inhibitor, was reported to generate protein aggregates by blocking the degradation of misfold protein.16 Addition of Bortezomib potentiated the formation of protein aggregates and apoptosis upon HSP70 inhibition, underscoring the significant role of protein aggregates in QUIZOM induced cytotoxicity (Supplementary Fig. 3D-3E). QUIZOM-induced protein aggregates formation was abolished by NR (Figure 3G), whereas further inhibition of metabolic respiration by PKM2 inhibition (Compound 3k) potentiated HSP70 inhibitor-induced apoptosis (Supplementary Fig. 3F), supporting the proposition of mitochondrial collapse promoting PAs-driven apoptosis (Figure 3H). Furthermore, increased protein aggregates was associated with DNA damage (Figure 3I). DNA damage was reduced by NR supplement, suggesting a causal relationship between mitochondrial function, protein aggregates formation and cytotoxicity (Figure 3J). The aforementioned transcriptome analysis revealed activation of p53 which might be triggered by protein aggregates-inducing DNA damage (Supplementary Fig. 3G). In fact, QUIZOM-induced apoptosis was p53-dependent, as demonstrated by its lack of effects in TP53 loss-of-function mutant MV4-11 isogenic line (Figure 3K). The role of HSP70 in QUIZOM-associated cytotoxicity was further examined by forced expression of HSP70 (HSP70-OE) in AML cell lines, which resulted in the reduced formation of protein aggregates and apoptosis upon QUIZOM treatment in vitro (Figure 3L-3M, Supplementary Fig. 3H) and mitigated the survival benefit of QUIZOM in animals engrafted by MV4-11 (Figure 3N).

QUIZOM activated proinflammatory response in FLT3-ITD AML
In addition to apoptosis, RNA-seq data showed the QUIZOM-induced increase in CDKN1A, a decrease in LMNB1 expression (Supplementary Fig. 4A) and a positive enrichment of pathways associated with senescence associated secretary phenotype (SASP) (Figure 4A-B and Supplementary Dataset 12). QUIZOM induced DNA damage, as shown by the increase in γH2AX in FLT3-ITD AML (Figure 3I). Furthermore,mitochontrial rupture led to an increase in the release of mitochondrial DNA (mtDNA) to cytoplasm (Figure 4C).17 These changes were associated with STING dimerization, 2’3’-cGAMP production and SASP cytokines (Figure 4D-4F and Supplementary Fig. 4B), characteristic of cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) activation. Treatment with cGAS inhibitor (RU.521), HSP70-OE and NR treatment ameliorated the increases in pro-inflammatory cytokines (Figure 4F), supporting the proposition that cGAS-STING activation resulted from the aberration of the aforementioned intracellular signal. Functionally, QUIZOM promoted phagocytosis by macrophages in co-culture with FLT3-ITD AML and the process was also ameliorated by NR or HSP70 overexpression (Figure 4G). Phagocytosis was also ameliorated by the treatment of CCL3 and CSF1 neutralizing antibodies (Figure 4H), suggesting that these cytokines might induce phagocytosis in response to QUIZOM treatment. Importantly, macrophage activation was observed in vivo, as evidenced by their increase in MHC II and CD40 expression (Figure 4I) when NSG mice engrafted by FLT3-ITD AML were treated with QUIZOM.

Phase 2 Clinical trial of QUIZOM in FLT3-ITD AML
The reported synergism and therapeutic mechanisms of QUIZOM laid the foundation of a Phase 2 clinical trial evaluating its clinical efficacy and toxicity in FLT3-ITD AML patients. Forty FLT3-ITD AML patients (35 R/R and 5 newly diagnosed) were recruited as previously described. Their clinicopathologic characteristics were shown in Supplementary Dataset 13. The median age was 57.5 years (range: 23-81). Among 35 R/R patients, 11 were primary refractory to “7+3” induction chemotherapy and 14 had two or more prior lines of chemotherapy. Fourteen patients had prior exposure to FLT3 inhibitors, including midostaurin (N=11) and sorafenib (N=3). Seven patients were at post-HSCT relapse (Supplementary Note 2). Median pre-treatment FLT3-ITD allelic ratio was 0.39 (range: 0.03 – 5.72). Targeted gene sequencing of 38 available diagnostic or pre-treatment BM samples showed a median of 3 mutations (range 1-6) (Figure 5A). Similar to other reported cohorts,18 recurrent mutations were seen in DNMT3A (49%), NPM1 (49%), TET2 (17%), RUNX1 (15%), IDH2 (15%) and WT1 (12%). A tyrosine kinase domain (TKD) mutation (p.D835V) of FLT3 was detected in one patient. However, there was no significant change in the top 10 common mutations in the cohort between pretreatment and relapse samples, indicating the genetic changes might not account for the emergence of QUIZOM resistance (Supplementary Fig. 5A) (Supplementary Fig. 5A).

Composite complete remission (CRc) rate was 83% (N=33), including 15% (N=6) CR and 68% (N=27) CRi (Figure 5B). Twenty-nine patients achieved remission after 1 cycle of QUIZOM, and 4 patients after 2 cycles. Median time to response is 23 days (Range: 17-62 days). The median number of cycles received was 3 (range 1-20). The CRc rates were 60% and 86% in upfront treatment and R/R group respectively. Higher CRc rate was observed in patients with mutated NPM1 (95% vs 67%, p=0.038), mutated DNMT3A (95% vs 67%, p=0.038) and wild-type WT1 (91% vs 20%, p=0.002) (Figure 5A) among other clinical parameters (Supplementary Dataset 14). The peripheral blood white cell count decreased within first 48-72 hours of treatment, compatible with induction of apoptosis by QUIZOM (Supplementary Fig. 5B).

Among 33 patients who achieved remission, 13 (39%) received allogeneic HSCT after a median of 143 days (Range: 53-367 days), including 3 from HLA-identical siblings, 4 from haploidentical relatives and 6 from matched unrelated donors (MUD). Twenty-two (63%) patients relapsed, including all who did not receive HSCT (N=19) and 3 patients at 4, 12 and 41 months after HSCT. One patient died of transplant-related mortality and another patient died of infective complication during remission. The median LFS and OS were 10 months (range: 0.7-68.2 months) and 12.9 months (range: 1.8-69.2 months). For patients not bridged to HSCT, the median LFS and OS were 3.3 months and 9.9 months respectively. For patients who were bridged to HSCT, the median LFS and OS were not reached (Figure 5C) at the time of analysis with a median follow up time of 4.7 years. Nine patients remained disease-free at the time of analysis with median LFS at 58.5 months (range: 20.5-68.5 months) and OS at 79 months (range: 22.5-106.3 months). Among 13 relapsed patients with available samples for evaluation, 8 (62%) patients had loss of FLT3-ITD clones at relapse. They had smaller pre-treatment ITD clone size (Supplementary Fig. 5C).

Univariate analysis showed that better overall survival was associated with age ≤ 60 years, achieving CRc, prior FLT3 inhibitor exposure, allogeneic HSCT and wild-type WT1. Superior LFS was associated with age ≤ 60 years and allogeneic HSCT (Supplementary Fig. 5D). Multivariate analysis was not performed due to small cohort size.

There were two early deaths on days 29 and 53 from QUIZOM treatment. They were both non-responding patients and causes of death were pneumonia and pulmonary haemorrhage, and pneumonia respectively, considered to be related to underlying leukaemia progression. Treatment-emergent adverse events were summarised in Supplementary Dataset 15. Grade 3/4 cytopenia and febrile neutropenia were common adverse events. Of note, 20% patients developed bacteraemia (grade 4 sepsis) requiring intravenous antibiotics. Importantly, prolongation of QTc was not observed at the current study dose.

Activation of immune response was associated with clinical QUIZOM response
To delineate the mechanism of QUIZOM responsiveness, single-cell RNA sequencing was performed in 45 samples from 19 unselected patients receiving QUIZOM, from whom serial samples (pre-treatment, remission and relapse/refractory stages) were available (Supplementary Fig. 5E). After integration and unsupervised clustering, 15 major cell populations were identified (Figure 5D-5E). Cell barcode calling was performed to identify AML cells carrying defined mutations (Supplementary Fig. 5F). 19,830 (19,169 with 1 mutation; 661 with ≥2 mutations) out of 439,320 cells were found positive, including mostly haematopoietic stem cell - multipotent progenitor (HSC-MPP), granulocyte-monocyte progenitor (GMP), megakaryocyte progenitor (MkP), and monocyte populations (Figure 5F). A machine learning framework was applied to define the leukaemic and immune populations with high precision and correlation with flow cytometry findings (Supplementary Fig. 5G-5I). The largest leukaemic cell cluster, HSC-MPP, exhibited strong transcriptomic similarity to HSC-MPP cells in previously reported FLT3-ITD samples (Supplementary Fig. 5J).19 QUIZOM treatment resulted in a more drastic reduction of HSC-MPP and significant decreases in GMP and MkP in responders compared to non-responders. Moreover, there were increases in immune populations including CD4-T, CD8-T, Natural Killer T cell (NKT cell), and Natural Killer cell (NK cells), which were not found in non-responders (Figure 5G, Supplementary Fig. 5K).

Single cell transcriptome was analysed to address if the aforementioned activation of immune and inflammatory responses to QUIZOM in the laboratory could be generalized to patients receiving such treatment. Specifically in the responders, QUIZOM induced immune activation, exemplified by the upregulation of interferon responsive genes and IRF2 regulon in the leukaemic HSC-MPP population (Figure 5H, Supplementary Fig. 5L). In addition, QUIZOM resulted in the activation of the T cell Receptor (TCR) pathway in CD4+ and CD8+ T cells, and cytotoxicity gene signature in CD8+ T cells at remission (Figure 5I-5K). Gene regulatory network analysis showed a remarkable increase in activity of EOMES and T-bet pertinent to T cell activation and differentiation in CD4+ and CD8+ T cells (Figure 5L-5M). Furthermore, QUIZOM induced a significant increase in terminally differentiated effector memory CD8+ T cells (TEMRA), decrease in effector memory CD8+ T cells, exhausted CD8+ T cells (TEX) and CD4+ Th17 cells (Figure 5N and Supplementary Fig. 5M). Collectively, the transcriptomic changes in both the leukaemic and immune cell compartments supported the proposition from laboratory studies that the immune system was remodelled by QUIZOM-mediated cytotoxicity on leukaemic blast and such mechanism might correlate with the clinical responsiveness to QUIZOM.

Identification of distinct leukaemic stem cell signature associated with QUIZOM-resistance
Despite the overall satisfactory response to QUIZOM, 4/19 patients did not respond. We further examined the transcriptomic changes after QUIZOM with a view to identify differential changes in the HSC-MPP population of responders compared to non-responders (Supplementary Fig. 6A). Significant and preferential changes were observed in the activity of 29 transcription factors in the HSC-MPP population among responders to QUIZOM (Supplementary Dataset 16), including the down-regulation of HSC genes, such as HOX cluster (Supplementary Fig. 6B).20 Furthermore, the primitive FLT3-ITD-bearing HSC-MPP blast was divided into 7 sub-clusters by unsupervised clustering. Intriguingly, non-responders showed persistence of sub-cluster 0 and 1 (resistant clusters) in the post-treatment samples (Figure 6A-6B, Supplementary Fig. 6C) indicating these clusters might underlie clinical drug resistance. These resistant sub-clusters exhibited up-regulation of gene expression pertinent to leukaemic stem cell, replicative quiescence and lipid metabolism, whereas those associated with protein translation and amino acid metabolism were reduced (Supplementary Fig. 6D-6E). Furthermore, HSC-MPP cells of non-responders on Day 28 of QUIZOM treatment showed a significantly lower pseudotime compared with the responders, attesting to its primitive nature (Figure 6C). We analysed Beat AML public RNA-seq cohort for the impact of LSC signature from this study on overall survival in AML patients. Based on the single cell matrix, AML patients were accorded with high, medium and low LSC fractions using CibersortX. Those with the highest LSC fraction were associated with the worst OS and those with the lowest LSC fraction showed the best OS (Figure 6D). Importantly, QUIZOM resistant LSCs displayed a transcriptome profile distinct from that of chemo-resistant LSC in paediatric AML21, suggesting diverse mechanisms of drug resistance in LSC (Supplementary Fig. 6F).

