Abstract
Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of
the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms
(IPMNs) are common lesions that may progress from low-grade dysplasia
(LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate
discrimination of IPMN-associated neoplastic grade is an unmet clinical
need. Targeted (semi)quantitative analysis of 100 metabolites and >1000
lipid species were performed on peri-operative pancreatic cyst fluid
and pre-operative plasma from IPMN and serous cystic neoplasm (SCN)
patients in a pancreas resection cohort (n = 35). Profiles were
correlated against histological diagnosis and clinical parameters after
correction for confounding factors. Integrated data modeling was used
for group classification and selection of the best explanatory
molecules. Over 1000 different compounds were identified in plasma and
cyst fluid. IPMN profiles showed significant lipid pathway alterations
compared to SCN. Integrated data modeling discriminated between IPMN
and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and
invasive cancer with up to 90.06% accuracy. Free fatty acids,
ceramides, and triacylglycerol classes in plasma correlated with
circulating levels of CA19-9, albumin and bilirubin. Integrated
metabolomic and lipidomic analysis of plasma or cyst fluid can improve
discrimination of IPMN from SCN and within PMNs predict the grade of
dysplasia.
Subject terms: Lipidomics, Metabolomics, Diagnostic markers, Tumour
biomarkers, Pancreatic cancer
Introduction
Pancreas cancer (PC) is expected to become the second most common cause
of cancer related death within the next decade^[49]1. Contrary to other
cancer types such as breast^[50]2 and colorectal^[51]3, whose prognosis
has been progressively improving over the time, pancreatic cancer
prognosis remains poor. This has in part been due to the lack of an
effective screening method with the ability to identify pancreatic
lesions that are at risk of progression and that appear before PC
develops^[52]4.
Pancreatic cystic neoplasms (PCNs) are increasingly diagnosed and
display a prevalence as high as 45% in the general population^[53]5.
Intraductal Papillary Mucinous Neoplasm (IPMN) account for half of all
PCNs and are increasingly considered possible precursor lesions of
PC^[54]6–[55]8. IPMNs can progress from low-grade dysplasia (LGD)
through high-grade dysplasia (HGD) to invasive cancer^[56]9,[57]10.
However, as the prevalence of IPMNs in the general population is higher
than the incidence of PC, only a minority of patients affected by IPMNs
will develop PC^[58]6,[59]8,[60]10. Therefore, the detection of IPMNs
by currently available imaging techniques is an opportunity for early
diagnosis of neoplastic precursor lesions and prevention of PC.
However, the development of a population-based screening program is
challenged by two factors: on one hand the costs of lifelong
surveillance, on the other hand the low pre-operative diagnostic
accuracy for pancreatic cystic lesions. These two problems partly
overlap, as due to the low diagnostic yield of conventional radiology,
many patients will undergo unnecessary lifelong follow-up with magnetic
resonance imaging and/or endoscopic ultrasound with associated high
health care costs that might become particularly unsustainable in the
near future^[61]11.
Current indications for surgery in IPMN patients are mainly based on
the pre-operative radiological imaging that suffers from low accuracy
(60–70%)^[62]12. Diagnostic yield can be slightly increased by adding
endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), which
allows for cytological and carcinoembryonic antigen (CEA)
analyses^[63]13. Nonetheless, fluid cytology does not allow
differentiation between different types of mucinous cysts or between
different grades of dysplasia in IPMN, and CEA is inaccurate to
discriminate benign mucinous cysts and cysts with high-grade dysplasia
or an associated invasive carcinoma^[64]14–[65]16. Thus, the
pre-operative diagnostic accuracy to distinguish between the various
benign or (pre-)malignant PCNs, such as IPMNs, is low, and there are no
methods available to discriminate between the different grades of
dysplasia associated with IPMNs. Correctly identifying PCNs and their
risk for progression to cancer is clinically crucial; as such, novel
biomarkers from blood or cyst fluid may allow for a more accurate
definition of IPMNs and improve their management and
treatment^[66]17–[67]20.
Metabolic reprogramming is an established hallmark of cancer^[68]21. In
addition to the carbohydrate and amino acid nutrients required by
growing cancer cells, the lipid-scavenger pathway and de novo fatty
acid synthesis are important for maintaining cancer cell proliferation
and survival in the tumor environment^[69]22,[70]23. Development and
progression of PC is associated with alterations in circulating
metabolic profiles^[71]24–[72]29. Whilst previous studies have compared
the metabolic profiles of PC patients and healthy
individuals^[73]25,[74]26,[75]28, few have examined IPMN patients or
considered the spectrum of IPMN severity, which is relevant for
pancreatic surgery management^[76]27,[77]30.
