Abstract
Tumors are intrinsically heterogeneous and it is well established that
this directs their evolution, hinders their classification and
frustrates therapy^[100]1–[101]3. Consequently, spatially resolved
omics-level analyses are gaining traction^[102]4–[103]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 metabolism^[104]10,[105]11. Through correlative mass
spectrometry imaging, we show that pantothenic acid (vitamin B[5])
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.
Subject terms: Cancer, Breast cancer, Metabolism
__________________________________________________________________
In this study, Kreuzaler et al. perform zonal analysis to study
metabolic heterogeneity in breast cancer and identify the metabolic
dependency on pantothenic acid (vitamin B5) in areas of the tumor that
show high expression levels of the oncogene MYC. Dietary restriction of
vitamin B5 reverses several MYC-driven metabolic changes and hampers
tumor progression.
Main
All tumors, including breast cancers, show profound clonal
heterogeneity^[106]12. These clones in turn have to adapt to the
ever-changing tumor microenvironment, frequently characterized by
fluctuating oxygen and nutrient supply^[107]13. This requires tumor
cells to constantly rewire their metabolic pathways and consequently
many tumors show a high degree of metabolic
flexibility^[108]14,[109]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 survival^[110]3,[111]10. While MYC is
recognized as a master regulator of metabolism, inducing glycolytic
flux and increasing glutaminolysis among
others^[112]11,[113]16,[114]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 characterized^[115]18. In brief, this model
allows us to create triple-negative mammary tumors, driven by WNT1, but
optionally also containing a MYC-ER^T2 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-ER^T2 (WM^low) express
tdTomato as a tracer, whereas the ones with MYC-ER^T2 (WM^high) express
enhanced green fluorescent protein (eGFP). Mixing the two clones
generates biclonal tumors (WM^mix) (Extended Data Fig. [116]1a).
Throughout the study, MYC-ER^T2 activation was performed acutely for a
duration of only 3 d unless otherwise stated.
Extended Data Fig. 1. WM tumors were analyzed with LC–MS analysis and spatial
metabolomics by DEFFI.
[117]Extended Data Fig. 1
[118]Open in a new tab
a, Overview of the transgenic background of the three WM tumors
subtypes. b, Lipid classes from the apolar fraction of WM^High, WM^Low
and WM^Mix tumors analyzed with LC–MS were selected based on their
association with WM^High and plotted on a radial plot using the R
package volcano3D. The radial angle ρ represents relative affiliation
of metabolites to the individual samples and the distance from center -
the relative amounts. Colors indicate statistically significant
affiliation to one or two samples (PG: phosphatidylglycerol, PS:
phosphatidylserine, PE: phosphatidylethanolamine, PC:
phosphatidylcholine, CL: cardiolipin). Significance was calculated with
an unpaired two-tailed t-test (WM^High, n = 4; WM^Low, n = 4; WM^Mix,
n = 8 tumors from independent animals). c, Selected metabolites from
the LC–MS analysis of the polar fractions of WM^High, WM^Low and WM^Mix
were plotted, showing increased levels of amino acids in WM^High
tumors. 10 mg of dry tissue from each sample were taken for the
extraction. Significance was calculated with an unpaired two-tailed
t-test (WM^High, n = 4; WM^Low, n = 4; WM^Mix, n = 8. Histidine:
WM^High vs WM^Mix, P = 0.02084; Lysine: WM^High vs WM^Low, P = 0.0135;
WM^High vs WM^Mix, P = 0.0448; Methionine: WM^High vs WM^Low,
P = 0.0044; Serine: WM^High vs WM^Low, P = 0.0445; WM^High vs WM^Mix,
P = 0.0095). d, Dot-plots represent the proportion of pixels in the
WM^Low and WM^High tumors respectively with intensities higher than the
median in the module eigenmetabolite. The graphs show that the Brown
module is more prevalent in the WM^Low tumors, while the Turquoise
module correlates with the WM^High tumors. Data represents tree
independent biological replicates (error bars: confidence interval). e,
Post DEFFI fluorescent microscopy of WM^High, WM^Low and WM^Mix tumors
is shown and ion colocalization analysis of DEFFI acquired images of
WM^High, WM^Low and WM^Mix tumors reveals a WM^High module (green) and
a WM^Low module (red). These are the two additional runs to the ones
shown in Fig. [119]1f. P value: *<0.05, ** 0.001, ***<0.0001,
****<0.00001. See also Fig. [120]1.
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. [121]1a and Extended Data Fig.
[122]1b,c)^[123]17,[124]19. Metabolic pathway analysis of WM^high
tumors showed significant increases in pathways related to cellular
growth, including serine and glycine metabolism, pyrimidine
biosynthesis as well as aminoacyl-tRNA biosynthesis (Fig. [125]1b).
With few exceptions, the metabolite levels found in WM^mix tumors range
in between the ones from the pure clonal tumors (Fig. [126]1a and
Extended Data Fig. [127]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.
Fig. 1. In situ segmentation of multiclonal mammary tumors.
[128]Fig. 1
[129]Open in a new tab
a, Polar and apolar fractions of WM^high, WM^low and WM^mix tumors were
analyzed with LC–MS (10 mg dry tissue from each sample was taken for
the extraction) and plotted on a radial plot using the R package
volcano3D. The radial angle ρ represents relative affiliation of
metabolites to the individual samples and the distance from center the
relative amounts. Colors indicate statistically significant affiliation
to one or two samples. Significance was calculated with an unpaired
two-tailed t-test (WM^high, n = 4; WM^low, n = 4; WM^mix, n = 8 tumors
from independent animals). b, Metabolic pathways analysis of the data
in a showing several significantly changed pathways and numbers of
identified members against the pathway size. c, Schematic of tissue
processing and DEFFI imaging. d,e, Levels and distribution of two
selected ions as measured by DEFFI (d) or LC–MS (e) show concordance
between the methodologies. Significance was calculated with an unpaired
two-tailed t-test (WM^high, n = 4; WM^low, n = 4; WM^mix, n = 8 tumors
from independent animals (WM^high versus WM^low, P = 0.00234; WM^high
versus WM^mix, P = 0.0089 (left); WM^high versus WM^low, P = 0.0047;
WM^high versus WM^mix, P = 0.0154 (right)). f, Post-DEFFI fluorescent
microscopy of WM^high, WM^low and WM^mix tumors show clonal
distribution (top). Ion colocalization analysis of DEFFI acquired
images of WM^high, WM^low and WM^mix tumors reveal a WM^high module
(green) and a WM^low module (red) (bottom). g, Circos plot representing
all metabolites found in the WM^high module. Nodes represent the
metabolites in the module. Node size is proportional to module
membership, which represents a given metabolite’s strength of the
colocalization index and is calculated as Pearson’s correlation between
the spatial intensities of the corresponding metabolite and the
module’s eigenmetabolites. Arc lines connect node pairs corresponding
to colocalized metabolites with Pearson’s correlation >0.7. The nodes
are color-grouped according to metabolite class based on Human
Metabolome Database putative annotations (|m/z error| < 10 ppm). All
box-and-whisker plots represent the following: line, median; box,
interquartile range (IQR); whiskers, 1.5 × IQR limited by
largest/smallest non-extreme value (NEV). In all DEFFI-MSI experiments
n = 3 tumors from independent animals for each WM tumor type. P values
indicated by *<0.05, ** 0.001, ***<0.0001, ****<0.00001.
[130]Source data
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)^[131]20 mass
spectrometric imaging (MSI) with fluorescence microscopy (Fig.
[132]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 WM^high and WM^low
tumors (for example Figure [133]1d,e). Notably, the WM^mix tumors
frequently showed a pattern of zonation, which followed their clonal
landscape (Fig. [134]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
image^[135]21,[136]22. We identified six ion modules, of which, based
on their distribution, one correlated with WM^high (module ‘Turquoise’)
and one with WM^low (module ‘Brown’) tumors (Fig. [137]1f, Extended
Data Fig. [138]1d,e and Supplementary Table [139]1). The two modules,
once projected onto the tumor tissues of the WM^mix 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 WM^high module, in accordance with our
data from the bulk analysis (Extended Data Fig. [140]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. [141]1g (connecting lines), Supplementary Table
[142]1 and Extended Data Table [143]1). Furthermore, in keeping with
the literature, eicosanoids represented a prominent lipid class in the
WM^high module^[144]23. Overall, these data underscore the robustness
of our imaging and segmentation approach.
Extended Data Table 1.
List of ions confirmed with MS/MS following LC–MS and/or DEFFI-MSI
pipeline
[145]graphic file with name 42255_2023_915_Tab1_ESM.jpg
[146]Open in a new tab
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. [147]1b) and
interrogated how well they correlated with the WM^high and WM^low
tumors as well as their affiliation to the WM^high 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. [148]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. [149]2c). As such, it
is required for a number of key metabolic pathways, including the Krebs
cycle and both fatty acid biosynthesis and oxidation^[150]24,[151]25.
Of note, in our correlative MSI, PA had a high overlap with the WM^high
clones in the WM^mix tumors (Fig. [152]2a), which also coincided with
an upregulation of PA as well as its main downstream product, CoA-SH,
in WM^high tumors as measured by bulk LC–MS analysis (Fig. [153]2b,c).
We thus decided to further study the involvement of the PA–CoA axis in
MYC-mediated metabolism.
Extended Data Fig. 2. PA correlates with MYC expression in mammary tumors.
[154]Extended Data Fig. 2
[155]Open in a new tab
a, Conditional probabilities of pixels with low, medium and high
amounts of the metabolites specified were calculated for WM^low and
WM^High tumors, showing a very high association of high levels of PA
with WM^High tumors. b, MS/MS spectra for confirmation of PA using in
situ tandem MS fragmentation of the PA precursor ion both from a
standard applied to the slide prior to imaging and from primary tumor
tissue (HCI002). Identical mass peaks as well as very similar relative
peak intensities confirm the primary peak as PA. c, Chemical structure
of CoA highlighting the part constituted by pantothenic acid. d, GC–MS
analysis for PA content in human PDXs (n = 5 tumors from independent
animals for each PDX). e, MYC expression and MYC pathway activation of
analyzed PDXs were plotted and show good general concordance between
MYC expression and pathway activation. f, Correlative DEFFI and IHC
staining showing the distribution of PA in relation to MYC staining in
human PDXs (n = 2 biological replicates for each PDX). g, Proportion of
pixels with higher than median levels of PA in MYC high and MYC low
areas measured by DEFFI on PDX samples. Data was extracted by automated
segmentation on immunostaining for MYC and the masks were projected
onto the measurements acquired in DEFFI. Areas of overt necrosis and
any staining and processing artefacts were excluded from the
quantification (Supplementary Figs. [156]1 and [157]2). The average
difference of the proportion of high levels of PA in areas high or low
for MYC respectively was found significantly different from zero by a
linear mixed effect model fitted with the ‘glmmTMB‘ package for R
(random intercepts were considered for run and tissue ID). The
algorithm was applied for all data from two independent biological
repeats of 4 independent PDXs. Solid black dot represents the average
of all runs. Error bar represents the confidence interval. h, Pairwise
P values of an unpaired two-sided Student’s t-test performed on the
GC–MS analysis of PA in PDX samples as plotted in Extended Data Fig. 2e
shows higher similarity between samples with the same MYC activation
status. i, Correlative DEFFI and IHC staining showing the distribution
of PA in relation to MYC staining in human core biopsies, showing
association of MYC with PA (n = 12 core biopsies). j, Proportion of
pixels with higher than median levels of PA in MYC high and Myc low
areas measured by DEFFI on human core biopsies. Data was extracted by
automated segmentation on immunostaining for MYC and these masks were
projected onto the measurements acquired in DEFFI. Areas of overt
necrosis and any staining and processing artefacts were excluded from
the quantification (Supplementary Fig. [158]3). The analysis was
performed as described in (g). The algorithm was applied on 4
independent human core biopsies. Solid black dot represents the average
of all samples. Error bar represents the confidence interval. k, Guided
by the EM, tumors were divided into subcellular compartments. The ROI
was applied to NanoSIMS measurements and shows ^15N derived from
^15N-PA infusions predominantly localizes to the mitochondria (cytosol
vs. mitochondria, p = 0.0005). Within the nuclear compartment, the
nucleolus shows more label incorporation (nucleus vs nucleolus,
p = 0.0124). Data represents one technical repeat of one sample. l,
Quantification of ^15N/^14N ratios in randomly selected WM^High, WM^Low
areas shows increased amounts of ^15N-PA-derived ^15N in WM^High areas.