Putative functions of LSC were examined. LSC preferentially expressed JUN and FOS (key regulators of quiescence, unfolded protein response)22, HSPA1B (encoding HSP70), and genes promoting lipid metabolism such as PLD1, MTMR3, NRR1H2, and SREBF123-25 (Figure 6E-6F and Supplementary Fig. 6G). LSC isolated from primary FLT3-ITD AML samples based on our LSC phenotypic markers CD33+CD44+CD69+ (Figure 6 F) [viz. triple positive (TP) cells] were more replicatively quiescent and less sensitive to QUIZOM than the bulk population (Figure 6G-6I). To validate the functional role of JUN/FOS and PLD1 (phospholipase D1, catalysing the hydrolysis of phosphatidylcholine (PC) to phosphatidate and choline) in the treatment response to QUIZOM, TP of FLT3-ITD primary AML samples were treated by QUIZOM combined with inhibitors to c-Fos/c-Jun (T-5224) and PLD1 inhibitor (VU0155069). T-5224 and VU0155069 synergized with QUIZOM in inducing apoptosis in the TP subpopulation (Figure 6J-K). Combination of PLD1 and QUIZOM resulted in profound suppression of leukaemic engraftment of primary AML (Figure 6L).

To investigate the molecular mechanisms of PLD1 in mediating LSC activity, untargeted metabolomic analysis of TP and bulk population was performed. Glycerophospholipid metabolism was the sole metabolic pathway enriched in TP compared with bulk population (Figure 6M and Supplementary Fig. 6H). In particular, pathway analysis of TP-enriched metabolites reveal the activation of phosphotidylcholine metabolism which was consistent with the upregulation of PLD1 in TP compared to bulk population (Supplementary Fig. 6I). Furthermore, PLD1 inhibition suppressed lipid uptake in TP but not in the bulk population (Figure 6N). PLD1 inhibition remodelled lipid metabolism, leading to increase in ROS and lipid peroxidation (Figure 6O-P). Furthermore, treatment with ferrostatin but not necrostatin mitigated the QUIZOM and PLD1 inhibition-induced cell death, underscoring the induction of ferroptosis by the latter treatment in the TP LSC subpopulation (Figure 6Q). PLD1 overexpression induced lipid uptake (Figure 6R) and ameliorated the QUIZOM-induced apoptosis (Figure 6S), supporting the role of PLD1 in mediating QUIZOM resistance in AML. Supplementation of phosphatidylcholine (PC), the substrate of PLD1, also induced QUIZOM resistance in AML (Supplementary Fig. 6J and 6K). Furthermore, PC significantly reduced QUIZOM-induced formation of protein aggregates (Supplementary Fig. 6L). Mechanistically, PLD1 inhibition preferentially reduced glycerophospholipid metabolism (Supplementary Fig. 6M) and resulted in accumulation of phosphatidylcholines via inhibiting its hydrolysis (Supplementary Fig. 6N). Among the phosphatidylcholines, diacyl-PUFA phosphatidylcholines (PC-PUFA ), a driver of ferroptosis,26 were increased. The observations supported the proposition that PLD1 inhibitor and QUIZOM showed synergizism in suppressing the growth of FLT3-ITD AML via simultaneous induction of apoptosis and ferroptosis.

The putative pathogenetic link between PLD1 expression and the JNK/JUN/HSPA1B axis in LSC population was evaluated (Supplementary Fig. 6O). Inhibition of PLD1 reduced phosphorylation of JNK (Figure 6T). Moreover, inhibition of JNK signaling, FOS/JUN transcriptional activity or PLD1 suppressed the expression HSPA1B (Figure 6U). In primary AML samples, the therapeutic effects of FOS/JUN and PLD1 inhibition on HSPA1B expression and protein aggregates formation were only observed in TP LSC but not the bulk population (Figure 6V-6W).

Methods
Detailed descriptions of materials and methods, including cell culture, xenotransplantation studies, translatome and transcriptome analyses, are described in the Supplementary Note 2.

Isotope-metabolic pulse labeling and drug treatments
AML cells were cultured for 24 hours, rinsed with PBS, and cultured in arginine, lysine and methionine depleted RPMI for 30 minutes and modified RPMI containing heavy lysine (13C6-13N2, 0.8 mM Lys8), heavy arginine (13C6-13N4, 0.8 mM Arg10), azidohomoalanine (1 mM AHA) and 10% dialyzed fetal bovine serum. They were treated with quizartinib (2 nM), omacetaxine mepesuccinate (OM, 10 nM), QUIZOM (quizartinib 2 nM + OM 10 nM), or 0.1% DMSO control for 4 hours and lysed. Newly synthesized proteins (NSPs) were isolated by click-chemistry, digested, subjected to tandem mass tag (TMT) multiplexed labelling and deep-scale liquid chromatography-mass spectrometry / mass spectrometry (LC-MS/MS) (Supplementary Note 2). Translatome data were quantified using Proteome Discoverer and MSstatsTMT. Changes in NSP upon drug treatments with a q-value < 0.05 and a log2 Fold change (FC) ≥0.38 or ≤-0.38 were further analyzed. Translatome analysis was described in details in the Supplementary Note 2.

Single cell RNA sequencing and data processing
Cryopreserved serial samples from AML patients enrolled in the QUIZOM trial were thawed and subjected to encapsulation and library preparation immediately with ChromiumTM Single Cell 5' v2 Reagent Kit. Paired-end 151 bp reads were generated by illumina NovaSeq 6000. Cell Ranger (version 6.0.0) was applied for quantification.57 Seurat was then used for downstream analysis.58 Cells with fewer than 200 detectable genes or with more than 15% counts mapped to mitochondrial genes were removed.59 Data normalization was carried out with SCTransform.60 Data integration was performed using “FindIntegrationAnchors” and “IntegrateData” functions in Seurat. Unsupervised clustering was conducted with Louvain algorithm and serial resolutions from 0.4 to 5.61 The stability of clusters was examined and an optimal resolution was determined by Clustree.62 Marker genes for each cluster were identified and published haematopoietic cell markers were used to annotate 15 major cell types.40,63 Details about single cell analysis were included in the Supplementary Note 2.

Patients and treatment
Relapse/refractory FLT3-ITD AML patients ≥18 years old and newly diagnosed FLT3-ITD AML patients ≥65 years old unfit for conventional chemotherapy were recruited into a phase II single-arm open-labelled study (ClinicalTrials.gov Identifier: NCT03135054) with informed consent and approval from Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (Ref no.: UW 17-099 and UW 05-183 T/846). Among the 45 patients recruited, one patient died before treatment and 4 patients subsequently withdrew from trial based on discretion of treating physicians.

Treatment comprised quizartinib 30 mg daily p.o. and OM at 2 mg daily i.v. for 7 days (1st cycle) or 5 days (subsequent cycles) for every 21-28 days until HSCT or disease progression. Patients achieving complete remission (CR) or complete remission with incomplete haematologic recovery (CRi) were referred for HSCT. Quizartinib was given as post-HSCT maintenance for at least 24 months after engraftment at doses ranging from 30 mg once per day twice weekly to 30 mg daily depending on tolerability.

Primary endpoint was composite complete remission (CRc) defined by CR or CRi according to ELN guideline 2022.2 Secondary endpoints were the percentage of patients who were bridged to HSCT, leukaemia-free (LFS) and overall survivals (OS) (Details refer to the Study Protocol).

Untargeted metabolomics analyses
Around 150 samples comprising FLT3-ITD cells under different combinations of QUIZOM treatments, as well as primary FLT3-ITD AML enriched with LSC, i.e. the triple positive (TP) cells or unselected AML cells in bulk, underwent untargeted metabolomics profiling by liquid chromatography-high resolution mass spectrometry. The untargeted metabolomics procedures was described in detail in the Supplementary Note 2.

Statistical analysis
Categorical variables were compared using Chi-square test and logistic regression whereas continuous variables were compared using t-test, Mann-Whitney U test and Wilcoxon signed rank test. Survival analysis was performed by Kaplan-Meier estimator. LFS and OS were visualised by Kaplan-Meier curves and described by median survival time. Statistical comparisons between two populations were performed by log-rank test. The impacts of various clinical factors on disease outcome were analyzed by univariate analysis using Cox regression.

