Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A unique subset of glycolytic tumour-propagating cells drives squamous cell carcinoma

Abstract

Head and neck squamous cell carcinoma (SCC) remains among the most aggressive human cancers. Tumour progression and aggressiveness in SCC are largely driven by tumour-propagating cells (TPCs). Aerobic glycolysis, also known as the Warburg effect, is a characteristic of many cancers; however, whether this adaptation is functionally important in SCC, and at which stage, remains poorly understood. Here, we show that the NAD+-dependent histone deacetylase sirtuin 6 is a robust tumour suppressor in SCC, acting as a modulator of glycolysis in these tumours. Remarkably, rather than a late adaptation, we find enhanced glycolysis specifically in TPCs. More importantly, using single-cell RNA sequencing of TPCs, we identify a subset of TPCs with higher glycolysis and enhanced pentose phosphate pathway and glutathione metabolism, characteristics that are strongly associated with a better antioxidant response. Together, our studies uncover enhanced glycolysis as a main driver in SCC, and, more importantly, identify a subset of TPCs as the cell of origin for the Warburg effect, defining metabolism as a key feature of intra-tumour heterogeneity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Sirt6 acts as a tumour suppressor in squamous cell carcinoma by negatively regulating aerobic glycolysis.
Fig. 2: Increased glycolysis enriches tumour-propagating cells in vivo.
Fig. 3: Glycolytic tumour-propagating cells uniquely upregulate glutathione metabolism via the oxidative pentose phosphate pathway to mitigate oxidative stress.
Fig. 4: A subset of tumour-propagating cells that are glycolytic supports glutathione metabolism and antioxidant response, functionally critical for tumour-propagating cell enrichment and tumourigenic potential in vivo.

Data availability

The RNA-seq data of the tumour subpopulations from mouse cutaneous tumours of Sirt6 WT or cKO animals have been submitted to the Gene Expression Omnibus (GEO) database under accession number GSE115953 (related to Fig. 2d–f and Extended Data Fig. 4e–h). The scRNA-seq data of TPCs from mouse cutaneous tumours of Sirt6 WT or cKO animals have been submitted to the GEO under accession GSE147031 (related to Fig. 4b–e and Extended Data Fig. 9a–j). There is no restriction on data availability. Human HNSCC scRNA-seq data from Puram et al.22 is available under accession GSE103322 (related to Fig. 4h and Extended Data Fig. 10c,d). Oncomine and TCGA datasets (related to Extended Data Figs. 1a–c and 2e) are available in cBioportal (https://www.cbioportal.org/). CCLE data (related to Extended Data Fig. 1e) are available in https://portals.broadinstitute.org/ccle/. Source data are provided with this paper.

Code availability

In-house codes were previously used in the published works. Appropriate references to the original works are provided.

References

  1. 1.

    Visvader, J. E. & Lindeman, G. J. Cancer stem cells: current status and evolving complexities. Cell Stem Cell 10, 717–728 (2012).

    CAS  PubMed  Google Scholar 

  2. 2.

    Frank, N. Y., Schatton, T. & Frank, M. H. The therapeutic promise of the cancer stem cell concept. J. Clin. Invest. 120, 41–50 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Malanchi, I. et al. Cutaneous cancer stem cell maintenance is dependent on β-catenin signalling. Nature 452, 650–653 (2008).

    CAS  PubMed  Google Scholar 

  4. 4.

    Lapouge, G. E. L. et al. Skin squamous cell carcinoma-propagating cells increase with tumour progression and invasiveness. EMBO J. 31, 4563–4575 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Boumahdi, S. et al. SOX2 controls tumour initiation and cancer stem-cell functions in squamous-cell carcinoma. Nature 10, 246–250 (2014).

    Google Scholar 

  6. 6.

    Siegle, J. M. et al. SOX2 is a cancer-specific regulator of tumour-initiating potential in cutaneous squamous cell carcinoma. Nat. Commun. 5, 4511 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  Google Scholar 

  8. 8.

    Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Google Scholar 

  10. 10.

    Warburg, O. On the origin of cancer cells. Science 123, 309–314 (1956).

    CAS  PubMed  Google Scholar 

  11. 11.