This is a biological replicate confirming the results in Fig. [159]2g,
h. Significance within this technical repeat was calculated with an
unpaired two-tailed t-test (P = 6.066e^−7). All box and whisker plots
represent the following: Line: median, box: IQR, whiskers: 1.5x IQR
limited by largest/smallest NEV. P value: *<0.05, ** 0.001, ***<0.0001,
****<0.00001. See also Fig. [160]2 and Appendix Figs. 1, 2, and 3.
[161]Source data
Fig. 2. Pantothenic acid correlates with MYC expression in mammary tumors.
[162]Fig. 2
[163]Open in a new tab
a, Post-DEFFI fluorescent microscopy of WM^high, WM^low and WM^mix
tumors shows clonal distribution (note, this is the same image as Fig.
[164]1f, repeated for illustrative purposes) (top). Single-ion DEFFI
image of PA (n = 3 for each tumor type) (bottom). b, LC–MS analysis of
WM tumors (10 mg dry tissue from each sample was taken for the
extraction) shows significant increase of PA in WM^high tumors (WM^high
versus WM^low, P = 0.00038; WM^high versus WM^mix, P = 0.0019). c,
LC–MS analysis of free CoA-SH in WM tumors shows an increase in WM^high
tumors (WM^high, n = 4; WM^low, n = 4; WM^mix, n = 8 tumors from
independent animals; WM^high versus WM^low, P = 0.0313; WM^high versus
WM^mix, P = 7.68 × 10^−5) (b,c). d, Correlative DEFFI and IHC staining
showing the distribution of PA in relation to MYC staining in human
PDXs (n = 2 biological replicates for each PDX). e, Correlative DEFFI
and IHC staining showing the distribution of PA in relation to MYC
staining in representative human core biopsy, showing association of
MYC with PA (n = 12 core biopsies). f, Workflow for correlative
fluorescence microscopy, EM and NanoSIMS analysis. g, Correlative
fluorescence microscopy, EM and NanoSIMS analysis shows ^15N derived
from ^15N-PA infusions predominantly localizing in MYC-high cells, with
subcellular localization in mitochondria and nucleoli. h, Cell-wise
quantification of ^15N/^14N ratios in the WM^high, WM^low areas shows
increased amounts of PA-derived labeled ^15N in WM^high cells. For
NanoSIMS 34 WM^high and 32 WM^low individual cells were analyzed from
one NanoSIMS run (P = 2.91 × 10^−9). All box-and-whisker plots
represent the following: line, median; box, IQR; whiskers, 1.5 × IQR
limited by largest/smallest NEV. Significance was calculated with an
unpaired two-tailed t-test. P values are represented by *<0.05, **
0.001, ***<0.0001, ****<0.00001.
[165]Source data
To investigate the clinical relevance of this finding, we first used a
panel of human patient-derived xenografts (PDXs) subcutaneously
transplanted into immunocompromised mice^[166]26. PDXs with the highest
MYC levels or a strong MYC transcriptional profile showed a robust
increase in PA levels (Extended Data Fig. [167]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. [168]2d, Extended Data Fig.
[169]2f–h and Supplementary Fig. [170]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.
[171]2e, Extended Data Fig. [172]2i,j and Supplementary Fig. [173]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 WM^high 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
angiogenesis^[174]18. To ascertain that the accumulation of PA in
WM^high areas of tumors is indeed driven by the WM^high 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)^[175]27, allowing
for subcellular localization of metabolite incorporation (Fig.
[176]2f). After administration of [^13C[3],^15N]PA, the fluorescence
image of the WM^mix sections was overlaid with the respective EM and
NanoSIMS images (Fig. [177]2g). As expected, mitochondria, which harbor
about 70% of a cell’s CoA (cellular concentration 10–50 µM)^[178]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.
[179]2k)^[180]29. Notably, WM^high cells incorporated significantly
more PA-derived ^15N compared to WM^low cells, strongly suggesting that
the increased levels of PA observed in WM^high areas of WM^mix tumors
are due to a cell-autonomous increase in uptake of PA, which is then
used to synthesize downstream metabolites such as CoA (Fig. [181]2h and
Extended Data Fig. [182]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 cycle^[183]30,[184]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 [^13C[6]]glucose or
[^13C[5]]glutamine (Extended Data Fig. [185]4a,c) and traced their
isotopically labeled downstream metabolites in situ through correlative
DEFFI-MSI and fluorescence microscopy. As anticipated, glutamine
catabolism in WM^high tumors was increased compared to their WM^low
counterparts, as seen by higher levels of glutamate M+5 isotopologue in
WM^high tumors following [^13C[5]]glutamine infusion (Fig. [186]3a and
Extended Data Fig. [187]4d). These results were largely in accordance
with bulk primary tumor tissue analysis by gas chromatography (GC)–MS
(Extended Data Fig. [188]3). Of note, the spatial component of MSI
allowed us to reveal clear metabolic zonation within tumor tissues,
where WM^high clones in the WM^mix tumors largely overlapped with areas
of increased presence of [^13C[5]]glutamine-derived glutamine and
glutamate isotopologues as well as either [^13C[6]]glucose- or
[^13C[5]]glutamine-derived Krebs cycle intermediates (Fig. [189]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 carrier^[190]32, 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 [^13C[6]]glucose- or
[^13C[5]]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 WM^high
clone was observed (Fig. [191]3c,d and Extended Data Fig. [192]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. [193]3e,f
and Extended Data Fig. [194]4g,h). WM^high clones thus have higher
levels of PA, which in turn correlates with a more active Krebs cycle.
Extended Data Fig. 4. PA correlates with areas of high MYC and anticorrelates
with lactagenic metabolism.
[195]Extended Data Fig. 4
[196]Open in a new tab
a-c, Labeled compounds utilized in DEFFI or NanoSIMS imaging as
indicated. d, Aligned post DEFFI fluorescent microscopy and glutamate
M + 5 isotopologues measured by DEFFI after [^13C[5]]glutamine infusion
in two repeat runs (Run 1 is displayed in Fig. [197]3a). e,f, Post
DEFFI fluorescent microscopy of WM^Mix tumors, shows increased PA in
WM^High regions. The bottom panels are binarised representations of the
labeled proxy compounds (lactate M + 3 isotopologue for lactagenic
metabolism, malate M + 1 isotopologue for increased Krebs cycle,
glutamate M + 5 for glutamine uptake) showing Krebs cycle activity
corresponds with higher PA and WM^High areas. Ion intensities were
corrected for natural abundance from individual metabolites and for
each pixel the metabolite with the higher value after maximal intensity
normalization was depicted. Note that for illustrative purposes some
panels are also displayed in Fig. [198]3c,d. g,h, Table showing the
pairwise correlation between different detected labeled compounds and
PA. The top value represents the correlation and the bottom value the p
value (n = 3 tumors from independent animals). i, j, Cell-wise
quantification of ^13C/^12C and ^15N/^14N ratios in WM^Mix tumors (run
2 and run 3 each representing a tumor from an independent biological
replicate, run 2: ^13C/^12C, p = 3.53e^−5; ^15N/^14N, P = 0.141; run 3:
^13C/^12C, P = 3e^−8; ^15N/^14N, P = 1.14e^−10) k,l, Cell-wise
quantification of ^13C/^12C and ^15N/^14N ratios in WM^Mix tumors
stratified by BrDu incorporation status (n = 3 tumors from independent
animals, ((k) run 1: Brdu^−, P = 1.2e^−11; BRDU^+, P = 0.0312; run 2,
P = 0.23; run 3, P = 5.94e^−9; (l): run 1, P = 0.0001; run 2,
P = 0.0004; run 3, P = 5.33e^−7). All box and whisker plots represent
the following: Line: median, box: IQR, whiskers: 1.5x IQR limited by
largest/smallest NEV. Significance was calculated with an unpaired
two-tailed t-test. P value: ****<0.00001. See also Fig. [199]3.
Fig. 3. Pantothenic acid correlates with areas of high MYC and anticorrelates
with lactagenic metabolism.
[200]Fig. 3
[201]Open in a new tab
a,b, DEFFI analysis of label incorporation into selected metabolites of
WM tumors post infusion with [^13C[5]]glutamine (a) or [^13C[6]]glucose
stripped for the natural abundance (b). c,d, Schematic of possible
routes of ^13C label incorporation after [^13C[6]]glucose (c), or
[^13C[5]]glutamine (d) infusion (top). Post-DEFFI fluorescent
microscopy of WM^mix tumors (bottom). Binarized representation of the
labeled proxy compounds (lactate M+3 isotopologue for lactagenic
metabolism and malate M+1 isotopologue for increased Krebs cycle)
showing Krebs cycle activity corresponds with higher PA and WM^high
areas (bottom). e,f, Dendrogram clustering between indicated proxy
compounds and PA in [^13C[6]]glucose (e) or [^13C[5]]glutamine (f)
infused tumors show a correlation for Krebs cycle proxies and no
correlation with lactate. g–i, Binarized images for glutamate M+5
isotopologue and lactate M+3 isotopologue (g) in six human PDXs shows
less labeled lactate in areas of increased PA (h), with a positive
correlation of PA with the former and no correlation with the latter
(i). Binarized images were generated as above (two biological
replicates from six PDXs were imaged). j, Correlative fluorescence
microscopy, EM and NanoSIMS analysis after injection of BrDu,
[^13C[6]]glucose and [amide-^15N]glutamine. More label is detected in
WM^high compared to WM^low. k,l, Cell-wise quantification of ^13C/^12C
and ^15N/^14N ratios in WM^mix tumors. The data represent one
biological replicate, two further are displayed in Extended Data Fig.
[202]4i,j (P = 5.24 × 10^−17 (k); P = 3.25 × 10^−5 (l)). All
box-and-whisker plots represent the following: line, median; box, IQR;
whiskers, 1.5 × IQR limited by largest/smallest NEV. Significance was
calculated with an unpaired two-tailed t-test. P values are represented
by *<0.05, ** 0.001, ***<0.0001, ****<0.00001. Note that fluorescence
images from WM^mix are depicted in both a and c, and b and d for better
readability.
[203]Source data
Extended Data Fig. 3. Activation of MYC leads to changes in carbon
utilization.
[204]Extended Data Fig. 3
[205]Open in a new tab
GC–MS analysis of WM tumors infused with [^13C[5]]glutamine (a) or
[^13C[6]]glucose (b) shows an increase in glutaminolysis in WM^High and
a trend towards more lactagenic metabolism in WM^Low tumors.
Significance was calculated with an unpaired two-tailed t-test
([^13C[5]]glutamine infusion WM^High, n = 3; WM^Low, n = 3; WM^Mix,
n = 4; [^13C[6]]glucose infusions: WM^High, n = 4 WM^Low, n = 4,
WM^Mix, n = 4 tumors from independent animals). All box and whisker
plots represent the following: Line: median, box: IQR, whiskers: 1.5x
IQR limited by largest/smallest NEV. P value: *<0.05, **< 0.001. See
also Fig. [206]3.
[207]Source data
To test whether this correlation was observed more widely, we
administered [^13C[5]]glutamine boluses to mice growing the
aforementioned panel of breast cancer PDXs. While fewer labeled
metabolites were detected overall, as this was a short-term bolus
injection, we did see a positive correlation between glutamate M+5 and
PA, which in turn correlates with MYC, but found no correlation of PA
with labeled lactate (Figs. [208]2d and [209]3g–i and Extended Data
Fig. [210]2f). Taken together, our results suggest that a MYC-driven
increase in PA uptake supports an increased Krebs cycle activity.
As above, we sought to investigate whether increased glucose and
glutamine uptake in WM^high areas was a cell-autonomous behavior by the
WM^high clones. We thus adapted our ultra-high-resolution
NanoSIMS-based correlative MSI protocol, by infusing WM^mix tumors with
a mixture of [^13C[6]]glucose and [amide-^15N]glutamine (Extended Data
Fig. [211]4b,c). Furthermore, we injected 5-bromodeoxyuridine (BrdU)
3 h before tumor collection, thus marking cells in S phase. This
approach allowed us to image the two WM clones via fluorescence
microscopy, while revealing cycling cells and tracing [^13C[6]]glucose-
and [amide-^15N]glutamine-derived stable-isotope labels at subcellular
resolution with the NanoSIMS (Fig. [212]3j). Of note, we saw strong
^15N-labeling in nucleoli, an area of active ribosomal RNA production.
This observation is consistent with the utilization of the amide
nitrogen atom of glutamine in nucleotide biosynthesis. Conversely,
glucose had a higher share of ^13C-labeling detected in the cytosolic
compartment, which is consistent with its role as a global carbon
donor. Notably, WM^high cells overall had a significantly higher amount
of label incorporation from both glucose and glutamine compared to
WM^low cells, even in tightly intermingled tumor regions, arguing for a
cell-autonomous effect of increased label uptake due to MYC activity
(Fig. [213]3k,l and Extended Data Fig. [214]4i,j). This effect
persisted, even when BrdU-positive cells, which have a particularly
high amount of label incorporation due to the biosynthetic needs of S
phase, were removed from the analysis, arguing for a MYC effect
irrespective of the acute cell cycle status of the cells (Extended Data
Fig. [215]4k,l). Overall, we conclude that heightened MYC activity
increases PA uptake and metabolism, thus facilitating a more active
Krebs cycle and metabolite uptake, in a cell-autonomous fashion.