This study aimed at defining the metabolomic and lipidomic makeup of
pancreatic cyst fluid and plasma in pancreas resection patients with
IPMN and serous cystic neoplasm (SCN).
Results
Study characteristics
This cohort study included 35 patients undergoing pancreas resection,
from whom pre-operative blood plasma (n = 21) and peri-operative cyst
fluid (n = 31) were collected (Supplementary Fig. [78]S1). Following
histological validation of resected tissues, four groups were assigned:
serous cystic neoplasm (SCN), IPMN with low-grade dysplasia (LGD), IPMN
with high-grade dysplasia (HGD), and invasive IPMN (Cancer) for which
clinical parameters are summarized in Table [79]1. As expected, the
IPMN group was older, of mixed gender, and had comparable BMI with SCN
controls. Cardiovascular disease (CVD) and diabetes were more common in
patients with IPMN. Compared to SCN, IPMN LGD and HGD showed no
significant elevation of circulating CA19-9, HbA1c, amylase, albumin,
bilirubin, or white blood cell count. Only Cancer had significantly
increased circulating CA19-9 or HbA1c levels.
Table 1.
Patient group characteristics.
Cyst fluid (n = 31) Plasma (n = 21)
SCN IPMN SCN IPMN
LGD HGD Cancer LGD HGD Cancer
Patients, % (n) 16.1 (5) 25.8 (8) 22.6 (7) 35.5 (11) 23.8 (5) 23.8 (5)
28.6 (6) 23.8 (5)
Female, % 100 50* 42.9* 27.3** 100 20* 33.3* 40
Alcohol use, % 60 25 28.6 18.2 40 20 16. 7 60
Smokers, % 40 0 0 9.1 40 0 0 40
CVD, % 20 71.4 71.4 54.6 20 60 50 60
Statins use, % 20 12.5 14.3 9.09 20 0 16.7 40
Diabetes, % 0 12.5 42.9 36.4 0 20 16.7 20
Age, years 48 66** 72*** 69*** 53 65 72.5** 69**
median (range) (34–58) (56–81) (66–75) (46–83) (34–68) (56–76) (66–75)
(65–83)
BMI, kg/m^2 29.64 27.51 27.21 24.97 28.01 32.16 24 25.69
median (range) (24.1–32.0) (21.8–36.6) (23.4–28.3) (20.2–29.7)
(24.1–31.0) (24.8–36.6) (21.5–28.3) (24.1–32.9)
HbA1c, mmol/mol 31 42.5 38 43* 33 44 38 51.5
median (range) (30–37) (35–48) (31–55) (31–67) (30–43) (37–48) (31–55)
(31–81)
S-CA 19–9, kE/L 11 18 11 376* 11 11 16 285**
median (range) (6.8–62) (6.4–182) (<1–115) (<1–1040) (7.9–62) (6.4–182)
(<1–115) (46–480)
Serum amylase, µkat/L 0.3 0.41 0.24 0.25 0.31 0.44 0.195 0.27
median (range) (0.19–1.64) (<0.13–0.65) (<0.13–0.93) (<0.13–0.87)
(0.19–1.64) (<0.13–0.54) (<0.13–0.72) (<0.13–0.54)
Albumin, g/L 36 36 36 31 38 37 34.5 31.5
median (range) (33–39) (26–38) (22–39) (19–38) (33–39) (36–39) (22–39)
(28–34)
Bilirubin, µmol/L 6 6.5 5 24 6 8 8 30
median (range) (3–18) (<3–13) (<3–315) (5–150) (3–7) (4–13) (4–315)
(12–119)
WBC, × 10^9/L 6.3 7.45 7.8 9.8 6.3 7.5 8.3 11.2**
median (range) (4.4–9.2) (5–9.4) (5.6–12.9) (5–13.9) (4.4–9.2)
(5.3–9.4) (7.2–12.9) (8–13.9)
[80]Open in a new tab
Statistical comparisons between each group and the control group (SCN)
were made using one-way ANOVA with Dunnett’s multiple comparisons test
for quantitative parameters and chi-square test for qualitative values;
*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001.
Metabolite profiling reveals alterations of lipid metabolism pathways
Cyst fluid and plasma were profiled single-blinded using a targeted and
(semi)quantitative liquid chromatography-tandem mass spectrometry
(LC-MS/MS) method. A total of 90 and 91 different metabolites were
measured in cyst fluid and plasma, respectively. A hierarchical
clustered heatmap of the metabolomic data showed no clear grouping of
metabolite profiles according to diagnose group (Fig. [81]1A,B).
