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Deficiency of metabolic regulator PKM2 activates the pentose phosphate pathway and generates TCF1+ progenitor CD8+ T cells to improve immunotherapy

Nature Immunology (2024)Cite this article

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Abstract

TCF1high progenitor CD8+ T cells mediate the efficacy of immunotherapy; however, the mechanisms that govern their generation and maintenance are poorly understood. Here, we show that targeting glycolysis through deletion of pyruvate kinase muscle 2 (PKM2) results in elevated pentose phosphate pathway (PPP) activity, leading to enrichment of a TCF1high progenitor-exhausted-like phenotype and increased responsiveness to PD-1 blockade in vivo. PKM2KO CD8+ T cells showed reduced glycolytic flux, accumulation of glycolytic intermediates and PPP metabolites and increased PPP cycling as determined by 1,2-13C glucose carbon tracing. Small molecule agonism of the PPP without acute glycolytic impairment skewed CD8+ T cells toward a TCF1high population, generated a unique transcriptional landscape and adoptive transfer of agonist-treated CD8+ T cells enhanced tumor control in mice in combination with PD-1 blockade and promoted tumor killing in patient-derived tumor organoids. Our study demonstrates a new metabolic reprogramming that contributes to a progenitor-like T cell state promoting immunotherapy efficacy.

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Fig. 1: A genetic screen targeting glycolytic enzymes identifies PKM as a potential regulator of T cell differentiation.
Fig. 2: PKM2 is upregulated upon T cell activation in vitro and in vivo, and its deletion results in a less effector-differentiated phenotype.
Fig. 3: Loss of PKM2 results in a progenitor-exhausted-like phenotype in CD8+ T cells in NSCLC.
Fig. 4: Loss of PKM2 results in a progenitor-exhausted-like phenotype in CD8+ T cells in melanoma.
Fig. 5: PKM2 deletion generates T cells with progenitor signatures which enhance the efficacy of PD-1 checkpoint blockade.
Fig. 6: PKM2 deletion in T cells results in decreased glycolytic flux and increased pentose phosphate pathway activity.
Fig. 7: Pentose phosphate pathway agonism in T cells generates a progenitor phenotype distinct from that induced by hexokinase blockade.
Fig. 8: Pentose phosphate pathway agonism results in tumor control in murine and human model systems.

Data availability

Source data for graphically presented data have been provided as Source Data files. Critical analysis outputs and metabolomic and carbon-tracing profiling data have been provided in the Supplementary Tables, with all other data supporting the findings available from the corresponding author on reasonable request. All other reagents are available either commercially or from the corresponding author on reasonable request. RNA sequencing data are available in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/gds). Associated accession numbers are: nontreated bulk RNA sequencing, GSE218141; anti-PD-1 bulk RNA sequencing, GSE114300 (ref. 8); bulk RNA sequencing of isolated adoptively co-transferred TILs, GSE216675; and bulk RNA sequencing of TCF1 reporter eGFP+ and eGFP− T cells from in vitro co-culture and metabolic manipulation, GSE238203. Mouse reference genomes are also available with the following NCBI RefSeq assembly numbers: mm9, GCF_000001635.18; GRCm38, GCF_000001635.20; and GRCm39, GCF_000001635.27. Source data are provided with this paper.

Code availability

Code for analysis of glycolytic screen data and RNA sequencing data were generated in R using field-standard previously reported packages and indicated parameters, which are described in the Methods and with the relevant source publications cited where appropriate. R code used in this manuscript is available on reasonable request from the corresponding author. Parameters and relevant filters for Partek Flow analyses, IPA and MetaboAnalyst analyses are described in the appropriate Methods sections.

References

  1. Restifo, N. P., Dudley, M. E. & Rosenberg, S. A. Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12, 269–281 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Klebanoff, C. A., Rosenberg, S. A. & Restifo, N. P. Prospects for gene-engineered T cell immunotherapy for solid cancers. Nat. Med. 22, 26–36 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rosenberg, S. A., Parkhurst, M. R. & Robbins, P. F. Adoptive cell transfer immunotherapy for patients with solid epithelial cancers. Cancer Cell 41, 646–648 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sharma, P. & Allison, J. P. Dissecting the mechanisms of immune checkpoint therapy. Nat. Rev. Immunol. 20, 75–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Markowitz, G. J. et al. Immune reprogramming via PD-1 inhibition enhances early-stage lung cancer survival. JCI Insight https://doi.org/10.1172/jci.insight.96836 (2018).

  9. Philip, M. & Schietinger, A. CD8(+) T cell differentiation and dysfunction in cancer. Nat. Rev. Immunol. 22, 209–223 (2022).

    Article  CAS  PubMed  Google Scholar 

  10. Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Siddiqui, I. et al. Intratumoral Tcf1(+)PD-1(+)CD8(+) T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 e110 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Miller, B. C. et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. van Loosdregt, J. & Coffer, P. J. The role of WNT signaling in mature T cells: T cell factor is coming home. J. Immunol. 201, 2193–2200 (2018).

    Article  PubMed  Google Scholar 

  14. Gattinoni, L. et al. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat. Med. 15, 808–813 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Escobar, G., Mangani, D. & Anderson, A. C. T cell factor 1: a master regulator of the T cell response in disease. Sci. Immunol. https://doi.org/10.1126/sciimmunol.abb9726 (2020).

  16. Gebhardt, T., Park, S. L. & Parish, I. A. Stem-like exhausted and memory CD8(+) T cells in cancer. Nat. Rev. Cancer 23, 780–798 (2023).

