Selected reaction monitoring for colorectal cancer diagnosis using a set of five serum peptides identified by BLOTCHIP®-MS analysis

  • Kazuhiko Uchiyama
  • Yuji Naito
  • Nobuaki Yagi
  • Katsura Mizushima
  • Yasuki Higashimura
  • Yasuko Hirai
  • Osamu Dohi
  • Tetsuya Okayama
  • Naohisa Yoshida
  • Kazuhiro Katada
  • Kazuhiro Kamada
  • Osamu Handa
  • Takeshi Ishikawa
  • Tomohisa Takagi
  • Hideyuki Konishi
  • Daisuke Nonaka
  • Kyoichi Asada
  • Lyang-Ja Lee
  • Kenji Tanaka
  • Yoshiaki Kuriu
  • Masayoshi Nakanishi
  • Eigo Otsuji
  • Yoshito Itoh
Original Article—Alimentary Tract

Abstract

Background

Colorectal cancer (CRC) is one of the most predominant types of cancer, and it is the fourth most common cause of cancer-related death and it is important to diagnose CRC in early stage to decrease the mortality by CRC. In our previous study, we identified a combination of five peptides as a biomarker candidate to diagnose CRC by BLOTCHIP®-MS analysis using a set of healthy control subjects and CRC patients (stage II–IV). The aim of the present study was to validate the serum biomarker peptides reported in our previous study using a second cohort and to establish their potential usefulness in CRC diagnosis.

Methods

A total of 56 patients with CRC (n = 14 each of stages I–IV), 60 healthy controls, and 60 patients with colonic adenoma were included in this study. The five peptides were extracted and analyzed by selected reaction monitoring using ProtoKey® Colorectal Cancer Risk Test Kit (Protosera, Inc., Amagasaki, Japan).

Results

The results clearly showed that the four CRC groups, stages I–IV, could be sufficiently discriminated from the control group and colonic polyp group. This five-peptide set could identify CRC at each stage compared to the control population in this validation cohort, including those with early-stage disease. The AUC values for each stage of CRC compared to the control population were 0.779, 0.946, 0.852, and 0.973 for stages I, II, III, and IV, respectively.

Conclusions

In this case–control validation study, we confirmed high diagnostic performance for CRC using five peptides that were identified in our previous study as serum biomarker candidates for the detection of CRC.

Keywords

Colorectal cancer Biomarker Peptidome BLOTCHIP®-MS analysis 

Notes

Acknowledgements

This work was supported by Grants-in-Aid for Scientific Research (KAKENHI) (B) to Y.N. (No. 16H05289) from the Japan Society for the Promotion of Science (JSPS), and by an Adaptable and Seamless Technology Transfer Program through target-driven R&D (to Y.N.) from the Japan Agency for Medical Research and Development (AMED), a Grant-in-Aid for Scientific Research (KAKENHI) (C) to K.U. (No. 15K08313) from the Japan Society for the Promotion of Science (JSPS), a Grant-in-Aid for Scientific Research (KAKENHI) (C) to T.T. (No. 16K09322) from the Japan Society for the Promotion of Science (JSPS). Statistical analysis was assisted by Hajime Yamakage (Satista Co., Ltd.).

Author contributions

Designed the experiments and wrote the paper: YN, NA, and KU. Analyzed the data: YN, NA, DN, KA, L-J L, KT and KU. Sample collection: YN, NA, KU, TO, NY, KK, KK, OH, TI, TT, HK, YK, NM, EO, and YI. Manipulation of samples: KM, YH, and YH. Overall supervision: NA, YN, and YI. All authors read and approved the final manuscript.

