Nutrient requirements of organ-specific metastasis in breast cancer

Abstract

Cancer metastasis is a major contributor to patient morbidity and mortality¹, yet the factors that determine the organs where cancers can metastasize are incompletely understood. Here we quantify the absolute levels of 124 metabolites in multiple tissues in mice and investigate how this relates to the ability of breast cancer cells to grow in different organs. We engineered breast cancer cells with broad metastatic potential to be auxotrophic for specific nutrients and assessed their ability to colonize different tissue sites. We then asked how tumour growth in different tissues relates to nutrient availability and tumour biosynthetic activity. We find that single nutrients alone do not define the sites where breast cancer cells can grow as metastases. In addition, we identify purine synthesis as a requirement for tumour growth and metastasis across many tissues and find that this phenotype is independent of tissue nucleotide availability or tumour de novo nucleotide synthesis activity. These data suggest that a complex interplay between multiple nutrients within the microenvironment dictates potential sites of metastatic cancer growth, and highlights the interdependence between extrinsic environmental factors and intrinsic cellular properties in influencing where breast cancer cells can grow as metastases.

Fig. 1. Nutrient levels in plasma, CSF and tissue interstitial fluid from mice.

a, Schematic of plasma, CSF and tissue interstitial fluid (IF) isolation from female NSG or B6 mice. Metabolites were quantified by liquid chromatography–mass spectrometry alongside a dilution series of chemical standards; 124 metabolites were quantified. TCA, tricarboxylic acid. Created in BioRender. Abbott, K. (2025) https://BioRender.com/tp4l6fd. b,c, PCA (b) and hierarchical clustering (c) of metabolites measured in plasma, CSF or tissue IF samples from NSG mice. Data represent n = 10 (plasma, liver IF, lung IF and pancreas IF), n = 6 (kidney IF and MFP IF) or n = 4 (CSF) biologically independent samples. Metabolite measurements were performed twice for plasma, liver, lung and pancreas IF samples and once for the remaining tissues; data from repeated measurements were pooled for analysis. Columns of the heatmap were z-score normalized. d, Bar plot showing the number of metabolites with significantly lower (depleted) or higher (elevated) levels in IF or CSF relative to plasma. Significance was determined by Welch’s t-test (two-sided, unequal variance) with fold change > 2 and P < 0.05. e, Loadings plot presenting the contribution of individual metabolite classes to the PCA components in b. The colours indicate pathway assignment in a. f–k, log₂ fold change of selected metabolites in tissue IF or CSF relative to plasma. Data are mean ± s.e.m., with n values as in b,c. l–n, Heatmaps of average log₂ fold change (FC) in metabolite concentrations relative to plasma. Scale bars indicate value ranges. o, Area under the curve (AUC) values from proliferation assays of MDA-MB-231 control (Ctrl) or indicated knockout (KO) cells cultured ± relevant rescue metabolites (Extended Data Fig. 6a–f). AUC values were normalized to control + rescue. Data are mean ± s.d.; n = 3 biologically independent samples. Representative plots are shown from one of two independent experiments with similar results. Statistical analysis was done by a Kruskal–Wallis test with Dunn’s multiple comparisons (two-sided). Arg, arginine; Cit, citrulline; Hx, hypoxanthine; Pro, proline; Ser, serine; Urd, uridine. PYCR denotes PYCR1/2/3 triple knockout. Source data

Fig. 2. Intracardiac implantation to determine where metabolite auxotrophs can grow as metastases.

