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Neue präklinische Modelle und Biomarker beim Prostatakarzinom

Novel preclinical models and biomarkers for prostate cancer

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Zusammenfassung

Das ultimative Ziel einer personalisierten onkologischen Therapie von Prostatakarzinompatienten beruht auf zwei wichtigen Voraussetzungen: zum einen der Entwicklung präklinischer (aber kliniknaher) Modellsysteme, zum anderen der Verfeinerung und Implementierung robuster und aussagekräftiger prognostischer und prädiktiver biologischer Marker. In den letzten Jahren konnten deutliche Fortschritte im grundlagenwissenschaftlichen Bereich gemacht werden, um diesen beiden Voraussetzungen näher zu kommen. Diese Entwicklungen sollen im Folgenden umrissen und anhand einiger herausragender Beispiele näher erläutert werden.

Abstract

The ultimate goal of a personalized approach to prostate cancer patient management relies on two prerequisites: the development of preclinical but clinically relevant model systems and robust prognostic and predictive biomarkers. The past several years have shown significant progress towards these two prerequisites which will be highlighted in this review using some notable examples.

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Literatur

  1. Tzelepi V et al (2012) Modeling a lethal prostate cancer variant with small-cell carcinoma features. Clin Cancer Res 18:666–677

    Article  PubMed  CAS  Google Scholar 

  2. Toivanen R et al (2013) A preclinical xenograft model identifies castration- tolerant cancer-repopulating cells in localized prostate tumors. Sci Transl Med 5:187ra71

    Article  PubMed  CAS  Google Scholar 

  3. Sharma NL et al (2013) The androgen receptor induces a distinct transcriptional program in castration-resistant prostate cancer in man. Cancer Cell 23:35–47

    Article  PubMed  CAS  Google Scholar 

  4. Burrell RA et al (2013) Replication stress links structural and numerical cancer chromosomal instability. Nature 494:492–496

    Article  PubMed  CAS  Google Scholar 

  5. Lunardi A et al (2013) A co-clinical approach identifies mechanisms and potential therapies for androgen deprivation resistance in prostate cancer. Nat Genet 45:747–755

    Article  PubMed  CAS  Google Scholar 

  6. Ding Z et al (2012) Telomerase reactivation following telomere dysfunction yields murine prostate tumors with bone metastases. Cell 148:896–907

    Article  PubMed  CAS  Google Scholar 

  7. Li ZG et al (2008) Androgen receptor-negative human prostate cancer cells induce osteogenesis in mice through FGF9-mediated mechanisms. J Clin Invest 118:2697–2710

    Article  PubMed  CAS  Google Scholar 

  8. Beltran H et al (2013) Targeted next-generation sequencing of advanced prostate cancer identifies Potential therapeutic targets and disease heterogeneity. Eur Urol 63:920–926

    Article  PubMed  CAS  Google Scholar 

  9. Börno ST et al (2012) Genome-wide DNA methylation events in TMPRSS2 – ERG fusion-negative prostate cancers implicate an EZH2-dependent mechanism with miR-26a hypermethylation. Cancer Discov 2(11):1024–1035

    Article  PubMed  Google Scholar 

  10. Taylor BS et al (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18:11–22

    Article  PubMed  CAS  Google Scholar 

  11. Tomlins SA (2005) Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310:644–648

    Article  PubMed  CAS  Google Scholar 

  12. Pettersson A et al (2012) The TMPRSS2:ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and metaanalysis. Cancer Epidemiol Biomarkers Prev 21:1497–1509

    Article  PubMed  Google Scholar 

  13. Barbieri CE et al (2012) Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat Genet 44:685–689

    Article  PubMed  CAS  Google Scholar 

  14. Baca SC et al (2013) Punctuated evolution of prostate cancer genomes. Cell 153:666–677

    Article  PubMed  CAS  Google Scholar 

  15. Weischenfeldt J et al (2013) Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23:159–170

    Article  PubMed  CAS  Google Scholar 

  16. Schaefer G et al (2013) Distinct ERG rearrangement prevalence in prostate cancer: higher frequency in young age and in low PSA prostate cancer. Prostate Cancer Prostatic Dis 16:132–138

    Article  PubMed  CAS  Google Scholar 

  17. Wu C et al (2012) Poly-gene fusion transcripts and chromothripsis in prostate cancer. Genes Chromosom Cancer 51:1144–1153

    Article  PubMed  CAS  Google Scholar 

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Einhaltung ethischer Richtlinien

Interessenkonflikt. N. Korzeniewski, M. Tapia-Laliena, Y. Tolstov, S. Pahernik, B. Hadaschik, M. Hohenfellner und S. Duensing geben an, dass kein Interessenkonflikt besteht. Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

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Correspondence to S. Duensing.

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Korzeniewski, N., Tapia-Laliena, M., Tolstov, Y. et al. Neue präklinische Modelle und Biomarker beim Prostatakarzinom. Urologe 52, 1256–1260 (2013). https://doi.org/10.1007/s00120-013-3310-6

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  • DOI: https://doi.org/10.1007/s00120-013-3310-6

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