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Journal of Mammary Gland Biology and Neoplasia

, Volume 7, Issue 4, pp 433–440 | Cite as

Clinical Applications of Proteomics: Proteomic Pattern Diagnostics

  • Emanuel F. PetricoinEmail author
  • Cloud P. Paweletz
  • Lance A. Liotta
Article

Abstract

Clinical proteomics is an exciting new subdiscipline of proteomics that involves bedside application of proteomic technologies. A new and potentially revolutionary technology and approach for early disease detection, surveillance, and monitoring is proteomic pattern diagnostics. Using this approach, high throughput mass spectrometry generates a proteomic fingerprint of a given body fluid, such as serum or nipple fluid aspirants (NAF), in less than 30 s. This information archive is then used by new types of bioinformatic pattern recognition algorithms to identify patterns of protein changes that can discriminate cancer from healthy and unaffected individuals. This entire process can take place in less than a minute and requires only a droplet of blood, NAF, or ductal lavage washings. The new concept that is introduced by this platform is that the underlying identities of the proteins that comprise the patterns are not known and do not need to be known; the pattern itself becomes the diagnostic.

proteomics patterns diagnostics nipple fluid breast cancer mass spectrometry genetic algorithms 

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REFERENCES

  1. 1.
    E. F. Petricoin, K. C. Zoon, E. C. Kohn, J. C. Barrett, and L. A. Liotta (2002). Clinical proteomics: Translating benchside promise into bedside reality. Nat. Rev. Drug Disc. 1:683–695.Google Scholar
  2. 2.
    L. A. Liotta and E. C. Kohn (May 17, 2001). The microenvironment of the tumour- host interface. Nature 411(6835):375–379.Google Scholar
  3. 3.
    B. L. Adam, A. Vlahou, O. J. Semmes, and G. L. Wright, Jr. (Oct, 2001). Proteomic approaches to biomarker discovery in prostate and bladder cancers. Proteomics 1(10):1264–1270.Google Scholar
  4. 4.
    D. Carter, J. F. Douglass, C. D. Cornellison, M. W. Retter, J. C. Johnson, A. A. Bennington, T. P. Fleming, S. G. Reed, R. L. Houghton, D. L. Diamond, and T. S. Vedvick (May 28, 2002). Purification and characterization of the mammaglobin/ lipophilin B complex, a promising diagnostic marker for breast cancer. Biochemistry 41(21):6714–6722.Google Scholar
  5. 5.
    C. Rosty, L. Christa, S. Kuzdzal, W. M. Baldwin, M. L. Zahurak, F. Carnot, D. W. Chan, M. Canto, K. D. Lillemoe, J. L. Cameron, C. J. Yeo, R. H. Hruban, and M. Goggins (Mar 15, 2002). Identification of hepatocarcinoma-intestinepancreas/ pancreatitis-associated protein I as a biomarker for pancreatic ductal adenocarcinoma by protein biochip technology. Cancer Res 62(6):1868–1875.Google Scholar
  6. 6.
    Z. Xiao, B. L. Adam, L. H. Cazares, M. A. Davis, J. W. Clements, P. F. Schellhammer, E. A. Dalmasso, and G. L. Wright, Jr. (Aug 15, 2001). Quantitation of serum prostate-specific membrane antigen by a novel protein biochip immunoassay discriminates benign from malignant prostate disease. Cancer Res , 61(16):6029–6033.Google Scholar
  7. 7.
    J. H. Kim, S. J. Skates, T. Uede, K. K. Wong, J. O. Schorge, C. M. Feltmate, R. S. Berkowitz, D. W. Cramer, and S. C. Mok (Apr 3, 2002). Osteopontin as a potential diagnostic biomarker for ovarian cancer. JAMA 287(13):1671–1679.Google Scholar
  8. 8.
    J. R. Harris, M. E. Lippman, U. Veronesi, W. Willett (1992). Breast cancer. N. Engl. J. Med. 327:319–328.Google Scholar
  9. 9.
    J. G. Elmore, M. B. Barton, V. M. Moceri, S. Polk, P. J. Arena, and S. W. Fletcher (1998). Ten-year risk of false positive screening mammogramsand clinical breast examinations.N. Engl. J. Med. 338: 1089–1096.Google Scholar
  10. 10.
    B. Fisher (1992). The evolution of paradigms for the management of breast cancer: A personal perspective. Cancer Res. 52:2371–2383.Google Scholar
  11. 11.
    M. Swift (1994). Ionizing radiation, breast cancer, and ataxia telangiectasia. J. Natl. Cancer. Inst. 86:1571–1572.Google Scholar
  12. 12.
    S. K. Sharan, M. Morimatsu, U. Albrecht, et al. (1997). Embryonic lethality and radiation hypersensitivity mediated by Rad51 in mice lacking Brca2. Nature 386:804–810.Google Scholar
  13. 13.
    E. Evron, W. C. Dooley, C. B. Umbricht, D. Rosenthal, N. Sacchi, E. Gabrielson, A. B. Soito, D. T. Hung, B. M. Ljung, N. E. Davidson, and S. Sukumar (2001). Detection of breast cancer in ductal lavage fluid by methylation-specific PCR. Lancet 337:1336–1337.Google Scholar
  14. 14.
    M. Wrensch, N. L. Petrakis, et al. (1993) Breast cancer risk associated with abnormal cytology in nipple aspirates of breast fluid and prior history of breast biopsy. Am. J. Epidemiol. 137:829–833.Google Scholar
  15. 15.
    E. R. Sauter, E. Ross, M. Daley, et al. (1997) Nipple aspirate fluid: A promising noninvasive method to identify cellular markers of breast cancer risk. Br. J. Cancer 76:494–501.Google Scholar
  16. 16.
    Y. Liu, J. L. Wang, H. Chang, S. H. Barsky, and M. Nguyen (2000). Breast-cancer diagnosis with nipple fluid bFGF. Lancet 356:567.Google Scholar
  17. 17.
    Y. Zhao, J. V. Sigitas, N. Klar, N. L. Sadowsky, C. M. Kaelin, and B. Smith (2001). Nipple fluid carcinoembryonic antigen and prostate antigen in cancer bearing and tumor-free breasts. J. Clin. Oncol. 19:1462- 1467.Google Scholar
  18. 18.
    E. R. Sauter, M. Daly, K. Linahan, H. Ehya, P. F. Engstrem, G. Bonney, E. A. Ross, H. Yu, and E. Diamandis. (1996). Prostate specific antigen levels in nipple aspirate fluid correlate with breast cancer risk. Cancer Epidemiol. Biomarkers Prev. 5:967–970.Google Scholar
  19. 19.
    E. B. King, K. L. Chew, N. L. Petrakis, and V. L. Ernster (1983). Nipple apirate cytology for the study of breast cancer precursor. J. Natl. Cancer Inst. 71:1115–1121.Google Scholar
  20. 20.
    L. Foretova, J. Garber, N. Sadowsky, S. Verselis, D. Joseph, A. Andrade, P. Gudrais, D. Fairclough, and F. Li. (1998). Carcinoembryonic antigen in breast nipple spirate fluid. Cancer Epidemiol. Biomarkers Prev. 7:195–198.Google Scholar
  21. 21.
    B. L. Adam, A. Vlahou, O. J. Semmes, and G. L. Wright Jr. (October 2001). Proteomic approaches to biomarker discovery in prostate and bladder cancers. Proteomics. 1 (10):1264–1270.Google Scholar
  22. 22.
    H. J. Issaq, T. D. Veenstra, T. P. Conrads, and D. Felschow (2002). The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification. Biochem. Biophys. Res. Commun. 292:587–592.Google Scholar
  23. 23.
    L. H. Cazares, B. Adom, M. Word, S. Nasim, P. Schellhommer, O. Semmes, and G. Wright, Jr. (August, 2002). Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ ionization mass spectroscopy. Clin. Cancer Res. 8(8): 2541–2552.Google Scholar
  24. 24.
    G. L. Wright, L. H. Cazares, S. M. Leung, S. Nasim, B. L. Adam, T. T. Yip, P. F. Schellhammer, L. Gong, and A. Vlahou (1999). Proteinchip((R)) surface enhanced laser desorption/ionization (SELDI) mass spectrometry: A novel protein biochip technology for detection of prostate cancer biomarkers in complex protein mixtures. Prostate Cancer Prostatic Dis. 2:264–276.Google Scholar
  25. 25.
    C. P. Paweletz, J. W. Gillispie, D. K. Ornstein, N. L. Simone, M. R. Brown, K. A. Cole, Q. H. Wang, J. Huang, N. Hu, T. Yip, W. E. Rich, E. C. Kohn, L. W. Marston, T. Weber, P. Taylor, M. R. Emmert-Buck, L. A. Liotta, and E. F. Petricoin III (2000). Rapid protein display profiling of cancer progression directly from human tissue using a protein biochip. Drug Dev. Res. 49:34–42.Google Scholar
  26. 26.
    C. P. Paweletz, B. Trock, M. Pennanen, T. Tsangaris, L. A. Liotta, and E. F. Petricoin III (2001). Protein patterns of nipple aspirate fluids obtained by SELDI-TOF aid in the diagnosis of breast cancer. Dis. Markers. 17(4):301–307.Google Scholar
  27. 27.
