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Proteomics of Cancer of Hormone-Dependent Tissues

  • Darren R. Tyson
  • David K. Ornstein
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 630)

Abstract

Serum and tissue biomarkers have begun to play an increasingly important role in the detection and management of many cancers of hormone-sensitive tissues. Specifically, the introduction of serum PSA measurements into clinical practice has dramatically altered detection and treatment of prostate cancer and serum tumor markers play a critical role in the management of testicular cancer. Serum biomarkers are used for ovarian and pancreatic cancers, but their usefulness is limited by poor specificity. Tissue biomarkers are used to help guide breast cancer treatment but are not widely used in other cancers. Even the “best” biomarkers such as PSA have substantial limitations. The discovery of new biomarkers for both early detection and prognosis of cancer is critical to the hope of better clinical outcomes. Recently there has been an expanding understanding of the underlying molecular etiology of cancer and molecular targeted therapies for some particularly aggressive cancers such as renal cell carcinoma have been developed. Better understanding of the molecular etiology of cancer and identification of additional therapeutic targets remain important research goals. Currently, there are very few patient-tailored therapies and there is a great need to better understand the molecular alterations associated with cancer and to use this information to design need cancer therapies and prevention strategies.

Advances in proteomic technologies have created tremendous opportunities for biomarker discovery and biological studies of cancer. The potential that proteomics will impact clinical practice is currently greater than ever, but there main several obstacles in making this a reality. A major hurdle to overcome continues to be the proper acquisition of patient tissues and body fluids for investigation and clinical diagnostics. Each cancer has specific issues in this regard and it is incumbent upon investigators and collaborating clinicians to understand the various nuances of tissue and biofluid procurement. This chapter not only reviews the clinical need and potential impact of proteomic studies of hormone-sensitive cancers, but details specific technologies and discusses the issues surrounding tissue/biofluid procurement.

Keywords

Breast Cancer Prostate Cancer Pancreatic Cancer Renal Cell Carcinoma Proteomic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Jemal A, Siegel R, Ward E et al. Cancer statistics. CA Cancer J Clin 2007; 57(1):43–66.PubMedGoogle Scholar
  2. 2.
    Thompson IM, Ankerst DP. Prostate-specific antigen in the early detection of prostate cancer. CMAJ 2007; 176(13):1853–1858.PubMedGoogle Scholar
  3. 3.
    Bast RC Jr, Badgwell D, Lu Z et al. New tumor markers: CA125 and beyond. Int J Gynecol Cancer 2005; 15Suppl 3:274–281.PubMedGoogle Scholar
  4. 4.
    Goggins M. Molecular markers of early pancreatic cancer. J Clin Oncol 2005; 23(20):4524–4531.PubMedGoogle Scholar
  5. 5.
    Neill M, Warde P, Fleshner N. Management of low-stage testicular seminoma. Urol Clin North Am 2007; 34(2): 127–136; abstract vii–viii.PubMedGoogle Scholar
  6. 6.
    Ocana A, Cruz JJ, Pandiella A. Trastuzumab and antiestrogen therapy: focus on mechanisms of action and resistance. Am J Clin Oncol 2006; 29(1):90–95.PubMedGoogle Scholar
  7. 7.
    Sim HG, Lange PH, Lin DW. Role of post-chemotherapy surgery in germ cell tumors. Urol Clin North Am 2007; 34(2):199–217; abstract ix.PubMedGoogle Scholar
  8. 8.
    Zolg W. The proteomic search for diagnostic biomarkers: lost in translation? Mol Cell Proteomics 2006; 5(1):1720–1726.PubMedGoogle Scholar
  9. 9.
    Ransohoff DF. Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer 2004; 4(4):309–314.PubMedGoogle Scholar
  10. 10.
    Strange K. The end of “naive reductionism”: rise of systems biology or renaissance of physiology? Am J Physiol Cell Physiol 2005; 288(5):C968–974.PubMedGoogle Scholar
  11. 11.
    Weston AD, Hood L. Systems biology, proteomics and the future of health care: toward predictive, preventative and personalized medicine. J Proteome Res 2004; 3(2):179–196.PubMedGoogle Scholar
  12. 12.
    Huang S, Wikswo J. Dimensions of systems biology. Rev Physiol Biochem Pharmacol 2006; 157:81–104.PubMedGoogle Scholar
  13. 13.
