ABSTRACT Metabolic reprogramming is a key hallmark of cancer, but less is known about metabolic plasticity of the same tumor at different sites. Here, we investigated the metabolic

adaptation of leukemia in two different microenvironments, the bone marrow and the central nervous system (CNS). We identified a metabolic signature of fatty acid synthesis in CNS leukemia,

highlighting stearoyl-CoA desaturase (SCD) as a key player. In vivo SCD overexpression increases CNS disease, whereas genetic or pharmacological inhibition of SCD decreases CNS load.

Overall, we demonstrated that leukemic cells dynamically rewire metabolic pathways to suit local conditions and that targeting these adaptations can be exploited therapeutically. Access

access to articles $119.00 per year only $9.92 per issue Learn more Buy this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local

taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING

VIEWED BY OTHERS THE GLYCOLYTIC GATEKEEPER PDK1 DEFINES DIFFERENT METABOLIC STATES BETWEEN GENETICALLY DISTINCT SUBTYPES OF HUMAN ACUTE MYELOID LEUKEMIA Article Open access 01 March 2022

INHIBITION OF THE SUCCINYL DEHYDROGENASE COMPLEX IN ACUTE MYELOID LEUKEMIA LEADS TO A LACTATE-FUELLED RESPIRATORY METABOLIC VULNERABILITY Article Open access 19 April 2022 LIPID METABOLISM:

THE POTENTIAL THERAPEUTIC TARGETS IN GLIOBLASTOMA Article Open access 17 March 2025 DATA AVAILABILITY RNA-seq data supporting this study’s findings have been deposited in GEO (accession

number: GSE135115). The GSE135115 SuperSeries is entitled “Gene expression profiles of MLL-AF4 and TEL-AML1 acute lymphoblastic leukemia blasts retrieved from central nervous system and

spleen”. This SuperSeries contains two series related to SEM and REH experiments as follows: GSE135113 “Gene expression profiles of MLL-AF4 acute lymphoblastic leukemia blasts retrieved from

central nervous system and spleen” and GSE135114 “Gene expression profiles of TEL-AML1 acute lymphoblastic leukemia blasts retrieved from central nervous system and spleen”. Previously

published human and primograft data re-analyzed here are available under accession codes GSE60926 and GSE89710. The source data associated with each figure are provided with the manuscript.

All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper. REFERENCES * Cairns, R. A.,

Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. _Nat. Rev. Cancer_ 11, 85–95 (2011). CAS  PubMed  Google Scholar  * DeBerardinis, R. J. & Chandel, N. S. Fundamentals

of cancer metabolism. _Sci. Adv._ 2, 1–18 (2016). Google Scholar  * Cha, J.-Y. & Lee, H.-J. Targeting lipid metabolic reprogramming as anticancer therapeutics. _J. Cancer Prev._ 21,

209–215 (2017). Google Scholar  * Gisselbrecht, C. Positron emission tomography – Guided therapy of aggressive non-Hodgkin lymphoma: Standard of care after the PETAL study? _J. Clin. Oncol._

36, 3272–3273 (2018). CAS  Google Scholar  * Caro, P. et al. Metabolic signatures uncover distinct targets in molecular subsets of diffuse large B-cell lymphoma. _Cancer Cell_ 22, 547–560

(2012). CAS  PubMed  PubMed Central  Google Scholar  * Kuntz, E. M. et al. Targeting mitochondrial oxidative phosphorylation eradicates therapy-resistant chronic myeloid leukemia stem cells.

_Nat. Med._ 23, 1234–1240 (2017). CAS  PubMed  PubMed Central  Google Scholar  * Nachmias, B. & Schimmer, A. D. Metabolic flexibility in Leukemia—adapt or die. _Cancer Cell_ 34, 695–696

(2018). CAS  PubMed  Google Scholar  * Olivares, O., Däbritz, J. H. M., King, A., Gottlieb, E. & Halsey, C. Research into cancer metabolomics: towards a clinical metamorphosis. _Semin.