    E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, Kohn EC, and L. A. Liotta (February 16, 2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 359(9306):572–577.Google Scholar
  28. 28.
    E. F. Petricoin III, G. B. Mills, E. S. Kohn, and L. Liotta (2002). Proteomic patterns in serum and identification of ovarian cancer. Lancet 360:170–171.Google Scholar
  29. 29.
    G. Ball, S. Mian, F. Holding, R. O. Allibone, J. Lowe, S. Ali, G. Li, S. McCardle, I. O. Ellis, C. Creaser, and R. C. Rees (March, 2002). An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics. 18(3):395–404.Google Scholar
  30. 30.
    J. Li, Z. Zhang, J. Rosenzweig, Y. Y. Wang, and D. W. Chan (August, 2002). Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin. Chem. 48(8):1296–1304.Google Scholar
  31. 31.
    B.-L. Adam, Y. Qu, J. W. Davis, M. D. Ward, M. A. Clements, L. H. Cazares, O. Semmes, P. Schellhammer, Y. Yasvi, Z. Feng, and G. Wright, Jr. (2002). Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 62:3609–3614.Google Scholar
  32. 32.
    E. F. Petricoin III, D. K. Ornstein, C. P. Paweletz, A. Ardekani, P. S. Hackett, B. A. Hitt, A. Velassco, C. Trucco, L. Wiegand, K. Wood, C. B. Simone, P. J. Levine, W. M. Linehan, M. R. Emmert-Buck, S. M. Steinberg, E. C. Kohn, and L. A. Liotta (October 16, 2002). Serum proteomic patterns for detection of prostate cancer. J. Natl. Cancer Inst. 94(20):1576–1578.Google Scholar
  33. 33.
    Y. Qu, B. L. Adam, Y. Yasui, M. D. Ward, L. H. Cazares, P. F. Schellhammer, Z. Feng, O. J. Semmes, and G. L. Wright Jr (October, 2002). Boosted decision tree analysis of surfaceenhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem. 48(10):1835–1843.Google Scholar
  34. 34.
    A. A. Alizadeh, M. B. Eisen, R. E. Davis, C. Ma, I. S. Lossos, A. Rosenwald, J. C. Boldrick, H. Sabet, T. Tran, X. Yu, J. I. Powell, L. Yang, G. E. Marti, T. Moore, J. Hudson, L. Lu, D. B. Lewis, R. Tibshirani, G. Sherlock, W. C. Chan, T. C. Greiner, D. D. Weisenburger, J. O. Armitage, R. Warnke, R. Levy, W. Wilson, M. R. Grever, J. C. Byrd, D. Botstein, P. O. Brown, and L. M. Staudt (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511.Google Scholar
  35. 35.
    T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286:531–537.Google Scholar
  36. 36.
    D. Lindahl, J. Palmer, and L. Edenbrandt (1999). Myocardial SPET: Artificial neural networks describe extent and severity of perfusion defects. Clin. Physiol. 19:497–503.Google Scholar
  37. 37.
    P. Lapuerta, G. J. L'Italien, S. Paul, R. C. Hendel, J. A. Leppo, and L. A. Fleisher (1998). Nerual network assessment of perioperative cardiac risk in vascular surgery patients. Med. Decis. Making 18:70–75.Google Scholar
  38. 38.
    J. H. Holland, (ed.) (1994). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, 3rd edn. MIT Press, Cambridge, MA.Google Scholar
  39. 39.
    T. Kohonen (1990). The self-organizing map. Proc. IEEE 78:1464—1480.Google Scholar
  40. 40.
    Kohonen. T. (1982). Self-organizing formation of topologically correct feature maps. Biol. Cybern. 43(1):59—69.Google Scholar
  41. 41.
    J. T. Tou and R. Gonzalez (eds.). Pattern classification by distance functions. In Pattern Recognition Principles, Addison Wesley, Reading, MA, pp. 75–109.Google Scholar
  42. 42.
    For full press release please go to: http://www.correlogic.com/ questlabcorp final.htmGoogle Scholar

Copyright information

© Plenum Publishing Corporation 2002

Authors and Affiliations

  • Emanuel F. Petricoin
    • 1
    Email author
  • Cloud P. Paweletz
    • 2
  • Lance A. Liotta
    • 2
  1. 1.FDA-NCI Clinical Proteomics Program, Division of Therapeutic ProteinsCenter for Biologic Evaluation and Research, Food and Drug AdministrationBethesda
  2. 2.FDA-NCI Clinical Proteomics Program, Laboratory of PathologyCenter for Cancer Research, National Cancer Institute, NIHBethesda

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