Abstract
Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1,2,3. Consequently, spatially resolved omics-level analyses are gaining traction4,5,6,7,8,9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.
Main
All tumors, including breast cancers, show profound clonal heterogeneity12. These clones in turn have to adapt to the ever-changing tumor microenvironment, frequently characterized by fluctuating oxygen and nutrient supply13. This requires tumor cells to constantly rewire their metabolic pathways and consequently many tumors show a high degree of metabolic flexibility14,15. Intrinsic oncogene-driven metabolic programs are thus integrated with environmental cues to shape the net local metabolism of tumor cells.
MYC is a proto-oncogene and a pleiotropic transcription factor, and clones with high MYC expression levels arise as subclones during malignant progression in a range of cancers, and generally correlate with higher grade and poor survival3,10. While MYC is recognized as a master regulator of metabolism, inducing glycolytic flux and increasing glutaminolysis among others11,16,17, the true metabolic signature of these malignant subclones in the pathophysiologically relevant context of multiclonality remains unknown. Conversely, unveiling the metabolic traits of MYC-high clones in situ would enable us to devise new therapeutic strategies targeted at the metabolism underlying malignant tumor progression.
In situ segmentation of multiclonal mammary tumors
To address this problem, we initially resorted to an inducible and traceable model of MYC heterogeneity in breast cancer, which we had previously developed and characterized18. In brief, this model allows us to create triple-negative mammary tumors, driven by WNT1, but optionally also containing a MYC-ERT2 construct, which expresses supraphysiological levels of the MYC-ER fusion protein and is activated by administration of tamoxifen. We term the tumors WM, reflecting the two oncogenes involved. The clones without MYC-ERT2 (WMlow) express tdTomato as a tracer, whereas the ones with MYC-ERT2 (WMhigh) express enhanced green fluorescent protein (eGFP). Mixing the two clones generates biclonal tumors (WMmix) (Extended Data Fig. 1a). Throughout the study, MYC-ERT2 activation was performed acutely for a duration of only 3 d unless otherwise stated.
Metabolomic characterization of the three WM tumor types confirmed previous observations seen when acutely switching on MYC in tumors, including an increase in the levels of several amino acids, as well as phosphatidylethanolamine (PE)/phosphatidylcholine (PC) and phosphatidylglycerol (PG) (Fig. 1a and Extended Data Fig. 1b,c)17,19. Metabolic pathway analysis of WMhigh tumors showed significant increases in pathways related to cellular growth, including serine and glycine metabolism, pyrimidine biosynthesis as well as aminoacyl-tRNA biosynthesis (Fig. 1b). With few exceptions, the metabolite levels found in WMmix tumors range in between the ones from the pure clonal tumors (Fig. 1a and Extended Data Fig. 1c), reflecting the partial contribution of the individual clonal populations. This highlights the difficulty in analyzing bulk tissue data of multiclonal tumors and the need for spatially resolved metabolomic data to disentangle the individual metabolic contributions and find possible vulnerabilities of the constituent clones.
We thus set out to define the metabolites and metabolic pathways most closely associated with the individual WM clones in situ. To this end we devised a strategy of multimodal correlative imaging that combined desorption electro-flow focusing ionization (DEFFI)20 mass spectrometric imaging (MSI) with fluorescence microscopy (Fig. 1c). Comparing metabolites co-detected in liquid chromatography–mass spectrometry (LC–MS) and DEFFI-MSI, we saw a good concordance between the methods for a number of metabolites with regards to their respective distribution in the WMhigh and WMlow tumors (for example Figure 1d,e). Notably, the WMmix tumors frequently showed a pattern of zonation, which followed their clonal landscape (Fig. 1d,f). To segment the tumors in an unbiased way, we adopted a recently published method of ion colocalization analysis that combines groups of ions into so-called modules based on their similar spatial behavior throughout the entire acquired image21,22. We identified six ion modules, of which, based on their distribution, one correlated with WMhigh (module ‘Turquoise’) and one with WMlow (module ‘Brown’) tumors (Fig. 1f, Extended Data Fig. 1d,e and Supplementary Table 1). The two modules, once projected onto the tumor tissues of the WMmix tumors, reflect the clonal landscape, highlighting the clonal specificity of our automated segmentation. Comparing the molecular features in the identified modules, we noticed that in the WMhigh module, in accordance with our data from the bulk analysis (Extended Data Fig. 1b), PGs and PE/PCs constituted the largest group of metabolites and that there was significant spatial correlation within, as well as between compound classes, a number of which were validated via tandem MS (MS/MS) in both methodologies (Fig. 1g (connecting lines), Supplementary Table 1 and Extended Data Table 1). Furthermore, in keeping with the literature, eicosanoids represented a prominent lipid class in the WMhigh module23. Overall, these data underscore the robustness of our imaging and segmentation approach.
Pantothenic acid correlates with MYC-high tumor areas
To identify metabolites that are central to Myc-mediated metabolic rewiring, we reasoned that a combination between the bulk metabolic analysis and the MSI data would give us the most relevant candidates. We thus extracted all statistically significantly changed metabolites identified by the bulk metabolic pathways analysis (Fig. 1b) and interrogated how well they correlated with the WMhigh and WMlow tumors as well as their affiliation to the WMhigh associated module ‘Turquoise’. By far the strongest correlation was observed for pantothenic acid (vitamin B5; henceforth PA), the chemical structure of which was confirmed by MS/MS using both a chemical standard and tumor tissues (Extended Data Fig. 2a,b). PA is the precursor for coenzyme A (CoA), a thiol that serves as an activator of carboxylic groups by forming thioesters (Extended Data Fig. 2c). As such, it is required for a number of key metabolic pathways, including the Krebs cycle and both fatty acid biosynthesis and oxidation24,25. Of note, in our correlative MSI, PA had a high overlap with the WMhigh clones in the WMmix tumors (Fig. 2a), which also coincided with an upregulation of PA as well as its main downstream product, CoA-SH, in WMhigh tumors as measured by bulk LC–MS analysis (Fig. 2b,c). We thus decided to further study the involvement of the PA–CoA axis in MYC-mediated metabolism.