However, principal component analysis (PCA) showed that cyst fluid but
not plasma from SCN was dissimilar to all other groups (Fig. [82]1C,D).
Figure 1.
[83]Figure 1
[84]Open in a new tab
Metabolomic profile. Heatmap of cyst fluid (A) and plasma (B)
metabolite concentrations. Projection of patient samples on the first
two principal components (PCA) for cyst fluid (C) and plasma (D)
datasets. Data (cyst, n = 31; plasma, n = 21) were adjusted for
confounding factors and features were subsequently standardized to have
a mean of zero and unit variance. Dendrograms were built using the
Euclidean distance matrix and Ward’s method.
We next applied quantitative metabolic pathway enrichment analysis,
using metabolite identities (see Supplementary Table [85]S1). Because
HGD and Cancer are target groups for resection, these groups were
combined (HGD/Cancer). Compared to SCN, 34 enriched pathways were found
in cyst fluid from HGD/Cancer and 12 enriched pathways from LGD at a
significance level of 0·05 (Supplementary Fig. [86]S2). Among these,
lipid pathways appeared to dominate, e.g. phosphatidylethanolamine
biosynthesis, phosphatidylcholine biosynthesis, taurine and hypotaurine
metabolism, phospholipid biosynthesis, beta oxidation of very long
chain fatty acids, fatty acid metabolism, oxidation of branched chain
fatty acids, and sphingolipid metabolism. Several of these were also
significantly enriched in plasma samples of HGD/Cancer or LGD compared
to SCN, including sphingolipid metabolism, phosphatidylethanolamine
biosynthesis, phosphatidylcholine biosynthesis (Supplementary
Fig. [87]S2).
Lipidomic profiling indicates a difference between IPMNs and SCN
As metabolic analysis of IPMN indicated altered lipid metabolism, and
considering the pancreatic exocrine function of secreting lipases that
might be affected in PCN patients, we next performed a high definition
lipid profiling of paired aliquots using the SCIEX Lipidyzer™
technology, where 1100 lipid molecular species were measured in all the
samples. Out of those 1100 measured lipid molecular species, we
successfully detected and quantified a total of 430 in cyst fluid and
941 in plasma. Heatmap visualization showed that triacylglycerol
(TAG)-related lipids are the most abundant type among all lipid classes
in both cyst fluid and plasma (Fig. [88]2A,B). Lipidomic profiles of
HGD and Cancer appear to be similar to each other, while LGD was
clustered slightly differently. Although the lipidomic profiles of
plasma and cyst fluid of the SCN group, as projected on the first two
principal components (PCA), show a different direction and shape of the
clouds of points when compared to IPMN groups, no evidently clear
separation of diagnose groups can be observed from the 2D plot
(Fig. [89]2C,D). The lipidomic analyses suggest alterations in lipid
compound composition in cyst fluid and plasma, pointing to the
possibility of discrimination of pancreatic disease severity,
confirming the pathway enrichments we observed. Looking at fold change
estimations (Fig. [90]3 and Supplementary Table [91]S1) it is possible
to observe a clear alteration of the TAG class in plasma samples in
both LGD and HGD/Cancer compared to SCN. In IPMN cyst fluid samples,
instead, free fatty acids (FFA) and ceramides (CER) appear to have, on
average, higher concentrations than those of SCN samples while having
lower amount of TAGs. Interestingly, the profile of the Cancer group is
similar to that of LGD in plasma and to that of HGD in cyst fluid,
while only TAGs differ significantly between HGD and LGD in both plasma
and cyst fluid (Supplementary Fig. [92]S3).
Figure 2.
[93]Figure 2
[94]Open in a new tab
Lipidomic profile. Heatmap of cyst fluid (A) and plasma (B) lipid
concentrations. Projection of patient samples on the first two
principal components (PCA) for cyst fluid (C) and plasma (D) datasets.
Data (cyst, n = 31; plasma, n = 21) were adjusted for confounding
factors and features were subsequently standardized to have a mean of
zero and unit variance. Dendrograms were built using the Euclidean
distance matrix and Ward’s method.
Figure 3.
[95]Figure 3
[96]Open in a new tab
Estimated fold changes of concentrations of all measured analytes
(including metabolite and lipid molecular species) between selected
groups. LGD compared to SCN in cyst fluid (A) and plasma (B).
