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Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development

Abstract

Most patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed with advanced disease and survive less than 12 months1. PDAC has been linked with obesity and glucose intolerance2,3,4, but whether changes in circulating metabolites are associated with early cancer progression is unknown. To better understand metabolic derangements associated with early disease, we profiled metabolites in prediagnostic plasma from individuals with pancreatic cancer (cases) and matched controls from four prospective cohort studies. We find that elevated plasma levels of branched-chain amino acids (BCAAs) are associated with a greater than twofold increased risk of future pancreatic cancer diagnosis. This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years before diagnosis, when occult disease is probably present. We show that plasma BCAAs are also elevated in mice with early-stage pancreatic cancers driven by mutant Kras expression but not in mice with Kras-driven tumors in other tissues, and that breakdown of tissue protein accounts for the increase in plasma BCAAs that accompanies early-stage disease. Together, these findings suggest that increased whole-body protein breakdown is an early event in development of PDAC.

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Figure 1: Plasma metabolites and risk of future pancreatic cancer diagnosis.
Figure 2: Plasma BCAA levels are elevated during subclinical disease.
Figure 3: BCAA elevations are derived from a long-term pool of amino acids.

References

  1. Vincent, A., Herman, J., Schulick, R., Hruban, R.H. & Goggins, M. Pancreatic cancer. Lancet 378, 607–620 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Michaud, D.S. et al. Physical activity, obesity, height, and the risk of pancreatic cancer. J. Am. Med. Assoc. 286, 921–929 (2001).

    Article  CAS  Google Scholar 

  3. Stolzenberg-Solomon, R.Z. et al. Insulin, glucose, insulin resistance, and pancreatic cancer in male smokers. J. Am. Med. Assoc. 294, 2872–2878 (2005).

    Article  CAS  Google Scholar 

  4. Wolpin, B.M. et al. Hyperglycemia, insulin resistance, impaired pancreatic beta-cell function, and risk of pancreatic cancer. J. Natl. Cancer Inst. 105, 1027–1035 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lynch, S.M. et al. Cigarette smoking and pancreatic cancer: a pooled analysis from the pancreatic cancer cohort consortium. Am. J. Epidemiol. 170, 403–413 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Klein, A.P. Genetic susceptibility to pancreatic cancer. Mol. Carcinog. 51, 14–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tan, B.H., Birdsell, L.A., Martin, L., Baracos, V.E. & Fearon, K.C. Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clin. Cancer Res. 15, 6973–6979 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Claudino, W.M., Goncalves, P.H., di Leo, A., Philip, P.A. & Sarkar, F.H. Metabolomics in cancer: a bench-to-bedside intersection. Crit. Rev. Oncol. Hematol. 84, 1–7 (2012).

    Article  PubMed  Google Scholar 

  9. Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kobayashi, T. et al. A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiol. Biomarkers Prev. 22, 571–579 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Wang, T.J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Brosnan, J.T. & Brosnan, M.E. Branched-chain amino acids: enzyme and substrate regulation. J. Nutr. 136, 207S–211S (2006).

    Article  CAS  PubMed  Google Scholar 

  13. Newgard, C.B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Floegel, A. et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62, 639–648 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Huxley, R., Ansary-Moghaddam, A., Berrington de Gonzalez, A., Barzi, F. & Woodward, M. Type-II diabetes and pancreatic cancer: a meta-analysis of 36 studies. Br. J. Cancer 92, 2076–2083 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hanley, J.A. & McNeil, B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982).

    Article  CAS  PubMed  Google Scholar 

  17. Pencina, M.J., D'Agostino, R.B., Sr., D'Agostino, R.B., Jr. & Vasan, R.S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157–172 discussion 207–112 (2008).

    Article  PubMed  Google Scholar 

  18. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hingorani, S.R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Bardeesy, N. et al. Both p16Ink4a and the p19Arf-p53 pathway constrain progression of pancreatic adenocarcinoma in the mouse. Proc. Natl. Acad. Sci. USA 103, 5947–5952 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jackson, E.L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. DuPage, M., Dooley, A.L. & Jacks, T. Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat. Protoc. 4, 1064–1072 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kirsch, D.G. et al. A spatially and temporally restricted mouse model of soft tissue sarcoma. Nat. Med. 13, 992–997 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Duell, E.J. et al. Pancreatitis and pancreatic cancer risk: a pooled analysis in the International Pancreatic Cancer Case-Control Consortium (PanC4). Ann. Oncol. 23, 2964–2970 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Guerra, C. et al. Chronic pancreatitis is essential for induction of pancreatic ductal adenocarcinoma by K-Ras oncogenes in adult mice. Cancer Cell 11, 291–302 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Gidekel Friedlander, S.Y. et al. Context-dependent transformation of adult pancreatic cells by oncogenic K-Ras. Cancer Cell 16, 379–389 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Brosnan, J.T. Interorgan amino acid transport and its regulation. J. Nutr. 133, 2068S–2072S (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Matthews, D.E., Marano, M.A. & Campbell, R.G. Splanchnic bed utilization of leucine and phenylalanine in humans. Am. J. Physiol. 264, E109–E118 (1993).

