Metabolomics (2017) 13:61
DOI 10.1007/s11306-017-1199-6
ORIGINAL ARTICLE
Discrimination of pancreatic cancer and pancreatitis by LC-MS
metabolomics
Anna Lindahl2 · Rainer Heuchel3 · Jenny Forshed2 · Janne Lehtiö2 · Matthias Löhr3 ·
Anders Nordström1,2
Received: 10 January 2017 / Accepted: 20 March 2017 / Published online: 1 April 2017
© The Author(s) 2017. This article is an open access publication
Abstract
Introduction Pancreatic ductal adenocarcinoma (PDAC)
is the fifth most common cause of cancer-related death in
Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in
the majority of cases malignancy is discovered too late for
curative treatment. There is at present no reliable diagnostic marker for PDAC available.
Objectives The aim of the study was to identify single
blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass
spectrometry (LC-MS).
Methods A discovery cohort comprising PDAC (n = 44)
and CP (n = 23) samples was analyzed by LC-MS followed
by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA))
statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure
high feature quality. Their discriminatory power was then
confirmed in an independent validation cohort including
PDAC (n = 20) and CP (n = 31) samples.
Electronic supplementary material The online version of this
article (doi:10.1007/s11306-017-1199-6) contains supplementary
material, which is available to authorized users.
* Anders Nordström
anders.nordstrom@umu.se
1
Department of Molecular Biology, Umeå University,
90187 Umeå, Sweden
2
Science for Life Laboratory, Department
of Oncology-Pathology, Karolinska Institutet, Stockholm,
Sweden
3
Department of Clinical Science, Intervention and Technology
(CLINTEC), Karolinska Institutet, Stockholm, Sweden
Results Glycocholic acid, N-palmitoyl glutamic acid and
hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLSDA resulted in a panel of five metabolites including the
aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.
Conclusion Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel
discriminating PDAC and CP in two independent cohorts.
Although further study is needed in larger cohorts, the
metabolites identified are potentially of use in PDAC
diagnostics.
Keywords Pancreatic cancer · Pancreatic ductal
adenocarcinoma · Pancreatitis · Metabolomics ·
Metabolism · LC-MS · Biomarkers · Serum · Plasma ·
Discovery · Validation
Abbreviations
RPLC-MS Reversed phase liquid chromatography coupled to mass spectrometry
PDAC
Pancreatic ductal adenocarcinoma
CP
Chronic pancreatitis
OPLS-DA Orthogonal partial least squares discriminant
analysis
1 Introduction
Pancreatic ductal adenocarcinoma (PDAC) is the fourth
most common cause of cancer-related death with a 5-year
survival rate of <5% (Siegel et al. 2016). In contrary to
many other forms of cancer the incidence of PDAC is rising and predicted to become the 2nd most common cause
of cancer-related death by 2030 (Rahib et al. 2014). Its
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lethality can be attributed to late diagnosis, almost complete resistance to conventional chemo- and radiotherapy,
and a lack of diagnostic biomarkers. Unlike breast or colorectal cancer, the only treatment with curative intent is
surgery, for which < 20% of patients qualify; but even then
the chance of survival is very low (Lohr 2006). Therefore,
PDAC should be treated as a medical emergency (Lohr
2014). Risk factors for PDAC include chronic pancreatitis
(CP) and around 5% of CP patients develop PDAC over
time (Pinho et al. 2014; Garrido-Laguna and Hidalgo
2015). Both PDAC and CP are characterized by an excessive and reactive stroma, or desmoplasia, which makes it
difficult to distinguish between the two conditions (Kloppel
and Adsay 2009). The one FDA-approved biomarker for
PDAC, carbohydrate antigen 19-9 (CA 19-9), is only useful
for prognosis and detection of disease recurrence (Fong and
Winter 2012). Therefore there is an urgent clinical need of
a non-invasive diagnostic biomarker for PDAC.
Metabolomics is the untargeted analysis of low molecular weight endogenous compounds, metabolites, present in
e.g. a defined biological sample such as tissue or a bodily
fluid as blood (Fiehn et al. 2000; Fiehn 2002; Spratlin et al.
2009; Dunn et al. 2011). The most widely used analytical
platform for metabolomics providing the largest metabolome coverage is liquid chromatography-mass spectrometry (LC-MS) (Want et al. 2007; Patti et al. 2012; Yin and
Xu 2014). Given the established connection between cell
metabolism and cancer (Hanahan and Weinberg 2011),
LC-MS metabolomics has been applied to identify metabolite markers for several cancers (Spratlin et al. 2009;
Nordstrom and Lewensohn 2010; Nicholson et al. 2012),
including PDAC (Daemen et al. 2015; Rios Peces et al.
2016). Specifically, there have been previous LC-MS based
metabolomics efforts to compare PDAC and CP blood samples (Urayama et al. 2010; Fukutake et al. 2015). There is
however still no screening test for PDAC available.
In the present study, we have performed LC-MS based
metabolomics on blood samples to compare the metabolic
profiles of PDAC and CP in two independent cohorts. Both
single discriminative metabolites and a panel of metabolites discriminating PDAC and CP were identified. Our
findings have potential clinical relevance for early diagnosis
of PDAC among CP patients to improve patient survival.
2 Materials and methods
2.1 Clinical samples
The study comprised a discovery- and a validation cohort
(Table 1). All patients had given informed consent in
accordance with the Helsinki Declaration (World Med
2013).
13
Table 1 Clinical data
Subjects (n)
Male/female
ratio
Age, median
(range)
a
n = 41,
b
n = 42
Discovery cohort
Validation cohort
PDAC
CP
PDAC
CP
44
21/20a
23
18/5
20
10/10
31
16/15
67 (30–102)b 36 (31–93) 70 (46–80) 69 (23–87)
Blood samples for the discovery cohort were collected
from German patients with PDAC (n = 44) and patients
with CP (n = 23). Serum samples were prepared at the same
location using a standardized protocol.