HGD/Cancer compared to SCN in cyst fluid (C) and plasma (D). HGD/Cancer
compared to LGD in cyst fluid (E) and plasma (F). Filled dots are fold
changes whose credibility interval does not overlap with the null
reference value of one-fold change, or zero on the plotted log scale.
Integrated metabolite and lipid data predict IPMN disease groups
Accurate classification of IPMN severity using novel biomarkers in cyst
fluid or plasma may facilitate the discrimination of low-risk from
high-risk patients. We therefore assessed the predictive capacity of
the integrated metabolome and lipidome profiles to classify samples
according to their corresponding disease group. As IPMN HGD and Cancer
are considered as high-risk lesions, we combined these into a single
group. Effects of clinical covariates (Table [97]1) were estimated and
subtracted from the raw data prior to analysis, and the only covariates
that improved the classification model were age and BMI. The result of
binary classifications and the performance of the CPPLS-DA model are
given in Table [98]2 and Supplementary Table [99]S2. The model
discriminated between SCN and IPMN with very high accuracy (100%) in
both cyst fluid and plasma samples. Choline, 2-aminoisobutyrate,
trimethylamine n-oxide, glycine, alanine, and glyceraldehyde were found
to be essential discriminatory molecules in both cyst fluid and plasma.
Furthermore, dimethylglycine was a discriminatory compound for cyst
fluid while serine and GABA were for plasma. Overall, the two biofluids
displayed similar predicting power, with cyst fluid-based
classification performing slightly better (accuracy of approximately
90%) when classifying the three groups SCN, LGD and HGD/Cancer.
Nevertheless, plasma-based classification could easily detect LGD
samples (90% accuracy) while cyst fluid molecules’ concentrations were
better for predicting HGD/Cancer samples (90% accuracy) (Table [100]2).
The model had a low sensitivity in discriminating the three IPMN groups
from each other when HGD and Cancer were considered separately. SCN
samples form a distinct cluster from IPMN samples, whereas the other
two groups overlap significantly in cyst fluid (Fig. [101]4A,B). The
top 15 molecules in cyst fluid and plasma ranked by their VIP scores
are presented in Fig. [102]4C,D. Interestingly, only a subset of
metabolites, without lipids, were sufficient to achieve best
performance. In particular, amino acids were the most important
molecules for the classification of plasma samples.
Table 2.
Performance measures of binary classifications with the chosen CPPLS-DA
model.
AUC^a Sensitivity Specificity Balanced accuracy^b
SCN vs All Cyst fluid 1.000 1.000 1.000 1.000
Plasma 0.950 0.800 0.875 0.837
LGD vs All Cyst fluid 0.935 0.875 0.913 0.894
Plasma 0.825 1.000 0.812 0.906
HGD-Cancer vs All Cyst fluid 0.949 0.889 0.923 0.906
Plasma 0.854 0.636 1.000 0.818
IPMN vs SCN Cyst fluid 1.000 1.000 1.000 1.000
Plasma 1.000 1.000 1.000 1.000
[103]Open in a new tab
^aArea Under the ROC Curve; ^b(Sensitivity + Specificity)/2.
Figure 4.
[104]Figure 4
[105]Open in a new tab
Canonical Powered Partial Least Squares and Discriminant Analysis
(CPPLS-DA) results. Projection of patient samples on the first two
principal components in cyst fluid (A) and plasma (B). Highest variable
importance in projection (VIP) scores in cyst fluid (C) and plasma (D).
Correlations to clinical pancreatic blood markers
We next asked whether there was any significant correlation between the
metabolites/lipids and the blood markers that are frequently used to
define the IPMN patients. Pearson correlation analysis identified
molecules in plasma showing strong correlations (r > 0.6 and adjusted
p-value ≤ 0.05) with circulating CA19-9 and albumin, and to some extent
with bilirubin (Supplementary Table [106]S3). CA19-9 was found to be
positively correlated with a total of 35 lipid molecules that were
classified as CER (n = 1), FFA (n = 4), Phosphatidylcholines (PCs)
(n = 10), Phosphatidylethanolamines (PE) (n = 10), Sphingomyelins (SM)
(n = 4), and TAG (n = 5) (Supplementary Table [107]S4a). In addition,
albumin and bilirubin levels were positively correlated with
4-pyridoxate, adenine, carnitine, cysteine, and lipid molecules
classified as CE (n = 1), FFA (n = 2), Lysophosphatidylcholines (LPC)
(n = 2), Lysophosphatidylethanolamines (LPE) (n = 1), PE (n = 1), CER
(n = 1), PC (n = 1), and TAG (n = 1) (Supplementary Table [108]S4a).