References

  1. 1.
    Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86.CrossRefPubMedGoogle Scholar
  2. 2.
    Pourhoseingholi MA. Increased burden of colorectal cancer in Asia. World J Gastrointest Oncol. 2012;4(4):68–70 Epub 2012/04/26.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Etzioni R, Urban N, Ramsey S, et al. The case for early detection. Nat Rev Cancer. 2003;3(4):243–52 Epub 2003/04/03.CrossRefPubMedGoogle Scholar
  4. 4.
    Edwards BK, Ward E, Kohler BA, et al. Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116(3):544–73 Epub 2009/12/10.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Allison JE, Tekawa IS, Ransom LJ, et al. A comparison of fecal occult-blood tests for colorectal-cancer screening. N Engl J Med. 1996;334(3):155–9 Epub 1996/01/18.CrossRefPubMedGoogle Scholar
  6. 6.
    Morikawa T, Kato J, Yamaji Y, et al. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population. Gastroenterology. 2005;129(2):422–8 Epub 2005/08/09.CrossRefPubMedGoogle Scholar
  7. 7.
    Parra-Blanco A, Gimeno-Garcia AZ, Quintero E, et al. Diagnostic accuracy of immunochemical versus guaiac faecal occult blood tests for colorectal cancer screening. J Gastroenterol. 2010;45(7):703–12 Epub 2010/02/17.CrossRefPubMedGoogle Scholar
  8. 8.
    Smith RA, Cokkinides V, Brooks D, et al. Cancer screening in the United States, 2011: a review of current American Cancer Society guidelines and issues in cancer screening. CA Cancer J Clin. 2011;61(1):8–30 Epub 2011/01/06.CrossRefPubMedGoogle Scholar
  9. 9.
    Kuipers EJ, Rosch T, Bretthauer M. Colorectal cancer screening–optimizing current strategies and new directions. Nature Rev Clin Oncol. 2013;10(3):130–42 Epub 2013/02/06.CrossRefGoogle Scholar
  10. 10.
    Boja ES, Rodriguez H. Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics. 2012;12(8):1093–110 Epub 2012/05/12.CrossRefPubMedGoogle Scholar
  11. 11.
    Cadeco S, Williamson AJ, Whetton AD. The use of proteomics for systematic analysis of normal and transformed hematopoietic stem cells. Curr Pharm Des. 2012;18(13):1730–50 Epub 2012/03/09.CrossRefPubMedGoogle Scholar
  12. 12.
    Garay JP, Gray JW. Omics and therapy—a basis for precision medicine. Mol Oncol. 2012;6(2):128–39 Epub 2012/03/27.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Heckman-Stoddard BM. Oncology biomarkers: discovery, validation, and clinical use. Semin Oncol Nurs. 2012;28(2):93–8 Epub 2012/05/01.CrossRefPubMedGoogle Scholar
  14. 14.
    Pan C, He N, Zhao M, et al. Subdividing the M1 stage of liver metastasis for nasopharyngeal carcinoma to better predict metastatic survival. Med Oncol. 2011;28(4):1349–55 Epub 2010/09/08.CrossRefPubMedGoogle Scholar
  15. 15.
    Fan NJ, Gao CF, Wang XL. Identification of regional lymph node involvement of colorectal cancer by Serum SELDI proteomic patterns. Gastroenterol Res Pract. 2011;2011:784967 Epub 2012/01/19.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Fan NJ, Gao CF, Zhao G, et al. Serum peptidome patterns for early screening of esophageal squamous cell carcinoma. Biotechnol Appl Biochem. 2012;59(4):276–82 Epub 2013/04/17.CrossRefPubMedGoogle Scholar
  17. 17.
    Fan NJ, Gao CF, Zhao G, et al. Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis. Diag Pathol. 2012;7:45 Epub 2012/04/24.CrossRefGoogle Scholar
  18. 18.
    Deng BG, Yao JH, Liu QY, et al. Comparative serum proteomic analysis of serum diagnosis proteins of colorectal cancer based on magnetic bead separation and MALDI-TOF mass spectrometry. Asian Pac J Cancer Prev. 2013;14(10):6069–75 Epub 2013/12/03.CrossRefPubMedGoogle Scholar
  19. 19.
    Baumann S, Ceglarek U, Fiedler GM, et al. Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin Chem. 2005;51(6):973–80 Epub 2005/04/23.CrossRefPubMedGoogle Scholar
  20. 20.
    Shin S, Cazares L, Schneider H, et al. Serum biomarkers to differentiate benign and malignant mammographic lesions. J Am Coll Surg. 2007;204(5):1065–71 (discussion 71-3).CrossRefPubMedGoogle Scholar
  21. 21.
    West-Norager M, Kelstrup CD, Schou C, et al. Unravelling in vitro variables of major importance for the outcome of mass spectrometry-based serum proteomics. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;847(1):30–7 Epub 2006/11/23.CrossRefPubMedGoogle Scholar
  22. 22.
    Uchiyama K, Naito Y, Yagi N, et al. Peptidomic analysis via one-step direct transfer technology for colorectal cancer biomarker discovery. J Proteomics Bioinform. 2015;5:1.Google Scholar
  23. 23.
    Picotti P, Aebersold R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods. 2012;9(6):555–66 Epub 2012/06/07.CrossRefPubMedGoogle Scholar
  24. 24.
    World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Seoul: From the 59th World Medical Association Assembly [database on the internet]. 2008. http://www.wma.net/en/30publications/10policies/b3/17c.pdf.
  25. 25.
    Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2014.Google Scholar
  26. 26.
    Bendix Carstensen MP, Esa Laara, Michael Hills. Epi: A package for statistical analysis in epidemiology. R package version 1.1.67. 2015.Google Scholar
  27. 27.
    Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5 Epub 1950/01/01.CrossRefPubMedGoogle Scholar
  28. 28.
    Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77 Epub 2011/03/19.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Issaq HJ, Xiao Z, Veenstra TD. Serum and plasma proteomics. Chem Rev. 2007;107(8):3601–20 Epub 2007/07/20.CrossRefPubMedGoogle Scholar
  30. 30.
    Dai Y, Hu C, Wang L, et al. Serum peptidome patterns of human systemic lupus erythematosus based on magnetic bead separation and MALDI-TOF mass spectrometry analysis. Scand J Rheumatol. 2010;39(3):240–6 Epub 2010/02/20.CrossRefPubMedGoogle Scholar
  31. 31.
    Petricoin EF, Belluco C, Araujo RP, et al. The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Nat Rev Cancer. 2006;6(12):961–7 Epub 2006/11/10.CrossRefPubMedGoogle Scholar
  32. 32.
    Villanueva J, Martorella AJ, Lawlor K, et al. Serum peptidome patterns that distinguish metastatic thyroid carcinoma from cancer-free controls are unbiased by gender and age. Mol Cell Proteomics. 2006;5(10):1840–52 Epub 2006/08/10.CrossRefPubMedGoogle Scholar
  33. 33.
    Voortman J, Pham TV, Knol JC, et al. Prediction of outcome of non-small cell lung cancer patients treated with chemotherapy and bortezomib by time-course MALDI-TOF-MS serum peptide profiling. Proteome science. 2009;7:34 Epub 2009/09/05.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Gianazza E, Chinello C, Mainini V, et al. Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors. J Proteomics. 2012;76:125–40.CrossRefPubMedGoogle Scholar
  35. 35.
    Padoan A, Seraglia R, Basso D, et al. Usefulness of MALDI-TOF/MS identification of low-MW fragments in sera for the differential diagnosis of pancreatic cancer. Pancreas. 2013;42(4):622–32 Epub 2012/12/29.CrossRefPubMedGoogle Scholar
  36. 36.
    Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359(9306):572–7 Epub 2002/02/28.CrossRefPubMedGoogle Scholar
  37. 37.
    Chen YD, Zheng S, Yu JK, et al. Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res. 2004;10(24):8380–5 Epub 2004/12/30.CrossRefPubMedGoogle Scholar
  38. 38.
    Liu XP, Shen J, Li ZF, et al. A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry. Cancer Invest. 2006;24(8):747–53 Epub 2006/12/13.CrossRefPubMedGoogle Scholar
  39. 39.
    Peng Y, Li X, Wu M, et al. New prognosis biomarkers identified by dynamic proteomic analysis of colorectal cancer. Mol BioSyst. 2012;8(11):3077–88 Epub 2012/09/22.CrossRefPubMedGoogle Scholar
  40. 40.
    Tanaka K, Tsugawa N, Kim YO, et al. A new rapid and comprehensive peptidome analysis by one-step direct transfer technology for 1-D electrophoresis/MALDI mass spectrometry. Biochem Biophys Res Commun. 2009;379(1):110–4 Epub 2008/12/17.CrossRefPubMedGoogle Scholar

