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Understanding the factors that govern tumour growth in metastatic sites could lead to more effective therapies for advanced cancer. Among the tumour microenvironment factors that contribute to where tumours can grow as metastases, nutrient availability is a key component2,3,4,[5](#…
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Understanding the factors that govern tumour growth in metastatic sites could lead to more effective therapies for advanced cancer. Among the tumour microenvironment factors that contribute to where tumours can grow as metastases, nutrient availability is a key component2,3,4,5,6,7,8,9. Because nutrient levels vary across tissues, they can limit where cancers grow as metastases3,4,10,11, forcing cancer cells to undergo metabolic adaptations that create new dependencies for colonization of different tissues. For instance, limited lipid availability in the brain results in a site-specific dependency on fatty acid synthesis or desaturation for brain metastasis of breast tumours12,13 or leukaemias14. Similarly, reduced brain serine levels sensitize breast cancer brain metastases to inhibition of the serine synthesis enzyme PHGDH15. Both pyruvate and asparagine availability can influence breast cancer metastasis to the lungs16,17. These studies raise the question of whether reduced availability of specific nutrients in certain tissues broadly predicts where metastatic cancer cells are likely to colonize and grow.
Here we sought to explore the relationship between tissue nutrient availability and metastatic potential in triple-negative breast cancer (TNBC). We quantified the absolute levels of 124 metabolites present in plasma and across six mouse tissues and constructed a series of nutrient auxotrophs to assess their metastatic potential to multiple tissues following intracardiac injections. Our analysis revealed that although levels of some metabolites correlate with metastatic potential, the levels of individual nutrients in isolation are insufficient to determine metastatic preference or the ability of specific nutrient auxotrophs to grow in a tissue site. Instead, our findings suggest that metastatic preference is driven by a combination of multiple nutrient levels and cell-intrinsic factors.
Nutrient availability across tissues
To assess nutrient availability across multiple tissue sites, including where TNBC cells can grow as primary tumours or metastases, we isolated plasma and interstitial fluid from the mammary fat pad (MFP), liver, lung, kidney and pancreas of female NOD-SCID-γ (NSG) and female C57BL/6J (B6) mice. We also collected cerebrospinal fluid (CSF) as a surrogate for the brain extracellular environment because acquiring sufficient interstitial fluid for analysis of multiple metabolites from normal brain tissue was not feasible. Quantitative mass spectrometry determined the absolute levels of 124 metabolites in the interstitial fluid from these different tissue sites, as well as in plasma (Fig. 1a and Extended Data Fig. 1a–j). We confirmed high correlations across biological replicates, stability of most metabolites during handling and consistency between plasma collection methods and across NSG and B6 strains (Extended Data Figs. 1k–3). Only a small subset of metabolites showed method-specific or strain-specific variation. For example, hypoxanthine increased after prolonged incubation on ice that exceeded collection time (Extended Data Fig. 2e), and some metabolites differed between NSG and B6 in specific tissues (Extended Data Fig. 3).
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.
Principal component analysis (PCA) and hierarchical clustering revealed that metabolites measured in tissue interstitial fluid samples cluster distinctly from those measured in plasma and the CSF (Fig. 1b,c and Extended Data Fig. 4a,b). When considering the differences in metabolite concentrations between tissue interstitial fluid and plasma, we found that although some metabolites were depleted in tissue interstitial fluid relative to plasma, numerous metabolites were present at higher concentration in interstitial fluid than in plasma (Fig. 1d and Extended Data Fig. 4c). By contrast, CSF showed lower levels of many metabolites than plasma, a pattern probably attributable to the selective permeability of the blood–brain barrier and that is consistent with previous studies examining CSF from both humans and mice18,19. Of note, we found that nucleotide-related metabolites, but not amino acids, were greater contributors to the PCA components separating the fluid samples in both mouse strains (Fig. 1e and Extended Data Fig. 4d). This suggests that levels of nucleotides and nucleotide precursors or salvage products are an important contributor to the differences in nutrient availability across tissue environments.