a, Schematic of intracardiac injection of control and auxotroph cells expressing Fluc into the left ventricle, enabling metastatic spread to the brain, liver, lung, ovary, bone, and kidney or adrenal glands. Colonization was quantified by bioluminescence imaging of harvested tissues at end point. MDA-MB-231–Fluc and HCC1806–Fluc cells were injected into NSG mice; EO771–Fluc cells were injected into B6 mice. Created in BioRender. Abbott, K. (2025) https://BioRender.com/73th1x3. b, Petal plots used to display metastatic patterns, where each petal represents a tissue and its length indicates growth of auxotrophs relative to controls. c, Petal plots showing the metastatic distributions of different metabolite auxotroph cells relative to control cells. Data are mean ± 95% confidence interval; n = 3–7 biologically independent mice per group, with exact numbers reported in the Source Data. Plots were derived from Extended Data Fig. 8. Bo, bone; Br, brain; Ki, kidney; Li, liver; Lu, lung; Ov, ovary. d, Scatter plots of average metabolite concentrations in tissue IF versus auxotroph dependency (log₂ fold depletion in tumour growth of knockout relative to control). MDA-MB-231 (black) and HCC1806 (blue) were compared with NSG tissue metabolite levels; EO771 (red) was compared with B6 levels. Brain values reflect CSF. Data are mean ± s.e.m.; n = 3–7 biologically independent mice per group with exact numbers reported in the Source Data. Pearson correlation coefficients (r) and exact P values are provided in the Source Data (two-sided tests; *P < 0.05 and **P < 0.01). PYCR denotes PYCR1/2/3 triple knockout. Experiments were performed once. Source data

Fig. 3. Metabolic dependencies of brain and MFP tumours.

a, Schematic of direct implantation of control or auxotroph cells expressing Gluc into the brain or MFP of mice, with tumour growth monitored over time via blood luminescence. MDA-MB-231–Gluc and HCC1806–Gluc cells were injected into NSG mice; EO771–Gluc cells were injected into B6 mice. Created in BioRender. Abbott, K. (2025) https://BioRender.com/e78ptc1. b, Petal plots illustrating auxotroph tumour growth relative to controls. Each petal represents a cell line and tumour site; petal length reflects relative tumour growth of auxotrophs versus controls. c, Petal plots showing growth distributions of auxotroph versus control tumours. Data are mean ± 95% confidence interval; n = 2–10 biologically independent mice per group with exact numbers reported in the Source Data. Plots were derived from Extended Data Fig. 11. Cell lines and tumour sites are as in b. d, Scatter plot correlating auxotroph dependency for brain growth based on route of cell delivery. Axes show log₂ fold change in tumour growth of knockout versus control cells following intracranial (x axis) or intracardiac (y axis) injections. Data are mean ± s.e.m.; n = 2–10 biologically independent mice per group (exact numbers are in Extended Data Figs. 8 and 11). Two-sided Pearson correlation coefficient and exact P value are provided in figure panel. e, Scatter plots of average metabolite concentrations in MFP IF and CSF (proxy for brain) versus auxotroph dependency for growth in each site (log₂ fold depletion of knockout relative to control). Symbols represent tissue metabolite concentrations. Data are mean ± s.e.m.; n = 3–7 biologically independent mice per group, with exact numbers reported in the Source Data. PYCR denotes PYCR1/2/3 triple knockout. Experiments were performed once. Source data

Fig. 4. Assessment of metabolite fate in primary and brain metastatic breast cancers.

a, Schematic of [U-¹³C]-glucose infusion to trace metabolite fate in female NSG mice bearing MDA-MB-231 tumours in the MFP or brain. Created in BioRender. Abbott, K. (2025) https://BioRender.com/os441ve. b, Fractional labelling of plasma glucose (m+0 and m+6) following [U-¹³C]-glucose infusion (0.4 mg min⁻¹, 10 h) in mice with MFP or brain tumours. Data are mean ± s.e.m.; n = 5 (MFP tumours) or n = 4 (brain tumours) biologically independent mice. c–i, Fractional labelling of the indicated metabolites measured by liquid chromatography–mass spectrometry. Separate cohorts of infused mice were implanted with tumours in either the MFP or brain, and both tumour and noncancerous tissues were collected from the same mice in each cohort. Data are mean ± s.e.m.; n = 5 (MFP tumour and noncancerous MFP), n = 4 (brain tumour) and n = 3 (noncancerous brain) biologically independent samples. Statistical analysis was performed using one-way analysis of variance with Holm–Sidak multiple comparisons test (two-sided). Experiments were performed once. Source data

Fig. 5. Correlating metabolic dependencies and metabolite levels with tissue-specific metastatic potential.