    Kugel, S. & Mostoslavsky, R. Chromatin and beyond: the multitasking roles for SIRT6. Trends Biochem. Sci. 39, 72–81 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhong, L. et al. The histone deacetylase Sirt6 regulates glucose homeostasis via Hif1α. Cell 140, 280–293 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Sebastian, C. et al. The histone deacetylase SIRT6 is a tumor suppressor that controls cancer metabolism. Cell 151, 1185–1199 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Kugel, S. et al. SIRT6 suppresses pancreatic cancer through control of Lin28b. Cell 165, 1401–1415 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Abel, E. L., Angel, J. M., Kiguchi, K. & DiGiovanni, J. Multi-stage chemical carcinogenesis in mouse skin: fundamentals and applications. Nat. Protoc. 4, 1350–1362 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Beck, B. et al. A vascular niche and a VEGF–Nrp1 loop regulate the initiation and stemness of skin tumours. Nature 478, 399–403 (2011).

    CAS  PubMed  Google Scholar 

  17. 17.

    Schober, M. & Fuchs, E. Tumor-initiating stem cells of squamous cell carcinomas and their control by TGF-β and integrin/focal adhesion kinase signaling. Proc. Natl Acad. Sci. USA 108, 10544–10549 (2011).

    CAS  PubMed  Google Scholar 

  18. 18.

    Oshimori, N., Oristian, D. & Fuchs, E. TGF-β promotes heterogeneity and drug resistance in squamous cell carcinoma. Cell 160, 963–976 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Brown, J. et al. TGF-β-induced quiescence mediates chemoresistance of tumor-propagating cells in squamous cell carcinoma. Cell Stem Cell 21, 650–664 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Randall, E. C. et al. Localized metabolomic gradients in patient-derived xenograft models of glioblastoma. Cancer Res. 80, 1258–1267 (2020).

    CAS  PubMed  Google Scholar 

  21. 21.

    Swales, J. G. et al. Quantitation of endogenous metabolites in mouse tumors using mass-spectrometry imaging. Anal. Chem. 90, 6051–6058 (2018).

    CAS  PubMed  Google Scholar 

  22. 22.

    Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lunt, S. Y. & Vander Heiden, M. G. Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annu. Rev. Cell Dev. Biol. 27, 441–464 (2011).

    CAS  PubMed  Google Scholar 

  24. 24.

    Hsu, P. P. & Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 134, 703–707 (2008).

    CAS  Google Scholar 

  25. 25.

    Zhang, W. C. et al. Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 148, 259–272 (2012).

    CAS  PubMed  Google Scholar 

  26. 26.

    Mao, P. et al. Mesenchymal glioma stem cells are maintained by activated glycolytic metabolism involving aldehyde dehydrogenase 1A3. Proc. Natl Acad. Sci. USA 110, 8644–8649 (2013).

    CAS  PubMed  Google Scholar 

  27. 27.

    Feng, W. et al. Targeting unique metabolic properties of breast tumor-initiating cells. Stem Cells 32, 1734–1745 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    DeBerardinis, R. J. & Chandel, N. S. We need to talk about the Warburg effect. Nat. Metab. 2, 127–129 (2020).

    PubMed  Google Scholar 

  29. 29.

    Almendro, V., Marusyk, A. & Polyak, K. Cellular heterogeneity and molecular evolution in cancer. Annu Rev. Pathol. 8, 277–302 (2013).

    CAS  PubMed  Google Scholar 

  30. 30.

    Rhodes, D. R. et al. Oncomine 3.0: genes, pathways and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9, 166–180 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Barretina, J. et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Lawrence, M. S. et al. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).

    CAS  Google Scholar 

  33. 33.

    Fitamant, J. et al. YAP inhibition restores hepatocyte differentiation in advanced HCC, leading to tumor regression. Cell Rep. 10, 1692–1707 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  35. 35.

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

    CAS  Google Scholar 

  36. 36.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  37. 37.

    Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Google Scholar 

  38. 38.

