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Metabolite profiling stratifies pancreatic ductaladenocarcinomas
into subtypes with distinctsensitivities to metabolic
inhibitorsAnneleen Daemena, David Petersonb,1, Nisebita Sahub,1,
Ron McCordb,1, Xiangnan Duc, Bonnie Liuc,Katarzyna Kowanetzb,
Rebecca Hongc, John Moffatd, Min Gaoc, Aaron Boudreaub, Rana
Mroueb, Laura Corsonc,Thomas O’Brienc, Jing Qingc, Deepak Sampathc,
Mark Merchantc, Robert Yauchb, Gerard Manninga, Jeffrey
Settlemanb,Georgia Hatzivassiliouc, and Marie Evangelistab,2
aBioinformatics and Computational Biology, Genentech, South San
Francisco, CA 94080; bDiscovery Oncology, Genentech, South San
Francisco, CA 94080;cTranslational Oncology, Genentech, South San
Francisco, CA 94080; and dBiochemical Pharmacology, Genentech,
South San Francisco, CA 94080
Edited by Ronald A. DePinho, University of Texas M.D. Anderson
Cancer Center, Houston, TX, and approved June 22, 2015 (received
for review January 29, 2015)
Although targeting cancer metabolism is a promising
therapeuticstrategy, clinical success will depend on an accurate
diagnosticidentification of tumor subtypes with specific metabolic
require-ments. Through broad metabolite profiling, we successfully
identifiedthree highly distinct metabolic subtypes in pancreatic
ductal adeno-carcinoma (PDAC). One subtype was defined by reduced
proliferativecapacity, whereas the other two subtypes (glycolytic
and lipogenic)showed distinct metabolite levels associated with
glycolysis, lipo-genesis, and redox pathways, confirmed at the
transcriptional level.The glycolytic and lipogenic subtypes showed
striking differences inglucose and glutamine utilization, as well
as mitochondrial function,and corresponded to differences in cell
sensitivity to inhibitors ofglycolysis, glutamine metabolism, lipid
synthesis, and redox balance.In PDAC clinical samples, the
lipogenic subtype associated with theepithelial (classical)
subtype, whereas the glycolytic subtypestrongly associated with the
mesenchymal (QM-PDA) subtype,suggesting functional relevance in
disease progression. Pharmaco-genomic screening of an additional
∼200 non-PDAC cell lines vali-dated the association between
mesenchymal status and metabolicdrug response in other tumor
indications. Our findings highlight theutility of broad metabolite
profiling to predict sensitivity of tumorsto a variety of metabolic
inhibitors.
metabolite profiling | metabolic subtypes in PDAC | glycolysis
|lipid synthesis | biomarkers for metabolic inhibitors
Metabolic reprogramming during tumorigenesis is an
essentialprocess in nearly all cancer cells. Tumors share a
commonphenotype of uncontrolled cell proliferation and must
efficientlygenerate the energy and macromolecules required for
cellulargrowth. The first example of metabolic reprogramming was
dis-covered more than 80 y ago by Otto Warburg: tumor cells can
shiftfrom oxidative to fermentative metabolism in the course of
onco-genesis (1). More recently, there has been a resurgence of
interestin targeting cancer metabolism (2–4) because it may not
only be ef-fective in inhibiting tumor growth, but may also provide
a therapeuticwindow (5, 6). For example, inactivation of lactate
dehydrogenase-A(LDHA), an enzyme that catalyzes the final step of
aerobic gly-colysis, thereby reducing pyruvate to lactate,
decreases tumorigenesisand induces regression of established tumors
in mouse models oflung cancer driven by oncogenic KRAS or epidermal
growth factorreceptor (EGFR) while minimally affecting normal cell
function(7). The finding that cancers have altered metabolism has
promptedsubstantial investigation, both preclinically and in
clinical trials, ofseveral metabolically targeted agents, including
those that elevatereactive oxygen species (ROS) or block
glycolysis, lipid synthesis,mitochondrial function, and glutamine
synthesis pathways (8).The identification of distinct metabolic
reprogramming events
or metabolic subtypes in cancer may inform patient selection
forinvestigational metabolic inhibitors and in the selection of
new
therapeutic targets (9, 10). Just as tumors vary greatly in
genomicalterations that impact signaling and regulatory pathways,
meta-bolic transformation is also heterogeneous and dependent on
tissuetype, proliferation rate, and isoenzyme use (9, 11). In
addition, theobserved differences in the dependence on and
utilization of themajor nutrients—glutamine and glucose—are linked
to oncogenicsignaling and the genomic features of a cancer cell
(12).Large-scale pharmacogenomic screening is a powerful method
for identifying biomarkers of drug response and can
acceleratethe search for new cancer therapies (13, 14). In this
study, weused broad baseline metabolite profiling in cell line
models ofpancreatic ductal adenocarcinoma (PDAC), a disease
contextpreviously associated with altered metabolism (15–18), to
iden-tify metabolic subtypes within PDAC and predict their
sensitivityto various metabolic inhibitors.