Many of the differences in metabolite concentrations across tissues align with known metabolic characteristics of each tissue. Most tissue interstitial fluid displayed lower glucose and higher lactate levels than plasma (Fig. 1f,g and Extended Data Fig. 4e,f), consistent with active glucose metabolism20. The liver stood out with higher levels of both glucose and lactate in interstitial fluid than plasma, consistent with its glycogenolytic and gluconeogenic capabilities, as well as its role in recycling lactate back to glucose21. In liver interstitial fluid, we also observed lower arginine and higher ornithine levels than plasma (Fig. 1h,i and Extended Data Fig. 4g,h), probably reflecting active arginase activity in this tissue22. Conversely, citrulline was depleted in kidney interstitial fluid (Extended Data Fig. 4i), aligning with human data23 and perhaps reflective of an active urea cycle in this tissue. Liver interstitial fluid contained relatively higher concentrations of branched-chain amino acids (isoleucine, leucine and valine) than other tissues (Fig. 1j,k and Extended Data Fig. 4j–l), which may reflect known low branched-chain amino acid aminotransferase activity in this organ relative to other tissues24.
Consistent with previous measurements18,19, the levels of many amino acids were lower in CSF than in circulation, with notable variation in amino acid levels measured in other tissues (Fig. 1l and Extended Data Fig. 1a). Unlike previous reports18,19, serine was not depleted in the CSF relative to plasma, although it was lower than other tissue interstitial fluid. This lack of serine depletion in CSF was consistent across both NSG and B6 mouse strains (Extended Data Fig. 4m), and may be influenced by other factors such as circadian rhythm25 or diet26. In addition, although many nucleotide species were elevated in tissue interstitial fluid and low in CSF compared with plasma (Fig. 1m,n and Extended Data Figs. 1d and 4n), the purine hypoxanthine was consistently high across all tissue interstitial fluid as well as in CSF, although some of this elevation may be due to a post-collection artefact (Extended Data Fig. 2e). Collectively, these findings denote many variances in nutrient availability across different tissues, which could constrain the ability of cancer cells to metastasize to particular organs.
Generation of nutrient auxotrophs
Cancer cells exposed to nutrient-limited microenvironments often adapt by upregulating metabolic pathways to synthesize the deficient metabolite10. To eliminate this adaptive response and isolate how nutrient availability constrains metastatic growth, we engineered widely metastatic TNBC cell lines to be auxotrophic for specific metabolites that vary across tissues. We selected the human lines MDA-MB-231 and HCC1806 (ref. 12) and the B6 mouse cell line EO771 (ref. 27), then used CRISPR–Cas9 to knock out genes essential for the synthesis of specific metabolites with known nutrient rescues (Extended Data Fig. 5a). Specifically, we targeted ASNS (asparagine)28,29, ASS1 (arginine)30,31, PHGDH (serine)15,32, PYCR1, PYCR2 and PYCR3 (proline)33, DHODH (pyrimidines, for example, uridine)34,35 and GART (purines, for example, inosine or hypoxanthine)36. We confirmed variable expression of each of these proteins in parental MDA-MB-231, HCC1806 and EO771 cells by western blot (Extended Data Fig. 5b,c); of note, MDA-MB-231 cells expressed lower levels of ASS1 and PHGDH, consistent with previous observations32,37. We also confirmed loss of relevant enzyme expression in each knockout line by western blot (Extended Data Fig. 5d–i), and further confirmed that each knockout line exhibited impaired proliferation in the absence of the relevant nutrient that corresponded to the intended auxotrophy (Fig. 1o and Extended Data Fig. 6a–f). Supplementation of the specific metabolite rescued proliferation of each auxotroph cell line to comparable levels as the parental line. EO771 cells were noted to occasionally stop proliferating when cultured in RPMI, which we attribute to glucose depletion, as refreshing the medium or supplementing glucose fully rescued sustained proliferation (Extended Data Fig. 6g).
