Skip to main content
Log in

Clinical phosphoproteomic profiling for personalized targeted medicine using reverse phase protein microarray

  • Perspectives
  • Published:
Targeted Oncology Aims and scope Submit manuscript

Abstract

Healthcare providers are increasingly incorporating information gleaned from genomics and proteomics in the diagnosis and treatment of cancer. These lines of inquiry are providing greater insight into why patients with similar histological tumor classification and staging often demonstrate dissimilar clinical outcomes, and are illuminating distinct diagnostic subgroups that are more responsive to specific treatment modalities. Clearer understanding of genes, gene products, and signaling pathways holds great promise for the personalization of molecular medicine. While the origins of oncologic disease are genetically encoded, the disease process is largely mediated through altered protein function. Recent investigations suggest that each individual patient’s tumor possesses unique kinase-driven cell signaling derangements, and that these derangements derive, in part, from the tumor’s relationship with its host microenvironment. Identification of signaling derangements and mapping functional protein–protein interactions via phosphoproteomic profiling offers great promise for the precise targeting of therapeutic agents, identifying new therapeutic targets, devising effective combinatorial therapies, monitoring treatment efficacy and toxicity, and ultimately predicting treatment outcome. This review focuses on advances in clinical phosphoproteomic profiling of cancer using the emerging technology of reverse phase protein microarrays, and highlights the translational roles this technology is playing in laying the foundations for personalized molecular therapeutics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Calvo KR, Liotta LA, Petricoin EF (2005) Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy. Biosci Rep 25:107–125

    PubMed  CAS  Google Scholar 

  2. Calvo KR, Petricion EF, Liotta L (2004) Genomics and Proteomics. In: DeVita VT, Hellman S, Rosenberg SA (eds) Cancer: principles and practice of oncology, 7th ed. Lippincott, Williams, and Wilkins, Hagerstown, Maryland

    Google Scholar 

  3. Dave SS, Wright G, Tan B et al (2004) Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 351:2159–2169

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  6. Singh D, Febbo PG, Ross K et al (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1:203–209

    PubMed  CAS  Google Scholar 

  7. Sorlie T, Tibshirani R, Parker J et al (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100:8418–8423

    PubMed  CAS  Google Scholar 

  8. Sotiriou C, Powles TJ, Dowsett M et al (2002) Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res 4:R3

    PubMed  Google Scholar 

  9. Staudt LM (2002) Gene expression profiling of lymphoid malignancies. Annu Rev Med 53:303–318

    PubMed  CAS  Google Scholar 

  10. Lynch TJ, Bell DW, Sordella R et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small cell lung cancer to gefitinib. N Engl J Med 350:2129–2139

    PubMed  CAS  Google Scholar 

  11. Paez JG, Janne PA, Lee JC et al (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497–1500

    PubMed  CAS  Google Scholar 

  12. Carr KM, Rosenblatt K, Petricoin EF et al (2004) Genomic and proteomic approaches for studying human cancer: prospects for true patient-tailored therapy. Hum Genomics 1:134–140

    PubMed  CAS  Google Scholar 

  13. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70

    PubMed  CAS  Google Scholar 

  14. Hunter T (2000) Signaling—2000 and beyond. Cell 100:113–127

    PubMed  CAS  Google Scholar 

  15. Liotta LA, Espina V, Mehta AI et al (2003) Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 3:317–325

    PubMed  CAS  Google Scholar 

  16. Liotta LA, Kohn EC (2001) The microenvironment of the tumor-host interface. Nature 411:375–379

    PubMed  CAS  Google Scholar 

  17. Geho DH, Bandle RW, Clair T et al (2005) Physiological mechanisms of tumor-cell invasion and migration. Physiology (Bethesda) 20:194–200

    CAS  Google Scholar 

  18. Thaker PH, Yazici S, Nilsson MB et al (2005) Antivascular therapy for orthotopic human ovarian carcinoma through blockade of the vascular endothelial growth factor and epidermal growth factor receptors. Clin Cancer Res 11:4923–4933

    PubMed  CAS  Google Scholar 

  19. Yokoi K, Kim SJ, Thaker P et al (2005) Induction of apoptosis in tumor-associated endothelial cells and therapy of orthotopic human pancreatic carcinoma in nude mice. Neoplasia 7:696–704

    PubMed  Google Scholar 

  20. Yokoi K, Thaker PH, Yazici S et al (2005) Dual inhibition of epidermal growth factor receptor and vascular endothelial growth factor receptor phosphorylation by AEE788 reduces growth and metastasis of human colon carcinoma in an orthotopic nude mouse model. Cancer Res 65:3716–3725

    PubMed  CAS  Google Scholar 

  21. Younes MN, Yigitbasi OG, Park YW et al (2005) Antivascular therapy of human follicular thyroid cancer experimental bone metastasis by blockade of epidermal growth factor receptor and vascular growth factor receptor phosphorylation. Cancer Res 65:4716–4727

    PubMed  CAS  Google Scholar 

  22. Ge H, Walhout AJ, Vidal M (2003) Integrating “omic” information: a bridge between genomics and systems biology. Trends Genet 19:551–560

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  24. Espina V, Geho D, Mehta AI et al (2005) Pathology of the future: molecular profiling for targeted therapy. Cancer Invest 23:36–46

    PubMed  CAS  Google Scholar 

  25. Nishizuka S, Charboneau L, Young L et al (2003) Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc Natl Acad Sci USA 100:14229–14234

