Dependence on mitochondrial respiration of malignant T cells reveals a new therapeutic target for angioimmunoblastic T-cell lymphoma.
Journal
Cell death discovery
ISSN: 2058-7716
Titre abrégé: Cell Death Discov
Pays: United States
ID NLM: 101665035
Informations de publication
Date de publication:
19 Jun 2024
19 Jun 2024
Historique:
received:
24
03
2024
accepted:
05
06
2024
revised:
01
06
2024
medline:
20
6
2024
pubmed:
20
6
2024
entrez:
19
6
2024
Statut:
epublish
Résumé
Cancer metabolic reprogramming has been recognized as one of the cancer hallmarks that promote cell proliferation, survival, as well as therapeutic resistance. Up-to-date regulation of metabolism in T-cell lymphoma is poorly understood. In particular, for human angioimmunoblastic T-cell lymphoma (AITL) the metabolic profile is not known. Metabolic intervention could help identify new treatment options for this cancer with very poor outcomes and no effective medication. Transcriptomic analysis of AITL tumor cells, identified that these cells use preferentially mitochondrial metabolism. By using our preclinical AITL mouse model, mimicking closely human AITL features, we confirmed that T follicular helper (Tfh) tumor cells exhibit a strong enrichment of mitochondrial metabolic signatures. Consistent with these results, disruption of mitochondrial metabolism using metformin or a mitochondrial complex I inhibitor such as IACS improved the survival of AITL lymphoma-bearing mice. Additionally, we confirmed a selective elimination of the malignant human AITL T cells in patient biopsies upon mitochondrial respiration inhibition. Moreover, we confirmed that diabetic patients suffering from T-cell lymphoma, treated with metformin survived longer as compared to patients receiving alternative treatments. Taking together, our findings suggest that targeting the mitochondrial metabolic pathway could be a clinically efficient approach to inhibit aggressive cancers such as peripheral T-cell lymphoma.
Identifiants
pubmed: 38897995
doi: 10.1038/s41420-024-02061-9
pii: 10.1038/s41420-024-02061-9
doi:
Types de publication
Journal Article
Langues
eng
Pagination
292Informations de copyright
© 2024. The Author(s).
Références
Jose C, Bellance N, Rossignol R. Choosing between glycolysis and oxidative phosphorylation: a tumor’s dilemma? Biochim Biophys Acta. 2011;1807:552–61.
doi: 10.1016/j.bbabio.2010.10.012
pubmed: 20955683
Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–33.
doi: 10.1126/science.1160809
van der Windt GJW, Pearce EL. Metabolic switching and fuel choice during T-cell differentiation and memory development. Immunol Rev. 2012;249:27–42.
doi: 10.1111/j.1600-065X.2012.01150.x
pubmed: 22889213
pmcid: 3645891
Zhang L, Yao Y, Zhang S, Liu Y, Guo H, Ahmed M, et al. Metabolic reprogramming toward oxidative phosphorylation identifies a therapeutic target for mantle cell lymphoma. Sci Transl Med. 2019;11:eaau1167.
doi: 10.1126/scitranslmed.aau1167
pubmed: 31068440
Patsoukis N, Weaver JD, Strauss L, Herbel C, Seth P, Boussiotis VA. Immunometabolic regulations mediated by coinhibitory receptors and their impact on T cell immune responses. Front Immunol. 2017;8:330.
doi: 10.3389/fimmu.2017.00330
pubmed: 28443090
pmcid: 5387055
Lunning MA, Vose JM. Angioimmunoblastic T-cell lymphoma: the many-faced lymphoma. Blood. 2017;129:1095–102.
doi: 10.1182/blood-2016-09-692541
pubmed: 28115369
Fujisawa M, Chiba S, Sakata-Yanagimoto M. Recent progress in the understanding of angioimmunoblastic T-cell lymphoma. J Clin Exp Hematop. 2017;57:109–19.
doi: 10.3960/jslrt.17019
pubmed: 29279549
pmcid: 6144190
Mondragón L, Mhaidly R, De Donatis GM, Tosolini M, Dao P, Martin AR, et al. GAPDH overexpression in the T cell lineage promotes angioimmunoblastic T cell lymphoma through an NF-κB-dependent mechanism. Cancer Cell. 2019;36:268–287.e10.
doi: 10.1016/j.ccell.2019.07.008
pubmed: 31447347
Mhaidly R, Krug A, Gaulard P, Lemonnier F, Ricci J-E, Verhoeyen E. New preclinical models for angioimmunoblastic T-cell lymphoma: filling the GAP. Oncogenesis. 2020;9:73.