ResultsBaseline Metabolite Profiling Identifies Three Metabolic
Subtypes inPDAC. We examined cell lines derived from naturally
occurringtumors because they recapitulate many aspects of the
tissue type
Significance
Targeting cancer metabolism requires personalized diagnosticsfor
clinical success. Pancreatic ductal adenocarcinoma (PDAC)
ischaracterized by metabolism addiction. To identify metabolic
de-pendencies within PDAC, we conducted broad metabolite
profilingand identified three subtypes that showed distinct
metaboliteprofiles associated with glycolysis, lipogenesis, and
redox path-ways. Importantly, these profiles significantly
correlated withenriched sensitivity to a variety of metabolic
inhibitors includinginhibitors targeting glycolysis,
glutaminolysis, lipogenesis, and re-dox balance. In primary PDAC
tumor samples, the lipid subtypewas strongly associatedwith an
epithelial phenotype, whereas theglycolytic subtype was strongly
associated with a mesenchymalphenotype, suggesting functional
relevance in disease progression.Our findingswill provide valuable
predictive utility for a number ofmetabolic inhibitors currently
undergoing phase I testing.
Author contributions: A.D., G.H., and M.E. designed research;
A.D., D.P., K.K., R.Y., G.M., J.S.,G.H., and M.E. performed
research; A.D., D.P., N.S., R. McCord, X.D., B.L., K.K., R.H.,
J.M.,M.G., A.B., R. Mroue, L.C., T.O., J.Q., and M.E. contributed
new reagents/analytic tools;A.D., D.P., N.S., R. McCord, X.D.,
B.L., K.K., R.H., J.M., M.G., A.B., R. Mroue, L.C., T.O.,
J.Q.,D.S., M.M., G.H., and M.E. analyzed data; and A.D. and M.E.
wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access
option.1D.P., R. McCord, and N.S. contributed equally to this
work.2To whom correspondence should be addressed. Email:
[email protected].
This article contains supporting information online at
www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental.
E4410–E4417 | PNAS | Published online July 27, 2015
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and genomic context of cancer (13, 14, 19, 20). Levels of
256metabolites were quantified in 38 pancreatic cancer cell lines
(fivebiological replicates per cell line) in logarithmic growth
phase usingmedia with physiological glucose and glutamine
concentrations(Datasets S1–S3). We applied nonnegative matrix
factorization(NMF) (21), a recently established approach for
consensus clus-tering (22–24), to 153 metabolites with reproducible
variation,allowing the capture of the strongest signal of metabolic
de-pendency (SI Materials and Methods). This analysis revealed
threestable and reproducible subtypes with adequate data
coherence(Fig. S1 A and B). The metabolite profiles of the cell
lines orderedby subtype are shown in Fig. 1A for metabolites with
distinct in-tensities in at least one subtype compared with the
other twosubtypes (F test, P < 0.05). These three subtypes
provided a usefuland interpretable basis for further analysis.
Metabolic Characterization Reveals a Slow Proliferating,
Glycolytic,and Lipogenic Subtype. The metabolite intensities within
eachsubtype were then mapped to known, previously
establishedmetabolic ontologies (Dataset S1 and SI Materials and
Methods)(25). One subtype (34% of all lines) was especially low in
aminoacids and carbohydrates (Fig. 1A, left subtype, and Fig. S1C).
Celllines in this subtype had an average doubling time that was
sig-nificantly higher (Fig. S1D) and were named the slow
proliferatingsubtype. Doubling times for cell lines from the other
two subtypeswere more similar (Fig. S1D); however, these two
subtypes dis-played strikingly distinct metabolic profiles,
independent of pro-liferation rate (SI Materials and Methods).
Thus, these metabolicsubtypes have unique metabolic profiles that
are independent ofgrowth rate.We further explored the metabolic
differences between the two
subtypes with similar proliferation rates. One subtype (27% of
alllines; Fig. 1A) exhibited, on average, elevated levels of
variouscomponents of the glycolytic and serine pathways, mainly
phos-phoenolpyruvate (PEP), glyceraldehyde-3-phosphate, lactate,
andserine (Fig. 1 B and C and Fig. S1E), and was named the
glycolyticsubtype. This subtype was also distinguished by lower
levels ofmetabolites important for redox potential such as
nicotinamideadenine dinucleotide (NAD) reduced (NADH), NAD
phos-phate (NADP), NAD phosphate reduced (NADPH),
glutathionedisulfide (GSSG), glutathione (GSH), and flavine adenine
di-nucleotide (Fig. 1 B and C, Fig. S1F, and Dataset S4). In
contrast,the other subtype (39% of all lines; Fig. 1A) was enriched
forvarious lipid metabolites such as palmitic acid (C16:0), oleic
acid(C18:cis[9]1), palmitoleic acid (C16:cis[9]1), and myristic
acid(C14:0) (Fig. 1 B and D and Dataset S4), as well as
mitochondrial[oxidative phosphorylation (OXPHOS)] metabolites
important forthe electron transport chain such as coenzyme Q10 and
coenzymeQ9 and components of the aspartate-malate shuttle such as
as-partate and glutamate (Fig. S1G and Dataset S4), and was
namedthe lipogenic subtype.
