Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

BMC Cancer

Dayan B Goodenowe³  Thayer White²  Yasuyo Yamazaki³  Bassirou Chitou³  Dushmanthi Jayasinghe³  Tolulope T Sajobi⁵  Wei Jin³  Masaki Mori⁴  Yuichiro Doki⁴  Morito Monden⁴  Hiroaki Nagano⁴  Elodie Pastural¹  Hidetoshi Eguchi⁴  Ichiro Takemasa⁴  Hirofumi Akita⁴  Shawn A Ritchie³ 

[1]Current address: Pan-Provincial Vaccine Enterprise Inc. (PREVENT), Saskatoon, SK, Canada

[2]Glycozym, Inc., Beverly, MA, USA

[3]Phenomenome Discoveries, Inc., Saskatoon, SK, Canada

[4]Department of Surgery, Osaka University Graduate School of Medicine, Osaka, Japan

[5]Current address: Department of Community Health Sciences, Hotchkiss Brain Institute Clinical Research Unit & Institute for Public Health, University of Calgary, Calgary, AB, Canada

关键词: Mass spectrometry;    Early detection;    Screening;    Metabolomics;    Metabolism;    Biomarker;    Pancreatic cancer;   

Others  :  1079574
DOI  :  10.1186/1471-2407-13-416

 received in 2013-08-30, accepted in 2013-09-02,  发布年份 2013

【 摘 要 】

Background

The prognosis of pancreatic cancer (PC) is one of the poorest among all cancers, due largely to the lack of methods for screening and early detection. New biomarkers for identifying high-risk or early-stage subjects could significantly impact PC mortality. The goal of this study was to find metabolic biomarkers associated with PC by using a comprehensive metabolomics technology to compare serum profiles of PC patients to healthy control subjects.

Methods

A non-targeted metabolomics approach based on high-resolution, flow-injection Fourier transform ion cyclotron resonance mass spectrometry (FI-FTICR-MS) was used to generate comprehensive metabolomic profiles containing 2478 accurate mass measurements from the serum of Japanese PC patients (n=40) and disease-free subjects (n=50). Targeted flow-injection tandem mass spectrometry (FI-MS/MS) assays for specific metabolic systems were developed and used to validate the FI-FTICR-MS results. A FI-MS/MS assay for the most discriminating metabolite discovered by FI-FTICR-MS (PC-594) was further validated in two USA Caucasian populations; one comprised 14 PCs, six intraductal papillary mucinous neoplasims (IPMN) and 40 controls, and a second comprised 1000 reference subjects aged 30 to 80, which was used to create a distribution of PC-594 levels among the general population.

Results

FI-FTICR-MS metabolomic analysis showed significant reductions in the serum levels of metabolites belonging to five systems in PC patients compared to controls (all p<0.000025). The metabolic systems included 36-carbon ultra long-chain fatty acids, multiple choline-related systems including phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, as well as vinyl ether-containing plasmalogen ethanolamines. ROC-AUCs based on FI-MS/MS of selected markers from each system ranged between 0.93 ±0.03 and 0.97 ±0.02. No significant correlations between any of the systems and disease-stage, gender, or treatment were observed. Biomarker PC-594 (an ultra long-chain fatty acid), was further validated using an independently-collected US Caucasian population (blinded analysis, n=60, p=9.9E-14, AUC=0.97 ±0.02). PC-594 levels across 1000 reference subjects showed an inverse correlation with age, resulting in a drop in the AUC from 0.99 ±0.01 to 0.90 ±0.02 for subjects aged 30 to 80, respectively. A PC-594 test positivity rate of 5.0% in low-risk reference subjects resulted in a PC sensitivity of 87% and a significant improvement in net clinical benefit based on decision curve analysis.

Conclusions

The serum metabolome of PC patients is significantly altered. The utility of serum metabolite biomarkers, particularly PC-594, for identifying subjects with elevated risk of PC should be further investigated.