To investigate the clinical relevance of this finding, we first used a panel of human patient-derived xenografts (PDXs) subcutaneously transplanted into immunocompromised mice26. PDXs with the highest MYC levels or a strong MYC transcriptional profile showed a robust increase in PA levels (Extended Data Fig. 2d,e). To further understand the relationship between MYC expression and PA in situ, we adapted the aforementioned correlative MSI approach to combine DEFFI imaging with immunohistochemistry (IHC). Indeed, correlative MSI confirmed that intertumorally and intratumorally, areas of high MYC displayed increased levels of PA (Fig. 2d, Extended Data Fig. 2f–h and Supplementary Fig. 2). Notably, DEFFI revealed that higher signal intensity of PA also strongly correlated with areas of high MYC expression in primary human breast cancer biopsies (Fig. 2e, Extended Data Fig. 2i,j and Supplementary Fig. 3) supporting the clinical significance and the general validity of our observed connection between MYC and PA metabolism.
Increased levels of metabolites as seen in WMhigh areas can be a cell-autonomous event, or in some cases they can be governed by field effects, such as a better overall vascularization due to MYC-mediated angiogenesis18. To ascertain that the accumulation of PA in WMhigh areas of tumors is indeed driven by the WMhigh cells, we devised a new, ultra-high-resolution, correlative MSI technique that combines fluorescence microscopy, electron microscopy (EM) and nanoscale secondary ion mass spectrometry (NanoSIMS)27, allowing for subcellular localization of metabolite incorporation (Fig. 2f). After administration of [13C3,15N]PA, the fluorescence image of the WMmix sections was overlaid with the respective EM and NanoSIMS images (Fig. 2g). As expected, mitochondria, which harbor about 70% of a cell’s CoA (cellular concentration 10–50 µM)28, were clear hotspots of PA-derived 15N incorporation, but also the nucleoli showed a substantial signal, which is consistent with recent reports of significant HDAC activity in nucleoli (Extended Data Fig. 2k)29. Notably, WMhigh cells incorporated significantly more PA-derived 15N compared to WMlow cells, strongly suggesting that the increased levels of PA observed in WMhigh areas of WMmix tumors are due to a cell-autonomous increase in uptake of PA, which is then used to synthesize downstream metabolites such as CoA (Fig. 2h and Extended Data Fig. 2l).
MYC-high areas expand the pools of Krebs cycle intermediates
PA, as the precursor of CoA, has a central role in allowing carbons from glycolysis to enter the Krebs cycle, as well as for the Krebs cycle itself in the conversion of α-ketoglutarate to succinate. As glucose and glutamine are two of the main carbon sources contributing to this cycle30,31, we wanted to gain a better insight into the spatial distribution of their utilization in our tumor models. We thus infused WM tumors with either [13C6]glucose or [13C5]glutamine (Extended Data Fig. 4a,c) and traced their isotopically labeled downstream metabolites in situ through correlative DEFFI-MSI and fluorescence microscopy. As anticipated, glutamine catabolism in WMhigh tumors was increased compared to their WMlow counterparts, as seen by higher levels of glutamate M+5 isotopologue in WMhigh tumors following [13C5]glutamine infusion (Fig. 3a and Extended Data Fig. 4d). These results were largely in accordance with bulk primary tumor tissue analysis by gas chromatography (GC)–MS (Extended Data Fig. 3). Of note, the spatial component of MSI allowed us to reveal clear metabolic zonation within tumor tissues, where WMhigh clones in the WMmix tumors largely overlapped with areas of increased presence of [13C5]glutamine-derived glutamine and glutamate isotopologues as well as either [13C6]glucose- or [13C5]glutamine-derived Krebs cycle intermediates (Fig. 3a,b). These 13C-labeled isotopologues had a distribution pattern that was largely opposite to the localization of 13C-labeled lactate. While lactate can act as a systemic carbon carrier32, the individual cell producing it usually excretes it as a waste product. Increased lactate production can be triggered by hypoxia or a so-called Warburg-like metabolism and shunts carbon units away from the Krebs cycle and oxidative phosphorylation. We thus utilized isotopically labeled lactate as a proxy for diminished Krebs cycle activity and lactagenic metabolism and isotopically labeled malate as a proxy for increased Krebs cycle activity in [13C6]glucose- or [13C5]glutamine-infused tumors. By binarizing the ion images for the predominant proxy compound after maximal intensity normalization, a clear association of increased Krebs cycle activity with the WMhigh clone was observed (Fig. 3c,d and Extended Data Fig. 4e,f). Pixel-wise correlations between different metabolites confirmed a close spatial association of Krebs cycle intermediates with PA, whereas lactate was poorly correlated with any of the compounds (Fig. 3e,f and Extended Data Fig. 4g,h). WMhigh clones thus have higher levels of PA, which in turn correlates with a more active Krebs cycle.