    Article  CAS  PubMed  Google Scholar 

  17. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1(-)CD8(+) tumor-infiltrating T cells. Immunity 50, 181–194 e186 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kishton, R. J., Sukumar, M. & Restifo, N. P. Metabolic regulation of T cell longevity and function in tumor immunotherapy. Cell Metab. 26, 94–109 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Reina-Campos, M., Scharping, N. E. & Goldrath, A. W. CD8(+) T cell metabolism in infection and cancer. Nat. Rev. Immunol. 21, 718–738 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van der Windt, G. J. & Pearce, E. L. Metabolic switching and fuel choice during T-cell differentiation and memory development. Immunol. Rev. 249, 27–42 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang, R. & Green, D. R. Metabolic reprogramming and metabolic dependency in T cells. Immunol. Rev. 249, 14–26 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hermans, D. et al. Lactate dehydrogenase inhibition synergizes with IL-21 to promote CD8(+) T cell stemness and antitumor immunity. Proc. Natl Acad. Sci. USA 117, 6047–6055 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pearce, E. L. et al. Enhancing CD8 T-cell memory by modulating fatty acid metabolism. Nature 460, 103–107 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sukumar, M. et al. Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function. J. Clin. Invest. 123, 4479–4488 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Choi, H. et al. Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep. 10, 1187–1201 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vardhana, S. A. et al. Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen. Nat. Immunol. 21, 1022–1033 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Horton, B. L. et al. Lack of CD8(+) T cell effector differentiation during priming mediates checkpoint blockade resistance in non-small cell lung cancer. Sci. Immunol. 6, eabi8800 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. DuPage, M. et al. Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression. Cancer Cell 19, 72–85 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).

    Article  CAS  PubMed  Google Scholar 

  31. Jurica, M. S. et al. The allosteric regulation of pyruvate kinase by fructose-1,6-bisphosphate. Structure 6, 195–210 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Gattinoni, L., Powell, D. J. Jr., Rosenberg, S. A. & Restifo, N. P. Adoptive immunotherapy for cancer: building on success. Nat. Rev. Immunol. 6, 383–393 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ban, Y. et al. Radiation-activated secretory proteins of Scgb1a1 (+) club cells increase the efficacy of immune checkpoint blockade in lung cancer. Nat. Cancer 2, 919–931 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Luckey, C. J. et al. Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells. Proc. Natl Acad. Sci. USA 103, 3304–3309 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ecker, C. et al. Differential reliance on lipid metabolism as a salvage pathway underlies functional differences of T cell subsets in poor nutrient environments. Cell Rep. 23, 741–755 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pucino, V. et al. Lactate buildup at the site of chronic inflammation promotes disease by inducing CD4(+) T cell metabolic rewiring. Cell Metab. 30, 1055–1074 e1058 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Seki, S. M. et al. Modulation of PKM activity affects the differentiation of T(H)17 cells. Sci. Signal https://doi.org/10.1126/scisignal.aay9217 (2020).

  39. Kono, M. et al. Pyruvate kinase M2 is requisite for Th1 and Th17 differentiation. JCI Insight https://doi.org/10.1172/jci.insight.127395 (2019).

  40. Angiari, S. et al. Pharmacological activation of pyruvate kinase M2 inhibits CD4(+) T cell pathogenicity and suppresses autoimmunity. Cell Metab. 31, 391–405 e398 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Stincone, A. et al. The return of metabolism: biochemistry and physiology of the pentose phosphate pathway. Biol. Rev. Camb. Philos. Soc. 90, 927–963 (2015).

    Article  PubMed  Google Scholar 

  42. Bouzier-Sore, A. K. & Bolanos, J. P. Uncertainties in pentose-phosphate pathway flux assessment underestimate its contribution to neuronal glucose consumption: relevance for neurodegeneration and aging. Front. Aging Neurosci. 7, 89 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Xia, J., Psychogios, N., Young, N. & Wishart, D. S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37, W652–W660 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Daneshmandi, S. et al. Blockade of 6-phosphogluconate dehydrogenase generates CD8(+) effector T cells with enhanced anti-tumor function. Cell Rep. 34, 108831 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ghergurovich, J. M. et al. A small molecule G6PD inhibitor reveals immune dependence on pentose phosphate pathway. Nat. Chem. Biol. 16, 731–739 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lu, C. et al. G6PD functions as a metabolic checkpoint to regulate granzyme B expression in tumor-specific cytotoxic T lymphocytes. J. Immunother. Cancer https://doi.org/10.1136/jitc-2021-003543 (2022).

  49. Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hwang, S. et al. Correcting glucose-6-phosphate dehydrogenase deficiency with a small-molecule activator. Nat. Commun. 9, 4045 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Raub, A. G. et al. Small-molecule activators of glucose-6-phosphate dehydrogenase (G6PD) bridging the dimer interface. ChemMedChem 14, 1321–1324 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Horikoshi, N. et al. Long-range structural defects by pathogenic mutations in most severe glucose-6-phosphate dehydrogenase deficiency. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2022790118 (2021).

  53. Garcia, A. A. et al. Stabilization of glucose-6-phosphate dehydrogenase oligomers enhances catalytic activity and stability of clinical variants. J. Biol. Chem. 298, 101610 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yang, Q. et al. TCF-1 upregulation identifies early innate lymphoid progenitors in the bone marrow. Nat. Immunol. 16, 1044–1050 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yao, C. et al. BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8(+) T cells. Nat. Immunol. 22, 370–380 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Roychoudhuri, R. et al. BACH2 regulates CD8(+) T cell differentiation by controlling access of AP-1 factors to enhancers. Nat. Immunol. 17, 851–860 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Rao, R. R., Li, Q., Gubbels Bupp, M. R. & Shrikant, P. A. Transcription factor Foxo1 represses T-bet-mediated effector functions and promotes memory CD8(+) T cell differentiation. Immunity 36, 374–387 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kim, M. V., Ouyang, W., Liao, W., Zhang, M. Q. & Li, M. O. The transcription factor Foxo1 controls central-memory CD8+ T cell responses to infection. Immunity 39, 286–297 (2013).

    Article  CAS  PubMed  Google Scholar 

  59. Delpoux, A., Lai, C. Y., Hedrick, S. M. & Doedens, A. L. FOXO1 opposition of CD8(+) T cell effector programming confers early memory properties and phenotypic diversity. Proc. Natl Acad. Sci. USA 114, E8865–E8874 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Podaza, E. et al. Novel co-culture strategies of tumor organoids with autologous T-cells reveal clinically relevant combinations of immune-checkpoint and targeted therapies. Preprint at bioRxiv https://doi.org/10.1101/2023.07.05.546622 (2023).

