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Microarrays

Technik und Potenzial beim Prostatakarzinom

Microarrays

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Zusammenfassung

Microarrays erlauben die simultane Analyse der Expression tausender Gene und liefern so ein Expressionsprofil der untersuchten Probe. Damit ist dieses Verfahren besonders geeignet, die komplexen genetischen Veränderungen maligner Tumoren zu charakterisieren. Die Auswertung durch sog. „unbeaufsichtigte hierarchische Clusteranalysen“ zeigt, dass charakteristische Expressionsprofile für Individuen, Organe und Gewebe sowie verschiedene Zelltypen bestehen. Bei Tumoren konnten „molekulare Signaturen“ im Vergleich zum Normalgewebe für unterschiedliche Tumorstadien und Risikogruppen, aber auch für das Ansprechen auf eine bestimmte Therapie beobachtet werden.

Beim Prostatakarzinom konnten zahlreiche tumorspezifische Expressionsveränderungen identifiziert werden. Von diesen scheinen insbesondere die Zelloberflächenprotease Hepsin und die α-Methyl-Acryl-CoA-Racemase Bedeutung für die Diagnosestellung zu erlangen. Darüber hinaus wurden Genexpressionsprofile identifiziert, die mit fortgeschrittenem Tumorstadium, geringer Differenzierung oder der Progression nach radikaler Prostatektomie assozziiert sind. Eine gesteigerte Expression von Enzymen der Steroidbiosynthese sowie des Androgenrezeptors ist Teil der „molekularen Signatur“ hormonrefraktärer Prostatakarzinome.

Abstract

Microarrays allow a simultaneous gene expression analysis of thousands of genes, providing an expression profile of the specimen investigated. Thus, this procedure is well suited to characterize the complex genetic alterations of malignant tumors. Using unsupervised hierarchical cluster analysis, characteristic expression profiles for individuals, organs and tissues, as well as for different cell types, can be identified. Molecular signatures have been observed in tumors compared to normal tissue, for different tumor stages, risk groups or response therapy. In prostate cancer, many tumor-specific gene expression alterations have been identified. Among these, the cell surface protease hepsin and α-methyl-acryl-CoA-racemase might gain importance as diagnostic tools. Moreover, gene expression profiles were identified which are associated with advanced tumor stage, poor differentiation or progress after radical prostatectomy. Increased expression of enzymes of steroid biosynthesis and the androgen receptor appears to be part of the molecular signature of hormone refractory prostate cancer.

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Literatur

  1. Alizadeh AA, Eisen MB, Davis RE et al. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403: 503–511

    PubMed  Google Scholar 

  2. Brazma A, Hingamp P, Quackenbush J et al. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29: 365–371

    CAS  PubMed  Google Scholar 

  3. Bubendorf L, Kolmer M, Kononen J et al. (1999) Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays. J Natl Cancer Inst 91: 1758–1764

    CAS  PubMed  Google Scholar 

  4. Bueno R, Loughlin K-R, Powell MH, Gordon GJ (2004) A diagnostic test for prostate cancer from gene expression profiling data. J Urol 171: 903–906

    CAS  PubMed  Google Scholar 

  5. Bull JH, Ellison G, Patel A et al. (2001) Identification of potential diagnostic markers of prostate cancer and prostatic intraepithelial neoplasia using cDNA microarray. Br J Cancer 84: 1512–1519

    Article  CAS  PubMed  Google Scholar 

  6. Chuaqui RF, Bonner RF, Best CJ et al. (2002) Post-analysis follow-up and validation of microarray experiments. Nat Genet 32 (Suppl): 509–514

    Article  CAS  Google Scholar 

  7. Chung CH, Bernard PS, Perou CM (2002) Molecular portraits and the family tree of cancer. Nat Genet 32 (Suppl): 533–540

    Article  CAS  PubMed  Google Scholar 

  8. Dhanasekaran SM, Barrette TR, Ghosh D et al. (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412: 822–826

    CAS  PubMed  Google Scholar 

  9. Dong JT (2001) Chromosomal deletions and tumor suppressor genes in prostate cancer. Cancer Metastasis Rev 20: 173–193

    Article  CAS  PubMed  Google Scholar 

  10. Duggan BJ, McKnight JJ, Williamson KE et al. (2003) The need to embrace molecular profiling of tumor cells in prostate and bladder cancer. Clin Cancer Res 9: 1240–1247

    CAS  PubMed  Google Scholar 

  11. Ellwood-Yen K, Graeber TG, Wongvipat J et al. (2003) Myc-driven murine prostate cancer shares molecular features with human prostate tumors. Cancer Cell 4: 223–238

    Article  CAS  PubMed  Google Scholar 

  12. Ernst T, Hergenhahn M, Kenzelmann M et al. (2002) Decrease and gain of gene expression are equally discriminatory markers for prostate carcinoma: a gene expression analysis on total and microdissected prostate tissue. Am J Pathol 160: 2169–2180