    Donner, A. J., Szostek, S., Hoover, J. M. & Espinosa, J. M. CDK8 is a stimulus-specific positive coregulator of p53 target genes. Mol. Cell 27, 121–133 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Heinrich, P. et al. Correcting for natural isotope abundance and tracer impurity in MS-, MS–MS- and high-resolution-multiple-tracer data from stable-isotope labeling experiments with IsoCorrectoR. Sci. Rep. 8, 17910 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Elia, I. et al. Breast cancer cells rely on environmental pyruvate to shape the metastatic niche. Nature 568, 117–121 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Young, J. D., Walther, J. L., Antoniewicz, M. R., Yoo, H. & Stephanopoulos, G. An elementary metabolite unit-based method of isotopically nonstationary flux analysis. Biotechnol. Bioeng. 99, 686–699 (2008).

    CAS  PubMed  Google Scholar 

  42. 42.

    Abdelmoula, W. M. et al. Automatic generic registration of mass spectrometry imaging data to histology using non-linear stochastic embedding. Anal. Chem. 86, 9204–9211 (2014).

    CAS  PubMed  Google Scholar 

  43. 43.

    Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).

    PubMed  Google Scholar 

  44. 44.

    Viola, P. & Wells, W. M. III Alignment by maximization of mutual information. Int. J. Comput. Vis. 24, 137–154 (1997).

    Google Scholar 

  45. 45.

    Klein, S., Staring, M., Andersson, P. & Pluim, J. P. W. Preconditioned stochastic gradient descent optimisation for monomodal image registration. Med. Image Comput. Comput. Assist. Inter. 14, 549–556 (2011).

    Google Scholar 

  46. 46.

    Thévenaz, P., Ruttimann, U. E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).

  47. 47.

    Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 176, 404 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank D. Lombard (University of Michigan) for the pTripZ-shSIRT6 construct, M. Weissenboeck (Institute of Molecular Pathology, Vienna) for the pMSCV-luc-PGK-Neo-IRES-eGFP construct, C. Benes (MGH Cancer Center) for the HSC2 cell line and P. Dotto (MGH Cutaneous Biology Department) for the SCC13 cell line. Human tumour-associated fibroblasts were a gift from S.A.B. (IRB, Spain). We also thank B. Berman, H. Cedar and T. Silva for providing the analysis on SIRT6 promoter methylation, and C. Villacorta-Martin for providing advice on scRNA-seq analysis. We thank all the members of the Mostoslavsky laboratory for helpful discussions and critical reading of the manuscript. We also thank the Flow Core facilities at the MGH Center for Regenerative medicine and at the MGH Department of Pathology. C.S. was supported by the Department of Defense Visionary Postdoctoral Award (CA120342) and is a recipient of the Marie Sklodowska-Curie Actions Individual Fellowship. R.M. is the Laurel Schwartz Endowed Chair in Oncology. This work was supported by National Institutes of Health grants R01CA175727, R01GM128448 and the Massachusetts Life Sciences Center (MLSC) Bits to Bytes Award (R.M.), and the Arthur, Sandra and Sarah Irving Fund for Gastrointestinal Immuno-Oncology (N.H.).

Author information

Affiliations

Authors

Contributions

J.-E.C. and R.M. conceptualized and designed the study; J-E.C. performed and analysed most of the experiments; J-E.C., C.S., and C.M.F. performed animal experiments; C.A.L. performed and analysed LC–MS metabolite assays; M.S-F., T.L., A.G., performed and analysed scRNA-seq with supervision of N.H.; B.G.C.L., W.M.A., and M.S.R. performed and analysed MALDI-MSI with supervision of N.Y.R.A.; M.C. and R.I.S. performed and analysed bulk RNA-seq; G.P. and S.A.B. designed xenotransplantation assays; G.R.W. performed and analysed bioluminescence imaging; J-E.C. and G.G.S. performed and analysed immunofluorescence imaging and western blot assays; R.B. performed and analysed gas chromatography–mass spectrometry assays; K.N.R. analysed the TCGA data; I.T. analysed scRNA-seq data from human HNSCC patient samples; S.V.S. and L.W.E. provided FFPE human samples for immunohistochemical analysis; J-E.C. and R.M. wrote the manuscript; R.M. supervised the project.

Corresponding author

Correspondence to Raul Mostoslavsky.