Depriving tumors of pantothenic acid reduces growth
The main downstream function of PA is mediated via CoA, although it is
also used as a prosthetic group in fatty acid biosynthesis. To
establish the requirement of PA and CoA for efficient tumor cell
growth, we starved 4T1 mammary tumor cells of PA, which halted their
proliferation and led to metabolite accumulation upstream of reactions
involving CoA, as well as signs of cellular stress (Fig. [216]4a and
Extended Data Fig. [217]5a,b). Notably, we could rescue this phenotype
promptly, by supplementing the cells with CoA, highlighting the need
for CoA as a downstream product of PA metabolism to sustain cell
growth.
Fig. 4. Tumors are dependent on pantothenic acid, whose import is regulated
by MYC through SLC5A6 expression.
[218]Fig. 4
[219]Open in a new tab
a, IncuCyte analysis of cell growth of the high MYC 4T1 cells with and
without PA (n = 3 technical replicates, error bars, mean ± s.e.m.;
representative image of two biological replicates is shown, Holm–Sidak
method P = 0.001248). b, Schematic of the experimental setup for diet
alteration. c, LC–MS quantification of PA in extracts from HCI002
tumors grown with and without PA (P = 4.3 × 10^−10). AU, arbitrary
units. d, Growth of orthotopically transplanted HCI002 tumors grown
with and without PA (c,d, control, n = 7; PA-free, n = 8 tumors from
independent animals, mean ± s.d., P = 0.0284). e, Cell proliferation in
tumors grown with and without PA quantified as BrdU-positive cells over
total cells (control/PA-free, n = 4 tumors from independent animals,
one section per tumor, P = 0.014). f,g, LC–MS analysis of HCI002 tumors
grown with or without PA receiving a bolus of [^13C[6]]glucose. CoA and
acetyl-CoA (f) and labeled acetyl-CoA (g) (control, n = 7; PA-free,
n = 8 tumors from independent animals; CoA, P = 0.00130; Ac-CoA,
P = 0.0057; Ac-CoA M+0, P = 0.054; Ac-CoA M+1, P = 0.00080; Ac-CoA M+2,
P = 0.0013). h, Total levels of selected metabolites from LC–MS
analysis of HCI002 tumors grown with and without PA. i, Levels and
fractional enrichment of ^13C-labeled selected metabolites from LC–MS
analysis of HCI002 tumors grown with and without PA receiving a bolus
of [^13C[6]]glucose. j, Western blot analysis of HCI002 tumors grown
with and without PA. k, qRT–PCR of WM^high and WM^low tumors (WM^high,
n = 6; WM^low, n = 5; WM^mix, n = 6 tumors from independent animals,
significant P values are 2.90 × 10^−11, 0.013, 0.043, 0.010, 0.0005 and
0.006). l Western blot analysis of WM tumors. m, Stratification of
tumors from the METABRIC dataset. n, IncuCyte growth analysis of the
low MYC 67NR cells with ectopic expression of SLC5A6 with and without
PA (n = 3 technical replicates; error bars, mean ± s.e.m.;
representative image of three biological replicates is shown). o, Tumor
growth of orthotopically transplanted 67NR cells with and without
SLC5A6 over-expression (67NR control, n = 7; 67NR SLC5A6 OE C1/2, n = 6
tumors from independent animals; error bars, mean ± s.d.). Significance
was calculated with an unpaired two-tailed t-test. All box-and-whisker
plots represent the following: line, median; box, IQR; whiskers,
1.5 × IQR limited by largest/smallest NEV. P values indicated by
*<0.05, ** 0.001, ***<0.0001, ****<0.00001.
[220]Source data
Extended Data Fig. 5. PA deprivation slows down mammary tumor growth in PDXs
and WM tumors.
[221]Extended Data Fig. 5
[222]Open in a new tab
a, Selected metabolites from GC^–MS analysis of 4T1 cells with and
without PA as well as CoA rescue. b, Western blot analysis of 4T1 cells
with and without PA and with increasing amounts of CoA as a rescue
(representative image of 3 biological repeats). c, Mouse weight during
PA deprivation in the PDX-cohort (control diet, n = 7; PA-free diet,
n = 8; error bar represents s.d.) d, Growth of WM^Mix tumors from mice
fed a PA-free or control diet (n = 5 tumors from independent animals;
error bar represent s.d., P = 0.02401). e Proliferation of WM^Mix
tumors with and without PA quantified as BrDU-positive cells over total
cells (n = 4 tumors from independent biological replicates, one section
per tumor; WM^low, P = 0.0304; WM^high, P = 0.0126). f, Densitometric
analysis of cleaved caspase 3 presence, normalised to actin in HCI002
tumors from mice fed PA-free or control diet (n = 5 tumor protein
extracts from independent biological replicates, P = 0.00171). g-h,
LC–MS analysis of WM^Mix tumors receiving a bolus of [^13C[6]]glucose
shows no significantly reduced amounts of free CoA-SH, Acetyl-CoA and
labeled Acetyl-CoA isotopologues upon PA deprivation (n = 5 tumors from
independent animals). i, Selected metabolites of LC–MS analysis of
HCI002 tumors show widespread reduction in polar metabolite levels in
tumors grown on PA-free diet. j, LC–MS analysis of HCI002 tumors
receiving a bolus of [^13C[6]]glucose shows widespread reduction in
label incorporation in selected metabolites upon PA deprivation, but
little difference in the fractional enrichment. k, LC–MS analysis of
WM^Mix tumors shows widespread reduction in polar metabolite levels in
tumors grown on PA-free diet. l, LC–MS analysis of WM^Mix tumors
receiving a bolus of [13C[6]]glucose shows widespread reduction in
label incorporation in selected metabolites upon PA deprivation, but
little difference in the fractional enrichment. m, LC–MS analysis of
the apolar fraction of HCI002 PDX tumors (n = 7 control, n = 8 PA-free)
shows accumulation of triglycerides (TG), following PA deprivation
(significance was calculated with a unpaired two-sided non-adjusted
Student’s t-test). n, LC–MS analysis of the apolar fraction of WM^Mix
tumors (n = 5 tumors from independent animals) shows accumulation of
diglycerides (DG) and triglycerides (TG), following PA deprivation
(significance was calculated with a unpaired two-sided non-adjusted
Student’s t-test). All box and whisker plots represent the following:
Line: median, box: IQR, whiskers: 1.5 x IQR limited by largest/smallest
NEV. Significance was calculated with an unpaired two-tailed t-test. P
value: *<0.05, ** 0.001. See also Fig. [223]4.
[224]Source data
Given these observations, we wondered whether we could exploit
therapeutically the reliance of tumor cells on this pathway. To reduce
their systemic PA levels, mice bearing triple-negative breast cancer
PDXs (HCI002, MYC high)^[225]33 or WM^mix tumors were fed a PA-free or
control diet from 5 weeks before tumor implantation until tumor
collection (Fig. [226]4b). The mice tolerated this treatment well
(Extended Data Fig. [227]5c) and it significantly reduced tumoral PA
levels (Fig. [228]4c). Notably, this coincided with a significant
reduction in tumor growth and cell proliferation in both the HCI002
PDXs as well as in either clonal populations of the WM^mix tumors (Fig.
[229]4d,e and Extended Data Fig. [230]5d,e). Note that under control
diet conditions, WM^high clones in WM^mix tumors have a strong tendency
toward increased proliferation, but this growth advantage is lost in
the absence of PA (Extended Data Fig. [231]5e)^[232]18. Last, in the
HCI002 tumors, PA deprivation also coincided with an increase in cell
death (Fig. [233]4j and Extended Data Fig. [234]5f). Efficient tumor
growth thus requires sufficient PA supply.
To understand how PA deprivation was affecting tumor metabolism, HCI002
tumors from PA-free diet or control diet-fed mice were analyzed via
LC–MS after a [^13C[6]]glucose infusion. As expected, in the case of
the HCI002 PDXs on a PA-free diet, the amount of free CoA, as well as
^13C-labeled acetyl-CoA, was significantly reduced (Fig. [235]4f,g).
This was not the case in the WM^mix tumors (Extended Data Fig.
[236]5g,h). This notwithstanding, in both tumor models the majority of
glycolytic intermediates, Krebs cycle intermediates, both essential and
non-essential amino acids and nucleotides were decreased when mice were
fed a PA-free diet (Fig. [237]4h and Extended Data Fig. [238]5i,k),
although, mirroring the CoA levels, this effect was more prominent in
the PDXs compared to WM^mix. Isotope tracing of [^13C[6]]glucose
revealed that total label incorporation into most compounds from
central carbon metabolism was significantly reduced; however, ^13C
fractional enrichment did not change in the majority of metabolites,
suggesting that tumor cells from mice on a PA-free diet do not
compensate for their reduced capacity of glucose uptake and catabolism
by metabolizing alternative compounds, but rather reduce the overall
pool size of metabolites, retaining similar relative fluxes between
glucose and other contributing carbon sources (Fig. [239]4i and
Extended Data Fig. [240]5j,l). Comparing the apolar fraction of these
tumors, revealed that the slower growing tumors propagated under
PA-free conditions showed a larger pool of storage lipids such as
diglycerides and triglycerides (Extended Data Fig. [241]5m,n).
Next, we sought to establish the adaptations in cellular signaling
following PA deprivation. Of note, the PDX tumors displayed a slight
reduction in c-MYC, but a robust reduction of the mTOR signaling
pathway, an indicator for nutrient availability, as exemplified by
reduced phosphorylation of its downstream targets p70S6K as well as S6K
(Fig. [242]4j). This strongly implies that as a result of reduced PA in
the diet, tumor cells signal a state of nutrient scarcity, which feeds
back onto this central signaling axis. Consistent with reduced levels
of many amino acids under PA-free conditions, ATF4, a known regulator
of amino acid biosynthesis, was significantly reduced. ATF4 is a
downstream target of mTOR signaling, the reduction of which might
explain this observation (Fig. [243]4j)^[244]34. Consistent with a
decreased glycolytic flux, the levels of the first enzyme of
glycolysis, hexokinase (HK2), were reduced in PA-free tumors.
Long-chain fatty acid CoA ligase, ACSL1, which is involved in both
β-oxidation and fatty acid biosynthesis, behaved inversely and
increased significantly under PA-free conditions, strengthening the
notion that some of the carbon units from glucose are being diverted
toward fatty acids. Notably, the expression of PDHE1, a component for
the pyruvate dehydrogenase complex that shunts pyruvate into the Krebs
cycle, was unchanged, indicating that flux regulation may precede this
final step of glycolysis.
We thus sought to gain mechanistic insights into the ability of Myc to
enhance PA uptake in a cell-autonomous manner. PA is transported into
cells alongside biotin and ɑ-lipoic acid by the multivitamin
transporter SLC5A6 (also known as SMVT)^[245]35. We investigated the
levels of SLC5A6 protein and messenger RNA in WM tumors and PDXs
(protein only), where we saw a strong correlation between the
transporter expression and MYC (Fig. [246]4k,l and Extended Data Fig.
[247]6a). Consistent with increased PA catabolism, WM^high tumors have
higher levels of pantothenic acid kinase 2 (PANK2) and
aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase
(AASDHPPT), an enzyme that transfers a phosphopantetheine from CoA onto
the acyl carrier domain of FASN (Fig. [248]4k). We confirmed that
SLC5A6 expression is regulated cell-autonomously by MYC in a
MYC-inducible cell line system, in which ectopic MYC expression is
induced by doxycycline treatment in 67NR murine mammary gland tumor
cells^[249]18. Ectopic MYC expression led to increased expression of
SLC5A6 at both gene and protein levels (Extended Data Fig. [250]6b,c).
Finally, the analyses of publicly available Chip-seq data^[251]36
revealed MYC binding to E-boxes in the Slc5a6 promoter region (Extended
Data Fig. [252]6d) confirming the direct transcriptional regulation of
Slc5a6 by MYC. Consistent with the connection between MYC and SLC5A6
observed in the preclinical models, we saw a clear correlation between
higher grade, ER-negative and MYC signature-high tumors and the SLC5A6
transporter (Fig. [253]4m) in the Molecular Taxonomy of Breast Cancer
International Consortium (METABRIC) dataset of breast cancer
samples^[254]37. These data prove a direct transcriptional activation
of SLC5A6 by MYC.
Extended Data Fig. 6. MYC upregulates SLC5A6 to increase PA uptake.
