Skip to main content
SpringerLink
Log in

Identifizierung von Biomarkern und therapeutischen Targets beim Nierenzellkarzinom mittels ProteinChip-Technologie

Identification of biomarkers and therapeutic targets for renal cell cancer using ProteinChip technology

  • Leitthema
  • Published:
Der Urologe Aims and scope Submit manuscript

Zusammenfassung

Zur komplexen Erfassung tumorbiologischer Veränderungen ist es notwendig, neben der DNA- und RNA-Ebene auch die Proteinebene zu analysieren. Mit der Methode der SELDI-TOF-MS (surface enhanced laser desorption/ionization time-of-flight mass spectrometry) steht nun ein Verfahren zur Verfügung, das die hochsensitive Detektion von spezifischen Proteinprofilen bei hohem Probendurchsatz erlaubt. Für die Nierentumoren konnten inzwischen spezifische Proteinmuster im Serum aufgezeigt werden, die die Grundlage für die Entwicklung spezifischer Biomarker darstellen. Erste Proteine, wie das „Serumamyloid Alpha“ (SAA) konnten identifiziert werden.

Die Analyse der Tumorgewebe wird zur Aufklärung der Tumorbiologie und zur Verbesserung der Subklassifizierung auch unter Berücksichtigung der Prognose führen. Proteomanalysen in Korrelation zur Therapie eröffnen die Möglichkeit der Aufklärung der biologischen Therapieeffekte einerseits und die Identifizierung von Biomarkern zur Patientenselektion und Therapieüberwachung andererseits. Erste Ansätze bei Nierenzelltumoren werden aufgezeigt.

Abstract

In order to understand tumour biology in its complexity, it is necessary to investigate the proteomics in addition to the DNA and RNA level. SELDI-TOF-MS represents a new technology allowing a highly sensitive high-throughput analysis to detect specific protein profiles. In renal cancer, it was possible to define specific protein patterns in serum. Several proteins have been identified, i.e. serum amyloid alpha (SAA).

Analysis of tumour tissues leads to a better understanding of tumour biology and provides the basis for differential classification and evaluation of prognosis. Investigation of the proteome concerning therapy results opens up the possibility of assessing downstream effects on the one hand and identifying biomarkers for selection of patients and therapy monitoring on the other hand. This review presents the first results for renal cancer.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Abb. 1
Abb. 2
Abb. 3
Abb. 4
Abb. 5

Literatur

  1. Adam BL, Qu Y, Davis JW et al. (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

    PubMed  Google Scholar 

  2. Banerjee H, Hawkins Z, Williams J et al. (2004) Search for a novel biomarker for the brain cancer astrocytoma by using surface enhanced laser desorption/ionisation (SELDI) technique. Cell Mol Biol (Noisy-le-grand) 50: 733–736

  3. Britten RA, Hardy C, Vlahou A, Gregory B, Giri PS, Drake R (2005) Identification of reproducible low mass SELDI protein profiles specific to cisplatin resistance in human ovarian cancer cells. Oncol Rep 14: 1323–1330

    PubMed  Google Scholar 

  4. Cadieux PA, Beiko DT, Watterson JD et al. (2004) Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS): a new proteomic urinary test for patients with urolithiasis. J Clin Lab Anal 18: 170–175

    Article  PubMed  Google Scholar 

  5. Clarke W, Silverman BC, Zhang Z, Chan DW, Klein AS, Molmenti EP (2003) Characterization of renal allograft rejection by urinary proteomic analysis. Ann Surg 237: 660–665

    Article  PubMed  Google Scholar 

  6. Combaret V, Bergeron C, Brejon S, Iacono I, Perol D, Negrier S, Puisieux A (2005) Protein chip array profiling analysis of sera from neuroblastoma patients. Cancer Lett 228: 91–96

    Article  PubMed  Google Scholar 

  7. Ernst G, Melle C, Schimmel B, Bleul A, von Eggeling F (2006) Proteohistography — Direct analysis of tissue with high sensitivity and high spatial resolution using ProteinChip technology. J Histochem Cytochem 54: 13–17

    Article  PubMed  Google Scholar 

  8. Fetsch PA, Simone NL, Bryant-Greenwood PK et al. (2002) Proteomic evaluation of archival cytologic material using SELDI affinity mass spectrometry: potential for diagnostic applications. Am J Clin Pathol 118: 870–876

    Article  PubMed  Google Scholar 

  9. Heike Y, Hosokawa M, Osumi S et al. (2005) Identification of serum proteins related to adverse effects induced by docetaxel infusion from protein expression profiles of serum using SELDI ProteinChip system. Anticancer Res 25: 1197–1203

    PubMed  Google Scholar 

  10. Junker K, Gneist J, Melle C, Driesch D, Schubert J, Claussen U, von Eggeling F (2005) Identification of protein pattern in kidney cancer using ProteinChip arrays and bioinformatics. Int J Mol Med 15: 285–290

    PubMed  Google Scholar 

  11. Khan N, Cromer CJ, Campa M, Patz EF Jr (2004) Clinical utility of serum amyloid A and macrophage migration inhibitory factor as serum biomarkers for the detection of nonsmall cell lung carcinoma. Cancer 101: 379–384

    Article  PubMed  Google Scholar 

  12. Kong F, Nicole White C et al. (2005) Using proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer. Gynecol Oncol 2005

  13. Kozak KR, Su F, Whitelegge JP, Faull K, Reddy S, Farias-Eisner R (2005) Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics 5: 4589–4596

