Abstract
Nutrition exerts considerable effects on health, and dietary interventions are commonly used to treat diseases of metabolic aetiology. Although cancer has a substantial metabolic component1, the principles that define whether nutrition may be used to influence outcomes of cancer are unclear2. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. By contrast, whether specific dietary interventions can influence the metabolic pathways that are targeted in standard cancer therapies is not known. Here we show that dietary restriction of the essential amino acid methionine—the reduction of which has anti-ageing and anti-obesogenic properties—influences cancer outcome, through controlled and reproducible changes to one-carbon metabolism. This pathway metabolizes methionine and is the target of a variety of cancer interventions that involve chemotherapy and radiation. Methionine restriction produced therapeutic responses in two patient-derived xenograft models of chemotherapy-resistant RAS-driven colorectal cancer, and in a mouse model of autochthonous soft-tissue sarcoma driven by a G12D mutation in KRAS and knockout of p53 (KrasG12D/+;Trp53−/−) that is resistant to radiation. Metabolomics revealed that the therapeutic mechanisms operate via tumour-cell-autonomous effects on flux through one-carbon metabolism that affects redox and nucleotide metabolism—and thus interact with the antimetabolite or radiation intervention. In a controlled and tolerated feeding study in humans, methionine restriction resulted in effects on systemic metabolism that were similar to those obtained in mice. These findings provide evidence that a targeted dietary manipulation can specifically affect tumour-cell metabolism to mediate broad aspects of cancer outcome.
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Data availability
The metabolomics data reported in this study have been deposited in Mendeley Data (https://doi.org/10.17632/zs269d9fvb.1).
Code availability
All computer code is available at: https://github.com/LocasaleLab/Dietary_methionine_restriction.
Change history
23 August 2019
This article was amended to correct the Peer review information, which was originally incorrect.
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Acknowledgements
We gratefully acknowledge support from the National Institutes of Health (NIH) R01CA193256, R21CA201963 and P30CA014236 (J.W.L.), R35CA197616 (D.G.K.), T32CA93240 (D.E.C.) and the Canadian Institutes of Health Research (CIHR, 146818) (X.G.). We thank M. L. Kiel and T. Hartman for assistance in designing the diets, and S. Heim for help with food preparation in the human study. The human study was partially supported by the Clinical Research Center at Penn State University (NIH M01RR10732). We gratefully acknowledge members of the Locasale laboratory for discussions and apologize to those whose work we could not cite owing to space constraints.
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X.G. and J.W.L. designed the study, wrote and edited the paper. D.E.C. and D.G.K. designed the sarcoma experiments and edited the paper. M.L. and D.S.H. designed and implemented the colorectal PDX models and edited the paper. X.G., M.L., D.E.C., G.A. and M.A.R. performed animal experiments. X.G., S.M.S. and M.A.R. performed all cell culture experiments. J.P.R. Jr, A. Ciccarella, A. Calcagnotto and S.N.N. conducted the human study. Z.D. conducted computational analyses with initial help from P.G.M. J.L. and S.J.M. assisted in mass spectrometry metabolomics experiments.
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J.W.L. and X.G. have patents related to targeting amino acid metabolism in cancer therapy. D.G.K. is a co-founder and has equity in XRAD Therapeutics, a company developing radiosensitizing agents. He also has patents related to radiosensitizing agents.
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Peer review information Nature thanks Danica Chen, Naama Kanarek and Alexei Vazquez for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Fig. 1 Dietary restriction of methionine rapidly and specifically alters methionine and sulfur metabolism but maintains overall metabolism in healthy C57BL/6J mice.
a, Dynamic patterns of the top three modes. Standardized concentration (the values are normalized to have mean = 0, s.d. = 1) in mode 1, mode 2 and mode 3. b, Heat map of metabolites in mode 2 and mode 3. PPP, pentose phosphate pathway. c, Volcano plot of metabolites in plasma collected at the end point. P values were determined by two-tailed Student’s t-test. d, Left, pathway analysis of significantly changed (*P < 0.05, two-tailed Student’s t-test) plasma metabolites by 21-day methionine-restricted diet. Right, fold change of altered metabolites in the top three most-affected pathways. Mean ± s.e.m., n = 5 mice per group. *P < 0.05, two-tailed Student’s t-test. e, Relative intensity of plasma amino acids and metabolites in one-carbon metabolism and redox balance at the end of the study. Mean ± s.e.m., n = 5 mice per group. *P < 0.05, two-tailed Student’s t-test. f, Relative intensity of the methionine-metabolism-related metabolites 2-keto-4-methylthiobutyrate and hypotaurine. Mean ± s.d., n = 5 mice per group. *P < 0.05, two-tailed Student’s t-test.