  61. Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598 e1512 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Jin, J. et al. Simplified method of the growth of human tumor infiltrating lymphocytes in gas-permeable flasks to numbers needed for patient treatment. J. Immunother. 35, 283–292 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Toriyama, K. et al. T cell-specific deletion of Pgam1 reveals a critical role for glycolysis in T cell responses. Commun. Biol. 3, 394 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Siska, P. J. et al. Suppression of Glut1 and glucose metabolism by decreased Akt/mTORC1 signaling drives T cell impairment in B Cell leukemia. J. Immunol. 197, 2532–2540 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Gemta, L. F. et al. Impaired enolase 1 glycolytic activity restrains effector functions of tumor-infiltrating CD8(+) T cells. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aap9520 (2019).

  67. Chang, C. H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Telang, S. et al. Small molecule inhibition of 6-phosphofructo-2-kinase suppresses T cell activation. J. Transl. Med. 10, 95 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Quinn, W. J. et al. Lactate limits T cell proliferation via the NAD(H) redox state. Cell Rep. 33, 108500 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mehta, M. M. et al. Hexokinase 2 is dispensable for T cell-dependent immunity. Cancer Metab. 6, 10 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Gu, M. et al. NF-κB-inducing kinase maintains T cell metabolic fitness in antitumor immunity. Nat. Immunol. 22, 193–204 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ma, R. et al. A Pck1-directed glycogen metabolic program regulates formation and maintenance of memory CD8(+) T cells. Nat. Cell Biol. 20, 21–27 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8(+) T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    Article  CAS  PubMed  Google Scholar 

  74. Shan, Q. et al. Tcf1 preprograms the mobilization of glycolysis in central memory CD8(+) T cells during recall responses. Nat. Immunol. 23, 386–398 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lu, S. et al. PKM2-dependent metabolic reprogramming in CD4(+) T cells is crucial for hyperhomocysteinemia-accelerated atherosclerosis. J. Mol. Med. 96, 585–600 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Walls, J. F. et al. Metabolic but not transcriptional regulation by PKM2 is important for natural killer cell responses. eLife https://doi.org/10.7554/eLife.59166 (2020).

  78. Kahan, S. M. et al. Intrinsic IL-2 production by effector CD8 T cells affects IL-2 signaling and promotes fate decisions, stemness, and protection. Sci. Immunol. 7, eabl6322 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Ji, Y. et al. Identification of the genomic insertion site of Pmel-1 TCR α and β transgenes by next-generation sequencing. PLoS ONE 9, e96650 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chang, K., Marran, K., Valentine, A. & Hannon, G. J. Packaging shRNA retroviruses. Cold Spring Harb. Protoc. 2013, 734–737 (2013).

    Article  PubMed  Google Scholar 

  82. Jacobi, A. M. et al. Simplified CRISPR tools for efficient genome editing and streamlined protocols for their delivery into mammalian cells and mouse zygotes. Methods 121, 16–28 (2017).

    Article  PubMed  Google Scholar 

  83. Kloss, C. C. et al. Dominant-negative TGF-β receptor enhances PSMA-targeted human CAR T cell proliferation and augments prostate cancer eradication. Mol. Ther. 26, 1855–1866 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

    Article  CAS  PubMed  Google Scholar 

  86. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. R Core Team. R: A Language and environment for statistical computing (R Foundation for Statistical Computing, 2022).

  88. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2016).

  89. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    Article  CAS  PubMed  Google Scholar 

  92. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  93. Korotkevic, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2019).

  94. Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  95. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  97. Blighe, K., Rana, S. & Lewis, M. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling. GitHub https://github.com/kevinblighe/EnhancedVolcano (2018).

  98. Chen, W. W., Freinkman, E., Wang, T., Birsoy, K. & Sabatini, D. M. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166, 1324–1337 e1311 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Xiang of the Genomics Resources Core Facility, J. McCormick, T. Baumgartner and P. Byrne of the Flow Cytometry Core Facility, K. Rhee of the Department of Microbiology and Immunology and G. Zhang of the Proteomics and Metabolomics Core Facility for their professional advice and technical expertise with RNA sequencing, FACS and steady-state and carbon-tracing metabolomics experiments. We thank A. Schietinger for helpful discussions and critical manuscript review. We thank S. B. Lee for animal colony management. We thank A. Irizarry for critical support in optimizing the PDTO platform. G.J.M. was supported by postdoctoral fellowships of National Cancer Institute T32 CA203702 and the National Center For Advancing Translational Sciences of the National Institutes of Health under award number KL2-TR-002385. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was also supported by funds from The Neuberger Berman Foundation Lung Cancer Research Center; a generous gift from Jay and Vicky Furman; and generous funds donated by patients in the Division of Thoracic Surgery to N.K.A. The funding organizations played no role in experimental design, data analysis or manuscript preparation.

Author information

Author notes

  1. Diamile A. Tavarez

    Present address: Regeneron Pharmaceuticals, Tarrytown, NY, USA

  2. Enrique Podaza

    Present address: Gritstone Bio, Boston, MA, USA

  3. Michael J. P. Crowley

    Present address: SalioGen Therapeutics, Lexington, MA, USA

  4. M. Laura Martin

    Present address: Altos Labs, Redwood City, CA, USA

  5. These authors contributed equally: Yi Ban, Diamile A. Tavarez, Liron Yoffe, Enrique Podaza.

Authors and Affiliations

  1. Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York, NY, USA

    Geoffrey J. Markowitz, Yi Ban, Diamile A. Tavarez, Liron Yoffe, Yongfeng He, Mitchell T. Martin, Michael J. P. Crowley, Dingcheng Gao, Timothy E. McGraw, Nasser K. Altorki & Vivek Mittal

  2. Neuberger Berman Lung Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Geoffrey J. Markowitz, Yi Ban, Diamile A. Tavarez, Yongfeng He, Mitchell T. Martin, Michael J. P. Crowley, Dingcheng Gao, Timothy E. McGraw, Nasser K. Altorki & Vivek Mittal

  3. Department of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, USA

    Geoffrey J. Markowitz, Yi Ban, Yongfeng He, Dingcheng Gao & Vivek Mittal

  4. HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA

    Liron Yoffe, Enrique Podaza & Olivier Elemento

  5. Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA

    Liron Yoffe, Enrique Podaza, M. Laura Martin, Olivier Elemento, Timothy E. McGraw, Nasser K. Altorki & Vivek Mittal

