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Mitochondrial inhibitors circumvent adaptive resistance to venetoclax and cytarabine combination therapy in acute myeloid leukemia

Abstract

Therapy resistance represents a major clinical challenge in acute myeloid leukemia (AML). Here we define a ‘MitoScore’ signature, which identifies high mitochondrial oxidative phosphorylation in vivo and in patients with AML. Primary AML cells with cytarabine (AraC) resistance and a high MitoScore relied on mitochondrial Bcl2 and were highly sensitive to venetoclax (VEN) + AraC (but not to VEN + azacytidine). Single-cell transcriptomics of VEN + AraC-residual cell populations revealed adaptive resistance associated with changes in oxidative phosphorylation, electron transport chain complex and the TP53 pathway. Accordingly, treatment of VEN + AraC-resistant AML cells with electron transport chain complex inhibitors, pyruvate dehydrogenase inhibitors or mitochondrial ClpP protease agonists substantially delayed relapse following VEN + AraC. These findings highlight the central role of mitochondrial adaptation during AML therapy and provide a scientific rationale for alternating VEN + azacytidine with VEN + AraC in patients with a high MitoScore and to target mitochondrial metabolism to enhance the sensitivity of AML cells to currently approved therapies.

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Fig. 1: Mitochondrial permeability transition pore is a key actor of early resistance to cytarabine in AML.
Fig. 2: Both AraC-induced resistance and bioenergetic capacity are rescued by VEN plus AraC combination therapy in a caspase-dependent manner.
Fig. 3: VEN improves anti-AML activity of cytarabine with different mechanisms of action from VEN + HMA in PDX models and patients.
Fig. 4: Mitochondrial OxPHOS gene signature/score and mitochondrial respiratory capacity significantly and specifically correlate with high sensitivity and response to VEN + AraC (but not VEN + HMA) in PDX models and patients with AML.
Fig. 5: Therapeutic regimen alternating VEN + AraC after VEN + AZA treatment is more efficient than multiple VEN + AZA cycles in AML in vitro and in vivo.
Fig. 6: Single-cell RNA-seq analysis uncovers specific cell states of resistance to VEN + AraC combination therapy.
Fig. 7: Resistance to VEN + AraC combination therapy is associated with ETCI remodeling and dependency in AML.

Data availability

LC–MS and IC–MS data present in Fig. 2 have been deposited on MassIVE and are acessible through accession no. MSV000087892 (http://massive.ucsd.edu/ProteoSAFe/status.jsp?task=a2860bbc66924ead868954a0d030f364). RNA-seq data from TUH AML patients presented in Extended Data Fig. 1 and in Fig. 3 are accessible through Gene Expression Omnibus accession no. GSE183329. RNA-seq data from patients with AML presented in Fig. 4 are available through the European Genome–phenome Archive (EGAS00001003820). This dataset is a restrictive dataset from the study ‘Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML’17. To access these data, contact I. Majewski (majewski@wehi.edu.au). Microarray data from CLDX MOLM14 presented in Fig. 6 are accessible through Gene Expression Omnibus accession no. GSE181792. The single-cell data discussed in Fig. 6 are accessible through Gene Expression Omnibus accession no. GSE178912. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The output bcl2 file was converted to FASTQ format by using CellRanger v.3.0.2 and then loaded in an R session with the Seurat 3.0 toolkit package86 involving the normalization and variance stabilization package sctransform87. For each PDX sample (TUH07 and TUH69), cells were filtered based on number of expressed genes (CTL, 300 < nFeatures < 5,500; AraC, 300 < nFeatures < 6,000; VEN, 300 < nFeatures < 6,500; AraC + VEN, 100 < nFeatures < 3,000). For TUH07 samples, percentage of mitochondrial reads were CTL, VEN, AraC, 0.04 < percent.mito < 0.15; AraC + VEN, 0.01 < percent.mito < 0.1. For TUH69 samples, percentage of mitochondrial reads were CTL, AraC, VEN, 0.02 < percent.mito < 0.15; AraC_VEN, 0.01 < percent.mito < 0.15. Single-Cell Signature Explorer was used for visualization of gene and signatures in the UMAP map88. Clustering was performed using Louvain algorithm implemented in Seurat, with a resolution of 0.1. Cluster markers were defined by Seurat package using a Wilcoxon statistical test. Markers with adjusted P value <0.05 were selected. GSEA for cluster markers were performed using the hypeR package89 using KEGG, Hallmark and GO databases. Area under the curve (AUC) scores for gene signatures were obtained using the R package AUCell, a method that uses the AUC to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell42. Next, we ran SCENIC42 analysis, which uses gene regulatory network inference, followed by a refinement step using cis-regulatory information, to generate a set of regulons (transcription factors and their target genes) in the scRNA-seq data. Python implementation, pySCENIC (https://github.com/aertslab/pySCENIC, v.0.9.19) was run using a Nextflow pipeline (https://github.com/aertslab/SCENICprotocol, v.0.2.0)42, which streamlined the main steps of GRN inference and refinement with pySCENIC, as well as the quantification of cellular activity. For each regulon, SCENIC calculates an AUC score for each cell and then defines a binary activity (ON/OFF) by setting up an automatic threshold. SCENIC clustering and heat maps were based on matrix of binary regulon activity. Comparison of regulon activities between clusters were based on continuous AUC score and involved a Mann–Whitney U-test and Benjamini–Hochberg correction for multiple tests. The code used in this study has been deposited in Zenodo and is available at https://zenodo.org/record/5137701#.YP7P1zqxVH4. Single-cell RNA data were processed using cellranger v.3.0.2. (https://www.10XGenomics.com/) and analyzed with the R package Seurat v.3.0 (https://satijalab.org/seurat/). GSEA for cluster markers was performed using the hypeR package11 using KEGG, Hallmark and GO databases. Gene expression signatures were extracted using Single-Cell Signature Explorer (https://fredsoftwares/products/single-cell-signature-explorer). AUC score for genes signatures were obtained using the R package AUCell. pySCENIC (https://github.com/aertslab/pySCENIC, v.0.9.19), was run using a Nextflow pipeline (https://github.com/aertslab/SCENICprotocol, v.0.2.0).