    Article  CAS  PubMed  Google Scholar 

  29. Cynober, L.A. Plasma amino acid levels with a note on membrane transport: characteristics, regulation, and metabolic significance. Nutrition 18, 761–766 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Acharyya, S. et al. Cancer cachexia is regulated by selective targeting of skeletal muscle gene products. J. Clin. Invest. 114, 370–378 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fearon, K.C., Glass, D.J. & Guttridge, D.C. Cancer cachexia: mediators, signaling, and metabolic pathways. Cell Metab. 16, 153–166 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Lecker, S.H. et al. Multiple types of skeletal muscle atrophy involve a common program of changes in gene expression. FASEB J. 18, 39–51 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. Lundholm, K., Bylund, A.C., Holm, J. & Schersten, T. Skeletal muscle metabolism in patients with malignant tumor. Eur. J. Cancer 12, 465–473 (1976).

    Article  CAS  PubMed  Google Scholar 

  34. Heymsfield, S.B. & McManus, C.B. Tissue components of weight loss in cancer patients. A new method of study and preliminary observations. Cancer 55, 238–249 (1985).

    Article  CAS  PubMed  Google Scholar 

  35. Dewys, W.D. et al. Prognostic effect of weight loss prior to chemotherapy in cancer patients. Eastern Cooperative Oncology Group. Am. J. Med. 69, 491–497 (1980).

    Article  CAS  PubMed  Google Scholar 

  36. Wigmore, S.J., Plester, C.E., Richardson, R.A. & Fearon, K.C. Changes in nutritional status associated with unresectable pancreatic cancer. Br. J. Cancer 75, 106–109 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tsoli, M. & Robertson, G. Cancer cachexia: malignant inflammation, tumorkines, and metabolic mayhem. Trends Endocrinol. Metab. 24, 174–183 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Son, J. et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature 496, 101–105 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature 497, 633–637 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harper, A.E., Miller, R.H. & Block, K.P. Branched-chain amino acid metabolism. Annu. Rev. Nutr. 4, 409–454 (1984).

    Article  CAS  PubMed  Google Scholar 

  41. Shah, S.H., Kraus, W.E. & Newgard, C.B. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126, 1110–1120 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Pannala, R., Basu, A., Petersen, G.M. & Chari, S.T. New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer. Lancet Oncol. 10, 88–95 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rich-Edwards, J.W., Corsano, K.A. & Stampfer, M.J. Test of the National Death Index and Equifax Nationwide Death Search. Am. J. Epidemiol. 140, 1016–1019 (1994).

    Article  CAS  PubMed  Google Scholar 

  44. Michaud, D.S. et al. Prediagnostic plasma C-peptide and pancreatic cancer risk in men and women. Cancer Epidemiol. Biomarkers Prev. 16, 2101–2109 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Wolpin, B.M. et al. Plasma 25-hydroxyvitamin D and risk of pancreatic cancer. Cancer Epidemiol. Biomarkers Prev. 21, 82–91 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Bao, Y. et al. A prospective study of plasma adiponectin and pancreatic cancer risk in five US cohorts. J. Natl. Cancer Inst. 105, 95–103 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Pannala, R. et al. Temporal association of changes in fasting blood glucose and body mass index with diagnosis of pancreatic cancer. Am. J. Gastroenterol. 104, 2318–2325 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Roberts, L.D., Souza, A.L., Gerszten, R.E. & Clish, C.B. Targeted metabolomics. Curr. Protoc. Mol. Biol. 98, 30.2 (2012).

    Article  Google Scholar 

  49. Townsend, M.K. et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin. Chem. 59, 1657–1667 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Bland, J.M. & Altman, D.G. Multiple significance tests: the Bonferroni method. BMJ 310, 170 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cochran, W.G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).

