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Methionine is a metabolic dependency of tumor-initiating cells

Nature Medicine volume 25, pages 825–837 (2019)Cite this article

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A Publisher Correction to this article was published on 21 May 2019

This article has been updated

Abstract

Understanding cellular metabolism holds immense potential for developing new classes of therapeutics that target metabolic pathways in cancer. Metabolic pathways are altered in bulk neoplastic cells in comparison to normal tissues. However, carcinoma cells within tumors are heterogeneous, and tumor-initiating cells (TICs) are important therapeutic targets that have remained metabolically uncharacterized. To understand their metabolic alterations, we performed metabolomics and metabolite tracing analyses, which revealed that TICs have highly elevated methionine cycle activity and transmethylation rates that are driven by MAT2A. High methionine cycle activity causes methionine consumption to far outstrip its regeneration, leading to addiction to exogenous methionine. Pharmacological inhibition of the methionine cycle, even transiently, is sufficient to cripple the tumor-initiating capability of these cells. Methionine cycle flux specifically influences the epigenetic state of cancer cells and drives tumor initiation. Methionine cycle enzymes are also enriched in other tumor types, and MAT2A expression impinges upon the sensitivity of certain cancer cells to therapeutic inhibition.

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Fig. 1: Metabolomic characterization of lung tumor-initiating cells and differentiated cells.
Fig. 2: The metabolic requirements of lung tumor-initiating cells.
Fig. 3: Metabolic labeling and tracking of methionine cycle flux.
Fig. 4: Functional and clinical relevance of methionine cycle enzymes in NSCLC.
Fig. 5: Small-molecule inhibition of the methionine cycle disrupts the tumorigenicity of lung tumor-initiating cells.

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Data availability

The metabolomics datasets generated or analyzed during this study are included in this published article in Supplementary Tables 2 and 3. Additional datasets are also available from the corresponding author upon reasonable request. Source data are available online for Figs. 1–5 and Extended Data Figs. 1 and 3–5.

Change history

  • 21 May 2019

    In the version of this article originally published, there is an error in Fig. 5a. Originally, ‘MAT2A’ appeared between ‘Methionine’ and ‘Homocysteine’. ‘MAT2A’ should have been ‘MTR’. The error has been corrected in the PDF and HTML versions of this article.

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Acknowledgements

We thank SingHealth Advanced Molecular Pathology Laboratory for their assistance in generating tumor microarray data, M.Y. Lee, K.H.E. Lim, X.H. Yeo, G.S. Tan, M. Nichane, L. Chen, W.A. Zaw, S.L. Khaw, M.S. Noghabi and R. Ettikan for technical assistance, and S.-C. Ng for critical comments. This research is supported by the National Research Foundation, Singapore (NRF-NRFF2015-04), the National Medical Research Council, Singapore (LCG17MAY004; NMRC/OFIRG/0064/2017; NMRC/TCR/007-NCC/2013; OFYIRG16nov013), the Agency for Science, Research and Technology, Singapore (1331AEG071; 334I00053; SPF 2012/001), and the Singapore Ministry of Education under its Research Centers of Excellence initiative. Z. Wang dedicates this manuscript to the memory of Joseph P. Calarco, a wonderful friend and scientist.

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  1. These authors contributed equally: Zhenxun Wang, Lian Yee Yip.

Authors and Affiliations

  1. Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Zhenxun Wang, Jia Hui Jane Lee, Zhengwei Wu, Hui Yi Chew, Heather Yin-Kuan Ang, Ju Yuan, Siming Ma, Li Shi Kimberly Choo, Xia Jiang, Qiang Yu, Axel M. Hillmer, Daniel Shao Weng Tan & Wai Leong Tam

  2. Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Lian Yee Yip, Pooi Kiat William Chong, Chin Chye Teo, Kai Lay Esther Peh, Nurhidayah Basri & Ying Swan Ho