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Acknowledgements

We thank all members of mice core facilities (UMS006, ANEXPLO, Inserm) in particular M. Lulka, C. Campi and all members of the CREFRE for their support and technical assistance and V. De Mas and E. Delabesse for the management of the Biobank BRC-HIMIP (Biological Resources Centres-Inserm Midi-Pyrénées Cytothèque des hémopathies malignes), which is supported by CAPTOR (Cancer Pharmacology of Toulouse-Oncopole and Région). We thank A. Melotti and IGE3 Genomic Platform (Geneva University) as well as F. Pont and F. Lopez (Pôle Technologique du CRCT, Inserm/U1037) and F. Martins (GET, GENOToul) for bulk DNA/RNA sequencing and single-cell RNA-seq procedures, respectively. We are grateful to the GENOToul Bioinformatics Platform Toulouse Midi-Pyrenees (Bioinfo Genotoul) for providing computing resources. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2019-T19001. 13C-isotope-tracing experiments were carried out at the Rutgers Cancer Institute of New Jersey. Metabolomics was performed at the MetaToul-MetaboHUB core facility (National Infrastructure of Metabolomics and Fluxomics) under supervision of L. Peyriga, F. Bellvert and J.-C. Portais. MetaToul is part of the national infrastructure MetaboHUB-ANR-11-INBS-0010 (French National infrastructure for Metabolomics and Fluxomics; www.metabohub.fr). MetaToul is supported by grants from the Région Midi-Pyrénées, the European Regional Development Fund, the SICOVAL, the Infrastructures en Biologie Santé et Agronomie, the Centre National de la Recherche Scientifique and the Institut National de la Recherche Agronomique Team. J.-E.S. is a member of OPALE Carnot Institute at the Organization for Partnerships in Leukemia. We thank A.-M. Benot, M. Serthelon and S. Nevouet for their daily help on the administrative and financial management of our team. The authors also thank N. Mazure for fruitful discussion about mitochondrial VDAC, M. Konopleva, I. Majewski and M. Selak for critical reading of the manuscript. This work was also supported by grants from the Programme Investissement d’Avenir PSPC (IMODI), the Laboratoire d’Excellence Toulouse Cancer (TOUCAN and TOUCAN2.0; contract ANR11-LABEX), INCA (PLBIO 2020-010, DIALAML), the Fondation Toulouse Cancer Santé, the Fondation ARC, the Ligue National de Lutte Contre le Cancer, the association Prolific and the association GAEL. A.S. is a fellow of the European Regional Development Fund through the Interreg V-A Spain-France-Andorra program, project PROTEOblood (EFA360/19). C.B. has a fellowship from the Fondation ARC.