Cell Dev. Biol._ 43, 52–64 (2015). PubMed  Google Scholar  * Frishman-Levy, L. & Izraeli, S. Advances in understanding the pathogenesis of CNS acute lymphoblastic leukaemia and potential

for therapy. _Br. J. Haematol._ 176, 157–167 (2017). PubMed  Google Scholar  * Pui, C. H. & Howard, S. C. Current management and challenges of malignant disease in the CNS in paediatric

leukaemia. _Lancet Oncol._ 9, 257–268 (2008). PubMed  Google Scholar  * Halsey, C. et al. The impact of therapy for childhood acute lymphoblastic leukaemia on intelligence quotients;

Results of the risk-stratified randomized central nervous system treatment trial MRC UKALL XI. _J. Hematol. Oncol._ 4, 1–12 (2011). Google Scholar  * Iyer, N. S., Balsamo, L. M., Bracken, M.

B. & Kadan-Lottick, N. S. Chemotherapy-only treatment effects on long-term neurocognitive functioning in childhood ALL survivors: a review and meta-analysis. _Blood_ 126, 346–353

(2015). CAS  PubMed  Google Scholar  * Price, R. A. & Johnson, W. W. The central nervous system in childhood leukemia: I. The arachnoid. _Cancer_ 31, 520–533 (1973). CAS  PubMed  Google

Scholar  * Williams, M. T. S. et al. The ability to cross the blood-cerebrospinal fluid barrier is a generic property of acute lymphoblastic leukemia blasts. _Blood_ 127, 1998–2006 (2016).

CAS  PubMed  Google Scholar  * Bartram, J. et al. High throughput sequencing in acute lymphoblastic leukemia reveals clonal architecture of central nervous system and bone marrow

compartments. _Haematologica_ 103, e110–e114 (2018). CAS  PubMed  PubMed Central  Google Scholar  * Kato, I. et al. Hypoxic adaptation of leukemic cells infiltrating the CNS affords a

therapeutic strategy targeting VEGFA. _Blood_ 129, 3126–3129 (2017). CAS  PubMed  PubMed Central  Google Scholar  * Buonamici, S. et al. CCR7 signalling as an essential regulator of CNS

infiltration in T-cell leukaemia. _Nature_ 459, 1000–1004 (2009). CAS  PubMed  PubMed Central  Google Scholar  * Krause, S. et al. Mer tyrosine kinase promotes the survival of

t(1;19)-positive acute lymphoblastic leukemia (ALL) in the central nervous system (CNS). _Blood_ 125, 820–830 (2015). CAS  PubMed  Google Scholar  * Cario, G. et al. High interleukin-15

expression characterizes childhood acute lymphoblastic leukemia with involvement of the CNS. _J. Clin. Oncol._ 25, 4813–4820 (2007). CAS  PubMed  Google Scholar  * Williams, M. T. S. et al.

Interleukin-15 enhances cellular proliferation and upregulates CNS homing molecules in pre-B acute lymphoblastic leukemia. _Blood_ 123, 3116–3127 (2014). CAS  PubMed  Google Scholar  *

Frishman-Levy, L. et al. Central nervous system acute lymphoblastic leukemia: role of natural killer cells. _Blood_ 125, 3420–3431 (2015). CAS  PubMed  PubMed Central  Google Scholar  *

Münch, V. et al. Central nervous system involvement in acute lymphoblastic leukemia is mediated by vascular endothelial growth factor. _Blood_ 130, 643–654 (2017). PubMed  Google Scholar  *

Spector, R., Robert Snodgrass, S. & Johanson, C. E. A balanced view of the cerebrospinal fluid composition and functions: focus on adult humans. _Exp. Neurol._ 273, 57–68 (2015). CAS 

PubMed  Google Scholar  * Hühmer, A. F., Biringer, R. G., Amato, H., Fonteh, A. N. & Harrington, M. G. Protein analysis in human cerebrospinal fluid: physiological aspects, current

progress and future challenges. _Dis. Markers_ 22, 3–26 (2006). PubMed  Google Scholar  * Damkier, H. H., Brown, P. D. & Praetorius, J. Cerebrospinal fluid secretion by the choroid

plexus. _Physiol. Rev._ 93, 1847–1892 (2013). CAS  PubMed  Google Scholar  * Méndez-Ferrer, S. et al. Mesenchymal and haematopoietic stem cells form a unique bone marrow niche. _Nature_ 466,

829–834 (2010). PubMed  PubMed Central  Google Scholar  * Morrison, S. J. & Scadden, D. T. The bone marrow niche for haematopoietic stem cells. _Nature_ 505, 327–334 (2014). CAS  PubMed

  PubMed Central  Google Scholar  * Olechnowicz, S. W. Z. & Edwards, C. M. Contributions of the host microenvironment to cancer-induced bone disease. _Cancer Res._ 74, 1625–1631 (2014).