a, Scatter plots correlating the metastatic potential of breast cancer cells to the lung with in vitro CRISPR dependencies of the indicated genes (left). Each dot represents one cell line. P values from two-sided Pearson correlation tests are shown in the panels. Heat map showing −log₁₀(P) values from correlations between metastatic potential to the indicated tissues and CRISPR dependency scores of the indicated genes (right). Data are from the DepMap portal. b, Scatter plots correlating metabolite concentrations in tissue IF with the metastatic potential of control cells following intracardiac injection. MDA-MB-231 (black) and HCC1806 (blue) values were compared with NSG tissue metabolite data; EO771 (red) was compared with B6 data. Brain values reflect CSF. Data are mean ± s.e.m.; n = 3–7 biologically independent mice per group, with exact numbers provided in the Source Data. Summary data used to derive metastatic potential values are presented in Extended Data Fig. 8. Pearson correlation coefficients (r) and exact P values are provided in the Source Data (two-sided tests). c, Volcano plots depicting Pearson correlation coefficients and P values for metabolites correlated with metastatic potential of control cell lines following intracardiac injection. The black circles indicate metabolites significantly correlated in all three cell lines. Significance was defined as |r| > 0.5 and P < 0.05 (two-sided). G1P, glucose-1-phosphate; GPC, glycerophosphocholine; 3M2OP, 3-methyl-2-oxopentanoic acid; PEtn, O-phosphoethanolamine. Source data

Extended Data Fig. 1. Metabolite concentrations in mouse plasma, CSF, and tissue interstitial fluid.

a-j, Heatmaps of metabolite concentrations in plasma, cerebrospinal fluid (CSF), and the indicated tissue interstitial fluids (IF) from female NSG mice measured by LC/MS. Metabolites are grouped by pathway; scale bars indicate concentration ranges. Data represent n = 10 (plasma, liver IF, lung IF, pancreas IF), n = 6 (kidney IF, MFP IF), or n = 4 (CSF) biologically independent samples. Metabolite measurements were performed twice for plasma, liver, lung and pancreas IF samples and once for the remaining tissues; data from repeated measurements were pooled for analysis. Metabolite concentrations below the limit of detection are shown in grey and labeled “nd” (not determined). k, Scatter plots comparing LC/MS metabolite concentrations (µM) in plasma or tissue IF from female NSG mice across two independent experiments to assess reproducibility. Data represent mean values from n = 4 (experiment 1) and n = 6 (experiment 2) biologically independent samples. Pearson correlation coefficients (r) and P values from two-sided tests, and the number of metabolites measured, are indicated in each panel. Source data

Extended Data Fig. 2. Analysis of post-collection metabolite stability in mouse plasma and liver interstitial fluid.

a, Heatmap showing log₂ fold change in metabolite levels over time relative to t = 0 in plasma and liver interstitial fluid (IF) samples incubated on ice for up to 30 min. Each value represents the mean of n = 4 biologically independent samples per time point from female C57BL/6 J (B6) mice. Experiments were performed once. b-c, Volcano plots of metabolites altered at 5, 10, or 30 min post-collection in plasma (b) or liver IF (c). Significance was defined as fold change ≥ 2 with P < 0.05 by two-sided Welch’s t-test with unequal variance. d, Bar plot showing the percentage of significantly altered metabolites at each time point in (b-c). e, Concentrations of hypoxanthine in plasma and liver IF over time. Data are mean ± SEM; n = 4 biologically independent samples per time point. Statistical analysis was performed using a Kruskal-Wallis test with Dunn’s multiple comparisons (two-sided). f, Scatter plots comparing plasma metabolite concentrations from cardiac puncture versus cheek bleed in female B6 and NSG mice. For B6, n = 7 (cardiac puncture) and n = 5 (cheek bleed); for NSG, n = 4 (cardiac puncture) and n = 6 (cheek bleed). Experiments were performed once. Each point represents the mean concentration of one metabolite across the indicated biological replicates. Pearson correlation coefficients (r) and P values from two-sided tests are shown in the panels. g, Volcano plots of plasma metabolite differences between collection methods in female B6 and NSG mice. Significance was defined as fold change ≥ 2 with P < 0.05 by two-sided Welch’s t-test with unequal variance. Sample sizes are as in (f). Source data

Extended Data Fig. 3. Comparison of tissue metabolite levels between C57BL/6 J and NSG mice.