The validation cohort included Swedish PDAC patients
(n = 20) and CP patients (n = 31). Plasma samples were
supplied by the Karolinska Institutet biobank and had been
prepared at different sites following a standardized procedure: whole blood samples were obtained at fasting early in
the morning, collected in sodium citrate tubes and immediately placed in −80 °C.
All plasma and serum samples were stored at −80 °C at
all times prior to analysis.
2.2 Sample preparation
All solvents used below were of HPLC grade (Fisher
Chemicals) and the water was Milli-Q (Millipore).
Plasma and serum samples were thawed on ice and
50 µl aliquots were mixed with 150 µl MeOH for protein
precipitation. Samples were then centrifuged for 15 min at
15800×g. The supernatant was transferred to a new tube
and then evaporated to complete dryness in a vacuum concentrator. Prior to LC-MS analysis, samples were reconstituted in 50 µl MeOH:H2O 1:1 and the two isotopically
labelled internal standards phenylalanine (D5) and palmitic
acid (D4) (Cambridge Isotope Laboratories) were added at
a final concentration of 5 µM.
2.3 LC-MS analysis
Metabolites were separated by reversed phase liquid chromatography and detected by electrospray ionization (ESI)
mass spectrometry in positive mode. All samples were run
in randomized order, randomized by the random number
function in Excel (Microsoft, USA).
For the discovery cohort, the analytical platform consisted of a 1200 HPLC-system (Agilent) connected on-line
to an LTQ Orbitrap Velos equipped with a HESI probe
(Thermo Scientific). 5 µl of each sample were injected onto
Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics
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a Kinetex C18 150 × 2.1 mm column, 2.6 µ, 100 Å (Phenomenex) and the following mobile phases were used at
a flow rate of 0.4 ml/min: H2O with 0.1% formic acid (A)
and 3:1 acetonitrile:isopropanol with 0.1% formic acid (B).
The linear gradient was 0 min, 5% B; 1 min, 5% B; 21 min,
95% B; 26 min, 95% B; 26.5 min, 5% B; 33 min, 5% B. MS
data was collected in centroid mode between m/z 80-1200
at an Orbitrap resolution of 60,000 using the following ESI
settings: Cap Temp, 350; Sheath Gas Flow, 45; Aux Gas
Flow, 15; Source Voltage, 3 kV; S-Lens RF Level, 60%.
For the validation cohort, the analytical platform consisted of a 1290 UHPLC-system (Agilent) connected online to a 6550 Q-ToF equipped with a JetStream source
(Agilent). 5 µl of each sample were injected onto a Kinetex C18 100 × 2.1 mm column, 2.6 µ, 100 Å (Phenomenex)
and the following mobile phases were used at a flow rate
of 0.5 ml/min: H2O with 0.1% formic acid (A) and 3:1
acetonitrile:isopropanol with 0.1% formic acid (B). The linear gradient was 0 min, 5% B; 7 min, 95% B; 9 min, 95%
B; 9.2 min, 5% B; 11 min, 5% B. MS data was collected
in centroid mode between m/z 70-1700 using the following
ESI settings: Gas Temp, 300; Gas Flow, 8; Nebulizer Pressure, 40; Sheet Gas Temp, 350; Sheet Gas Flow, 11; Vcap,
4000; Fragmentor, 100; Skimmer1, 45; OctapoleRFPeak,
750. The analysis procedure has also been described previously (Staubert et al. 2015; Lindahl et al. 2016).
The peak heights for the internal standards were continuously monitored during analysis to ensure MS signal stability (Supplementary table 1). In the validation cohort the
signal stability was monitored by injection of a pooled QC
sample for every sixth sample during the LC-MS analysis.
Examples of extracted ion chromatograms are illustrated in
Supplementary Fig. 1.
Targeted MS/MS data of a pooled sample was also collected for identification purposes of discriminative features
in both cohorts using the ESI settings described above
(Supplementary Fig. 2). Samples were analyzed on two different instruments for reasons of instrument availability at
the time of receiving the different cohorts to the laboratory.
Sample-wise median normalization (Trezzi et al. 2015)
was used to correct for technical variation in e.g. sample preparation and MS signal intensity. Next, a two-step
peak filtering approach was applied. First, peaks with
an intensity level ≥ 60,000 were retained. Second, only
peaks present in at least 75% of samples in at least one
group were used for the following statistical analyses.
2.4 Discovery cohort data preprocessing
Fig. 1 Schematic overview of data analysis workflow. Univariate (left) and multivariate (right) analyses comparing PDAC and CP
were performed in parallel, starting with the abundance filtered discovery cohort data set (step 1). The large number of metabolite features (n = 4578) is due to superfluous features e.g. adducts and isotopes. Following statistical analysis of the discovery cohort (step 2a
and b), selected metabolite features were examined manually in the
raw data and low quality features (e.g. adducts, isotopes, low intensity peaks) were excluded (step 3). High quality features that were
not present in the validation cohort were excluded as well, together
with features that could not be identified. The discriminative capacity
of the remaining identified features, now referred to as metabolites,
was confirmed in the validation cohort by statistical analysis (step 4a
and b). Finally, metabolites that were regulated in opposite directions
in the two cohorts were excluded (step 5), resulting in three single
metabolite markers and a panel of five metabolite markers
Discovery raw data files were converted to .cdf format using
the file converter tool in Xcalibur version 2.2 (Thermo Scientific). Peak detection, integration and alignment were
performed with the open-source software XCMS version
1.30.3 (Smith et al. 2006). The following XCMS settings
were used: For peak detection and integration with the centWave algorithm, ppm = 10, snthr = 5, peakwidth = c(5.15),
mzdiff = −0.01, prefilter = c(6,100), fitgauss = TRUE; for
alignment with Obiwarp, distFunc = “cor”, profStep = 1,
grouping parameters bw = 1, mzwid = 0.005, minfrac = 0.5.