Experimental data were presented as mean ± standard error of the mean (SEM) from at least three independent experiments. Mouse survival after AML cell line engraftment was evaluated by Kaplan-Meier analysis. All statistical tests were carried out using GraphPad Prism 8.0 or R. p-values were shown in all figures. A full licence of Biorender is owned to publish.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present a compelling study and were responsive to the previous reviews. My only comment is with respect to their response to Reviewer 1 Comment 1. In the Supplementary Materials the authors state on lines 156-160, \"The resulting p-values were adjusted using the Benjamini-Hochberg method4 to account for multiple testing. Gene set enrichment analysis (GSEA) was performed based on the fold change of gene effect score produced by limma. The normalized enrichment score (NES) and -log10(adjusted p-value) from GSEA were visualized as volcano plot in Figure 1A.\" and on lines 357-358, \"For the GO terms, multiple testing was controlled using the false discovery rate 357 (FDR) approach, with an adjusted p-value, i.e. q-value < 0.05 to consider significance.\" Lines 396-397, \"Adjusted p-396 values were obtained using the Benjamini-Hochberg method.\" lines 403-404, \"FDR was 403 set at 0.05 and adjusted p-value < 0.05 was considered statistically significant.\" Please remove \"adjusted p-value\" from all of these sentences. FDRs are not adjusted p-values which has been the source of confusion in the applied literature.", "reviewer_2": "The authors have responded to my criticisms.", "reviewer_4": "The manuscript by Zheng et al. entitled “Combination treatment with quizartinib and omacetaxine mepesuccinate reveals metabolic vulnerabilities and mechanisms of drug resistance in FLT3-ITD AML” presents an in-depth study evaluating the therapeutic effects and resistance mechanisms of the combination therapy QUIZOM (quizartinib and omacetaxine mepesuccinate) in FLT3-ITD acute myeloid leukemia (AML). The study is methodologically sound, and offers a compelling narrative linking mitochondrial dysfunction, proteostasis collapse, and immune activation to therapeutic efficacy, while also identifying a lipid metabolism-dependent leukemic stem cell (LSC) population driving resistance. The authors integrate data from functional genomics, translatomics, untargeted metabolomics, single-cell transcriptomics, and a Phase II clinical trial. They convincingly demonstrate that QUIZOM disrupts mitochondrial proteostasis and activates pro-inflammatory signaling, with therapeutic efficacy and immune modulation in patients. Importantly, a phosphatidylcholine metabolism–driven LSC population is implicated in QUIZOM resistance.\n\nThe manuscript is comprehensive and impactful. The authors have also provided thoughtful and detailed responses to previous reviewer comments. However, several new scientific points and clarifications are necessary to fully support the claims and broaden the manuscript’s translational relevance.\n\nMajor Comments\n1. Can phosphatidylcholine (PC) supplementation induce QUIZOM resistance in AML?\nThe manuscript identifies PLD1-mediated PC metabolism as a key pathway associated with QUIZOM resistance. However, it remains unclear whether supplementation with phosphatidylcholine itself is sufficient to induce resistance in AML cell lines, mirroring the effect of PLD1 overexpression.\nRecommendation: Authors should test or at least discuss the potentials of exogenous PC for its effect on QUIZOM sensitivity and whether it recapitulates the protective effect seen with PLD1 overexpression (e.g., Figure 6S). This would help establish the functional significance of PC metabolism in resistance.\n2. Does PC supplementation reduce protein aggregate formation after QUIZOM treatment?\nThe authors show that PLD1 and HSP70 expression reduce protein aggregates in resistant cells. However, it is unknown whether PC supplementation itself could mitigate proteotoxic stress induced by QUIZOM.\nRecommendation: If such data exist, they should be presented. Otherwise, the authors should speculate on this mechanism as part of the discussion that link lipid metabolism to proteostasis.\n3. Does QUIZOM regulate PLD1 expression directly?\nThe manuscript suggests PLD1 expression is a feature of the resistant LSC phenotype, but it remains unclear whether PLD1 levels are modulated by QUIZOM treatment in parental or bulk AML populations.\nRecommendation: Please clarify whether QUIZOM increases or suppresses PLD1 expression. Clarify whether QUIZOM treatment induces or suppresses PLD1 expression in bulk or triple positive (TP) AML populations.\n4. Is PLD1 upregulation at relapse associated with additional somatic mutations?\nThe study highlights PLD1 as a resistance marker, but does not clearly address whether its upregulation is associated with clonal evolution at relapse.\nRecommendation: Please evaluate whether PLD1 upregulation correlates with acquisition of additional mutations in relapse samples, or clarify if it reflects transcriptional reprogramming of pre-existing clones.\n5. How can the described metabolic remodeling on immune microenvironment be potentially harnessed for rational treatment design?\nThe authors provide compelling evidence for cGAS-STING activation and CD8+ T cell responses in clinical responders. However, the broader implications of metabolic remodeling on the immune microenvironment deserve further discussion.\nRecommendation: The authors should expand their discussion on the role of tumor metabolism in shaping immune responses, including how mitochondrial collapse, SASP cytokines, and lipid metabolism may influence anti-leukemic immunity. This would help contextualize the clinical significance of QUIZOM-induced immune modulation and provide rationale for combination with immune checkpoint inhibitors or metabolic adjuvants.\n6. Did QUIZOM inhibit glycolysis in addition to mitochondrial respiration?\nWhile the authors show that QUIZOM suppresses mitochondrial function and glucose uptake, it is unclear whether glycolysis is directly inhibited. Given that AML cells can display metabolic plasticity, additional metabolic profiling is warranted. Specifically, extracellular acidification rate (ECAR) via Seahorse assays and/or glucose uptake and lactate production assays would be informative.\nRecommendation: Please clarify whether glycolytic activity was evaluated and if QUIZOM also impairs glycolysis. If not, this should be acknowledged as a limitation.\n\nMinor Points\n• Explicitly mention the source and composition of the NR and PLD1 inhibitor used in rescue experiments in the Methods.\n• While the inclusion of a Phase II trial is commendable, the high CRi vs CR rate and transfusion dependence post-remission (as mentioned in the rebuttal) should be more transparently discussed in the main text.\n• A schematic summary of the proposed mechanism of resistance involving PLD1, lipid metabolism, proteostasis, and immune escape could aid reader understanding."}, "author_response": "Responses to reviewers' comments:\nReviewer #1:\nWe are grateful to the reviewer’s comments, which have helped us clarify our data\nanalyses and presentation. Our responses to these comments were given as follows:\n1. Line 140-141: The authors state, “P-value was reported after Benjamini-Hochberg\nadjustment to account for multiple testings” is not a correct statement because the FDR is\nnot an adjusted p-value but a different entity. This should read something like, “To account\nfor multiple hypothesis testing, the Benjamini-Hochberg false discovery rate (FDR) method\nwas applied to the resulting p-values.” It would also be useful if the authors stated at the\nend of the sentence what FDR threshold was applied to consider genes as significant.\nThanks for the revision. The manuscript was revised as follows:\nSupplementary Method. Page 8, Line 319:\n“To account for multiple hypothesis testing, the Benjamini-Hochberg false discovery rate (FDR)\nmethod was applied to the resulting p-values. FDR was set at 0.05 and adjusted p-value < 0.05\nwas considered statistically significant.”\n2. Lines 157-158 & 160-162: The authors state, “The expression of mitochondrial genes was\nquantified and cells with more than 15% counts mapped to mitochondrial genes were\nremoved from further analysis.” Then they state, “Datasets were then merged and\nnormalized using ‘SCTransform’ v2 function in Seurat with regression on mitochondrial\ngene percentage.” The authors should clarify what type of regression model was applied\nbecause the percentage is bounded by 0 naturally and then bounded by 15 due to their\nfiltering, so linear regression would not be appropriate to directly apply. Also, they should\nexplain why they filtered then modelled mitochondrial gene percentage.\nThe reviewer pointed out correctly that linear regression model should not apply where\ndependent variables were bounded. Therefore, we used a regularized negative binomial\nregression model in the normalization step, an approach which has been reported by the\nSCTransform.19 In this normalization process, the Unique Molecular Identifier (UMI) was the\ndependent variable and the mitochondrial gene percentage was an independent variable\n(covariate), thereby potential impact of mitochondrial gene content (0-15%) could be negated\nin the normalized expression value of genes in each cell.19,20 Those cells with more than 15%\nmitochondrial genes were excluded as they might be unhealthy and unreliable for\ndownstream analysis.21,22 The 15% cut-off was widely adopted in previous publications.23,24\nTherefore, we have revised the text as follows:\nSupplementary Methods, Page 9, Line 324:\n“A regularized negative binomial regression model was used in the normalization step, an\napproach which has been reported by the SCTransform.20 In this normalization process, the\nUnique Molecular Identifier (UMI) was the dependent variable and the mitochondrial gene\npercentage was an independent variable (covariate), thereby potential impact of\nmitochondrial gene content (0-15%) could be negated in the normalized expression value of\ngenes in each cell.19,20 Those cells with more than 15% mitochondrial genes were excluded as\nthey might be unhealthy and unreliable for downstream analysis.22,23 The 15% cut-off was\nwidely adopted in previous publications.24,25\n3. Line 162-163: The authors should explain what is meant by “top 3000 integration\nfeatures.”\nIn this study, we have analyzed and integrated the 45 single cell transcriptome datasets\narising from patient samples. First, a subset of 3000 “highly variable genes” were selected\nfrom each dataset based on their standardized variance. They represented mostly genes that\nwere associated with distinct cell populations or functional states in each dataset and hence\nmight be more biologically relevant. The “highly variable genes ” that were selected from\nmultiple datasets will be ranked based on their prevalence. For example, “highly variable\ngenes” that occurred in all 45 datasets will be ranked the top and others that occurred in only\n44 samples will be ranged the second etc. Another 3000 genes at the top ranks based on this\nalgorithm will be selected. They represented genes that were most relevant and recurrent in\nour datasets. This workflow has been described and has become the standard pipeline of\nSeurat.26\nThese points were now incorporated in the revised manuscript as follows:\nSupplementary Methods, Page 9, Line 332:\n“We have then integrate the 45 single cell transcriptome datasets arising from patient\nsamples. First, a subset of 3000 “highly variable genes” were selected from each dataset\nbased on their standardized variance using “FindVariableFeatures” function in Seurat.26 They\nrepresented mostly genes that were associated with distinct cell populations or functional\nstates in each dataset and hence might be more biologically relevant. The “highly variable\ngenes ” that were selected from multiple datasets will be ranked based on their prevalence.\nFor example, “highly variable genes” that occurred in all 45 datasets will be ranked the top\nand others that occurred in only 44 samples will be ranged the second etc. Another 3000\ngenes at the top ranks based on this algorithm will be selected using\n“SelectIntegrationFeatures” in Seurat.65”\n4. Line 166: Please provide a reference for the Louvain algorithm.\nThe reference for the Louvain algorithm was provided as follows:\nSupplementary Methods, Page 10, Line 390:\n“Unsupervised clustering with Louvain algorithm35,36 and resolution=0.4 using Seurat’s\nfunction “FindClusters” were applied to the HSC-MPP population and 7 clusters were\nidentified.”\n5. Line 197: The authors state that a secondary endpoint was “rate of patients bridged to\nHSCT.” Because “rate” is used that would more often be interpreted as the time to HSCT\ninstead of as a binary outcome. Because they reported the frequency and percentage of\npatients received HSCT, this sentence should be rephrased.