    PubMed  CAS  Google Scholar 

  26. Zhang DH, Wong LL, Tai LK et al (2005) Overexpression of CC3/TIP30 is associated with HER-2/neu status in breast cancer. J Cancer Res Clin Oncol 131:603–608

    PubMed  CAS  Google Scholar 

  27. Zhang DH, Tai LK, Wong LL et al (2005) Proteomics of breast cancer: enhanced expression of cytokeratin19 in human epidermal growth factor receptor type 2 positive breast tumors. Proteomics 5:1797–1805

    PubMed  CAS  Google Scholar 

  28. Ideker T, Thorsson V, Ranish JA et al (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292:929–934

    PubMed  CAS  Google Scholar 

  29. Bray D (2003) Molecular networks: the top-down view. Science 301:1864–1865

    PubMed  CAS  Google Scholar 

  30. Schwikowski B, Uetz P, Fields S (2000) A network of protein–protein interactions in yeast. Nat Biotechnol 18:1257–1261

    PubMed  CAS  Google Scholar 

  31. Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 100:12123–12128

    PubMed  CAS  Google Scholar 

  32. Manning G, Whyte DB, Martinez R et al (2002) The protein kinase complement of the human genome. Science 298:1912–1934

    PubMed  CAS  Google Scholar 

  33. Pawson T (2002) Regulation and targets of receptor tyrosine kinases. Eur J Cancer 38 Suppl 5:S3–S10

    PubMed  Google Scholar 

  34. Irish JM, Hovland R, Krutzik PO et al (2004) Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118:217–228

    PubMed  CAS  Google Scholar 

  35. Bichsel VE, Liotta LA, Petricoin EF (2001) Cancer proteomics: from biomarker discovery to signal pathway profiling. Cancer J 7:69–78

    PubMed  CAS  Google Scholar 

  36. Tyers M, Mann M (2003) From genomics to proteomics. Nature 422:193–197

    PubMed  CAS  Google Scholar 

  37. Gavin AC, Aloy P, Grandi P et al (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440:631–636

    PubMed  CAS  Google Scholar 

  38. Krogan NJ, Cagney G, Yu H et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440:637–643

    PubMed  CAS  Google Scholar 

  39. Hermjakob H, Montecchi-Palazzi L, Bader G et al (2004) The HUPO PSI’s molecular interaction format—a community standard for the representation of protein interaction data. Nat Biotechnol 22:177–183

    PubMed  CAS  Google Scholar 

  40. Human Proteome Organization http://www.HUPO.org

  41. Orchard D, Kersey P, Hermjakob H et al (2003) The HUPO proteomics standards initiative meeting: towards common standards for exchanging proteomics data. Compar Funct Genom 4:16–19

    CAS  Google Scholar 

  42. Orchard S, Hermjakob H, Apweiler R (2003) The proteomics standards initiative. Proteomics 3:1374–1376

    PubMed  CAS  Google Scholar 

  43. Araujo RP, Liotta LA (2006) A control theoretic paradigm for cell signaling networks: a simple complexity for a sensitive robustness. Curr Opin Chem Biol 10:81–87

    PubMed  CAS  Google Scholar 

  44. Alberti S, Parodi S (2003) Signaling protein networks as targets of new antineoplastic drugs. Int J Biol Markers 18:57–61

    PubMed  CAS  Google Scholar 

  45. Araujo RP, Petricion EF, Liotta L (2006) Illuminating the cancer cell's control circuitry: paving the way to individualized therapeutic strategies. Current Signal Transduction Therapeutics (in press)

  46. Carlson JM, Doyle J (2002) Complexity and robustness. Proc Natl Acad Sci USA 99 Suppl 1:2538–2545

    PubMed  Google Scholar 

  47. Stelling J, Sauer U, Szallasi Z et al (2004) Robustness of cellular functions. Cell 118:675–685

    PubMed  CAS  Google Scholar 

  48. Druker BJ, Sawyers CL, Kantarjian H et al (2001) Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. N Engl J Med 344:1038–1042

    PubMed  CAS  Google Scholar 

  49. Pegram MD, Konecny G, Slamon DJ (2000) The molecular and cellular biology of HER2/neu gene amplification/overexpression and the clinical development of herceptin (trastuzumab) therapy for breast cancer. Cancer Treat Res 103:57–75

    PubMed  CAS  Google Scholar 

  50. Petricoin EF, Zoon KC, Kohn EC et al (2002) Clinical proteomics: translating benchside promise into bedside reality. Nat Rev Drug Discov 1:683–695

    PubMed  CAS  Google Scholar 

  51. Petricoin EF, Bichsel VE, Calvert VS et al (2005) Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy. J Clin Oncol 23:3614–3621

    PubMed  CAS  Google Scholar 

  52. Araujo RP, Petricoin EF, Liotta LA (2005) A mathematical model of combination therapy using the EGFR signaling network. Biosystems 80:57–69

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  54. Brown RE (2005) Morphoproteomics: exposing protein circuitries in tumors to identify potential therapeutic targets in cancer patients. Expert Rev Proteomics 2:337–348

    PubMed  CAS  Google Scholar 

  55. Tangrea MA, Wallis BS, Gillespie JW et al (2004) Novel proteomic approaches for tissue analysis. Expert Rev Proteomics 1:185–192

    PubMed  CAS  Google Scholar 

  56. Oda K, Matsuoka Y, Funahashi A et al (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Molecular Systems Biology. DOI 10.1038/msb4100014

  57. Petricoin E, Wulfkuhle J, Espina V et al (2004) Clinical proteomics: revolutionizing disease detection and patient tailoring therapy. J Proteome Res 3:209–217