Isotopes and adducts were not removed.
2.5 Univariate analysis
Both univariate and multivariate statistical analysis were
applied in parallel (Fig. 1).
PDAC vs. CP
1.
4578 features
2a.
Student’s t-test
2b.
OPLS-DA
254 features
259 features
3.
Feature quality control
Detection in validation cohort
17 metabolites
19 metabolites
4a.
Student’s t-test
Validation cohort
4b.
OPLS-DA
Validation cohort
11 metabolites
19 metabolites
Feature quality
5. control
Fold-change direction
3 single
metabolite markers
5 metabolite
marker panel
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Univariate analysis was performed in the software
GraphPad Prism version 6.07 (GraphPad), where PDAC
and CP were compared using a Student’s t test with
Welch’s correction. In the discovery cohort, metabolite features with p-value > 0.05 were excluded in order to limit the
number of features to be examined in the raw data.
Box plots and bar graphs were created in GraphPad
Prism.
2.6 Multivariate analysis
For multivariate analysis, data sets were imported into the
software SIMCA version 14 (Umetrics). Unit variance scaling was applied to give metabolite features with low and
high variation between samples equal importance, and log
transformation was applied to shift data towards Gaussian
distribution. Principal component analysis (PCA) was initially applied to all variables extracted using XCMS (Supplementary Fig. 3). Subsequently was orthogonal partial
least squares-discriminant analysis (OPLS-DA) (Bylesjo
et al. 2006) with default SIMCA settings was used to identify metabolite feature patterns discriminating PDAC and
CP. The features with the highest discriminatory power
between classes were selected based on the variable importance for the projection (VIP) plot. Features significant
with a 95% confidence interval, based on the cross- validation, were selected for further analysis. For the discovery
cohort, the feature selection was done in the same way with
the additional criterion that the VIP-values should be >1.5.
For refined OPLS-DA models, i.e. those built on
selected metabolite features only, cross-validation settings
were changed from the default 1/7 to 1/5 of samples constituting the internal prediction set. The purpose was to
minimize the bias of the refined model towards the selected
metabolite features.
All score- and loading scatter plots were created in
SIMCA.
2.7 Feature quality control
The discriminative metabolite features selected in the discovery cohort had to meet several criteria to ensure feature
quality, regardless of the method of statistical analysis.
First, features were manually examined in the raw data. Isotopes, adducts and poor quality features, e.g. low intensity
peaks were excluded following manual inspection of peak
shape. This removed features judged by the software to be
chromatographic peaks, but which they human eye rapidly
can assign to be column bleed or similar artefacts. Second,
for the univariate part of the analysis workflow, features
were re-integrated in the raw data using the Qual and Quan
Browsers in Xcalibur version 2.2 (Thermo Scientific) and
their statistical significance was confirmed with a Student´s
13
t test (p < 0.05). Third, ion chromatograms of the selected
features were extracted from the validation cohort raw data
files using the software MassHunter Qual version B.06.00
(Agilent); discovery cohort features that were either not
detected or of low quality in the validation cohort were
excluded. Fourth, unidentifiable metabolite features were
excluded.
Peak areas of the identified metabolites that met quality
control criteria were integrated in the validation cohort raw
data files using the “Agile” setting in MassHunter Qual.
The median normalized peak area values were used for further uni- and multivariate analyses to confirm the results
from the discovery cohort.
2.8 Metabolite identification
Accurate mass measurements were subject to database
searches in the public databases METLIN (Smith et al.
2005) and Human Metabolome Database (Wishart et al.
2013) as well as an in-house library comprising 384 synthetic standards. Database hits were then confirmed by
retention time match (in-house library only) and MS/
MS spectral match from pooled samples (Supplementary
Fig. 2). In a few cases additional synthetic standard compounds were acquired and analyzed on the same platform
to confirm compound identity.
For the discovery cohort, samples were also fractionated
by LC and relevant fractions were then analyzed by direct
infusion to acquire MSn structural information (van der
Hooft et al. 2012).
3 Results
3.1 Univariate analysis identifies three single
metabolites discriminating PDAC and CP
After abundance peak filtering, the discovery cohort data
set contained 4578 metabolite features including isotopes, adducts and low quality features (Fig. 1). 254 of
these features had a p-value < 0.05 as determined by Student’s t test with Welch’s correction. After (i) raw data
examination, (ii) re-integration and confirmation of statistical significance, (iii) detection in validation cohort
and (iv) feature identification, 17 metabolites remained.
11 of these metabolites had a p-value < 0.05 (Student’s t
test) in the validation cohort as well. Fold-change calculations showed that the eight metabolites with the lowest intensity were down-regulated in PDAC compared to
CP in the discovery cohort, as opposed to up-regulated in
PDAC compared to CP in the validation cohort (Fig. 2).
