The Glycolytic Gatekeeper PDK1 defines different metabolic states between genetically distinct subtypes of human acute myeloid leukemia

Acute myeloid leukemia remains difficult to treat due to strong genetic heterogeneity between and within individual patients. Here, we show that Pyruvate dehydrogenase kinase 1 (PDK1) acts as a targetable determinant of different metabolic states in acute myeloid leukemia (AML). PDK1low AMLs are OXPHOS-driven, are enriched for leukemic granulocyte-monocyte progenitor (L-GMP) signatures, and are associated with FLT3-ITD and NPM1cyt mutations. PDK1high AMLs however are OXPHOSlow, wild type for FLT3 and NPM1, and are enriched for stemness signatures. Metabolic states can even differ between genetically distinct subclones within individual patients. Loss of PDK1 activity releases glycolytic cells into an OXPHOS state associated with increased ROS levels resulting in enhanced apoptosis in leukemic but not in healthy stem/progenitor cells. This coincides with an enhanced dependency on glutamine uptake and reduced proliferation in vitro and in vivo in humanized xenograft mouse models. We show that human leukemias display distinct metabolic states and adaptation mechanisms that can serve as targets for treatment.

The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Jan Jacob Schuringa January 24th 2022 no software was used for data collection The following software was used for data analyses: Corel draw 2019: Corel TM (www.coreldraw.com) was used for the generation of Figures. Prism 8 Graphpad (www.graphpad.com) was used for statistical tests for all bar graphs FlowJo v10.0.6 TreeStar (www.flowjo.com) was used for ana;yse flow cytometry data LC-MS/MS data was analyzed using Analyst 1.7 (AbSciex, Framingham, MA, USA) MetIDQ™ Biocrates Life Sciences (www.biocrates.com) was used for analyzing Mass Spectrometry Based Targeted Metabolomics Assay generated data. MyIQ Bio-Rad (www.bio-rad.com) was used for analyzing Q-RT-PCR data ClustVis (BETA) (https://biit.cs.ut.ee/clustvis/) was used to generate clustering heatmaps. statistical package for the social sciences (SPSS) 19.0 was used for statistical tests For GSEA studies, the GenePattern GSEAPreranked v4.1.0 module was used and statistical results are included as part of the default paramaters.

October 2018
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Source data are provided with this paper. All data are also available from the corresponding author on request. Fig.1A: LFQ proteome data is provided as Supplemental Table 2 and is available under PRIDE PXD030463. Furthermore, the following publicly available datasets were used: GSE13159 (Mile) and TCGA Cancer Genome Atlas Research3 (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) Cell biological experiments were performed in triplicate (or more when indicated in the Figures). No statistical method was used to predetermine sample size, but our sample size was similar to previous studies since in our experience this is sufficient to determine reproducible results. For the quantitative proteome studies, 6 healthy PB CD34+ samples and 42 AML patient samples were included. Also here, no statistical method was used to pre-determine sample size.
No data were excluded from analyses All data are from a minimum of 3 independent experiments Samples were randomized during data collection. For MOLM13, MV4-11 and HL60 in vivo engraftment studies, animals were randomly assigned to treatment or not treatment groups based on IVIS results ensuring that the average engraftment levels in each group were comparable. For the HuScaffold in vivo KBM7 and PDX models , mice were randamly assigned to treatment or non-treatment groups.
Investigators were not blinded to the experiments as the researchers need to collect samples based on the treatment and cell type information. Commercially available cell lines were obtained from the ATCC or DSMZ. Cell lines were not externally authenticated thereafter.

CD34-APC BD
Our cell lines routinely tested negative for mycoplasma contamination.
No cell lines used in this study were found in the database of commonly misidentified cell lines that is maintained by ICLAC and NCBI Biosample.
6-to 8-week-old female NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) mice were used for the experiments. All animals were housed under specific pathogen free conditions in individually ventilated cages during the whole experiment and were maintained according to the Guide for Care and Use of Laboratory Animals of the National Research Council, USA, and to the National Council of Animal Experiment Control recommendations.
The study did not involve wild animals The study did not involve samples collected from the field All our animal studies were performed in accordance with national and institutional guidelines. All experiments were approved by the Animal Ethics Committee of the University of Sao Paulo and by Central Animal Facility University Medical Center Groningen (#067/2018).