  6. Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA

    Mitchell T. Martin, Michael J. P. Crowley, Juan R. Cubillos-Ruiz & Vivek Mittal

  7. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Tito A. Sandoval, Dingcheng Gao, Olivier Elemento, Juan R. Cubillos-Ruiz, Timothy E. McGraw, Nasser K. Altorki & Vivek Mittal

  8. Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, NY, USA

    Tito A. Sandoval & Juan R. Cubillos-Ruiz

  9. Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY, USA

    Juan R. Cubillos-Ruiz

  10. Department of Biochemistry, Weill Cornell Medicine, New York, NY, USA

    Timothy E. McGraw

Authors
  1. Geoffrey J. Markowitz
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  2. Yi Ban
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  3. Diamile A. Tavarez
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  4. Liron Yoffe
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  5. Enrique Podaza
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  6. Yongfeng He
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  7. Mitchell T. Martin
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  8. Michael J. P. Crowley
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  9. Tito A. Sandoval
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  10. Dingcheng Gao
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  11. M. Laura Martin
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  12. Olivier Elemento
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  13. Juan R. Cubillos-Ruiz
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  14. Timothy E. McGraw
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  15. Nasser K. Altorki
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  16. Vivek Mittal
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Contributions

G.J.M. and V.M. conceptualized the project and designed the experiments. G.J.M. performed the mouse experiments with assistance from Y.B., D.A.T., Y.H. and M.T.M. G.J.M., E.P., M.L.M. and O.E. designed the immunocompetent human PDTO platform and E.P. performed experiments with the samples from patients. Y.B., D.A.T., Y.H., M.T.M. and D.G. provided suggestions and technical support for experiments. D.G. aided in sorting cells. M.J.P.C. and L.Y. provided code and support for RNA sequencing analyses. T.A.S. provided technical support for Seahorse analyses. J.R.C.-R. and T.E.M. provided guidance on analysis and interpretation of metabolic data generated in the study. J.R.C.-R. provided guidance on interpretation of T cell differentiation data generated in the study. V.M. and N.K.A. provided project oversight and support. G.J.M. wrote the manuscript. G.J.M. and V.M. edited the manuscript with input from other authors. All authors discussed the results and conclusions drawn from the studies.

Corresponding author

Correspondence to Vivek Mittal.

Ethics declarations

Competing interests

J.R.C.-R. is a scientific consultant for NextRNA Therapeutics and Autoimmunity Biologic Solutions and holds patents on IRE1α modulation for the treatment of disease. O.E. is supported by Janssen, J&J, Astra-Zeneca, Volastra and Eli Lilly research grants. He is a scientific advisor and equity holder in Freenome, Owkin, Volastra Therapeutics and One Three Biotech and a paid scientific advisor to Champions Oncology and Pionyr Immunotherapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: S. Houston in collaboration with the Nature Immunology team.

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

Extended data

Extended Data Fig. 1 Glycolysis signatures are enriched in tumor-infiltrating T cells from larger tumors, both in mice treated with IgG control and in mice treated with anti-PD-1.

a, Gene set enrichment analysis using the KEGG Glycolysis / Gluconeogenesis dataset for differences in glycolytic gene signatures in tumor-infiltrating CD8 + T cells between: anti-PD-1-treated progressing (Group 3) and anti-PD-1-treated regressing (Group 2) tumors at early timepoints; anti-PD-1-treated progressing (Group 6) and anti-PD-1-treated regressing (Group 4) tumors at later timepoints; and anti-PD-1-treated progressing (Group 6) and anti-PD-1-treated partially regressing (Group 5) tumors at later timepoints. b, Heatmap showing normalized expression of selected genes from the KEGG Glycolysis / Gluconeogenesis dataset across groups. Numbers: (a-b) n = 3-8 biological replicates per group. Statistics: (a) GSEA by fgsea package in R.

Extended Data Fig. 2 Expression and presentation of Ova257-264 in ova-GFP-expressing cell lines, characterization of antigen-specific T cell phenotypes elicited by in vitro co-culture with antigen-expressing tumor cells, and pyruvate kinase isoform expression characteristics in CD8 + T cells.

a,b, Contour plots displaying PD-L1 and MHC I-Ova257-264 (left) and GFP and MHC I-Ova257-264 (right) in HKP1-ova-GFP cells (a) or B16F10-ova-GFP cells (b) with (blue) or without (red) overnight stimulation with 20 ng/mL IFNγ. c, Schematic for co-culture experiment. d, Histograms of IFNγ and GzmB expression on days 3-10 of the experiment. e, Mean fluorescence intensities of checkpoint proteins evaluated in naïve T cells and on days 5-9 of the experiment. f, Representative contour plots displaying Tox and TCF1 expression in naïve T cells (blue) and co-cultured T cells (red) at days 3-14 of the experiment. g, PKM1 and PKM2 isoform counts from bulk RNA Sequencing analysis of lung-infiltrating CD8 + T cells at different times and tumor burdens post-implantation. h, Histograms of fluorescence of PKM1 (solid) or isotype control (dotted) of naïve OT-I+Thy1.1 + T cells (blue) or from co-culture with HKP1-ova-GFP cells (red) at days 3-14 post-initial stimulation. i, Histogram of CD4 expression in CD4 + T cells with shRNA hairpins targeting CD4 (blue) or not transduced (red). j, Histogram of PKM2 expression in CD8 + T cells with shRNA hairpins targeting PKM (blue) or CD4 (red). k-l, Histograms of PKM2 (e) and PKM1 (f) expression in CD8 + T cells with sgRNA guides targeting PKM2 (blue) or non-targeting controls (red). m, Western blots for Total PKM, PKM2, PKM1, alpha-tubulin, and Histone H3 in protein lysates from intact or fractionated cells with either non-targeting control (PKM2wt) or PKM2-specific (PKM2ko) guides. Numbers: (d) n = 1 biological replicate per timepoint, experiment repeated twice; (e-f) n = 2 biological replicates per timepoint, experiment repeated twice. (g) Biological replicates: Naïve n = 3, Day 7 n = 6, Low n = 6, High n = 5. (m) n = 1 biological replicate fractionated, experiment repeated twice. Statistics: (g) Two-way ANOVA, Sidak’s multiple comparisons. Data in (g) are presented as mean ± standard deviation. Abbreviations: APC, Allophycocyanin; PECy7, Phycoerythrin-Cyanine7; PE, Phycoerythrin; PB, Pacific Blue; FITC, Fluorescein isothiocyanate; PCP5.5, Peridinin Chlorophyll-A Protein-Cyanine5.5.