These eight metabolites were phospholipids. After
exclusion of the phospholipids, the three metabolites
Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics
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3.2 Multivariate analysis identifies a panel of five
metabolites discriminating PDAC and CP
Hexanoylcarnitine
N-palmitoyl glutamic acid
Glycocholic acid
PE(9:0/9:0)
LPC(16:0)
LPE(20:5)
LPC(18:2)
LPC(18:1)
LPC(20:4)
Discovery cohort
LPC(20:3)
Validation cohort
LPC(22:4)
-0.5
0
1
2
2 .2
17
Fold-change (PDAC over CP)
Fig. 2 Fold-change comparison between cohorts. Following confirmation of metabolite significance in the validation cohort (Fig. 1, step
4a and b), fold-change calculations revealed that eight metabolites
were regulated in opposite directions in the discovery- and validation
cohorts. All eight metabolites were phospholipids that were downregulated in PDAC compared to CP in the discovery cohort, but upregulated in PDAC compared to CP in the validation cohort. Consequently all phospholipids were excluded as potential single metabolite
markers
glycocholic acid, hexanoylcarnitine and N-palmitoyl glutamic acid remained as single discriminative markers for
PDAC compared to CP (Fig. 3). In the discovery cohort,
applying an FDR cut-off of 5% to the 254 metabolites did
not yield any significant metabolites. However the manual inspection and re-integration together with the validation in an independent patient cohort strengthens the
findings.
Normalized peak area (log10)
Hexanoylcarnitine
p = 0.013
4 Discussion
The PDAC incidence rate is expected to increase worldwide over the next years (Rahib et al. 2014), making the
need for improved diagnostic tools even more acute. One
group at risk of PDAC development is CP patients (Pinho
et al. 2014) who would clearly benefit from the discovery
of novel markers for PDAC diagnosis.
Glycocholic acid
106
1 05
259 of the 4578 metabolite features in the discovery cohort
(Fig. 1, step 2b) were selected as potential markers for
PDAC as determined by the VIP-plot in the initial OPLSDA model (Fig. 4a). 19 metabolites remained after feature
quality control, detection in validation cohort and metabolite identification. These 19 metabolites were significant in
the validation cohort OPLS-DA model (Fig. 4b). A comparison of the loading scatter plots for the discovery- and
validation cohort OPLS-DA models showed that a group of
14 metabolites was regulated in opposite directions in the
two cohorts (Fig. 4c, d). As in the univariate analysis, all
14 were phospholipids that were down-regulated in PDAC
compared to CP in the discovery cohort but up-regulated in
PDAC in the validation cohort.
After exclusion of the phospholipids the five metabolites
N-palmitoyl glutamic acid, glycocholic acid, hexanoylcarnitine, chenodeoxyglycocholate and PAGN remained. They
were used to build a refined validation cohort OPLS-DA
model (Fig. 5a, b) to evaluate their discriminatory power
in the absence of phospholipids. The predictive ability of
the refined model as indicated by the Q2(cum) value was
0.513, i.e. approximately 50% of samples were correctly
classified, and the R2X(cum) value was 0.736. The statistical significance of the model as indicated by cross-validated ANOVA was p = 8.2E−07.
p = 0.0066
p = 0.041
N-palmitoyl glutamic acid
105
p = 0.0058
p = 9.9E-05
1 04
104
103
1 03
102
101
Fig. 3 Single metabolite markers discriminating PDAC and CP.
After identification of significant features in the discovery cohort
(n = 67), feature quality control and confirmation of significance in
the validation cohort (n = 51), three single metabolite markers for
p = 4.0E-04
PDAC discovery
CP discovery
PDAC validation
CP validation
PDAC compared to CP remained. All three were up-regulated in
PDAC. Statistical test: Welch’s unequal variances t test. Box plot settings: Range, minimum to maximum value; line at median
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OPLS-DA discovery cohort
Score scatter plot
A
OPLS-DA validation cohort
Score scatter plot
B
PDAC
CP
to1
to1
PDAC
CP
t1
t1
OPLS-DA discovery cohort
Loading scatter plot
PDAC
D
pos1o
CP
pos1o
C
OPLS-DA validation cohort
Loading scatter plot
Phospholipids
PDAC
CP
Phospholipids
pq1
pq1
Fig. 4 Reversed metabolite fold-change direction in discovery- and
validation cohorts. a Score scatter plot for the initial OPLS-DA model
of 4578 metabolite features in the discovery cohort (Fig. 1, step 2b).
Blue dots represent PDAC samples (scores, n = 44); white dots represent CP samples (n = 23); t1 on the x-axis, first component; to1 on the
y-axis, first of two orthogonal components. Model parameters: CVgroups, 7; R2X(cum) 0.39; Q2(cum) 0.22; CV-ANOVA p = 0.02. The
model was used to select 259 features for further analysis. b Score
scatter plot for the OPLS-DA model in the validation cohort based on
the 19 metabolites remaining from the discovery cohort (Fig. 1, step
4b). Orange dots represent PDAC samples (n = 20); white dots represent CP samples (n = 31); to1 on the y-axis, first (and only) orthogo-
nal component. Model parameters: CV-groups, 7; R2X(cum) 0.648;
Q2(cum) 0.699; CV-ANOVA p = 1.7E-11. c Loading (metabolite)
scatter plot for the discovery cohort OPLS-DA model. The phospholipids are situated to the right of the vertical line, close to the dummy
CP variable, indicating that they are down-regulated in PDAC compared to CP. d Loading scatter plot for the validation cohort OPLSDA model. The phospholipids are situated to the left of the vertical
line, close to the dummy PDAC variable; hence they are up-regulated
in PDAC compared to CP. Based on this reversal of their fold-change
directions, the 14 phospholipids were excluded from the final metabolite marker panel
Any preclinical biomarker study at the discovery stage
should include an inflammatory control of the target organ
to increase the chance of identifying disease-specific markers (Chechlinska et al. 2010; Lindahl et al. 2016). Here,
CP serves as an inflammatory control of the pancreas for
PDAC-specific marker discovery. In addition, CP is a risk
factor for PDAC development and the two diseases share
several inflammatory parameters. It is therefore imperative
to identify PDAC-unique markers specific for the discrimination of PDAC and CP.