    PubMed  CAS  Google Scholar 

  58. Allred DC, Mohsin SK, Fuqua SA (2001) Histological and biological evolution of human premalignant breast disease. Endocr Relat Cancer 8:47–61

    PubMed  CAS  Google Scholar 

  59. Bonner RF, Emmert-Buck M, Cole K et al (1997) Laser capture microdissection: molecular analysis of tissue. Science 278:1481–1483

    PubMed  CAS  Google Scholar 

  60. Cowherd SM, Espina VA, Petricoin EF et al (2004) Proteomic analysis of human breast cancer tissue with laser-capture microdissection and reverse-phase protein microarrays. Clin Breast Cancer 5:385–392

    PubMed  CAS  Google Scholar 

  61. Emmert-Buck MR, Bonner RF, Smith PD et al (1996) Laser capture microdissection. Science 274:998–1001

    PubMed  CAS  Google Scholar 

  62. Fuller AP, Palmer-Toy D, Erlander MG et al (2003) Laser capture microdissection and advanced molecular analysis of human breast cancer. J Mammary Gland Biol Neoplasia 8:335–345

    PubMed  Google Scholar 

  63. Mojica WD, Rapkiewicz AV, Liotta LA et al (2005) Manual exfoliation of fresh tissue obviates the need for frozen sections for molecular profiling. Cancer 105:483–491

    PubMed  CAS  Google Scholar 

  64. Sugiyama Y, Sugiyama K, Hirai Y et al (2002) Microdissection is essential for gene expression profiling of clinically resected cancer tissues. Am J Clin Pathol 117:109–116

    PubMed  CAS  Google Scholar 

  65. Wulfkuhle JD, McLean KC, Paweletz CP et al (2001) New approaches to proteomic analysis of breast cancer. Proteomics 1:1205–1215

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  67. Nielsen UB, Cardone MH, Sinskey AJ et al (2003) Profiling receptor tyrosine kinase activation by using Ab microarrays. Proc Natl Acad Sci USA 100:9330–9335

    PubMed  Google Scholar 

  68. Petricoin EF, Liotta LA (2004) Proteomic approaches in cancer risk and response assessment. Trends Mol Med 10:59–64

    PubMed  CAS  Google Scholar 

  69. Baak JP, Path FR, Hermsen MA et al (2003) Genomics and proteomics in cancer. Eur J Cancer 39:1199–1215

    PubMed  CAS  Google Scholar 

  70. Ornstein DK, Petricoin EF (2004) Proteomics to diagnose human tumors and provide prognostic information. Oncology (Williston Park) 18:521–529; discussion 529–532

    Google Scholar 

  71. Petricoin EF, Liotta LA (2003) Mass spectrometry-based diagnostics: the upcoming revolution in disease detection. Clin Chem 49:533–534

    PubMed  CAS  Google Scholar 

  72. Wulfkuhle JD, Sgroi DC, Krutzsch H et al (2002) Proteomics of human breast ductal carcinoma in situ. Cancer Res 62:6740–6749

    PubMed  CAS  Google Scholar 

  73. Rosenblatt KP, Bryant-Greenwood P, Killian JK et al (2004) Serum proteomics in cancer diagnosis and management. Annu Rev Med 55:97–112

    PubMed  CAS  Google Scholar 

  74. Charboneau L, Scott H, Chen T et al (2002) Utility of reverse phase protein arrays: applications to signaling pathways and human body arrays. Brief Funct Genomic Proteomic 1:305–315

    PubMed  CAS  Google Scholar 

  75. Wulfkuhle J, Espina V, Liotta L et al (2004) Genomic and proteomic technologies for individualization and improvement of cancer treatment. Eur J Cancer 40:2623–2632

    PubMed  CAS  Google Scholar 

  76. Gulmann C, Sheehan KM, Kay EW et al (2006) Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J Pathol 208:595–606

    PubMed  CAS  Google Scholar 

  77. Ekins RP, Chu F (1994) Developing multianalyte assays. Trends Biotechnol 12:89–94

    PubMed  CAS  Google Scholar 

  78. Templin MF, Stoll D, Schrenk M et al (2002) Protein microarray technology. Trends Biotechnol 20:160–166

    PubMed  CAS  Google Scholar 

  79. Nishizuka S, Washburn NR, Munson PJ (2006) Evaluation of ordinary flatbed scanners for quantitative density analysis. Biotechniques 40:442–448

    Article  PubMed  CAS  Google Scholar 

  80. Espina V, Woodhouse EC, Wulfkuhle J et al (2004) Protein microarray detection strategies: focus on direct detection technologies. J Immunol Methods 290:121–133

    PubMed  CAS  Google Scholar 

  81. Stoll D, Templin MF, Schrenk M et al (2002) Protein microarray technology. Front Biosci 7:c13–c32

    PubMed  CAS  Google Scholar 

  82. Utz PJ (2005) Protein arrays for studying blood cells and their secreted products. Immunol Rev 204:264–282

    PubMed  CAS  Google Scholar 

  83. Wilson DS, Nock S (2003) Recent developments in protein microarray technology. Angew Chem Int Ed Engl 42:494–500

    PubMed  CAS  Google Scholar 

  84. Zhu H, Snyder M (2003) Protein chip technology. Curr Opin Chem Biol 7:55–63

    PubMed  CAS  Google Scholar 

  85. Paweletz CP, Gillespie JW, Ornstein DK et al (2000) Rapid protein display profiling of cancer progression directly from human tissue using a protein biochip. Drug Develop Res 49:34–42