Differences Between Glycolytic and Lipogenic Subtypes Are
ConfirmedTranscriptionally. We next determined whether differences
in me-tabolite levels observed between the glycolytic and lipogenic
subtypescould be explained by differences in the expression of
genesknown to be associated with the metabolic ontologies (Dataset
S5and SI Materials and Methods). Consistent with the differences
inmetabolite levels, expression of genes associated with glycolysis
andthe pentose phosphate pathway were found to be relatively
ele-vated in cell lines from the glycolytic subtype (Fig. 1 E and
F, Fig.S1 H and I, and Dataset S6). For example, most glycolytic
linesdemonstrated higher expression of neuron-specific enolase
[ENO2;adjusted P = 0.0016; Fig. 1 E and F], along with higher
levels of itsproduct PEP, whereas other enolase homologs were not
differen-tially expressed (Fig. S1J). We also noted that protein
(and notmRNA) abundance of the lactate transporter,
monocarboxylatetransporter 1 (MCT1) was elevated in the glycolytic
lines compared
with the lipogenic lines (P < 0.05; Fig. 1E and Fig. S1K). In
con-trast, cell lines within the lipogenic subtype were enriched
for ex-pression of lipogenesis genes involved in cholesterol and de
novolipid synthesis including 7-dehydrocholesterol reductase
(DHCR7),stearoyl-CoA desaturase (SCD), and fatty acid synthase
(FASN)(adjusted P < 0.1; Fig. 1 E and F, Fig. S1 H and L, and
DatasetS6). Thus, PDAC-derived cell lines can be clustered by
theirmetabolite profiles and these differences appear to be
determinedin part by differences in gene expression.
Glycolytic and Lipogenic Subtypes Use Glucose and Glutamine in
aDifferent Manner. The metabolic and transcriptional profiles
sug-gested that these two subtypes may differ in their use of
glucoseand glutamine, the most abundant carbon sources available
tocancer cells. We predicted that the lipogenic subtype
wouldpreferentially use glucose for the tricarboxylic acid (TCA)
cycleand lipid synthesis, whereas the glycolytic subtype would use
glu-cose more for aerobic glycolysis, and consequently, use more
glu-tamine for TCA anaplerosis. 13C metabolic mass
isotopomerdistribution analysis (MIDA) using either
[U-13C5]glutamine or[U-13C6]glucose revealed a significant increase
in the contribution of[U-13C6]glucose to TCA metabolites in
representative cell linesfrom the lipogenic subtype relative to the
glycolytic subtype (Fig.2A; P < 0.05). In contrast,
representative glycolytic lines in-corporated [U-13C5]glutamine
into TCA metabolites at signifi-cantly higher levels than lines
from the lipogenic subtype (Fig. 2B;P< 0.05). Moreover,
lipogenic cell lines incorporated 14C-glucose intolipid metabolites
at a significantly higher level than cell lines fromthe glycolytic
subtype (Fig. 2C; P < 0.01). Consistent with theseobservations,
lipogenic lines showed on average higher O2 con-sumption (Fig. 2D;
P < 0.01) and a greater mitochondrial content[Mitotracker and
tetramethylrhodamine ethyl ester (TMRE) in-tensity] compared with
glycolytic subtype lines (Fig. 2E; P < 0.01;Dataset S7). Thus,
cell lines from the glycolytic and lipogenicsubtypes appear to use
glucose and glutamine in a different manner.
Glycolytic and Lipogenic Cell Lines Show Distinct Sensitivity
toVarious Metabolic Inhibitors in Vitro. Based on their distinct
meta-bolic wiring, we predicted that glycolytic and lipogenic cell
lineswould show differential sensitivity to inhibitors targeting
aerobicglycolysis (oxamate and the LDHA inhibitor GNE-140)
(26),glutaminolysis
[bis-2-(5-phenylacetimido-1,2,4,thiadiazol-2-yl)ethylsulfide
(BPTES)], and de novo lipid synthesis [FASN inhibitorGSK1195010
(27), SCD inhibitor (28), cerulenin, and orlistat].Indeed, as
predicted, the glycolytic subtype was enriched for linesthat were
sensitive to the LDHA inhibitor, oxamate, and BPTES,whereas the
lipogenic subtype was enriched for lines that weresensitive to
inhibitors targeting lipid synthesis (Fig. 3A and Fig.S2A; P <
0.05; Dataset S7 and SI Materials and Methods). More-over,
glycolytic cell lines showed higher rates of fatty acid (FA)uptake
(Fig. S2B) and increased sensitivity to media with reducedlipid
content (Fig. S2C), suggesting these lines may be more re-liant on
FA pathways for generating lipids.Maintaining redox balance is
another key requirement for can-
cer cells (29). The differences in redox-related metabolites
betweenglycolytic and lipogenic cell lines suggested that they may
also showdifferential sensitivity to ROS-inducing agents or
inhibitors of en-zymes or transporters important for maintaining
glutathione syn-thesis and NADP/NADPH balance in cells. Indeed, we
found thatcell lines within the glycolytic subtype showed enhanced
sensitivityto a variety of such agents including inhibitors of
gamma-gluta-mylcysteine synthetase [buthionine sulphoximine
(BSO)],and the cystine transporter xCT
{(S)-4-carboxyphenylglycine[(S)-4-CPG]} (Fig. 3B and Dataset S7).In
addition to short-term (3 d) culture assays, we tested the
efficacy profile of LDHA inhibitor, oxamate, and the SCD
in-hibitor in long-term (12 d) culture assays and observed
similarresults (Fig. 3 A and C).