CAS  PubMed  PubMed Central  Google Scholar  * Eckhoff, E. M., Queudeville, M., Debatin, K.-M. & Meyer, L. H. A novel B cell precursor ALL cell line (018Z) with prominent neurotropism

and isolated CNS leukemia in a NOD/SCID/huALL xenotransplantation model. _Blood_ 114, 1630–1630 (2009). Google Scholar  * van der Velden, V. H. J. et al. New cellular markers at diagnosis

are associated with isolated central nervous system relapse in paediatric B-cell precursor acute lymphoblastic leukaemia. _Br. J. Haematol._ 172, 769–781 (2016). PubMed  Google Scholar  *

Theodoropoulos, P. C. et al. Discovery of tumor-specific irreversible inhibitors of stearoyl-CoA desaturase. _Nat. Chem. Biol._ 12, 218–225 (2016). CAS  PubMed  PubMed Central  Google

Scholar  * Metallo, C. M. et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. _Nature_ 481, 380–384 (2012). CAS  Google Scholar  * Angelucci, C. et al. Pivotal

role of human stearoyl-CoA desaturases (SCD1 and 5) in breast cancer progression: oleic acid-based effect of SCD1 on cell migration and a novel pro-cell survival role for SCD5. _Oncotarget_

9, 24364–24380 (2018). PubMed  PubMed Central  Google Scholar  * Hess, D., Chisholm, J. W. & Igal, R. A. Inhibition of stearoyl-CoA desaturase activity blocks cell cycle progression and

induces programmed cell death in lung cancer cells. _PLoS ONE_ 5, e11394 (2010). PubMed  PubMed Central  Google Scholar  * Wang, J. et al. High expression of stearoyl-CoA desaturase 1

predicts poor prognosis in patients with clear-cell renal cell carcinoma. _PLoS ONE_ 11, e0166231 (2016). PubMed  PubMed Central  Google Scholar  * Chen, L. et al. Stearoyl-CoA

desaturase-1-mediated cell apoptosis in colorectal cancer by promoting ceramide synthesis. _Sci. Rep._ 6, 1–11 (2016). Google Scholar  * Kim, S. J., Choi, H., Park, S. S., Chang, C. &

Kim, E. Stearoyl-CoA desaturase (SCD) facilitates proliferation of prostate cancer cells through enhancement of androgen receptor transactivation. _Mol. Cells_ 31, 371–377 (2011). CAS 

PubMed  PubMed Central  Google Scholar  * Zhang, H., Li, H., Ho, N., Li, D. & Li, S. Scd1 plays a tumor-suppressive role in survival of leukemia stem cells and the development of chronic

myeloid leukemia. _Mol. Cell Biol._ 32, 1776–1787 (2012). CAS  PubMed  PubMed Central  Google Scholar  * Southam, A. D. et al. Drug redeployment to kill leukemia and lymphoma cells by

disrupting SCD1-mediated synthesis of monounsaturated fatty acids. _Cancer Res._ 75, 2530–2540 (2015). CAS  PubMed  Google Scholar  * Imamura, K. et al. Discovery of novel and potent

stearoyl coenzyme a desaturase 1 (SCD1) inhibitors as anticancer agents. _Bioorganic Med. Chem._ 25, 3768–3779 (2017). CAS  Google Scholar  * Folger, O. et al. Predicting selective drug

targets in cancer through metabolic networks. _Mol. Syst. Biol._ 7, 1–10 (2011). Google Scholar  * Miyazaki, M., Man, W. C. & Ntambi, J. M. Targeted disruption of stearoyl-CoA

desaturase1 gene in mice causes atrophy of sebaceous and meibomian glands and depletion of wax esters in the eyelid. _J. Nutr._ 131, 2260–2268 (2001). CAS  PubMed  Google Scholar  * Brown,

J. M. & Rudel, L. L. Stearoyl-coenzyme A desaturase 1 inhibition and the metabolic syndrome: considerations for future drug discovery. _Curr. Opin. Lipidol._ 21, 192–197 (2010). CAS 

PubMed  PubMed Central  Google Scholar  * Prieto, C. et al. NG2 antigen is involved in leukemia invasiveness and central nervous system infiltration in MLL-rearranged infant B-ALL.