    CAS  Google Scholar 

  86. Paweletz CP, Liotta LA, Petricoin EF (2001) New technologies for biomarker analysis of prostate cancer progression: laser capture microdissection and tissue proteomics. Urology 57:160–163

    PubMed  CAS  Google Scholar 

  87. Gillespie JW, Ahram M, Best CJ et al (2001) The role of tissue microdissection in cancer research. Cancer J 7:32–39

    PubMed  CAS  Google Scholar 

  88. Gillespie JW, Gannot G, Tangrea MA et al (2004) Molecular profiling of cancer. Toxicol Pathol 32 Suppl 1:67–71

    PubMed  CAS  Google Scholar 

  89. Stillman BA, Tonkinson JL (2000) FAST slides: a novel surface for microarrays. Biotechniques 29:630–635

    PubMed  CAS  Google Scholar 

  90. Haab BB, Dunham MJ, Brown PO (2001) Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol 2:research0004.0001–0004.0013

    Google Scholar 

  91. Bobrow MN, Harris TD, Shaughnessy KJ et al (1989) Catalyzed reporter deposition, a novel method of signal amplification. Application to immunoassays. J Immunol Methods 125:279–285

    PubMed  CAS  Google Scholar 

  92. Winters ME, Lowenthal M, Feldman AL et al (2006) The future of cancer diagnostics: proteomics, immunoproteomics, and beyond. ASM, Washington, District of Columbia

    Google Scholar 

  93. Geho D, Lahar N, Gurnani P et al (2005) Pegylated, steptavidin-conjugated quantum dots are effective detection elements for reverse-phase protein microarrays. Bioconjug Chem 16:559–566

    PubMed  CAS  Google Scholar 

  94. Speer R, Wulfkuhle JD, Liotta LA et al (2005) Reverse-phase protein microarrays for tissue-based analysis. Curr Opin Mol Ther 7:240–245

    PubMed  CAS  Google Scholar 

  95. Joliffe T (1986) Principle components analysis. Springer, Berlin Heidelberg New York

    Google Scholar 

  96. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2:418–427

    PubMed  CAS  Google Scholar 

  97. Ringner M, Peterson C, Khan J (2002) Analyzing array data using supervised methods. Pharmacogenomics 3:403–415

    PubMed  CAS  Google Scholar 

  98. Shannon W, Culverhouse R, Duncan J (2003) Analyzing microarray data using cluster analysis. Pharmacogenomics 4:41–52

    PubMed  CAS  Google Scholar 

  99. Lucas P (2004) Bayesian analysis, pattern analysis, and data mining in health care. Curr Opin Crit Care 10:399–403

    PubMed  Google Scholar 

  100. Nishizuka S, Chen ST, Gwadry FG et al (2003) Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling. Cancer Res 63:5243–5250

    PubMed  CAS  Google Scholar 

  101. Grubb RL, Calvert VS, Wulkuhle JD et al (2003) Signal pathway profiling of prostate cancer using reverse phase protein arrays. Proteomics 3:2142–2146

    PubMed  CAS  Google Scholar 

  102. Wulfkuhle JD, Aquino JA, Calvert VS et al (2003) Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics 3:2085–2090

    PubMed  CAS  Google Scholar 

  103. Vuong GL, Weiss SM, Kammer W et al (2000) Improved sensitivity proteomics by postharvest alkylation and radioactive labeling of proteins. Electrophoresis 21:2594–2605

    PubMed  CAS  Google Scholar 

  104. Calvert V, Tang Y, Boveia V et al (2004) Development of multiplexed protein profiling and detection using near infrared detection of reverse-phase protein microarrays. Clinical Proteomics 1:81–90

    CAS  Google Scholar 

  105. Chan SM, Ermann J, Su L et al (2004) Protein microarrays for multiplex analysis of signal transduction pathways. Nat Med 10:1390–1396

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  107. Geho DH, Petricoin EF, Liotta LA (2004) Blasting into the microworld of tissue proteomics: a new window on cancer. Clin Cancer Res 10:825–827

    PubMed  CAS  Google Scholar 

  108. Jones RB, Gordus A, Krall JA et al (2006) A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439:168–174

    PubMed  CAS  Google Scholar 

  109. Geho DH, Lahar N, Ferrari M et al (2004) Opportunities for nanotechnology-based innovation in tissue proteomics. Biomed Microdevices 6:231–239

    PubMed  CAS  Google Scholar 

  110. Simpson R (2003) Proteins and proteomics. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York

    Google Scholar 

  111. Hanash S (2004) HUPO initiatives relevant to clinical proteomics. Mol Cell Proteomics 3:298–301

    PubMed  CAS  Google Scholar 

  112. The Signalling Gateway. Antibodies tested by the AfCS Antibody Laboratory http://www.signaling-gateway.org/data/antibody/cgi-bin/targets.cgi

  113. Abminer http://www.discover.nci.nih.gov/abminer/

  114. Major SM, Nishizuka S, Morita D et al (2006) AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies. BMC Bioinformatics 7:192

    PubMed  Google Scholar 

  115. Gulmann C, Espina V, Petricoin E et al (2005) Proteomic analysis of apoptotic pathways reveals prognostic factors in follicular lymphoma. Clin Cancer Res 11:5847–5855

    PubMed  CAS  Google Scholar 

  116. Sheehan KM, Calvert VS, Kay EW et al (2005) Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol Cell Proteomics 4:346–355