In the present study, we have compared the metabolic
profiles of PDAC and CP in blood in two independent
cohorts using untargeted LC-MS. The fact that both cohorts
did not consist of either serum or plasma is a drawback of
the study. However, that they represent completely independent patients strengthens the validity of the proposed
biomarkers. Previous reports comparing the metabolite
profiles of serum and plasma samples have found good correlation between the two sample matrices (Denery et al.
2011; Yu et al. 2011; Ishikawa et al. 2014). We have identified three single discriminative metabolites as well as a
five-metabolite panel of markers, since a marker panel can
increase specificity and sensitivity compared to single discriminative metabolites (Wang et al. 2011; Wingren et al.
13
Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics
5 metabolite marker panel in validation cohort
OPLS-DA score scatter plot
Page 7 of 10 61
5 metabolite marker panel in validation cohort
OPLS-DA loading scatter plot
B
A
Chenodeoxyglycocholate
Glycocholic acid
PDAC
CP
N-palmitoyl glutamic acid
pos1o
to1
PAGN
PDAC
CP
Hexanoylcarnitine
t1
pq1
Fig. 5 A marker panel of five metabolites discriminates PDAC and
CP. a Score scatter plot for the refined OPLS-DA model of the five
discriminative metabolites with consistent fold-change directions in
the validation cohort (Fig. 1, step 4b and 5). Orange dots represent
PDAC samples (n = 20); white dots represent CP samples (n = 31);
t1 on the x-axis, first component; to1 on the y-axis, first (and only)
orthogonal component. Model parameters: CV-groups, 5; R2X(cum)
0.736; Q2(cum) 0.513; CV-ANOVA p = 8.2E-07. b Corresponding
loading scatter plot. All five metabolites in the marker panel show
increased levels in PDAC compared to CP
2012). All five metabolites identified here can be easily
measured in a clinical MS-lab.
PDAC and CP blood samples have been compared by
LC-MS metabolomics analysis in two previous studies
(Urayama et al. 2010; Fukutake et al. 2015). One of these
targeted free amino acids in plasma. The advantage of a
targeted approach is that sample preparation and analysis
is optimized for a specific class of metabolites, but at the
cost of covering only a limited part of the metabolome.
Untargeted LC-MS on the other hand has the largest potential to identify novel metabolites due to increased metabolome coverage (Patti et al. 2012). In the second previous
study comparing PDAC and CP, untargeted LC-MS was
applied to plasma samples. Contrary to the present study,
clinical data on e.g. disease stage and smoking status was
available, which is important to avoid known confounding
factors; however, the sample size (n = 10) was very limited
which makes the results less generalizable. Other studies
have applied different analytical metabolomics platforms
to discriminate PDAC and CP in bodily fluids. In a study
using gas chromatography-mass spectrometry, a majority
of the serum discriminative metabolites were amino acids
(Kobayashi et al. 2013), but included also some metabolites that would not have been particularly well captured in
our LC-MS set-up such as some sugar species including,
arabinose, ribulose and 1,5-Anhydro-D-glucitol and some
organic acids such as uric acid, nonanoic acid and caprylic
acid. In the mentioned study, CP samples were however
included in the validation cohort only. Amino acids were
also connected to future risk of pancreatic cancer in a
metabolomics study applying LC-MS to prediagnostic
plasma (Mayers et al. 2014). In the present study a number of amino acids were found dysregulated in PDAC compared to CP in the validation cohort only (data not shown),
but lacking confirmation in a second cohort these results
were excluded. Further, two studies have used nuclear
magnetic resonance to study plasma and urine samples,
respectively, but have not validated their results in a second
cohort (Zhang et al. 2012) or had only three CP samples
out of a total of 25 samples included in the benign control
group (Davis et al. 2013).
In the present study, the levels of a number of phospholipids were altered in PDAC compared to CP in both
cohorts. Phospholipids were also found dysregulated in
previous studies (Urayama et al. 2010; Ritchie et al. 2013;
Sakai et al. 2016). However, we excluded all phospholipids as potential markers since they were down-regulated in
PDAC in the discovery cohort as opposed to up-regulated
in the validation cohort (Figs. 2, 4c, d). A possible explanation for these results is that different blood sample matrices,
i.e. serum and plasma, were used in the two cohorts. Serum
and plasma samples from the same individual are known to
display different concentration levels for some metabolites,
including phospholipids, due to the differences in sample
handling (Yu et al. 2011). Noteworthy, as systemic phospholipid levels are altered by inflammatory responses in
general, they are unlikely candidates for disease specific
markers (Lindahl et al. 2016).
As mentioned above, the two bile acids glycocholic acid
and chenodeoxyglycocholate were significantly increased
in PDAC compared to CP in the present study (Figs. 3, 5b).
A possible explanation for increased levels of circulating
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bile acids is tumor growth into the bile duct. Another theory includes bile acid reflux into the pancreas, leading to
pancreatitis and eventually malignant cell transformation;
a direct carcinogenic effect of bile acids is also possible
(Feng and Chen 2016). Bile acid levels in prediagnostic
plasma have also been connected to future risk of pancreatic cancer (Mayers et al. 2014), supporting our results and
reasoning.
Two of the discriminative metabolite markers identified here, N-palmitoyl glutamic acid and hexanoylcarnitine (Figs. 3, 5b), are fatty acid conjugates with an amino
acid and carnitine, respectively. N-acyl amino acids were
only recently discovered as a novel group of compounds
and little is known about their function (Hanus et al. 2014).
However, a recent study in mice showed that certain N-acyl
amino acids regulate cell metabolism through mitochondrial uncoupling (Long et al. 2016). Acylcarnitines in turn
are vital for the transport of fatty acids into the mitochondrial matrix (Indiveri et al. 2011). Short-chain acylcarnitines including hexanoylcarnitine were previously found
increased in serum in type 2 diabetes compared to normal
glucose tolerance (Mai et al. 2013); both CP and PDAC
may cause diabetes (Pinho et al. 2014).