_Leukemia_ 32, 633–644 (2018). CAS  PubMed  Google Scholar  * Pieters, R. et al. Outcome of infants younger than 1 year with acute lymphoblastic leukemia treated with the interfant-06

protocol: results from an international phase III randomized study. _J. Clin. Oncol._ 37, 2246–2256 (2019). CAS  PubMed  Google Scholar  * Vriens, K. et al. Evidence for an alternative fatty

acid desaturation pathway increasing cancer plasticity. _Nature_ 566, 403–406 (2019). PubMed  PubMed Central  Google Scholar  * Ono, A. et al. Feedback activation of AMPK-mediated autophagy

acceleration is a key resistance mechanism against SCD1 inhibitor-induced cell growth inhibition. _PLoS ONE_ 12, e0181243 (2017). PubMed  PubMed Central  Google Scholar  * Hagedorn, N. et

al. Submicroscopic bone marrow involvement in isolated extramedullary relapses in childhood acute lymphoblastic leukemia: a more precise definition of “isolated” and its possible clinical

implications, a collaborative study of the Resistant Disease Committee. _Blood_ 110, 4022–4029 (2007). CAS  PubMed  Google Scholar  * Yuneva, M. O. et al. The metabolic profile of tumors

depends on both the responsible genetic lesion and tissue type. _Cell Metab._ 15, 157–170 (2012). CAS  PubMed  PubMed Central  Google Scholar  * Hensley, C. T. et al. Metabolic heterogeneity

in human lung tumors. _Cell_ 164, 681–694 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Kerr, E. M., Gaude, E., Turrell, F. K., Frezza, C. & Martins, C. P. Mutant Kras copy

number defines metabolic reprogramming and therapeutic susceptibilities. _Nature_ 531, 110–113 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Sciacovelli, M. & Frezza, C.

Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. _FEBS J._ 284, 3132–3144 (2017). CAS  PubMed  PubMed Central  Google Scholar  * Burrell, R. A., McGranahan, N.,

Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. _Nature_ 501, 338–345 (2013). CAS  PubMed  Google Scholar  * Sanjana, N. E., Shalem, O.

& Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. _Nat. Methods_ 11, 783–784 (2014). CAS  PubMed  PubMed Central  Google Scholar  * Ibrahimi, A. et al. Highly

efficient multicistronic lentiviral vectors with peptide 2A sequences. _Hum. Gene Ther._ 20, 845–860 (2009). CAS  PubMed  Google Scholar  * Agnese, S. T., Spierto, F. W. & Hannon, W. H.

Evaluation of four reagents for delipidation of serum. _Clin. Biochem._ 16, 98–100 (1983). CAS  PubMed  Google Scholar  * Tumanov, S. et al. Calibration curve-free GC–MS method for

quantitation of amino and non-amino organic acids in biological samples. _Metabolomics_ 12, 1–13 (2016). CAS  Google Scholar  * Mackay, G. M., Niels, L. Z., Broek, J. F. van den &

Gottlieb, E. in _Metabolic Analysis Using Stable Isotopes_ (ed., Metallo, C. M.) 171–196 (Elsevier, 2015). * Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the

comparative CT method. _Nat. Protoc._ 3, 1101–1108 (2008). CAS  PubMed  Google Scholar  * Martin, M. & N, T. Cutadapt removes adapter sequences from high-throughput sequencing reads.

_EMBnet.journal_ 17, 10–12 (2011). * Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. _Nat. Biotechnol._ 34, 525–527 (2016). CAS 

PubMed  Google Scholar  * Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. _Genome Biol._ 15, 1–21 (2014). Google

Scholar  * Warnes, G. R. et al. gplots: Various R programming tools for plotting data. R package v.3.0-1. http://CRAN.R-project.org/package=gplots (2015). Download references

ACKNOWLEDGEMENTS We thank the patients and their families who generously donated the samples used in this study to the NHS Greater Glasgow and Clyde Biorepository, Laboratory Medicine

Building, Queen Elizabeth University Hospital, the Bloodwise Childhood Leukemia Cell Bank, the Glasgow Neuroimmunology Biobank and the West of Scotland CSF Biobank. In addition, we thank J.