    PubMed  CAS  Google Scholar 

  117. Wulfkuhle JD, Liotta LA, Petricoin EF (2003) Proteomic applications for the early detection of cancer. Nat Rev Cancer 3:267–275

    PubMed  CAS  Google Scholar 

  118. Zhu H, Bilgin M, Snyder M (2003) Proteomics. Annu Rev Biochem 72:783–812

    PubMed  CAS  Google Scholar 

  119. Petricoin EF, Ornstein DK, Liotta LA (2004) Clinical proteomics: applications for prostate cancer biomarker discovery and detection. Urol Oncol 22:322–328

    PubMed  CAS  Google Scholar 

  120. Petricoin EF, Liotta LA (2004) Clinical proteomics: application at the bedside. Contrib Nephrol 141:93–103

    PubMed  CAS  Google Scholar 

  121. Posadas EM, Simpkins F, Liotta LA et al (2005) Proteomic analysis for the early detection and rational treatment of cancer—realistic hope? Ann Oncol 16:16–22

    PubMed  CAS  Google Scholar 

  122. Tirumalai RS, Chan KC, Prieto DA et al (2003) Characterization of the low molecular weight human serum proteome. Mol Cell Proteomics 2:1096–1103

    PubMed  CAS  Google Scholar 

  123. Geho DH, Liotta LA, Petricoin EF et al (2006) The amplified peptidome: the new treasure chest of candidate biomarkers. Curr Opin Chem Biol 10:50–55

    PubMed  CAS  Google Scholar 

  124. Liotta LA, Petricoin EF (2006) Serum peptidome for cancer detection: spinning biologic trash into diagnostic gold. J Clin Invest 116:26–30

    PubMed  CAS  Google Scholar 

  125. Winters M, Mehta A, Petricoin EF et al (2005) Supra-additive growth inhibition by a celecoxib analogue and carboxyamido-triazole is primarily mediated through apoptosis. Cancer Res 65:3853–3860

    PubMed  CAS  Google Scholar 

  126. Mircean C, Shmulevich I, Cogdell D et al (2005) Robust estimation of protein expression ratios with lysate microarray technology. Bioinformatics 21:1935–1942

    PubMed  CAS  Google Scholar 

  127. Wei Q, Wang LE, Sturgis EM et al (2005) Expression of nucleotide excision repair proteins in lymphocytes as a marker of susceptibility to squamous cell carcinomas of the head and neck. Cancer Epidemiol Biomarkers Prev 14:1961–1966

    PubMed  CAS  Google Scholar 

  128. Awada A, Mano M, Hendlisz A et al (2004) New anticancer agents and therapeutic strategies in development for solid cancers: a clinical perspective. Expert Rev Anticancer Ther 4:53–60

    PubMed  CAS  Google Scholar 

  129. Cantley LC (2002) The phosphoinositide 3-kinase pathway. Science 296:1655–1657

    PubMed  CAS  Google Scholar 

  130. Chang F, Lee JT, Navolanic PM et al (2003) Involvement of PI3K/Akt pathway in cell cycle progression, apoptosis, and neoplastic transformation: a target for cancer chemotherapy. Leukemia 17:590–603

    PubMed  CAS  Google Scholar 

  131. Chen YL, Law PY, Loh HH (2005) Inhibition of PI3K/Akt signaling: an emerging paradigm for targeted cancer therapy. Curr Med Chem Anticancer Agents 5:575–589

    PubMed  CAS  Google Scholar 

  132. Cheng JQ, Lindsley CW, Cheng GZ et al (2005) The Akt/PKB pathway: molecular target for cancer drug discovery. Oncogene 24:7482–7492

    PubMed  CAS  Google Scholar 

  133. Cox MC, Permenter M, Figg WD (2005) Angiogenesis and prostate cancer: important laboratory and clinical findings. Curr Oncol Rep 7:215–219

    PubMed  CAS  Google Scholar 

  134. Dai Y, Grant S (2004) Small molecule inhibitors targeting cyclin-dependent kinases as anticancer agents. Curr Oncol Rep 6:123–130

    PubMed  Google Scholar 

  135. Dudek AZ, Pawlak WZ, Kirstein MN (2003) Molecular targets in the inhibition of angiogenesis. Expert Opin Ther Targets 7:527–541

    PubMed  CAS  Google Scholar 

  136. Fischer U, Schulze-Osthoff K (2005) New approaches and therapeutics targeting apoptosis in disease. Pharmacol Rev 57:187–215

    PubMed  CAS  Google Scholar 

  137. Ghoul A, Servoa M, Benhadji KA et al (2006) Protein kinase c α and δ are members of a large kinase family of high potential for novel anticancer targeted therapy. Targeted Oncology 1:42–53

    Google Scholar 

  138. Giuliani N, Lunghi P, Morandi F et al (2004) Downmodulation of ERK protein kinase activity inhibits VEGF secretion by human myeloma cells and myeloma-induced angiogenesis. Leukemia 18:628–635

    PubMed  CAS  Google Scholar 

  139. Glade-Bender J, Kandel JJ, Yamashiro DJ (2003) VEGF blocking therapy in the treatment of cancer. Expert Opin Biol Ther 3:263–276

    PubMed  CAS  Google Scholar 

  140. Hafner C, Reichle A, Vogt T (2005) New indications for established drugs: combined tumor-stroma-targeted cancer therapy with PPARgamma agonists, COX-2 inhibitors, mTOR antagonists and metronomic chemotherapy. Curr Cancer Drug Targets 5:393–419