  3. School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

    Jia Hui Jane Lee & Wai Leong Tam

  4. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Zhengwei Wu & Wai Leong Tam

  5. Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Wan Teck Lim, Eng Huat Tan & Daniel Shao Weng Tan

  6. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Wan Teck Lim

  7. Duke–NUS Medical School, Singapore, Singapore

    Wan Teck Lim

  8. Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore

    Tony Kiat Hon Lim & Angela Takano

  9. Merck Sharp and Dohme Translational Medicine Research Centre, Singapore, Singapore

    Bing Lim

  10. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Wai Leong Tam

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  1. Zhenxun Wang
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Contributions

L.Y.Y., P.K.W.C., C.C.T., K.L.E.P., N.B. and Y.S.H. performed metabolomic studies. Z. Wang, J.H.J.L., H.Y.-K.A., L.S.K.C., H.Y.C., X.J. and Z. Wu performed molecular, cell-based and mouse xenograft experiments. A.T., A.M.H., Q.Y., E.H.T., W.T.L., T.K.H.L., J.Y., S.M. and D.S.W.T. provided key reagents and interpreted the data. Z. Wang, B.L. and W.L.T. designed the research and wrote the manuscript.

Corresponding authors

Correspondence to Bing Lim or Wai Leong Tam.

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Extended data

Extended Data Fig. 1 Metabolomic characterization of lung tumor-initiating cells and differentiated cells.

a, Mean tumor mass of implanted cells. Error bars, s.d.; n = 6 tumors. b, Left, mean percentage of CD166+ cells. Error bars, s.d.; n = 4 biologically independent experiments. Right, representative flow cytometry plots, independently repeated four times. Unstained cells are in blue. c, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 6 biologically independent experiments. d, Mean ECAR. Error bars, s.d.; n = 6 biologically independent experiments. e,f, Top, immunoblots of the indicated cells, independently repeated three times. Bottom, mean ATP levels. Formate was supplemented at 0.5 mM. Error bars, s.d.; n = 6 (e) and n = 5 (f) biologically independent experiments. g, Top, mean tumor mass of implanted cells. Error bars, s.d.; n = 5 tumors. Bottom, mean tumor volume. Error bars, s.e.m.; n = 4 tumors. Mean tumor mass and volume for Control-knockdown (a and Fig. 1c) are included. h, Left, mean percentage of CD166+ cells. Error bars, s.d.; n = 4 biologically independent experiments. Right, representative flow cytometry plots, independently repeated four times. Unstained cells are in blue. i, Mean tumor mass and volume of implanted cells. Error bars, s.d. (left) and s.e.m. (right); n = 4 tumors. Mean tumor mass and volume for TS and Adh cells (a and Fig. 1c) are included. j, Immunoblots of cells grown in the indicated conditions. Independent blots were repeated at least three times with similar results. Histone H3 is used as a loading control. k, Mean abundance of metabolites in cells sorted by CD166 expression from three different tumors. Error bars, s.d. *P < 0.05, one-sided multiple t test corrected for multiple comparisons by the Holm–Sidak method. Exact P values (vs. CD166– cells) are as follows: methionine, 0.0102522; SAM, 0.01934. l, Immunoblots of sorted cells. Independent blots were repeated at least three times with similar results. GAPDH and total histone H3 were used as loading controls.

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Extended Data Fig. 2 The metabolic requirements of lung tumor-initiating cells.