Goodfellow, H. Willison, S. Bhatti and Y. Yousafzai for assistance with obtaining primary samples and C. Orange and L. Stevenson for help with histology. Histology slides were scanned by the

University of Glasgow slide scanning and image analysis service at the Queen Elizabeth University Hospital. RNA-seq was performed by the Glasgow Polyomics research facility at the

University of Glasgow. We also thank K. Keeshan and the Biological Services Unit, Cancer Research UK Beatson Institute for animal assistance. We thank G. Cazzaniga for supplying PDXs, V.

Saha for providing reporter plasmids and L. Akimov, I. Muler, H. Fishman, A. Rein and E. Vax for technical assistance. This work was supported by the Chief Scientist Office (O.O. and C.H.,

grant ETM/374), Fondazione Italiana per la Ricerca sul Cancro *FIRC (A.M.S.), the William and Elizabeth Davies Foundation (A.C., Clinical Research Fellowship), the Laura and Ike Perlmutter

Fund (E.G. and I.A.), the German Israel Foundation (S.I. and C.E.), the Norman and Sadie Lee Foundation (S.I.), the Israel Science Foundation 1775/12 (E.G. and I.M.), European Union ERA NET

TRASCALL program (S.I.), Israel Cancer Research Foundation City of Hope collaborative program (S.I.) and Cancer Research UK (J.J.K. and G.M.). This project has received funding from the

European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement META-CAN No 766214 (S.I.F., J.F-G., I.M. and E.G.). AUTHOR INFORMATION Author

notes * Angela Maria Savino Present address: Molecular Pharmacology Program, Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for

Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA * Jurre J. Kamphorst Present address: Rheos Medicines, Cambridge, MA, USA * These authors contributed

equally: Angela Maria Savino, Sara Isabel Fernandes, Orianne Olivares. AUTHORS AND AFFILIATIONS * Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Angela Maria Savino, Shani Barel,

 Ifat Geron, Liron Frishman & Shai Izraeli * Sheba Medical Center, Ramat Gan, Israel Angela Maria Savino, Shani Barel, Ifat Geron, Liron Frishman, Yehudit Birger & Shai Izraeli * The

Ruth and Bruce Rappaport Faculty of Medicine, Technion–Israel Institute of Technology, Haifa, Israel Sara Isabel Fernandes, Jonatan Fernández-García, Ifat Abramovich, Inbal Mor & Eyal

Gottlieb * Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK Orianne Olivares, Antony

Cousins, Elke K. Markert & Christina Halsey * Schneider Children’s Medical Center of Israel, Petach Tiqva, Israel Anna Zemlyansky, Yehudit Birger & Shai Izraeli * Cancer Research UK

Beatson Institute, Glasgow, UK Elke K. Markert, Sergey Tumanov, Gillian MacKay & Jurre J. Kamphorst * Charite University, Berlin, Germany Cornelia Eckert * Glasgow Polyomics, College of

Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK Pawel Herzyk * Institute of Molecular, Cell and Systems Biology, College of Medical Veterinary and Life Sciences,

University of Glasgow, Glasgow, UK Pawel Herzyk * Centro Ricerca Tettamanti, Fondazione MBBM, Universita degli Studi di Milano-Bicocca, Monza, Italy Michela Bardini * Molecular Pharmacology

Program, Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New

York, NY, USA Ersilia Barin & Michael G. Kharas * Donald B. and Catherine C. Marron Cancer Metabolism Center, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York,

NY, USA Sudha Janaki-Raman & Justin R. Cross * Beckman Research Institute, City of Hope, Duarte, CA, USA Shai Izraeli Authors * Angela Maria Savino View author publications You can also

search for this author inPubMed Google Scholar * Sara Isabel Fernandes View author publications You can also search for this author inPubMed Google Scholar * Orianne Olivares View author

publications You can also search for this author inPubMed Google Scholar * Anna Zemlyansky View author publications You can also search for this author inPubMed Google Scholar * Antony