\nPoints are well taken. The manuscript was revised as follows:\nPage 4, Line 134:\n“Secondary endpoints were the percentage of patients who were bridged to HSCT, leukaemia-\nfree (LFS) and overall survivals (OS).”\n6. Figure 1A: The authors should indicate in the caption what the plotted points represent\n(genes or pathways) and the graph should include a y-axis label.\nThank you for the comment. The figure and the legend have been revised as follows.\nFigure 1A\nLegend to Figure 1A\n“Analysis of genome-wide CRISPR-Cas9 knockout data (DepMap) revealed that FLT3-ITD AML\ncell lines were more vulnerable to the knockout of genes involved in translation, FLT3\nsignaling, respiratory electron transport, and DNA repair process (see also Supplementary\nTable 5 and 6). Each dot represents one Reactome pathway. A cut-off of Benjamini-Hochberg\nadjusted p-value < 0.05 was used to select the statistically significant pathways. NES,\nnormalized enrichment score.”\n7. Results; Figures: MOLM-14 is discussed in the Results section on line 209 but is not\nincluded as a cell line in Supplementary Methods lines 299-303 and MOLM-14 is not\ndepicted in these figures. Did the authors examine MOLM-14 and get negative results?\nMOLM-14 was mentioned in the Results because it was included in the DepMap database.\nThis cell line was not available in our institute. Instead, we made use of MOLM-13 and MV4-\n11, which were remarkably similar to MOLM-14 in their sensitivity towards therapeutic\nagents.1-3\nThe revised manuscript should now read:\nSupplementary Methods, Page 2, Line 49:\n“MOLM-14, which was included in the DepMap database, was not available in our institute.\nHowever, its sensitivity to therapeutic agents was remarkably similar to those of MOLM-13\nand MV4-11.1-3”\n8. Figures 1, 2, 3, 4, 6: The captions or figures should include a translation for V, Q, O and\nQO somewhere so that the tables can stand alone.\nWe apologize for the oversight. V, Q, O and QO stands for:\nV = Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate\nThese were added to the legends to Figure 1, 2, 3, 4 and 6, which should now read:\n“V: Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate”\n9. Extended Table 13: The data reported seem to appear one line below the column A label\nstarting with No. of ITD close(s), No. (%). Also the percentages reported in the main text of\nthe manuscript (lines 306-338) are generally rounded to the nearest tenth while the\npercentages reported in the table are rounded to the nearest whole number. Because the\nsample size is 40, rounding to the nearest whole number is more appropriate.\nWe apologized for the oversight and the Supplementary Table 13 has now been revised.\nThe manuscript should now read:\nPage 8, Line 288:\n“Composite complete remission (CRc) rate was 83% (N=33), including 15% (N=6) CR and 68%\n(N=27) Cri (Figure 5B)… The CRc rates were 60% and 86% in upfront treatment and R/R group\nrespectively. ”\n10. Lines 353-354, Figure 5D and 5E; Supplementary Methods lines 550-552: Due to the\nsmall sample size and that the effective sample size in a Cox PH model is the number of\nevents, and that typically we require ~20 events per variable included in the model, a\nmultivariate model with 4 variables is not reasonable for this dataset (27 OS events; 22 LFS\nevents).\nThank you very much for the insightful comment. We agreed that multivariate model analysis\nwas limited by the small sample size of the cohort. We have now removed the multivariate\nanalysis from Figure 5D and its legends and the manuscript should now read:\nPage 8, Line 312:\n“Univariate analysis showed that better overall survival was associated with age ≤ 60 years,\nachieving CRc, prior FLT3 inhibitor exposure, allogeneic HSCT and wild-type WT1. Superior LFS\nwas associated with age ≤ 60 years and allogeneic HSCT (Extended Data Fig. 5C). Multivariate\nanalysis was not performed due to small cohort size.”\nSupplementary Methods, Page 13, Line 503:\n“The impacts of various clinical factors on disease outcome were analyzed by univariate\nanalysis using Cox regression. All statistical tests were carried out using Microsoft Excel 2019,\nGraphPad Prism 8.0 or “survivalAnalysis” package in R.”\n11. Figure 6D: It is unclear how the authors arrived at 7 clusters for these data.\nWe applied unsupervised clustering with Louvain algorithm and resolution=0.4 using Seurat’s\nfunction “FindClusters” to the HSC-MPP population and identified 7 clusters. The resolution\nwas determined using Clustree, which allowed us to examine the stability of clusters at serial\nresolutions from 0.2 to 2, and at a resolution of 0.4, the clusters were most stable. The\nmanuscript was revised as follows:\nSupplementary Methods, Page 10, Line 390:\n“Unsupervised clustering with Louvain algorithm35,36 and resolution=0.4 using Seurat’s\nfunction “FindClusters” were applied to the HSC-MPP population and 7 clusters were identified.\nThe resolution was determined using Clustree36, which allowed one to examine the stability of\nclusters at serial resolutions from 0.2 to 2, and at a resolution of 0.4, the clusters were most\nstable “\n12. Supplementary Methods, lines 314-315: The sentence, “The difference in gene knock-\nout effect was calculated using limma after Benjamini-Hochberg adjustment” does not\nmake sense. Perhaps the authors used limma to identify differences due to the knock-out\neffect and then adjusted for multiple comparisons by calculating Benjamini-Hochberg false\ndiscovery rate using the resulting p-values.\nPoint well taken. The manuscript was revised as follows.\nSupplementary Methods, Page 2, Line 72:\n“The difference in gene knock-out effect was identified by limma (linear models for microarray\ndata), a statistical package for analyzing RNA-seq data. The resulting p-values were adjusted\nusing the Benjamini-Hochberg method4 to account for multiple testing.”\n13. Supplementary Methods, line 550: More clearly rephrase the sentence, “Survivals were\nestimated by Kaplan-Meier method.”\nThe sentence was rewritten as follows.\nSupplementary Methods, Page 13, Line 500:\n“Survival analysis was performed by Kaplan-Meier estimator. LFS and OS were visualised by\nKaplan-Meier curves and described by median survival time. Statistical comparisons between\ntwo populations was performed by log-rank test.”\nReviewer #2:\nMajor criticisms:\nWhat is missing is the tolerability of this regimen- other than the woefully-inadequate\nphrase “Treatment was generally tolerated”. Were there early deaths? What were common\nadverse events? What was the median time to response? Given that there was a high\noverall response rate, but low overall true CR rate, the implication is that these were\npatients with essentially aplastic marrows that required stem cell rescue in order to survive.\nPoints well taken. There were two early deaths due to leukemia progression and persistent\ncytopenia: One patient died of pneumonia and pulmonary haemorrhage on day 29 from\nQUIZOM. Day 20 bone marrow showed non-remission. Another patient died of pneumonia\non day 53 from QUIZOM. Bone marrow examination after day 20 also showed non-remission.\nThere was no early mortality among responders. Common adverse events include\nmyelosuppression, febrile neutropenia and infection. A table of treatment-emergent adverse\nevents is included in the revised text (Supplementary Table 15). Over 97% patients had grade\n3/4 cytopenia that was either pre-existing or treatment-related. The median time to response\nis 23 days (Range: 17-62 days).\nThe reviewer pointed out correctly that the high CRi rate and low true CR rate suggested\nmarrow aplasia. In fact, 22 out of 27 (81%) patients with CRi, and overall 22 out of 33 (67%)\nresponding patients, remained transfusion-dependent post-QUIZOM treatment before\nrelapse or alloHSCT.\nThe manuscript has been revised as follows:\nSupplementary Methods, Page 7, Line 278:\n“If BM blasts were above 5%, a second BM examination would be performed at the end of the\nsecond cycle. Median time to response was defined by time from start of treatment to the day\nof bone marrow examination showing clearance of blasts (<5%), either CR or CRi.”\nSupplementary Methods, Page 7, Line 284:\n“Adverse events (AEs) were reported from start of treatment till 30 days from last\nadministration of study drug. AEs were considered treatment-emergent (TEAEs) as assessed\nby the investigators to be related to study drug and either new or worsening AEs by at least\none grade compared to baseline. Adverse events were graded according to the Common\nTerminology Criteria for Adverse Events (CTCAE) Version 4.0. Early deaths were defined by\ndeath within 60 days of starting the treatment.”\nPage 8, Line 288:\n“Composite complete remission (CRc) rate was 83% (N=33), including 15% (N=6) CR and 68%\n(N=27) CRi (Figure 5B). Twenty-nine patients achieved remission after 1 cycle of QUIZOM, and\n4 patients after 2 cycles. Median time to response is 23 days (Range: 17-62 days).”\nPage 8, Line 316:\n“There were two early deaths on days 29 and 53 from QUIZOM treatment. They were both\nnon-responding patients and causes of death were pneumonia and pulmonary haemorrhage,\nand pneumonia respectively, considered to be related to underlying leukaemia progression.\nTreatment-emergent adverse events were summarised in Supplementary Table 15. Grade 3/4\ncytopenia and febrile neutropenia were common adverse events. Of note, 20% patients\ndeveloped bacteraemia (grade 4 sepsis) requiring intravenous antibiotics. Importantly,\nprolongation of QTc was not observed at the current study dose.”\nSupplementary Table 15. Treatment-emergent adverse events (TEAEs) from start to 30 days\nafter QUIZOM.\nGrading\n1 2 3 4 5\nMyelosuppression\nAnemia* 0 (0%) 1 (3%) 27 (68%) 0 (0%) 0 (0%)\nPlatelet count decreased* 0 (0%) 1 (3%) 1 (3%) 26 (65%) -\nNeutrophil count decreased* 0 (0%) 0 (0%) 0 (0%) 24 (60%) -\nInfection\nFebrile neutropenia - - 24 (60%) 0 (0%) 0 (0%)\nSepsis - - - 8 (20%) 0 (0%)\nLung infection - 2 (5%) 4 (10%) 1 (3%) 2 (5%)\nUpper respiratory tract\n0 (0%)\ninfection - 1 (3%) 0 (0%) 0 (0%)\nEnterocolitis 0 (0%) 1 (3%) 0 (0%) 0 (0%) 0 (0%)\nJoint infection - 2 (5%) 0 (0%) 0 (0%) 0 (0%)\nSkin infection 0 (0%) 0 (0%) 1 (3%) 0 (0%) 0 (0%)\nUrinary tract infection - 0 (0%) 1 (3%) 0 (0%) 0 (0%)\nBleeding\nDuodenal ulcer 0 (0%) 1 (3%) 0 (0%) 0 (0%) 0 (0%)\nEpistaxis 0 (0%) 2 (5%) 1 (3%) 0 (0%) 0 (0%)\nBronchopulmonary\n0 (0%)\nhemorrhage 0 (0%) 0 (0%) 0 (0%) 1 (3%)\nHematuria 1 (3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)\nRectal hemorrhage 2 (5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)\nVitreous hemorrhage 0 (0%) 1 (3%) 0 (0%) 0 (0%) -\nBruising 1 (3%) 0 (0%) - - -\nHematoma 2 (5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)\nOthers\nHypotension 0 (0%) 1 (3%) 0 (0%) 0 (0%) 0 (0%)\nVomiting 2 (5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)\nMalaise 1 (3%) 0 (0%) - - -\nRash maculopapular 2 (5%) 1 (3%) 0 (0%) - -\nBone pain 2 (5%) 0 (0%) 0 (0%) - -\nALT increased 7 (18%) 0 (0%) 0 (0%) 0 (0%) -\nAST increased 6 (15%) 0 (0%) 0 (0%) 0 (0%) -\nPancreatitis - 1 (3%) 0 (0%) 0 (0%) 0 (0%)\nThe number and frequency (in parentheses) of patients with TEAEs were shown. The grading\nwas based on Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0. Adverse\nevents were either new or worsening of existing conditions by at least one grade. For\nrecurrent adverse events, only the highest grade was counted. Adverse events obviously not\nrelated to treatment were excluded as assessed by investigators.\n*98%, 98% and 100% patients had pre-existing or treatment-emergent grade 3/4 cytopenia\nafter QUIZOM treatment\nPage 13, Line 502:\n“Despite the high CRc rate to QUIZOM, most cases failed hematological recovery. In fact, two\nthirds of the responding patients remained transfusion-dependent until allo-HSCT.”\nMinor criticisms:\nThe methodology used a number of in the experiments in incompletely described. For\nFigure 2A there is no reference in the Supplementary Methods to “OP-Puro”, for Figures 2J-\nK, there is no description of the assay that I can find.\nWe apologize for the oversight. We have now revised the manuscript and included the\ndescription of different assays with references cited in the Supplementary Methods, which\nshould now read:\nSupplementary Methods, Page 3, Line 89:\n“Measurement of the rate of protein synthesis\nThe rate of protein synthesis was measured using Click-iT™ Plus OPP Alexa Fluor™ 488 Protein\nSynthesis Assay Kit as reported previously.5 In brief, AML cells were incubated with Click-iT®\nOPP and drug at 37oC for 30 minutes. The cells were then fixed, permeabilized, reacted with\nClick-iT® Plus OPP cocktail and resuspended in PBS with DAPI for FACS analysis.”\nSupplementary Methods, Page 3, Line 109:\n“Measurement of intracellular ROS\nIntracellular ROS levels were determined according to the manufacturer's protocol, as\ndetailed in previous studies.7 In brief, cells treated with drugs were stained with CellROX™ Deep\nRed at 37°C for 30 minutes. After washing twice with PBS, the cells were resuspended in RPMI\n1640 medium containing DAPI and then analyzed by FACS.