a, Left, mean number of colonies. Error bars, s.d.; n = 4 biologically independent experiments. Right, mean tumor mass. Error bars, s.d.; n = 9 (no methionine, no serine/glycine), n = 6 (no glutamine) tumors. Mean TS tumor masses (Extended Data Fig. 1a) were included. b, Imaging experiments were independently repeated three times. Top, lesion from TS-implanted lung (l) and normal bronchiole (r). Black bars, 50 μm. Middle, GFP-positive lesion (l) and normal bronchiole (r). Scale bars, 40 μm. Bottom, mean number of GFP-positive lesions. Error bars, s.d.; n = 5 lungs. ****P < 0.0001 by unpaired two-sided Student’s t test. c, Mean tumor mass. Error bars, s.d.; n = 5 tumors. d, Top, mean percentage of CD166+ cells. n = 4 biologically independent experiments. Error bars, s.d. Bottom, representative flow cytometry plots independently repeated four times. Unstained cells are in blue. e, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 6 biologically independent experiments. f, Left, mean number of colonies. Error bars, s.d.; n = 5 (no methionine + homocysteine), n = 3 (no methionine + SAM), n = 6 (48/48) biologically independent replicates. Right, mean tumor mass. Error bars, s.d.; n = 9 (no methionine + SAM, 48/48), n = 5 (no methionine + homocysteine) tumors. Tumor masses for no methionine (a) and TS cells (Extended Data Fig. 1a) are included. ****P < 0.0001, two-sided Student’s t test with Welch’s correction. P values (vs. no methionine): no methionine + homocysteine, 0.0505; no methionine + SAM, <0.0001; 48/48, <0.0001. g, Mean α-ketoglutarate/succinate ratios. Error bars, s.d.; n = 4 biologically independent experiments. h, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 6 biologically independent experiments. i, Top, representative flow cytometry plots independently repeated four times. Left, Complete condition. Right, Thymidine treated positive control. Bottom, mean percentage of cells in G2/M. Error bars, s.d.; n = 4 biologically independent experiments. j, Mean percentage of Annexin V+ cells. Error bars, s.d.; n = 4 biologically independent experiments. k, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 6 biologically independent experiments.

Extended Data Fig. 3 Metabolic labeling and tracking of methionine cycle flux.

a, Changes to [13C]methionine through the methionine cycle. Red circles, 13C; blue triangle, ATP; +, positive charge; black circle, 12C; black letters, enzymes. b, Top, cells were starved of methionine (16 h; l) or not starved (r) before [13C]methionine pulse–chase. Bottom, metabolite species detected are indicated on the right, and proportional abundance (% APE) is indicated on the left. Data represent the mean ± s.e.m.; n = 3 technical replicates. Technical replicates are shown to demonstrate technical consistency. n = 2 (bottom left) and n = 3 (bottom right) biologically independent experiments. c,d, Immunoblots of the indicated cells. β-actin (c) and total histone H3 (d) were used as loading controls. Independent blots were repeated at least three times with similar results. e, Mean tumor mass of implanted cells. Tumor masses from control- and GLDC-knockdown cells (Extended Data Fig. 1a) are included. Error bars, s.d.; n = 6 tumors. f, Immunoblots of the indicated lines. β-actin was used as a loading control. Independent blots were repeated at least three times with similar results. g, Percent APE of metabolite species derived from deuterated homocysteine. Data represent the mean ± s.e.m.; n = 3 technical replicates. Technical replicates are shown to demonstrate technical consistency. n = 3 biologically independent experiments. h, Mean tumor mass. Tumor masses from control- and GLDC-knockdown cells (Extended Data Fig. 1a) are included. Error bars, s.d.; n = 7 tumors. i, Mean ATP levels in the indicated cells supplemented with formate (0.5 mM), methyl-THF (20 μM) or adenosine (200 μM). Error bars, s.d.; n = 6 biologically independent experiments; **P < 0.005, Student’s two-sided t test with Welch’s correction. Exact P values (vs. GLDC shRNA) are as follows: GLDC shRNA + methyl-THF, 0.7947; GLDC shRNA + adenosine, 0.0011; GLDC shRNA + formate, 0.0010.