Source data

Extended Data Fig. 3 Reduced proliferation, sustained elevated TCF1 expression with PKM2 knockout (PKM2KO).

a-g, Activated OT-I+Thy1.1 + T cells electroporated with guides targeting PKM2 (sgPKM2) or non-targeting controls (sgNTC), labeled with CellTrace Violet, then co-cultured with HKP1-ova-GFP until 4 days post-initial stimulation. a, Representative contour plots for Annexin V (Ann V) and CellTrace Violet expression in either control (red) or PKM2KO T cells (blue). b-c, Quantification of percent viable T cells (b) and CellTrace Violet mean fluorescence intensity (MFI) (c) in sgNTC (red) and sgPKM2 (blue) T cells. d, Representative contour plots for CD44 and CD62L expression in either control (red) or PKM2KO (blue) T cells. e-g, Quantification of: CD44 + CD62L+ as percent of Annexin V- Thy1.1+ singlets (e); Annexin V- cells as percent of CD44 + CD62L- or CD44 + CD62L+ Thy1.1+ singlets (f); and CellTrace Violet MFI in viable CD44 + CD62L- or CD44 + CD62L+ Thy1.1+ singlets (g) in sgNTC (red) and sgPKM2 (blue) T cells. h-j, Activated OT-I+Thy1.1 + T cells electroporated with guides targeting PKM2 (sgPKM2) or non-targeting controls (sgNTC), co-cultured with HKP1-ova-GFP for 6-9 days post-initial stimulation. h-i, Representative contour plots for Tox and TCF1 staining in control (red) and PKM2KO (blue) T cells at 6 (h) and 9 (i) days post-initial stimulation. j, Quantification of TCF1 lo and TCF1 hi populations from (h-i). k-n, Activated pmel-1+Thy1.1 + T cells electroporated with guides targeting PKM2 (sgPKM2) or non-targeting controls (sgNTC), co-cultured with B16F10-ova-GFP until 6 days post-initial stimulation. k-m, Representative contour plots for Tox and TCF1 (k), CD44 and CD62L (l), and TIM3 and SlamF6 (m) in control (red) or PKM2KO (blue) T cells. n, Quantification of TCF1 hi, CD62L + , and SlamF6+ populations from (k-m). Numbers: (a-g) n = 6 biological replicates per group, (h-j) n = 2–3 biological replicates per guide, 4-6 biological replicates per group, (k-n) n = 6 biological replicates per group. Statistics: (b,c,e) Unpaired two-tailed t-tests; (f,g), Two-way ANOVA, Šídák multiple comparisons; (j,n) Multiple unpaired two-tailed t-tests, Holm-Šídák multiple comparisons. Data in (b-c,e-g,j,n) are presented as mean ± standard deviation. Abbreviations: Ann V, Annexin V; APC, Allophycocyanin; FITC, Fluorescein isothiocyanate; PECy7, Phycoerythrin-Cyanine7; PE, Phycoerythrin; PB, Pacific Blue; APCCy7, Allophycocyanin-Cyanine7; AF647, Alexa Fluor 647.

Source data

Extended Data Fig. 4 Anti-PD-1 treatment results in rapid tumor control and phenotypic alterations in PKM2 wild-type (PKM2WT) and PKM2 knockout (PKM2KO) T cells.

a-c, Adoptive transfers of either PKM2WT (red) or PKM2KO (blue) activated OT-I+Thy1.1 + CD8 + T cells were performed into lymphodepleted C57Bl/6 mice 7 days after HKP1-ova-GFP implantation, with anti-PD-1 or IgG at days 10 and 14, and mice harvested at day 16 post-tumor-implantation. a, Bioluminescence imaging as counts (left) or fold of day 6 luminescence (right). b,c, Counts (c) and population quantification (d) of adoptively transferred T cells gated on TCF1 expression in draining lymph nodes (dLN) (left) or tumors (right). d–r, Adoptive co-transfers of activated PKM2WT (red) or PKM2KO (blue) OT-I+Thy1.1 + CD8 + T cells distinguished by Thy1.1 zygosity were performed into lymphodepleted C57Bl/6 mice 7 days after HKP1-ova-GFP implantation, with anti-PD-1 or IgG at days 10 and 14, and mice harvested at days 13 and 15 post-tumor-implantation. d, Bioluminescence imaging quantification in anti-PD-1- (purple) or IgG-treated (orange) mice. e-n, Quantification of populations of PKM2WT and PKM2KO CD8 + T cells from tumors: frequencies as percentages of donor (e), CD44/CD62L population proportions (g), Klrg1/CD127 population proportions (i), TCF1 gated populations (k), and IFNγ + TNFα+ proportions after restimulation (m). Representative contour plots from day 13 for each analysis (f,h,j,l,n). o–r, Quantification of Ki67 (o) and IL-2 (q) mean fluorescence intensity, normalized to average fluorescence from activated control samples, in PKM2WT and PKM2KO CD8 + T cells from tumors, with Ki67 further gated on TCF1 expression. Representative histograms for Ki67 expression from day 13 (p) and IL-2 expression from day 15 (r), with TCF1 hi (solid contours) and TCF1 lo (dotted lines) populations for Ki67. Numbers: Biological replicates per group: (a-c) n = 10; (d–r) n = 11-13, aggregate of two experiments. Statistics: (a-c) Two-way ANOVA, Tukey’s multiple comparisons; (e,k,o,r) Paired and unpaired two-tailed t-tests; (g,i,m) Multiple paired and unpaired two-tailed t-tests, Holm-Šídák multiple comparisons. Data in (a-e,g,i,k,m,o,q) are presented as mean ± standard deviation. Abbreviations: BV711, Brilliant Violet 711; BV785, Brilliant Violet 785; AF488, Alexa Fluor 488; PECy7, Phycoerythrin-Cyanine7; BV421, Brilliant Violet 421; PE, Phycoerythrin; APC, Allophycocyanin; PCP5.5, Peridinin Chlorophyll-A Protein-Cyanine5.5; FITC, Fluorescein isothiocyanate.