【 授权许可】

   
2013 Ritchie et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Klapman J, Malafa MP: Early detection of pancreatic cancer: why, who, and how to screen. Cancer Control 2008, 15(4):280-287.
• [2]Helmstaedter L, Riemann JF: Pancreatic cancer–EUS and early diagnosis. Langenbeck’s archives of surgery / Deutsche Gesellschaft für Chirurgie 2008, 393(6):923-927.
• [3]Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ: Cancer statistics, 2009. CA Cancer J Clin 2009, 59(4):225-249.
• [4]Ritchie SA, Tonita J, Alvi R, Lehotay D, Elshoni H, Myat S, McHattie J, Goodenowe DB: Low-serum GTA-446 anti-inflammatory fatty acid levels as a new risk factor for colon cancer. Int J Cancer 2013, 132:355-362.
• [5]Hoffe S, Balducci L: Cancer and age: general considerations. Clin Geriatr Med 2012, 28(1):1-18.
• [6]Parkin DM: 1. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer 2011, 105(Suppl 2):1-4.
• [7]Irigaray P, Newby JA, Clapp R, Hardell L, Howard V, Montagnier L, Epstein S, Belpomme D: Lifestyle-related factors and environmental agents causing cancer: an overview. Biomed Pharmacother 2007, 61(10):640-658.
• [8]Willett WC: Diet and cancer: one view at the start of the millennium. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2001, 10(1):3-8.
• [9]Prasad S, Sung B, Aggarwal BB: Age-associated chronic diseases require age-old medicine: role of chronic inflammation. Prev Med 2012, 54:S29-S37.
• [10]Dítě P, Hermanová M, Trna J, Novotný I, Růžička M, Liberda M, Bártková A: The role of chronic inflammation: chronic pancreatitis as a risk factor of pancreatic cancer. Digestive diseases (Basel, Switzerland) 2012, 30(3):277-283.
• [11]Aharoni A, Vos CR, Verhoeven H, Maliepaard C, Kruppa G, Bino RJ, Goodenowe D: Nontargeted metabolome analysis by use of Fourier Transform Ion Cyclotron Mass Spectrometry. Omics 2002, 6(3):217-234.
• [12]Dettmer K, Aronov P, Hammock B: Mass spectrometry-based metabolomics. Mass Spectrom Rev 2007, 26(1):51-78.
• [13]Spratlin J, Serkova N, Eckhardt S: Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 2009, 15(2):431-440.
• [14]Malhotra RK, Indrayan A: A simple nomogram for sample size for estimating sensitivity and specificity of medical tests. Indian J ophthalmology 2010, 58(6):519-522.
• [15]Ritchie S, Ahiahonu P, Jayasinghe D, Heath D, Liu J, Lu Y, Jin W, Kavianpour A, Yamazaki Y, Khan A, et al.: Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection. BMC medicine 2010, 8:13. BioMed Central Full Text
• [16]Goodenowe D, Cook L, Liu J, Lu Y, Jayasinghe D, Ahiahonu P, Heath D, Yamazaki Y, Flax J, Krenitsky K, et al.: Peripheral ethanolamine plasmalogen deficiency: a logical causative factor in Alzheimer’s disease and dementia. J Lipid Res 2007, 48(11):2485-2498.
• [17]Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res 2001, 125(1–2):279-284.
• [18]Vickers AJ, Cronin AM, Elkin EB, Gonen M: Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMCMed Informatics Decis making 2008, 8:53. BioMed Central Full Text
• [19]Vickers AJ, Elkin EB: Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006, 26(6):565-574.
• [20]Cureton EE, D’Agostino RB: Factor analysis: An applied approach. Hillsdale, NJ: Erlbaum; 1983.
• [21]Kim J-O, Mueller CW: Factor analysis, statistical methods and practical issues. (Quantitative applications in the social sciences.). Beverly Hills: Sage; 1978.
• [22]Breiman L: Random forests. Mach Learn 2001, 45:5-32.