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Extended Data Fig. 4 Functional and clinical relevance of methionine cycle enzymes in NSCLC.

a, Immunoblots of the indicated enzymes. β-actin was used as a loading control. Independent blots were repeated at least three times with similar results. b, MTHFR immunohistochemistry (performed once) of a tumor microarray (n = 47) containing paired tumor and normal sections. Top, representative staining intensity. White bar, 20 µm. Bottom, box-and-whisker plots comparing the intensity of tumor and normal sections. Intensity was defined as the product of the maximum immunostaining intensity and the percentage of tumor cells stained per section. Box, twenty-fifth to seventy-fifth percentile; the median value coincides with the seventy-fifth percentile; whiskers indicate the minima and maxima. **P = 0.0005, paired Student’s two-sided t test. t = 3.776, d.f. = 46. c, MTHFR immunohistochemistry (performed once) of an NSCLC tumor microarray (n = 153). Top, representative staining intensity. White bar, 200 µm. Bottom, contingency table correlating staining intensity with NSCLC grade. Chi-squared test P value (P = 0.2297) is indicated at the bottom right. χ2 = 8.116, d.f. = 6. d,e, Immunoblots of MAT2A in the indicated cells or tumors. GAPDH was used as a loading control. Independent blots were repeated at least three times with similar results. f, Proliferation curves generated from mean cell viability of the indicated lines. Error bars, s.d.; n = 10 biologically independent experiments.

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Extended Data Fig. 5 Small-molecule inhibition of the methionine cycle disrupts the tumorigenicity of lung tumor-initiating cells.

a, Mean tumor mass of implanted cells. Error bars, s.d.; n = 6 (D9, DMSO), n = 9 (FIDAS, FIDAS + SAM) tumors. b, Left, mean percentage of CD166+ cells. Error bars, s.d.; n = 4 biologically independent experiments. Right, representative flow cytometry plots independently repeated four times. Unstained cells are in blue. c,d, Immunoblots of the indicated cells. β-catenin was used as a loading control. Independent blots were repeated at least three times with similar results. e, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 10 biologically independent experiments. f, Mean percentage of Annexin V+ cells. Error bars, s.d.; n = 4 (DMSO, D9), n = 3 (FIDAS) biologically independent experiments. g,h, Proliferation curves generated from mean cell viability. Error bars, s.d.; n = 10 biologically independent experiments. i, Mean tumor mass of implanted cells. Error bars, s.d.; n = 7 (control), n = 6 (FIDAS) and n = 9 (cisplatin) tumors. j, Mean number of GFP+ lesions. Error bars, s.d.; n = 5 lungs. ****P < 0.0001, two-sided unpaired Student’s t test. k, Individual weight plots of nine mice undergoing the indicated treatment. l, Mean MAT2A mRNA levels in normal versus tumor tissue, including glioblastoma (TCGA), colorectal cancer (TCGA), nasopharyngeal carcinoma81, leukemia82,83, lymphoma84, ovarian carcinoma85, melanoma86, prostate adenocarcinoma87 and breast cancer88. ****P < 0.0001, ***P < 0.001, **P ≤ 0.01, *P ≤ 0.05, Student’s unpaired two-sided t test with Welch’s correction. P values and numbers of normal and tumor samples are as follows: brain: P < 0.0001; n = 10 and n = 547; nasopharynx, P = 0.0005; n = 10 and n = 31; skin: P = 0.0015; n = 7 and n = 63; lymphatic system, P < 0.0001; n = 20 and n = 40; bone marrow (childhood acute lymphatic leukemia): P = 0.0492; n = 8 and n = 566; bone marrow (chronic lymphatic leukemia): P = 0.0003; n = 11 and n = 100; ovary: P < 0.0001; n = 10 and n = 185; breast, P = 0.0215; n = 5 and n = 59; prostate: P = 0.0010; n = 20 and n = 69; colon: P < 0.0001; n = 22 and n = 215. m, Immunoblots of the indicated cells. Independent blots were repeated at least three times with similar results.

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Wang, Z., Yip, L.Y., Lee, J.H.J. et al. Methionine is a metabolic dependency of tumor-initiating cells. Nat Med 25, 825–837 (2019). https://doi.org/10.1038/s41591-019-0423-5

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