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C.B. and J.-E.S. conceived the study, designed the experiments and interpreted the results. J.-E.S. designed the research and supervised the study. C.B. developed experimental tools, performed in vitro experiments, performed in vivo treatment studies and analyzed data. E.S. developed experimental tools, performed mitochondria purifications, OxPHOS enzymatic activities and SCENITH analysis. E.S., N.G., M.S., G.C., T.F., E.B., M.G., P.M., C.L., A.S., N.A., L.J. and E.K. contributed to in vivo experiments. E.S. A.S., M.G. and M.P. collected primary samples, collected the patient information for the retrospective analysis. A.S. and M.G. contributed to animal care. Y.W. and X.S. performed LC–MS analysis. L.S. performed IC–MS experiments and analysis and contributed to LC–MS analyses. L.P.P. contributed to LC–MS analyses. L.S. contributed to data interpretation. G.C. performed NMR analysis. M.S. collected MOLM14 in vivo samples for RNA-seq. H.A.L. and C.M. sequenced single-cell RNA-seq and single-cell DNA-seq data. J.T. sequenced RNA-seq data. A.B., M.T., N.P. and T.K. performed bioinformatics analysis. C.J. performed Duolink PLA. R.J. provided SCENITH substrates and help for data analysis. Q.F. and J.K. provided matched patients with AML for metabolic correlations analyses. I.T. and A.H.W. provided RNA-seq and data for clinical patients. J.J. and F.C. provided conceptual input to data interpretation. F.V. and C.R. provided clinical expertise. F.R. and J.C.M developed single-cell tools. N.N. performed Affymetrix experiments.

Corresponding author

Correspondence to Jean-Emmanuel Sarry.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Michael Andreeff, Daniel Herranz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 AML cells rely on mitochondrially bound BCL2 to sustain OxPHOS-dependent resistance to cytarabine.

(a, b) Mitochondrial membrane potential (MMP) using TMRE probe (a) and mitochondrial using MTG probe (b), on primary AML sample from Toulouse University Hospital (TUH) at diagnosis and relapse (n=16 patients). **P=0.0013; P=0.1591 (c) Cumulative total cell tumor burden assessed in mice bone marrow and spleen at day 0 (D0) and at day 8 (D8) after AraC treatment. The experiment was performed 10 times: in 9 different PDXs (60 mg/kg) and one CLDX (MOLM14, 30 mg/kg). Each dot is a mouse (MOLM14, CTL n=5 mice, AraC n=7 mice, **P =0.0025; TUH52, CTL n=4 mice, AraC n=7 mice, P=0.4121; TUH19, CTL n=6 mice, AraC n=4 mice, **P=0.0095; TUH57, CTL n=5 mice, AraC n=7 mice, **P=0.0025; TUH11, CTL n=10 mice, AraC n=10 mice, ****P < 0.0001; TUH55, CTL n=4 mice, AraC n=4 mice, *P=0.0286; TUH10, CTL n=7 mice, AraC n=6 mice, **P=0.0012; TUH06, CTL n=5 mice, AraC n=4 mice, *P=0.0159; TUH23, CTL n=5 mice, AraC n=5 mice, **P=0.0079; TUH30, CTL n=7 mice, AraC n=6 mice, **P=0.0012;). PDXs and CLDX are ranked from lower responders (Fold Change, FC, <10) to higher responders (FC > 10). (d) BCL2 expression at diagnosis and relapse after intensive chemotherapy according to Hackl et al. 2011 (n=10 patients). *P=0.0488. (e) BCL2 gene expression in AML cells compared with ‘normal’ hematopoietic stem cells (HSC) and peripheral blood mononuclear cells (PBMC) using Bloopool database (n=44 HSC/PBMC and n=1309 AML patients). ***P=0.0004. (f) Western-Blotting for BCL2, MCL1 and actinin in 2 normal peripheral blood mononuclear cells (PBMC) and 7 different AML cells lines. The experiment was performed twice with independent experiments and a representative example is shown. (g, h) BCL2 expression in MOLM14 CLDX after in vivo AraC treatment (30 mg/kg) (experiment performed twice with independent experiments; representative image shown) (g), and in PDXs after in vivo AraC treatment (60 mg/kg) in NSG mice (experiment was performed in 4 independent PDXs) (h). (i) Western-Blotting for BCL2 and HSP90 after AraC treatment for 24H. n=9 different primary AML samples. (j) Western-Blotting for BCL2 and actinin in MOLM14 transduced with CTL or BCL2 shRNAs. The experiment was performed 4 times and a representative example is shown. (k) Basal oxygen consumption rates of MOLM14 transduced with CTL or BCL2 shRNAs (n=4 independent experiments). *P=0.0286. (l) Citrate, α-ketoglutarate (α-KG), fumarate (Fum) and malate amounts measured by IC/MS in MOLM14 cells transduced with CTL or BCL2 shRNAs. Each dot is an experimental replicate of three independent experiments (n=3). (Citrate, ****P < 0.0001; α-KG, **P=0.0042; Fum, **P=0.0017; Malate, **P=0.0035) (m) EC50 for AraC of MOLM14 transduced with CTL or BCL2 shRNAs. (n=3 independent experiments). P values: *P=0.0139. (n, o) Loss of mitochondrial membrane potential (n) using TMRE staining (*P=0.0138; **P=0.0014; ***P=0.0004) and percent of viable cells (o) (*P=0.0022; ***P=0.0004; ****P < 0.0001), following 24H of AraC treatment in MOLM14 transduced with CTL or BCL2 shRNAs. (n=4 independent experiments). (p) EC50 for venetoclax of patient resistant (R, IC50 > 0.25 μM) vs sensitive (S, IC50 < 0.25 μM) in the two different cohorts TUH (n=22 patients) and BeatAML (n=59 patients). ****P < 0.0001. (q) BCL2 gene expression in TUH (n=7 patients) and BeatAML (n=59 patients) cohorts with resistant (R, IC50 > 0.25 μM) vs sensitive (S, IC50 < 0.25 μM) patients to venetoclax, based on the median sensitivity of the BeatAML cohort. *P=0.0471; ****P < 0.0001. (r, s) Gene Set Enrichment Analysis (GSEA) of gene signatures from Molecular Signature Data Base (MSigDB) and Gene Ontology Biological Processes (GOBP) in patients from the BeatAML cohort (n=59 patients) (r) and TUH cohort (n=7 patients) (s), resistant (R, IC50 > 0.25 μM) vs sensitive (S, IC50 < 0.25 μM) to venetoclax. Data in C, E, K, L, M, N, O, P and Q are displayed as mean ± s.e.m. Data analysis in A, B, D is by two-tailed paired Wilcoxon test and in C, E, K, L, M, N, O, P, Q by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance.