Source data

Extended Data Fig. 5 Characterization of sorted tumor-specific PKM2WT and PKM2KO T cells from tumors from mice treated with IgG control or anti-PD-1.

a–l, Activated OT-I+Thy1.1 + PKM2WT (NTC-2, red) or PKM2KO (Pkm2-8, blue) CD8 + T cells distinguished by Thy1.1 zygosity were adoptively co-transferred into lymphodepleted C57Bl/6 mice 7 days after orthotopic implantation of HKP1-ova-GFP tumors. 3 doses of IgG control or anti-PD-1 were administered on days 10, 14, and 17 after tumor implantation. T cells were sorted from tumors based on Thy1.1 zygosity, phenotyped by flow cytometry, and bulk RNA sequenced. a, Gating strategy for identifying adoptively co-transferred PKM2WT (Thy1.1 homozygous, red) and PKM2KO (Thy1.1 heterozygous, blue) T cells sorted back from tumors. b, Histograms of PKM2 (left) and PKM1 (right) expression in activated CD8 + T cells electroporated with guides targeting PKM2 (Pkm2-8, blue) or non-targeting control (NTC-2, red). c, Bioluminescence imaging (BLI) group averages (top) and curves from individual mice (bottom) to measure tumor burden in mice treated with IgG control (orange) or anti-PD-1 (purple) from harvests on days 12 (left) and 16 (right) post-tumor implantation. d-e, Flow cytometric analysis of PKM2WT (NTC-2, red) and PKM2KO (Pkm2-8, blue) CD8 + T cells for percentage of donor pool (d) and for CD44 + CD62L+ percentage (e) with indicated treatments and harvest timepoints. f-l, Gene set enrichment analyses examining: signatures for effector and memory cells at days 12 (left two) and 16 (right two) (f), progenitor and terminal exhaustion signatures at days 12 (g) and 16 (h), and hallmark signatures at days 12 (i) and 16 (j) in PKM2KO compared with PKM2WT; and hallmark signatures in anti-PD-1-treated mice compared with IgG at days 12 (k) and 16 (l). Numbers: (c) n = 6 biological replicates per group. (d-l) n = 3 biological replicates per group. Statistics: (d-e) paired two-tailed t-tests; (f-l) GSEA by fgsea. Data in (c) are presented as mean ± standard deviation. Abbreviations: BV711, Brilliant Violet 711; BV785, Brilliant Violet 785; SSC-A, Side Scatter-Area; FSC-A, Forward Scatter-Area; FSC-H, Forward Scatter-Height; FSC-W, Forward Scatter-Width; SSC-H, Side Scatter-Height; SSC-W, Side Scatter-Width; DAPI, 4’,6-Diamidino-2-phenylindole; FITC, Fluorescein isothiocyanate; PE, Phycoerythrin.

Source data

Extended Data Fig. 6 No effect of PKM2 deletion on fatty acid or glutamine oxidation, but accumulation of glycolytic intermediates and pentose phosphate pathway (PPP) metabolites.

a-d, Activated OT-I+ Thy1.1 + CD8 + T cells electroporated with guides targeting PKM2 (blue) or non-targeting controls (red), were co-cultured with HKP1-ova-GFP, sorted at day 6 post-initial stimulation, and underwent stress tests measuring oxygen consumption (a,c) and extracellular acidification (b,d) for fatty acid oxidation (a,b) or glutamine oxidation (c,d). e–l, Activated OT-I+Thy1.1 + CD8 + T cells were electroporated with guides targeting PKM2 (blue) or non-targeting control red), and either cultured continuously on anti-CD3ε/anti-CD28 coated plates or co-cultured with HKP1-ova-GFP tumor cells. Viable T cells were sorted, and 203 polar metabolites profiled by liquid chromatography/mass spectrometry (LC/MS). Normalized metabolite abundance is presented as fold of control abundance. Metabolites involved in glycolysis (e-h) and the PPP (i–l) are shown for T cells in the listed conditions. m-v, Activated OT-I+Thy1.1 + CD8 + T cells were electroporated with guides targeting PKM2 (blue) or non-targeting control (red), and co-cultured with HKP1-ova-GFP. Viable T cells were sorted on day 4 post-initial stimulation and labeled with 1,2 13C glucose for 2 h, and 204 polar metabolites profiled by LC/MS for abundance and labeling. Normalized metabolite abundance is presented as fold of average control abundance. (m) Metaboanalyst identification of impacted pathways. (n-v) Quantification for all 13C-labeled metabolites in glycolysis (n), the PPP (o), and the tricarboxylic acid (TCA) cycle (p), and different labeled isotopes of metabolites with significantly altered enrichment in PKM2 knockout T cells compared to control: glyceraldehyde-3-phosphate (q), 3-phosphoglyceric acid (r), phosphoenolpyruvic acid (s), ribose-5-phosphate (t), sedoheptulose 7-phosphate (u), and oxoglutaric acid (v). Numbers: Biological replicates: (a-b) n = 5–6 per guide; (c-d) n = 8-9 per guide; (e–l) n = 6 for NTC-2 24 h post, n = 4 for PKM2-8 24 h post, n = 4 for NTC-2 48 h co-culture, n = 4 for PKM2-8 48 h co-culture, n = 4 for NTC-2 96 h plate, n = 4 for PKM2-8 96 h plate, n = 3 for NTC-2 96 h co-culture, and n = 3 for PKM2-8 96 h co-culture, aggregate of two experiments; (m-v) n = 7 per group. Statistics: (e–l,n-v) Multiple unpaired two-tailed t-tests. Data in (a–l,n-v) are presented as mean ± standard deviation.