The levels of the nitrogenous metabolite PAGN were
also increased in PDAC compared to CP in the present
study (Fig. 5b). PAGN is a product of gut microbiota-host
co-metabolism found in urine and blood (Barrios et al.
2015), and there are indications that the gut microbiota has
an important role in carcinogenesis (Schwabe and Jobin
2013).
In conclusion, this work has identified five metabolites
capable of discriminating PDAC and CP in blood either as
single or as a panel of markers. Findings were validated in
a separate patient cohort. To determine the potential clinical benefit of these markers, further evaluation in larger
clinical studies is needed. Nevertheless, we believe that
these metabolites are potentially useful for early diagnosis
of PDAC in CP patients.
Acknowledgements A.N. acknowledges The Swedish Foundation for Strategic Research (FFL4) and the Jane and Dan Olsson
Foundation for funding. ML acknowledges ALF and Richard Julin
Stiftelse for financial support and RH acknowledges Cancerfonden
(CAN2008/982, CAN2013/780) for financial support.
The Swedish Metabolomics Centre is acknowledged for support with LC-MS
analysis.
Conflict of interest The authors declare that they have no conflict of
interests in relation to the work described.
Ethical approval This study was approved by regional etic committee in Gothenburg, Sweden and by Die ethic-kommission-II, Fakultät
für klinische Medizin Mannheim der Ruprecht-Karls-Universität Heidelberg, Germany, and was conducted in accordance with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards.
13
Informed consent Blood samples were collected after informed
consent from all patients.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
References
Barrios, C., M. Beaumont, T. Pallister, J. Villar, J. K. Goodrich, A.
Clark, J. Pascual, R. E. Ley, T. D. Spector, J. T. Bell, C. Menni
(2015). Gut-microbiota-metabolite axis in early renal function decline. Plos ONE 10(8), e0134311, doi:10.1371/journal.
pone.0134311.
Bylesjo, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis:
Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20, 341–351. doi:10.1002/
cem.1006
Chechlinska, M., Kowalewska, M., & Nowak, R. (2010). Systemic
inflammation as a confounding factor in cancer biomarker
discovery and validation. Nature Reviews Cancer, 10, 2–13.
doi:10.1038/nrc2782.
Daemen, A., Peterson, D., Sahu, N., McCord, R., Du, X., Liu, B.,
Kowanetz, K., Hong, R., Moffat, J., Gao, M., Boudreau, A.,
Mroue, R., Corson, L., O’Brien, T., Qing, J., Sampath, D., Merchant, M., Yauch, R., Manning, G., Settleman, J., Hatzivassiliou, G., & Evangelista, M. (2015). Metabolite profiling stratifies
pancreatic ductal adenocarcinomas into subtypes with distinct
sensitivities to metabolic inhibitors. Proceedings of the National
Academy of Sciences of the United States of America, 112,
E4410–E4417. doi:10.1073/pnas.1501605112.
Davis, V. W., Schiller, D. E., Eurich, D., Bathe, O. F., & Sawyer, M.
B. (2013). Pancreatic ductal adenocarcinoma is associated with
a distinct urinary metabolomic signature. Annals of Surgical
Oncology, 20, S415–S423. doi:10.1245/s10434-012-2686-7.
Denery, J. R., Nunes, A. A., & Dickerson, T. J. (2011). Characterization of differences between blood sample matrices in untargeted metabolomics. Analytical Chemistry, 83, 1040–1047.
doi:10.1021/ac102806p.
Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A.,
Haselden, J. N., Nicholls, A. W., Wilson, I. D., Kell, D. B.,
Goodacre, R., & Human Serum Metabolome, C. (2011). Procedures for large-scale metabolic profiling of serum and plasma
using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083.
doi:10.1038/nprot.2011.335.
Feng, H. Y., & Chen, Y. C. (2016). Role of bile acids in carcinogenesis of pancreatic cancer: An old topic with new perspective.
World Journal of Gastroenterology, 22, 7463–7477. doi:10.3748/
wjg.v22.i33.7463.
Fiehn, O. (2002). Metabolomics–the link between genotypes and phenotypes. Plant molecular biology, 48, 155–171.
Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R.
N., & Willmitzer, L. (2000). Metabolite profiling for plant
functional genomics. Nature Biotechnology, 18, 1157–1161.
doi:10.1038/81137.
Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics
Fong, Z. V., & Winter, J. M. (2012). Biomarkers in pancreatic cancer diagnostic, prognostic, and predictive. Cancer Journal, 18,
530–538. doi:10.1097/PPO.0b013e31827654ea.
Fukutake, N., M. Ueno, N. Hiraoka, K. Shimada, K. Shiraishi, N.
Saruki, T. Ito, M. Yamakado, N. Ono, A. Imaizumi, S. Kikuchi,
H. Yamamoto, K. Katayama (2015). A novel multivariate index
for pancreatic cancer detection based on the plasma free amino
acid profile. Plos ONE 10(7), e0132223. doi:10.1371/journal.
pone.0132223.
Garrido-Laguna, I., & Hidalgo, M. (2015). Pancreatic cancer:
from state-of-the-art treatments to promising novel therapies.
Nature Reviews. Clinical Oncology, 12, 319–334. doi:10.1038/
nrclinonc.2015.53.
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next
generation. Cell, 144, 646–674. doi:10.1016/j.cell.2011.02.013.
Hanus, L., Shohami, E., Bab, I., & Mechoulam, R. (2014). N-Acyl
amino acids and their impact on biological processes. BioFactors, 40, 381–388. doi:10.1002/biof.1166.