Ethics declarations

Competing interests

R.M. has a financial interest in Galilei Biosciences, a company developing activators of the mammalian SIRT6 protein. R.M.’s interests were reviewed and are managed by MGH and MGB HealthCare in accordance with their conflict-of-interest policies. N.H. has equity in BioNTech and Related Sciences. N.Y.R.A. is a key opinion leader to Bruker Daltonics. The other authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.

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

Extended data

Extended Data Fig. 1 SIRT6 acts as a tumor suppressor in human HNSCC.

Extended Data Fig. 1 (related to Fig. 1). a, Kaplan-Meier survival analysis of HNSCC patients based on SIRT6 copy number (log-rank test, p=0.0133) b, SIRT6 expression level in human HNSCC compared to matched normal tissue (t-test, p=1.89e-6), or by tumor grade, respectively from the Oncomine c, SIRT6 copy number change in human HNSCC compared to normal tissue (t-test, p=1.29e-13), or by metastasis feature from the TCGA listed in the Oncomine. d, Representative SIRT6 immunostaining from human normal foreskin, differentiated HNSCC characterized by keratin pearl (KP), and undifferentiated HNSCC (T, tumour). Scale bars indicate 100µm. Immunostaining was performed two times with similar results. e, SIRT6 gene copy number change in human cancer cell lines from the CCLE. Source data

Extended Data Fig. 2 Dysplastic cancer cells presented increased glycolysis in an in vivo model of cutaneous SCC and in human HNSCC in the context of SIRT6.

Extended Data Fig. 2 (related to Fig. 1)a, H&E stained images of early SCC found only among Sirt6 cKO tumours. Scale bars indicate 200µm b, PCNA (top) and GLUT1 (bottom) immunostaining in normal untreated skin, P27 anagen animal back skin, and dysplastic skin treated with DMBA/TPA. Dashed line indicates either hair follicle or epidermis. For normal skin, both images came from the same untreated mouse, but not immediate adjacent skin sections. Scale bars indicate 100µm. Immunostaining was performed ten times for GLUT1 and six times for PCNA with similar results. c, Glut1 and Ldha expression in normal skin and skin tumors from Sirt6-deficient animals at 21 weeks after DMBA treatment. Data indicate mean ± S.E.M. Each dot represents one biologically independent tumour sample. (n = 1, 2, 6, 5, respectively, from left to right in each graph) Student’s t-tests were performed (two-sided). d, GLUT1, phospho-PDH (Ser293), and MPC1 immunostaining in Sirt6-deleted large papilloma samples. Scale bars indicate 100µm. e, SIRT6, GLUT1, PDK1, and LDHB expression levels in human HNSCC compared by tumour grade from Ginos et al. listed in the Oncomine. Immunostaining against GLUT1, p-PDH, and MPC1 was performed ten, three, and two times, respectively, with similar results. f, Representative SIRT6 and GLUT1 immunostaining from human differentiated HNSCC and undifferentiated HNSCC. Immunostaining was performed two times with similar results. Scale bars indicate 100µm. * p<0.05 ** p<0.01. Source data

Extended Data Fig. 3 Metabolic features of tumour basal cells.

Extended Data Fig. 3 (related to Fig. 2). a, GLUT1 and Keratin5 (left panel), and GLUT1 and Keratin10 (right panel) immunofluorescence in skin tumour samples treated with DMBA/TPA. Scale bars indicate 200µm. Data indicate mean ± S.E.M. b, CD34 and Keratin5 (left panel), and CD34 and Keratin10 (right panel) immunofluorescence in skin tumor samples treated with DMBA/TPA. Scale bars indicate 200µm. Data indicate mean ± S.E.M. Immunostaining was performed three times with similar results. Quantification is done by at least three independent 20x images with hundreds of positive cells. c, Representative whole tumour immunofluorescence with GLUT1, CD34, and Keratin 10 in skin tumour samples treated with DMBA/TPA. Images were taken at 40x and stitched with the software in Zeiss confocal microscope. Correlation value is calculated in Matlab. Scale bars indicate 300µm. Immunostaining was performed two times with similar results. d, Representative flow cytometry plot of GLUT1-A647 and CD34-BV421 co-stained tumor cells after gating live, YFP positive, a6 integrin high cells. Statistics, sample sizes (n) and numbers of replications are presented in Methods, ‘Statistics and reproducibility’.