Extended Data Fig. 2 Dietary restriction of methionine alters methionine metabolism in PDX models of colorectal cancer.
a, Information on original tumours from patients with colorectal cancer. b–e, Data from the prevention study shown in Fig. 1f. n = 8 mice per group (4 female and 4 male). b, Food intake. Mean ± s.e.m. *P < 0.05, two-tailed Student’s t-test. c, Volcano plots of metabolites in tumour, plasma and liver. P values were determined by two-tailed Student’s t-test. d, Left, Venn diagrams of significantly changed (*P < 0.05, two-tailed Student’s t-test) metabolites in tumour, plasma and liver by methionine restriction and pathway analysis (false discovery rate < 0.5) of the commonly changed metabolites. Right, fold changes of intensity of tumour metabolites in cysteine and methionine metabolism, and taurine and hypotaurine metabolism, induced by methionine restriction. Mean ± s.d. *P < 0.05, two-tailed Student’s t-test. n = 8 mice per group (4 female and 4 male). e, Relative fold change of intensity of amino acids. Mean ± s.d. *P < 0.05, two-tailed Student’s t-test. n = 8 mice per group (4 female and 4 male).
Extended Data Fig. 3 Methionine restriction leads to specific cell-intrinsic metabolic alterations in tumours.
To determine whether the effect of methionine restriction on tumour growth is systemic, cell autonomous or both, we conducted an integrated analysis of global changes in the metabolic network across tumour, plasma and liver within each model from the prevention study shown in Fig. 1f. n = 8 mice per group (4 female and 4 male). a, Spearman’s rank correlation coefficients of fold change of metabolites in tumour, plasma and liver induced by methionine restriction exhibited strong correlations between each tissue pair, with the highest correlation between the tumour and the plasma in both CRC119 and CRC240. b, Multidimensional scaling analysis of metabolite fold change in response to methionine restriction. In both models, the fold change of metabolites in tumours showed a higher similarity with those in the plasma than with those in the liver. c, d, Liver was the most-affected tissue in both models. c, The effect of methionine restriction on metabolism in tumour, plasma and liver, evaluated by taking the log10 of the fold change. Box limits are the 25th and 75th percentiles, centre line is the median, and the whiskers are the minimal and maximal values. d, Numbers of metabolites significantly altered (*P < 0.05, two-tailed Student’s t-test) by methionine restriction. n = 8 mice per group (4 female and 4 male). e, Schematic defining methionine-related metabolites (metabolized from or to methionine within four reaction steps) and methionine-unrelated metabolites. f, g, A higher proportion of altered metabolites was methionine-related in plasma and tumour compared to liver, in which metabolites altered by methionine restriction were nearly equally distributed between methionine-related and methionine-unrelated groups. f, Fraction of significantly (*P < 0.05, two-tailed Student’s t-test) altered metabolites for methionine-related and methionine-unrelated metabolites in tumour, liver and plasma. g, Numbers of total and significantly altered metabolites for methionine-related and methionine-unrelated metabolites in tumour, liver and plasma. P values were determined by one-sided Fisher’s exact test.
Extended Data Fig. 4 Methionine restriction inhibits cell proliferation, and most substantially alters cysteine and methionine metabolism in primary colorectal cancer cells.
a, Relative cell numbers in CRC119 and CRC240 primary tumour cells treated with different doses of methionine for 72 h. Mean ± s.d. n = 3 biologically independent samples; similar results were obtained from 3 independent experiments. *P < 0.05, two-tailed Student’s t-test. b, Volcano plots of metabolites in cells cultured in 0 or 100 μM methionine for 24 h. P values were determined by two-tailed Student’s t-test. c, Left, Venn diagram of significantly changed (*P < 0.05, two-tailed Student’s t-test) metabolites in CRC119 and CRC240 primary cells cultured with no methionine versus control (100 μM methionine), and pathway analysis of metabolites that were commonly changed. Right, fold change of metabolites in cysteine and methionine metabolism, and pyrimidine metabolism, in CRC119 and CRC240 primary cells treated with 0 or 100 μM methionine. Mean ± s.d. n = 3 biologically independent samples. *P < 0.05, two-tailed Student’s t-test. d, Relative fold change of intensity of amino acids by methionine deprivation in CRC119 and CRC240 primary cells. Mean ± s.d. n = 3 biologically independent samples. *P < 0.05, two-tailed Student’s t-test.
Extended Data Fig. 5 Dietary restriction of methionine sensitizes PDX models of colorectal cancer to 5-FU chemotherapy.
a, Volcano plots of metabolites in plasma and liver altered by the combination of dietary restriction of methionine and 5-FU treatment. P values were determined by two-tailed Student’s t-test. b, Effect of 5-FU treatment alone, and a combination of methionine restriction and 5-FU treatment, on metabolites in tumour, plasma and liver, evaluated by taking the log10 of the fold change. Box limits are the 25th and 75th percentiles, centre line is the median, and the whiskers are the minimal and maximal values. The data represents metabolites in liver (337), plasma (282) and tumour (332) from n = 8 mice per group. c, Numbers of metabolites significantly changed by methionine restriction, 5-FU treatment or the combination of methionine restriction and 5-FU treatment in plasma, tumour and liver. *P < 0.05, two-tailed Student’s t-test. d, Pathway analysis of metabolites significantly changed (*P < 0.05, two-tailed Student’s t-test) by methionine restriction, 5-FU treatment or the combination of dietary methionine restriction and 5-FU treatment (false discovery rate < 0.5). e–g, Relative intensity of metabolites related to cysteine and methionine metabolism, and nucleotide metabolism, in tumour (e) and redox balance in liver (f) and plasma (g). Mean ± s.e.m. n = 8 mice per group. *P < 0.05, two-tailed Student’s t-test. h, Spearman’s rank correlation coefficients of methionine restriction and 5-FU-induced fold change of metabolites in tumour, plasma and liver from mice on dietary methionine restriction and with 5-FU treatment.