[255]Extended Data Fig. 6
[256]Open in a new tab
a, Western blot analysis for MYC and SLC56A6 in human PDXs. b, qRT-PCR
analysis of 67NR cells with Doxycycline-inducible MYC shows
transcriptional upregulation of Slc5A6 upon MYC induction (n = 3
extracted RNA samples from independent biological replicates, error bar
represents s.d., P = 0.0013). c, Western blot analysis of 67NR cells
with Doxycycline-inducible MYC shows an upregulation of SLC5A6 upon MYC
induction (representative image of 3 biological repeats). d, Publicly
available Myc Chip-Seq data of pre-tumoral lymphocytes as well as overt
lymphoma in an Eμ-Myc-driven model^[257]72 show invasion of an E-box
cluster localized in the Slc5A6 promoter. The data represents three
biological replicates (error bar represents s.d., P = 0.0042). e,
Western blot analysis of 4T1 and 67NR cells shows more MYC and SLC5A6
in 4T1 cells compared to 67NR cells. f, qPCR analysis of recombinant
SLC5A6 expression (n = 5, error bar represents s.d.). g, GC–MS analysis
of 67NR control and 67NR SLC5A6 over-expressing cells treated with
stable-isotope labeled PA, shows an increased uptake of the labeled PA,
and a higher baseline level of unlabeled PA in SLC5A6 over-expressing
cells (n = 3 extracts from independent biological replicates, error bar
represents s.d., PA M + 4, P = 1.5e^−5, 0.0003, 4.30e^−6; PA,
P = 0.00032, 1.5e^−5, 0.00038, 4.3e^−6). h, GC–MS analysis of 67NR
control and SLC5A6 over-expressing cells either starved for PA or
rescued with CoA, shows no significant difference in the control cells
between any of the treatments, while the SLC5A6 cells show a
significant drop in citrate after PA deprivation that is readily
rescued with CoA, implying metabolic reprogramming through SLC5A6
over-expression (n = 3 extracts from independent biological replicates,
error bar represents s.d., control vs -PA, P = 0.0083; -PA vs -PA+CoA,
P = 0.037). i, GC–MS analysis of stable-isotope labeled (M + 4) and
endogenous PA in tumors grown from 67NR control cells or 67NR SLC5A6
over-expressing cells show an increased uptake of both endogenous and
exogenous labeled PA (n = 6 control, n = 7 SLC5A6 OE extracts from
tumors from independent biological replicates, error bar represents
s.d., P = 0.0049, P = 0.0033). All box and whisker plots represent the
following: Line: median, box: IQR, whiskers: 1.5x IQR limited by
largest/smallest NEV. Significance was calculated with an unpaired
two-tailed t-test. P value: *<0.05, ** 0.001. See also Fig. [258]4.
[259]Source data
Last, we set out to understand whether SLC5A6 was indeed responsible
for cellular PA homeostasis and how this affected cellular metabolism
and growth potential. The 4T1 cells and 67NR cells are sister cell
lines from the same spontaneous murine breast cancer. The former has
higher levels of both MYC and SLC5A6 compared to the latter (Extended
Data Fig. [260]6e). We had previously noted that unlike the 4T1 cells
under PA deprivation, the 67NR cells under the same condition did not
promptly react with growth retardation and did not have profound
changes in cellular metabolism (Fig. [261]4a,n and Extended Data Fig.
[262]6h). We thus over-expressed SLC5A6 in these cells (Extended Data
Fig. [263]6f). The baseline levels of PA were drastically increased,
and the rapid uptake of a stable-isotope-labeled PA concomitant with a
reduction of the unlabeled counterpart proved that this pool is highly
dynamic (Extended Data Fig. [264]6g). SLC5A6 thus governs intracellular
PA levels and can act as a bottleneck for PA import. Of note, SLC5A6
over-expressing cells had an increased proliferative capacity under PA
replete conditions, but became dependent on PA, as its withdrawal
inhibited their proliferation below the baseline of the control cells
(Fig. [265]4n). Furthermore, their metabolism now responded to PA
withdrawal similarly to the 4T1 cells (Extended Data Figs. [266]5a and
[267]6h), arguing for a metabolic adaptation to high PA levels. To see
whether these observations could be reproduced in vivo, we
orthotopically transplanted two clones of the SLC5A6 over-expressing
67NR cells into Balb/c mice. Both clones initially grew faster, with
one clone reaching statistical significance and the other one showing a
strong trend (Fig. [268]4o), but this growth advantage was lost as the
tumors reached around 1.5 cm^3 in size. These results are consistent
with the notion that at the onset of tumor growth, proliferation is the
main driver in size gain, whereas later the establishment of a
supportive microenvironment becomes more important. Indeed, all tumors
showed widespread necrosis at the time of collection, arguing for
insufficient support by the microenvironment. Last, we measured the
levels of PA as well as the uptake of labeled [^13C[3],^15N]PA in the
tumors and saw a significant increase in both, showing that also in
vivo SLC5A6 governs intracellular PA levels (Extended Data Fig.
[269]6i).
Discussion
In summary, we have shown that MYC increased PA levels within tumors
through direct upregulation of the transporter SLC5A6 and that this is
required to underpin the proliferative and biosynthetic programs of MYC
(Extended Data Fig. [270]7). The need for such metabolic programs in
tumors represents a tractable metabolic vulnerability.
Extended Data Fig. 7. Model.
[271]Extended Data Fig. 7
[272]Open in a new tab
MYC is upregulated in higher grade tumor areas. It can bind to the
SLC5A6 promoter and increase its transcription. This in turn increases
PA intake, and enhances CoA synthesis, which facilitates a biosynthetic
metabolism by enhancing Krebs Cycle activity, while shunting
metabolites away from lactagenic metabolism. Whether fatty acid
biosynthesis is also enhanced is not subject of this study, but
consistent with our model (image created with [273]BioRender.com).
Some historical data had noted the effect of PA on tumor
growth^[274]38; our study, however, provides a functional link between
high MYC expression, PA and downstream Krebs cycle activity. This is
particularly pertinent, as vitamin supplementation is usually given to
patients with cancer to counterbalance vitamin deficiencies caused by
the side effects of chemotherapy on the gastrointestinal tract;
however, our work as well as recent work from other groups^[275]39,
show that under certain circumstances reducing vitamin availability to
the tumor either by nutrient deprivation or possibly by blocking the
cognate transporter might be advantageous. A very recent study has
shown that a PI3K–Akt axis governs CoA biosynthesis from PA. Combined
with our data, this represents an instance of metabolic oncogene
cooperation, where MYC provides the cell with the ability to import the
starting material (PA), whereas PI3K enhances downstream biosynthesis,
in this case CoA^[276]40.
Of note, another recent study suggests that anti-tumorigenic T cells
require high levels of CoA^[277]41. In our study we cannot account for
T cell activity, as most of our models needed to be on an
immunocompromised background to avoid rejection. T cells engage a very
proliferative and biosynthetic metabolism once activated and are likely
to engage a similar metabolic adaptation as the MYC-high tumor cells.
It is thus an enticing thought that competition for the same nutrients
might, in part, explain the lack of a therapeutic effect under
immunotherapy. Consequently, being able to stratify based on known
metabolic dependencies, and being able to predict antagonistic
metabolic programs, will facilitate the development of bespoke
therapies for a given tumor subtype. Last, we show how the utilization
of in situ metabolomics and correlative imaging provide deeper insights
into the local tumor biology, such as the finding of reduced
lactagenesis in MYC-high tumor areas, which in turn helps to identify
metabolic requirements driven by specific oncogenic profiles and their
interplay with the microenvironment. Deploying this technology to other
tumor systems will allow to extract more metabolic vulnerabilities that
correlate with subclonal mutations or specific tumor subregions and
thus lead to more targeted metabolic interventions.
Methods
Mice
Husbandry
All procedures and animal husbandry were carried out in accordance with
the UK Home Office under the Animals (Scientific Procedures) Act 1986,
and the Crick Animal Welfare and Ethical Review Body, which is
delivered as part of the Biological Research Facility Strategic
Oversight Committee (BRF-SOC), under project license P609116C5. Mice
were caged in individually ventilated cages, on a 12-h light–dark
cycle, at ambient temperature and a humidity of 55 ± 10%, with food and
water ad libitum. The maximum tumor size permitted under the project
license P609116C5 is 1.5 cm in diameter, which was never exceeded.
WM tumor generation
To generate spontaneous non-recombined tumors as a source of biclonal
tumors, Rosa26-CAG-lox-STOP-lox-MYC-ERT2/ Rosa26-mTmG/MMTV-Wnt1 mice
were used. The transgenes used to generate the cross were on the
following backgrounds: MMTV-Wnt1, FVB/N; Rosa26-mTmG, C57BL/6J and
Rosa26-CAG-lox-STOP-lox-MYC-ERT2:Balb/c. The tumors arose spontaneously
between 4 and 8 months of age. Once palpable, tumors were desensitized
to tamoxifen by daily intraperitoneal (i.p.) injection (100 µl,
10 mg ml^−1 tamoxifen (Sigma) in olive oil with 10% ethanol). At
1.5 mm^3 tumors were excised, cut into small fragments and
cryopreserved in FBS (Gibco, A5256701, lot 2575507) with 10%
dimethylsulfoxide (DMSO). To generate secondary tumors, fragments were
surgically implanted into the number four fat pad of female NOD/Scid
mice ((NOD.CB17-Prkdcscid/NCrCrl) at the age of 6–8 weeks. Once tumors
were palpable, mice were treated as above. At 1.5 mm^3 tumors were
excised and digested in 5 ml additive-free DMEM with 1 mg ml^−1
Collagenase/Dispase (Roche) for 1 h under shaking. Tumor cells were
suspended in high-glucose DMEM (Thermo Fisher Scientific, 11960044)
with 2 mM glutamine and 10% FBS (Gibco, A5256701, lot 2575507). To
generate WM^high cells, Cre-expressing attenuated adenovirus
(Ad5CMV-Cre, University of Iowa, VVC-U of Iowa-5) and Polybrene
(8 μg ml^−1), was added overnight. WM^low cells were not infected, but
otherwise treated the same. Cells were washed extensively and eGFP- or
tdTomato-positive cells were sorted with an Avalon sorter (BioRad). A
total of 150,000 cells were then surgically implanted into the number
four fat pad of 6–8-week-old NOD/Scid mice either as pure clonal
populations or 1:1 mixtures. MYC-ER^T2 was activated 3 d before
collection by twice-daily i.p. injections of tamoxifen (100 µl,
10 mg ml^−1 tamoxifen). On the day of collection, 3 h were left between
the injection and further procedures, to have a fully active MYC-ER^T2
construct. Tumors were collected at a size of 1.5 mm^3. Mice were
injected with BrdU (100 µl i.p., 10 mg ml^−1 (Sigma)) 3 h before
killing.
PDX tumor generation
The following previously described tumors were propagated via surgical
fragment implant into 5-week-old female NSG mice
subcutaneously^[278]26: HCI002, HCI009, STG143, STG195, STG335 and
STG201; at the age of 5 weeks. Tumors took 2–10 months to form and were
collected at a size of 1.5 mm^3.
Diet modifications
A PA-free diet was formulated on the basis of a standard diet
(composition is shown in Supplementary Table [279]2). PA absence was
verified by LC–MS analysis. The control diet had the same composition
as the PA-free diet, but with PA added. To avoid any interference with
juvenile growth and puberty, diets were changed at 6 weeks of age.
Diets were then introduced progressively over 4 d by increasing the
amount of the PA-free/control diet and reducing the amounts of standard
chow diet. In the meantime, tumor fragments were implanted into other
Scid mice to grow WM tumors for in vitro recombination (WM only) and
reimplantation or HCI002 tumors for reimplantation. Tumors were
dissociated into a single-cell suspension and 750,000 (PDX) or 150,000
(WM tumors) cells were orthotopically implanted into the fourth
inguinal fat pad of NOD/Scid mice. Mice were typically on a PA-free or
control diet for 5 weeks before tumor implantation and remained on the
diet until the tumors were collected. Dietary modification had no
effect on mobility and responsiveness of animals nor their weight gain.
67NR cell-derived tumor generation
The 67NR control and SLC5A6 over-expressing cells (150,000 cells in 80%
Matrigel) were transplanted into female Balbc/CJ mice at the age of 7
weeks.
Stable-isotope labeling in vivo
The following compounds were utilized for stable-isotope labeling:
[^13C[6]]glucose, [^13C[5]]glutamine, [amide-15N]glutamine and calcium
pantothenate ([13C3,15N]β-alanyl), all from Goss Scientific. We either
performed bolus injections of the compounds to study acute consumption
or infusions, to measure the steady-state label incorporation or
repeated boluses over many days to study label accumulation (PA
([^13C[3],^15N]β-alanyl)). Mice were not fasted before stable-isotope
administration and all experiments were performed in the early
afternoon.