a, Scatter plots comparing metabolite concentrations across plasma, CSF and tissue interstitial fluid (IF) from female C57BL/6 J (B6) and female NSG mice. For B6 mice: n = 15 (CSF) and n = 5 (plasma, all tissue IF). For NSG mice: n = 10 (plasma, liver IF, lung IF, pancreas IF), n = 6 (kidney IF, MFP IF), and n = 4 (CSF). Metabolite measurements for B6 mice were performed twice for CSF and once for plasma and all tissue IF samples; data from repeated measurements were pooled for analysis. Metabolite measurements for NSG mice were performed twice for plasma, liver IF, lung IF and pancreas IF and once for kidney IF, MFP IF and CSF; data from repeated measurements were pooled for analysis. Each point represents the mean concentration of one metabolite in the indicated tissue. The number of metabolites measured is indicated in each panel. Pearson correlation coefficients (r) and P values from two-sided tests are shown in the panels. b, Volcano plots of metabolites differing between female B6 and NSG mice in plasma, CSF, or tissue IF. Significance was defined as fold change ≥ 2 with P < 0.05 by two-sided Welch’s t-test with unequal variance. Sample sizes are as in (a). 2HB, 2-hydroxybutyrate; 2HG, 2-hydroxyglutarate; GAA, guanidinoacetic acid. Source data

Extended Data Fig. 4. Nutrient levels in plasma, CSF, and tissue interstitial fluid from C57BL/6 J mice.

a-b, Principal component analysis (PCA) (a) and hierarchical clustering (b) of metabolites measured in plasma, cerebrospinal fluid (CSF), and tissue interstitial fluid (IF) from female C57BL/6 J (B6) mice. Data represent n = 15 (CSF) or n = 5 (plasma and all tissue IF) biologically independent samples. Metabolite measurements for B6 mice were performed twice for CSF and once for plasma and all tissue IF samples; data from repeated measurements were pooled for analysis. Columns of the heatmap were z-score normalized. c, Bar plot showing the number of metabolites with significantly lower (depleted) or higher (elevated) levels in IF or CSF relative to plasma. Significance was determined by Welch’s t-test (two-sided, unequal variance) with fold change >2 and P < 0.05. d, Loadings plot presenting the contribution of individual metabolite classes to the PCA components in (a). Colors indicate pathway assignment. e-l, Log₂ fold change of selected metabolites in tissue IF or CSF relative to plasma in female B6 or NSG mice. For B6: n = 15 (CSF) or n = 5 (plasma and all tissue IF). For NSG: n = 10 (plasma, liver IF, lung IF, pancreas IF), n = 6 (kidney IF, MFP IF), or n = 4 (CSF). Data are mean ± SEM. m-n, Log₂ fold change in amino acid (m) or nucleotide (n) concentrations between paired CSF and plasma samples from B6 (n = 14) or NSG (n = 4) mice. Data are mean ± SEM. Metabolite measurements for B6 mice were performed twice, and data from repeated measurements were pooled for analysis. Metabolite measurements for NSG mice were performed once. Statistical analysis by one-sample Student’s t-test (two-sided); exact P values provided in Source Data (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). Source data

Extended Data Fig. 5. Validation of amino acid and nucleotide synthesis gene knockouts in breast cancer cells.

a, Schematic of amino acid and nucleotide synthesis pathways, highlighting enzymes targeted to generate auxotrophs. Genes knocked out in breast cancer cells are shown in red; relevant metabolites synthesized are shown in blue. Asp: aspartate; DHO: dihydroorotate; Glu: glutamate; Gln: glutamine; IMP: inosine monophosphate; P5C: 1-pyrroline-5-carboxylic acid; PRPP: phosphoribosyl diphosphate; UMP: uridine monophosphate. Created in BioRender. Abbott, K. (2025) https://BioRender.com/lz9ywec. b, Western blot analysis of parental MDA-MB-231, EO771, and HCC1806 breast cancer cells for expression of the indicated proteins. c, Quantification of western blots in (b). Signal intensity of each protein was normalized to vinculin loading control. Data are mean ± SD; n = 3 biologically independent samples. Statistical analysis was performed using one-way ANOVA with Holm–Sidak multiple comparisons test (two-sided). d-i, Western blot analysis of non-targeting control (Ctrl) or knockout (KO) auxotroph clonal cells for expression of the indicated proteins in MDA-MB-231, HCC1806, or EO771 cells. PYCR KO represents PYCR1/2/3 triple KO. For gel source data, see Supplementary Fig. 1. Representative blots are shown from one of two independent experiments with similar results. Source data