Extended Data Fig. 6 Inhibition of cell growth mediated by methionine restriction is largely due to interruptions to the production of nucleosides and redox balance.
a, The synergic effects of methionine restriction and 5-FU treatment in CRC119 primary cells and HCT116 cells were evaluated by cell counting. Mean ± s.e.m. n = 3 biological replicates. *P < 0.05 by two-tailed Student’s t-test. b, The rescue effect of choline, formate, homocysteine, homocysteine with vitamin B12, nucleosides and NAC, alone or in combination, on the inhibition of HCT116 cell proliferation mediated by methionine restriction. Mean ± s.e.m., n = 9 biologically independent samples from 3 independent experiments. *P < 0.05 versus control, ^P < 0.05 versus methionine restriction, #P < 0.05 versus 5-FU treatment, $P < 0.05 versus methionine restriction + 5-FU treatment, by two-tailed Student’s t-test. c, Mass intensity for [M + 1] dTTP and [M + 1] methionine in HCT116 cells from the experiment described in Fig. 2h. Mean ± s.d. n = 3 biologically independent samples. *P < 0.05 versus control and #P < 0.05 versus methionine restriction, by two-tailed Student’s t-test.
Extended Data Fig. 7 Dietary restriction of methionine sensitizes mouse models of RAS-driven autochthonous sarcoma to radiation.
a, Volcano plots of metabolites in tumour, plasma and liver, and pathway analysis of metabolites significantly changed (*P < 0.05, two-tailed Student’s t-test) by dietary restriction of methionine alone (false discovery rate < 1). b, Spearman’s rank correlation coefficients of fold change of metabolites in tumour, plasma and liver induced by methionine restriction. c, Volcano plots of metabolites in tumour, plasma and liver, and pathway analysis of metabolites significantly changed (*P < 0.05, two-tailed Student’s t-test) by dietary restriction of methionine and radiation (false discovery rate < 0.5). d, Spearman’s rank correlation coefficients of fold change of metabolites in tumour, plasma and liver induced by methionine restriction and radiation. e, Relative intensity of metabolites related to cysteine and methionine metabolism, and energy balance in tumours. Mean ± s.d. n = 7 mice per group, except for the methionine-restriction group (n = 6). *P < 0.05 versus control, by two-tailed Student’s t-test. f, g, The largest effects on metabolism occurred in the combination of diet and radiation. f, Effect of methionine restriction and radiation alone, or in combination, on metabolites in tumour, plasma and liver, evaluated by taking the log10 of fold change. Box limits are the 25th and 75th percentiles, centre line is the median, and the whiskers are the minimal and maximal values. The data represent metabolites in liver (319), plasma (308) and tumour (332) from n = 7 mice per group, except for the methionine-restriction group (n = 6). g, Numbers of metabolites significantly changed (*P < 0.05, two-tailed Student’s t-test) by methionine restriction and radiation alone, or in combination.
Extended Data Fig. 8 Dietary restriction of methionine can be achieved in humans.
a, Heat map of significantly changed (*P < 0.05, two-tailed Student’s t-test) plasma metabolites by dietary intervention, in six human subjects. b, Volcano plot of plasma metabolites. P values were determined by two-tailed Student’s t-test. c, Pathway analysis of altered (*P < 0.05, two-tailed Student’s t-test) plasma metabolites. d, Relative intensity of amino acids in plasma. n = 6 biologically independent humans. *P < 0.05 by two-tailed Student’s t-test.
Extended Data Fig. 9 Comparative metabolic effects of methionine restriction across mouse models and humans.
a, Spearman’s rank correlation coefficients of fold changes of methionine-related metabolites induced by methionine restriction (defined in Extended Data Fig. 3f) in plasma samples from non-tumour bearing C57BL/6J mice, CRC119 and CRC240 mouse models, the sarcoma mouse model and healthy human subjects. b, Spearman’s rank correlation coefficients among different models in a, ranked from the highest to the lowest.
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Gao, X., Sanderson, S.M., Dai, Z. et al. Dietary methionine influences therapy in mouse cancer models and alters human metabolism. Nature 572, 397–401 (2019). https://doi.org/10.1038/s41586-019-1437-3
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DOI: https://doi.org/10.1038/s41586-019-1437-3
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