Extended Data Fig. 4 Analysis of tumor-propagating cell enrichment and transcriptome.

Extended Data Fig. 4 (related to Fig. 2). a, Representative flow sorting scheme from mouse skin tumours to isolate α6 integrinhigh/CD34+ cells in FACSAria II b, Percentage of α6 integrinhigh/CD34+ cells from Sirt6 WT or Sirt6-deleted skin tumours with or without DCA treatment. Skin tumours were grouped based on their tumour size and genotype for comparison. In the group of skin tumors bigger than 2.5mm from Sirt6 F/F; K14-cre+; YFP+ animals, if we exclude the lowest value from the group (marked with a star sign), the difference in the enrichment α6 integrinhigh/CD34+ cells between Sirt6 WT and Sirt6-deleted skin tumours that were bigger than 2.5mm becomes statistically significant (** p=0.0086) Data indicate mean ± S.E.M. Each dot represents one biologically independent tumour sample. (n = 2, 6, 3, 6, 5, 3 respectively, from left to right in each graph). c, Blood (from tail vein) lactate level assessed by GC-MS in DCA-treated animals and control animals Data indicate mean ± S.E.M. Each dot represents one biologically independent tumour sample. (n = 6, 5, 3 respectively, from left to right in each graph) Student’s t-tests were performed (two-sided). d, Western blot analysis of p-PDH (Ser293) from DCA-treated skin samples and vehicle control. e, An MDS plot of all the samples with top 500 DEGs f, Correlation plots between biological duplicates of KO tumor subpopulations g, Representative gene list and corresponding fold changes in expression from Sirt6 WT or cKO TPCs and its negative counterparts (α6high/CD34-) for functional gene categories associated with lipid metabolism (top) and amino acid transport (bottom). Data indicate mean. h, Representative gene list and corresponding fold changes in expression from Sirt6 WT or cKO TPCs for functional gene categories associated with each biological process. For g and h, data are from at least two biological replicates (n = 3 for WT and n = 2 for Sirt6 cKO), presented by mean. * p<0.05 ** p<0.01 *** p<0.0001. Source data

Extended Data Fig. 5 Overexpression of SIRT6 WT negatively affects glycolytic gene expression, and slows down glycolytic flux, while minimally affecting the mitochondrial TCA cycle in HSC2 cells.

Extended Data Fig. 5 (related to Fig. 3). a, Chromatin fraction and whole cell lysates were extracted respectively and were analyzed by Western blot in doxycycline-inducible SIRT6 WT or H133Y overexpressing HSC2 cells in addition to EV (empty vector) control. b, Glycolysis stress test was performed using the Seahorse bioanalyzer following the manufacturer’s instruction in HSC2 cells pretreated with doxycycline for 24hrs. ECAR value was normalized by cell number of each well using the Cyquant cell proliferation assay kit. Data indicate mean ± S.D. Each dot represents one biologically independent sample and s.d. (n = 9 for S6HY and n = 16 for S6WT). Student’s t-tests were performed (two-sided). c,e, Relative enrichment of fully labeled glycolysis intermediates (c) or labeled TCA cycle intermediates (e) after incubation with U-13C-glucose at a given time point either in SIRT6 WT or H133Y overexpressing HSC2 cells (26hr post doxycycline). Data indicate mean ± S.D. Data are from three biological replicates. d, Annexin V and PI staining in HSC2 cells 24hr post dox, analyzed by MACSQuant VYB Data indicate mean ± S.E.M. Data are from three biological replicates. *** p<0.001. Source data

Extended Data Fig. 6 SIRT6 is an H3K56Ac and H3K9Ac deacetylase, regulating glycolysis.