Negative correlations were noted between albumin and cystathionine,
D-Ribose-5-P, inosinic acid or inosine monophosphate (IMP), and
taurochenodeoxycholate and some lipids within the classes of PC
(n = 9), TAG (n = 6) and CER (n = 6), while bilirubin negatively
correlated with few metabolite and lipids (Supplementary
Table [109]S4b).
Discussion
Pancreatic IPMNs are common precancerous lesions^[110]6–[111]8. Today,
only some radiological and clinical parameters are used to identify
patients with high risk for cancer progression or malignancy, for
example, main pancreatic duct dilatation, cyst diameter, rate of
progression and elevated serum CA19-9^[112]14–[113]16. Unfortunately,
no high-accuracy tools are available that determine the IPMN-associated
grade of dysplasia or that offer accurate differential diagnosis from
other benign and low-risk PCNs (i.e. SCNs). These diagnostic
limitations negatively affect patient management and treatment. Until
recently, metabolites involved in IPMN disease progression have been
scarcely studied^[114]27. A holistic view of the plasma and cyst fluid
metabolic profile may aid the discovery of biomarkers capable of
improving pre-operative PCN diagnosis.
We have shown that an integrated metabolomics and lipidomics approach
can be used to 1) discriminate between IPMN and SCN and 2) determine
the IPMN-associated grade of dysplasia. Our analysis offered superior
predictive accuracy compared to conventional cross-sectional imaging or
EUS-FNA^[115]15. The LOOCV balanced accuracy of discriminating
Cancer/HGD from SCN and LGD was 90.6% for cyst fluid and 81.8% for
plasma. When discriminating IPMN as a whole from SCN, accuracy reached
100% for both plasma and cyst fluid. The availability of accurate
plasma-based tests could represent a major advantage for patients who
do not require invasive procedures like EUS-FNA, which are associated
with risk of complications and low-diagnostic accuracy^[116]31.
While previous PC metabolome studies measured around 50-100 metabolites
per case^[117]24,[118]26,[119]29,[120]32, our high-definition
integrated approach measured around 100 metabolites from 15 different
biological classes and 1000 lipid molecular species from 13 different
lipid classes, largely covering the important metabolome spectrum, i.e.
sugars, nucleotides, amino acids, and lipids. Recent elegant metabolome
studies^[121]24,[122]28 pointed out that a number of compounds,
including very long-chain fatty acids, phospholipids, and taurine, were
differentially present in PC patients or PC tissue compared to healthy
subjects or parenchyma tissue, respectively. This agrees with our
findings of enriched taurine and fatty acid metabolism pathways and
phospholipid biosynthesis pathways in cyst fluid of pre-malignant or
early malignant cases, e.g. and LGD and Cancer/HGD, as compared to SCN.
Moreover, our study also has shown significant alterations of different
classes of molecules, mainly TAGs, detected in plasma of Cancer/HGD,
and LGD, compared to SCN. We did not observe significant alterations of
molecules in plasma when comparing HGD/Cancer with LGD, suggesting
these disease groups display a more comparable plasma profile.
The tumor marker CA19-9 is used for predicting malignancy of IPMN and
monitoring PC progression, but its use as a definitive diagnostic
marker, especially detecting IPMN HGD, is limited^[123]33,[124]34.
Combining additional blood markers with CA19-9 was recently shown to
improve early detection of PC^[125]33, and building a broader molecular
profile around CA19-9 in IPMN patients may enhance the diagnostic
accuracy. The lipid metabolites strongly associated with CA19-9 in this
cohort are therefore of interest and need to be examined further,
possibly together with metabolites correlating with serum albumin and
bilirubin. Our findings that plasma, but not cyst fluid metabolites,
strongly correlated with these three markers, suggest that systemic,
rather than local factors, may have an influence on development and
progression of IPMN.
A strength of this paper is the investigation of a surgical cohort of
patients, with definitive histology and assessment of the grade of
dysplasia. This allowed us to accurately match the actual
IPMN-associated grade of dysplasia with our data, avoiding problems of
misdiagnosis that occur in more than one third of the patients
undergoing EUS-FNA with cyst fluid analysis^[126]12. In addition, we
used a validated targeted and (semi) quantitative analysis through a
robust and reliable LC-MS/MS approach with strict quality
management^[127]35. We furthermore tested several linear mixed models
to assess many covariate parameters (see Table [128]1), and while use
of statins was considered as possible confounding factor, it did not
improve the final classification model which only adjusted for age and
BMI.