Indiveri, C., Iacobazzi, V., Tonazzi, A., Giangregorio, N., Infantino,
V., Convertini, P., Console, L., & Palmieri, F. (2011). The mitochondrial carnitine/acylcarnitine carrier: Function, structure and
physiopathology. Molecular Aspects of Medicine, 32, 223–233.
doi:10.1016/j.mam.2011.10.008.
Ishikawa, M., Maekawa, K., Saito, K., Senoo, Y., Urata, M., Murayama, M., Tajima, Y., Kumagai, Y., & Saito, Y. (2014). Plasma
and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS ONE, 9, e91806.
doi:10.1371/journal.pone.0091806.
Kloppel, G., & Adsay, N. V. (2009). Chronic pancreatitis and the differential diagnosis versus pancreatic cancer. archives of pathology & laboratory. Medicine, 133, 382–387.
Kobayashi, T., S. Nishiumi, A. Ikeda, T. Yoshie, A. Sakai, A. Matsubara, Y. Izumi, H. Tsumura, M. Tsuda, H. Nishisaki, N. Hayashi,
S. Kawano, Y. Fujiwara, H. Minami, T. Takenawa, T. Azuma, M.
Yoshida (2013). A novel serum metabolomics-based diagnostic
approach to pancreatic cancer. Cancer Epidemiology Biomarkers
& Prevention 22, 571–579 doi:10.1158/1055-9965.epi-12-1033.
Lindahl, A., Forshed, J., & Nordstrom, A. (2016). Overlap in serum
metabolic profiles between non-related diseases: Implications
for LC-MS metabolomics biomarker discovery. Biochemical
and Biophysical Research Communications, 478, 1472–1477.
doi:10.1016/j.bbrc.2016.08.155.
Lohr, J. M. (2014). Personal view Pancreatic cancer should be treated
as a medical emergency. Bmj-British Medical Journal 349,
g5261 doi:10.1136/bmj.g5261.
Lohr, M. (2006). Is it possible to survive pancreatic cancer? Nature
clinical practice gastroenterology &. Hepatology, 3, 236–
237. doi:10.1038/ncpgasthep0469.
Long, J. Z., Svensson, K. J., Bateman, L. A., Lin, H., Kamenecka, T.,
Lokurkar, I. A., Lou, J., Rao, R. R., Chang, M. R., Jedrychowski,
M. P., Paulo, J. A., Gygi, S. P., Griffin, P. R., Nomura, D. K., &
Spiegelman, B. M. (2016). The secreted enzyme PM20D1 regulates lipidated amino acid uncouplers of mitochondria. Cell, 166,
424–435. doi:10.1016/j.cell.2016.05.071.
Mai, M., A. Tonjes, P. Kovacs, M. Stumvoll, G. M. Fiedler, A. B.
Leichtle (2013). Serum levels of acylcarnitines are altered in prediabetic conditions. Plos ONE 8(12), e82459. doi:10.1371/journal.pone.0082459.
Mayers, J. R., Wu, C., Clish, C. B., Kraft, P., Torrence, M. E.,
Fiske, B. P., Yuan, C., Bao, Y., Townsend, M. K., Tworoger,
S. S., Davidson, S. M., Papagiannakopoulos, T., Yang, A.,
Dayton, T. L., Ogino, S., Stampfer, M. J., Giovannucci, E.
L., Qian, Z. R., Rubinson, D. A., Ma, J., Sesso, H. D., Gaziano, J. M., Cochrane, B. B., Liu, S. M., Wactawski-Wende, J.,
Manson, J. E., Pollak, M. N., Kimmelman, A. C., Souza, A.,
Pierce, K., Wang, T. J., Gerszten, R. E., Fuchs, C. S., Vander
Page 9 of 10 61
Heiden, M. G., & Wolpin, B. M. (2014). Elevation of circulating branched-chain amino acids is an early event in human
pancreatic adenocarcinoma development. Nature Medicine, 20,
1193–1198. doi:10.1038/nm.3686.
Nicholson, J. K., Holmes, E., Kinross, J. M., Darzi, A. W., Takats,
Z., & Lindon, J. C. (2012). Metabolic phenotyping in clinical
and surgical environments. Nature, 491, 384–392. doi:10.1038/
nature11708.
Nordstrom, A., & Lewensohn, R. (2010). Metabolomics: Moving
to the clinic. Journal of Neuroimmune Pharmacology, 5, 4–17.
doi:10.1007/s11481-009-9156-4.
Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews.
Molecular Cell Biology, 13, 263–269. doi:10.1038/nrm3314.
Pinho, A. V., Chantrill, L., & Rooman, I. (2014). Chronic pancreatitis: A path to pancreatic cancer. Cancer Letters, 345, 203–
209. doi:10.1016/j.canlet.2013.08.015.
Rahib, L., Smith, B. D., Aizenberg, R., Rosenzweig, A. B., Fleshman, J. M., & Matrisian, L. M. (2014). Projecting cancer
incidence and deaths to 2030: The unexpected burden of
thyroid, liver, and pancreas cancers in the United States.
Cancer Research, 74, 2913–2921. doi:10.1158/0008-5472.
can-14-0155.
Rios Peces, S., Diaz Navarro, C., Marquez Lopez, C., Caba, O., Jimenez-Luna, C., Melguizo, C., Prados, J. C., Genilloud, O., Vicente
Perez, F., & Perez Del Palacio, J. (2016). Untargeted LC-HRMSbased metabolomics for searching new biomarkers of pancreatic
ductal adenocarcinoma: A pilot study. Journal of biomolecular
screening: the official journal of the Society for Biomolecular
Screening. doi:10.1177/1087057116671490.
Ritchie, S. A., H. Akita, I. Takemasa, H. Eguchi, E. Pastural, H.
Nagano, M. Monden, Y. Doki, M. Mori, W. Jin, T. T. Sajobi, D.