\nMeasurement of protein aggregates\nThe formation of protein aggregates was evaluated using TPE-MI, NTPAN-MI and\nPROTEOSTAT® Protein aggregation assay (Enzo Life Sciences).8-10 AML cells were treated with\ndrugs for 1 day, washed, and then stained according to the manufactures’ protocol. For TPE-\nMI and NTPAN-MI, treated cells were washed with PBS and stained with the dye in HBSS at\n37°C for 30 minutes. Stained cells were then washed and analyzed by FACS. For PROTEOSTAT®\nProtein aggregation assay, treated cells were washed and lyzed. Abundance of protein\naggregates was determined according to the standard curve and examined by microplate\nreader. The amount of protein aggregates was further normalized by the abundance of\nprotein concentration examined by PierceTM BCA protein assay. ”\nReviewer #3:\nEvaluating the combination of HHT/omacetaxine and quizartinib is certainly reasonable\nenough, but this reviewer was hoping to find within this manuscript a new and exciting\nmessage suitable for the readers of Nature Cancer.\nWe thank the reviewer for this comment. In this manuscript, we adopted a staged and\nunbiased approach to identify the vulnerabilities of FLT3-ITD AML pertinent to protein\ntranslation and reported the hitherto undescribed therapeutic mechanisms based on\nperturbed metabolism and proteostasis. To prove the principle, we initiated a Phase II\nclinical trial and demonstrated, in a cohort including heavily pre-treated patients, high\nremission rate and long-term survival over 70% among HSCT recipients. Using serially\ncollected samples, we demonstrated for the first time the mechanistic link of metabolic\nremodelling and immune activation in clinical responders to perturbed proteostasis and the\nmechanism of drug resistance with particular reference to PLD1/JNK/HSPA1B activation in a\nprimitive leukaemia population. These findings were summarized in the graphical abstract.\nWe believed that these information will bring important insights to both leukaemia biologists\nand clinical haematologists and will provide a solid and mechanistic foundation based on\nwhich future clinical trials can be designed.\nTo strengthen the manuscript for the readers of Nature Cancer, we have performed a series\nof new experiments to highlight the following novel concepts with respects to therapeutic\ndevelopments in FLT3-ITD AML:\n1. FLT3-ITD AML is vulnerable to perturbation of mitochondria-assisted protein refolding via\nsuppression of HSP70 signaling (Figure 2E-2T, Extended Data Fig. 2J-2R, Figure 3 and Extended\nData Fig. 3);\n2. Perturbation of mitochondria by QUIZOM in FLT3-ITD AML resulted in pro-inflammatory\nsignals and cGAS-STING activation (Figure 4, Extended Data Fig. 4, Figure 5H-5N);\n3. Drug resistance in FLT3-ITD AML was mediated by persistence and emergence of resistant\nclones manifesting LSC signature with enhanced chaperone protein refolding activity and\nspecific metabolic dependence. These phenotypes were mitigated by c-Fos/c-Jun and PLD1\ninhibition, thereby overcoming drug resistance (Figure 3G, 3J, 3L-3N, Figure 6J-6W, Extended\nData Fig. 6J-6K) .\nDetails of these experiments and the results have been incorporated in the revised\nmanuscript and our responses were listed in the relevant points that followed.\nPerhaps most importantly the clinical data do not convince that this combination has great\nfuture potential. Quizartinib monotherapy in relapsed/refractory patients (two cohorts –\none aged >60 and another aged >18 relapsed after salvage or HSCT) gave CR rates of ~46-\n56% with median LFS of around 3 months and median OS of 6 months (as per PMID\n29859851). This compares with ~3.5months LFS and 9.5 months OS in the current study\nwhich features arguably a more favourable patient population (e.g. median age here is 57.5\nversus median age of 69 in cohort 1 of the Lancet Oncology paper). Thus the leukaemia free\nsurvival with QUIZOM here in patients who did not receive transplant was indistinguishable\nfrom historic data for quizartinib monotherapy.\nPoints very well taken. The phase II clinical trial in the current study (QUIZOM) was intended\nto test the concept and therapeutic efficacy of combining a FLT3 inhibitor and a protein\ntranslation inhibitor. Importantly, the clinical samples have helped us examine the\ntherapeutic mechanisms and identify potential targets of drug resistance.\nIn general, we find it difficult to compare outcome between clinical trials as the drug dose,\nregimen and patient characteristics were different. Of note, the doses of quizartinib used\nwere also different. In the Lancet Oncology paper, the first cohort of patients was treated\nwith 200 mg daily dose continuously, and subsequent cohorts received reducing doses to 135\nmg, 90 mg and 60 mg daily. In QUIZOM cohort, patients were treated with 30 mg daily\nquizartinib continuously and simultaneously with omacetaxine mepesuccinate.\nHowever, in response to the reviewer’s comments, we have tabulated the characteristics and\ntreatment outcome of the relapse/refractory patients treated with single agent quizartinib\nfrom the Lancet Oncology paper by Cortes et al published in 201835 and data from QUIZOM\nfocusing on relapse/refractory (R/R) patients as follows:\nLancet Oncology Lancet Oncology QUIZOM study\nCohort 1 Cohort 2 R/R\nAgent tested Quizartinib Quizartinib QUIZOM**\nQuizartinib dose 200 mg 135-60 mg 30 mg\nNumber 112 136 35\nSex (M:F, %) 56:56 (50%) 69:67 (51%) 11:24 (31%)\nMedian age, median 69 (66–73, not 50 (39-59, not 55 (45-61, 23-71)\n(IQR, range) provided) provided)\nPrevious alloHSCT 0 40 (29%) 7 (20%)\nNo. of previous 1 2 1: 21 (60%)\nline(s) of therapies 2: 11 (31%)\n3: 3 (9%)\nCRc, n (%) 63 (56%) 62 (46%) 30 (86%)\nCR 3 (3%) 5 (4%) 6 (17%)\nCRp+CRi 60 (54%) 57 (41%) 24 (69%)\nWithout HSCT\nLFS 12.1 weeks Not mentioned 13.2 weeks\nOS 25.4 weeks 20.1 weeks# 39.6 weeks\nWith HSCT\nLFS - Not mentioned Not reached*\nOS - 30.1 weeks# Not reached*\n* Median follow up time for post-HSCT patients is 4.7 years.\n# From 28-day landmark analysis; ** Combination of Quizartinib and Omacetaxine\nmepesuccinate.\nCompared with QUIZOM, patients in Cohort 1 was older, had less heavy treatments before\nand prior HSCT was excluded. Cohort 2 was more comparable in age and prior treatments,\nincluding HSCT. However, consistently patients receiving QUIZOM showed higher CRc and\nCR rate and better OS.\nWe believed that comparison between trials was difficult and that has not been the intention.\nThe QUIZOM treatment did show favourable response and long-term survival for those who\nwere bridged to HSCT. Importantly, single cell transcriptome data arising from the trial\nshowed for the first time immune activation through cGAS-STING pathway in FLT3-ITD AML\nwith metabolic remodelling and perturbed proteostasis via the activation of\nPLD1/JNK/HSPA1B axis in mediating drug resistance. Moreover, the information arising\nprovided a solid foundation for future therapeutic consideration based on immune activation\nand perturbed proteostasis.\nThese new messages was incorporated in the revised manuscript.\nPage 12, Line 466:\n“Third, the leukaemia inhibitory activity of QUIZOM was successfully translated into clinical\napplication. Specifically, patients with R/R AML or unfit for conventional treatment showed a\nhigh response rate to QUIZOM. Notwithstanding the difference in study design and patient\ncohort, our results compared very favorably with those of gilteritinib4 and quizartinib35\nmonotherapy and the regimen provided an effective bridging therapy to HSCT (Supplementary\nTable 17). Specifically, 7 patients recruited into this study had relapse after first HSCT and 4\nwere effectively bridged to the second HSCT, attesting to the safety and tolerability of the\nregimen. With post-HSCT quizartinib maintenance, 3 remained in remission after 5 years,\nunderscoring the efficacy of the QUIZOM regimen.”\nPage 12, Line 476:\n“Finally, single-cell transcriptome analysis of the serial samples from the clinical cohort\nrevealed potential mechanisms of QUIZOM responsiveness and subsequent refractoriness.\nSpecific molecular changes in responders included down-regulation of HOX gene activity in\nAML at remission, suggesting that the latter underwent critical loss of leukemic-maintaining\nsignals that accounted for the therapeutic efficacy.44 Moreover, immune activation at\nremission was evident by the enrichment of CD8-TEMRA, increased cytotoxic signature and\nreduction of CD8-TEX.45-47 However, macrophage population was not annotated, due to its\nrarity in BM samples.15 The results were consistent with our preclinical studies that QUIZOM\ninduced immune activation via cGAS-STING pathway. Furthermore, the data have provided\ninsights to the QUIZOM refractoriness. Specifically, subclusters in the non-responders’ HSC-\nMPP population showing strong LSC signature might account for the drug resistance.48,49\nGenes associated with quiescence (JUN), protein folding (HSPA1B) and lipid metabolism (PLD1)\nwere abundant in the LSC population that might be crucial to the maintenance of leukaemia\nstemness. Interestingly, LSC with quiescent phenotype and active lipid metabolism have been\nfound to confer resistance to chemotherapy and venetoclax, suggesting potential therapeutic\nstrategy via targeting LSC quiescence and lipid metabolism.50 Notably, PLD1 might act as a\nsignal transducer and drive JNK/JUN pathway via modulating lipid mediator (phosphatidic\nacid). The latter induced HSP70 expression and conferred drug resistance. Inhibition of PLD1\nremodeled phosphatidylcholine metabolism and resulted in the accumulation of PC-PUFA2\nwhich triggered ferroptosis in FLT3-ITD AML. PLD1 was also found to crosstalk with Wnt/β-\ncatenin and S6K/AKT pathway and promoted the progression of different solid tumors\nsuggesting its essential role in cancer stemness.51,52 Most importantly, our data validated the\ntherapeutic efficacy of c-Fos/c-Jun and PLD1 inhibitors that can sensitize LSC to QUIZOM,\nproviding a novel regimen for FLT3-ITD AML. ”\nThe data presented in figures 2-4 while touching on a multitude of downstream cellular\nevents, do not really go into depth for any of them.\nPoints very well taken. We have since then re-focused the mechanistic studies on the\ntherapeutic mechanisms of QUIZOM and gathered evidence that the latter suppressed HSP70\nexpression and perturbed mitochondrial functions and integrity, leading to disrupted\nproteostasis, DNA damage and activation of pro-inflammatory responses. These experiments\nwere incoroporated in the revised manuscript, which should now read:\nOn disruption of mitochondrial function and structure:\nPage 5, Line 204:\n“QUIZOM decreased mitochondrial mass and membrane potential in FLT3-ITD AML (Figure\n2F-2G). There was ultrastructural evidence of mitochondria rupture (Figure 2H-2I).\nFunctionally, QUIZOM significantly reduced glucose uptake, basal mitochondrial respiration\nand ATP level (Extended Data Fig. 2K-2N). Moreover, it suppressed the maximal respiratory\ncapacity, underscoring a significant reduction of functional mitochondria (Extended Data Fig.\n2M-2N). Moreover, it suppressed the maximal respiratory capacity, underscoring a significant\nreduction of functional mitochondria (Extended Data Fig. 2M-2N). An untargeted\nmetabolomics was performed and principal component analysis (PCA) confirmed a unique\nspectrum of metabolic remodeling by QUIZOM, which was distinct from those of single drug\ntreatment (Extended Data Fig. 2O). In particular, QUIZOM significantly reduced glucose,\namino acid and nucleotide metabolism which were reported to be essential in supporting the\nsurvival of FLT3-ITD AML (Figure 2K).19-21 However, pathway analysis of metabolites with\nsignificant changes upon QUIZOM treatment showed that lipid metabolism was not affected\nindicating the dispensable role of lipid metabolism in mitochondrial respiration in parental\nFLT3-ITD AML (Extended Data Fig. 2P). Nicotinamide riboside (NR), a precursor of NAD+\ncofactor, could ameliorate the growth inhibitory effect of QUIZOM (Figure 2L-M). NR has been\nshownt o promote mitochondrial biogenesis and mitochondrial respiration.22,23 In this study,\nNR alleviated the adverse effects of QUIZOM on mitochondria and ATP level (Figure 2N-Q).\nUntargeted metabolomics showed that NR induced increase in sugar and glycerophospholipid\nmetabolism in FLT3-ITD AML upon treatment with QUIZOM (Extended Data Fig. 2Q). Similar\neffects of QUIZOM and NR rescue were seen in primary FLT3-ITD AML samples (Figure 2R-T).”