To generate proline auxotrophs, deletion of the mitochondrial (PYCR1 and PYCR2) and the cytoplasmic (PYCR3) genes encoding pyrroline-5-carboxylate reductase was required, as the presence of any of these genes has been reported to enable proline synthesis33 and could support proliferation following proline withdrawal (Extended Data Figs. 5g and 6d). As arginine starvation in parental cells arrests proliferation via mTOR inactivation38, addition of citrulline was required for control cells to proliferate without arginine (Fig. 1o and Extended Data Fig. 6b); however, ASS1-null cells could not grow without arginine even if citrulline was added, validating that ASS1-knockout cells lost the ability to generate arginine from citrulline. For GART, despite the presence of some species detected at 110 kDa (ref. 39) in MDA-MB-231 and HCC1806 using an anti-GART antibody (Extended Data Fig. 5i), depletion of the 50-kDa monomeric form was sufficient to result in hypoxanthine auxotrophy in all three cell lines (Extended Data Fig. 6f). To assess the specificity of these auxotrophies, we also tested HCC1806 ASNS-knockout, ASS1-knockout, PHGDH-knockout and PYCR1, PYCR2, PYCR3 triple knockout lines in media lacking other amino acids targeted in this study. Each knockout line maintained robust proliferation when deprived of amino acids unrelated to its engineered enzyme deficiency, supporting the specificity of the observed auxotrophies (Extended Data Fig. 6h–k). One exception was that ASNS-knockout cells showed reduced proliferation in citrulline-depleted media, suggesting a potential interaction with arginine synthesis pathways. Overall, these data confirm that the genetic modifications render cells dependent on external supplementation of the expected metabolites for proliferation and survival in standard culture conditions.
In constructing the nutrient auxotrophs, we identified three distinct categories of metabolite requirements. First, removing asparagine or proline from the culture medium reduced proliferation of all cell lines when the relevant synthesis genes were knocked out (Extended Data Fig. 6a,d). Second, withdrawal of arginine in all cell lines, or serine in MDA-MB-231 cells, reduced proliferation even when the synthesis genes were intact (Extended Data Fig. 6b,c). Third, nucleotides, which are typically absent from standard culture media, could be supplemented to rescue cell dependency on DHODH or GART (Extended Data Fig. 6e,f). These findings indicate that for some nutrients, cellular synthesis capacity is important for maximal proliferation regardless of environmental availability. Nevertheless, this panel of metabolite auxotrophs with varying metabolite synthesis requirements enable testing of how metabolite synthesis influences the ability of cells to colonize different tissues with different nutrient levels.
Single nutrients do not predict metastasis
Before testing the auxotrophs in vivo, we confirmed that MDA-MB-231, HCC1806 and EO771 cells tagged with firefly luciferase (Fluc) metastasize to multiple organs after intracardiac injection, consistent with previous reports12,40 (Extended Data Fig. 7). We then injected Fluc-tagged auxotrophs and control cells and compared their ability to form tissue-specific metastases (Fig. 2a–c and Extended Data Fig. 8). Nucleotide auxotrophs showed the strongest defects, as DHODH loss impaired metastasis in all sites for MDA-MB-231 and HCC1806, and in the liver and kidney or adrenal gland for EO771, whereas GART was essential across all tissues in all three lines (Extended Data Fig. 8a,b). Despite abundant hypoxanthine in tissue fluids and CSF (Fig. 1n and Extended Data Figs. 1d and 4n), GART remained essential, arguing that individual nutrient requirements are not sufficient to define the tissues to which these cancer cells can metastasize.
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.
In contrast to nucleotides, amino acid dependencies were heterogeneous and cell line specific (Fig. 2c and Extended Data Fig. 8c–f). ASNS was required for metastasis to bone and kidney or adrenal gland in MDA-MB-231 and HCC1806 but not in EO771, whereas effects on brain metastasis were modest, with only slight impairment in MDA-MB-231 and EO771 and no effect in HCC1806. Loss of ASS1 most consistently impaired bone metastasis across lines, although the magnitude was modest, whereas effects in other tissues varied. PHGDH dependency also differed, as MDA-MB-231 required PHGDH broadly, consistent with previous reports of serine synthesis dependence in brain metastasis15, but HCC1806 showed dependency only in lung and bone, and EO771 displayed minimal reliance. These findings demonstrate that low serine levels in the brain18,19 do not universally confer PHGDH dependence. Finally, proline synthesis was broadly required in MDA-MB-231 but variably so in HCC1806 and EO771, although all three lines showed some dependency on PYCR1, PYCR2 and PYCR3 (PYCR1/2/3) for brain metastasis. Collectively, these results indicate that, unlike nucleotide synthesis, amino acid auxotrophy does not reliably predict tissue-specific metastatic capacity. Instead, dependencies differ across cell lines and tissues, highlighting the combined influence of cell-intrinsic traits and local environments.