Boluses were performed as previously described^[280]15.
Stable-isotope-labeled compounds were diluted in saline and injected
intravenously (i.v.) into the tail vein of the mice. [^13C[6]]glucose
was administered in a single bolus of 0.4 mg g^−1 body weight in
approximately 100 μl final volume. [^13C[5]]glutamine has limited
solubility and was thus administered in two boluses 15 min apart for a
total amount of 0.34 mg g^−1 body weight. In both cases, 15 min after
the last injection, tissues and blood were collected. Tumors and
control tissues were swiftly excised and either snap-frozen in liquid
nitrogen or fixed. Blood was extracted by cardiac puncture and serum
was separated for metabolite analyses.
In the case of PA ([^13C[3],^15N]β-alanyl), when used in mice to label
WM tumors for NanoSIMS analysis 100-μl boluses of 3 mg ml^−1 PA were
administered during five consecutive days. Tumor collection was
performed 5 h after the last administration. Conversely, in the mice
transplanted with 67NR SLC over-expressing and control cells, a single
bolus of PA ([^13C[3],^15N]β-alanyl) at 0.086 mg g^−1 body weight was
administered 15 min before tumor collection.
Infusions of isotopically labeled nutrients were performed under
general anesthesia (isoflurane) using an Aladdin AL-1000 pump (World
Precision Instruments) following previously published
protocols^[281]42. For [^13C[6]]glucose, mice received a bolus of
0.4 mg g^−1 body weight, followed by a 0.012 mg g^−1 body weight per
minute infusion for 3 h. For glutamine-derived stable-isotope
infusions, mice received a bolus of 0.187 mg g^−1 body weight, followed
by a 0.005 mg g^−1 body weight per min infusion for 3 h. For
co-infusion of [^13C[6]]glucose and [amide-^15N]glutamine, a solution
of 40 mg ml^−1 glutamine and 96 mg ml^−1 glucose was prepared and mice
received a bolus of 0.187 mg of glutamine and 0.442 mg glucose per gram
of body weight. This was followed by an infusion of 0.005 mg glutamine
and 0.012 mg glucose per gram of body weight per minute during 3 h. At
the end of the infusion, tissue and blood were obtained as described
for the boluses.
Where indicated, mice were injected with BrdU (100 µl i.p., 10 mg ml^−1
(Sigma)) 3 h before killing.
Cell lines and culture conditions
The 4T1 and 67NR mouse mammary gland tumor cell lines were obtained
from the Francis Crick Institute cell culture facility and were
described previously. The 67NR–tet–cMYC–IRES–eGFP cells were described
previously^[282]18,[283]43.
The cell lines were authenticated using a standard protocol for
identification of mouse cell lines using short tandem-repeat profiling.
The profile is compared back to any available on commercial cell banks
(such as ATCC and Cellosaurus). The species is confirmed by using a
primer system, based on the Cytochrome C Oxidase Subunit 1 gene from
mitochondria.
Cells were cultured in high-glucose DMEM (Thermo Fisher Scientific,
11960044) with 2 mM glutamine and 10% FBS (Gibco, A5256701, lot
2575507). Deviations are mentioned under the respective methods.
For in vitro PA uptake assays, cells were cultured in DMEM with 10 µM
labeled PA ([^13C[3],^15N]β-alanyl).
Human breast biopsies
The institutional review board approved collecting all samples for this
study at Imperial College Healthcare National Health Service Trust
(Imperial College Healthcare Tissue Bank HTA license no. 12275 and
Tissue Bank sub-collection no. SUR-ZT-14-043). The REC no. (REC Wales
approval) is 17/WA/0161. All patients provided their consent to use
their samples in this study. All methods were performed according to
institutional and ethical guidelines. Patients undergoing surgery were
recruited and 12 tissue samples were taken from a range of subtypes,
which were identified in the histopathology assessment (Extended Data
Table [284]2). Data were only obtained on patients who had consented to
the utilization of tissue for research. Tumors had to be of a
macroscopic size ≥2 cm to allow for adequate research tissue without
compromising the clinical diagnosis. Where feasible, tissue was
provided from the center of the tumor from non-necrotic areas. Upon
collection, all tissue samples were stored at −80 °C.
Extended Data Table 2.
List of human breast cancer biopsies
[285]graphic file with name 42255_2023_915_Tab2_ESM.jpg
[286]Open in a new tab
Metabolite extraction and analysis
Metabolite extraction
Snap-frozen tumors were ground with a liquid nitrogen-cooled mortar and
pestle and subsequently lyophilized overnight in a FreeZone 4.5 Freeze
Dry System (Labconco). Metabolites were extracted following published
protocols^[287]44. Then, 5–10 mg of dried powder was weighed and
extracted with 1.8 ml 2:1 chloroform:methanol. First, 0.6 ml methanol
containing scyllo-inositol (10 nmol) and [^13C[5]–^15N]valine (5 µM
final) as internal standards was added. This was followed by 1.2 ml
chloroform containing margaric acid (C17:0, 10–20 μg). Samples were
vortexed thoroughly and sonicated three times (8 min each) in a
sonication bath at 4 °C. Samples were subsequently spun at 18,000g for
20 min and the supernatants were vacuum-dried in a rotational vacuum
concentrator RVC 2–33 CD (Christ). Pellets were extracted with a
methanol:water solution (2:1 v/v) as above and the two supernatants
were combined and dried. Phase separation was performed with a 1:3:3
solution of chloroform:methanol:water. Polar (containing polar
metabolites) and apolar (containing lipids) phases were stored
separately at −80 °C until further processing. For the analysis of
metabolites in blood, 5 μl serum was processed as above, without
vortexing and sonication.
For in vitro assays, cells were plated in triplicate in six-well plates
(250,000 cells per well) and cultured overnight before changing the
medium to the indicated experimental conditions. At collection, cells
were washed with PBS and snap-frozen. Subsequently, cells were scraped
in 0.75 ml methanol, 0.25 ml chloroform and 0.25 ml water containing
1 nmol scyllo-inositol as an internal standard. Samples were vortexed,
sonicated for 3 × 8-min bursts at 4 °C and incubated overnight at 4 °C.
Samples were subsequently pelleted by centrifugation at 20,000g for
20 min at 4 °C. Phase separation was performed with a 1:3:3 solution of
chloroform:methanol:water through the addition of 0.25 ml water to the
supernatant. Polar and apolar phases were separated by centrifugation
at 20,000g for 5 min at 4 °C. The polar phase was vacuum-dried in a
rotational vacuum concentrator RVC 2–33 CD (Christ) for GC–MS analysis
as detailed below.
GC–MS analysis
GC–MS analysis was performed following published protocols^[288]15.
Part of the polar fraction was dried and washed twice with methanol.
The first wash contained l-Nor-leucine (1.6 nmol per sample) as a run
standard. For derivatization, metabolites were incubated with
methoximation (Sigma, 20 µl, 20 mg ml^−1 in pyridine) overnight
followed by trimethylsilylation (20 µl
N,O-bis(trimethylsilyl)trifluoroacetamide reagent (BSTFA) containing 1%
trimethylchlorosilane (TMCS), Thermo Fisher). Samples were then
analyzed using an Agilent 7890A-5975C GC–MS system. Splitless injection
(injection temperature 270 °C) onto a 30 m + 10 m × 0.25 mm DB-5MS + DG
column (Agilent J&W) was used, using helium as the carrier gas, in
electron ionization mode. The initial oven temperature was 70 °C
(2 min), followed by temperature gradients to 295 °C at 12.5 °C per min
and then to 320 °C at 25 °C per min (held for 3 min). Under these
conditions, glutamine and glutamic acid can spontaneously cyclize to
pyroglutamic acid. The latter was thus used to assess glutamine levels
in the tissue samples or plasma extracts, due to its preponderant
abundance in blood. Metabolites were identified based on a mix of
authentic standards, using the MassHunter software (Agilent,
v.10.0.368). Label incorporation was calculated by subtracting the
natural abundance of stable isotopes from the observed
amounts^[289]15,[290]45. Briefly, The level of enrichment of individual
isotopologs (m+x) of metabolites was estimated as the percentage of the
metabolite pool containing x ^13C atoms after correction for natural
abundance:
[MATH: Enrichmentofm+xx=Cm+xΣ
Cm+0+Cm+1…+Cm+i×100% :MATH]
The percentage carbons enriched for a metabolite with i isotopologs was
calculated by:
[MATH: 13Cmet=m=1i+2<
mfrac>m+2i…+im+1i :MATH]
LC–MS analysis
LC–MS analysis of the polar extracts was carried out following
published protocols^[291]46. Samples were injected into a Dionex
UltiMate LC system (Thermo Scientific) with a ZIC-pHILIC
(150 mm × 4.6 mm, 5-μm particle) column (Merck Sequant). Solvent B was
acetonitrile (Optima HPLC grade, Sigma-Aldrich) and solvent A was 20 mM
ammonium carbonate in water (Optima HPLC grade, Sigma-Aldrich). A
15-min elution gradient of 80% solvent A to 20% solvent B was used,
followed by a 5 min wash of 95:5 solvent A to solvent B and 5 min
re-equilibration. Other parameters were a flow rate of 300 µl min^−1;
column temperature of 25 °C; injection volume of 10 µl; and autosampler
temperature of 4 °C. MS was performed with positive/negative polarity
switching using an Q Exactive Orbitrap (Thermo Scientific) with a HESI
II probe. MS parameters were spray voltage of 3.5 kV and 3.2 kV for
positive and negative modes, respectively; probe temperature of 320 °C;
sheath and auxiliary gases were 30 and 5 AU, respectively; full scan
range was 70 to 1050 m/z with settings of auto gain control (AGC)
target and resolution as Balanced and High (3 × 10^6 and 70,000),
respectively. Data were recorded using Xcalibur software (Thermo
Scientific), v.3.0.63. Mass calibration was performed for both ESI
polarities before analysis using the standard Thermo Scientific Calmix
solution. To enhance calibration stability, lock-mass correction was
also applied to each analytical run using ubiquitous low-mass
contaminants. Quality control samples were prepared by pooling equal
volumes of each sample and analyzed throughout the run to provide a
measurement of the stability and performance of the system. To confirm
the identification of important features, some quality control samples
were run in data-dependent top-N (ddMS2-top-N) mode, with acquisition
parameters as follows: resolution of 17,500; AGC target under 2 × 10^5;
isolation window of m/z 0.4; and stepped collision energy 10, 20 and 30
in high-energy collisional dissociation mode. Qualitative and
quantitative analysis was performed using Free Style v.1.5 and
Tracefinder v.4.1 software (Thermo Scientific) according to the
manufacturer’s workflows. For putative annotation, a CEU Mass Mediator
tool was employed^[292]47.
LC–MS analysis of the apolar extracts was carried out following
published protocols^[293]48. Lipids were separated by injecting 10-μl
aliquots onto a 2.1 × 100 mm, 1.8-μm C18 Zorbax Eclipse plus column
(Agilent) using a Dionex UltiMate 3000 LC system (Thermo Scientific).
Solvent A was 10 mM ammonium formate in water (Optima HPLC grade,
Fisher Chemical) and solvent B was water:acetonitrile:isopropanol,
5:20:75 (v/v/v) with 10 mM ammonium formate (Optima HPLC grade, Fisher
Chemical). A 20-min elution gradient of 45% to 100% solvent B was used,
followed by a 5 min wash of 100% solvent B and 3 min re-equilibration.
Other parameters were a flow rate of 600 μl min^−1; a column
temperature of 60 °C; and an autosampler temperature of 10 °C. MS was
performed with positive/negative polarity switching using a Q Exactive
Orbitrap (Thermo Scientific) with a HESI II probe. MS parameters were a
spray voltage of 3.5 kV and 2.5 kV for positive and negative modes,
respectively; a probe temperature of 275 °C; sheath and auxiliary gases
were 55 and 15 arbitrary units, respectively; full scan range was 150
to 2,000 m/z with settings of AGC target and resolution as Balanced and
High (3 × 106 and 70,000), respectively. Data were recorded using
Xcalibur software (Thermo Scientific), v.3.0.63. Mass calibration was
performed for both ESI polarities before analysis using the standard
Thermo Scientific Calmix solution. To enhance calibration stability,
lock-mass correction was also applied to each analytical run using
ubiquitous low-mass contaminants. To confirm the identification of
significant features, pooled quality control samples were run in
data-dependent top-N (ddMS2-top-N) mode, with acquisition parameters of
mass resolution of 17,500; AGC target under 2 × 105; isolation window
of m/z 0.4; and stepped collision energy of 10, 20 and 30 in
high-energy collisional dissociation mode. Qualitative and quantitative
analyses were performed using Free Style v.1.5 (Thermo Scientific),
Progenesis QI v.2.4.6911.27652 (Nonlinear Dynamics) and LipidMatch
v.2.02 (Innovative Omics)^[294]49. Radial plot representations were
performed in R (v.3.6.2) using the R package volcano3D v.2.08 (ref.