\nOn disruption of mitochondrial-assisted protein refolding and apoptosis\nPage 6, Line 228:\n“QUIZOM was shown to reduce the amount of chaperone protein HSP70 (Figure 2C) and\ninduce the formation of protein aggregates (PAs) in two independent assay platforms (Figure\n3A-3B, Extended Data Fig. 3A-3C). Pharmacologic inhibition of HSP70 similarly enhanced the\nformation of PAs (Figure 3C-3D) and induced apoptosis (Figure 3E-3F) in AML cell lines and\nprimary samples. Bortezomib, a proteasomal inhibitor, was reported to generate PAs by\nblocking the degradation of misfolded protein.24 Addition of Bortezomib potentiated the\nformation of PAs and apoptosis upon HSP70 inhibition, underscoring the significant role of\nPAs in QUIZOM induced cytotoxicity (Extended Data Fig. 3D-3E). QUIZOM-induced PAs\nformation was abolished by NR (Figure 3G), whereas further inhibition of metabolic\nrespiration by PKM2 inhibition (Compound 3k) potentiated HSP70i-induced apoptosis\n(Extended Data Fig. 3F), supporting the proposition of mitochondrial collapse promoting PAs-\ndriven apoptosis (Figure 3H). Furthermore, increased PAs was associated with DNA damage\n(Figure 3I). DNA damage was reduced by NR supplement, suggesting a causal relationship\nbetween mitochondrial function, PAs formation and cytotoxicity (Figure 3J). The\naforementioned transcriptome analysis revealed activation of p53 which might be triggered\nby PA-inducing DNA damage (Extended Data Fig. 3G). In fact, QUIZOM-induced apoptosis was\np53-dependent, as demonstrated by its lack of effects in TP53 loss-of-function mutant MV4-\n11 isogenic line (Figure 3K). The role of HSP70 in QUIZOM-associated cytotoxicity was further\nexamined by forced expression of HSP70 (HSP70-OE) in AML cell lines, which resulted in the\nreduced formation of PAs and apoptosis upon QUIZOM treatment in vitro (Figure 3L-3M,\nExtended Data Fig. 3H) and mitigated the survival benefit of QUIZOM in animals engrafted by\nMV4-11 (Figure 3N).”\nOn QUIZOM induced activation of proinflammatory response\nPage 7, Line 253:\n“In addition to apoptosis, RNA-seq data showed the QUIZOM-induced increase in CDKN1A, a\ndecrease in LMNB1 expression (Extended Data Fig. 4A) and a positive enrichment of pathways\nassociated with senescence associated secretary phenotype (SASP) (Figure 4A-B and\nSupplementary Table 12). QUIZOM induced DNA damage, as shown by the increase in γH2AX\nin FLT3-ITD AML (Figure 3I). Furthermore,mitochontrial rupture led to an increase in the\nrelease of mitochondrial DNA (mtDNA) to cytoplasm (Figure 4C).25 These changes were\nassociated with STING dimerization, 2’3’-cGAMP production and SASP cytokines (Figure 4D-\n4F and Extended Data Fig. 4B), characteristic of cGAS-STING activation. Treatment with cGAS\ninhibitor (RU.521), HSP70-OE and NR treatment ameliorated the increases in pro-\ninflammatory cytokines (Figure 4F), supporting the proposition that cGAS-STING activation\nresulted from the aberration of the aforementioned intracellular signal. Functionally,\nQUIZOM promoted phagocytosis by macrophages in co-culture with FLT3-ITD AML and the\nprocess was also ameliorated by NR or HSP70 overexpression (Figure 4G). Phagocytosis was\nalso ameliorated by the treatment of CCL3 and CSF1 neutralizing antibodies (Figure 4H),\nsuggesting that these cytokines might induce phagocytosis in response to QUIZOM treatment.\nImportantly, macrophage activation was observed in vivo, as evidenced by their increase in\nMHC II and CD40 expression (Figure 4I) when NSG mice engrafted by FLT3-ITD AML were\ntreated with QUIZOM.”\nThey single cell RNAseq data showing in figure 6 while extensive does not really provide\nany significant new insights\nThanks for the criticism. In our humble opinion, single cell RNAseq data in this study have\nserved criticial functions and shed important mechanistic insights to drug resistance in FLT3-\nITD AML.\nFirst, the data showed immune activation of T-cells and suppression of leukaemia cell\ncompartment, confirming clinical relevance of the aforementioned laboratory observations.\nSecond, serial examination of samples and comparison between responders and non-\nresponders have led to identification of LSC population that might account for drug resistance.\nThird, we have now examined the metabolomics of this LSC population and identified key\nresistant mechanisms pertaining to activation of phosphatidylcholine metabolism via\nupregulation of PLD1 in LSCs. PLD1 was now shown to be a critical regulator of\nphosphatidylcholine metabolism in QUIZOM-resistant LSCs, promoting resistance by\nsuppressing ferroptosis. The mechanistic link between PLD1 and the JNK/JUN/HSPA1B axis in\nLSCs was also identified. The results were incorporated in the revised manuscript as follows:\nPage 10, Line 385:\n“Putative functions of LSC were examined. LSC preferentially expressed JUN and FOS (key\nregulators of quiescence, unfolded protein response)30, HSPA1B (encoding HSP70), and genes\npromoting lipid metabolism such as PLD1, MTMR3, NR1H2, and SREBF131-33 (Figure 6E-6F and\nExtended Data Fig. 6G). LSC isolated from primary FLT3-ITD AML samples based on our LSC\nphenotypic markers CD33+CD44+CD69+ (Figure 6F) [viz. triple positive (TP) cells] were more\nreplicatively quiescent and less sensitive to QUIZOM than the bulk population (Figure 6G-6I).\nTo validate the functional role of JUN/FOS and PLD1 (phospholipase D1, catalysing the\nhydrolysis of phosphatidylcholine (PC) to phosphatidate and choline) in the treatment\nresponse to QUIZOM, TP of FLT3-ITD primary AML samples were treated by QUIZOM\ncombined with inhibitors to c-Fos/c-Jun (T-5224) and PLD1 inhibitor (VU0155069). T-5224 and\nVU0155069 synergized with QUIZOM in inducing apoptosis in the TP subpopulation (Figure 6J-\nK). Combination of PLD1 and QUIZOM resulted in profound suppression of leukaemic\nengraftment of primary AML (Figure 6L).\nTo investigate the molecular mechanisms of PLD1 in mediating LSC activity, untargeted\nmetabolomic analysis of TP and bulk population was performed. Glycerophospholipid\nmetabolism was the sole metabolic pathway enriched in TP compared with bulk population\n(Figure 6M and Extended Data Fig. 6H). In particular, pathway analysis of TP-enriched\nmetabolites reveal the activation of phosphotidylcholine metabolism which was consistent\nwith the upregulation of PLD1 in TP compared to bulk population (Extended Data Fig. 6I).\nFurthermore, PLD1 inhibition suppressed lipid uptake in TP but not in the bulk population\n(Figure 6N). PLD1 inhibition remodelled lipid metabolism, leading to increase in ROS and lipid\nperoxidation (Figure 6O-P). Furthermore, treatment with ferrostatin but not necrostatin\nmitigated the QUIZOM and PLD1i induced apoptosis, underscoring the induction of ferroptosis\nby the latter treatment in the TP LSC subpopulation (Figure 6Q). PLD1 overexpression induced\nlipid uptake (Figure 6R) and ameliorated the QUIZOM-induced apoptosis (Figure 6S),\nsupporting the role of PLD1 in mediating QUIZOM resistance in AML. Mechanistically, PLD1\ninhibition preferentially reduced glycerophospholipid metabolism (Extended Data Fig. 6J) and\nresulted in accumulation of phosphatidylcholines via inhibiting its hydrolysis (Extended Data\nFig. 6K). Among the phosphatidylcholines, diacyl-PUFA phosphatidylcholines (PC-PUFA ), a\n2\ndriver of ferroptosis,34 were increased. The observations supported the proposition that PLD1\ninhibitor and QUIZOM showed synergizism in suppressing the growth of FLT3-ITD AML via\nsimultaneous induction of apoptosis and ferroptosis.\nThe putative pathogenetic link between PLD1 expression and the JNK/JUN/HSPA1B axis in LSC\npopulation was evaluated (Extended Data Fig. 6L). Inhibition of PLD1 reduced\nphosphorylation of JNK (Figure 6T). Moreover, inhibition of JNK signaling, FOS/JUN\ntranscriptional activity or PLD1 suppressed the expression HSPA1B (Figure 6U). In primary\nAML samples, the therapeutic effects of FOS/JUN and PLD1 inhibition on HSPA1B expression\nand PAs formation were only observed in TP LSC but not the bulk population (Figure 6V-6W).”\nPage 12, Line 476:\n“Finally, single-cell transcriptome analysis of the serial samples from the clinical cohort\nrevealed potential mechanisms of QUIZOM responsiveness and subsequent refractoriness.\nSpecific molecular changes in responders included down-regulation of HOX gene activity in\nAML at remission, suggesting that the latter underwent critical loss of leukemic-maintaining\nsignals that accounted for the therapeutic efficacy.44 Moreover, immune activation at\nremission was evident by the enrichment of CD8-TEMRA, increased cytotoxic signature and\nreduction of CD8-TEX.45-47 However, macrophage population was not annotated, due to its\nrarity in BM samples.15 The results were consistent with our preclinical studies that QUIZOM\ninduced immune activation via cGAS-STING pathway. Furthermore, the data have provided\ninsights to the QUIZOM refractoriness. Specifically, subclusters in the non-responders’ HSC-\nMPP population showing strong LSC signature might account for the drug resistance.48,49\nGenes associated with quiescence (JUN), protein folding (HSPA1B) and lipid metabolism (PLD1)\nwere abundant in the LSC population that might be crucial to the maintenance of leukaemia\nstemness. Interestingly, LSC with quiescent phenotype and active lipid metabolism have been\nfound to confer resistance to chemotherapy and venetoclax, suggesting potential therapeutic\nstrategy via targeting LSC quiescence and lipid metabolism.50 Notably, PLD1 might act as a\nsignal transducer and drive JNK/JUN pathway via modulating lipid mediator (phosphatidic\nacid). The latter induced HSP70 expression and conferred drug resistance. Inhibition of PLD1\nremodeled phosphatidylcholine metabolism and resulted in the accumulation of PC-PUFA\n2\nwhich triggered ferroptosis in FLT3-ITD AML. PLD1 was also found to crosstalk with Wnt/β-\ncatenin and S6K/AKT pathway and promoted the progression of different solid tumors\nsuggesting its essential role in cancer stemness.51,52 Most importantly, our data validated the\ntherapeutic efficacy of c-Fos/c-Jun and PLD1 inhibitors that can sensitize LSC to QUIZOM,\nproviding a novel regimen for FLT3-ITD AML.”\nThe same tubulin loading control seems to have been used in western blots in Figures 1J,\n2E, 2G and 4B.\nWe thanks the reviewer for raising this point. We have adhered to strict laboratory practice\nand each experiment has been performed with its own housekeeping control. Data as shown\nin Figure 1J, 2E, 2G and 4B were generated from the same set of experiments, hence the same\ntubulin loading control. To avoid misunderstanding, we have repeated the immunoblotting\nof tubulin for each figure (Extended Data Fig. 1D, Figure 2C, Extended Data Fig. 2I, Figure 3I,\nFigure 4D).\nOther comments\n(i) Throughout there was little or no information as to what statistical test was performed\nin each figure panel and what error bars referred to.\nWe apologize for the oversight. The specific statistical test and the meaning of the error bars,\nwhich indicated the standard error of the mean (SEM), were now shown in the legends to\neach relevant figure panels.\n(ii) There was frequent use of acronyms which were undefined.\nWe have now clearly defined the acronyms throughout the manuscript, which should now\nread:\nPage 3, Line 96:\n“Newly synthesized proteins (NSPs) were isolated by click-chemistry, digested, subjected to\nTMT multiplexed labelling and deep-scale Liquid chromatography-mass spectrometry/mass\nspectrometry (LC-MS/MS) analysis (Supplementary methods).”\nPage 7, Line 260:\n“…characteristic of cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING)\nactivation.”\nPage 9, Line 332:\n“…including mostly hematopoietic stem cell – multipotent progenitor (HSC-MPP),\nGranulocyte-monocyte progenitor (GMP), Megakaryocyte progenitor (MkP), and monocyte\npopulations (Figure 5F)…”\nPage 9, Line 341:\n“…increases in immune populations including CD4-T, CD8-T, Natural Killer T cell (NKT cell), and\nNatural Killer cell (NK cells), which were not found in non-responders (Figure 6C, Extended\nData Figure 6I)…”\nPage 9, Line 349:\n“QUIZOM resulted in the activation of the T cell Receptor (TCR) pathway in CD4+ and CD8+ T\ncells, and cytotoxicity gene signature in CD8+ T at remission (Figure 5I-5K).”\nPage 13, Line 505:\n“…and FLT3-wildtype (FLT3-WT) leukemia relapse was often encountered.…”\nLegend to Figure 1\n“V: Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate”\nLegend to Figure 2\n“V: Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate; NR: Nicotinamide riboside; SSC: side scatter; FSC: forward scatter; 7-AAD: 7-\naminoactinomycin D; MCL1-OE: MCL1-overexpression”\nLegend to Figure 3\n“V: Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate; H: HSP70-IN-1; NR: Nicotinamide riboside; HSP70-OE: HSP70-overexpression”\nLegend to Figure 4\n“V: Vehicle; Q: Quizartinib; O: Omacetaxine mepesuccinate; QO: Quizartinib + Omacetaxine\nmepesuccinate; cGASi: cGAS inhibitor RU.521; NR: Nicotinamide riboside; HSP70-OE: HSP70-\noverexpression”\nLegend to Figure 5\n“EM: effector memory; TEMRA: terminally differentiated effector memory; TEX: exhausted T”\nLegend to Figure 6\n“V: Vehicle; QO: Quizartinib + Omacetaxine mepesuccinate; P/PLD1i: PLD1 inhibitor VU\n0155069; JNKi: JNK inhibitor JNK-IN-8; c-Fos/c-Juni: c-Fos/c-Jun inhibitor T-5224; PLD1-OE:\nPLD1-overexpression”\nExtended Data Fig. 5B\nFLT3-ITD AR: FLT3-ITD allelic ratio\n(iii) RNA sequencing datasets were not made available for the reviewer.