Extended Data Fig. 6 (related to Fig. 3). a, Chromatin fractions and whole cell lysates were extracted respectively and were analyzed by Western blot in doxycycline-inducible SIRT6 knockdown SCC13 cells as well as control cells over time. b, Chromatin immunoprecipitation assay against H3K9Ac was performed, followed by qPCR analysis on the promoter regions of the known SIRT6 target glycolysis genes. Data indicate mean ± S.D. Data are from two independent experiments with two experimental replicates. Student’s t-tests were performed (two-sided). c,d, Immunofluorescence staining against H3K56Ac and CD34 in DMBA/TPA-treated Sirt6 WT or cKO tumours. More concentrated antibody condition was used in the samples shown in d for H3K56Ac. Scale bars indicate 100µm. Immunostaining was performed three times with similar results. e, Glucose uptake was measured after 2-NBDG incubation in SCC13 cells followed by FACSAria II analysis. Data indicate mean ± S.D. Cells were cultured in the presence of doxycycline to induce knockdown of SIRT6, and then either kept in Dox (Dox ON) or withdrew from Dox for 3 days (Dox OFF). Data are from three independent experiments with two experimental replicates. Student’s t-tests were performed (two-sided). f-h, Glucose and lactate concentration, and the ratio of these two were measured in media of SCC13 cells pretreated with doxycycline for 3 days by using kits from Biovision. Data indicate mean ± S.D. Data are from four biological replicates. Student’s t-tests were performed (two-sided). i, Glycolysis stress test was performed using the Seahorse bioanalyzer following the manufacturer’s instruction in SCC13 cells pretreated with doxycycline for at least 3 days. ECAR value was normalized by cell number of each well using the Cyquant cell proliferation assay kit. Data indicate mean ± S.D. Each dot represents one biologically independent sample, presented by the mean and s.d. (n = 14 for shCtrl and n = 14 for shSIRT6). Student’s t-tests were performed (two-sided). j, Ratio of NAD+/NADH was assessed by a kit from Abcam in SCC13 cells pretreated with doxycycline for 3 days. Data are from three biological replicates. k, Relative reactive oxygen species level analyzed by LSRII using CellROX deepred in SCC13 cells 3 days post doxycycline. Data indicate mean ± S.D. Data are from two independent experiments with two experimental replicates. Student’s t-tests were performed (two-sided). l,m, Relative abundance of the indicated metabolites either in control (shCtrl) or SIRT6 knockdown (shSIRT6) SCC13 cells 3 days post doxycycline. Data indicate mean. Data are from at least two biological replicates (n = 2 for shCtrl and n = 3 for shSIRT6). * p<0.05, ** p<0.01, *** p<0.001. Source data

Extended Data Fig. 7 Glycolytic metabolism enhances cell survival and cell proliferation.

Extended Data Fig. 7 (related to Fig. 3). a, Apoptosis assay in HSC2 cells pretreated with doxycycline for at least 4 days. Propidium iodide (PI) and annexin V double positive cells were considered as dead cells. Data indicate mean ± S.D. Data are from three independent experiments (n = 10 each sample). Student’s t-tests were performed (two-sided). b, Cell proliferation assay in SCC13 cells pretreated with doxycycline for at least 3 days with or without DCA over time. Data indicate mean ± S.E.M. (left). Western blot analysis of whole cell lysates was performed at day 5 in the cell proliferation assay (right). Data are from three independent experiments. Two-way ANOVA test was performed. c, Schematic presentation of skin xenotransplantation assay in severely immunocompromised NSG (NOD/SCID/Il2rg-/-) mice. Doxycycline was administered in drinking water. HPKs; human primary keratinocytes, TAFs; tumour-associated fibroblasts d, In vivo growth of SCC13 cells was monitored by bioluminescence imaging over time. Normalized total flux ratio of each mouse was used to track in vivo tumor growth (left). Representative bioluminescence images of tumours with control or SIRT6-deficient SCC13 cells at day 61 (right). Two-way ANOVA test was performed. e, Knockdown of SIRT6 by in vivo doxycycline administration in tumours was confirmed by immunohistochemistry. Scale bars indicate 100µm. Immunostaining was performed two times with similar results. * p<0.05, *** p<0.001. Source data

Extended Data Fig. 8 Metabolic heterogeneity within tumors as determined by MALDI-MSI.