Daemen et al. PNAS | Published online July 27, 2015 | E4411
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Glucose
G6P
F6P
F1,6BP
Glyceraldehyde 3-P
3-Phosoglycerate
PEP
Ribulose-5-P G1P
2,6BP
Glycerol-3-P
Serine
Pyruvate Lactate
Glutamine
Glutamate
a-KG
Citrate Isocitrate Citrate
a-KG
Acetyl-CoA LipidsPDH
Oxaloacetate Aspartate
ENO2
MCT1
HMGCS1 MVD
FDFT1 DHCR7 SCD5DL DHCR24
DGAT1 SCD FASN ACAT2
PSPH
PDK1
p < 0.05 p < 0.1 p < 0.2
Glycolytic Lipogenic
Succinate Malate
Oxaloacetate
Fumarate
Cholines
Glycolysis / PPP
Carbohydrates
Other complex lipids
OXPHOS /mitochondria−related REDOX
Fatty acids
Amino acids
Sphingolipids
JG score
−5 0 5 10
Glyc
olytic
Lipog
enic
-1.0
-0.5
0.0
0.5
1.0
1.5
log2
Ole
ic a
cid
(RU
) ***
Glyc
olytic
Lipog
enic
-2.0
-1.0
0.0
1.0
2.0
log2
Pal
mito
leic
aci
d (R
U)
***
Glyc
olytic
Lipog
enic
-2.0
-1.0
0.0
1.0
2.0
3.0
log2
Myr
istic
aci
d (R
U)
***
Glyc
olytic
Lipog
enic
-2.0
-1.0
0.0
1.0
2.0
log2
Pal
mito
leic
aci
d (R
U)
***
Glyc
olytic
Lipog
enic
2
4
6
8
10
log2
SC
D (R
PKM
+1) *
Glyc
olytic
Lipog
enic
4
5
6
7
8
log2
FA
SN (R
PKM
+1)
*
Glyc
olytic
Lipog
enic
1
2
3
4
5
6
log2
EN
O2
(RPK
M +
1) ***
Glyc
olytic
Lipog
enic
4
5
6
7
8lo
g2 D
HC
R7
(RPK
M +
1)
**
Glyc
olytic
Lipog
enic
6.0
8.0
10.0
12.0
14.0
log2
PEP
(RU
) **
Glyc
olytic
Lipog
enic
0.0
2.0
4.0
6.0
log2
GSS
G (R
U) **
Glyc
olytic
Lipog
enic
-6.0
-4.0
-2.0
0.0
2.0
4.0
log2
GA
P (R
U) *
D
E
B
C
F
B
AsPC1
YAPCCFPAC
1KP
3TCC
PAN2
Hs 766TCapan
2Panc 02.03
Panc 04.03
Panc 03.27
Panc 05.04
KLM1
Panc 10.05
MIA Paca
2
KP2
PSN1HUP
T3PK
45HPL45PA
TU8988T
KP4PK
45PSW
1990PA
TU8902
HPACKCI
MO
H1
PANC1
SUIT2
PATU
8988S
HPAFII
SU.86.86PK
8PK
1DAN
GHUP
T4BxPC
3PK
59KP
3L
Car
bo-
hydr
ates
A
min
o ac
ids
REDOX
Fatty
ac
ids
Gly
coly
sis
PP
P
0
3.9
1.9
+2.
1
+4.
2
Slow Proliferating Glycolytic Lipogenic
A
Fig. 1. Identification of distinct metabolic subtypes in PDAC
through baseline metabolite profiling. (A) Hierarchical clustering
of identifiable metaboliteswith significant intensity differences
between any of the three subtypes (F test, P < 0.05; 99
metabolites). Cell lines were grouped by subtype, with the orderper
subtype defined by unsupervised clustering. Log2 intensity ratio
data per metabolite are scaled across all cell lines to mean = 0
and SD = 1. Blue indicateslow scaled intensity, and yellow
indicates high for each metabolite. Highlighted in gray are
functionally related metabolites. Slow proliferating lines
arelabeled in gray, glycolytic lines in purple, and lipogenic in
cyan. (B) Relative enrichment of the eight metabolic ontology
classes in the glycolytic and lipogenicsubtypes, represented by JG
score (47). Positive scores represent ontologies enriched for
metabolites with high intensities in the glycolytic subtype.
SeeDataset S1 for a description and list of metabolites per
ontology and Dataset S4 for the list of differentially expressed
metabolites. (C) Normalized metaboliteintensity levels for
metabolites involved in glycolysis/pentose phosphate and redox
pathways that were differentially expressed between glycolytic
andlipogenic lines. RU stands for relative unit, with intensity
levels normalized to a reference pool of samples for metabolites
from the Broad Profiling platform(Dataset S2) and to a universal
13C-labeled internal standard for metabolites from the Energy
platform (Dataset S3). (D) Normalized metabolite intensity
levelsfor metabolites involved in lipid synthesis that were
differentially expressed between glycolytic and lipogenic lines.
(E) Detailed metabolite map with genesdifferentially expressed
between cell lines from the glycolytic vs. lipogenic subtype
indicated with various shades of color depending on P value
corrected formultiple testing. For MCT1, P value is based on
protein expression level. We refer to Dataset S6 for a list of
differentially expressed genes. (F) Expression levelsof ENO2,
DHCR7, SCD, and FASN involved in glycolysis and lipid synthesis
that were differentially expressed between glycolytic and lipogenic
lines (DatasetS6). Asterisks denote a statistically significant
difference by unpaired t test with Welch’s correction (*P <
0.05, **P < 0.01, ***P < 0.001).