Source data

Extended Data Fig. 2 Selective BCL2 inhibitor venetoclax restores sensitivity of AML cells to AraC.

(A,B,C) Concentration response for the MOLM14 (a), U937 (b) and OCI-AML3 (c) AML cell lines upon exposure to simultaneous combinations of venetoclax (VEN) and aracytine (AraC) assessed by annexin V/7AAD staining following 24H of treatment. Scale bar: cell viability 0 to 100%. The experiment was performed 3 times and a representative example is shown. (d–f) Combination index data analysis of the concentration response data in A,B and C panels. Synergy effect correspond to a CI < 1, strong synergy effect CI < 0.3 and antagonist effect CI > 1. (n=3 independent experiments). (g) Percent of viable MOLM14, U937, KG1a and OCI-AML3 cells following treatment with venetoclax (0.5 μM) and aracytine (0.5 μM) for 24H. Values are mean ± s.e.m (n=5 independent experiments). Data analysis is by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance. P values: MOLM14, CTL/VEN **P=0.0079, CTL/AraC *P=0.0159, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC **P=0.0079; U937, CTL/VEN P=0.3095, CTL/AraC *P=0.0317, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.3095; KG1a, CTL/VEN **P=0.0079, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC **P=0.0079; OCIAML3, CTL/VEN *P=0.0159, CTL/AraC P=0.3095, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.2222. (h, i) Immunoblotting of cell lines (H) and primary AML cells (I) with the indicated antibodies following 24 h of treatment with venetoclax (Cell lines: 0.5 μM; Primary cells: 50 nM) and aracytine (Cell lines: 0.5 μM, Primary cells :25 μM). The experiment was performed twice with independent experiments and a representative example is shown.

Source data

Extended Data Fig. 3 Venetoclax disrupts mitochondrially bound BCL2 and decreases mitochondrial activities without affecting mitochondrial protein expression levels.