    Chen R, Yi EC, Donohoe S et al. Pancreatic cancer proteome: the proteins that underlie invasion, metastasis and immunologic escape. Gastroenterology 2005; 129(4):1187–1197.PubMedGoogle Scholar
  14. 14.
    Liotta LA, Kohn EC. The microenvironment of the tumour-host interface. Nature 2001; 411(6835):375–379.PubMedGoogle Scholar
  15. 15.
    Bonner RF, Emmert-Buck M, Cole K et al. Laser capture microdissection: molecular analysis of tissue. Science 1997; 278(5342):1481, 1483.PubMedGoogle Scholar
  16. 16.
    Emmert-Buck MR, Bonner RF, Smith PD et al. Laser capture microdissection. Science 1996; 274(5289):998–1001.PubMedGoogle Scholar
  17. 17.
    Kang JS, Calvo BF, Maygarden SJ et al. Dysregulation of annexin 1 protein expression in high-grade prostatic intraepithelial neoplasia and prostate cancer. Clin Cancer Res 2002; 8(1):117–123.PubMedGoogle Scholar
  18. 18.
    Paweletz CP, Ornstein DK, Roth MJ et al. Loss of annexin 1 correlates with early onset of tumorigenesis in esophageal and prostate carcinoma. Cancer Res 2000; 60(22):6293–6297.PubMedGoogle Scholar
  19. 19.
    Yee DS, Narula N, Ramzy I et al. Reduced annexin II protein expression in high-grade prostatic intraepithelial neoplasia and prostate cancer. Arch Pathol Lab Med 2007; 131(6):902–908.PubMedGoogle Scholar
  20. 20.
    Burgemeister R. New aspects of laser microdissection in research and routine. J Histochem Cytochem 2005; 53(3):409–412.PubMedGoogle Scholar
  21. 21.
    Wulfkuhle JD, Sgroi DC, Krutzsch H et al. Proteomics of human breast ductal carcinoma in situ. Cancer Res 2002; 62(22):6740–6749.PubMedGoogle Scholar
  22. 22.
    Xu BJ, Caprioli RM, Sanders ME et al. Direct analysis of laser capture microdissected cells by MALDI mass spectrometry. J Am Soc Mass Spectrom 2002; 13(11):1292–1297.PubMedGoogle Scholar
  23. 23.
    Zang L, Palmer Toy D, Hancock WS et al. Proteomic analysis of ductal carcinoma of the breast using laser capture microdissection, LC-MS and 160/180 isotopic labeling. J Proteome Res 2004; 3(3):604–612.PubMedGoogle Scholar
  24. 24.
    Zhang DH, Tai LK, Wong LL et al. Proteomics of breast cancer: enhanced expression of cytokeratin 19 in human epidermal growth factor receptor type 2 positive breast tumors. Proteomics 2005; 5(7):1797–1805.PubMedGoogle Scholar
  25. 25.
    Nakagawa T, Huang SK, Martinez SR et al. Proteomic profiling of primary breast cancer predicts axillary lymph node metastasis. Cancer Res 2006; 66(24):11825–11830.PubMedGoogle Scholar
  26. 26.
    Neubauer H, Clare SE, Kurek R et al. Breast cancer proteomics by laser capture microdissection, sample pooling, 54-cm IPG IEF and differential iodine radioisotope detection. Electrophoresis 2006; 27(9):1840–1852.PubMedGoogle Scholar
  27. 27.
    Yang F, Foekens JA, Yu J et al. Laser microdissection and microarray analysis of breast tumors reveal ER-alpha related genes and pathways. Oncogene 2006; 25(9):1413–1419.PubMedGoogle Scholar
  28. 28.
    Umar A, Luider TM, Foekens JA et al. Nano LC-FT-ICR MS improves proteome coverage attainable for approximately 3000 laser-microdissected breast carcinoma cells. Proteomics 2007; 7(2):323–329.PubMedGoogle Scholar
  29. 29.
    Cowherd SM, Espina VA, Petricoin EF 3rd, Liotta LA. Proteomic analysis of human breast cancer tissue with laser-capture microdissection and reverse-phase protein microarrays. Clin Breast Cancer, 2004; 5(5):385–392.PubMedGoogle Scholar
  30. 30.