    PubMed  CAS  Google Scholar 

  141. Juin P, Geneste O, Raimbaud E et al (2004) Shooting at survivors: Bcl-2 family members as drug targets for cancer. Biochim Biophys Acta 1644:251–260

    PubMed  CAS  Google Scholar 

  142. Kim D, Cheng GZ, Lindsley CW et al (2005) Targeting the phosphatidylinositol-3 kinase/Akt pathway for the treatment of cancer. Curr Opin Investig Drugs 6:1250–1258

    PubMed  CAS  Google Scholar 

  143. Lee S, Choi EJ, Jin C et al (2005) Activation of PI3K/Akt pathway by PTEN reduction and PIK3CA mRNA amplification contributes to cisplatin resistance in an ovarian cancer cell line. Gynecol Oncol 97:26–34

    PubMed  CAS  Google Scholar 

  144. Lenz HJ (2005) Antiangiogenic agents in cancer therapy. Oncology (Williston Park) 19:17–25

    Google Scholar 

  145. Luo J, Manning BD, Cantley LC (2003) Targeting the PI3K-Akt pathway in human cancer: rationale and promise. Cancer Cell 4:257–262

    PubMed  CAS  Google Scholar 

  146. Majumder PK, Sellers WR (2005) Akt-regulated pathways in prostate cancer. Oncogene 24:7465–7474

    PubMed  CAS  Google Scholar 

  147. Mita MM, Mita A, Rowinsky EK (2003) The molecular target of rapamycin (mTOR) as a therapeutic target against cancer. Cancer Biol Ther 2:S169–S177

    PubMed  CAS  Google Scholar 

  148. Mitsiades CS, Mitsiades N, Hideshima T et al (2005) Proteasome inhibitors as therapeutics. Essays Biochem 41:205–218

    PubMed  CAS  Google Scholar 

  149. Rabindran SK (2005) Antitumor activity of HER2 inhibitors. Cancer Lett 227:9–23

    PubMed  CAS  Google Scholar 

  150. Ribatti D, Vacca A, Merchionne F et al (2005) Antiangiogenesis by chemotherapeutic agents. Mini Rev Med Chem 5:313–317

    PubMed  CAS  Google Scholar 

  151. Ringel MD, Hayre N, Saito J et al (2001) Overexpression and overactivation of Akt in thyroid carcinoma. Cancer Res 61:6105–6111

    PubMed  CAS  Google Scholar 

  152. Ross JS, Schenkein DP, Pietrusko R et al (2004) Targeted therapies for cancer 2004. Am J Clin Pathol 122:598–609

    PubMed  CAS  Google Scholar 

  153. Samuels Y, Velculescu VE (2004) Oncogenic mutations of PIK3CA in human cancers. Cell Cycle 3:1221–1224

    PubMed  CAS  Google Scholar 

  154. Senderowicz AM (2004) Targeting cell cycle and apoptosis for the treatment of human malignancies. Curr Opin Cell Biol 16:670–678

    PubMed  CAS  Google Scholar 

  155. Shelton JG, Steelman LS, Abrams SL et al (2005) The epidermal growth factor receptor gene family as a target for therapeutic intervention in numerous cancers: what’s genetics got to do with it? Expert Opin Ther Targets 9:1009–1030

    PubMed  CAS  Google Scholar 

  156. Tibes R, Trent J, Kurzrock R (2005) Tyrosine kinase inhibitors and the dawn of molecular cancer therapeutics. Annu Rev Pharmacol Toxicol 45:357–384

    PubMed  CAS  Google Scholar 

  157. Traxler P (2003) Tyrosine kinases as targets in cancer therapy—successes and failures. Expert Opin Ther Targets 7:215–234

    PubMed  CAS  Google Scholar 

  158. Voorhees PM, Orlowski RZ (2006) The proteasome and proteasome inhibitors in cancer therapy. Annu Rev Pharmacol Toxicol 46:189–213

    PubMed  CAS  Google Scholar 

  159. Wakelee HA, Schiller JH (2005) Targeting angiogenesis with vascular endothelial growth factor receptor small-molecule inhibitors: novel agents with potential in lung cancer. Clin Lung Cancer 7(Suppl 1):S31–S38

    Article  PubMed  Google Scholar 

  160. Zhang Z, Li M, Rayburn ER et al (2005) Oncogenes as novel targets for cancer therapy (part I): growth factors and protein tyrosine kinases. Am J Pharmacogenomics 5:173–190

    PubMed  CAS  Google Scholar 

  161. Zhang Z, Li M, Rayburn ER et al (2005) Oncogenes as novel targets for cancer therapy (part II): Intermediate signaling molecules. Am J Pharmacogenomics 5:247–257

    PubMed  CAS  Google Scholar 

  162. Zhang Z, Li M, Rayburn ER et al (2005) Oncogenes as novel targets for cancer therapy (part III): transcription factors. Am J Pharmacogenomics 5:327–338

    PubMed  CAS  Google Scholar 

  163. Zhang Z, Li M, Rayburn ER et al (2005) Oncogenes as novel targets for cancer therapy (part IV): regulators of the cell cycle and apoptosis. Am J Pharmacogenomics 5:397–407

    PubMed  CAS  Google Scholar 

  164. Timar J, Ladanyi A, Petak I et al (2003) Molecular pathology of tumor metastasis III. Target array and combinatorial therapies. Pathol Oncol Res 9:49–72

    Article  PubMed  CAS  Google Scholar 

  165. Moniz M, Yeatermeyer J, Wu TC (2005) Control of cancers by combining antiangiogenesis and cancer immunotherapy. Drugs Today (Barc) 41:471–494