(a) Purified mitochondria from U937, MOLM14, KG1A immunoblotted with the indicated antibodies. The experiment was performed once for 3 different AML cell lines. (b) Spare respiratory capacity of MOLM14, U937, KG1A and OCIAML3. OCIAML3, CTL/VEN *P=0.0305, CTL/AraC P=0.0635, CTL/VEN+AraC *P=0.0248, AraC/VEN+AraC **P=0.0019; U937, CTL/VEN *P=0.0203, CTL/AraC *P=0.0309, CTL/VEN+AraC P=0.5490, AraC/VEN+AraC *P=0.0329; MOLM14, CTL/VEN **P=0.0052, CTL/AraC *P=0.0361, CTL/VEN+AraC P=0.1431, AraC/VEN+AraC *P=0.0125; KG1A, CTL/VEN *P=0.0169, CTL/AraC *P=0.0106, CTL/VEN+AraC ***P=0.0009, AraC/VEN+AraC **P=0.0027. (c,d) Western-Blotting for ETC complexes and HSP90 in cell lines (c) and primary AML samples (d). Two independent experiments were performed and a representative example is shown. (e,f) Mitochondrial mass using MTG probe in cell lines (e) and in PDXs (f). OCIAML3, CTL/AraC **P=0.0079, CTL/VEN+AraC P=0.6825, AraC/VEN+AraC P=0.8413; U937, CTL/AraC P=0.1270, CTL/VEN+AraC P=0.1270, AraC/VEN+AraC P=0.3095; MOLM14, CTL/AraC P=0.1270, CTL/VEN+AraC P=0.1270, AraC/VEN+AraC P=0.8413; KG1A, CTL/AraC P=0.6825, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC **P=0.0079; TUH07, D0/AraC P=0.0519, D0/VEN+AraC P=0.0823, AraC/VEN+AraC P=0.0931; TUH35, D0/AraC **P=0.0022, D0/VEN+AraC ***P=0.0007, AraC/VEN+AraC P=0.0926; TUH30, D0/AraC **P=0.0016, D0/VEN+AraC **P=0.0016, AraC/VEN+AraC P=0.2222. (g,h) Mitochondrial membrane potential using TMRE staining in 4 different cell lines (g) and in 3 different PDXs (h). OCIAML3, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC *P=0.0159; U937, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P > 0.9999; MOLM14, CTL/AraC P=0.1270, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.1508; KG1A, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.8413; TUH07, D0/AraC P=0.3853, D0/VEN+AraC P=0.7554, AraC/VEN+AraC P=0.6688; TUH35, D0/AraC **P=0.0087, D0/VEN+AraC ***P=0.0007, AraC/VEN+AraC P=0.0813; TUH30, D0/AraC **P=0.0031, D0/VEN+AraC **P=0.0016, AraC/VEN+AraC P=0.1508. (i) Mitochondrial calcium content using rhodamin2 probe. OCIAML3, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.6905; U937, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.5476; MOLM14, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC P=0.8413; KG1A, CTL/AraC **P=0.0079, CTL/VEN+AraC **P=0.0079, AraC/VEN+AraC **P=0.0079. In B for OCIAML3 and KG1A n=3 independent experiments, for U937 and MOLM14 n=4 independent experiments. In E, G and I n=5 independent experiments. Mice number in F and H: TUH07, D0 n=5, AraC n=6, VEN+AraC n=6; for TUH35, D0 n=6, AraC n=5, VEN+AraC n=8; TUH30, D0 n=8, AraC n=5, VEN+AraC n=5. Data in B, E, F, G, H and I are displayed as mean ± s.e.m. Data analysis in B, E, F, G, H and I is by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance.

Source data

Extended Data Fig. 4 Venetoclax blocks AraC-induced OXPHOS and metabolic phenotypes.