However, this study has also some limitations. Firstly, there is
possible selection bias because we analyzed a small and homogenous
cohort of patients which might not be fully representative of the
entire population and thus might potentially restrict the predictive
power of lipidomic/metabolomics profiling to certain groups within the
general population. In this relatively small cohort all SCNs cases are
female, which is not surprising as the prevalence rate in the general
population for SCNs is 9-16% of all cystic lesions while approximately
75% of patients with SCNs are women^[129]36. Although this gender
imbalance can be certainly considered a limitation we observed that,
after adjusting for BMI and age, the effect of sex did not have any
impact on out-of-sample model prediction accuracy. Therefore, we
believe that our classification accuracy is mainly a consequence of
differences between diseases while we expect unbalances in sex
distribution to have only a very negligible influence. Moreover, we did
not include other types of mucinous tumors of the pancreas, such as
mucinous cystic neoplasm (MCN). However, such lesions are easier to
recognize, due the peculiar epidemiological and radiological features
(typically in body and tail of the pancreas, almost exclusively in
young females) that make diagnosis not particularly
challenging^[130]37. Additionally, the current study might suffer from
a possible sampling bias, considering that fluid was aspirated from one
or two accessible cysts, despite IPMNs often being multifocal and
occurring in different locations of the pancreas (head/body/tail).
Therefore, one cannot exclude the possibility that metabolic profiles
of the sampled cysts might not be representative of all cystic lesions.
Lastly, we did not investigate possible factors that might have
potential effects on (lipid) metabolism, such as specific genetic
mutations (e.g GNAS or KRAS gene)^[131]38,[132]39.
This study has comprehensively mapped the metabolite and lipid makeup
of cyst fluid and plasma from PCN patients with defined pathology,
using integrated metabolomics and lipidomics. Our findings have
clinical implications and may support assay development for
differential diagnostics of PCNs to improve patient management. Future
studies are needed to test larger patient cohorts using the proposed
model, to better understand associations between metabotypes and IPMN
malignancy risks.
Methods
Study population and ethical considerations
In this prospective cohort study, patients undergoing pancreatic
surgery for suspected pancreatic cystic neoplasm (PCN) with
post-surgically validated intraductal papillary mucinous neoplasm
(IPMN) and serous cystic neoplasm (SCN) from February 2016 to January
2017 at Karolinska University Hospital, Sweden, were included. Excluded
were cases without a cystic component, non-IPMNs, or those without cyst
fluid in the resected pancreas (Supplementary Fig. [133]S1). This study
follows the Helsinki convention and good clinical practice with
permission of the Ethical Review Board Stockholm and the Karolinska
Biobank Board (Dnr 2015/1580-31/1). Written informed consent was
obtained from all patients.
Pancreatic cyst fluid collection
Fresh resection specimens were received at the pathology laboratory
within 20 minutes of surgical removal, in sterile conditions and on
ice. Macroscopic assessment to identify the cystic lesion and main
pancreatic duct was done by a specialist pancreatic pathologist. Fluid
from the main pancreatic duct was collected using a syringe without
needle. When the cystic lesion was readily identified in the intact
specimen, the fluid was aspirated using a syringe with needle. For
specimens in which the cystic lesion was not readily accessible from
the surface the specimen was cut or when the cyst content was too
viscous content was aspirated using a syringe without needle. Aspirated
fluid was stored at −80 °C until further analysis.
Peripheral blood collection and plasma isolation
Venous whole blood was collected in K[2] EDTA Vacutainer® tubes (BD,
Stockholm, Sweden) immediately prior to surgery. Within four hours of
collection, blood was processed using Ficoll Paque Plus (GE Life
Sciences, Uppsala, Sweden) gradient-density centrifugation following
manufacturer’s instructions and the plasma fraction was stored at
−80 °C until further analyses.
Histopathological diagnosis and cyst fluid classification
Resection specimens were fixed in 4% formaldehyde and processed for
routine histopathological diagnosis. The cystic lesions were classified
by light microscopic examination of hematoxylin-eosin stained slides by
a specialized pancreatic pathologist as IPMN or SCN^[134]40. The grade
of dysplasia in IPMN was assessed using a 2-grade (high/low) scale,
according to current international standard^[135]41. To make the cyst
fluid classification more representative of the neoplastic epithelium
that produces it, specimens showing <5% high-grade dysplasia (HGD) were
classified as low-grade dysplasia (LGD). Specimens with concomitant
invasive carcinoma were classified as “Cancer” and considered as a
separate class for further analyses.