Source data

Extended Data Fig. 7 Multiple rounds of pentose phosphate pathway activity yield different labeling patterns from 1,2 13C glucose carbon tracing and higher order charges on metabolites.

a-c, Sample schematics from rounds 1 and 2; more rounds could further alter the labeling patterns. Filled circles indicate 13C, and colored outlines of circles allow tracking of carbons. a, Labeling pattern from 1 round of glycolysis and the pentose phosphate pathway. b, Labeling pattern of metabolites generated in the first round of the non-oxidative pentose phosphate pathway incorporating labeled glyceraldehyde-3-phosphate generated during glycolysis (black underline in panel a). Note the emergence of m + 4 F6P. c, Labeling pattern of metabolites generated from a second round of pentose phosphate pathway activity (recycling), with labeled fructose-6-phosphate from round one of the pentose phosphate pathway undergoing isomerization back to glucose-6-phosphate and entering the oxidative pentose phosphate pathway; and incorporating labeled glyceraldehyde-3-phosphate generated during glycolysis (black underline in panel a). Blue brackets indicate grouped reactants and products for the reactions indicated by the blue arrow. Green brackets indicate reactants for the reactions indicated by green arrows, and orange brackets for the reactions indicated by orange arrows. Labeled reactant variants (A, B, 1, 2, α, and β) combine to yield labeled variant product sets (A1, A2, B1, B2, α1, α2, β1, and β2). Note the emergence of m + 3 F6P and the different positions of 13C in metabolites, allowing for future rounds of pentose phosphate pathway activity to have more higher order charges. d, Labeling pattern for fructose-6-phosphate generated under 6 different conditions with a maximum of two rounds of pentose phosphate pathway activity. Note that charges ranging from m + 0 to m + 4 can be generated using metabolites from glycolysis and pentose phosphate pathway activity, with 13C at a variety of positions, allowing for future variant labeling patterns. Abbreviations: G6P, glucose-6-phosphate; 6PG, 6-phosphogluconate; Ru5P, ribulose-5-phosphate; Xu5P, xylulose-5-phosphate; R5P, ribose-5-phosphate; F6P, fructose-6-phosphate; G3P, glyceraldehyde-3-phosphate; S7P, sedoheptulose 7-phosphate; FBP, fructose 1,6-bisphosphate; E4P, erythrose-4-phosphate; 1,3-BPG, 1,3-bisphosphglycerate; 3PG, 3-phosphoglycerate; PEP, phosphoenolpyruvate; PGI, phosphoglucoisomerase.

Extended Data Fig. 8 Pharmacologic activation of the oxidative pentose phosphate pathway results in modest cell death, proliferative inhibition, and altered T cell differentiation.

a-g, OT-I+Thy1.1 + T cells were activated, labeled with CellTrace Violet, treated with either DMSO (red) or 3 µM glucose-6-phosphate dehydrogenase agonist AG1 (blue), and co-cultured with HKP1-ova-GFP until 4 days post-initial stimulation. a, Representative contour plots for Annexin V (Ann V) and CellTrace Violet expression. b-c, Quantification of viability (b) and CellTrace Violet mean fluorescence intensity (MFI) (c). d, Representative contour plots for CD44 and CD62L expression. e-g, Quantification of: CD44 + CD62L+ as percent of Annexin V- Thy1.1+ (e); Annexin V- viable T cells as percent of CD44/CD62L populations in Thy1.1+ cells (f); and CellTrace Violet MFI in viable CD44/CD62L populations in Thy1.1+ cells (g) in T cells from either DMSO control (red) or AG1 (blue) treatment. h-r, Human PBMCs from two different patients, 19-164 and 19-176, were labeled with CellTrace Violet, and T cells isolated and expanded with either DMSO (red) or AG1 (blue) for 3 days. h, Representative contour plots for Annexin V and CellTrace Violet expression in CD45 + CD3 + CD8 + T cells. i-j, Quantification of viability (b) and CellTrace Violet MFI in viable CD8 + T cells (c). k, Representative contour plots for CD45RA and CD62L expression. l–n, Quantification of: CD45RA/CD62L populations as percent of Annexin V- CD45 + CD3 + CD8+ (l); Annexin V- viable T cells as percent of CD45RA/CD62L populations in CD45 + CD3 + CD8+ cells (m); and CellTrace Violet MFI in viable CD45RA/CD62L populations (n). o, Representative contour plots for CD45RO and CD62L expression. p-r, Quantification of: CD45RO/CD62L populations as percent of Annexin V- CD45 + CD3 + CD8+ (p); Annexin V- viable T cells as percent of CD45RO/CD62L populations in CD45 + CD3 + CD8+ cells (q); and CellTrace Violet MFI in viable CD45RO/CD62L populations (r). Numbers: (a-g) n = 6 biological replicates per group. (h-r) n = 2–3 replicates per patient, 2 patients. Statistics: (b,c,e) Unpaired two-tailed t-tests; (f,g,l,m,n,p,q,r), Two-way ANOVA, Šídák multiple comparisons; (i,j) Multiple unpaired two-tailed t-tests, Holm-Šídák multiple comparisons. Data in (b-c,e-g,i-j,l–n,p-r) are presented as mean ± standard deviation. Abbreviations: Ann V, Annexin V; APC, Allophycocyanin; FITC, Fluorescein isothiocyanate; PECy7, Phycoerythrin-Cyanine7; APCCy7, Allophycocyanin-Cyanine7.

Source data

Extended Data Fig. 9 Pharmacologic blockade of the oxidative phase but not of the non-oxidative phase of the pentose phosphate pathway results in loss of effector differentiation.

a-c, Flow cytometry analysis of OT-I+ Thy1.1 + T cells activated for 48 h, co-cultured for 2 days with HKP1-ova-GFP tumor cells at a 5:1 effector:target ratio, then co-cultured for an additional 2 days until day 6 post-initial stimulation while being treated with either DMSO control (black), glucose-6-phosphate dehydrogenase inhibitor G6PDi-1 (periwinkle), 6-phosphogluconate dehydrogenase inhibitor 6-aminonicotinamide (6-AN, blue), or the transketolase inhibitor oxythiamine (red). a, Representative contour plots for Tox and TCF1 staining in T cells treated with DMSO (top left), G6PDi-1 (top right), 6-AN (bottom left), or oxythiamine (bottom right), with gates for populations with differential TCF1 expression. b, Quantification of populations from (a) as percent of viable CD8+ Thy1.1 + T cells. c, Mean fluorescence intensities (MFIs) for transcription factors in co-cultured T cells from (a). d-e, Flow cytometry analysis of OT-I+ Thy1.1 + T cells activated for 24 h, electroporated with guides targeting PKM2 (Pkm2-8) or non-targeting controls (NTC-2), expanded for another 24 h, co-cultured with HKP1-ova-GFP tumor cells at a 5:1 effector:target ratio for 2 days, then co-cultured for an additional 2 days until day 6 post-initial stimulation while being treated with either DMSO control (black), G6PDi-1 (periwinkle), 6-AN (blue), or a lower (light red) or higher (dark red) dose of oxythiamine. d-e, Quantification of TCF1 hi (d) or IFNγ+ TNFα+ (e) populations as percent of viable CD8+ Thy1.1 + T cells from co-culture. Numbers: (b–d) n = 4 biological replicates per group, experiment repeated four times; (e-n) n = 2 biological replicates per group. Statistics: (b–d) Multiple unpaired two-tailed t-tests, Holm-Šídák multiple comparisons. Data in (b–e) are presented as mean ± standard deviation. Abbreviations: APC, Allophycocyanin; PE, Phycoerythrin; AF488, Alexa Fluor 488; BV605, Brilliant Violet 605; PECy7, Phycoerythrin-Cyanine7.

Source data

Extended Data Fig. 10 Gating strategies for identification and characterization of populations of interest.

Acquired flow cytometry data was initially gated using a similar workflow: size exclusion (SSC-A vs. FSC-A) -> singlet identification on forward scatter (FSC-H vs. FSC-W) -> singlet identification on side scatter (SSC-H vs. SSC-W) -> live CD8+ cell identification (either SSC-H vs. Live/Dead stain followed by Thy1.1 vs. CD8, or Live/Dead stain vs. CD8 followed by Thy1.1 vs. CD8 or Thy1.1 vs. Thy1.2) -> downstream analysis. a-b, Representative gating strategies for evaluating cytokine production (a) and transcription factor-expressing populations (b) in vitro. c-f, Representative gating strategies for evaluating PKM2 expression (c), surface marker expression (d), transcription factor expression (e), and cytokine-producing populations (f) after in vivo adoptive co-transfer of PKM2WT and PKM2KO T cells. Abbreviations: SSC-A, Side Scatter-Area; SSC-H, Side Scatter-Height; SSC-W, Side Scatter-Width; FSC-A, Forward Scatter-Area; FSC-H, Forward Scatter-Height; FSC-W, Forward Scatter-Width; BV711, Brilliant Violet 711; FITC, Fluorescein isothiocyanate; PE, Phycoerythrin; PCP5.5, Peridinin Chlorophyll-A Protein-Cyanine5.5; PB, Pacific Blue; APCCy7, Allophycocyanin-Cyanine7; BV785, Brilliant Violet 785; BV605, Brilliant Violet 605; AF488, Alexa Fluor 488; PECy7, Phycoerythrin-Cyanine7; APC, Allophycocyanin.

Supplementary information

Reporting Summary

Supplementary Table 1

Supplementary Table 1: Gene expression of the 343 DEGs in CD8+ T cells isolated from larger tumors in the treatment-naive (GSE218141) and anti-PD-1-treated (GSE114300) RNA-seq datasets.

Supplementary Table 2

Supplementary Table 2: KEGG pathway analysis from the treatment-naive (GSE218141) and anti-PD-1-treated (GSE114300) RNA-seq datasets focused on comparisons between CD8+ T cells isolated from larger tumors and other phenotypes.

Supplementary Table 3

Supplementary Table 3: Differential gene expression between PKM2 knockout and PKM2 wild-type T cells from the adoptive co-transfer experiment (GSE216675).

Supplementary Table 4

Supplementary Table 4: Hallmark pathway analysis between PKM2 knockout and PKM2 wild-type T cells from the adoptive co-transfer experiment (GSE216675) either combined or split by day.

Supplementary Table 5

Supplementary Table 5: Differential gene expression between T cells from anti-PD-1-treated and IgG-treated mice from the adoptive co-transfer experiment (GSE216675).

Supplementary Table 6

Supplementary Table 6: Hallmark pathway analysis between T cells from anti-PD-1-treated and IgG-treated mice from the adoptive co-transfer experiment (GSE216675) either combined or split by day.

Supplementary Table 7

Supplementary Table 7: Hallmark and Gene Ontology: Biological Processes (GO:BP) pathway analysis for differential effects of anti-PD-1 treatment based on donor T cell genotype from the adoptive co-transfer experiment (GSE216675).

Supplementary Table 8

Supplementary Table 8: Data for metabolite abundance and normalization steps from the steady-state polar metabolite profiling LC–MS dataset.

Supplementary Table 9

Supplementary Table 9: Data for isotope abundance and normalization steps from the 1,213C glucose carbon tracing and polar metabolite profiling LC–MS dataset.

Supplementary Table 10

Supplementary Table 10: Differential gene expression comparing TCF1 reporter eGFP+ and TCF1 reporter eGFP− cells from the in vitro co-culture under treatment with different metabolic manipulations (GSE238203).

Supplementary Table 11

Supplementary Table 11: Differential gene expression comparing AG1-treated TCF1 reporter eGFP+, 2-DG-treated TCF1 reporter eGFP+ and DMSO-treated TCF1 reporter eGFP+ cells from the in vitro co-culture under treatment with different metabolic manipulations (GSE238203).

Supplementary Table 12

Supplementary Table 12: Differential gene expression comparing AG1-treated and DMSO-treated cells from the in vitro co-culture under treatment with different metabolic manipulations (GSE238203).

Supplementary Table 13

Supplementary Table 13: IPA analysis for upstream regulators conserved between PKM2 knockout and AG1 or 2-DG treatment and their respective controls.

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Markowitz, G.J., Ban, Y., Tavarez, D.A. et al. Deficiency of metabolic regulator PKM2 activates the pentose phosphate pathway and generates TCF1+ progenitor CD8+ T cells to improve immunotherapy. Nat Immunol (2024). https://doi.org/10.1038/s41590-024-01963-1

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