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Functional Confirmation of the Glycolytic and Lipogenic Subtype
inVivo. To translate these findings in vivo and generate
proof-of-concept findings for our two metabolic subtypes, we
evaluatedxenografts of MIA Paca-2, a glycolytic cell line, and
HPAC, alipogenic cell line, for their sensitivity to glycolysis vs.
lipid syn-thesis inhibition. Because oxamate and LDHA inhibitors
havepoor pharmacokinetics in mice (26), we inhibited glycolysis
byengineering MIA Paca-2 and HPAC cells to express a
doxycline(DOX)-inducible shRNA against LDHA. MIA Paca-2
xenografttumors treated with DOX showed undetectable levels of
LDHA(Fig. 3D) and 68% tumor growth inhibition (TGI) compared
withtumors expressing LDHA (Fig. 3E). In contrast, administration
of
an SCD inhibitor showed no efficacy (Fig. 3E), although
phar-macodynamic inhibition of SCD was seen (Fig. 3F). In
contrast,HPAC xenograft tumors showed minimal sensitivity to
LDHAknockdown (9% TGI; Fig. S2 D and E) but showed significanttumor
growth inhibition to SCD inhibitor treatment (52% TGI)(30). Thus,
glycolytic and lipogenic subtypes are functionally dis-tinct and
show differential sensitivity to glycolytic and lipidbiosynthesis
inhibition.
Glycolytic and Lipogenic Subtypes Are Associated with
KnownSubtypes of PDAC, Driven by Mesenchymal Status. We next setout
to determine how our defined metabolic subtypes associated
C Glycoly
tic
Lipog
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/mg
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Lipog
enic
0.00
0.02
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OCAR
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oles
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l)
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TMR
E In
tens
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MIA Paca-2HUP-T3SW 1990 SUIT-2HPACPA-TU-8988S
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Glycolytic Lipogenicy y p g
** **
Glyc
olytic
Lipog
enic
10
20
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% G
luta
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e (M
5) fr
om
13C
-Glu
tam
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Glyc
olytic
Lipog
enic
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% M
alat
e (M
4) fr
om
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-Glu
tam
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Glyc
olytic
Lipog
enic
0
10
20
30
40
% a
-KG
(M5)
from
13
C-G
luta
min
e
* **
**
A
C
D
E
B
Fig. 2. Functional characterization of glycolytic and lipogenic
subtypes. (A) Comparison of relative contribution of glucose
oxidation to the TCA metabolites,determined by M2 labeling from
[U-13C6]glucose for citrate, αKG, malate, and aspartate between
glycolytic and lipogenic cell lines. (B) Comparison of
relativecontribution of reductive glutamine metabolism to TCA
metabolites, determined by M5 labeling from [U-13C5]glutamine for
αKG and glutamate, and M4labeling for malate between glycolytic and
lipogenic cell lines. (C) Comparison of relative contribution of
glucose metabolism to de novo lipid synthesisbetween glycolytic and
lipogenic cell lines. Cells were labeled with 1 μCi/mL D[U-14C]
glucose for 6 h, and lipids were extracted. The incorporation of
14C intolipids was determined by scintillation counting. (D)
Comparison of oxygen consumption rates (OCRs) between glycolytic
and lipogenic cell lines. (E) Com-parison of relative mitochondria
number (Mitotracker intensity per cell) and potential/fitness (TMRE
per cell) between glycolytic and lipogenic cell lines. ForA–E, the
mean and SD between cell lines belonging to the glycolytic subtype
vs. lipogenic subtype is plotted where each cell line is shown as
one dot,representing the mean of three replicates. Data are
normalized to sample protein content (A–C) or cell number (D and
E). Asterisks denote a statisticallysignificant difference by
unpaired t test with Welch’s correction (*P < 0.05, **P <
0.01, ***P < 0.001).
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with primary PDAC tumor samples from patients. Three
clinicalsubtypes of PDAC were recently identified through
molecularprofiling of PDAC tumors: classical (characterized by high
ex-pression of adhesion-associated and epithelial genes),
quasi-mesen-chymal (QM-PDA, characterized by
mesenchyme-associatedgenes), and exocrine-like (22). Because
exocrine-like cell lineshave not been reported, we simplified the
three-subtype PDACsignature to a 42-gene expression signature that
distinguishesclassical from QM-PDA (22), and applied it to our cell
line panel.We found that all cell lines within the glycolytic
subtype associatedwith the quasimesenchymal subtype, whereas most
lipogenic lines
were associated with the classical subtype (Fig. 4A; P =
0.0006;Dataset S7). Thus, our metabolite subtypes derived from
pan-creatic cell lines strongly correlate with known subtypes
ofPDAC tumors, with the glycolytic subtype strongly associatingwith
mesenchymal features and the lipogenic subtype associ-ating with
epithelial features.
Metabolic and Mesenchymal Markers Predict Response to
Glycolyticand Glutaminolytic Inhibitors in PDAC and Other Tumor
Types. Car-cinomas with mesenchymal features (including PDAC) tend
to bemore aggressive and typically have an overall poorer
prognosis
A C
D
F
B
E
Fig. 3. Glycolytic and lipogenic cell lines show distinct
sensitivity to various metabolic inhibitors both in vitro and in
vivo. (A) Comparison of IC50 values to variousmetabolic inhibitors
between representative glycolytic vs. lipogenic cell lines in
short-term (3 d) viability assays. Saturated IC50 values correspond
to cell lines wherean IC50 was not reached at the maximum drug
concentration. The mean and SD between cell lines belonging to the
glycolytic vs. lipogenic subtype is plottedwhere each cell line is
shown as one dot, representing the mean of three replicates.