Chemicals
All metabolite standards used in the analysis were purchased from
Sigma-Aldrich (Helsinki, Finland), while isotopically labelled
metabolite internal standards (IS) were obtained from Cambridge Isotope
Laboratory (Tewksbury, MA, USA). For lipidomics, kits containing 50
labelled internal standards across 13 lipids classes were purchased
from SCIEX (Framingham, MA, USA). Ammonium formate, ammonium acetate,
and ammonium hydroxide were obtained from Sigma-Aldrich (Helsinki,
Finland). Formic acid (FA), acetonitrile (ACN), methanol (HiPerSolv
CHROMANORM, LCMS grade), ethyl acetate (HPLC grade), 2-propanol,
1-propanol, and dichloromethane were purchased from VWR International
(Helsinki, Finland). Deionized water, up to a resistivity of 18 MΩ⋅cm,
was purified with a Barnstead Easypure RoDi water purification system
(Thermo Scientific, Marietta, OH, USA). Whole blood was purchased from
the Finnish Red Cross blood service (Helsinki, Finland) from which
serum samples were prepared and used as internal quality control
samples.
Metabolomic analysis
Metabolomic analysis of samples was performed using liquid
chromatography-mass spectrometry as previously described in the
supplementary data of Nandania et al.^[136]35. Briefly, 15 labeled
internal standards were used to estimate quantitative levels of 100
metabolites. To 100 µL thawed sample (plasma or cyst fluid), 10 µL of
labelled internal standard mixture was added, and then metabolites were
extracted using protein precipitation by adding acetonitrile +1% formic
acid (1:4, sample:solvent). The collected extracts were dispensed in
Ostro 96-well plates (Waters Corporation, Milford, USA) and filtered by
applying a vacuum at a delta pressure of 300–400 mbar for 2.5 min on
robot’s vacuum station. Filtered sample extract (5 µL) was injected in
an Acquity UPLC-system coupled to a Xevo TQ-S triple quadrupole mass
spectrometer (Waters Corporation, Milford, MA, USA) which was operated
in both positive and negative polarities with switching time of 20
milliseconds. Multiple Reaction Monitoring (MRM) acquisition mode was
selected for the quantification of metabolites. MassLynx 4.1 software
was used for data acquisition, data handling and instrument control.
Data processing was done using TargetLynx 4.1 software.
Lipidomic analysis
Lipids were extracted with liquid-liquid extraction (LLE) method using
ethyl acetate and methanol. In borosilicate glass tubes, to 100 µL
thawed sample (plasma or cyst fluid), 1 mL methanol and 1 mL water was
added. Then, 100 μL of labelled internal standard mixture (prepared as
per SCIEX LIPIDYZER manual’s instructions) was added and allowed to
equilibrate with the samples. To each tube 3.5 mL of ethyl acetate was
added after which tubes were put on a rotator shaker for 15 min at 30
RPM, followed by centrifugation at 3000 RPM for 10 min. After
centrifugation, the upper layer of ethyl acetate was collected and
dried under N[2] gas. Dried samples were reconstituted with 250 μL of
mobile phase (dichloromethane:methanol (50:50) containing 10 mM
ammonium acetate) for injection. Lipid separation and quantitation was
performed on the SCIEX Lipidyzer^™ platform using a SCIEX 5500 QTRAP®
mass spectrometer (SCIEX, Washington, D.C., USA) with SelexION®
Differential ion mobility (DMS) technology by directly infusing 50 μL
of extracted samples with a mobile phase at flow rate of 70 µL/min. Two
acquisition methods, with and without SelexION® technology, were used
to cover 13 lipid classes using flow injection analysis. The lipid
molecular species were measured using MRM strategy in both positive and
negative polarities. Positive ion mode was used for the detection of
lipid classes – sphingomyelins (SM), diacylglycerols (DAG), cholesteryl
esters (CE), ceramides (CER), triacylglycerols (TAG), and negative ion
mode was used for the detection of lipid classes –
lysophosphatidylethanolamines (LPE), lysophosphatidylcholines (LPC),
phosphatidylcholines (PC), phosphatidylethanolamines (PE) and free
fatty acids (FFA). Lipidomics Workflow Manager software was used for
acquisition of samples, automated data-processing, signal detection and
lipid species concentration calculations.
Statistical analysis
All data analyses were performed with R 3.5.1 and Stan
2.17.1^[137]42,[138]43. Concentration values in μmol/L were assumed to
follow a lognormal distribution and were therefore log-transformed as a
preliminary normalization operation. Missing values were removed from
the dataset following the “modified 80% rule”, according to which a
variable is discarded if the relative frequency of missing values is
more than 0.8 in all clinical groups^[139]44. Remaining missing values
were imputed with the QRILC function from R package imputeLCMD^[140]45.