• [23]Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD: Validity of prognostic models: when is a model clinically useful? Semin Urol Oncol 2002, 20(2):96-107.
• [24]Rousson V, Zumbrunn T: Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case–control studies. BMC Med Informatics and Decis Making 2011, 11:45. BioMed Central Full Text
• [25]Djulbegovic B, Desoky AH: Equation and nomogram for calculation of testing and treatment thresholds. Med Decis Making 1996, 16(2):198-199.
• [26]Hata S, Sakamoto Y, Yamamoto Y, Nara S, Esaki M, Shimada K, Kosuge T: Prognostic impact of postoperative serum CA 19–9 levels in patients with resectable pancreatic cancer. Annals of surgical oncology 2012, 19(2):636-641.
• [27]Humphris JL, Chang DK, Johns AL, Scarlett CJ, Pajic M, Jones MD, Colvin EK, Nagrial A, Chin VT, Chantrill LA, et al.: The prognostic and predictive value of serum CA19.9 in pancreatic cancer. Ann Oncol 2012, 23(7):1713-1722.
• [28]Kim JE, Lee KT, Lee JK, Paik SW, Rhee JC, Choi KW: Clinical usefulness of carbohydrate antigen 19–9 as a screening test for pancreatic cancer in an asymptomatic population. J Gastroenterol Hepatol 2004, 19(2):182-186.
• [29]Zubarik R, Gordon SR, Lidofsky SD, Anderson SR, Pipas JM, Badger G, Ganguly E, Vecchio J: Screening for pancreatic cancer in a high-risk population with serum CA 19–9 and targeted EUS: a feasibility study. Gastrointestinal endoscopy 2011, 74(1):87-95.
• [30]Singh S, Tang S, Sreenarasimhaiah J, Lara LF, Siddiqui A: The clinical utility and limitations of serum carbohydrate antigen (CA19-9) as a diagnostic tool for pancreatic cancer and cholangiocarcinoma. Dig Dis Sci 2011, 56(8):2491-2496.
• [31]Locker G, Hamilton S, Harris J, Jessup J, Kemeny N, Macdonald J, Somerfield M, Hayes D, Bast R: ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol 2006, 24(33):5313-5327.
• [32]Pastural E, Ritchie S, Lu Y, Jin W, Kavianpour A, Khine Su-Myat K, Heath D, Wood PL, Fisk M, Goodenowe DB: Novel plasma phospholipid biomarkers of autism: mitochondrial dysfunction as a putative causative mechanism. Prostaglandins Leukot Essent Fatty Acids 2009, 81(4):253-264.
• [33]Sohn TA, Yeo CJ, Cameron JL, Iacobuzio-Donahue CA, Hruban RH, Lillemoe KD: Intraductal papillary mucinous neoplasms of the pancreas: an increasingly recognized clinicopathologic entity. Ann Surg 2001, 234(3):313-321. discussion 321–312
• [34]Ritchie S, Heath D, Yamazaki Y, Grimmalt B, Kavianpour A, Krenitsky K, Elshoni H, Takemasa I, Miyake M, Sekimoto M, et al.: Reduction of novel circulating long-chain fatty acids in colorectal cancer patients is independent of tumor burden and correlates with age. BMC gastroenterology 2010, 10:140. BioMed Central Full Text
• [35]Raimondi S, Lowenfels AB, Morselli-Labate AM, Maisonneuve P, Pezzilli R: Pancreatic cancer in chronic pancreatitis; aetiology, incidence, and early detection. Best Pract Res Clin Gastroenterol 2010, 24(3):349-358.
• [36]Tonack S, Jenkinson C, Cox T, Elliott V, Jenkins RE, Kitteringham NR, Greenhalf W, Shaw V, Michalski CW, Friess H, et al.: iTRAQ reveals candidate pancreatic cancer serum biomarkers: influence of obstructive jaundice on their performance. Br J Cancer 2013, 108(9):1846-1853.
• [37]Ritchie SA, Jayasinghe D, Davies GF, Ahiahonu P, Ma H, Goodenowe DB: Human serum-derived hydroxy long-chain fatty acids exhibit anti-inflammatory and anti-proliferative activity. J Exp Clin Cancer Res 2011, 30(1):59. BioMed Central Full Text