Asterisks denote a statistically significant difference by
Mann–Whitney test(*P < 0.05, **P < 0.01, ***P < 0.001).
(B) Comparison of IC50 values to various ROS-inducing agents
between representative glycolytic vs. lipogenic cell lines in
short-term (3 d) viability assays, similar to A. (C) Comparison of
sensitivity to oxamate, LDHA, or SCD inhibitors between
representative glycolytic vs. lipogenic cell lines inlonger-term
(12 d), low seeding density growth assays. (D) Western blots
showing 98% in vivo knockdown of LDHA levels in MIA Paca-2
xenografts administeredwith doxycycline (1 mg/mL) for 8 d vs. 5%
sucrose. (E) In vivo knockdown of LDHA (n = 10 for each group)
results in 68% TGI, 95% confidence interval [48, 83] inthe MIA
Paca-2 shLDHA model of a glycolytic subtype tumor, whereas
treatment with an SCD inhibitor (75 mg/kg, orally, BID) resulted in
no significant change intumor volume. (F) Confirmed pharmacodynamic
inhibition of lipid metabolism by SCD inhibitor. The SCD inhibitor
reduces desaturation of palmitate and stearatein MIA Paca-2 shLDHA
xenograft tumor tissues and in mouse liver and plasma (n = 5 per
group). Data are presented as mean ± SD.
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EMT H
igh
EMT L
ow
100
1000
BSO
IC50
(
EMT H
igh
EMT L
ow0
10
20
30
BPT
ES IC
50 (
M)
EMT H
igh
EMT L
ow0
20
40
60
Oxa
mat
e IC
50 (m
M) **** **** ****
MIA Paca 2KP4
PSN1PA TU 8988T
PK 45PPK 45H
PL45SW 1990
DAN GHUP T3PANC 1
PA TU 8902BxPC 3
KP 2SU.86.86
PK 8SUIT 2PK 59KP 3L
KCI MOH1HPAC
HUP T4HPAF II
PA TU 8988S
Epithelial / Mesenchymal Score
2 1 0 1 2
GlycolyticLipogenic
Classical (Epithelial)
QMPDA (Mesenchymal)
Epithelial / Mesenchymal Score
Glycolysis / PPP
PPARA
Lipids
Amino acids
OXPHOS
JG score
5 0 5
PPARA
Glycolysis / PPP
Lipids
Amino acids
OXPHOS
JG score
15 10 5 0 5
Up in resistant
Up in resistant
LDHAi BPTES
Up in sensitive
JG score
Up in sensitive
JG score
Glycolytic Subtype Lipogenic Subtype Glucose Glucose
Pyruvate Lactate
Serine
Pentose Pathway
Mitochondria (TCA cycle)
Pyruvate Lipid synthesis
Mitochondria (TCA cycle)
Glutamine
Mesenchymal Tumors Sensitive to:
-Glycolytic, Glutamine inhibitors, ROS-inducing agents
Epithelial Tumors Sensitive to:
- Lipid inhibitors
Vim H
igh
Vim Lo
w0
10
20
30
BPT
ES IC
50 (
M)
Vim H
igh
Vim Lo
w0
100
200
300
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500
Oxa
mat
e IC
50 (m
M)
Vim H
igh
Vim Lo
w
100
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BSO
IC50
(nM
)
**** **** **** **** **** ****
ENO2 / Lipid Ave
ENO2
Lipid Ave
MVD
HMGCS1
FDFT1
DHCR7
DGAT1
0−4.
4
−2.
4
+1.
7
+3.
8
A
C
F
B
D
E
Fig. 4. Metabolic and mesenchymal markers predict response to
glycolytic and glutaminolytic inhibitors in PDAC and other tumor
types. (A) Epithelial/mes-enchymal score for the glycolytic and
lipogenic cell lines based on a 42-gene set characteristic of the
classical and QM-PDA subtypes (22). The score is defined as
thedifference in average expression of QM-PDA vs. classical genes,
with a positive score indicative of QM-PDA and a negative score of
classical. Cell lines arecolored by metabolic subtype, with
glycolytic lines in purple and lipogenic lines in cyan. All
glycolytic cell lines are of the QM-PDA subtype, whereas lipogenic
celllines are associated with the classical subtype (Fisher’s exact
test, P = 0.0006). (B) Relative enrichment of the five curated
metabolism gene sets in cell lines that aresensitive (positive JG
score) or resistant (negative JG score) to LDHA inhibitor or BPTES
in a pan-cancer panel of 204 and 167 nonpancreatic cell lines,
respectively,after exclusion of cell lines with intermediate
response. See Dataset S5 for a list of genes per gene set. (C)
Metabolic dependency preference in the panel of ∼200nonpancreatic
cell lines is based on the ratio of ENO2 expression to the average
expression of five lipid genes, and labeled on top of the heatmap
as glycolytic inpurple (ratio > third quartile), lipogenic in
cyan (ratio< lower quartile), and undefined type in gray (ratio
between lower and third quartile). Shown are expression(log2 RPKM +
1) of glycolytic gene ENO2, five lipid genes DGAT1, DHCR7, FDFT1,
HMGCS1, and MVD, average expression of the five lipid genes (Lipid
Ave), andthe ratio of ENO2 to average lipid expression (ENO2/Lipid
Ave). Data from Dataset S8. (D) High expression of a pan-cancer EMT
signature (EMT) associates withsensitivity to oxamate, BPTES, and
BSO across a variety of tumor types. EMT low is defined by RPKM
values < lower quartile, EMT high = RPKM values >
thirdquartile. Asterisks denote a statistically significant
difference by Mann–Whitney test (*P < 0.05, **P < 0.01, ***P
< 0.001, ****P < 0.0001). (E) High expression ofmesenchymal
marker vimentin (Vim) associates with sensitivity to oxamate,
BPTES, and BSO across a variety of tumor types. Vim low is defined
by RPKM values <lower quartile, Vim high = RPKM values >
third quartile. Asterisks as per D. (F) Model of preferential
glucose and glutamine utilization in the glycolytic vs.lipogenic
subtype.