Copyright information

© Japanese Society of Gastroenterology 2018

Authors and Affiliations

  • Kazuhiko Uchiyama
    • 1
  • Yuji Naito
    • 1
    • 2
  • Nobuaki Yagi
    • 1
    • 3
  • Katsura Mizushima
    • 1
  • Yasuki Higashimura
    • 1
  • Yasuko Hirai
    • 1
  • Osamu Dohi
    • 1
  • Tetsuya Okayama
    • 1
  • Naohisa Yoshida
    • 1
  • Kazuhiro Katada
    • 1
  • Kazuhiro Kamada
    • 1
  • Osamu Handa
    • 1
  • Takeshi Ishikawa
    • 1
  • Tomohisa Takagi
    • 1
  • Hideyuki Konishi
    • 1
  • Daisuke Nonaka
    • 4
  • Kyoichi Asada
    • 4
  • Lyang-Ja Lee
    • 4
  • Kenji Tanaka
    • 4
  • Yoshiaki Kuriu
    • 5
  • Masayoshi Nakanishi
    • 5
  • Eigo Otsuji
    • 5
  • Yoshito Itoh
    • 1
  1. 1.Molecular Gastroenterology and HepatologyKyoto Prefectural University of MedicineKyotoJapan
  2. 2.Department of Endoscopy and Ultrasound MedicineKyoto Prefectural University of MedicineKyotoJapan
  3. 3.Department of Gastroenterology, Murakami Memorial HospitalAsahi UniversityGifuJapan
  4. 4.Membrane Protein and Ligand Analysis Center, Protosera Inc.AmagasakiJapan
  5. 5.Division of Digestive Surgery, Department of SurgeryKyoto Prefectural University of MedicineKyotoJapan

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