Extended Data Fig. 6. Validation of auxotroph cells by assessing proliferation with or without rescue metabolites.

a-f, Percent confluence of control (Ctrl) or knockout (KO) clonal cells cultured in medium with or without the relevant rescue metabolites. ASNS KO (a) and PYCR KO (d) were cultured in DMEM; ASS1 KO (b) and PHGDH KO (c) in RPMI lacking arginine or serine, respectively; DHODH KO (e) and GART KO (f) in RPMI. Rescue metabolite concentrations: 1.15 mM arginine (Arg), 379 µM asparagine (Asn), 1 mM citrulline (Cit), 100 µM hypoxanthine (Hx), 174 µM proline (Pro), 286 µM serine (Ser), 100 µM uridine (Urd). Data are mean ± SD; n = 3 biologically independent samples. g, Percent confluence of EO771 cells in standard RPMI (11 mM glucose), RPMI containing 25 mM glucose (DMEM-equivalent), or standard RPMI refreshed every 48 h. Data are mean ± SD; n = 3 biologically independent samples. h-k, Percent confluence of HCC1806 Ctrl or auxotroph KO cells (ASNS KO, ASS1 KO, PHGDH KO, PYCR KO) cultured in DMEM lacking the indicated metabolites. h, ± Asn. i, Without Arg, ± Cit. j, ± Ser. k, ± Pro. Rescue metabolite concentrations: 398 µM Arg, 379 µM Asn, 1 mM Cit, 174 µM Pro, 400 µM Ser. Data are mean ± SD; n = 3 biologically independent samples. Asn, asparagine; Arg, arginine; Cit, citrulline; Ser, serine; Pro, proline; Urd, uridine; Hx, hypoxanthine; PYCR KO, PYCR1/2/3 triple KO. Representative plots are shown from one of two independent experiments with similar results. Source data

Extended Data Fig. 7. Assessment of breast cancer cell metastasis.

a, Whole-body bioluminescence imaging (BLI, photons/sec) of MDA-MB-231-Fluc, HCC1806-Fluc, and EO771-Fluc control cells over time following intracardiac injection into female NSG (MDA-MB-231, HCC1806) or C57BL/6 J (EO771) mice. Data are mean ± SEM; n = 7 (MDA-MB-231, NSG), n = 5 (HCC1806, NSG), and n = 7 (EO771, B6) biologically independent mice. Experiments were performed once. Data are from the same experiments shown in Extended Data Fig. 8c. b, BLI and quantification of metastatic burden in organs of mice injected as in (a). Data are mean ± SEM. c, Representative images of mice and tissues showing tumor burden after intracardiac injection as in (a). Color scales reflect BLI radiance; minimum and maximum values are shown next to each set of images. Source data

Extended Data Fig. 8. Metastasis of breast cancer auxotrophs.

Whole-body bioluminescence imaging (BLI, photons/sec) and tissue-specific BLI of female NSG (MDA-MB-231, HCC1806) or C57BL/6 J (EO771) mice injected with control (Ctrl) or auxotroph cells. Each panel corresponds to one gene knockout: a, DHODH KO; b, GART KO; c, ASNS KO; d, ASS1 KO; e, PHGDH KO; f, PYCR KO (PYCR1/2/3 triple KO). Within each panel, data are shown for all three cell lines, where left plots track whole-body BLI over time and right plots show quantitative analysis of tissue-specific metastatic burden. The final BLI time point corresponds to the experimental endpoint when tissues were harvested. Depletion values indicate fold reduction in metastatic load relative to Ctrl: ○, <2-fold; +, 2–5 fold; ++, 5–10 fold; +++, >10-fold. Two bones, kidneys, or ovaries were analyzed per mouse. Data are mean ± SEM; exact n values are shown in each panel (biologically independent mice). Experiments were performed once. Statistical analysis was performed using two-sided unpaired Welch’s t-test with Holm-Sidak multiple comparisons correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). Exact P values are provided in the Source Data. The same control mice were used as follows: MDA-MB-231 in (b, c; d, e); HCC1806 in (a, d, f; b, c, e); EO771 in (a, b; d, e). Groups separated by a semicolon denote distinct cohorts of control mice. Source data