doi: 10.1038/s41389-020-00259-x
pubmed: 32796826
pmcid: 7427806
Krug A, Tosolini M, Madji Hounoum B, Fournié J-J, Geiger R, Pecoraro M, et al. Inhibition of choline metabolism in an angioimmunoblastic T-cell lymphoma preclinical model reveals a new metabolic vulnerability as possible target for treatment. J Exp Clin Cancer Res. 2024;43:43.
doi: 10.1186/s13046-024-02952-w
pubmed: 38321568
pmcid: 10845598
Iqbal J, Wright G, Wang C, Rosenwald A, Gascoyne RD, Weisenburger DD, et al. Gene expression signatures delineate biological and prognostic subgroups in peripheral T-cell lymphoma. Blood. 2014;123:2915–23.
doi: 10.1182/blood-2013-11-536359
pubmed: 24632715
pmcid: 4014836
Roth RB, Hevezi P, Lee J, Willhite D, Lechner SM, Foster AC, et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics. 2006;7:67–80.
doi: 10.1007/s10048-006-0032-6
pubmed: 16572319
Audet-Walsh É, Yee T, McGuirk S, Vernier M, Ouellet C, St-Pierre J, et al. Androgen-dependent repression of ERRγ reprograms metabolism in prostate cancer. Cancer Res. 2017;77:378–89.
doi: 10.1158/0008-5472.CAN-16-1204
pubmed: 27821488
Patsoukis N, Bardhan K, Chatterjee P, Sari D, Liu B, Bell LN, et al. PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat Commun. 2015;6:6692.
doi: 10.1038/ncomms7692
pubmed: 25809635
Argüello RJ, Combes AJ, Char R, Gigan J-P, Baaziz AI, Bousiquot E, et al. SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution. Cell Metab. 2020;32:1063–1075.e7.
doi: 10.1016/j.cmet.2020.11.007
pubmed: 33264598
pmcid: 8407169
Klein Geltink RI, Edwards-Hicks J, Apostolova P, O’Sullivan D, Sanin DE, Patterson AE, et al. Metabolic conditioning of CD8+ effector T cells for adoptive cell therapy. Nat Metab. 2020;2:703–16.
doi: 10.1038/s42255-020-0256-z
pubmed: 32747793
Michalek RD, Gerriets VA, Jacobs SR, Macintyre AN, MacIver NJ, Mason EF, et al. Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J Immunol. 2011;186:3299–303.
doi: 10.4049/jimmunol.1003613
pubmed: 21317389
Buck MD, Sowell RT, Kaech SM, Pearce EL. Metabolic instruction of immunity. Cell. 2017;169:570–86.
doi: 10.1016/j.cell.2017.04.004
pubmed: 28475890
pmcid: 5648021
Leone RD, Powell JD. Metabolism of immune cells in cancer. Nat Rev Cancer. 2020;20:516–31.
doi: 10.1038/s41568-020-0273-y
pubmed: 32632251
pmcid: 8041116
Molina JR, Sun Y, Protopopova M, Gera S, Bandi M, Bristow C, et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat Med. 2018;24:1036–46.
doi: 10.1038/s41591-018-0052-4
pubmed: 29892070
Bachy E, Camus V, Thieblemont C, Sibon D, Casasnovas R-O, Ysebaert L, et al. Romidepsin plus CHOP versus CHOP in patients with previously untreated peripheral T-cell lymphoma: results of the Ro-CHOP phase III study (Conducted by LYSA). J Clin Oncol. 2022;40:242–51.
doi: 10.1200/JCO.21.01815
pubmed: 34843406
Krug A, Tari G, Saidane A, Gaulard P, Ricci J-E, Lemonnier F, et al. Novel T follicular helper-like T-cell lymphoma therapies: from preclinical evaluation to clinical reality. Cancers (Basel). 2022;14:2392.
doi: 10.3390/cancers14102392
pubmed: 35625998
Yap TA, Daver N, Mahendra M, Zhang J, Kamiya-Matsuoka C, Meric-Bernstam F, et al. Complex I inhibitor of oxidative phosphorylation in advanced solid tumors and acute myeloid leukemia: phase I trials. Nat Med. 2023;29:115–26.
doi: 10.1038/s41591-022-02103-8
pubmed: 36658425
Oestreich KJ, Read KA, Gilbertson SE, Hough KP, McDonald PW, Krishnamoorthy V, et al. Bcl-6 directly represses the gene program of the glycolysis pathway. Nat Immunol. 2014;15:957–64.
doi: 10.1038/ni.2985
pubmed: 25194422
pmcid: 4226759
Ray JP, Staron MM, Shyer JA, Ho P-C, Marshall HD, Gray SM, et al. The Interleukin-2-mTORc1 Kinase Axis Defines the Signaling, Differentiation, and Metabolism of T Helper 1 and Follicular B Helper T Cells. Immunity. 2015;43:690–702.