    Article  PubMed  Google Scholar 

  14. Krieg RC, Fogt F, Braunschweig T, Herrmann PC, Wollscheidt V, Wellmann A (2004) ProteinChip Array analysis of microdissected colorectal carcinoma and associated tumor stroma shows specific protein bands in the 3.4 to 3.6 kDa range. Anticancer Res 24: 1791–1796

    PubMed  Google Scholar 

  15. Le L, Chi K, Tyldesley S, Flibotte S, Diamond DL, Kuzyk MA, Sadar MD (2005) Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions. Clin Chem 51: 695–707

    Article  PubMed  Google Scholar 

  16. Li J, Zhang Z, Rosenzweig J, Wang YY, Chan DW (2002) Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem 48: 1296–1304

    PubMed  Google Scholar 

  17. Liu W, Guan M, Wu D, Zhang Y, Wu Z, Xu M, Lu Y (2005) Using tree analysis pattern and SELDI-TOF-MS to discriminate transitional cell carcinoma of the bladder cancer from noncancer patients. Eur Urol 47: 456–462

    Article  PubMed  Google Scholar 

  18. Melle C, Kaufmann R, Hommann M, Bleul A, Driesch D, Ernst G, von Eggeling F (2004) Proteomic profiling in microdissected hepatocellular carcinoma tissue using ProteinChip technology. Int J Oncol 24: 885–891

    PubMed  Google Scholar 

  19. Mueller J, von Eggeling F, Driesch D, Schubert J, Melle C, Junker K (2005) ProteinChip technology reveals distinctive protein expression profiles in the urine of bladder cancer patients. Eur Urol 47: 885–894

    Article  PubMed  Google Scholar 

  20. O’Riordan E, Goligorsky MS (2005) Emerging studies of the urinary proteome: the end of the beginning? Curr Opin Nephrol Hypertens 14: 579–585

    PubMed  Google Scholar 

  21. Petricoin EF 3rd, Ornstein DK, Paweletz CP et al. (2002) Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 94: 1576–1578

    PubMed  Google Scholar 

  22. Petricoin EF, Ardekani AM, Hitt BA et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359: 572–577

    Article  PubMed  Google Scholar 

  23. Pusztai L, Gregory BW, Baggerly KA et al. (2004) Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer 100: 1814–1822

    Article  PubMed  Google Scholar 

  24. Rogers MA, Clarke P, Noble J, Munro NP, Paul A, Selby PJ, Banks RE (2003) 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 63: 6971–6983

    PubMed  Google Scholar 

  25. Rossi L, Martin BM, Hortin GL et al. (2005) Inflammatory protein profile during systemic high dose interleukin-2 administration. Proteomics 5: 2385–2395

    Article  PubMed  Google Scholar 

  26. Schaub S, Rush D, Wilkins J et al. (2004) Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J Am Soc Nephrol 15: 219–227

    Article  PubMed  Google Scholar 

  27. Schaub S, Wilkins J, Weiler T, Sangster K, Rush D, Nickerson P (2004) Urine protein profiling with surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry. Kidney Int 65: 323–332

    Article  PubMed  Google Scholar 

  28. Tolson J, Bogumil R, Brunst E et al. (2004) Serum protein profiling by SELDI mass spectrometry: detection of multiple variants of serum amyloid alpha in renal cancer patients. Lab Invest 84: 845–856

    Article  PubMed  Google Scholar 

  29. Won Y, Song HJ, Kang TW, Kim JJ, Han BD, Lee SW (2003) Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons. Proteomics 3: 2310–2316

    Article  PubMed  Google Scholar 

  30. Wong YF, Cheung TH, Lo KW, Wang VW, Chan CS, Ng TB, Chung TK, Mok SC (2004) Protein profiling of cervical cancer by protein-biochips: proteomic scoring to discriminate cervical cancer from normal cervix. Cancer Lett 211: 227–234

    Article  PubMed  Google Scholar 

  31. Yokoi K, Shih LC, Kobayashi R et al. (2005) Serum amyloid A as a tumor marker in sera of nude mice with orthotopic human pancreatic cancer and in plasma of patients with pancreatic cancer. Int J Oncol 27: 1361–1369

    PubMed  Google Scholar 

Download references

Danksagung

Wir danken Dr. Andreas Wiesner Ciphergen Biosystems, Inc. für die Bereitstellung von ausgewählten Abbildungen.

Interessenkonflikt:

Der korrespondierende Autor weist auf eine Verbindung mit folgender Firma/Firmen hin: Grundlage der Technik ist das nur von der Firma „Ciphergen“ produzierte System.

Author information

Authors and Affiliations

  1. Klinik für Urologie, Universitätsklinik der FSU, Jena

    K. Junker, F. von Eggeling, J. Müller, T. Steiner & J. Schubert

  2. Core Unit Chip-Application, Institut für Humangenetik und Anthropologie, FSU Jena

    F. von Eggeling

  3. Klinik für Urologie, Klinikum der FSU, Lessingstraße 1, 07743, Jena

    K. Junker

Authors
  1. K. Junker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. von Eggeling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Steiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Schubert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Junker.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Junker, K., von Eggeling, F., Müller, J. et al. Identifizierung von Biomarkern und therapeutischen Targets beim Nierenzellkarzinom mittels ProteinChip-Technologie. Urologe 45, 305–315 (2006). https://doi.org/10.1007/s00120-006-1001-2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00120-006-1001-2

Schlüsselwörter

Keywords

Search

Navigation