Extended Data Fig. 8 (related to Fig. 3). a, tSNE analysis of all the metabolites analyzed by MALDI-MSI in each sample. Rainbow spectrum represents how close each pixel to the others is within each sample. There was no input of histological information of tissue to perform this dimensionality reduction. Data are always reproducible every time the code was run. The key point for this stability is the random seed point in the t-SNE algorithm is set to zero and that maintained reproducibility. The t-SNE analysis was done on MATLAB 2018a that was installed on a workstation operating with Windows 10. b, H&E staining in the same section as MALDI-MSI. Images were taken at 40x and stitched with the software in Zeiss microscope. Scale bars indicate 1mm. c,e, MALDI-MSI data of glucose-6-phosphate and citrate in DMBA/TPA-treated tumors and adjacent skin Scale bars indicate 1mm. Data are from two biologically independent tumour samples, consisting of more than a thousand of pixel datapoints per sample. d,f, Quantification of MALDI-MSI signal intensity of each metabolite from CD34+ and CD34- areas. The non-parametric Wilcoxon rank-sum test (two-sided and 95% significance level) was used after checking normality distribution using Kolmogorov–Smirnov test. Details of the box plots are listed in the Source Data file 4. g, Co-registration images (the left two) of G-6-P and citrate on top of immunofluorescence stained image in tumor 1. Overlay image (the very right) of G-6-P and citrate with a correlation value of distribution between two metabolites. Scale bars indicate 300µm. Data are from two biologically independent tumour samples, consisting of more than hundred thousands of pixel data points per sample. h, tSNE analysis of CD34+ area in tumour 1. Data are always reproducible every time the code was run. The key point for this stability is the random seed point in the t-SNE algorithm is set to zero and that maintained reproducibility. The t-SNE analysis was done on MATLAB 2018a that was installed on a workstation operating with Windows 10. Source data

Extended Data Fig. 9 Single cell RNA-seq analysis of TPCs from 2 WT and 2 KO tumors.

Extended Data Fig. 9 (related to Fig. 4). a, Dimensionality reduction analysis of prospectively isolated TPCs from 2 WT and 2 KO tumours using UMAP (resolution = 0.5), color-coded by clusters (top) or samples (bottom) b, Dimensionality reduction analysis of prospectively isolated TPCs from 2 WT and 2 KO tumours using tSNE, color-coded by clusters (top) or samples (bottom) c, Principal component analysis of prospectively isolated TPCs from 2 WT and 2 KO tumours, color-coded by samples d, Bar graph indicating cluster frequency by mouse samples. e,f, Dimensionality reduction analysis of prospectively isolated TPCs from 1 WT and 2 KO tumours using UMAP (resolution = 0.5, e) or tSNE (f), color-coded by samples g,h, Violin plots to show expression levels of Cd34 (g) or a6 integrin (h), shown by clusters (top) and samples (bottom) i,j, Trajectory analysis of TPCs, shown by clusters (i) or pseudotime (j).

Extended Data Fig. 10 Antioxidant protection in TPCs.

Extended Data Fig. 10 (related to Fig. 4). a, Percentage of α6 integrinhigh/CD34+ cells from Sirt6 WT or Sirt6-deleted skin tumours with or without NAC treatment under DCA administration Data indicate mean ± S.E.M. Each dot represents one biologically independent tumour sample. (n = 5, 3, 10, 7, respectively, from left to right in each graph) Student’s t-tests were performed (two-sided). b, Representative bright field images of tumorspheres (Day 10) in SCC13 cells. Scale bars indicate 500µm. Bright field imaging was performed two times with similar results. c,d, Violin plots, showing the distribution and mean of glycolytic scores (c) and antioxidant gene signature scores (d), across single cancer cells from 10 HNSCC tumors published in Puram et al. Tumors were ordered by their glycolytic scores and are further classified into three subtypes, demonstrating that the classical subtype is associated with high glycolytic and antioxidant gene signature scores. Statistics, sample sizes (n) and numbers of replications are presented in Methods, ‘Statistics and reproducibility’.

Supplementary information

Reporting Summary

Supplementary Tables 1–9

Gene lists from RNA-seq, pool size data from LC–MS, and primer sequences for Methods.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 7

Unprocessed western blots.

Source Data Extended Data Fig. 8

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Choi, JE., Sebastian, C., Ferrer, C.M. et al. A unique subset of glycolytic tumour-propagating cells drives squamous cell carcinoma. Nat Metab 3, 182–195 (2021). https://doi.org/10.1038/s42255-021-00350-6

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing