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«Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have beenon November 23, 2010 as ...»

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Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712

Author manuscripts have beenon November 23, 2010 as publication but have not yet been edited.

Published OnlineFirst peer reviewed and accepted for 10.1158/1055-9965.EPI-10-0712

FEASIBILITY OF IDENTIFYING PANCREATIC CANCER BASED ON SERUM

METABOLOMICS

Oliver F. Bathe, MD, MSc1, 2, Rustem Shaykhutdinov,PhD3, Karen Kopciuk, PhD4, Aalim M. Weljie, PhD3, Andrew McKay, MD, MSc5, Francis R. Sutherland, MD1, Elijah Dixon, MD, MPH1, Nicole Dunse, BN1, Dina Sotiropoulos, MN1, Hans J. Vogel, PhD3 Departments of Surgery, 2Oncology, 3Biological Sciences Mathematics and Statistics, University of Calgary, Calgary, AB, Canada Department of Surgery, University of Manitoba, Winnipeg, MB, Canada.

Running Title: Metabolomic profile of pancreatic cancer Research Support: Alberta Cancer Board, Joanne Bowie Endowment Fund. HJV is a Scientist of the Alberta Heritage Foundation for Medical Research.

KEYWORDS: Pancreatic cancer, metabolomics, diagnosis, biomarker, NMR spectroscopy

Address correspondence to:

Oliver F. Bathe, MD Tom Baker Cancer Centre, Division of Surgical Oncology, 1331 -29th St NW, Calgary, AB, Canada T2N 4N2 Phone: 403-521-3179 Fax: 403-283-1651 Email: bathe@ucalgary.ca Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Copyright © 2010 American Association for Cancer Research Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

ABSTRACT Background. We postulated that the abundance of various metabolites in blood would facilitate the diagnosis of pancreatic and biliary lesions, which could potentially prevent unnecessary surgery.

Methods. Serum from patients with benign hepatobiliary disease (N=43) and from patients with pancreatic cancer (N=56) were examined by 1H NMR spectroscopy to quantify 58 unique metabolites. Data were analyzed by “targeted profiling” followed by supervised pattern recognition and orthogonal partial least squares-discriminant analysis (O-PLS-DA) of the most significant metabolites, which enables comparison of the whole sample spectrum between groups.

Results. The metabolomic profile of patients with pancreatic cancer was significantly different from that of patients with benign disease (AUROC=0.8372). Overt diabetes mellitus (DM) was identified as a possible confounding factor in the pancreatic cancer group. Thus, diabetics were excluded from further analysis. In this more homogeneous pancreatic cancer group, compared to benign cases, serum concentrations of glutamate and glucose were most elevated, on multivariate analysis. In benign cases, creatine and glutamine were most abundant. To examine the usefulness of this test, a comparison was made to age- and gender-matched controls with benign lesions that mimic cancer, controlling also for presence of jaundice and diabetes (N=14/group). The metabolic profile in patients with pancreatic cancer remained distinguishable from patients with benign pancreatic lesions (AUROC=0.8308).

Conclusions. The serum metabolomic profile may be useful for distinguishing benign from malignant pancreatic lesions.

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Impact. Further studies will be required to study the effects of jaundice and diabetes. A more comprehensive metabolomic profile will be evaluated using mass spectrometry.

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

INTRODUCTION

Pancreatic cancer is the fourth most common cause of cancer mortalities in North America (1). The 5 year survival rate is only 5.1% (1). The only chance of potential longterm control of this devastating disease is resection (2). Unfortunately, early diagnosis is difficult. The symptoms most frequently associated with pancreatic cancer (weight loss, malaise, fatigue and pain) are vague and nonspecific. Jaundice is a later manifestation of the disease. This sign is also nonspecific, as it may be secondary to a benign biliary stricture or to gallstones. Early pancreatic cancer is invisible on routine radiographic studies. None of the radiographic findings of more advanced disease (including bile duct stricture and pancreatic mass) is pathognomonic (Figure 1). Benign lesions mimicking pancreatic cancer include pancreatitis and pancreatic cysts. Finally, obtaining a tissue diagnosis is extremely difficult. Bile duct brushings (in the event of a biliary stricture) only have a yield of 23 – 41% (3, 4). The diagnostic rate of EUS-guided biopsies for pancreatic masses is only about 71% (5). While their sensitivity is about 85%, negative predictive value is only about 64% (6). Therefore, negative biopsies are not particularly informative and do not aid in clinical decision-making (2).





There are several consequences to this inherent difficulty in obtaining a confident diagnosis in lesions mimicking pancreatic cancer. Firstly, 7 – 16% (and as high as 25%) of patients who undergo a Whipple procedure or a radical pancreatectomy are found on final pathology to have benign lesions (7-11). These operations are extensive procedures associated with a high morbidity and a mortality rate. In the US, the overall in-hospital mortality rate for pancreatic resections is 7.6% (12). Secondly, clinicians who encounter the non-specific signs associated with pancreatic cancer are often reluctant to refer the

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

patient for a surgical opinion because they would like to avoid the morbidity of surgery if the patient has benign disease. This conservative, expectant approach may cause a delay in treatment that can result in the loss of any opportunity for potentially curative surgery.

In recent surgical series, 20% of patients with pancreatic cancer have resectable disease (13), although it is difficult to discern how many of those patients are found to have unresectable disease because of delays in diagnosis. Our current inability to make a definitive and early diagnosis therefore has substantial impact on the outcomes of a significant proportion of patients.

To improve the diagnostic accuracy of pancreatic cancer, it will be important to identify diagnostic biomarkers. While protein biomarkers represent the main focus of many groups’ efforts, many proteins are undetectable in serum, due to their low abundance or due to interference by high abundance proteins. Metabolite levels may change even when protein levels do not. Metabolomics describes the “quantitative measurement of time-related multiparametric metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification” (14). The biomarkers of interest consist of metabolites, small molecules which are intermediates and products of metabolism, including molecules associated with energy storage and utilization;

precursors to proteins and carbohydrates; regulators of gene expression; and signaling molecules. Thus, like the proteome, the metabolome represents a functional portrait of the cell or the organism. Changes in metabolism result in alterations of the abundance of groups of metabolites. Therefore, identification of the patterns of changes in metabolites would provide insight on the functional changes that occur due to any given condition.

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

The metabolomic signature therefore represents a biomarker of considerable interest, albeit one that has been studied relatively little.

Recently, others have demonstrated the capability to distinguish malignant and non-malignant disease states based on the metabolomic profile of tissue and blood (15-17). Our intent was to determine whether the metabolomic signature in serum could be utilized to distinguish pancreatic cancer from benign hepatobiliary diseases.

MATERIALS AND METHODS

Sample Collection The study was approved by the Conjoint Health Research Ethics Board at the University of Calgary. Clinically annotated serum samples were collected from patients encountered through a hepatobiliary surgery practice. Samples were obtained from patients with known pancreatic adenocarcinoma; or benign pancreatic conditions (including benign masses and chronic pancreatitis). Additional controls consisted of patients undergoing elective cholecystectomy for biliary colic. Patients with any acute inflammation or sepsis were specifically excluded from this latter group. All patients provided consent to participate. Surgical pathology and follow-up data were available for all patients. This enabled more assured classification of patients to benign or malignant categories.

The collection and processing of samples was standardized as much as possible.

Samples were collected by venipuncture or through a central line (taking care to exclude

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Biosciences), which contains a clot activator and a gel for serum separation. Samples were stored at room temperature until processing. Samples were processed within 6 hours of collection, then frozen at -20oC until the time of analysis. All samples except two were collected from patients in the operating room, shortly after induction of general anesthesia, prior to any surgical manipulation. Two patients (one with pancreatic adenocarcinoma, one with benign disease) had samples collected under fasting conditions.

NMR Spectroscopy All experiments were performed on a Bruker Avance 600 spectrometer (Bruker Biospin, Milton, Canada) operating at 600.22 MHz and equipped with a 5 mm TXI probe at 298 K. All one-dimensional 1H NMR spectra of aqueous samples were acquired using a standard Bruker noesypr1d pulse sequence in which the residual water peak was irradiated during the relaxation delay of 1.0 s and during the mixing time of 100 ms. A total of 1024 scans were collected into 63536 data points over a spectral width of 12195 Hz with a 90˚ pulse width and a 5 s repetition time. A line broadening of 0.1Hz was applied to the spectra prior to Fourier transformation, phasing, and baseline correction.

Additional two-dimensional NMR experiments were performed for the purpose of confirming chemical shift assignments, including total correlation spectroscopy (2D 1HC TOCSY) and heteronuclear single quantum coherence spectroscopy (2D 1H-13C HSQC), using standard Bruker pulse programs.

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and spin-spin coupling constants with those of model compounds in Human Metabolome Database (HMDB) (18) and Chenomx NMR Suite 5.1 software (Chenomx Inc.,

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Downloaded from cebp.aacrjournals.org on October 16, 2016. © 2010 American Association for Cancer Research.

Author Manuscript Published OnlineFirst on November 23, 2010; DOI: 10.1158/1055-9965.EPI-10-0712 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Edmonton, Canada). Metabolites were quantified using the targeted profiling approach as implemented in the Chenomx software (19).

Data Analysis Preprocessing of the raw data was carried out for each experiment by normalizing the concentration of each metabolite to a total concentration of all metabolites in the sample.

Normalized metabolite concentrations were then log transformed, centered and scaled.

Data were examined for missing or influential values and for their symmetry. Descriptive statistics compared the measured clinical and patient features between the patients with benign disease and those with pancreatic cancer. Two-sample t-tests were used for continuous variables and exact methods for categorical ones. All tests were two-sided and statistical significance was declared for p-values less than 0.05 unless otherwise noted.

Selection of potentially important metabolites was carried out using two sample t-tests which assumed unequal variances. A p-value threshold of 0.3 was used to select metabolites for inclusion in the supervised orthogonal partial least squares discriminate analyses (OPLS-DA).

The metabolite concentration data matrix was analyzed by pattern recognition methods within SIMCA-P (Version 12.0, Umetrics, Umea, Sweden). A supervised OPLS-DA approach was chosen (20). This allows for a direct comparison of the variance between degree of disease status (benign or malignant; y variable) and metabolite concentrations (x variables). To calculate AUROC, specificity and sensitivity were determined based on sample class prediction during the seven-fold cross validation (Ypredcv, in the SIMCA-P software). Calculation of AUROC was performed using the GNU R ROCR package (21).

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