\nWe have deposited the RNA-seq and single-cell RNA-seq data in the Sequence Read Archive\n(SRA) under the accession number PRJNA1054981 and have created a reviewer link according\nto the instruction of SRA. The link is:\nhttps://dataview.ncbi.nlm.nih.gov/object/PRJNA1054981?reviewer=rr8c11dau2n1a84lv753\ns9i22f\n(iv) The proteomics datasets do not appear to have been uploaded loaded to a repository\nfor review.\nWe have deposited the proteomics data in the PRIDE database under the accession number\nPXD047567. The link is:\nhttps://www.ebi.ac.uk/pride/archive/projects/PXD047567\n(v) The main methods should feature ethics statements for the clinical trial and also the use\nof the primary payment patient samples.\nThe ethics statements for clinical trial and the use of primary patient samples have been\nincluded in the revised manuscript, which should now read:\nSupplementary Method, Page 2, Line 58\n“Ethics statements\nThe research was conducted in accordance with Declaration of Helsinki. The clinical trial was\napproved by Institutional Review Board of the University of Hong Kong/Hospital Authority\nHong Kong West Cluster (HKU/HA HKW IRB) (Ref no.: UW 17-099). Informed consent was\nobtained from all patients. The collection and use of primary bone marrow or peripheral blood\nsamples from AML patients was also approved by HKU/HA HKW IRB (Ref no.: UW 05-183\nT/846)”\n(vi) The methods should include dates for the clinical trial recruitment window.\nThe dates for the clinical trial recruitment were included in the revised manuscript as follows:\nPage 7, Line 275:\n“Forty patients including 35 relapse/refractory and 5 newly diagnosed FLT3-ITD AML were\nrecruited and treated with the combination of quizartinib and omacetaxine mepesuccinate\nbetween November 2017 and September 2020.”\n(vii) Details on the DEPMAP analysis were missing and it was not possible to determine\nwhat had been done to generate figure panel 1A.\nPoints well taken. We have further clarified the method used for DepMap analysis.\nThe manuscript should now read:\nSupplementary Methods, Page 2, Line 67:\n“Meta data of 50 AML cell lines included in the DepMap project was prepared and their\nmutations were downloaded from DepMap 23Q2 release. Gene effect score derived from\nCRISPR knock-out screen of the same release was downloaded and merged. Mutation profile\nand gene effect score were available in 33 cell lines, which were further analyzed. Three cell\nlines carried FLT3-ITD mutations, viz. MOLM-13, MOLM-14, and MV4-11, and the other 30 cell\nlines were FLT3 wild type. The difference in gene knock-out effect was identified by limma\n(linear models for microarray data), a statistical package for analyzing RNA-seq data. The\nresulting p-values were adjusted using the Benjamini-Hochberg method to account for\nmultiple testing. Gene set enrichment analysis (GSEA) was performed based on the fold\nchange of gene effect score produced by limma. The normalized enrichment score (NES) and -\nlog10(adjusted p-value) from GSEA were visualized as volcano plot in Figure 1A.”\n(viii) Line 339 says 35 patients but in the data above it says 33. Which is it?\nWe apologize for the typo. The manuscript has been revised as follows:\nPage 8, Line 298:\n\"Among 33 patients who achieved remission, 13 (39%) received allogeneic HSCT after …\"\n(ix) The study should include a full table of adverse events as is normal in a clinical trial\nreport. This is missing.\nThanks for the comments. A table of treatment-emergent adverse events has now been\nincluded in the revised text (also added as Supplementary Table 15).\nPlease refer to answer for question from Reviewer 2.\n(x) Figure 1A is missing a Y axis label.\nWe apologize for the oversight. Figure 1A has now been revised. Please refer to our response\nto Point 6 of Reviewer 1.\n(xi) Figure 1B it is not clear that cell viability is being shown. The legend refers to\nproliferation.\nWe have revised the legend to Extended Data Fig. 1A (Figure 1B in the older version) and\nclarified that the cell viability as examined by PrestoBlueTM Cell Viability Reagent was being\nshown.\nLegend to Extended Data Fig. 1A\n“(A) Quizartinib and OM displayed stronger growth suppression on FLT3-ITD human AML cell\nlines compared to FLT3-WT AML cell lines examined by PrestoBlueTM Cell Viability Reagent for\ncell viability (Paired t test, SEM shown as error bar, n=3).”\n(xii) Authors refer to synergy but it appears that the results often show an additive rather\nthan synergistic effect.\nAccording to the Combination Index and EOBA analysis of quizartinib and OM treatment in\nAML cells, the cytotoxic effect between the 2 drugs were synergistic (Extended Data Fig. 1C).\nWe have further analysed the synergism of apoptotic induction upon QUIZOM treatment in\nMOLM-13 and MV4-11. EOBA of apoptosis synergism in MOLM-13 and MV4-11 were 36.0%\nand 30.5% indicating QUIZOM treatment synergistically induced apoptosis in AML. We also\nperformed Combination Index (CI) and confirmed QUIZOM is synergistic in suppressing the\ngrowth of AML cell lines (Figure 1D).\nWe have revised the text and figures to better describe the synergism of QUIZOM in AML as\nfollow:\nPage 5, Line 166:\n“Combination treatment (QUIZOM) synergistically reduced protein synthesis and induced\napoptosis in human FLT3-ITD AML cell lines (Figure 1D-1F, Extended Data Fig. 1C-1E) and\nisogenic Ba/F3-FLT3-ITD cell line (Figure 1G-1I). ”\n(xiii) Had the authors thought to include other cell lines with FLT3-ITD mutations such as\nMUTZ-11 and PL-21?\nThank you for the suggestion. MUTZ-11 or PL-21 were not included in this study as they were\nalso not included in the CRISPR dataset in DepMap from which we identified the\nvulnerabilities of FLT3-ITD AML. These cell lines were not available in our institution.\nTo ascertain the specific effects of QUIZOM on FLT3-ITD AML, we generated the isogenic lines\n(Ba/F3 and 32D) carrying FLT3-WT or FLT3-ITD and performed parallel experiments. The\nresults were shown in Figure 1G-I.\nThe manuscript has been revised as follows:\nPage 5, Line 166:\n“Combination treatment (QUIZOM) synergistically reduced protein synthesis and induced\napoptosis in human FLT3-ITD AML cell lines (Figure 1D-1F, Extended Data Fig. 1C-1E) and\nisogenic Ba/F3-FLT3-ITD cell lines (Figure 1G-1I). ”\n(xiv) In Figure 1 legend the dose used for quizartinib seems to vary by 1000-fold (nm vs uM).\nWe apologized for the oversight. The legend to Figure 1K has been revised, which should now\nread:\nPage 20, Line 731:\n“(E) QUIZOM (2nM quizartinib and 5nM OM) treatment showed the strongest inhibition on\nprotein synthesis in FLT3-ITD AML cell lines (Paired t test, SEM shown as error bar, n=3).”\n(xv) Authors should show MCL1 over expression western blot with panel 2F.\nThe Western blot showing MCL1 overexpression was shown in the new Extended Data Fig. 2E.\n(xvi) Some orthogonal evidence that protein aggregates are generated by the combination\nwould be appreciated (Figure 2J).\nWe appreciated the reviewer’s comments. We have now performed two additional sets of\nexperiments based on NTPAN-MI9 and the Aggresome Detection Kit (Abcam ab139486) to\nstrength the evidence that protein aggregates were induced by the combination treatment.\nThe manuscript has been revised as follows:\nPage 6, Line 228:\n“QUIZOM was shown to reduce the amount of chaperone protein HSP70 (Figure 2C) and\ninduce the formation of protein aggregates (PAs) in multiple independent assay platforms\n(Figure 3A-3B, Extended Data Fig. 3A-3C).”\n(xvii) A table showing the genetics of the primary samples used in in vitro studies would be\nappreciated.\nA table summarized the genetics of all primary samples used in in vitro studies was added as\nSupplementary Table 20.\nSupplementary Table 20. Genetics of the primary samples used in in vitro studies\nUPN Gene Nucleotide Change Amino Acid Change VAF\nIDH1 c.394C>T p.R132C 0.42\nFBXW7 c.257C>T p.S86L 0.42\nDNMT3A c.2683G>A p.V895M 0.38\nFLT3 c.1805_1806ins36 p.L601_K602 0.32\nNPM1 c.860_863dup p.W288fs 0.38\nDNMT3A c.2409-1G>T 0.45\nFLT3 c.1743_1805dup p.T582_K602dup 0.09\nNPM1 c.860_863dup p.W288fs 0.4\nFLT3 c.1754_1795dup p.S585_E598dup 0.32\nNPM1 c.860_863dup p.W288fs 0.41\nFLT3 c.1773_1793dup p.Y597_E598dup 0.83\nPTPN11 c.1504T>C p.S502P 0.07\nNPM1 c.860_863dup p.W288fs 0.44\nFLT3 c.1758_1793dup p.D586_Y597dup 0.3\nATM c.8987+2T>G 0.45\nTET2 c.3662G>A p.C1221Y 0.06\nNPM1 c.860_863dup p.W288fs 0.05\nFLT3 c.1758_1793dup p.D586_Y597dup 0.04\nFLT3 c.1740_1817dup p.Val581_Pro606dup 0.1\nFLT3 c.1793A>T p.Glu598Val 0.55\nIDH1 c.395G>A p.Arg132His 0.49\nNPM1 c.860_863dupTCTG p.Trp288Cysfs*12 0.31\nNPM1 c.860_863dup p.W288fs 0.39\nFLT3 c.1792_1815dup p.E598_605dup 0.73\nTET2 c.5681C>T p.R1894L 0.43\nTET2 c.1301_1304dup p.H435fs 0.39\nRUNX1 c.1003C>T p.Q335* 0.4\nFLT3 c.1771_1836dup p.Y591_F612dup 0.44\nSRSF2 c.284C>G p.P95R 0.49\nIDH2 c.419G>A p.R140Q 0.45\nSRSF2 c.284C>T p.P95L 0.48\nFLT3 c.1788_1832dup p.Y597_E611dup 0.26\nBCORL1 c.1784_1803dup p.K602fs 0.67\nNPM1 c.860_863dup p.W288fs 0.39\nFLT3 c.1789_1824dup p.Y597_E608dup 0.28\nNPM1 c.863_864insCATG p.W288fs 0.4\nFLT3 c.1790_1828dup p.Y597_N609dup 0.37\nFLT3 c.1744_1806dup p.T582_K602dup 0.14\n(xviii) The lines are not aligned in table 13 where chemotherapy treatments are listed.\nThe Supplementary Table 13 has been revised.\nResponses to reviewers’ comments\nReviewer #1:\nThe authors present a compelling study and were responsive to the previous reviews. My only\ncomment is with respect to their response to Reviewer 1 Comment 1. In the Supplementary\nMaterials the authors state on lines 156-160, \"The resulting p-values were adjusted using the\nBenjamini-Hochberg method4 to account for multiple testing. Gene set enrichment analysis\n(GSEA) was performed based on the fold change of gene effect score produced by limma. The\nnormalized enrichment score (NES) and -log10(adjusted p-value) from GSEA were visualized as\nvolcano plot in Figure 1A.\" and on lines 357-358, \"For the GO terms, multiple testing was\ncontrolled using the false discovery rate 357 (FDR) approach, with an adjusted p-value, i.e. q-value\n< 0.05 to consider significance.\" Lines 396-397, \"Adjusted p-396 values were obtained using the\nBenjamini-Hochberg method.\" lines 403-404, \"FDR was 403 set at 0.05 and adjusted p-value < 0.05\nwas considered statistically significant.\" Please remove \"adjusted p-value\" from all of these\nsentences. FDRs are not adjusted p-values which has been the source of confusion in the applied\nliterature.\nPoints were well taken. The manuscript was revised as follows:\nLegend to Figure 1A:\n“Multiple testing was controlled by Benjamini-Hochberg procedure with the false discovery rate (FDR) set\nat 0.05”\nSupplementary Materials, Page 2, Line 73\n“Multiple testing was controlled by Benjamini-Hochberg procedure with the false discovery rate (FDR) set\nat 0.05.4 Gene set enrichment analysis (GSEA) was performed based on the fold change of gene effect\nscore produced by limma. The normalized enrichment score (NES) and -log10(p-value) from GSEA were\nvisualized as volcano plot in Figure 1A.”\nSupplementary Materials, Page 7, Line 274\n“For the GO terms, multiple testing was controlled by Benjamini-Hochberg procedure with the false\ndiscovery rate (FDR) set at 0.05.”\nSupplementary Materials, Page 8, Line 313\n“FDR was set at 0.05.”\nSupplementary Materials, Page 8, Line 320\n“FDR was set at 0.05 to consider statistical significance.”\nReviewer #2:\nThe authors have responded to my criticisms.\nWe thank the reviewer for the previous comments and criticisms that have helped us improve the\nmanuscript substantially.\nReviewer #4:\nThe manuscript by Zheng et al. entitled “Combination treatment with quizartinib and\nomacetaxine mepesuccinate reveals metabolic vulnerabilities and mechanisms of drug resistance\nin FLT3-ITD AML” presents an in-depth study evaluating the therapeutic effects and resistance\nmechanisms of the combination therapy QUIZOM (quizartinib and omacetaxine mepesuccinate)\nin FLT3-ITD acute myeloid leukemia (AML). The study is methodologically sound, and offers a\ncompelling narrative linking mitochondrial dysfunction, proteostasis collapse, and immune\nactivation to therapeutic efficacy, while also identifying a lipid metabolism-dependent leukemic\nstem cell (LSC) population driving resistance. The authors integrate data from functional\ngenomics, translatomics, untargeted metabolomics, single-cell transcriptomics, and a Phase II\nclinical trial. They convincingly demonstrate that QUIZOM disrupts mitochondrial proteostasis\nand activates pro-inflammatory signaling, with therapeutic efficacy and immune modulation in\npatients. Importantly, a phosphatidylcholine metabolism–driven LSC population is implicated in\nQUIZOM resistance.\nThe manuscript is comprehensive and impactful. The authors have also provided thoughtful and\ndetailed responses to previous reviewer comments. However, several new scientific points and\nclarifications are necessary to fully support the claims and broaden the manuscript’s translational\nrelevance.\nMajor Comments\n1. Can phosphatidylcholine (PC) supplementation induce QUIZOM resistance in AML?\nThe manuscript identifies PLD1-mediated PC metabolism as a key pathway associated with\nQUIZOM resistance. However, it remains unclear whether supplementation with\nphosphatidylcholine itself is sufficient to induce resistance in AML cell lines, mirroring the effect\nof PLD1 overexpression.