(a) An integrated analysis of IC/MS data based on Metaboanalyst software (pathway tool) for a simplified view of contributing pathways of MOLM14 cells. (b,c) The glycolytic metabolites: glucose 6-phosphate (G6P) (n=6 independent experiments), fructose 6-phosphate (F6P), 2,3-bisphosphoglycerate (2/3PG), amounts (n=3 independent experiments) (b), and the pentose phosphate pathway (PPP) metabolites (n=3 independent experiments): 6-phosphogluconate (6PG), ribose 1-phosphate (Rib1P), sedoheptulose 7-phosphate (Sed7P) amounts (c) measured by IC/MS in MOLM14 cells. Data are represented in fold change to control. P values: G6P, CTL/VEN P=0.3648, CTL/AraC *P=0.0338, CTL/VEN+AraC P=0.1367, AraC/VEN+AraC *P=0.0261; F6P, nsP > 0.1111; 2/3PG, nsP > 0.1111; 6PG, nsP > 0.1111; Rib1P, nsP > 0.1111; Sed7P, nsP > 0.1111; Aspartate, CTL/VEN P=0.0877, CTL/AraC *P=0.0106, CTL/VEN+AraC *P=0.0267, AraC/VEN+AraC *P=0.0170; Glutamate, CTL/VEN P=0.3735, CTL/AraC *P=0.0186, CTL/VEN+AraC P=0.4883, AraC/VEN+AraC P=0.0837. (d) Aspartate and glutamate amount measured by IC/MS in MOLM14 cells. Data are represented in fold change to control. (n=4 independent experiments). (e,f,g) Basal and maximal JATP production in KG1A (e), OCIAML3 (f) and U937 (g) cells, divided into JATPglyc and JATPox using a Seahorse XF24 Extracellular Analyzer. Aggregate data from 4 independent experiments. (h) The contribution of glucose to aspartate and glutamate production in MOLM14 cells (n=2 independent experiments). Data in A, B, C, D, E, F, G, and H are assessed 24H post in vitro treatment with venetoclax (VEN, 0.5 μM) and/or cytarabine (AraC, 0.5 μM). Data in B, C, D, E, F, G, and H are displayed as mean ± s.e.m. Data analysis in B, C and D is by two-tailed paired t-test. *P < 0.05, **P < 0.01, ****P < 0.0001, ns:non-significant.

Source data

Extended Data Fig. 5 Venetoclax improves anti-AML effects of AraC without additional in vivo toxicity.

(a) Percent of loss of mitochondrial membrane potential in human AML cells in the bone marrow of leukemic mice were assessed by flow cytometry using fluorescent TMRE probe staining. P values: TUH10, D0/AraC P=0.9898, D0/VEN+AraC ****P < 0.0001, AraC/VEN+AraC **** P < 0.0001; TUH28, D0/AraC P=0.0513, D0/VEN+AraC ***P=0.0006, AraC/VEN+AraC P=0.0734; TUH35, D0/AraC P=0.8182, D0/VEN+AraC ***P=0.0013, AraC/VEN+AraC *P=0.0127; TUH59, D0/AraC *P=0.0317, D0/VEN+AraC *P=0.0159, AraC/VEN+AraC **P=0.0079; TUH30, D0/AraC P=0.2844, D0/VEN+AraC *P=0.0451, AraC/VEN+AraC P=0.1508; TUH07, D0/AraC P=0.5368, D0/VEN+AraC **P=0.0043, AraC/VEN+AraC P=0.1320. (b,c) Change in mice weight (P values: nsP > 0.1111) (b) and murine CD45 count in bone marrow and spleen (P values: nsP > 0.1111, ****P < 0.0001) (c). (d) Change in peripheral blood indices (hematocrite, white blood cells, red blood cells and platelets) in mice during the different treatments of PDX TUH35. P values: Hematocrite and WBC, D0/AraC **P=0.0022, D0/VEN+AraC ***P=0.0007, AraC/VEN+AraC P=0.3233; RBC, nsP > 0.1111; Platelets, D0/AraC *P=0.0411, D0/VEN+AraC **P=0.0013, AraC/VEN+AraC *P=0.0293. In A, B, C and D for TUH10, D0 n=12 mice, AraC n=13 mice, VEN+AraC n=13 mice; for TUH28, D0 n=7 mice, AraC n=6 mice, VEN+AraC n=7 mice; for TUH35, D0 n=6 mice, AraC n=6 mice, VEN+AraC n=8 mice; TUH59, D0 n=4 mice, AraC n=5 mice, VEN+AraC n=5 mice; for TUH30, D0 n=8 mice, AraC n=5 mice, VEN+AraC n=5 mice; for TUH07, D0 n=5 mice, AraC n=6 mice, VEN+AraC n=6 mice. Data in A, B, C and D are assessed post in vivo treatment with venetoclax (100 mg/kg) and aracytine (30 mg/kg). (e,f) Apoptosis induction post 24H of in vitro treatment with venetoclax (50 nM) and aracytine (25 μM) (E) and immunoblotting before treatments with the indicated antibodies (F) of primary AML cells (n=18 patients). Data in A, B, C, and D are displayed as mean ± s.e.m. Data analysis in A, B, C, and D is by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance. *P < 0.05, **P < 0.01, ****P < 0.0001, ns:non-significant.

Source data

Extended Data Fig. 6 Patients with a high MitoScore are predicted to be high responders to VEN plus AraC and not to VEN plus AZA doublet therapy.