To assess variability and reproducibility, we repeated the in vivo metastasis experiment for four HCC1806 auxotroph lines (ASNS, ASS1, PHGDH and PYCR1/2/3), which had shown variable tissue-specific growth. Metastatic burden was quantified by bioluminescence imaging, and auxotroph-to-control fold changes were compared across experiments (Extended Data Fig. 9a–e). Of 24 tissue comparisons, 22 were consistent, supporting the reproducibility of the assay. The only differences were modestly better growth of ASS1-knockout cells in the brain and ovary in the repeat experiment. These results indicate that the heterogeneous metastatic phenotypes reflect biological differences rather than experimental variability.
To test whether auxotrophs retained their phenotype after metastasis formation, we isolated HCC1806 ASNS-knockout brain metastases and ASS1-knockout liver metastases following intracardiac injection. Western blotting confirmed that neither enzyme was re-expressed (Extended Data Fig. 9f,g). In culture, ASNS-knockout cells proliferated only with asparagine, and ASS1-knockout cells failed to grow without arginine, even with citrulline added (Extended Data Fig. 9h–k), both consistent with their original in vitro phenotypes. Thus, auxotrophy is preserved in vivo, and growth in nutrient-depleted tissues is not due to re-expression of the deleted enzyme.
To evaluate whether nutrient availability predicts tissue-specific auxotroph dependencies, we correlated metabolite levels with metastatic outgrowth (Fig. 2d). We hypothesized that auxotrophs would struggle to grow in tissues lacking the corresponding nutrient but would grow normally in tissues where it was abundant: for example, PHGDH-null cells in serine-poor versus serine-rich sites. Only a few significant correlations were observed: proline concentration correlated positively with PYCR1/2/3 dependency in HCC1806 cells, whereas arginine showed a negative correlation with ASS1 dependency in MDA-MB-231 cells. Broader analysis revealed few associations, most unrelated to the engineered auxotrophy (Extended Data Fig. 10a–d). Thus, single-nutrient levels alone do not reliably predict metabolic dependencies in metastasis.
We also considered whether the low but non-zero levels of amino acids measured in vivo might partially sustain auxotrophs. For example, arginine is present at 3 µM in liver interstitial fluid and asparagine at 4 µM in CSF (Extended Data Fig. 10e,f). In titration assays, ASS1-null cells showed a half-maximal inhibitory concentration (IC50) of 2.5 µM arginine (with citrulline present), and ASNS-null cells showed an IC50 of 10 µM asparagine (Extended Data Fig. 10g–j). These concentrations are at or below those measured in vivo, suggesting that residual nutrient availability could allow some proliferation but does not fully support proliferation of the auxotrophs in culture. Thus, additional support from the tumour microenvironment, such as stromal exchange or protein scavenging, must further enable cancer cell proliferation in nutrient-limited tissues.
Auxotroph tumour growth in the MFP or brain
Because nucleotide synthesis auxotrophs fail to grow in multiple tissues, it might be required for survival in circulation. To test this possibility, and to ask whether nutrient availability more directly constrains tumour growth at the implantation site itself, we implanted auxotrophs into the brain or MFP, bypassing dissemination through the circulation (Fig. 3a–c and Extended Data Fig. 11). We chose these sites because the levels of many nutrients, especially amino acids, are lower in the brain18,19, and previous work has suggested that tumours preferentially grow in their primary-site tissue environment41. To monitor tumour growth, we engineered auxotrophs to express Gaussia luciferase (Gluc)42, enabling noninvasive tracking by blood luminescence. We also implanted EO771 auxotrophs into the brain to compare metabolic dependencies in that site across all three lines.
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.
We first compared how the route of cell delivery to the brain influenced gene dependency for tumour growth in that site. Our analysis revealed a statistically significant but modest correlation between gene dependencies observed with intracranial and intracardiac injections (Fig. 3d and Extended Data Fig. 12a), indicating that the overall patterns of gene dependency are independent of the method of cell delivery to the brain. However, we found that dependencies were generally stronger when cells were directly implanted via intracranial injection. In addition, we observed some notable outliers in which the route of injection affected gene dependency. For instance, HCC1806 cells demonstrated a strong dependency on ASS1 when injected intracranially, but this dependency was reduced following intracardiac injection. EO771 cells lacking DHODH or PHGDH were able to form metastatic brain tumours when injected in the heart but not when directly implanted in the brain.