    Takeshima Y, Amatya VJ, Daimaru Y et al. Heterogeneous genetic alterations in ovarian mucinous tumors: application and usefulness of laser capture microdissection. Hum Pathol 2001; 32(11):1203–1208.PubMedGoogle Scholar
  31. 31.
    Jones MB, Krutzsch H, Shu H et al. Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer. Proteomics 2002; 2(1):76–84.PubMedGoogle Scholar
  32. 32.
    Shekouh AR, Thompson CC, Prime W et al. Application of laser capture microdissection combined with two-dimensional electrophoresis for the discovery of differentially regulated proteins in pancreatic ductal adenocarcinoma. Proteomics 2003; 3(10):1988–2001.PubMedGoogle Scholar
  33. 33.
    Unwin RD, Craven RA, Harnden P et al. Proteomic changes in renal cancer and co-ordinate demonstration of both the glycolytic and mitochondrial aspects of the Warburg effect. Proteomics 2003; 3(8):1620–1632.PubMedGoogle Scholar
  34. 34.
    Peehl DM. Primary cell cultures as models of prostate cancer development. Endocr Relat Cancer 2005; 12(1):19–47.PubMedGoogle Scholar
  35. 35.
    Everley PA, Bakalarski CE, Elias JE et al. Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation. J Proteome Res 2006; 5(5):1224–1231.PubMedGoogle Scholar
  36. 36.
    Craven RA, Stanley AJ, Hanrahan S et al. Proteomic analysis of primary cell lines identifies protein changes present in renal cell carcinoma. Proteomics 2006; 6(9):2853–2864.PubMedGoogle Scholar
  37. 37.
    Chen R, Pan S, Brentnall TA et al. Proteomic profiling of pancreatic cancer for biomarker discovery. Mol Cell Proteomics 2005; 4(4):523–533.PubMedGoogle Scholar
  38. 38.
    Sarto C, Valsecchi C, Mocarelli P. Renal cell carcinoma: handling and treatment. Proteomics 2002; 2(11):1627–1629.PubMedGoogle Scholar
  39. 39.
    Issaq HJ, Veenstra TD. The role of electrophoresis in disease biomarker discovery. Electrophoresis 2007; 28(12):1980–1988.PubMedGoogle Scholar
  40. 40.
    Diamandis EP. Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations. Mol Cell Proteomics 2004; 3(4)367–378.PubMedGoogle Scholar
  41. 41.
    Domon B, Broder S. Implications of new proteomics strategies for biology and medicine. J Proteome Res 2004; 3(2):253–260.PubMedGoogle Scholar
  42. 42.
    Hortin GL. The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome. Clin Chem 2006; 52(7):1223–1237.PubMedGoogle Scholar
  43. 43.
    Schiffer E, Mischak H, Novak J. High resolution proteome/peptidome analysis of body fluids by capillary electrophoresis coupled with MS. Proteomics 2006; 6(20):5615–5627.PubMedGoogle Scholar
  44. 44.
    van der Merwe DE, Oikonomopoulou K, Marshall J et al. Mass spectrometry: uncovering the cancer proteome for diagnostics. Adv Cancer Res 2007; 96:23–50.PubMedGoogle Scholar
  45. 45.
    Semmes OJ, Malik G, Ward M. Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer. J Cell Biochem 2006; 98(3):496–503.PubMedGoogle Scholar
  46. 46.
    McLean JA, Ridenour WB, Caprioli RM. Profiling and imaging of tissues by imaging ion mobility-mass spectrometry. J Mass Spectrom 2007; 42(8):1099–1105.PubMedGoogle Scholar
  47. 47.
    Chaurand P, Sanders ME, Jensen RA et al. Proteomics in diagnostic pathology: profiling and imaging proteins directly in tissue sections. Am J Pathol 2004; 165(4):1057–1068.PubMedGoogle Scholar
  48. 48.
    Schwartz SA, Reyzer ML, Caprioli RM. Direct tissue analysis using matrix-assisted laser desorption/ionization mass spectrometry: practical aspects of sample preparation. J Mass Spectrom 2003; 38(7):699–708.PubMedGoogle Scholar
  49. 49.
    Chaurand P, Schwartz SA, Caprioli RM. Assessing protein patterns in disease using imaging mass spectrometry. J Proteome Res 2004; 3(2):245–252.PubMedGoogle Scholar
  50. 50.