\nRecommendation: Authors should test or at least discuss the potentials of exogenous PC for its\neffect on QUIZOM sensitivity and whether it recapitulates the protective effect seen with PLD1\noverexpression (e.g., Figure 6S). This would help establish the functional significance of PC\nmetabolism in resistance.\nWe thank the reviewer for the insightful suggestion. We have since then examined the effect of\nphosphatidylcholine supplement and found that it significantly reduced QUIZOM-mediated growth\nsuppression and apoptosis induction (Extended Data Fig. 6J and 6K). These new observations further\nstrengthened our proposition in the initial submission that activation of PLD1-governed PC\nmetabolism can confer QUIZOM resistance in AML.\nThe manuscript has been revised as follows:\nResults, Page 11, Line 413\n“Supplementation of phosphatidylcholine (PC), the substrate of PLD1, also induced QUIZOM resistance\nin AML (Extended Data Fig. 6J and 6K)”\nExtended Data Fig. 6J\nExtended Data Fig. 6K\nLegend to Extended Data Fig. 6J and 6K\n“(J-K) Supplement of phosphatidylcholine (PC) (10uM) ameliorated (J) growth suppression and (K)\napoptosis induction by QUIZOM (2nM quizartinib and 5nM OM) in MV4-11 and MOLM-13 cell lines (Paired\nt test, SEM shown as error bar, n=3).”\n2. Does PC supplementation reduce protein aggregate formation after QUIZOM treatment?\nThe authors show that PLD1 and HSP70 expression reduce protein aggregates in resistant cells.\nHowever, it is unknown whether PC supplementation itself could mitigate proteotoxic stress\ninduced by QUIZOM.\nRecommendation: If such data exist, they should be presented. Otherwise, the authors should\nspeculate on this mechanism as part of the discussion that link lipid metabolism to proteostasis.\nThank you for the insightful suggestion. In fact, we had data to show that phosphatidylcholine\nsupplement reduced the abundance of protein aggregates in FLT3-ITD AML cell lines upon QUIZOM\ntreatment (Extended Data Fig. 6L), underscoring the importance of protein aggregates in mediating\nthe therapeutic effects of QUIZOM.\nThe manuscript has been revised as follows:\nResults, Page 11, Line 414\n“Furthermore, PC significantly reduced QUIZOM-induced formation of protein aggregates (Extended\nData Fig. 6L).”\nExtended Data Fig. 6L\nLegend to Extended Data Fig. 6L\n“(L) Supplement of phosphatidylcholine (PC) (10uM) ameliorated the formation of protein aggregates by\nQUIZOM (2nM quizartinib and 5nM OM) in MV4-11 and MOLM-13 cell lines (Paired t test, SEM shown as\nerror bar, n=3).”\n3. Does QUIZOM regulate PLD1 expression directly?\nThe manuscript suggests PLD1 expression is a feature of the resistant LSC phenotype, but it\nremains unclear whether PLD1 levels are modulated by QUIZOM treatment in parental or bulk\nAML populations.\nRecommendation: Please clarify whether QUIZOM increases or suppresses PLD1 expression.\nClarify whether QUIZOM treatment induces or suppresses PLD1 expression in bulk or triple\npositive (TP) AML populations.\nThank you for the comment. We have examined the transcriptome data in AML cell lines upon\nQUIZOM treatment and found that PLD1 expression was increased in FLT3-ITD AML. It suggested the\nmetabolic remodeling might be a compensatory pathway upon the inhibition of mitochondrial\nrespiration and protein folding by QUIZOM. Therefore, upregulation of PLD1 at relapse might be the\nconsequence of both QUIZOM-mediated transcriptomic change and selection of PLD1-high LSC\nsubclone during QUIZOM treatment in patients.\nWe have incorporated this information and the revised manuscript should now read:\nDiscussion, Page 13, Line 512:\n“More importantly, additional somatic mutation was not observed at relapse, instead, the transcriptome\ndata revealed that QUIZOM treatment upregulated PLD1 expression in FLT3-ITD AML cell lines. It\nsuggested that QUIZOM resistance at relapse might be the consequence of both QUIZOM-mediated\ntranscriptomic change and selection of PLD1-high LSC subclone during QUIZOM treatment in patients.”\n4. Is PLD1 upregulation at relapse associated with additional somatic mutations?\nThe study highlights PLD1 as a resistance marker, but does not clearly address whether its\nupregulation is associated with clonal evolution at relapse.\nRecommendation: Please evaluate whether PLD1 upregulation correlates with acquisition of\nadditional mutations in relapse samples, or clarify if it reflects transcriptional reprogramming of\npre-existing clones.\nWe have examined the somatic mutations at relapse. However, there was no significant change in\ngene mutations between pretreatment and relapse samples. The new data analysis was now shown in\nExtended Data Fig. 5A.\nWe have incorporated this information and the revised manuscript should now read:\nResults, Page 7, Line 286\n“However, there was no significant change in the top 10 common mutations in the cohort between\npretreatment and relapse samples, indicating the genetic changes might not account for the emergence\nof QUIZOM resistance (Extended Data Fig. 5A).”\nDiscussion, Page 13, Line 512\n“More importantly, additional somatic mutation was not observed at relapse, instead…..”\nExtended Data Fig. 5A\nLegend to Extended Data Fig. 5A\nNo significant changes were observed in the top 10 most common mutations in the cohort between\npre-treatment and relapse samples. Y-axis represents log10 (“the percentage of cells carrying the\nspecified mutations” + 1) (Paired t test, n=9).\n5. How can the described metabolic remodeling on immune microenvironment be potentially\nharnessed for rational treatment design?\nThe authors provide compelling evidence for cGAS-STING activation and CD8+ T cell responses in\nclinical responders. However, the broader implications of metabolic remodeling on the immune\nmicroenvironment deserve further discussion.\nRecommendation: The authors should expand their discussion on the role of tumor metabolism\nin shaping immune responses, including how mitochondrial collapse, SASP cytokines, and lipid\nmetabolism may influence anti-leukemic immunity. This would help contextualize the clinical\nsignificance of QUIZOM-induced immune modulation and provide rationale for combination with\nimmune checkpoint inhibitors or metabolic adjuvants.\nPoints very well taken. We have now revised the Discussion to highlight the clinical significance of\nQUIZOM with particular reference to the role of QUIZOM-driven metabolic remodeling, immune\nmodulation and lipid-mediated QUIZOM resistance. The manuscript should now read:\nDiscussion, Page 11, Line 444\n“First, we found that the overreactive translational activity in FLT3-ITD AML supported the protein\nrefolding machinery via overexpressing HSP70. Combined inhibition of FLT3-ITD signal and protein\nsynthesis (QUIZOM treatment) synergistically reduced HSP70 expression and therefore suppressed the\nprotein refolding activity, exerted proteostatic stress, and subsequently induced DNA damage by\naccumulation of misfolded protein, i.e. protein aggregates. Simultaneously, QUIZOM disrupted\nmitochondria which generates ATP for maintaining the bioenergetic pathway for supporting leukaemic\nsurvival and also the proper function of ATP-dependent HSP7037. Therefore, QUIZOM potentiated the\naccumulation of protein aggregates via ablating mitochondria-HSP70 axis. Induction of protein\naggregates by HSP70 inhibition has been reported in lymphoma and colon cancer.38,39 Therapeutic\ntargeting of HSP70 has been reported in FLT3-ITD AML38 but the mechanism is unclear. Our results\nsupported the proposition of a novel mitochdonria-HSP70-protein aggregates-DNA damage axis…...”\nDiscussion, Page 12, Line 461\n“Second, we demonstrated the cell extrinsic effect of QUIZOM treatment in FLT3-ITD AML. QUIZOM\ndisrupted the proteostatic and mitochondrial homeostasis that resulted in the formation of protein\naggregates in FLT3-ITD AML. The mitochondrial collapse and protein aggregates then potentiated the\nactivation of cGAS-STING pathway and subsequent increase in the expression of SASP cytokines.”\nDiscussion, Page 13, Line 496\n“Specifically, our single-cell RNAseq data on the clinical cohorts revealed that FLT3-ITD AML is extremely\nheterogeneous. Subclusters in the non-responders’ HSC-MPP population showing strong LSC signature\nmight account for the drug resistance.48,49 Genes associated with quiescence (JUN), protein folding\n(HSPA1B) and lipid metabolism (PLD1) were abundant in the LSC population that might be crucial to the\nmaintenance of leukaemia stemness. However, amino acid associated oxidative phosphorylation and\nprotein translation were not active in that cluster.”\nDiscussion, Page 13, Line 507\n“The latter support the chaperone-mediated protein refolding capacity by inducing HSP70 expression and\nconferred drug resistance. Inhibition of PLD1 remodeled phosphatidylcholine metabolism and resulted\nin the accumulation of PLD1 substrate, PC-PUFA , which triggered ferroptosis in FLT3-ITD AML.”\n“Collectively, our work suggested that CD8 T cell activation or PLD1 inhibition abrogated alternative\nchaperone protein refolding machinery and might improve the treatment outcome of QUIZOM in FLT3-\nITD AML.”\n6. Did QUIZOM inhibit glycolysis in addition to mitochondrial respiration?\nWhile the authors show that QUIZOM suppresses mitochondrial function and glucose uptake, it\nis unclear whether glycolysis is directly inhibited. Given that AML cells can display metabolic\nplasticity, additional metabolic profiling is warranted. Specifically, extracellular acidification rate\n(ECAR) via Seahorse assays and/or glucose uptake and lactate production assays would be\ninformative.\nRecommendation: Please clarify whether glycolytic activity was evaluated and if QUIZOM also\nimpairs glycolysis. If not, this should be acknowledged as a limitation.\nThank you for the suggestion. From the untargeted metabolomics data of QUIZOM-treated AML cell\nlines (Figure 2K), lactate was significantly suppressed in both MV4-11 and MOLM-13. Further, in\nExtended Data Fig. 2K, QUIZOM also reduced glucose uptake, examined by 2-NBDG. We have added\nextracellular acidification rate (ECAR) analysis via Seahorse assay (Extended Data Fig. 2M and 2N)\nshowing the reduction of both basal and maximal glycolysis by QUIZOM. Collectively, the results\nsuggested the suppression of glycolysis upon QUIZOM treatment. We have incorporated the\ncomments in the revised manuscript, which should now read:\nResults, Page 6, Line 205:\n“Functionally, QUIZOM significantly reduced both glycolysis (glucose uptake and extracellular\nacidification rate (ECAR)), mitochondrial respiration and ATP level (Extended Data Fig. 2K-\n2P)……”\nExtended Data Fig. 2M\nExtended Data Fig. 2N\nLegend to Extended Data Fig. 2M and 2N\n“(M) Representative image showing the Seahorse Glycolytic stress analysis of QUIZOM\ntreatment in MV4-11 and MOLM-13.\n(N) QUIZOM reduced both basal and maximum ECAR in MV4-11 and MOLM-13 (Paired t test,\nSEM shown as error bar, n=3).”\nMinor Points\n• Explicitly mention the source and composition of the NR and PLD1 inhibitor used in rescue\nexperiments in the Methods.\nWe have revised Supplementary Table 1 and now included the information of NR and PLD1 inhibitor.\n• While the inclusion of a Phase II trial is commendable, the high CRi vs CR rate and transfusion\ndependence post-remission (as mentioned in the rebuttal) should be more transparently\ndiscussed in the main text.\nPoint was well taken. The manuscript was revised as follow.\nPage 13, Line 523\n“There were limitations to the present study. Despite the high CRc rate to QUIZOM, most cases failed\nhematological recovery. In fact, two thirds of the responding patients remained transfusion-dependent\nuntil allo-HSCT, limiting the benefits in transplantation-ineligible patients. Future trials should consider\ndose modifications and measurable residual disease-guided strategies to avoid treatment-related marrow\naplasia.”\nA schematic summary of the proposed mechanism of resistance involving PLD1, lipid metabolism,\nproteostasis, and immune escape could aid reader understanding.\nThank you for the suggestion. We have revised the graphical abstract and Extended Data Fig.\n6O for better understanding of QUIZOM resistance involving metabolism, proteostasis and\nimmune escape."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my previous comments and I have no further concerns.", "reviewer_4": "The revised manuscript by Zheng et al. has fully addressed my remaining concerns and has been substantially strengthened through the inclusion of new data and thoughtful revisions to the text. These improvements significantly enhance the clarity and impact of the study. The findings provide novel mechanistic insights with broad relevance to the fields of cancer biology and therapeutics, and are likely to be of wide interest to the readership of Nature Communications. I therefore recommend the manuscript for publication."}, "author_response": ""}]

Decision letter