Extended Data Fig. 9. Reproducibility of tissue-specific depletion patterns and validation of HCC1806 auxotrophs.

a-d, Upper panels, whole-body bioluminescence imaging (BLI, photons/sec) of female NSG mice injected with HCC1806-Fluc control (Ctrl) or auxotroph cells. Final BLI values correspond to the endpoint when tissues were harvested. Lower panels, tissue-specific BLI showing metastatic burden of auxotrophs. Depletion values indicate fold reduction versus Ctrl: ○, <2-fold; +, 2–5 fold; ++, 5–10 fold; +++, >10-fold. Data are mean ± SEM; exact n values are shown in each panel (biologically independent mice). Two bones, kidneys, or ovaries were analyzed per mouse. Experiments were performed once. Statistical analysis by two-sided unpaired Welch’s t-test with Holm-Sidak multiple comparisons correction. PYCR KO denotes PYCR1/2/3 triple KO. e, Comparison of ASNS, ASS1, PHGDH, or PYCR KO depletion across two independent experiments. Depletion values represent tissue-specific BLI of KO relative to Ctrl. Experiment 1 corresponds to Extended Data Fig. 8; experiment 2 corresponds to (a-d). Data are mean ± SEM; P values from two-sided unpaired Welch’s t-tests with Benjamini–Krieger–Yekutieli correction for multiple comparisons; ns: not significant. f-g, Western blots confirming loss of ASNS in brain (f) and ASS1 in liver (g) metastases from KO versus Ctrl cells. Cells were isolated from biologically independent metastases in (a-d). For gel source data, see Supplementary Fig. 1. h-k, Proliferation of metastasis-derived auxotrophs ex vivo. h-i, Percent confluence of ASNS KO brain lines in RPMI lacking (-Asn) or supplemented (+ Asn, 379 µM). j-k, Percent confluence of ASS1 KO liver lines in RPMI lacking (-Arg), with (+Cit, 1 mM), or (+Arg, 1.15 mM). Data are mean ± SD; n = 3 biologically independent samples per condition. Each line was derived from an independent metastatic outgrowth from different mice in (a-d). In f-k, representative plots are shown from one of two independent experiments with similar results. Source data

Extended Data Fig. 10. Correlations of auxotroph metastatic potential with tissue metabolite levels.

a-b, Scatter plots correlating tissue metabolite concentrations with MDA-MB-231 dependency on GART for metastatic growth. a, Creatine. b, Glucose-6-phosphate (Glucose-6P). The x-axis shows tissue metabolite concentrations; the y-axis shows log2 fold change of knockout (KO) versus control (Ctrl). Symbols denote metabolite concentrations in specific tissues; brain values reflect CSF. Data are mean ± SEM; n = biologically independent mice as in Extended Data Fig. 8. Pearson correlation coefficients (r) and exact P values (two-sided) are shown in figure panels. c, Volcano plots of Pearson correlation coefficients and P values for correlations between metabolite levels and metastatic potential of auxotroph cell lines following intracardiac injection, calculated as in (a-b). d, Fraction of metabolites showing significant positive (pos) or negative (neg) correlations with metastatic potential of the indicated auxotroph cell lines from (c). e-f, Absolute concentrations of arginine (e) or asparagine (f) in liver IF and CSF replotted from Extended Data Fig. 1a. Data are mean ± SD; n = 6 biologically independent mice (liver IF) and n = 4 biologically independent mice (CSF). Dotted lines mark in vitro IC50 values from (i-j). g-h, Percent confluence of HCC1806 Ctrl or KO cells cultured across a titration series of rescue metabolites. g, Ctrl and ASS1 KO cells in RPMI-Arg with 1 mM citrulline plus increasing arginine. h, Ctrl and ASNS KO cells in DMEM with increasing asparagine. Data are mean ± SD; n = 3 biologically independent samples. i-j, Area under the curve (AUC) from proliferation assays in (g-h), plotted against log₁₀-transformed arginine (i) or asparagine (j) concentrations. Dotted lines indicate IC50 values. Data are mean ± SD; n = 3 biologically independent samples. In g-j, representative plots are shown from one of two independent experiments with similar results. Source data

Extended Data Fig. 11. Tumor growth of auxotroph versus control cells following intracranial or MFP implantations.