    Article  Google Scholar 

  52. Wacholder, S. et al. Performance of common genetic variants in breast-cancer risk models. N. Engl. J. Med. 362, 986–993 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. DeLong, E.R., DeLong, D.M. & Clarke-Pearson, D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988).

    Article  CAS  PubMed  Google Scholar 

  54. Gail, M.H. et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J. Natl. Cancer Inst. 81, 1879–1886 (1989).

    Article  CAS  PubMed  Google Scholar 

  55. Gail, M.H. & Benichou, J. Validation studies on a model for breast cancer risk. J. Natl. Cancer Inst. 86, 573–575 (1994).

    Article  CAS  PubMed  Google Scholar 

  56. Saslow, D. et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J. Clin. 57, 75–89 (2007).

    Article  PubMed  Google Scholar 

  57. Gail, M.H. et al. Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J. Natl. Cancer Inst. 91, 1829–1846 (1999).

    Article  CAS  PubMed  Google Scholar 

  58. Costantino, J.P. et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J. Natl. Cancer Inst. 91, 1541–1548 (1999).

    Article  CAS  PubMed  Google Scholar 

  59. Tice, J.A. et al. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann. Intern. Med. 148, 337–347 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Barlow, W.E. et al. Prospective breast cancer risk prediction model for women undergoing screening mammography. J. Natl. Cancer Inst. 98, 1204–1214 (2006).

    Article  PubMed  Google Scholar 

  61. Rockhill, B., Spiegelman, D., Byrne, C., Hunter, D.J. & Colditz, G.A. Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J. Natl. Cancer Inst. 93, 358–366 (2001).

    Article  CAS  PubMed  Google Scholar 

  62. Chen, J. et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J. Natl. Cancer Inst. 98, 1215–1226 (2006).

    Article  PubMed  Google Scholar 

  63. Cook, N.R. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115, 928–935 (2007).

    Article  PubMed  Google Scholar 

  64. Pepe, M.S., Janes, H., Longton, G., Leisenring, W. & Newcomb, P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am. J. Epidemiol. 159, 882–890 (2004).

    Article  PubMed  Google Scholar 

  65. Cook, N.R., Buring, J.E. & Ridker, P.M. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann. Intern. Med. 145, 21–29 (2006).

    Article  CAS  PubMed  Google Scholar 

  66. Ridker, P.M., Buring, J.E., Rifai, N. & Cook, N.R. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. J. Am. Med. Assoc. 297, 611–619 (2007).

    Article  CAS  Google Scholar 

  67. Roberts, N.J. et al. The predictive capacity of personal genome sequencing. Sci. Transl. Med. 4, 133ra158 (2012).

    Article  Google Scholar 

  68. van Ravesteyn, N.T. et al. Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk. Ann. Intern. Med. 156, 609–617 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Chatterjee, N. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400–405 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Danesh, J. et al. The Emerging Risk Factors Collaboration. analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur. J. Epidemiol. 22, 839–869 (2007).

    Article  CAS  PubMed  Google Scholar 

  71. Ridker, P.M., Hennekens, C.H., Buring, J.E. & Rifai, N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N. Engl. J. Med. 342, 836–843 (2000).

    Article  CAS  PubMed  Google Scholar 

  72. Ridker, P.M. Clinical application of C-reactive protein for cardiovascular disease detection and prevention. Circulation 107, 363–369 (2003).

    Article  PubMed  Google Scholar 

  73. Pradhan, A.D. et al. Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: prospective analysis from the Women's Health Initiative observational study. J. Am. Med. Assoc. 288, 980–987 (2002).

    Article  CAS  Google Scholar 

  74. Ridker, P.M. et al. C-reactive protein levels and outcomes after statin therapy. N. Engl. J. Med. 352, 20–28 (2005).

    Article  CAS  PubMed  Google Scholar 

  75. Greenland, P. et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 56, e50–e103 (2010).

    Article  PubMed  Google Scholar 

  76. Ridker, P.M. et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008).

    Article  CAS  PubMed  Google Scholar 

  77. Ridker, P.M. et al. Reduction in C-reactive protein and LDL cholesterol and cardiovascular event rates after initiation of rosuvastatin: a prospective study of the JUPITER trial. Lancet 373, 1175–1182 (2009).

    Article  CAS  PubMed  Google Scholar 

  78. Kaptoge, S. et al. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N. Engl. J. Med. 367, 1310–1320 (2012).

    Article  PubMed  Google Scholar 

  79. Wormser, D. et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet 377, 1085–1095 (2011).

    Article  PubMed  Google Scholar 

  80. Di Angelantonio, E. et al. Lipid-related markers and cardiovascular disease prediction. J. Am. Med. Assoc. 307, 2499–2506 (2012).