(a) Gene set enrichment analysis (GSEA) of the gene ontology biological processes (GOBP) data base at diagnosis in patient receiving the duplet therapy venetoclax plus low dose acracytine (LDAC) from the study VIALE-C (NCT03069352). RNA-seq data available through the European Genome-phenome Archive (EGAS00001003820). Dotted line at −1.3 (log10(0.05)) indicates the threshold limit of the q-value below which the gene signatures are significantly enriched. (n=19 patients). (b–d) Kaplan-Meier plots of overall survival (OS) for patients with AML treated with venetoclax in association with LDAC (n=19 patients) based on two different OxPHOS score category (MOOTHA_MITOCHONDRIA geneset, B; FARGE_HIGH_OXPHOS geneset, C, and FAO score category (FATTY_ACID_METABOLISM geneset, D). Data analysis is by log-rank Mantel-Cox’s t-test. (e) Heatmap of gene-expression values depicting CPT1a and BCL2 family members in AML patients treated with venetoclax in association with LDAC (n=19 patients) and classified either by their MitoScore (Low vs High) or by their initial response (CR+CRi vs RD). Low to high expression is represented by a change of colour from orange to green, respectively. (f) OncoPrint of genetic alterations in patients with AML treated with venetoclax in association with either LDAC (VIALE-C, n=19 patients) or HMA (VIALE-A, n=12 patients) classified in MitoScoreLow and MitoScoreHigh. (g) Data of oxygen consumption rate (OCR) in the primary sample TUH161. (n=1 with 3 technical replicates). Representation of the spare respiratory capacity (SRC) calculation.

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Extended Data Fig. 7 Single-cell transcriptomic analysis confirms the enrichment of lipids-related gene signatures in AraC-specific residual cell clusters.

(a,b) Cumulative total cell tumor burden of human viable CD45+CD33+CD44+ AML cells for PDX TUH07 (A) and TUH69 (B) was assessed in bone marrow and spleen at day 0 (D0) and at day 8 (D8) for the three different treatments by flow cytometry. Data are assessed post in vivo treatment with venetoclax (100 mg/kg) and aracytine (30 mg/kg). Data are displayed as mean ± s.e.m. TUH07, D0 n=4 mice, VEN n=6, AraC n=5 mice, VEN+AraC n=6 mice; TUH69, D0 n=5 mice, VEN n=5, AraC n=5 mice, VEN+AraC n=8 mice. P values: TUH07, D0/VEN **P=0.0095, D0/AraC *P=0.0159, D0/VEN+AraC **P=0.0095, AraC/VEN+AraC **P=0.0043; TUH69, D0/VEN P=0.1508, D0/AraC P=0.3095, D0/VEN+AraC **P=0.0016, AraC/VEN+AraC **P=0.0451. Data analysis in A and B is by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance. *P < 0.05, **P < 0.01, ****P < 0.0001, ns:non-significant. (c) Unsupervised hierarchical clustering was done by principal component analysis (PCA) in PDX TUH07 and TUH69. 9 different clusters were identified. (d) UMAP visualization of single cell from PDX TUH07 and TUH69 colored per patient (n=31,604 cells). (e) Visualization of the repartition of the total cell count per cluster with distinction of cells coming from each PDX. (f) Visualization of the repartition of the total cell count per cluster with the different conditions highlighted (Day0; Day8 following VEN; AraC; VEN plus AraC). (g) Visualization of single cell scores for CD36 gene enrichment on violin plots. The center line represents the median (Q2) and the box spans the 25th (Q1) to 75th percentiles (Q3). Minimum (Q0) and maximum (Q4) values are outside the box. D0 (Q0: −4.2108; Q1: −0.9785; Q2: 0.347; Q3: 2.3348; Q4: 5.1356), VEN (Q0: −2.7858; Q1: 0.669; Q2: 2.4531; Q3: 3.0013; Q4: 5.2046), AraC (Q0: −2.9885; Q1: −0.454; Q2: 1.8428; Q3: 3.0775; Q4: 5.019), VEN+AraC (Q0: −1.5777; Q1: 1.0628; Q2: 2.2119; Q3: 2.8247; Q4: 4.7953). (h,i) Visualization on the UMAP plot of single cell scores for OxPHOS gene signature enrichment. (H) and fatty acid metabolism gene signature enrichment (I) using Single-Cell Signature Explorer.

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Extended Data Fig. 8 VEN plus AraC specific residual cell clusters are phenotypically heterogeneous in vivo.