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(22, 31, 32). Given the strong association between
quasi-mesenchymalstatus and glycolytic dependency in the PDAC
lines, we askedwhether this association might also exist in other
tumor types.We screened ∼200 nonpancreatic cancer cell lines,
representingvarious tumor types, for sensitivity to inhibitors of
aerobic gly-colysis and glutaminolysis, as well as to ROS-inducing
agents(Dataset S8). As in PDAC (Fig. 1E and Fig. S1H), we found
thatcell lines most sensitive to the LDHA inhibitor, oxamate,
andBPTES were associated with a glycolytic signature, whereas
celllines that were most resistant to these inhibitors were
associatedwith an OXPHOS signature (Fig. 4B and Fig. S3A). We
nextassigned each cell line to a metabolic subtype (glycolytic
vs.lipogenic) using the glycolytic and lipid genes that were
mostdifferentially expressed in the PDAC metabolic subtypes
(ad-justed P < 0.05). Using the ratio of expression of
glycolytic geneENO2 to the average expression of lipid genes
diacylglycerolO-acyltransferase 1 (DGAT1), DHCR7,
farnesyl-diphosphatefarnesyltransferase 1 (FDFT1),
3-hydroxy-3-methylglutaryl-CoAsynthase 1 (HMGCS1), and mevalonate
(diphospho) decarbox-ylase (MVD) clearly distinguished
nonpancreatic cell lines bymetabolic dependency preference (Fig.
4C). A glycolytic pref-erence in nonpancreatic lines associated
with sensitivity to LDHAinhibitor, oxamate, and BPTES (Fig. S3B; P
< 0.05; Dataset S8). Inaddition, consistent with our findings in
the PDAC tumors, mes-enchymal tumors [according to a pan-cancer
epithelial-mesenchy-mal transition (EMT) signature (33) or
vimentin] were moresensitive to the LDHA inhibitor, oxamate, BPTES,
and ROS-inducing agents [BSO and (S)-4-CPG] across a variety of
tumortypes (Fig. 4 D and E and Fig. S3C; P < 0.001; Dataset S8).
Asimilar discrepant dependency was observed in the slow
pro-liferating PDAC cell lines, with six lines more glycolytic
and/ormesenchymal and six lines more lipogenic and/or epithelial,
despitetheir slower proliferation (Fig. S3 D and E). Thus,
mesenchymaltumors, regardless of indication, appear to share common
metabolicvulnerabilities, and agents that block glycolysis,
glutamine metabo-lism, or redox balance may be particularly
effective. These resultssupport a model in which metabolic
plasticity with regard to bio-energetic pathways is limited, and,
consequently, unique metabolicdependencies exist in tumors that can
be exploited for cancertherapy based on tumor subtype.
DiscussionUsing broad metabolite profiling, we successfully
stratifiedPDAC-derived cell lines into discrete metabolic subtypes.
Pre-vious metabolic profiling studies have been conducted in
tumorsand in cell lines of the NCI-60 panel with different end
points(9). However, this study is the first, to our knowledge, to
suc-cessfully identify metabolic subtypes through profiling of a
largenumber of samples within one tissue type and to
demonstratethat each subtype is enriched for drug sensitivity to
uniqueclasses of metabolic inhibitors.Although metabolic clustering
accounted for a substantial frac-
tion of the drug response variation observed across cancer
celllines, some heterogeneity in drug response within the
lipogenicsubtype remained (see SI Text and Figs. S4 and S5 for a
discussionon heterogeneity). Some cell lines were clearly
“hard-wired” forlipogenesis and showed sensitivity to all lipid
inhibitors tested,whereas the more refractory lines appeared to be
capable ofswitching to alternative pathways, perhaps those
involving fatty aciduptake. Further understanding of the nature and
plasticity of met-abolic networks in these cancer cells will be
required to more ac-curately predict their sensitivity to specific
classes of metabolicinhibitors. In addition, although we
successfully translated our invitro findings in vivo, additional
factors within the tumor microen-vironment (tumor-stroma signaling,
angiogenesis, and hypoxia) willinfluence sensitivity and adaptation
to metabolic inhibition in vivo.Our study also identified PEP as
one of the most differentially
expressed metabolites between glycolytic and lipogenic cell
lines.