Cousins View author publications You can also search for this author inPubMed Google Scholar * Elke K. Markert View author publications You can also search for this author inPubMed Google

Scholar * Shani Barel View author publications You can also search for this author inPubMed Google Scholar * Ifat Geron View author publications You can also search for this author inPubMed 

Google Scholar * Liron Frishman View author publications You can also search for this author inPubMed Google Scholar * Yehudit Birger View author publications You can also search for this

author inPubMed Google Scholar * Cornelia Eckert View author publications You can also search for this author inPubMed Google Scholar * Sergey Tumanov View author publications You can also

search for this author inPubMed Google Scholar * Gillian MacKay View author publications You can also search for this author inPubMed Google Scholar * Jurre J. Kamphorst View author

publications You can also search for this author inPubMed Google Scholar * Pawel Herzyk View author publications You can also search for this author inPubMed Google Scholar * Jonatan

Fernández-García View author publications You can also search for this author inPubMed Google Scholar * Ifat Abramovich View author publications You can also search for this author inPubMed 

Google Scholar * Inbal Mor View author publications You can also search for this author inPubMed Google Scholar * Michela Bardini View author publications You can also search for this author

inPubMed Google Scholar * Ersilia Barin View author publications You can also search for this author inPubMed Google Scholar * Sudha Janaki-Raman View author publications You can also

search for this author inPubMed Google Scholar * Justin R. Cross View author publications You can also search for this author inPubMed Google Scholar * Michael G. Kharas View author

publications You can also search for this author inPubMed Google Scholar * Eyal Gottlieb View author publications You can also search for this author inPubMed Google Scholar * Shai Izraeli

View author publications You can also search for this author inPubMed Google Scholar * Christina Halsey View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS A.M.S., S.I.F., O.O., E.G., C.H., P.H. and S.I. designed the study. A.M.S., S.I.F., O.O., A.C., A.Z., S.B., I.G., L.F., Y.B., C.E., M.B., E.B. and S.J.R. provided the samples

and performed most of the experiments. A.M.S., S.I.F., O.O., A.C., P.H., E.K.M., J.G.-F., C.H., I.M., J.R.C., M.G.K. and E.G. analyzed and interpreted the data. S.T., I.A., J.J.K. and G.M.

performed and analyzed the MS experiments. A.M.S., S.I.F., O.O., C.H., E.G., I.M. and S.I. wrote the manuscript. CORRESPONDING AUTHORS Correspondence to Eyal Gottlieb, Shai Izraeli or

Christina Halsey. ETHICS DECLARATIONS COMPETING INTERESTS E.G. is a board member and a shareholder of Metabomed Ltd., Israel, J.J.K. is an employee and shareholder of Rheos Medicines Inc.

M.G.K. is a consultant for Accent Therapeutics and M.G.K.’s laboratory receives some financial support from 28-7. These disclosures are not directly related to these studies. All other

authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional

affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 LEUKEMIC INFILTRATES IN MURINE CNS AND BONE MARROW. A, H&E staining of paraffin-embedded leukemic (ALL) murine skull and brain. **

indicates leukemic infiltrate in calvarial bone marrow, dashed line indicates leptomeningeal space filled with leukemic infiltrate B, H&E staining of paraffin embedded murine femur

confirming widespread dense leukemic infiltrate throughout the marrow cavity. Images are representative of 8 mice. EXTENDED DATA FIG. 2 FATTY ACID SYNTHESIS-RELATED GENES ARE UPREGULATED IN

ALL CELLS DERIVED FROM THE CNS OF XENOGRAFT MODELS. A, Schematic of the RNA Sequencing workflow. Two batches of five NSG mice were xenografted with human ALL cell lines SEM [t(4;11) MLL-AFF1

(MLL-AF4)]. Post engraftment, cells were collected from central nervous system (CNS) and spleens. Before RNA extraction, CNS or spleen ALL cells from each batch were pooled to reach the

quantities required for polyA-tailed RNA sequencing. After extraction, RNA was sequenced and analyzed as described in material and methods. B, Top 20 differentially expressed coding genes in

CNS compared to spleen from RNA sequencing, excluding genes with a base mean <10, ranked according to their adjusted p-value in SEM (I) and REH (II) cells. Gene function was assigned

using NCBI Gene and linked resources. The reported p-value(s) result from a two-sided DESeq2’s Wald test and were FDR-adjusted by the Benjamini-Hochberg procedure. C, I-II Enrichment plots

of metabolism of lipids and lipoproteins (REACTOME) and oxidative phosphorylation (KEGG) for REH cell line extracted from the CNS and spleen of engrafted mice (n=2 groups of 5 mice each).