doi: 10.1016/j.immuni.2015.08.017
pubmed: 26410627
pmcid: 4618086
Nishizawa S, Sakata-Yanagimoto M, Hattori K, Muto H, Nguyen T, Izutsu K, et al. BCL6 locus is hypermethylated in angioimmunoblastic T-cell lymphoma. Int J Hematol. 2017;105:465–9.
doi: 10.1007/s12185-016-2159-z
pubmed: 27921272
Sun L, Suo C, Li S-T, Zhang H, Gao P. Metabolic reprogramming for cancer cells and their microenvironment: beyond the Warburg effect. Biochim Biophys Acta Rev Cancer. 2018;1870:51–66.
doi: 10.1016/j.bbcan.2018.06.005
pubmed: 29959989
Imahashi N, Basar R, Huang Y, Wang F, Baran N, Banerjee PP, et al. Activated B cells suppress T-cell function through metabolic competition. J Immunother Cancer. 2022;10:e005644.
doi: 10.1136/jitc-2022-005644
pubmed: 36543374
pmcid: 9772692
Chen Z, Zhu Q, Deng X, Yao W, Zhang W, Liu W, et al. Angioimmunoblastic T-cell lymphoma with predominant CD8+ tumor-infiltrating T-cells is a distinct immune pattern with an immunosuppressive microenvironment. Front Immunol. 2022;13:987227.
doi: 10.3389/fimmu.2022.987227
pubmed: 36325319
pmcid: 9618886
Chang C-H, Qiu J, O’Sullivan D, Buck MD, Noguchi T, Curtis JD, et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell. 2015;162:1229–41.
doi: 10.1016/j.cell.2015.08.016
pubmed: 26321679
pmcid: 4864363
Chao R, Nishida M, Yamashita N, Tokumasu M, Zhao W, Kudo I, et al. Nutrient condition in the microenvironment determines essential metabolisms of CD8+ T cells for enhanced IFNγ production by metformin. Front Immunol. 2022;13:864225.
doi: 10.3389/fimmu.2022.864225
pubmed: 35844589
pmcid: 9277540
Veeramachaneni R, Yu W, Newton JM, Kemnade JO, Skinner HD, Sikora AG, et al. Metformin generates profound alterations in systemic and tumor immunity with associated antitumor effects. J Immunother Cancer. 2021;9:e002773.
doi: 10.1136/jitc-2021-002773
pubmed: 34230113
pmcid: 8261884
Wabitsch S, McCallen JD, Kamenyeva O, Ruf B, McVey JC, Kabat J, et al. Metformin treatment rescues CD8+ T-cell response to immune checkpoint inhibitor therapy in mice with NAFLD. J Hepatol. 2022;77:748–60.
doi: 10.1016/j.jhep.2022.03.010
pubmed: 35378172
pmcid: 9391315
Leca J, Fortin J, Mak TW. Illuminating the cross-talk between tumor metabolism and immunity in IDH-mutated cancers. Curr Opin Biotechnol. 2021;68:181–5.
doi: 10.1016/j.copbio.2020.11.013
pubmed: 33360716
Zhang H, Schaefer A, Wang Y, Hodge RG, Blake DR, Diehl JN, et al. Gain-of-function RHOA mutations promote focal adhesion kinase activation and dependency in diffuse gastric cancer. Cancer Discov. 2020;10:288–305.
doi: 10.1158/2159-8290.CD-19-0811
pubmed: 31771969
Kakiuchi M, Nishizawa T, Ueda H, Gotoh K, Tanaka A, Hayashi A, et al. Recurrent gain-of-function mutations of RHOA in diffuse-type gastric carcinoma. Nat Genet. 2014;46:583–7.
doi: 10.1038/ng.2984
pubmed: 24816255
Sakata-Yanagimoto M, Enami T, Yoshida K, Shiraishi Y, Ishii R, Miyake Y, et al. Somatic RHOA mutation in angioimmunoblastic T cell lymphoma. Nat Genet. 2014;46:171–5.
doi: 10.1038/ng.2872
pubmed: 24413737
Cortes JR, Ambesi-Impiombato A, Couronné L, Quinn SA, Kim CS, da Silva Almeida AC, et al. RHOA G17V induces T follicular helper cell specification and promotes lymphomagenesis. Cancer Cell. 2018;33:259–273.e7.
doi: 10.1016/j.ccell.2018.01.001
pubmed: 29398449
pmcid: 5811310
Ng SY, Brown L, Stevenson K, deSouza T, Aster JC, Louissaint A, et al. RhoA G17V is sufficient to induce autoimmunity and promotes T-cell lymphomagenesis in mice. Blood. 2018;132:935–47.
doi: 10.1182/blood-2017-11-818617
pubmed: 29769264
pmcid: 10251505
Leca J, Lemonnier F, Meydan C, Foox J, El Ghamrasni S, Mboumba D-L, et al. IDH2 and TET2 mutations synergize to modulate T Follicular Helper cell functional interaction with the AITL microenvironment. Cancer Cell. 2023;41:323–339.e10.
doi: 10.1016/j.ccell.2023.01.003
pubmed: 36736318
Birsoy K, Wang T, Chen WW, Freinkman E, Abu-Remaileh M, Sabatini DM. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell. 2015;162:540–51.
doi: 10.1016/j.cell.2015.07.016
pubmed: 26232224
pmcid: 4522279
Baccelli I, Gareau Y, Lehnertz B, Gingras S, Spinella J-F, Corneau S, et al. Mubritinib targets the electron transport chain complex I and reveals the landscape of oXPHOS dependency in acute myeloid leukemia. Cancer Cell. 2019;36:84–99.e8.
doi: 10.1016/j.ccell.2019.06.003
pubmed: 31287994
Xu Y, Xue D, Bankhead A, Neamati N. Why all the fuss about oxidative phosphorylation (OXPHOS)? J Med Chem. 2020;63:14276–307.
doi: 10.1021/acs.jmedchem.0c01013
pubmed: 33103432
pmcid: 9298160
Triggle CR, Mohammed I, Bshesh K, Marei I, Ye K, Ding H, et al. Metformin: is it a drug for all reasons and diseases? Metabolism. 2022;133:155223.
doi: 10.1016/j.metabol.2022.155223
pubmed: 35640743
Bridges HR, Jones AJY, Pollak MN, Hirst J. Effects of metformin and other biguanides on oxidative phosphorylation in mitochondria. Biochem J. 2014;462:475–87.
doi: 10.1042/BJ20140620
pubmed: 25017630
Wang NF, Jue TR, Holst J, Gunter JH. Systematic review of antitumour efficacy and mechanism of metformin activity in prostate cancer models. BJUI Compass. 2023;4:44–58.
doi: 10.1002/bco2.187
pubmed: 36569495
Krukowski K, Ma J, Golonzhka O, Laumet GO, Gutti T, van Duzer JH, et al. HDAC6 inhibition effectively reverses chemotherapy-induced peripheral neuropathy. Pain. 2017;158:1126–37.
doi: 10.1097/j.pain.0000000000000893
pubmed: 28267067
pmcid: 5435512
Ellinghaus P, Heisler I, Unterschemmann K, Haerter M, Beck H, Greschat S, et al. BAY 87-2243, a highly potent and selective inhibitor of hypoxia-induced gene activation has antitumor activities by inhibition of mitochondrial complex I. Cancer Med. 2013;2:611–24.
doi: 10.1002/cam4.112
pubmed: 24403227
pmcid: 3892793
Foretz M, Guigas B, Viollet B. Metformin: update on mechanisms of action and repurposing potential. Nat Rev Endocrinol. 2023;19:460–76.
doi: 10.1038/s41574-023-00833-4
pubmed: 37130947
Wang Y, Maurer MJ, Larson MC, Allmer C, Feldman AL, Bennani NN, et al. Impact of metformin use on the outcomes of newly diagnosed diffuse large B-cell lymphoma and follicular lymphoma. Br J Haematol. 2019;186:820–8.
doi: 10.1111/bjh.15997
pubmed: 31135975
pmcid: 6731132
Chiche J, Reverso-Meinietti J, Mouchotte A, Rubio-Patiño C, Mhaidly R, Villa E, et al. GAPDH expression predicts the response to R-CHOP, the tumor metabolic status, and the response of DLBCL patients to metabolic inhibitors. Cell Metab. 2019;29:1243–1257.e10.
doi: 10.1016/j.cmet.2019.02.002
pubmed: 30827861
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
doi: 10.1186/1471-2105-12-323
pubmed: 21816040
pmcid: 3163565
de Leval, Rickman L, Thielen DS, Reynies C, de A, Huang Y-L, et al. The gene expression profile of nodal peripheral T-cell lymphoma demonstrates a molecular link between angioimmunoblastic T-cell lymphoma (AITL) and follicular helper T (TFH) cells. Blood. 2007;109:4952–63.
doi: 10.1182/blood-2006-10-055145
pubmed: 17284527
Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50:D687–D692.
doi: 10.1093/nar/gkab1028
pubmed: 34788843
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.
doi: 10.1073/pnas.0506580102
pubmed: 16199517
pmcid: 1239896