Tumor growth and survival were measured in NSG mice implanted with MDA-MB-231-Gluc or HCC1806-Gluc cells, or in C57BL/6 J mice implanted with EO771-Gluc cells. Control (Ctrl) and knockout (KO) auxotroph lines were implanted either in the brain or mammary fat pad (MFP), and tumors were monitored over time by secreted Gaussia luciferase (Gluc). Survival in intracranial injections was assessed in the same cohorts. EO771 experiments were performed for brain tumors only. a-c, DHODH KO; d-f, GART KO; g-i, ASNS KO; j-l, ASS1 KO; m-o, PHGDH KO; p-r, PYCR KO (PYCR1/2/3 triple KO). For each gene set, the three panels show MDA-MB-231, HCC1806, and EO771. Within each panel, subplots are arranged (left, middle, right) as brain tumor growth, brain survival, and MFP tumor growth (MDA-MB-231 and HCC only). Experiments were performed once. Data are mean ± SEM for tumor growth and Kaplan-Meier survival curves for survival analysis; n = biologically independent mice indicated in each panel. Tumor growth was analyzed by two-way ANOVA across timepoints and groups, and survival by log-rank (Mantel–Cox) test. Exact P values (two-sided) are provided in figure panels. The same control mice were used as follows: for MDA-MB-231, brain (a, d, g, j, m, p) and MFP (a, d, g, j, m, p); for HCC1806, brain (b, h, k, q; e, n) and MFP (b, h, k, q; e, n); for EO771, brain (c, i, l; f, o, r). Groups separated by a semicolon denote distinct cohorts of control mice. Source data

Extended Data Fig. 12. Auxotroph dependency in brain tumors; plasma metabolite labeling in mice bearing brain versus MFP tumors.

a, Relative dependency of metabolite auxotroph cells on specific metabolic genes for growth in the brain, expressed as log₂ fold change of knockout (KO) relative to control (Ctrl) following intracranial or intracardiac injection. Data are mean ± SEM. Group sizes and instances where the same control mice were used across panels are indicated in Extended Data Figs. 8 and 11. PYCR denotes PYCR1/2/3 triple KO. b, Tumor growth in NSG mice implanted with MDA-MB-231-Gluc cells in the mammary fat pad (MFP) or brain. Tumor burden was monitored over time using secreted Gaussia luciferase (Gluc). Data are mean ± SEM; n = 5 biologically independent mice (MFP) or n = 4 biologically independent mice (brain). At the experimental endpoint, following [U-¹³C]-glucose infusion, tumors and matched noncancerous tissues were harvested from the same mice (MFP tumor with non-tumor MFP tissue; brain tumor with adjacent brain). c-e, Plasma metabolite analyses from the mice in (b) following [U-¹³C]-glucose infusion. c, Total ion counts for the indicated metabolites at endpoint (white bars, MFP tumor–bearing mice; black bars, brain tumor-bearing mice). d-e, Isotopolog distributions showing fractional labeling of the indicated metabolites. In (b-e), data are mean ± SEM; n = 5 biologically independent mice (MFP tumor-bearing mice) and n = 4 (brain tumor-bearing mice). Experiments were performed once. Source data

Extended Data Fig. 13. Metabolite labeling in MDA-MB-231 brain and MFP tumors.