Jayasinghe, B. Chitou, Y. Yamazaki, T. White, D. B. Goodenowe
(2013). Metabolic system alterations in pancreatic cancer patient
serum: potential for early detection. Bmc Cancer 13(1), 416.
doi:10.1186/1471-2407-13-416.
Sakai, A., Suzuki, M., Kobayashi, T., Nishiumi, S., Yamanaka, K.,
Hirata, Y., Nakagawa, T., Azuma, T., & Yoshida, M. (2016).
Pancreatic cancer screening using a multiplatform human serum
metabolomics system. Biomarkers in Medicine, 10, 577–586.
doi:10.2217/bmm-2016-0020.
Schwabe, R. F., & Jobin, C. (2013). The microbiome and cancer.
Nature Reviews Cancer, 13, 800–812. doi:10.1038/nrc3610.
Siegel, R. L., K. D. Miller, A. Jemal (2016). Cancer statistics, 2016.
Ca-a Cancer Journal for Clinicians 66, 7–30. doi:10.3322/
caac.21332.
Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., Custodio, D. E., Abagyan, R., & Siuzdak, G. (2005).
METLIN: A metabolite mass spectral database. Therapeutic
drug monitoring, 27, 747–751.
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G.
(2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and
identification. Analytical Chemistry, 78, 779–787. doi:10.1021/
ac051437y.
Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009). Clinical
applications of metabolomics in oncology: a review. Clinical
cancer research: an official journal of the American Association for Cancer Research, 15, 431–440. doi:10.1158/1078-0432.
CCR-08-1059.
Staubert, C., Bhuiyan, H., Lindahl, A., Broom, O. J., Zhu, Y. F.,
Islam, S., Linnarsson, S., Lehtio, J., & Nordstrom, A. (2015).
Rewired metabolism in drug-resistant leukemia cells a metabolic switch hallmarked by reduced dependence on exogenous
glutamine. Journal of Biological Chemistry, 290, 8348–8359.
doi:10.1074/jbc.M114.618769.
13
61
Page 10 of 10
Trezzi, J. P., Vlassis, N., & Hiller, K. (2015). The role of metabolomics in the study of cancer biomarkers and in the development
of diagnostic tools. Advances in Experimental Medicine and
Biology, 867, 41–57. doi:10.1007/978-94-017-7215-0_4.
Urayama, S., Zou, W., Brooks, K., & Tolstikov, V. (2010). Comprehensive mass spectrometry based metabolic profiling of blood
plasma reveals potent discriminatory classifiers of pancreatic
cancer. Rapid Communications in Mass Spectrometry, 24, 613–
620. doi:10.1002/rcm.4420.
van der Hooft, J. J. J., Vervoort, J., Bino, R. J., & R. C. H. de Vos
(2012). Spectral trees as a robust annotation tool in LC-MS
based metabolomics. Metabolomics, 8, 691–703. doi:10.1007/
s11306-011-0363-7.
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P.,
McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez,
C., O’Donnell, C. J., Carr, S. A., Mootha, V. K., Florez, J. C.,
Souza, A., Melander, O., Clish, C. B., & Gerszten, R. E. (2011).
Metabolite profiles and the risk of developing diabetes. Nature
Medicine, 17, 448–483. doi:10.1038/nm.2307.
Want, E. J., Nordstrom, A., Morita, H., & Siuzdak, G. (2007). From
exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. Journal of Proteome Research 6, 459–
468 doi:10.1021/pr060505+.
Wingren, C., Sandstrom, A., Segersvard, R., Carlsson, A., Andersson, R., Lohr, M., & C. A. K. Borrebaeck (2012). Identification
of serum biomarker signatures associated with pancreatic cancer. Cancer Research, 72, 2481–2490. doi:10.1158/0008-5472.
can-11-2883.
Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y.
F., Djoumbou, Y., Mandal, R., Aziat, F., Dong, E., Bouatra, S.,
13
A. Lindahl et al.
Sinelnikov, I., Arndt, D., Xia, J. G., Liu, P., Yallou, F., Bjorndahl,
T., Perez-Pineiro, R., Eisner, R., Allen, F., Neveu, V., Greiner,
R., & Scalbert, A. (2013). HMDB 3.0-The human metabolome
database in 2013. Nucleic Acids Research, 41, D801–D807.
doi:10.1093/nar/gks1065.
World Med A. (2013). World medical association declaration of helsinki ethical principles for medical research involving human
subjects. Jama-Journal of the American Medical Association
310, 2191–2194 doi:10.1001/jama.2013.281053.
Yin, P., & Xu, G. (2014). Current state-of-the-art of nontargeted
metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications.
Journal of chromatography. A, 1374, 1–13. doi:10.1016/j.
chroma.2014.11.050.
Yu, Z. H., G. Kastenmuller, Y. He, P. Belcredi, G. Moller, C. Prehn,
J. Mendes, S. Wahl, W. Roemisch-Margl, U. Ceglarek, A. Polonikov, N. Dahmen, H. Prokisch, L. Xie, Y. X. Li, H. E. Wichmann, A. Peters, F. Kronenberg, K. Suhre, J. Adamski, T. Illig,
R. Wang-Sattler (2011). Differences between human plasma
and serum metabolite profiles. Plos ONE. doi:10.1371/journal.
pone.0021230.
Zhang, L., Jin, H. F., Guo, X. G., Yang, Z., Zhao, L., Tang, S. H.,
Mo, P., Wu, K. C., Nie, Y. Z., Pan, Y. L., & Fan, D. M. (2012).
Distinguishing pancreatic cancer from chronic pancreatitis and
healthy individuals by H-1 nuclear magnetic resonance-based
metabonomic profiles. Clinical Biochemistry, 45, 1064–1069.
doi:10.1016/j.clinbiochem.2012.05.012.