• [38]Serhan C: Novel eicosanoid and docosanoid mediators: resolvins, docosatrienes, and neuroprotectins. Curr Opin Clin Nutr Metab Care 2005, 8(2):115-121.
• [39]Yao X, Zeng M, Wang H, Fei S, Rao S, Ji Y: Metabolite detection of pancreatic carcinoma by in vivo proton MR spectroscopy at 3T: initial results. La Radiologia medica 2012, 117(5):78-788.
• [40]Yamada T, Okajima F, Ohwada S, Kondo Y: Growth inhibition of human pancreatic cancer cells by sphingosylphosphorylcholine and influence of culture conditions. Cell Mol Life Sci 1997, 53(5):435-441.
• [41]Afrasiabi E, Blom T, Balthasar S, Tornquist K: Antiproliferative effect of sphingosylphosphorylcholine in thyroid FRO cancer cells mediated by cell cycle arrest in the G2/M phase. Mol Cell Endocrinol 2007, 274(1–2):43-52.
• [42]Fang F, He X, Deng H, Chen Q, Lu J, Spraul M, Yu Y: Discrimination of metabolic profiles of pancreatic cancer from chronic pancreatitis by high-resolution magic angle spinning 1H nuclear magnetic resonance and principal components analysis. Cancer Sci 2007, 98(11):1678-1682.
• [43]Longnecker DS, Chandar N, Sheahan DG, Janosky JE, Lombardi B: Preneoplastic and neoplastic lesions in the pancreas of rats fed choline-devoid or choline-supplemented diets. Toxicol Pathol 1991, 19(1):59-65.
• [44]Ilcol YO, Gurun MS, Taga Y, Ulus IH: Choline increases serum insulin in rat when injected intraperitoneally and augments basal and stimulated aceylcholine release from the rat minced pancreas in vitro. Eur J Biochem 2003, 270(5):991-999.
• [45]Terés S, Lladó V, Higuera M, Barceló-Coblijn G, Martin ML, Noguera-Salvà MA, Marcilla-Etxenike A, García-Verdugo JM, Soriano-Navarro M, Saus C, et al.: Normalization of sphingomyelin levels by 2-hydroxyoleic acid induces autophagic cell death of SF767 cancer cells. Autophagy 2012, 8(10):1542-1544.
• [46]Skaff O, Pattison D, Davies M: The vinyl ether linkages of plasmalogens are favored targets for myeloperoxidase-derived oxidants: a kinetic study. Biochemistry 2008, 47(31):8237-8245.
• [47]Labadaridis I, Moraitou M, Theodoraki M, Triantafyllidis G, Sarafidou J, Michelakakis H: Plasmalogen levels in full-term neonates. Acta Paediatr 2009, 98(4):640-642.
• [48]Singh H, Beckman K, Poulos A: Exclusive localization in peroxisomes of dihydroxyacetone phosphate acyltransferase and alkyl-dihydroxyacetone phosphate synthase in rat liver. J Lipid Res 1993, 34(3):467-477.
• [49]Brites P, Waterham H, Wanders R: Functions and biosynthesis of plasmalogens in health and disease. Biochim Biophys Acta 2004, 1636(2–3):219-231.
• [50]Nagan N, Zoeller R: Plasmalogens: biosynthesis and functions. Prog Lipid Res 2001, 40(3):199-229.
• [51]Merchant T, Center NY, Oncology D, Minsky B, Lauwers G, Pathology D, Diamantis P, Haida T, Glonek T, Laboratory MR: Esophageal cancer phospholipids correlated with histopathologic findings: a 31P NMR study. NMR Biomed 1999, 12(4):1-5.
• [52]Smith R, Lespi P, Luca M, Bustos C, Marra F, Alaniz M, Marra C: A reliable biomarker derived from plasmalogens to evaluate malignancy and metastatic capacity of human cancers. Lipids 2008, 43(1):79-89.
• [53]Bittman R, Qin D, Wong D, Tigyi G, Samadder P, Arthur G: Synthesis and antitumor properties of a plasmalogen methyl ether analogue. Tetrahedron 2001, 57(20):4277-4282.
• [54]Canadian Cancer Statistics 2011. Published by the Canadian Cancer Society; http://www.cancer.ca/en/cancer-information/cancer-101/canadian-cancer-statistics-publication webcite