Profile of the running ES score & Positions of the Gene Set Members on the Rank Ordered List. III Statistically significant biological functions in REH cells isolated from CNS and spleen

of xenografted mice. p-values for positive association with a signature (enrichment) were calculated by permutation test. Plotted are signatures with significant fold-changes in enrichment

between the CNS versus spleen groups (log2 scale). Red bars indicate signatures with positive log fold-change (gain) in CNS versus spleen, blue bars indicate negative log fold-change (loss)

in CNS versus spleen samples. Source data EXTENDED DATA FIG. 3 SCHEMATIC OF THE FATTY ACID METABOLISM. Glucose or glutamine-derived citrate or free Acetyl-CoA serve as precursor for

saturated fatty acid, further un-saturated to provide either triglycerides or phospholipids. Saturated fatty acids can also enter cycle of degradation within the mitochondrion through

beta-oxidation. ACLY: ATP- Citrate Lyase; FASN: Fatty Acid Synthase; SCD: Stearoyl-CoA Desaturase; ACC: Acetyl CoA Carboxylase; CPT: Carnitine Palmitoyltransferase; HMGCR:

3-Hydroxy-3-Methylglutaryl-CoA Reductase; SQLE: Squalene epoxidase; TCA: Tricarboxylic acid cycle; FA: Fatty acid; FAO: Fatty acid oxidation. EXTENDED DATA FIG. 4 QUANTITATIVE PCR VALIDATION

OF TOP RANKED GENES DIFFERENTIALLY EXPRESSED IN CNS BLASTS COMPARED TO SPLEEN IN SEM AND REH CELLS. A, and 018z cells (B) p (two tailed) = one sample T and Wilcoxon test. Results are

normalized to 36B4 human housekeeping genes and presented as LogFold2 change enrichment of comparing CNS to spleen for the SEM-REH samples; human HPRT was used as housekeeping gene and

enrichment of comparing CNS to BM was calculated for 018z. n=7 for _SCD_, _FASN_, _ACLY_, _CPT1a_; n=6 for _CPT1b_; n=5 for _CPT2_ in (A). n=12 for _LDLR_, _HMGCR_, _FASN_, _ACLY_, _CPT1a_,

_CPT1b_; n=11 for _SQLE_; n=7 for _SCD_, _ABCA1_ in B, For box-and-whisker plots, boxes represent 25th and 75th percentiles, center lines indicate median values and whiskers represent

minimum and maximum values. C, Western blot of SCD and FASN proteins in SEM cells retrieved from the CNS and spleen of mice (n=4 mice). Source data EXTENDED DATA FIG. 5 ANALYSIS OF AVAILABLE

PUBLIC HUMAN DATABASES. Left side of each panel: Boxplots showing single genes differentially regulated in samples of BM from patients at diagnosis (n=22) and relapse (n=20) and CNS samples

at relapse (n=8) from public database GSE60926, unpaired analysis. Right side of each panel: Patient-derived xenograft samples established by transplantation of patient ALL cells onto NSG

mice, single dots indicate paired bone marrow and CNS. Public database GSE89710. A, ABCA1: ATP-binding cassette transporter subfamily A member 1; B, ACC: Acetyl-CoA carboxylase; C, ACLY: ATP

Citrate Lyase; D, CPT1A: Carnitine Palmitoyltransferase 1A; E, CPT1B: Carnitine Palmitoyltransferase 1B; F, CPT2: Carnitine Palmitoyltransferase 2; G, FASN: Fatty acid synthase; H, HMGCR:

3-Hydroxy-3-Methylglutaryl-CoA Reductase; I, LDLR: Low density lipoprotein receptor; J, SQLE: Squalene. FDR – false discovery rate. For box-and-whisker plots, boxes represent 25th and 75th

percentiles, center lines indicate median values and whiskers represent minimum and maximum values. Source data EXTENDED DATA FIG. 6 RATIOS OF MONOUNSATURATED FATTY ACIDS TO THEIR SATURATED