    CAS  Google Scholar 

  81. Ayala, J.E. et al. Standard operating procedures for describing and performing metabolic tests of glucose homeostasis in mice. Dis. Model. Mech. 3, 525–534 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Clasquin, M.F., Melamud, E. & Rabinowitz, J.D. LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr. Protoc. Bioinformatics 37, 14.11 (2012).

    Google Scholar 

  83. Antoniewicz, M.R. et al. Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab. Eng. 9, 277–292 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Fernandez, C.A., Des Rosiers, C., Previs, S.F., David, F. & Brunengraber, H. Correction of 13C mass isotopomer distributions for natural stable isotope abundance. J. Mass Spectrom. 31, 255–262 (1996).

    Article  CAS  PubMed  Google Scholar 

  85. Andrikopoulos, S., Blair, A.R., Deluca, N., Fam, B.C. & Proietto, J. Evaluating the glucose tolerance test in mice. Am. J. Physiol. Endocrinol. Metab. 295, E1323–E1332 (2008).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We would like to acknowledge A. Deik and K. Bullock of the Broad Institute for assistance with LC-MS sample analyses and the Tang Histology facility in the Koch Institute Swanson Biotechnology Center for assistance processing mouse tissues. Cambridge Isotope Laboratories supplied 13C-BCAA diets for mouse labeling studies. We would like to thank C. Newgard and A. Goldberg for their thoughtful discussions regarding this manuscript. We would also like to thank the participants and staff of the Health Professionals Follow-Up Study (HPFS), Nurses' Health Study (NHS), Physicians' Health Study I (PHS) and Women's Health Initiative-Observational Study (WHI-OS) for their contributions as well as the cancer registries of the following states for their help: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Indiana, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Washington and Wyoming. NHS and HPFS are supported by US National Institutes of Health (NIH) grants P01 CA87969, P01 CA55075, P50 CA127003, R01 CA124908, R01 CA49449 and 1UM1 CA167552. PHS is supported by NIH grants CA 97193, CA 34944, CA 40360, HL 26490 and HL 34595. The WHI program is funded by the NIH through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C and HHSN271201100004C. We acknowledge additional support from grant F30 CA183474 to J.R.M.; from a Nestle Research Center award to the Broad Institute; from R01 DK081572 grant to T.J.W. and R.E.G.; from the Robert T. and Judith B. Hale Fund for Pancreatic Cancer, Perry S. Levy Fund for Gastrointestinal Cancer Research and Pappas Family Research Fund for Pancreatic Cancer to C.S.F.; from the Burroughs Wellcome Fund, Damon Runyon Cancer Research Foundation, the Smith Family and the Stern Family to M.G.V.H.; and from NIH/NCI grant K07 CA140790, the American Society of Clinical Oncology Conquer Cancer Foundation, the Howard Hughes Medical Institute and Promises for Purple to B.M.W. M.G.V.H. additionally acknowledges support from P30-CA14051 and P01-CA117969, and major support for this project was provided by the Howard Hughes Medical Institute to B.M.W. and the Lustgarten Foundation to C.S.F., M.G.V.H. and B.M.W.

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All authors participated in the analysis and interpretation of data. C.W., C.B.C., P.K., C.Y., Y.B., M.K.T. and B.M.W. performed the statistical analyses of the human data. C.B.C., M.K.T., S.S.T., T.J.W., R.E.G. and B.M.W. evaluated the platform for analysis of plasma metabolites in large cohort studies. C.B.C., J.R.M., A.S. and K.P. performed metabolite profiling experiments. P.K., Y.B., M.K.T., S.S.T., S.O., M.J.S., E.L.G., Z.R.Q., D.A.R., J.M., H.D.S., J.M.G., B.B.C., S.L., J.W.-W., J.E.M., M.N.P. and C.S.F. assisted in data acquisition, management and interpretation from the four cohort studies. J.R.M. and M.E.T. conducted all mouse experiments with assistance from B.P.F., S.M.D., T.P., A.Y., T.L.D. and A.C.K. C.W., C.B.C., J.R.M., C.S.F., M.G.V.H. and B.M.W. designed the study and drafted the manuscript with input from all authors.

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Correspondence to Matthew G Vander Heiden or Brian M Wolpin.

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Mayers, J., Wu, C., Clish, C. et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med 20, 1193–1198 (2014). https://doi.org/10.1038/nm.3686

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