(a–c) Visualization on the UMAP plot of hematopoietic cell signatures that best match the three VEN+AraC clusters (#6, #7, #8) using Single Cell Explorer. Cluster #6 is monocyte-like based on gene signature created from Wu et al. (Journal of Hematology & Oncology, 2020). (A); Cluster #7 is HSC like based on Van Galen gene signature (Van Galen et al. Cell, 2019). (B) and cluster #8 is Myelo-Erythroid Progenitor (MEP) like based on gene signature created from Velten et al. NCB, 2017 (C). (d) UMAP plot displaying SCENIC clusters identified based on Gene Regulatory Network inference. Colors indicate SCENIC clusters. (e) Heatmap displaying binary activity of regulons identified by SCENIC Gene regulatory network analysis.

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Extended Data Fig. 9 VEN plus AraC specific residual cell clusters are transcriptionally (and not genetically) heterogeneous in vivo.

(a–c) Volcano plots showing active regulons identified by SCENIC gene regulatory network analysis for the Seurat clusters #6 (A), #7 (B), #8 (C). (D,E) UMAP plot displaying binary activity of top regulons MITF (d) and TP53 (e). (f) Repartition of clonal prevalence in PDX TUH69 per condition (Day0; Day8 following VEN; AraC; VEN+AraC) using single-cell mutational analysis via Mission Bio Tapestri.

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Extended Data Fig. 10 Mitochondrial inhibitors enhance anti-AML effects of VEN plus AraC in vitro.

(a,b) Percent of loss of mitochondrial membrane potential in MOLM14 (A) and U937 (B) AML cell lines following 24 h of treatment with venetoclax plus aracytine (0.5 μM; 0.5 μM) in combination or not with an electron transfer chain (ETC) complex I inhibitors (IACS-010759, 1 μM; ONC-212, 1 μM; CPI-613, 200 μM). Loss of mitochondrial membrane potential was assessed by flow cytometry using fluorescent TMRE probe staining. Data are displayed as mean ± s.e.m (n=4 independent experiments). P values: MOLM14, CTL/VEN *P=0.0327, CTL/AraC P=0.2002, CTL/VEN+AraC **P=0.0017, AraC/VEN+AraC **P=0.0016, IACS/VEN+IACS **P=0.0015, IACS/AraC+IACS **P=0.0022, IACS/VEN+AraC+IACS ****P < 0.0001, AraC+IACS/VEN+AraC+IACS ***P=0.0003, ONC/VEN+ONC ***P=0.0004, ONC/AraC+ONC *P=0.0155, ONC/VEN+AraC+ONC ***P=0.0001, AraC+ONC/VEN+AraC=ONC ****P < 0.0001, CPI/VEN+CPI P=0.0543, CPI/AraC+CPI **P=0.0047, CPI/VEN+AraC+CPI ***P=0.0002, AraC+CPI/VEN+AraC=CPI **P=0.0020; U937, CTL/VEN P=0.2558, CTL/AraC P=0.8183, CTL/VEN+AraC *P=0.0375, AraC/VEN+AraC *P=0.0472, IACS/VEN+IACS *P=0.0138, IACS/AraC+IACS P=0.1499, IACS/VEN+AraC+IACS **P=0.0081, AraC+IACS/VEN+AraC=IACS *P=0.0187, ONC/VEN+ONC ****P < 0.0001, ONC/AraC+ONC P=0.1076, ONC/VEN+AraC+ONC **P=0.0020, AraC+ONC/VEN+AraC=ONC **P=0.0021, CPI/VEN+CPI P=0.1345, CPI/AraC+CPI *P=0.0320, CPI/VEN+AraC+CPI *P=0.0136, AraC+CPI/VEN+AraC=CPI P=0.0953. Data analysis in A and B is by two-tailed unpaired Student’s t-test with Welch’s correction for unequal variance. *P < 0.05, **P < 0.01, ****P < 0.0001, ns:non-significant. (c) Gating strategy for flow cytometry analysis. Cells from the bone marrow compartment of NSG mice are selected according to the forward scatter (FSC) and side scatter (SSC) area (A) parameters. Then, doublet exclusion is performed using height (H) versus area (A) parameters of FSC and live/dead discrimination is applied using annexinV dye. The AML blast population is CD45+CD33+. The tumor burden is determined using the beads and blasts true count.

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Bosc, C., Saland, E., Bousard, A. et al. Mitochondrial inhibitors circumvent adaptive resistance to venetoclax and cytarabine combination therapy in acute myeloid leukemia. Nat Cancer 2, 1204–1223 (2021). https://doi.org/10.1038/s43018-021-00264-y

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