Considering the lack of balance for basic clinical parameters
(Table [141]1), all values were adjusted for the effects of confounding
factors using a linear mixed model. Let
[MATH:
ygij :MATH]
be the log-concentration of molecule j observed at sample i belonging
to phenotypic group g. The basic assumption of our model is that
observations are conditionally independent and normally distributed,
with same standard deviation σ but different mean value μ:
[MATH:
ygij|μgij,σ~N(μgij,σ2) :MATH]
Covariates to include in the model were selected by the highest
out-of-sample point-wise predictive accuracy^[142]46. Parameter μ was
then defined as the linear combination
[MATH:
μgij=θ+φi+λ
j+ηgj+γ
1jagei+<
mi>γ2jbm
ii :MATH]
where θ is the grand mean,
[MATH: φi
:MATH]
is the general effect of sample i,
[MATH: λj
:MATH]
is the effect of molecule j,
[MATH:
ηgj
:MATH]
is the effect of phenotypic group g on molecule j, and
[MATH:
γv(v=1,2) :MATH]
are effects varying with molecules. Fold change of molecule j between
groups g and h was therefore defined as
[MATH: e(ηgj−ηh
j)
:MATH]
. All coefficients associated with the same discrete category are
constrained to sum to zero in order to make the model identifiable. We
assigned to each unknown parameter weakly informative prior
distributions as follows:
[MATH: θ~t(3,0,10) :MATH]
[MATH:
φi~N(0,α2),i=1,…,n :MATH]
[MATH:
λj~N(0,β2),j=1,…,m :MATH]
[MATH:
γvj~N(0,ωv2),v=1,2,j=1,…,m :MATH]
[MATH:
ηgj~N(0,ζg2),g=1,…,k,j=1,…,m :MATH]
[MATH:
σ~halft<
mo
stretchy="false">(3,0,10) :MATH]
[MATH:
α~halft<
mrow>(3,0,10) :MATH]
[MATH:
β~halft<
mrow>(3,0,10) :MATH]
[MATH:
ωv~halft(3,0,10),v=1,2 :MATH]
[MATH:
ζg~halft(3,0,10),g=1,…,k :MATH]
where t refers to the three-parameters Student’s t-distribution and
halft to the same distribution but truncated at 0 and defined only on
the positive values^[143]47. Total number of phenotypic groups k
depended on the particular statistical analysis being conducted.
Estimated fold changes and their corresponding 95% credibility
intervals, computed from 20,000 posterior samples, are available in
Supplementary Table [144]S1. Prior to visualization, classification,
and enrichment analyses, the dataset was adjusted for the confounding
covariates “age” and “BMI” and subsequently standardized to a mean of
zero and unit variance. Principal Component Analysis (PCA) was applied
to the adjusted data for exploratory data purposes. Heatmaps and data
projection on the first two principal components were used to visualize
the dataset. Classification was performed using a Canonical Powered
Partial Least Squares Discriminant Analysis (CPPLS-DA), fitted with the
pls R package^[145]48,[146]49. Classification performance was measured
with a Leave-One-Out Cross Validation (LOO-CV) strategy, and balanced
accuracy (average between sensitivity and specificity of the
classifier) is reported^[147]50. Best explanatory molecules were
selected according to their Variable Importance in Projection (VIP)
ranking scores according to the following iteration scheme^[148]51. At
each step of the algorithm the performance of the model was recorded
with a LOO-CV strategy and the molecules were sorted according to their
VIP score. Subsequently, 5% of the molecules with the lowest VIP score
were discarded and this operation was repeated until the number of
molecules allowed model identifiability. The model with the highest
performance was ultimately selected. Pathway enrichment analysis (QEA)
was performed with the free web service MetaboAnalyst 4.0^[149]52. All
figures were generated in R 3.5.1^[150]42.
Ethical considerations
This study follows the Helsinki convention and good clinical practice.
This study was conducted at Karolinska University Hospital under
permission of the Ethical Review Board Stockholm and the Karolinska
Biobank Board (Dnr 2015/1580-31/1). Written informed consent was
obtained from all patients.
Supplementary information
[151]Supplementary Material^ (1.1MB, pdf)
[152]Supplementary Table S1^ (1.9MB, xlsx)
[153]Supplementary Table S2^ (23.5KB, xlsx)
[154]Supplementary Table S3^ (661.8KB, xlsx)
Acknowledgements