    CAS  Google Scholar 

  166. Retter AS, Figg WD, Dahut WL (2003) The combination of antiangiogenic and cytotoxic agents in the treatment of prostate cancer. Clin Prostate Cancer 2:153–159

    PubMed  CAS  Google Scholar 

  167. van Cruijsen H, Giaccone G, Hoekman K (2005) Epidermal growth factor receptor and angiogenesis: opportunities for combined anticancer strategies. Int J Cancer 117:883–888

    PubMed  Google Scholar 

  168. Wachsberger P, Burd R, Dicker AP (2003) Tumor response to ionizing radiation combined with antiangiogenesis or vascular targeting agents: exploring mechanisms of interaction. Clin Cancer Res 9:1957–1971

    PubMed  CAS  Google Scholar 

  169. Belluco C, Mammano E, Petricoin E et al (2005) Kinase substrate protein microarray analysis of human colon cancer and hepatic metastasis. Clin Chim Acta 357:180–183

    PubMed  CAS  Google Scholar 

  170. Herrmann PC, Gillespie JW, Charboneau L et al (2003) Mitochondrial proteome: altered cytochrome c oxidase subunit levels in prostate cancer. Proteomics 3:1801–1810

    PubMed  CAS  Google Scholar 

  171. Rapkiewicz AV, Espina V, Zujewski JA et al (2006) The needle in the haystack: application of breast fine needle aspirate samples to quantitative protein microarray technology (manuscript in process)

  172. Rudelius M, Pittaluga S, Nishizuka S et al (2006) Constitutive activation of AKT contributes to the pathogenesis and survival of mantle cell lymphoma. Blood (in press)

  173. Zha H, Raffeld M, Charboneau L et al (2004) Similarities of prosurvival signals in Bcl-2-positive and Bcl-2-negative follicular lymphomas identified by reverse phase protein microarray. Lab Invest 84:235–244

    PubMed  CAS  Google Scholar 

  174. Graff JR, Konicek BW, McNulty AM et al (2000) Increased AKT activity contributes to prostate cancer progression by dramatically accelerating prostate tumor growth and diminishing p27Kip1 expression. J Biol Chem 275:24500–24505

    PubMed  CAS  Google Scholar 

  175. Marshall CJ (1995) Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell 80:179–185

    PubMed  CAS  Google Scholar 

  176. Zimmermann S, Moelling K (1999) Phosphorylation and regulation of Raf by Akt (protein kinase B). Science 286:1741–1744

    PubMed  CAS  Google Scholar 

  177. Tolcher AW, Reyno L, Venner PM et al (2002) A randomized phase II and pharmacokinetic study of the antisense oligonucleotides ISIS 3521 and ISIS 5132 in patients with hormone-refractory prostate cancer. Clin Cancer Res 8:2530–2535

    PubMed  CAS  Google Scholar 

  178. Ahram M, Best CJ, Flaig MJ et al (2002) Proteomic analysis of human prostate cancer. Mol Carcinog 33:9–15

    PubMed  CAS  Google Scholar 

  179. Ornstein DK, Gillespie JW, Paweletz CP et al (2000) Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 21:2235–2242

    PubMed  CAS  Google Scholar 

  180. Scheffler IE (1999) Mitochondria. Wiley-Liss, New York

    Google Scholar 

  181. Kadenbach B, Huttemann M, Arnold S et al (2000) Mitochondrial energy metabolism is regulated via nuclear-coded subunits of cytochrome c oxidase. Free Radic Biol Med 29:211–221

    PubMed  CAS  Google Scholar 

  182. Vijayasarathy C, Biunno I, Lenka N et al (1998) Variations in the subunit content and catalytic activity of the cytochrome c oxidase complex from different tissues and different cardiac compartments. Biochim Biophys Acta 1371:71–82

    PubMed  CAS  Google Scholar 

  183. Glaichenhaus N, Leopold P, Cuzin F (1986) Increased levels of mitochondrial gene expression in rat fibroblast cells immortalized or transformed by viral and cellular oncogenes. Embo J 5:1261–1265

    PubMed  CAS  Google Scholar 

  184. Krieg RC, Knuechel R, Schiffmann E et al (2004) Mitochondrial proteome: cancer-altered metabolism associated with cytochrome c oxidase subunit level variation. Proteomics 4:2789–2795

    PubMed  CAS  Google Scholar 

  185. Armstrong JS (2006) Mitochondria: a target for cancer therapy. Br J Pharmacol 147:239–248

    PubMed  CAS  Google Scholar 

  186. Parr RL, Dakubo GD, Thayer RE et al (2006) Mitochondrial DNA as a potential tool for early cancer detection. Hum Genomics 2:252–257

    PubMed  CAS  Google Scholar 

  187. Bellacosa A, De Feo D, Godwin AK et al (1995) Molecular alterations of the AKT2 oncogene in ovarian and breast carcinomas. Int J Cancer 64:280–285

    PubMed  CAS  Google Scholar 

  188. Hu L, Zaloudek C, Mills GB et al (2000) In vivo and in vitro ovarian carcinoma growth inhibition by a phosphatidylinositol 3-kinase inhibitor (LY294002). Clin Cancer Res 6:880–886

    PubMed  CAS  Google Scholar 

  189. Philp AJ, Campbell IG, Leet C et al (2001) The phosphatidylinositol 3′-kinase p85alpha gene is an oncogene in human ovarian and colon tumors. Cancer Res 61:7426–7429

    PubMed  CAS  Google Scholar 

  190. Shayesteh L, Lu Y, Kuo WL et al (1999) PIK3CA is implicated as an oncogene in ovarian cancer. Nat Genet 21:99–102

    PubMed  CAS  Google Scholar 

  191. Yuan ZQ, Sun M, Feldman RI et al (2000) Frequent activation of AKT2 and induction of apoptosis by inhibition of phosphoinositide-3-OH kinase/Akt pathway in human ovarian cancer. Oncogene 19:2324–2330

    PubMed  CAS  Google Scholar 

  192. Altomare DA, Wang HQ, Skele KL et al (2004) AKT and mTOR phosphorylation is frequently detected in ovarian cancer and can be targeted to disrupt ovarian tumor cell growth. Oncogene 23:5853–5857

    PubMed  CAS  Google Scholar 

  193. Westfall SD, Skinner MK (2005) Inhibition of phosphatidylinositol 3-kinase sensitizes ovarian cancer cells to carboplatin and allows adjunct chemotherapy treatment. Mol Cancer Ther 4:1764–1771

    PubMed  CAS  Google Scholar 

  194. Partridge EE, Barnes MN (1999) Epithelial ovarian cancer: prevention, diagnosis, and treatment. CA Cancer J Clin 49:297–320

    PubMed  CAS  Google Scholar 

  195. Raspollini MR, Amunni G, Villanucci A et al (2004) c-KIT expression and correlation with chemotherapy resistance in ovarian carcinoma: an immunocytochemical study. Ann Oncol 15:594–597

    PubMed  CAS  Google Scholar 

  196. Sattler M, Salgia R (2004) Targeting c-KIT mutations: basic science to novel therapies. Leuk Res 28(Suppl 1):S11–S20

    PubMed  CAS  Google Scholar 

  197. Boyce EA, Kohn EC (2005) Ovarian cancer in the proteomics era: diagnosis, prognosis, and therapeutics targets. Int J Gynecol Cancer 15(Suppl 3):266–273

    PubMed  Google Scholar 

  198. Yannelli JR, Wroblewski JM (2004) On the road to a tumor cell vaccine: 20 years of cellular immunotherapy. Vaccine 23:97–113

    PubMed  CAS  Google Scholar 

  199. Zbar AP (2004) The immunology of colorectal cancer. Surg Oncol 13:45–53

    PubMed  CAS  Google Scholar 

  200. Lebowitz PF, Eng-Wong J, Swain SM et al (2004) A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 10:6764–6769

    PubMed  CAS  Google Scholar 

  201. Pandolfi PP (2004) Breast cancer—loss of PTEN predicts resistance to treatment. N Engl J Med 351:2337–2338

    PubMed  CAS  Google Scholar 

  202. Kersting C, Tidow N, Schmidt H et al (2004) Gene dosage PCR and fluorescence in situ hybridization reveal low frequency of egfr amplifications despite protein overexpression in invasive breast carcinoma. Lab Invest 84:582–587

    PubMed  CAS  Google Scholar 

  203. Martin MD, Hilsenbeck SG, Mohsin SK et al (2006) Breast tumors that overexpress nuclear metastasis-associated 1 (MTA1) protein have high recurrence risks but enhanced responses to systemic therapies. Breast Cancer Res Treat 95:7–12

    PubMed  CAS  Google Scholar 

  204. Page MJ, Amess B, Townsend RR et al (1999) Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. Proc Natl Acad Sci USA 96:12589–12594

    PubMed  CAS  Google Scholar 

  205. Khor TO, Gul YA, Ithnin H et al (2004) Positive correlation between overexpression of phospho-BAD with phosphorylated Akt at serine 473 but not threonine 308 in colorectal carcinoma. Cancer Lett 210:139–150

    PubMed  CAS  Google Scholar 

  206. Del Poeta G, Venditti A, Del Principe MI et al (2003) Amount of spontaneous apoptosis detected by Bax/Bcl-2 ratio predicts outcome in acute myeloid leukemia (AML). Blood 101:2125–2131

    PubMed  Google Scholar 

  207. Korsmeyer SJ, Shutter JR, Veis DJ et al (1993) Bcl-2/Bax: a rheostat that regulates an anti-oxidant pathway and cell death. Semin Cancer Biol 4:327–332

    PubMed  CAS  Google Scholar 

  208. Wendel HG, De Stanchina E, Fridman JS et al (2004) Survival signaling by Akt and eIF4E in oncogenesis and cancer therapy. Nature 428:332–337

    PubMed  CAS  Google Scholar 

  209. Davis DW, McConkey DJ, Abbruzzese JL et al (2003) Surrogate markers in antiangiogenesis clinical trials. Br J Cancer 89:8–14

    PubMed  CAS  Google Scholar 

  210. Alexander H, Bartlett DL, Libutti SK (2000) National Cancer Institute experience with regional therapy for unresectable primary and metastatic cancer of the liver or peritoneal cavity. Humana, Totowa, New Jersey

    Google Scholar 

  211. Grover A, Alexander HR (2004) The past decade of experience with isolated hepatic perfusion. Oncologist 9:653–664

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gerhard S. Mundinger.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mundinger, G.S., Espina, V., Liotta, L.A. et al. Clinical phosphoproteomic profiling for personalized targeted medicine using reverse phase protein microarray. Targ Oncol 1, 151–167 (2006). https://doi.org/10.1007/s11523-006-0025-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11523-006-0025-2

Keywords

Navigation