PRECURSORS. Ratios of total levels of oleic/stearic acids in total fatty acids extracts in (A) SCD-high and (C) SCD-low 018z cells, in comparison to respective controls (CTL) (n=4

independent experiments for each condition). Ratios of relative levels of oleic/stearic acids in free fatty acids extracts in (B) 018z overexpressing or (D) downregulating SCD, comparatively

to corresponding controls (CTL). n=5 independent experiments for each condition, p(two-tailed)=unpaired parametric Student’s t-test. Error bars represent mean ± s.d. Source data EXTENDED

DATA FIG. 7 INCREASED SCD ACTIVITY AND EXPRESSION UPON GENETIC MODIFICATION. A, Ratio of relative level of oleic/stearic acids in free fatty acids extracts from cells isolated from CNS of

mice engrafted with SCD overexpressing (SCD-high, n=3 mice) and control (CTL, n=4 mice) 018z cells. p(two-tailed)=unpaired parametric Student’s t-test. B, Gene expression level of SCD after

overexpression in REH cells (n=2 independent experiments). Error bars represent mean ± s.d. Source data EXTENDED DATA FIG. 8 EFFECT OF FBS DELIPIDATION ON RELATIVE CONCENTRATIONS OF

METABOLITES, USING FUMED SILICA. Relative levels of listed metabolites in lipidated (Lip) or delipidated (Delip) FBS – (A) stearic, (B) oleic, (C) palmitic, (D) palmitoleic, (E) arachidonic

and (F) linoleic acids, (G) glucose, (H) lactate, (I) glutamine and (J) glutamate. Total fatty acids were extracted by saponification and the polar metabolites were extracted in 50%

methanol, 30% acetonitrile, 20% water (“Reg extraction”). Source data EXTENDED DATA FIG. 9 BODY WEIGHT OF MICE TREATED WITH SCD1 INHIBITOR. Body weight variation of NSG mice transplanted

with PDX (A) 1, (B) 2, (C) 3, or (D) 4, treated with SW203668 (Treated, n=5 mice) or vehicle (Control, n=5 mice). Error bars represent mean ± s.d. Source data EXTENDED DATA FIG. 10 EXAMPLE

OF THE GATING STRATEGY USED FOR FLOW CYTOMETRY ANALYSIS. (A) Gating for live cells. (B) Gating to exclude doublets and cell aggregates. (C) Identification of human and mouse CD45+ specific

populations. SUPPLEMENTARY INFORMATION SUPPLEMENTARY TABLES 1–5 REPORTING SUMMARY SUPPLEMENTARY VIDEO Phenotypic representation of SCD overexpression in ALL cells. SOURCE DATA SOURCE DATA

FIG. 1 Statistical source data. SOURCE DATA FIG. 2 Statistical source data. SOURCE DATA FIG. 3 Statistical source data. SOURCE DATA FIG. 3 Unprocessed western blots. SOURCE DATA FIG. 4

Statistical source data. SOURCE DATA FIG. 4 Unprocessed western blots. SOURCE DATA FIG. 5 Statistical source data. SOURCE DATA FIG. 6 Statistical source data. SOURCE DATA EXTENDED DATA FIG.

2 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 4 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 4 Unprocessed western blots. SOURCE DATA EXTENDED DATA FIG. 5 Statistical

source data. SOURCE DATA EXTENDED DATA FIG. 6 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 7 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 8 Statistical source data.

SOURCE DATA EXTENDED DATA FIG. 9 Statistical source data. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Savino, A.M., Fernandes, S.I., Olivares, O. _et

al._ Metabolic adaptation of acute lymphoblastic leukemia to the central nervous system microenvironment depends on stearoyl-CoA desaturase. _Nat Cancer_ 1, 998–1009 (2020).

https://doi.org/10.1038/s43018-020-00115-2 Download citation * Received: 28 June 2019 * Accepted: 14 August 2020 * Published: 28 September 2020 * Issue Date: October 2020 * DOI:

https://doi.org/10.1038/s43018-020-00115-2 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not

currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative