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Application of sector protein microarrays to clinical samples

Clinical Proteomics volume 1, pages 91–99 (2004)Cite this article

Abstract

Many protein functions are conferred by posttranslational modifications, which allow proteins to perform specific cellular tasks. Protein microarrays enable specific detection of posttranslational modifications not attainable by gene arrays. Reverse-phase protein microarrays have been widely adopted for use with clinical biopsy specimens because they have many advantages including highly reproducible printing of cellular lysates onto array surfaces, buit-in dilution curves, and direct detection using one antibody per analyte. This results in high-sensitivity, broad dynamic range, and favorable precision. Reverse-phase arrays have been restricted to a one slide/one antibody format. Although this is suitable for analyzing treatment effects over populations of samples, it is not well suited to individual patient assessments. One means of reaching this goal is the sector array format. Through the sector array, multiple antibody probes can be multiplexed on a single slide containing replicate immobilized aliquots from one patient. Thus, on one slide, a complete set of analytes can be characterized and used to support a therapy decision. This article describes a method for constructing sector arrays and demonstrates feasibility and adequate sensitivity applied to apoptosis related pathways.

References

  1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57–70.

    Article  PubMed  CAS  Google Scholar 

  2. Hunter T. The Croonian Lecture 1997. The phosphorylation of proteins on tyrosine: its role in cell growth and disease. Philos. Trans R Soc Lond B Biol Sci 1998;353:583–605.

    Article  PubMed  CAS  Google Scholar 

  3. Liotta LA, Kohn EC. The microenvironment of the tumour-host interface. Nature 2001;411: 375–379.

    Article  PubMed  CAS  Google Scholar 

  4. Rockett JC. Chip, chip, array! Three chips for post-genomic research. Drug Discov Today 2002;7:458–459.

    Article  PubMed  Google Scholar 

  5. Schwartz DR, Kardia SL, Shedden KA, et al. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas Cancer Res 2002;62:4722–4729.

    PubMed  CAS  Google Scholar 

  6. Shipp MA, Ross KN, Tamayo P, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002;8:68–74.

    Article  PubMed  CAS  Google Scholar 

  7. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001;98: 10,869–10,874

    Article  CAS  Google Scholar 

  8. Welsh JB, Zarrinkar PP, Sapinoso LM, et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci USA 2001; 98:1176–1181.

    Article  PubMed  CAS  Google Scholar 

  9. Zou TT, Selaru FM, Xu Y, et al. Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon. Oncogene 2002;21:4855–4862.

    Article  PubMed  CAS  Google Scholar 

  10. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001;98:13,790–13,795.

    Article  CAS  Google Scholar 

  11. Liotta LA, Espina V, Mehta AI, et al. Protein microarrays: Meeting analytical challenges for clinical applications. Cancer Cell 2003;3: 317–325.

    Article  PubMed  CAS  Google Scholar 

  12. Paweletz CP, Charboneau L, Bichsel VE, et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 2001;20:1981–1989.

    Article  PubMed  CAS  Google Scholar 

  13. Charboneau L. Utility of reverse phase protein arrays: Applications to signaling pathways and human body arrays. Briefings in Functional Genomics and Proteomics 2002;1:305–315.

    Article  PubMed  CAS  Google Scholar 

  14. Blume-Jensen P, Hunter T. Oncogenic kinase signalling. Nature 2001;411:355–365.

    Article  PubMed  CAS  Google Scholar 

  15. Bowden ET, Barth M, Thomas D, Glazer RI, Mueller SC. An invasion-related complex of cortactin, paxillin and PKCmu associates with invadopodia at sites of extracellular matrix degradation. Oncogene 1999;18:4440–4449.

    Article  PubMed  CAS  Google Scholar 

  16. Celis JE, Gromov P. Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 2003;3:9–15.

    Article  PubMed  CAS  Google Scholar 

  17. Hunter T. Signaling—2000 and beyond. Cell 2000;100:113–127.

    Article  PubMed  CAS  Google Scholar 

  18. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature 2000;407:651–654.

    Article  PubMed  CAS  Google Scholar 

  19. Liotta LA, Kohn EC, Petricoin EF. Clinical proteomics: personalized molecular medicine. Jama 2001;286:2211–2214.

    Article  PubMed  CAS  Google Scholar 

  20. Petricoin EF, Zoon KC, Kohn EC, Barrett JC, Liotta LA. Clinical proteomics: translating benchside promise into bedside reality. Nat Rev Drug Discov 2002;1:683–695.

    Article  PubMed  CAS  Google Scholar 

  21. Liotta L, Petricoin E. Molecular profiling of human cancer. Nat Rev Genet 2000;1:48–56.

    Article  PubMed  CAS  Google Scholar 

  22. MacBeath G. Protein microarrays and proteomics. Nat Genet 2002;32 Suppl:526–532.

    Article  PubMed  CAS  Google Scholar 

  23. Zhu H, Snyder M. Protein chip technology. Curr Opin Chem Biol 2003;7:55–63.

    Article  PubMed  CAS  Google Scholar 

  24. Haab BB, Dunham MJ, Brown PO. Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol 2001; 2:RESEARCH0004.

    Google Scholar 

  25. Lal SP, Christopherson RI, dos Remedios CG. Antibody arrays: an embryonic but rapidly growing technology. Drug Discov Today 2002; 7:S143-S149.

    Article  PubMed  CAS  Google Scholar 

  26. Templin MF, Stoll D, Schrenk M, Traub PC, Vohringer CF, Joos TO. Protein microarray technology. Trends Biotechnol 2002;20:160–166.

    Article  PubMed  CAS  Google Scholar 

  27. Wilson DS, Nock S. Recent developments in protein microarray technology. Angrew Chem Int Ed Engl 2003;42:494–500.

    Article  CAS  Google Scholar 

  28. MacBeath G, Schreiber SL. Printing proteins as microarrays for high-throughput function determination. Science 2000;289:1760–1763.

    PubMed  CAS  Google Scholar 

  29. Humphery-Smith I, Wischerhoff E, Hashimoto R. Protein arrays for assessment of target selectivity. Drug Discovery World 2002;4: 17–27.

    Google Scholar 

  30. Petach H, Gold L. Dimensionality is the issue: use of photoaptamers in protein microarrays. Current Opinion in Biotechnology 2002;13: 309–314.

    Article  PubMed  CAS  Google Scholar 

  31. Schaeferling M, Schiller S, Paul H, et al. Application of self-assembly techniques in the design of biocompatible protein microarray surfaces. Electrophoresis 2002;23:3097–3105.

    Article  PubMed  CAS  Google Scholar 

  32. Weng S, Gu K, Hammond PW, et al. Generating addressable protein microarrays with PROfusion covalent mRNA-protein fusion technology. Proteomics 2002;2:48–57.

    Article  PubMed  CAS  Google Scholar 

  33. Espina V, Mehta AI, Winters ME, et al. Protein microarrays: molecular profiling technologies for clinical specimens. Proteomics 2003;3: 2091–2100.

    Article  PubMed  CAS  Google Scholar 

  34. King G, Payne S, Walker F, Murray GI. A highly sensitive detection method for immunohistochemistry using biotinylated tyramine. J Pathol 1997;183:237–241.

    Article  PubMed  CAS  Google Scholar 

  35. Kukar T, Eckenrode S, Gu Y, et al. Protein microarrays to detect protein-protein interactions using red and green fluorescent proteins. Anal Biochem 2002;306:50–54.

    Article  PubMed  CAS  Google Scholar 

  36. Morozov VN, Gavryushkin AV, Deev AA. Direct detection of isotopically labeled metabolites bound to a protein microarray using a charge-coupled device. J Biochem Biophys Methods 2002;51:57–67.

    Article  PubMed  CAS  Google Scholar 

  37. Schweitzer B, Roberts S, Grimwade B, et al. Multiplexed protein profiling on microarrays by rolling-circle amplification. Nat Biotechnol 2002;20:359–365.

    Article  PubMed  CAS  Google Scholar 

  38. Wiese R. Analysis of several fluorescent detector molecules for protein microarray use. Luminescence 2003;18:25–30.

    Article  PubMed  CAS  Google Scholar 

  39. Graf R, Friedl P. Detection of immobilized proteins on nitrocellulose membranes using a biotinylation-dependent system. Anal Biochem 1999;273:291–297.

    Article  PubMed  CAS  Google Scholar 

  40. Bobrow MN, Harris TD, Shaughnessy KJ, Litt GJ. Catalyzed reporter deposition, a novel method of signal amplification. Application to immunoassays. J Immunol Methods 1989;125: 279–285.

    Article  PubMed  CAS  Google Scholar 

  41. Bobrow MN, Shaughnessy KJ, Litt GJ. Catalyzed reporter deposition, a novel method of signal amplification. II. Application to membrane immunoassays. J Immunol Methods 1991;137:103–112.

    Article  PubMed  CAS  Google Scholar 

  42. Hunyady B, Krempels K, Harta G, Mezey E. Immunohistochemical signal amplification by catalyzed reporter deposition and its application in double immunostaining. J Histochem Cytochem 1996;44:1353–1362.

    Google Scholar 

  43. Tonkinson JL, Stillman BA. Nitrocellulose: a tried and true polymer finds utility as a post-genomic substrate. Front Biosci 2002;7:c1–12.

    Article  PubMed  CAS  Google Scholar 

  44. Jessani N, Liu Y, Humphrey M, Cravatt BF. Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness. Proc Natl Acad Sci USA 2002; 99:10,335–10,340.

    Article  CAS  Google Scholar 

  45. Knezevic V, Leethanakul C, Bichsel VE, et al. Proteomic profiling of the cancer microenvironment by antibody arrays. Proteomics 2001; 1:1271–1278.

    Article  PubMed  CAS  Google Scholar 

  46. Miller JC, Zhou H, Kwekel J, et al. Antibody microarray profiling of human prostate cancer sera: Antibody screening and identification of potential biomarkers. Proteomics 2003;3:56–63.

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, FDA-NCI Clinical Proteomics Program, Bethesda, MD

    Virginia Espina, Lance A. Liotta & David Geho

  2. Center for Biologics Evaluation and Research, Office of Tissue, Cell and Gene Therapy, Food and Drug Administration, FDA-NCI Clinical Proteomics Program, Bethesda, MD

    Emanuel F. Petricoin III

Authors
  1. Virginia Espina
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  2. Emanuel F. Petricoin III
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  3. Lance A. Liotta
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  4. David Geho
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Correspondence to Virginia Espina.

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Espina, V., Petricoin, E.F., Liotta, L.A. et al. Application of sector protein microarrays to clinical samples. Clin Proteom 1, 91–99 (2004). https://doi.org/10.1385/CP:1:1:091

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  • DOI: https://doi.org/10.1385/CP:1:1:091

Key Words

  • Protein microarray
  • molecular profiling
  • individual targeted
  • sector arrays
  • clinical analysis

Clinical Proteomics

ISSN: 1559-0275