    Cornett DS, Mobley JA, Dias EC et al. A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer. Mol Cell Proteomics 2006; 5(10):1975–1983.PubMedGoogle Scholar
  51. 51.
    Merchant M, Weinberger SR. Recent advancements in surface-enhanced laser desorption/ionization-time of flight-mass spectrometry. Electrophoresis 2000; 21(6):1164–1177.PubMedGoogle Scholar
  52. 52.
    Verma M, Wright GL Jr, Hanash SM et al. Proteomic approaches within the NCI early detection research network for the discovery and identification of cancer biomarkers. Ann N Y Acad Sci 2001; 945:103–115.PubMedGoogle Scholar
  53. 53.
    von Eggeling F, Junker K, Fiedle W et al. Mass spectrometry meets chip technology: a new proteomic tool in cancer research? Electrophoresis 2001; 22(14):2898–2902.Google Scholar
  54. 54.
    Paweletz CP, Trock B, Pennanen M et al. Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer. Dis Markers 2001; 17(4):301–307.PubMedGoogle Scholar
  55. 55.
    Pawlik TM, Fritsche H, Coombes KR et al. Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry. Breast Cancer Res Treat 2005; 89(2):149–157.PubMedGoogle Scholar
  56. 56.
    Sauter ER, Zhu W, Fan XJ et al. Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer. Br J Cancer 2002; 86(9):1440–1443.PubMedGoogle Scholar
  57. 57.
    Cazares LH, Adam BL, Ward MD et al. Normal, benign, preneoplastic and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ionization mass spectrometry. Clin Cancer Res 2002; 8(8):2541–2552.PubMedGoogle Scholar
  58. 58.
    Cheung PK, Woolcock B, Adomat H et al. Protein profiling of microdissected, prostate tissue links growth differentiation factor 15 to prostate carcinogenesis. Cancer Res 2004; 64(17):5929–5933.PubMedGoogle Scholar
  59. 59.
    Hara T, Honda K, Ono M et al. Identification of 2 serum biomarkers of renal cell carcinoma by surface enhanced laser desorption/ionization mass spectrometry. J Urol 2005; 174(4 Pt 1):1213–1217.PubMedGoogle Scholar
  60. 60.
    Rogers MA, Clarke P, Noble J et al. Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility. Cancer Res 2003; 63(20):6971–6983.PubMedGoogle Scholar
  61. 61.
    Kohli M, Siegel E, Bhattacharya S et al. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for determining prognosis in advanced stage hormone relapsing prostate cancer. Cancer Biomark 2006; 2(6):249–258.PubMedGoogle Scholar
  62. 62.
    Menard C, Johann D, Lowenthal M et al. Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis. Cancer Res 2006; 66(3):1844–1850.PubMedGoogle Scholar
  63. 63.
    Suriano R, Lin Y, Ashok BT et al. Pilot study using SELDI-TOF-MS based proteomic profile for the identification of diagnostic biomarkers of thyroid proliferative diseases. J Proteome Res 2006; 5(4):856–861.PubMedGoogle Scholar
  64. 64.
    Nakamura K, Yoshikawa K, Yamada Y et al. Differential profiling analysis of proteins involved in anti-proliferative effect of interferon-alpha on renal call carcinoma cell lines by protein biochip technology. Int J Oncol 2006; 28(4):965–970.PubMedGoogle Scholar
  65. 65.
    Petricoin EF, Ardekani AM, Hitt BA et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002; 359(9306):572–577.PubMedGoogle Scholar
  66. 66.
    Conrads TP, Zhou M, Petricoin EF et al. Cancer diagnosis using proteomic patterns. Expert Rev Mol Diagn 2003; 3(4):411–420.PubMedGoogle Scholar
  67. 67.
    Ornstein DK, Rayford W, Fusaro VA et al. Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml. J Urol 2004; 172(4 Pt 1):1302–1305.PubMedGoogle Scholar
  68. 68.
    Petricoin EF 3rd, Ornstein DK, Paweletz CP et al. Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 2002; 94(20):1576–1578.PubMedGoogle Scholar
  69. 69.
    Lowenthal MS, Mehta AI, Frogale K et al. Analysis of albumin-associated peptides and proteins from ovarian cancer patients. Clin Chem 2005; 51(10):1933–1945.PubMedGoogle Scholar
  70. 70.
    Pieper R, Gatlin CL, McGrath AM et al. Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots. Proteomics 2004; 4(4):1159–1174.PubMedGoogle Scholar
  71. 71.