    CAS  PubMed  Google Scholar 

  13. Giordano TJ, Shedden KA, Schwartz DR et al. (2001) Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles. Am J Pathol 159: 1231–1238

    CAS  PubMed  Google Scholar 

  14. Henshall SM, Afar DEH, Hiller J et al. (2003) Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse. Cancer Res 63: 4196–4203

    CAS  PubMed  Google Scholar 

  15. Hippo Y, Taniguchi H, Tsutsumi S et al. (2002) Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 62: 233–240

    CAS  PubMed  Google Scholar 

  16. Holzbeierlein J, Lal P, LaTulippe E et al. (2004) Gene expression analysis of human prostate carcinoma during hormonal therapy identifies androgen-responsive genes and mechanisms of therapy resistance. Am J Pathol 164: 217–227

    CAS  PubMed  Google Scholar 

  17. Jazaeri AA, Yee CJ, Sotiriou C et al. (2002) Gene expression profiles of BRCA1-linked, BRCA2-linked, and sporadic ovarian cancers. J Natl Cancer Inst 94: 990–1000

    Google Scholar 

  18. Jiang Z, Woda BA, Rock KL et al. (2001) P504S: a new molecular marker for the detection of prostate carcinoma. Am J Surg Pathol 25: 1397–1404

    Article  CAS  PubMed  Google Scholar 

  19. Lapointe J, Li C, Higgins JP et al. (2004) Gene expression profiles identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA 101: 811–816

    Article  CAS  PubMed  Google Scholar 

  20. Latil A, Bieche I, Chene L et al. (2003) Gene expression profiling in clinically localized prostate cancer: a four-gene expression model predicts clinical behaviour. Clin Cancer Res 9: 5477–5488

    CAS  PubMed  Google Scholar 

  21. LaTulippe E, Satagopan J, Smith A et al. (2002) Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease. Cancer Res 62: 4499–4506

    CAS  PubMed  Google Scholar 

  22. Lin YM, Furukawa Y, Tsunoda T et al. (2002) Molecular diagnosis of colorectal tumors by expression profiles of 50 genes expressed differentially in adenomas and carcinomas. Oncogene 21: 4120–4128

    Article  CAS  PubMed  Google Scholar 

  23. Luo J, Duggan DJ, Chen Y et al. (2001) Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res 61: 4683–4688

    CAS  PubMed  Google Scholar 

  24. Luo J, Zha S, Gage WR et al. (2002) Alpha-methylacyl-CoA racemase: a new molecular marker for prostate cancer. Cancer Res 62: 2220–2226

    CAS  PubMed  Google Scholar 

  25. Magee J-A, Araki T, Patil S et al. (2001) Expression profiling reveals hepsin overexpression in prostate cancer. Cancer Res 61: 5692–5696

    CAS  PubMed  Google Scholar 

  26. Mohr S, Leikauf GD, Keith G, Rihn BH (2002) Microarrays as cancer keys: an array of possibilities. J Clin Oncol 20: 3165–3175

    Article  CAS  PubMed  Google Scholar 

  27. Perou CM, Sorlie T, Eisen MB et al. (2000) Molecular portraits of human breast tumours. Nature 406: 747–752

    CAS  PubMed  Google Scholar 

  28. Rhodes DR, Sanda MG, Otte AP, Chinnaiyan AM, Rubin MA (2003) Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer. J Natl Cancer Inst 95:661–668

    Article  CAS  PubMed  Google Scholar 

  29. Rosenwald A, Wright G, Chan WC et al. (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346: 1937–1947

    Google Scholar 

  30. Ross DT, Scherf U, Eisen MB et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 24: 227–235

    Article  CAS  PubMed  Google Scholar 

  31. Schulz WA, Burchardt M, Cronauer MV (2003) Molecular biology of prostate cancer. Mol Hum Reprod 9: 437–448

    Article  CAS  PubMed  Google Scholar 

  32. van’t Veer LJ, Dai H, van de Vijver MJ et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536

    PubMed  Google Scholar 

  33. van de Vijver MJ, He YD, van’t Veer LJ et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009

    Article  PubMed  Google Scholar 

  34. Varambally S, Dhanasekaran SM, Zhou M et al. (2002) The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 419: 624–629

    Article  CAS  PubMed  Google Scholar 

  35. Welsh JB, Sapinoso LM, Su AI et al. (2001) Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 61: 5974–5978

    CAS  PubMed  Google Scholar 

  36. Xu J, Stolk J-A, Zhang X et al. (2000) Identification of differentially expressed genes in human prostate cancer using subtraction and microarray. Cancer Res 60: 1677–1682

    CAS  PubMed  Google Scholar 

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Correspondence to M.-O. Grimm.

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Grimm, MO., Hartmann, F.H. & Schulz, W.A. Microarrays. Urologe [A] 43, 653–658 (2004). https://doi.org/10.1007/s00120-004-0578-6

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