a, Normalized peak areas for glucose metabolism metabolites in MFP, MFP tumor, brain, or brain tumor tissues isolated at endpoint from the mice shown in Fig. 4 at endpoint. b, Isotopolog distributions of glucose metabolism metabolites measured by LC/MS in MDA-MB-231 tumors (brain or MFP) and matched noncancerous tissues from NSG mice infused with [U-¹³C]-glucose. c, Normalized peak areas for amino acids in MFP, MFP tumor, brain, or brain tumor tissues isolated as in (a). d, Isotopolog distributions of amino acids in MDA-MB-231 tumors (brain or MFP) and matched noncancerous tissues from NSG mice infused with [U-¹³C]-glucose. e, Normalized peak areas for nucleotides in MFP, MFP tumor, brain, or brain tumor tissues isolated from the mice shown in Fig. 4 at endpoint. f, Isotopolog distributions of nucleotides in MDA-MB-231 tumors (brain or MFP) and matched noncancerous tissues from NSG mice infused with [U-¹³C₆]-glucose. In (a-f), data are mean ± SEM; n = 5 biologically independent mice (MFP tumor, MFP), 3 (noncancerous brain), and 4 (brain tumor). g, Isotopolog distributions of the indicated metabolites in MDA-MB-231 control (Ctrl) or DHODH knockout (KO) cells cultured 24 h in medium containing [U-¹³C]-glucose and 100 µM uridine. h, Normalized peak areas for metabolites shown in (g). i-j, Isotopolog distributions of pyrimidines (i) and purines (j) in MDA-MB-231 Ctrl or DHODH KO cells cultured as in (g). k, Normalized peak areas for metabolites shown in (i-j). In (g-k), data are mean ± SD; n = 3 biologically independent samples. Experiments were performed once. Source data

Extended Data Fig. 14. Dietary depletion of serine and glycine alters tissue metabolite levels and site-specific metastasis.

a, Volcano plot of plasma metabolite changes in female NSG mice fed a control (Ctrl) or serine/glycine-deficient (-SG) diet for 15 days. Significance was defined as fold change ≥ 2 with P < 0.05 by two-sided Welch’s t-test with unequal variance; n = 5 mice per group. b, Plasma concentrations of serine and glycine from (a). Data are mean ± SD; n = 5 per group. c-d, Principal component analysis (PCA) (c) and hierarchical clustering (d) of metabolites in brain, kidney, liver, and lung from Ctrl or -SG diet-fed mice. n = 5 per tissue per diet. Heatmap columns were z-score normalized. e, Volcano plots showing tissue metabolite differences between diets, thresholds as in (a). n = 5 per group. f, Normalized peak areas for serine and glycine in tissues from (e). Data are mean ± SEM. g, Body weight of mice maintained on Ctrl or -SG diet. HCC1806-Fluc cells were injected after 15 days, and mice remained on their respective diets until endpoint. Data are mean ± SEM; n = 10 per group. h, Whole-body bioluminescence imaging (BLI, photons/sec) of the same mice shown in (g) tracking metastatic progression. Final values correspond to endpoint when tissues were harvested. Data are mean ± SEM; n = 10 per group. i, Tissue-specific BLI at endpoint following intracardiac injection of HCC1806-Fluc cells in Ctrl or -SG diet-fed mice. Data are mean ± SEM; n = 10 per group (two bones, kidneys, or ovaries analyzed per mouse). Statistical significance for (b, f, i) was assessed by two-sided unpaired Welch’s t-test with Holm-Sidak multiple comparisons correction. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant. Exact P values (two-sided) are provided in Source Data. Experiments were performed once. Source data

Extended Data Fig. 15. Correlation of metastatic potential with gene expression, CRISPR dependency, and metabolite levels.

a-b, Scatter plots correlating breast cancer cell line metastatic potential with (a) RNA expression (log₂ transcripts per million, TPM) or (b) in vitro CRISPR dependency of the indicated genes, from the Dependency Map portal. Each point represents a cell line. c, Scatter plot comparing Pearson correlation (r) values for EO771 metastatic potential across tissues with corresponding metabolite concentrations measured in interstitial fluid from female NSG mice (x-axis) versus C57BL/6 J mice (B6, y-axis). Each point is a metabolite–tissue pair. Points in red were significant in B6 only, points in blue in NSG only, and points in black in both cohorts (significance defined as |r | > 0.5, P < 0.05). d, Scatter plot correlating tissue carnosine concentrations with metastatic potential of control (Ctrl) cells following intracardiac injection (black, MDA-MB-231; blue, HCC1806; red, EO771). Symbols denote metabolite concentrations in specific tissues; brain values reflect CSF. Data are mean ± SEM; n = biologically independent mice as in Extended Data Fig. 8. Pearson correlation coefficients (r) and exact P values (two-sided) are provided in Source Data. Source data