    Pisitkun T, Johnstone R, Knepper MA. Discovery of urinary biomarkers. Mol Cell Proteomics 2006; 5(10):1760–1771.PubMedGoogle Scholar
  72. 72.
    M’Koma AE, Blum DL, Norris JL et al. Detection of preneoplastic and neoplastic prostate disease by MALDI profiling of urine. Biochem Biophys Res Commun 2007; 353(3):829–834.Google Scholar
  73. 73.
    Rehman I, Azzouzi AR, Catto JW et al. Proteomic analysis of voided urine after prostatic massage from patients with prostate cancer: a pilot study. Urology 2004; 64(6):1238–1243.PubMedGoogle Scholar
  74. 74.
    Perroud B, Lee J, Valkova N et al. Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer 2006; 5:64.PubMedGoogle Scholar
  75. 75.
    Alexander H, Stegner AL, Wagner-Mann C et al. Proteomic analysis to identify breast cancer biomarkers in nipple aspirate fluid. Clin Cancer Res 2004; 10(22):7500–7510.PubMedGoogle Scholar
  76. 76.
    Pawlik TM, Hawke DH, Liu Y et al. Proteomic analysis of nipple aspirate fluid from women with early-stage breast cancer using isotope-coded affinity tags and tandem mass spectrometry reveals differential expression of vitamin D binding protein. BMC Cancer 2006; 6:68.PubMedGoogle Scholar
  77. 77.
    Gronborg M, Bunkenborg J, Kristiansen TZ et al. Comprehensive proteomic analysis of human pancreatic juice. J Proteome Res 2004; 3(5):1042–1055.PubMedGoogle Scholar
  78. 78.
    Stingl J, Eirew P, Ricketson I et al. Purification and unique properties of mammary epithelial stem cells. Nature 2006; 439(7079):993–997.PubMedGoogle Scholar
  79. 79.
    Fata JE, Werb Z, Bissell MJ. Regulation of mammary gland branching morphogenesis by the extracellular matrix and its remodeling enzymes. Breast Cancer Res 2004; 6(1):1–11.PubMedGoogle Scholar
  80. 80.
    Celis JE, Moreira JM, Cabezon T et al. Identification of extracellular and intracellular signaling components of the mammary adipose tissue and its interstitial fluid in high risk breast cancer patients: toward dissecting the molecular circuitry of epithelial-adipocyte stromal cell interactions. Mol Cell Proteomics 2005; 4(4):492–522.PubMedGoogle Scholar
  81. 81.
    Litvinov IV, Vander Griend DJ, Xu Y et al. Low-calcium serum-free defined medium selects for growth of normal prostatic epithelial stem cells. Cancer Res 2006; 66(17):8598–8607.PubMedGoogle Scholar
  82. 82.
    Gulmann C, Sheehan KM, Kay EW et al. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics and therapy guidance in cancer. J Pathol 2006; 208(5):595–606.PubMedGoogle Scholar
  83. 83.
    Drukier AK, Ossetrova N, Schors E et al. High-sensitivity blood-based detection of breast cancer by multi photon detection diagnostic proteomics. J Proteome Res 2006; 5(8):1906–1915.PubMedGoogle Scholar
  84. 84.
    Yurkovetsky Z, Ta’asan S, Skates S et al. Development of multimarker panel for early detection of endometrial cancer. High diagnostic power of prolactin. Gynecol Oncol 2007; 107(1):58–65.PubMedGoogle Scholar
  85. 85.
    Casiano CA, Mediavilla-Varela M, Tan EM. Tumor-associated antigen arrays for the serological diagnosis of cancer. Mol Cell Proteomics 2006; 5(10):1745–1759.PubMedGoogle Scholar
  86. 86.
    Qin S, Qiu W, Ehrlich JR et al. Development of a “reverse capture” autoantibody microarray for studies of antigen-autoantibody profiling. Proteomics 2006; 6(10):3199–3209.PubMedGoogle Scholar
  87. 87.
    Varambally S, Yu J, Laxman B et al. Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. Cancer Cell 2005; 8(5):393–406.PubMedGoogle Scholar
  88. 88.
    Wozny W, Schroer K, Schwall GP et al. Differential radioactive quantification of protein abundance ratios between benign and malignant prostate tissues: cancer association of annexin A3. Proteomics 2007; 7(2):313–322.PubMedGoogle Scholar
  89. 89.
    Lexander H, Palmberg C, Hellman U et al. Correlation of protein expression, Gleason score and DNA ploidy in prostate cancer. Proteomics 2006; 6(15):4370–4380.PubMedGoogle Scholar
  90. 90.
    Ahram M, Best CJ, Flaig MJ et al. Proteomic analysis of human prostate cancer. Mol Carcinog 2002; 33(1):9–15.PubMedGoogle Scholar
  91. 91.
    Hwang SI, Thumar J, Lundgren DH et al. Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene 2007; 26(1):65–76.PubMedGoogle Scholar
  92. 92.
    Hood BL, Darfler MM, Guiel TG et al. Proteomic analysis of formalin-fixed prostate cancer tissue. Mol Cell Proteomics 2005; 4(11):1741–1753.PubMedGoogle Scholar
  93. 93.
    Ornstein DK, Gillespie JW, Paweletz CP et al. Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 2000; 21(11):2235–2242.PubMedGoogle Scholar
  94. 94.
    Wright ME, Tsai MJ, Aebersold R. Androgen receptor represses the neuroendocrine transdifferentiation process in prostate cancer cells. Mol Endocrinol 2003; 17(9):1726–1737.PubMedGoogle Scholar
  95. 95.
    Martin DB, Gifford DR, Wright ME et al. Quantitative proteomic analysis of proteins released by neoplastic prostate epithelium. Cancer Res 2004; 64(1):347–355.PubMedGoogle Scholar
  96. 96.
    Rowland JG; Robson JL, Simon WJ et al. Evaluation of an in vitro model of androgen ablation and identification of the androgen responsive proteome in LNCaP cells. Proteomics 2007; 7(1):47–63.PubMedGoogle Scholar
  97. 97.
    Meehan KL, Sadar MD. Quantitative profiling of LNCaP prostate cancer cells using isotope-coded affinity tags and mass spectrometry. Proteomics 2004; 4(4):1116–1134.PubMedGoogle Scholar
  98. 98.
    Zhang D, Tai LK, Wong LL et al. Proteomic study reveals that proteins involved in metabolic and detoxification pathways are highly expressed in HER-2/neu-positive breast cancer. Mol Cell Proteomics 2005; 4(11):1686–1696.PubMedGoogle Scholar
  99. 99.
    Lopez MF, Mikulskis A, Kuzdzal S et al. A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples. Clin Chem 2007; 53(6):1067–1074.PubMedGoogle Scholar
  100. 100.
    Wulfkuhle JD, Aquino JA, Calvert VS et al. Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics 2003; 3(11):2085–2090.PubMedGoogle Scholar
  101. 101.
    Bhattacharyya S, Siegel ER, Petersen GM et al. Diagnosis of pancreatic cancer using serum proteomic profiling. Neoplasia 2004; 6(5):674–686.PubMedGoogle Scholar
  102. 102.
    Yu KH, Rustgi AK, Blair IA. Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry. J Proteome Res 2005; 4(5):1742–1751.PubMedGoogle Scholar
  103. 103.
    Honda K, Hayashida Y, Umaki T et al. Possible detection of pancreatic cancer by plasma protein profiling. Cancer Res 2005; 65(22):10613–10622.PubMedGoogle Scholar
  104. 104.
    Lin Y, Goedegebuure PS, Tan MC et al. Proteins associated with disease and clinical course in pancreas cancer: a proteomic analysis of plasma in surgical patients. J Proteome Res 2006; 5(9):2169–2176.PubMedGoogle Scholar
  105. 105.
    Deng R, Lu Z, Chen Y et al. Plasma proteomic analysis of pancreatic cancer by 2-dimensional gel electrophoresis. Pancreas 2007; 34(3):310–317.PubMedGoogle Scholar
  106. 106.
    Crnogorac-Jurcevic T, Gangeswaran R, Bhakta V et al. Proteomic analysis of chronic pancreatitis and pancreatic adenocarcinoma. Gastroenterology 2005; 129(5):1454–1463.PubMedGoogle Scholar
  107. 107.
    Sitek B, Luttges J, Marcus K et al. Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma. Proteomics 2005; 5(10):2665–2679.PubMedGoogle Scholar
  108. 108.