^[295]50).
For the detection of CoA-SH and acetyl-CoA we used an Agilent 1200 LC
system equipped with an Agilent Poroshell 120 EC-C18, 2.7 μm,
4.6 × 50 mm column was used. Then, 10 μl cleared samples were injected
on the column. Mobile phase A was 40 mM ammonium formate at pH 6.8 and
mobile phase B was LC–MS grade acetonitrile. Metabolites were separated
at a flow rate 0.5 ml min^−1, using the following elution conditions:
0–1 min, 2% B; 1–10 min, 2–98% B; and 10–12 min, 98% B. An Agilent
accurate mass 6230 time-of-flight apparatus was employed. Dynamic mass
axis calibration was achieved by continuous infusion of a reference
mass solution using an isocratic pump connected to a dual Agilent Jet
Stream ESI source, operated in the positive-ion mode. ESI capillary,
nozzle and fragmentor voltages were set at 3,000 V, 2,000 V and 110 V,
respectively. The nebulizer pressure was set at 40 psi and the nitrogen
drying gas flow rate was set at 10 l min^−1. The drying gas temperature
was maintained at 200 °C. The sheath gas temperature and flow rate were
350 °C and 11 l min^−1. The MS acquisition rate was 1.0 spectra s^−1
and m/z data ranging from 50–1,200 were stored. The instrument
routinely enabled accurate mass spectral measurements with an error of
<5 ppm. Data were collected in the 2 GHz (extended dynamic range) mode
and stored in centroid format.
Western blot
Western blots were run using standard protocols for non-native protein
detection. The following antibodies were used: MYC: AbCam, ab32072,
1:1,000 dilution; SLC5A6: Proteintech 26407-1-AP, 1:1,000 dilution;
actin: Merck A3854 (HRP coupled), 1:25,000 dilution; cleaved caspase 3,
Cell Signaling cat. no. 9664, 1:1,000 dilution; ATF4: AbCam, ab23760,
1:1,000 dilution; PDHE1: AbCam ab110330, 1:1,000 dilution; p70S6K: Cell
Signaling, cat. no. 9202, 1:1,000 dilution; p-p70S6K: Cell Signaling,
cat. no. 9234, 1:1,000; p-S6: Cell Signaling, cat. no. 4834s, 1:1,000
dilution; S6k: Cell Signaling, cat. no. 2217, 1:1,000 dilution; HK2:
Cell Signaling, cat. no. 2867S, 1:1,000 dilution; AcSL1: Cell
Signaling, cat. no. 4047, 1:1,000 dilution; p-PERK (Thr980): Cell
Signaling, cat. no. 3179, 1:1,000 dilution; PERK: Cell Signaling, cat.
no. 3192, 1:1,000 dilution; p-eIF2ɑ (Ser51): Cell Signaling, cat. no.
3398, 1:1,000 dilution; eIF2ɑ: Cell Signaling, cat. no. 5324, 1:1,000
dilution. The secondary antibodies were anti-rabbit-HRP, GE Healthcare,
cat. no. NA934-1ML, 1:7,500 dilution; and anti-mouse-HRP, Invitrogen,
cat. no. 62-6520, 1:7,500 dilution.
RNA extraction and quantitative PCR
The 67NR-tet-cMYC cells were plated at 1.5 × 10^5 cells in a six-well
plate and cultured overnight before induction of MYC with doxycycline
for 24 h. Cells were collected in TRIzol (Thermo Fisher). Similarly,
3 mg tumor tissue was homogenized in TRIzol. Total cellular RNA was
extracted according to the manufacturer’s instructions. Subsequently,
RNA was treated with DNase (Thermo Fisher, EN0525) and 1 µg RNA was
incubated with 0.5 µM random primers. Complementary DNA was generated
using Superscript III Reverse Transcriptase (Invitrogen, 18080044) and
treatment with RNase OUT (Invitrogen, 10777019) according to the
manufacturer’s instructions. Quantitative PCR reactions were performed
in a ViiA 7 Real-Time PCR System (Thermo Fisher). PCR reactions were
performed using Applied Biosystems Power SYBR Green PCR Master Mix
(Thermo Fisher) in 10-µl reactions containing 5 pmol forward and
reverse primer at 200 ng of cDNA. PCR conditions were an initial
denaturation and polymerase activation for 10 min at 95 °C, followed by
40 cycles of 15 s and 95 °C and 60 s at 60 °C. These amplification
cycles were followed by a melt-curve denaturation. Fold change and
absolute abundance were determined using the 2^ΔΔCT and 2^ΔCT methods,
respectively.
Primer sequences were designed using NCBI-PrimerBLAST:
c-MYC: (F) 5′-CCTTCTCTCCGTCCTCGGAT-3′, (R) 5′-TCTTGTTCCTCCTCAGAGTCG-3′;
Slc5a6: (F) 5′-TTCACTGGCAACTGTCACGA-3′, (R) 5′-AGATACTGAGTGCTGCCTGG-3′;
Pank1: (F) 5′-AAGAACAGGCCGCCATTCC-3′, (R) 5′-CTGCCGTGATATCCTTCGT-3′;
Pank2: (F) 5′-TCACAGGCACCAGTCTTGGA-3′, (R) 5′-CCTGGCAAGCCAAACCTCT-3′;;
Ppcdc: (F) 5′-CGTGCGTGTTGAGGTCATAG-3′, (R) 5′-
GCCTGGGTCTAGAATCTGTCA-3′; Aasdhppt: (F) 5′-AAAGGGAAAGCCGGTTCTTG-3′, (R)
5′-ATTGAACCACGACCTGGAAA-3′; Slc1a5: (F) 5′-GCCTTCCGCTCTTTTGCTAC-3′, (R)
5′-GACGATAGCGAAGACCACCA-3′,
SLC5A6 exogenous: (F)TGGGCTGCTGTTACTTCCTG, (R) CACCCTCACGGTCTTGTTGA,
The sequence for primers for Slc38a1 were obtained from Bian et
al.^[296]51: (F) 5′-TACCAGAGCACAGGCGACATTC-3′, (R)
5′-ATGGCGGCACAGGTGGAACTTT-3′.
Cloning
Using Gateway cloning (Invitrogen, Thermo Fisher), SLC5A6
(pDONR221_SLC5A6 was a gift from the RESOLUTE Consortium and G.
Superti-Furga (Addgene plasmid #132194)) was inserted into the
retroviral plasmid, pBabe-Puro (pBABE-Puro-gateway was a gift from M.
Meyerson (Addgene plasmid #51070)).
Retroviral gene transfer
Phoenix-AMPHO cells were seeded at 1.0 × 10^6 cells in 6-cm plates and
cultured overnight. Cells were subsequently transfected with 2 µg
pBabe-SLC5A6 plasmid using PEI. After 16 h, the medium was replaced
with 5 ml DMEM + 10% FBS and cells were cultured for an additional 48 h
before collection of virus-containing supernatant. The supernatant was
centrifuged at 1,000g for 5 min at 4 °C and filtered through a 0.45-µm
syringe filter. Before transduction, 67NR cells were grown to 80%
confluency and treated with 8 µg ml^−1 Polybrene before infection with
0.5 ml virus-containing medium. After 24 h of transduction, cells were
replated with 3 mg ml^−1 puromycin for 72 h and maintained in the
culture medium at 1 μg ml^−1.
Growth curves
Cells were washed with PBS before seeding at indicated confluency in
high-glucose DMEM (custom made at the Francis Crick Institute) with 10%
dialyzed FBS^[297]52 with or without 10 µM d-calcium pantothenate
(Sigma, P5155) in 48-well plates. Cells cultured in the absence of
pantothenate were cultured with or without Coenzyme A Trilithium Salt
(EMD Millipore, 234101) at the indicated concentrations. Cells were
imaged recurrently with the Incucyte S3 Live Cell Analysis System and
confluency was measured using the Incucyte S3 Software (Sartorius)
v.2021C.
Sample preparation and DEFFI-MSI
Snap-frozen tumors were mounted onto cryosectioning chucks with a
freezing drop of ice. The tumors were sectioned individually into 10-μm
slices at −21 °C and thaw-mounted onto SuperFrost glass slides of
75 mm × 25 mm (Thermo Fisher Scientific). Sections were dried with a
flow of nitrogen, placed in slide boxes and vacuum packed. They were
stored at −80 °C until they were used for analysis. One set of PDXs was
instead embedded in hydroxypropyl-methylcellulose (40–60 cP, 2% in
H[2]O) and polyvinylpyrrolidone (average molecular weight 360,000) 7.5
and 2.5 g, respectively in 100 ml H[2]O and cryosectioned^[298]53. The
downstream processing was as described above.
For human breast cancer biopsies each sample was cryosectioned into
10-μm thick parallel sections using a Cryostat Leica CM 1950 (Leica)
set to −27 °C in the chamber and −25 °C in the sample holder. Tissue
sections were put onto SuperFrost plus glass slides (Thermo Fisher
Scientific). The slides were vacuum packed and stored at −80 °C until
DEFFI-MSI analysis.
Imaging was carried out on the DESI imaging source (Prosolia)
consisting of a two-dimensional sample holder moving stage coupled to a
XEVO G2-XS QToF (Waters CorporationA), with the ion block temperature
set at 150 °C. The DESI sprayer was converted to a DEFFI sprayer
according to Wu et al. ^[299]20 by pulling the solvent capillary
inwards^[300]20. A custom-built inlet capillary was heated up to 500 °C
to assist the desolvation of secondary droplets. All images were
acquired using HDImaging v.1.4 software in combination with MassLynx
v.4.1 (Waters Corporation). Imaging parameters were set as follows:
N[2]gas pressure at 5 bar; 95:5 v/v methanol/water solvent was
delivered by a nanoAcquity binary solvent manager (Waters Corporation)
set at a flow rate of 1.5 µl min^−1; sprayer voltage at 4.5 kV; sprayer
angle of 75°; sprayer to surface distance of 2 mm; sprayer to inlet
capillary distance of 1 mm; sprayer inlet capillary collection angle of
10°; emitter orifice distance of 150 µm; and orifice diameter of
200 µm. MS parameters were set as follows: m/z 50–1,000, one scan per
second, horizontal acquisition speed at 100 μm s^−1 with a lateral
resolution of 100 μm, acquired in negative sensitivity MS mode. PA
standard (100 ppm in 50:50 (v/v) methanol:water) was spiked onto tissue
and left at room temperature for 15 min to dry. DEFFI-MS/MS imaging was
then performed in negative sensitivity mode (precursor ion m/z of
218.10; collision energy of 12 v; one scan per second; and
100 µm × 100 µm pixel size). Once acquisition was finished, all tissue
sections were stored at −80 °C before staining.
After DEFFI acquisitions, samples were rehydrated. For WM tumors, the
slides were immediately cover-slipped and fluorescence was imaged on an
Olympus VC120 slide scanner. PDX samples were also rehydrated and fixed
for 10 min in 4% PFA. Staining was performed following standard IHC
protocols.
DEFFI image analysis
DEFFI-MSI peaks used for the statistical modeling were extracted from
the raw spectra. As a first step, peaks were centroided using the
method described by Inglese et al.^[301]22. To include possible
shoulder peaks, we selected those with a prominence greater than five.
Peaks prominences were estimated using the ‘find_peaks’ function
available in the Scipy package v.1.6.3 for Python^[302]54. A first
intra-run peak matching was performed using MALDIquant v.1.19.3 (ref.
^[303]55) relaxed method with a search window of 50 ppm. This allowed
SPUTNIK spatial filtering to remove signals that were detected outside
the tissue or were associated with scattered images (number of
eight-connected pixels <9) (ref. ^[304]56). Subsequently to the peak
filtering, all non-tissue pixels were discarded. The filtered tissue
peaks were mapped back to their original raw m/z values and mass
recalibrated using the method described by Inglese et al.^[305]22. The
recalibrated intra-run peaks were therefore matched using MALDIquant
v.1.19.3 relaxed method. Matching of tissue peaks was performed with a
search window of 50 ppm. If more than one peak was found in the search
windows in the same spectrum, only the one with the highest intensity
was retrieved. After this step, each run was assigned a set of common
masses. To match the peak masses among different runs, for each
DEFFI-MSI run r∈R, a representative list of peaks was calculated as the
set of ordered pairs X[r] = {(m/z[p,r], Y[p,r]): p∈P[r] }, where
[MATH:
Yp,r
=∑i=1Ny<
mrow>i,p,r/N :MATH]
is the arithmetic mean of the pixel-wise intensities y[i,r] of the peak
p with mass-to-charge ratio m/z[p,r] and P[r] is the set of common m/z
values of the run r. The m/z values of the representative peaks lists
of all R runs X′ = {X[1], X[2],…,X[R]} were matched together using the
MALDIquant v.1.19.3 relaxed method with a tolerance of 20 ppm to
generate a set P′ of inter-run common m/z values. Thus, this set of
masses was common to all runs. The set of inter-run common m/z values
was therefore filtered using a consensus approach. We removed all peaks
p′∈P′ if the corresponding mean intensity Y[p′,r] was equal to zero in
at least one run r∈R. Final intensity matrices were median-scaling
normalized (using median of non-zero intensities) and batch-effect
corrected using ComBat (from SVA package v.3.34.0)^[306]57, with the
batch equal to the acquisition run.