ENO2, which converts 2-phosphoglycerate (2-PG) to PEP, wasalso
one of the most differentially expressed genes between thesetwo
subtypes, suggesting that inhibitors of ENO2 may be par-ticularly
effective against glycolytic tumors. Enolases act down-stream of
phosphoglycerate mutase (PGAM1) and regulatepyruvate kinase (PK) M2
isoform (PKM2), genes that are par-ticularly active in glycolytic
tumors and have recently attractedattention for their role in
serine biosynthesis through regulationof 3-phosphoglycerate
dehydrogenase (PHGDH) (34). ENO2 hasalso been proposed as a target
in ENO1-deleted glioblastomas(35). Our findings further
substantiate the biological rationalefor targeting ENO2 in a subset
of cancers.Finally, we demonstrated that the observed metabolic
sub-
types correlate with epithelial vs. (quasi)-mesenchymal cell
statesboth in PDAC and other cancer types. We propose a model
(Fig.4F) in which mesenchymal tumors are metabolically wired
topreferentially use glucose for glycolysis and lactate
productionand use glutamine for generating TCA metabolites,
whereasepithelial tumors preferentially use glucose for the TCA
cycleand de novo lipogenesis. Moreover, our analysis suggests
thatmesenchymal tumors may be more vulnerable to
ROS-inducingagents, potentially through differences in NADPH
balance andantioxidant responses (36).Such differences in metabolic
vulnerabilities between epithelial
and mesenchymal states could arise from the activation of
signalingpathways associated with these states. For example,
epithelialsubtypes have previously been shown to be enriched for
activatingmutations in receptor tyrosine kinases (RTK) such as EGFR
(37)and PI3K/AKT signaling pathways (23), leading to activation of
themechanistic target of rapamycin (mTOR). mTOR increases
bothprotein synthesis and lipogenesis through mechanisms
includingenzyme phosphorylation and transcriptional activation of
EIF1A(38) and SREBP1 (39–41). In contrast, mesenchymal states
areassociated with increased c-Myc expression and HIF1A, which
havebeen shown to drive a glycolytic profile (42, 43). Regardless
of thenature or mechanism of action for the metabolic variation we
ob-served, our data provide valuable predictive utility and
therebyinform clinical evaluation of a variety of metabolic
inhibitors suchas MCT and glutaminase inhibitors currently
undergoing phase Itesting across a variety of tumor
indications.
Materials and MethodsDetailed materials and methods can be found
in SI Materials and Methods.All cell lines listed in Dataset S9
were grown in RPMI (without glucose,without glutamine) media (US
Biological #R9011) supplemented with 6 mMglucose, 2 mM glutamine,
5% FBS, 100 μg/mL penicillin, and 100 U/mLstreptomycin. Metabolite
profiling was performed as previously described(44). For flux
analysis, cells were cultured for ∼18 h in RPMI with 10%(vol/vol)
dialyzed FBS supplemented with either 3 mM D[U-13C]glucose or1 mM
L[U-13C]glutamine. Data analysis was carried out with the
MultiQuantsoftware. For short-term viability assays, cells were
plated using optimalseeding densities in 384-well plates. The
following day, cells were treatedwith LDHA inhibitor GNE-140 (26),
oxamate (Sigma cat# O2751), SCDinhibitor (28), FASN inhibitor
GSK1195010 (27), cerulenin, orlistat, BSO,S-4-CPG, aminooxyacetic
acid (AOA), and BPTES (45), using a 6-pt dose titra-tion scheme.
After 72 h, cell viability was assessed using the
CellTiter-GloLuminescence Cell Viability assay. Absolute inhibitory
concentration (IC)values were calculated using four-parameter
logistic curve fitting and areaverages from a minimum of two
independent experiments. For long-termgrowth assays, glycolytic
cell lines (MIA Paca-2, SW 1990, PSN1, and HUP-T3)and lipogenic
cell lines (PA-TU-8902, PK-8, KP-3L, and SUIT-2) were seeded ina
6-well dish at 3,000 cells per well overnight (RPMI, 5% serum, 2 mM
glu-tamine) and then treated in media with indicated concentrations
of oxamate,SCD inhibitor, or DMSO for 12 d at 37 °C and 5%CO2.
Fatty uptake assays wereperformed using the Free Fatty Acid Uptake
Assay Kit (ab176768) accordingto the manufacturer’s protocol.
Reduced serum experiments were carriedout using 3% delipidated
serum (SeraCare 502099) and 1% FBS (SeraCareCC5010-500). Seahorse
Bioscience assays were used for oxygen consump-tion. All procedures
involving animals were reviewed and approved by theInstitutional
Animal Care and Use Committee (IACUC) at Genentech and
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carried out in an AAALAC (Association for the Assessment and
Accreditation ofLaboratory Animal Care) accredited facility. All
statistical analyses were per-formed in R 3.0.0 (46). The optimal
number of metabolic subtypes wasobtained with nonnegative matrix
factorization, using the NMF package. TheDESeq2 package was used
for differential expression analysis. Metabolic on-tology and gene
set enrichment analyses were based on GSEAlm.
ACKNOWLEDGMENTS. We thank Richard Bourgon, Eva Lin, Billy
Lam,Yihong Yu, and Arjan Gower for help with cell-based drug
screens anddata analysis, Mandy Kwong for advice on 13C metabolic
mass iso-topomer distribution analysis (MIDA), Allison Bruce for
assistance withthe metabolic diagram, and Metanomics Health
(Lisette Leonhardt,Ulrike Rennefarhrt, Oliver Schmitz, and Hajo
Schiewe) for technical sup-port on metabolite profiling.
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