    Gronborg M, Kristiansen TZ, Iwahori A et al. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol Cell Proteomics 2006; 5(1):157–171.PubMedGoogle Scholar
  109. 109.
    Marengo E, Robotti E, Cecconi D et al. Identification of the regulatory proteins in human pancreatic cancers treated with Trichostatin A by 2D-PAGE maps and multivariate statistical analysis. Anal Bioanal Chem 2004; 379(7–8):992–1003.PubMedGoogle Scholar
  110. 110.
    Cecconi D, Donadelli M, Scarpa A et al. Proteomic analysis of pancreatic ductal carcinoma cells after combined treatment with gemcitabine and trichostatin A. J Proteome Res 2005; 4(6):1909–1916.PubMedGoogle Scholar
  111. 111.
    Hwa JS, Park HJ, Jung JH et al. Identification of proteins differentially expressed in the conventional renal cell carcinoma by proteomic analysis. J Korean Med Sci 2005; 20(3):450–455.PubMedGoogle Scholar
  112. 112.
    Poznanovic S, Wozny W, Schwall GP et al. Differential radioactive proteomic analysis of microdissected renal cell carcinoma tissue by 54 cm isoelectric focusing in serial immobilized pH gradient gels. J Proteome Res 2005; 4(6):2117–2125.PubMedGoogle Scholar
  113. 113.
    Zhuang Z, Huang S, Kowalak JA et al. From tissue phenotype to proteotype: sensitive protein identification in microdissected tumor tissue. Int J Oncol 2006; 28(1):103–110.PubMedGoogle Scholar
  114. 114.
    Perego RA, Bianchi C, Corizzato M et al. Primary cell cultures arising from normal kidney and renal cell carcinoma retain the proteomic profile of corresponding tissues. J Proteome Res 2005; 4(5):1503–1510.PubMedGoogle Scholar
  115. 115.
    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):1840–1852.PubMedGoogle Scholar
  116. 116.
    Brown LM, Helmke SM, Hunsucker SW et al. Quantitative and qualitative differences in protein expression between papillary thyroid carcinoma and normal thyroid tissue. Mol Carcinog 2006; 45(8):613–626.PubMedGoogle Scholar
  117. 117.
    Torres-Cabala C, Bibbo M, Panizo-Santos A et al. Proteomic identification of new biomarkers and application in thyroid cytology. Acta Cytol 2006; 50(5):515–528.Google Scholar
  118. 118.
    Eriksson J, Fenyo D. Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs. Nat Biotechnol 2007; 25(6):651–655.PubMedGoogle Scholar
  119. 119.
    Park Y, Downing SR, Kim D et al. Simultaneous and exact interval estimates for the contrast of two groups based on an extremely high dimensional variable: application to mass spec data. Bioinformatics 2007; 23(12):1451–1458.PubMedGoogle Scholar
  120. 120.
    Lubovac Z, Gamalielsson J, Olsson B. Combining functional and topological properties to identify core modules in protein interaction networks. Proteins 2006; 64(4):948–959.PubMedGoogle Scholar
  121. 121.
    Bernaschi M, Castiglione F, Ferranti A et al. ProtNet: a tool for stochastic simulations of protein interaction networks dynamics. BMC Bioinformatics 2007; 8 Suppl 1:S4.PubMedGoogle Scholar
  122. 122.
    Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA et al. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc Natl Acad Sci USA 2007; 104(14):5860–5865.PubMedGoogle Scholar
  123. 123.
    Shannon P, Markiel A, Ozier O et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11):2498–2504.PubMedGoogle Scholar
  124. 124.
    Tangrea MA, Chuaqui RF, Gillespie JW et al. Expression microdissection: operator-independent retrieval of cells for molecular profiling. Diagn Mol Pathol 2004; 13(4):207–212.PubMedGoogle Scholar
  125. 125.
    Buckanovich RJ, Sasaroli D, O’Brien-Jenkins A et al. Use of immuno-LCM to identify the in situ expression profile of cellular constituents of the tumor microenvironment. Cancer Biol Ther 2006; 5(6):635–642.PubMedGoogle Scholar

Copyright information

© Landes Bioscience and Springer Science+Business Media 2008

Authors and Affiliations

  1. 1.Departments of Urology and Physiology and BiophysicsUniversity of California Irvine Medical CenterOrangeUSA
  2. 2.Department of UrologyUniversity of California IrvineMedical CenterOrangeUSA

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