WGCNA v.1.70-3 (ref. ^[307]58) was employed to determine a consensus
metabolic network following a similar approach described by Inglese et
al.^[308]21. Only spectra corresponding to WM^high and WM^low tumors,
not WM^mix were used to estimate the network. Signed hybrid^[309]58
adjacencies, corresponding to the only positive Pearson’s correlations
between all pairs of features, were calculated from the individual
runs, using a consensus soft power equal to the smallest value
corresponding to an R^2 > 0.85 across all runs. Tested values for the
soft power ranged from 1 to 20. The adjacencies were subsequently
normalized using the ‘single quantile’ method available in the WGCNA
v.1.70-3 package for R, with the reference quantile set equal to 0.95
and combined into a consensus adjacency. Each element of the consensus
adjacency matrix corresponded to the minimum adjacency value across the
runs.
The consensus topological overlap matrix^[310]59,[311]60, estimated
from the consensus adjacency, was used to determine the network modules
through hierarchical clustering (average linkage).
An initial set of clusters were determined using the dynamicTreeCut
algorithm^[312]61, with the ‘hybrid’ algorithm and a smallest cluster
size equal to 20. Module eigenmetabolites (MEs) were calculated from
each module as the first principal-component scores of the merged runs
spectra, using the only module features. The scores sign was reversed
if Pearson’s correlation with the average image (calculated assigning
to each pixel the mean intensity of their peaks) was negative. Modules
with MEs correlated (Pearson’s correlation) more than 0.85 were
considered identical and merged into a single module. Final MEs were
recalculated from the merged modules. Association between modules and
MYC was estimated using a linear regression model. First, for each
module, the WM^high and WM^low values of the ME were binarized into
‘low-ME’ and ‘high-ME’ using the inter-run median value as threshold.
Then, the proportion of ‘high-ME’ was calculated within each tissue
section and used as the dependent variable of the regression model,
whereas the binary tissue MYC condition of each pixel was used as
independent variable. Because of its proportion nature, the dependent
variable was modeled as a β-distributed
(link = ‘logit’)^[313]62,[314]63. ME models with a significantly
different from zero slope (partial Wald test P < 0.05,
Benjamini–Hochberg correction) were considered statistically associated
to MYC. Finally, ME values for the WM^mix spectra were calculated
projecting their module peak intensities on the corresponding loadings
estimated from the WM^high and WM^low spectra.
All DEFFI single-ion images were maximal intensity normalized for
individual ions within each run and scales were adjusted to values of
0–1 with the color palette adjusted with thresholding intensity at the
99.9th percentile to avoid artifacts caused by outliers, unless
specified.
Dendrogram for ion colocalization of labeled metabolites
Mass-to-charge ratios (m/z) of selected metabolites were searched in
the list of inter-run matched common m/z values. Theoretical m/z values
were calculated from the deprotonated mass adding k times the δ m/z
corresponding to the mass difference between ^13C and ^12C
(ΔC = 1.003355 m/z). The value of k varied from zero to five, where
k > 0 represented the isotopic forms. Features within an error of
10 ppm from the theoretical m/z value were considered as candidate
matches. In the case of multiple candidates, the one corresponding to
the highest mean intensity across all pixels was selected.
Matched features intensities corresponding to isotopes (k > 0) were
adjusted for the natural isotopic abundance to estimate the intensity
corresponding to the only labeled metabolites. The natural isotopic
abundance was estimated from the observed raw monoisotopic intensities,
with the probability of observing k ^13C atoms, given N carbon atoms,
modeled as binomially distributed, Pr(X = k) = Binom(k, N, P), where
P = 0.01109 represents the probability of observing natural ^13C
isotopes. Pixel intensities corresponding to the labeled ^13C component
were therefore calculated subtracting the estimated natural ^13C
intensity from the raw intensity of the same pixel. Negative
intensities could result from measurement errors. In these cases, we
set the ^13C intensity to zero, following the assumption that either no
labeled molecules were detected or their abundance fell below the limit
of detection of the instrument.
All three runs were included in the calculation. Intra-run Pearson’s
correlations were estimated between the corrected intensities of the
matched features. Subsequently, an inter-run consensus correlation
matrix was calculated by taking the minimum values among the runs. Only
WM^mix pixels were used for the calculation of the correlations.
The null hypothesis that consensus correlations between ion pairs is
zero was tested using a permutation test. In each of 10,000
permutations, consensus correlations were calculated after shuffling
the pixel intensities of the individual ion images. P values were
calculated as
[MATH: (∑i<
mi>Ii+1)/10,001 :MATH]
, where I[i] is the indicator function, which is equal to 1 if the
absolute permuted correlation is greater or equal than the original
absolute correlation, 0 otherwise. P values of all pairwise
correlations were adjusted using the Benjamini–Hochberg method.
A hierarchical clustering, with average linkage, was employed to
partition the matched metabolites. One minus the consensus correlation
was used as a distance measure. Clusters were identified by cutting the
dendrogram at the level corresponding to two clusters.
Automated segmentation and co-registration of PDX samples and human biopsies
Levels of MYC were identified by IHC (see below) in PDX samples and
human biopsies. The IHC signal was deconvoluted using the inbuilt DAB
staining deconvolution algorithm of the QuPath software package. The
pixels devoid of any DAB staining from the deconvoluted IHC images were
then removed by thresholding the green image channel at an intensity of
230. The remaining pixels were subsequently assigned as MYC positive.
The image was then re-binned by a factor of 16 by summing the total
number of MYC-positive pixels in any given 16 × 16 area using the
MATLAB function ‘blockproc’ (Mathworks, image-processing toolbox). The
resulting images (the MYC percentage proportions figures) were then
clustered using the k-means clustering algorithm using Euclidean
distance and k = 2 to differentiate regions that are high in MYC stain
versus low. The binary image of the cluster that was highest in MYC
signal versus those with low and no MYC was then extracted and
registered to the MSI image. Areas of overt necrosis, staining and
processing artifacts were excluded from the analysis by drawing masks.
The masks were applied to the single-ion images for PA from MSI
analysis as well as to the deconvoluted MYC staining before
binarization, and within each tumor the average difference of mean
levels of PA between regions of high and low MYC was assessed by a
linear mixed-effects model fitted with the ‘glmmTMB‘ v.1.1.4 package
for R (random intercepts were considered for run and tissue ID). We
then plotted the observed proportions connected by a line per sample,
with a black dot representing the predicted mean of the two groups. The
error bars represent the confidence interval of the predictions for the
two groups (Supplementary Fig. [315]1).
Immunohistochemistry/immunofluorescence
Immunofluorescence staining was performed against BrDU, eGFP and Tomato
using the following antibodies. BrDU (BD, 347580, 1:100 dilution); eGFP
*(Abcam, ab6683, 1:100 dilution); and RFP (Rockland, 600-401-379, 1:500
dilution). Secondary antibodies were Alexa Fluor-647 donkey anti-mouse,
A-31571, Alexa Fluor-555 donkey anti-rabbit, A-31572, Alexa Fluor-488
donkey anti-goat, A-11055, all at 1:500 dilution. Slides were blocked
for 10 min in 4% PFA at room temperature and rehydrated in PBS–Tween
(0.1%) for 10 min. Slides were then transferred into a citric buffer
(10 mM at pH 6.0) and boiled in a microwave for 15 min. Slides were
cooled under running tap water and blocking was performed in PBS–Tween
(0.1%) with 3% BSA for 1 h at room temperature. Primary antibodies were
incubated overnight, whereas secondary antibodies were incubated for
1 h at room temperature. Nuclei were stained with Hoechst
(0.1 μg ml^−1, Sigma). To assess proliferation in WM tumors, three
visual fields per sample were segmented by clones (WM^high and WM^low)
and total nuclei as well as BrdU-positive nuclei were quantified using
the cell counting plugin in ImageJ. To assess proliferations in
PA-deprived and control HCI002 PDXs, BrdU staining was carried out as
above, slides were scanned with an Olympus VC120 slide scanner and
nuclei as well as BrdU-positive cells were quantified on the whole
slide using the Qpath cell-counting feature. IHC staining for c-MYC was
carried out on frozen sections post-DEFFI for PDX samples and on the
consecutive slice for human biopsies. The secondary antibody was a
biotinylated goat anti-rabbit (BA-1000, Vector, 1:250 dilution) for
45 min at room temperature. The ABC kit (PK-6100 from Vector) was
incubated for 30 min and DAB for 10 min.
NanoSIMS
Electron microscopy embedding
For NanoSIMS analysis, WM^mix tumors were grown as described above. At
3 h before collection, BrdU (100 μl, 10 mg ml^−1) was administered to
the mice i.p. Either boluses of stable-isotope labeled calcium
pantothenate ([^13C[3],^15N]β-alanyl) or a co-infusion of
[^13C[6]]glucose and [amide-^15N]glutamine were administered to the
mice as described above. Once removed tumors were fixed overnight in
freshly prepared 4% paraformaldehyde in 0.1 M phosphate buffer (PB) at
pH 7.4 and stored at 4 °C. Following initial fixation, samples were
embedded in 2% low-melting-point agarose (A4018-50G, Sigma-Aldrich) in
0.1 M PB and 150-μm sections were collected using a vibrating knife
ultramicrotome (VT1200S, Leica), using a speed of 1 mm s^−1 and an
amplitude of 0.75 mm. Excess agarose was removed from the sections and
tissue was stored in a 24-well plate in 0.1 M PB at 4 °C.
Sections were then transferred onto a glass slide, cover-slipped in PB
and the whole section imaged in a single plain with a confocal
microscope (Leica, Falcon SP7) at ×100 magnification (objective HCPL
APO CS2 ×10, NA 0.4). Regions of interest (ROIs) were chosen and a
z-stack of approximately 70-μm depth at ×100 magnification was imaged
over an area of approximately 1.5 mm^2.
Imaged sections were then removed from the glass slide and embedded
using a protocol adapted from the NCMIR method^[316]64. Sections were
post-fixed in 4% paraformaldehyde/2.5% glutaraldehyde in 0.1 M PB at pH
7.4 for 1 h at room temperature. Samples were then washed in 0.1 M PB
(5 × 3 min) before being post-fixed in 2% reduced osmium (2% osmium
tetroxide/1.5% potassium ferricyanide) at 4 °C for 1 h. Sections were
washed (5 × 3 min in dH[2]O), stained in 1% thiocarbohydrazide for
20 min at room temperature, washed again (5 × 3 min in dH[2]O) and
stained in 2% osmium tetroxide for 30 min at room temperature. Finally,
samples were washed (5 × 3 min in dH[2]O) before being left overnight
in 1% uranyl acetate at 4 °C. The following day sections were washed
(5 × 3 min in dH[2]O) and then stained en bloc with lead aspartate (pH
5.5) for 30 min at 60 °C. After a final wash (5 × 3 min in dH[2]O),
sections were dehydrated using a graded series of alcohol (20%, 50%,
75%, 90%, 100% × 2, 20 min each) followed by infiltration with Durcupan
resin (44610-1EA, Sigma-Aldrich) (1:1 resin:ethanol overnight and 100%
resin for 24 h). Sections were then flat embedded using Aclar (L4458,
Agar Scientific) and polymerized at 60 °C for 48 h.