We found that nucleotide synthesis was invariably required, as loss of DHODH or GART impaired brain and MFP tumour growth in all three lines and extended survival (Fig. 3c and Extended Data Fig. 11a–f). These data are also consistent with a broad requirement for de novo nucleotide synthesis to grow in tissues despite high extracellular nucleotide levels (Fig. 1m,n). By contrast, amino acid dependencies were more variable, as was observed in intracardiac experiments. MDA-MB-231 required ASNS in both the brain and MFP, whereas HCC1806 showed no ASNS dependence in the brain and EO771 only showed a modest effect (Fig. 3c and Extended Data Fig. 11g–i). ASS1 was required for HCC1806 tumours in both the brain and the MFP, for MDA-MB-231 only in the MFP, and was dispensable in EO771 (Extended Data Fig. 11j–l). PHGDH was essential in MDA-MB-231 across sites but variably required in HCC1806 and EO771 (Extended Data Fig. 11m–o). Proline synthesis (PYCR1/2/3) was required in the brain for HCC1806, modestly in MDA-MB-231, but not for EO771 (Extended Data Fig. 11p–r). A consistent link between tissue nutrient availability and tumour growth was not observed (Fig. 3e). Together, these data show that direct implantation recapitulates the heterogeneous auxotroph dependencies observed with intracardiac injection and reinforces that single-nutrient levels do not reliably predict tissue tumour growth requirements.
MFP and brain tumour metabolic activity
To further study the relationship between nutrient levels in tissues and the ability to synthesize specific nutrients to allow tumour growth in a given tissue, we traced the fate of 13C-labelled glucose in MDA-MB-231-derived brain or MFP tumours in mice (Fig. 4a). We monitored the fraction of m+6-labelled glucose in plasma over the course of the infusion and observed that labelling approached steady state by the final time point (Fig. 4b and Extended Data Fig. 12b). We observed that plasma glucose labelling was slightly lower in mice bearing brain tumours than in those with MFP tumours, whereas 13C-labelling of pyruvate, lactate and amino acids in plasma was largely similar between cohorts (Extended Data Fig. 12c–e). These findings suggest that tumour location may modestly influence systemic glucose metabolism.
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.
Within tissues, lactate and tricarboxylic acid cycle metabolite labelling was higher in the brain and brain tumours than in the MFP (Extended Data Fig. 13a,b), consistent with high glucose utilization in the brain43 and greater biosynthetic demands imposed by low amino acid availability behind the blood–brain barrier15,19. Accordingly, labelling of asparagine, glycine, serine and proline was higher in brain tumours than in MFP tumours (Fig. 4c–f and Extended Data Fig. 13c,d). However, growth of auxotrophs lacking ASNS, PHGDH or PYCR1/2/3 was similarly affected in both sites (Fig. 3c), arguing that elevated amino acid synthesis in brain tumours does not necessarily indicate increased dependency.
For nucleotides, MFP tumours synthesized more purine and pyrimidine nucleotides than normal tissue, whereas brain and brain tumours showed lower synthesis, closer to normal MFP (Fig. 4g–i and Extended Data Fig. 13e,f). Regardless, DHODH and GART were required for tumour growth in both sites (Fig. 3c), again indicating that synthesis activity does not predict pathway dependency. Although glucose-derived labelling was reduced in brain tumours, total nucleotide levels were largely similar between brain and MFP tumours (Extended Data Fig. 13e), suggesting compensation through uptake or salvage. Supporting this, DHODH-knockout MDA-MB-231 cells cultured with uridine showed reduced pyrimidine labelling from 13C-glucose but maintained overall nucleotide levels (Extended Data Fig. 13g–k), providing evidence that salvage can sustain nucleotide pools when de novo synthesis is impaired.
Together, these results demonstrate that although metabolic activity differs between brain and MFP tumours, neither individual nutrient levels nor pathway activity reliably predict auxotroph dependencies or metastatic potential. Rather, it implies that tumou