Targeted single-section large area montaging
Polymerized sections were removed from the Aclar and blocks were
prepared from the ROI. The ROI was identified by aligning the overview
×100 confocal image of the whole section to an overview image of the
now-embedded section, acquired using a stereo microscope (MC205C
stereo, DMC 4500 Camera, Leica Microsystems) in Photoshop (Adobe). The
identified area was then removed from the resin with a small bit of
excess on each side using a razor blade and the excised block was
mounted on a metal pin (10-006002-50, Labtech) using silver epoxy
(604057, CW2400 adhesive, Farnell), which was polymerized at 60 °C for
1 h^[317]65. The sample was removed from the oven and any excess resin
and silver epoxy was trimmed using a glass knife on an ultramicrotome
(EM UC7, Leica Microsystems). The resin block was shaped into a square
with one corner removed so that it was asymmetric, to aid in
orientation of the block in the serial block-face (SBF) scanning
electron microscope (SEM). Finally, the block face was trimmed until
the tissue was reached. The block was then sputter coated with 10 nm
platinum (Q150S, Quorum Technologies) and loaded into a 3View2XP
(Gatan) attached to a Sigma VP SEM (Zeiss) with focal charge
compensation (Zeiss). The SBF SEM was used as a ‘smart trimming’ tool,
allowing visual assessment of the tissue structure during cutting until
the EM images of the block face, collected using a BSE detector (3View
detector, Gatan), matched with the structures imaged in the ×100
confocal z-stack. When the z-plane containing the ROI had been reached,
the sample was removed from the SBF SEM, the block was re-trimmed using
a glass knife to a sub-area of the ROI approximately 400 μm × 200 μm
and 200-nm sections were cut from the block face using an
ultramicrotome (EM UC7, Leica Microsystems) and a 6-mm histo diamond
knife (DiATOME). Sections were collected onto silicon wafers (G3390,
Agar Scientific) using an eyelash and dried on a hotplate at 70 °C for
10 min. The silicon wafers were then mounted onto SEM stubs
(10-002012-100, Labtech Electron Microscopy) using adhesive carbon tabs
(15-000412, Labtech Electron Microscopy) and loaded into a Quanta FEG
250 SEM (Thermo Fisher Scientific). Tiled images of the whole section
were collected using Maps software (v.1.1.8.603, Thermo Fisher
Scientific) using a low-voltage high-contrast backscattered electron
detector (vCD, Thermo Fisher Scientific). Images were collected using a
voltage of 2.5 kV, a spot size of 3, a dwell time of 5 ms, a working
distance of 6 mm and a pixel resolution of 10 nm. Individual images
from the tiled sequence were exported to TIFF files and aligned into a
single image using the TrackEM2 plugin in Fiji^[318]66. Both the
exported single image and the corresponding resin section on a silicon
wafer were then sent to NanoSIMS for targeted analysis, where they were
loaded into the NanoSIMS (Cameca NanoSIMS 50L, Cameca/Ametek). Before
analysis, the pulse height distributions of the electron multiplier
detectors were analyzed and their voltage gains and thresholds adjusted
if necessary. They were then further examined by measuring the C^− and
CN^− count rate on adjacent detectors used to measure the ^13C/^12C and
^12C^15N/^12C^14N isotope ratios. This step is imperative to measure
accurate isotope ratios. The NanoSIMS 50L has seven detectors which
were then moved to the appropriate radii in the magnetic sector with
fixed magnetic field to measure the following masses: ^12C, ^13C,
^12C^14N, ^12C^15N, ^31P, ^79Br and ^81Br. Images were typically
acquired at 50 μm × 50 μm field width or 25 μm × 25 μm with a 300-μm D1
aperture (D1-2) to provide a mosaic correlating with regions previously
analyzed by fluorescence and scanning EM. Nono-SIMS images were
acquired using Cameca NanoSIMS N550L software v.4.4. and were processed
and quantitative data extracted using the OpenMIMS plugin for ImageJ.
The acquisition modes were overlaid using the BigWarp plugin in Fiji.
For better visualization, the pseudocolor look-up-table (LUT) was
changed in the [^13C[6]]glucose and [^15N]amido-glutamine co-infused
samples.
MYC signature in PDXs
Based on the microarray expression data from the PDXs
used^[319]26,[320]67 we downloaded the c6 oncogenic signatures from the
Molecular Signatures Database^[321]68,[322]69 and applied the GSVA
package, v.1.48.3 (ref. ^[323]70) to infer sample specific MYC pathway
activation. As the PDX cohort might not represent all types of breast
cancers, we included the 1,980 METABRIC samples from elswehere^[324]37
and quantile-normalized them together before converting the expression
values into z scores. For pathways that had a subset of genes that
should be upregulated and another subset that should be downregulated,
the final score was obtained as upregulated − downregulated. We
compared the scores with the log-intensity expression with a
scatter-plot.
Metabolic pathway analysis
Global feature extraction was carried out using Progenesis QI
(Nonlinear Dynamics) tool following parameters used for the LC–MS
analysis method (FWHM, 70,000, minimum chromatographic peak width of
0.166 min, min. intensity threshold of 10^5). Features with coefficient
of variation below <30% across replicates from at least one class of
the WM^high, WM^low or WM^mix tumor samples were further used for
untargeted metabolic pathway analysis using MetaboAnalyst v.5.0 (ref.
^[325]71). A feature table consisting of 1,562 input m/z features from
polar and apolar sample analysis of both polarities were processed
using the mummichog algorithm. A feature significance cutoff of 0.05 P
value for a Student’s t-test between WM^high and WM^low was used and
enriched pathways were identified using Homo sapiens (KEGG) pathway
library (mass accuracy threshold of 10 ppm). For calculation of the
enrichment score for the identified pathway, the number of significant
feature hits from individual pathways with fold change difference
higher in either WM^high or WM^low samples was estimated. The
enrichment score is then calculated with a modified mummichog algorithm
by the significant hits coming from individual sample types rather than
all significant hits and by using Fisher’s exact test P value for
pathway enrichment to scale the enrichment score. Enriched pathways
that consist of pathway size >1 in the library were retained for the
analysis.
Conditional probability for ion coexpression with WM^high clones
The list of metabolites associated with enriched pathways in WM^high
tumors were compared to the DEFFI-MS data for deprotonated ions.
Conditional probabilities P (metabolite level | myc level) were
estimated from the multi-run merged normalized and
batch-effect-corrected DEFFI data. Metabolite intensities were
discretized into low, mid and high levels by thresholding to the
t[1] = 33th and t[2] = 66th percentiles (y ≤ t[1]⇒low,
t[1] < y ≤ t[2]⇒mid, y > t[2]⇒high). The conditional probabilities were
calculated as a fraction of pixels with either low, mid or high
intensity, stratified by MYC level. Only pixels from WM^low and WM^high
were used for the estimation. The calculated conditional probabilities
were represented as a heat map using R v.3.6.2 (ref. ^[326]72) and the
ComplexHeatmap v.2.2.0 (ref. ^[327]73) package.
METABRIC dataset analysis
Transcript levels of SLC5A6 were evaluated in human breast cancer
samples from the publicly available METABRIC microarray gene expression
dataset^[328]37. Samples were subsequently annotated with molecular
subtype based on Prediction Analysis of Microarray 50
allocations^[329]74, estrogen receptor status, grade, size, number of
lymph nodes positive and proliferation (expression of Aurora Kinase A).
Thereafter, samples were ordered according to MYC signature expression
(average transcript levels of 335 genes), retrieved from a previously
published core MYC expression signature (consisting of 398
genes)^[330]75. Data analyses were undertaken using R v.3.6.1 (ref.
^[331]72), the ComplexHeatmap v.2.2.0 (ref. ^[332]73) and Circlize
v.0.4.15 (ref. ^[333]76) packages.
Chip-seq
Publicly available data from Sabo et al.^[334]36 were downloaded from
the Gene Expression Omnibus repository under accession no.
[335]GSE51011. An E-box rich region inside the murine Slc5a6 promoter
was identified using the genomic sequence extracted from the Ensembl
database and the respective region was identified in the Chip-seq raw
data. The respective binding intensities of MYC to this region were
plotted for pre-tumor as well as tumor cells.
Statistics
Pairwise comparisons were generally carried out using the Student’s
t-test in Excel or using the R package ggplot2 v.3.4.0 and ggpubr
v.0.4.0. No statistical methods were used to predetermine sample sizes
but our sample sizes are similar to those reported in previous
publications^[336]15,[337]18.
When possible, mice from the same litter were randomized into groups.
To avoid litter effects, different cell types (for example WM^high,
WM^low and WM^mix) were implanted into mice from different litters in a
random fashion. This was also the case in all studies with diet
alteration. Before changing the diet, litters were mixed and
randomized. Samples were randomized for metabolomics analysis. In
preclinical model experiments, tumor size and mouse weight measurements
were blinded as well as treatment regimes. DESI/DEFFI imaging was
carried out blind and agnostic to the underlying tumor genotypes. Data
analyses were not performed blind to the conditions of the experiments.
Data were only excluded due to technical failure (for example over
confluence, at start of an experiment, bad melting curve in qRT–PCR).
No data were systematically excluded. Only mice with failed tumor
grafting or tumor-unrelated health issues were excluded. Mice with
failed metabolite administration due to improper cannulation and mice
with failed tumor grafting were excluded.
For the NanoSIMS study with calcium pantothenate (dual label ^13C and
^15N), the ^13C trace was recorded, but not analyzed, as it failed to
show a signal above background, due to a higher natural abundance of
^13C compared to ^15N.
Statistical methods embedded in R-code-based image analysis, such as
the ion colocalization analysis and the correlation dendrogram, are
indicated in the respective sections.
All box-and-whisker plots represent the following: line, median; box,
IQR; whiskers, 1.5 × IQR limited by largest/smallest NEV.
Data distribution was assumed to be normal but this was not formally
tested.
Reporting summary
Further information on research design is available in the [338]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[339]Supplementary Information^ (842.1KB, pdf)
Supplementary Figs. 1, 2 and 3 and Supplementary Table 2.
[340]Reporting Summary^ (4MB, pdf)
[341]Supplementary Table 1^ (129KB, xlsx)
List of ions constituting metabolic modules identified in WM tumors.
Source data
[342]Source Data Fig. 1^ (516.4KB, xlsx)
LC–MS analysis data of polar and apolar metabolites in WM tumors.
[343]Source Data Fig. 2^ (13.6KB, xlsx)
LC–MS analysis of CoA levels in WM tumors and quantification of
single-cell PA-derived ^15N/^14N ratio detected by NanoSIMS in WM
tumors.
[344]Source Data Fig. 3^ (63KB, xlsx)
Quantification of single-cell ^13C/^12C and ^15N/^14N ratios detected
by NanoSIMS in WM tumors.
[345]Source Data Fig. 4^ (148.3KB, xlsx)
LC–MS analysis of polar metabolite in HCI002 tumors grown with and
without PA. LC–MS analysis of CoA and acetyl-CoA in HCI002 tumors grown
with and without PA. Analysis of the indicated gene expressions in
WM^high and WM^low tumors. HCI002 tumor growth with and without PA.
Growth of tumors from 67NR cells with and without ectopic expression of
SLC5A6.
[346]Source Data Fig. 4^ (21.9MB, pdf)
Uncut western blots for Fig. [347]4j,l.
[348]Source Data Extended Data Fig. 2^ (17.3KB, xlsx)
GC–MS data of PA level analysis in PDXs. Quantification of subcellular
^15N/^14N ratios detected by NanoSIMS in WM tumors. Quantification of
single-cell PA-derived ^15N/^14N ratio detected by NanoSIMS in WM
tumors (second biological replicate).
[349]Source Data Extended Data Fig. 3^ (36.7KB, xlsx)
GC–MS analysis of polar metabolites from WM tumors labeled with
^13C-glutamine. Values are nmol of metabolite per mg dry tissue. GC–MS
analysis of polar metabolites from WM tumors labeled with ^13C-glucose.
Values are nmol of metabolite per mg dry tissue.
[350]Source Data Extended Data Fig. 5^ (346.3KB, xlsx)
GC–MS analysis of extracts from 4T1 cells grown either with or without
PA as well as rescued with CoA. Values are area under the curve. LC
analysis of CoA and acetyl-CoA in WM tumors grown with and without PA.
LC–MS analysis of polar metabolite levels in WM tumors grown with and
without PA. LC–MS analysis of apolar metabolite levels in HCI002 tumors
grown with and without PA. LC–MS analysis of apolar metabolite levels
in WM tumors grown with and without PA. Growth of WM tumors in the
presence and absence of PA. Quantification of BrdU signal in WM tumors
grown in the presence and absence of PA.
[351]Source Data Extended Data Fig. 6^ (43.3KB, xlsx)
GC–MS analysis of polar metabolites in 67NR cells with SLC5A6
over-expression. GC–MS analysis of the effect of PA deprivation on
polar metabolites in 67NR cells. GC–MS analysis of the effect of PA
deprivation on polar metabolites in 67NR cells with SLC5A6
over-expression. GC–MS analysis of polar metabolites in tumors from
67NR cells with and without the ectopic expression of SLC5A6.
Expression of genes upon the induction of ectopic MYC expression in
67NR cells.
[352]Source Data Extended Data Fig. 6^ (4.6MB, pdf)
Uncut western blots for Extended Data Fig. [353]6a,c,e.
Acknowledgements