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Prognostic and Predictive Markers, and Stratifications Tables, for the Detection and Treatment of Renal Cell Carcinoma

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Urologic Oncology

Abstract

Despite knowledge of disease stage, grade, and histological subtype (HS), patient outcome in RCC remains elusive. Therefore, a vast number of predictive and prognostic models as well as biological markers have been proposed. Many show promise in stratifying the survival curves or discriminating between stage distributions, while others achieved independent predictor status in specific end points of interest.

There is an increased interest in composite biomarker, such as the BioScore (Parker et al., Cancer 115(10):2092–2103, 2009), which has increased accuracy compared to other models. The search continues for an ideal model that is relevant, simple to use and understand, and that will be able to distinguish between different patient diseases and characteristics. The future in prognostic factors and predictive models lies in finding biomarkers that will assist in choosing select target therapies, immunotherapies, and chemotherapies. To improve patient prognosis, treatment sequences, with targeted agents and novel drugs, need to be individualized by using the patient’s genomic classifications.

Presently, Immune oncology agents (IO), notably immune checkpoint inhibitors targeting PD-1, are the most promising in treatment of RCC. It is postulated that a preestablished immune response can optimize immune checkpoint inhibition therapy. Therefore, even though past studies found few desired results, vaccinations may hold the key to future therapeutic success (Curtis et al., Curr Oncol Rep 18(9):57, 2016; Hammers H, Curr Opin Urol 26(6):543–547, 2016).

This chapter will focus on prognostic models specifically for metastatic RCC and models for all types of RCC. Second, this chapter will focus on molecular biomarkers, including tissue-based biomarkers, blood-based biomarkers, and immune system biomarkers.

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Authors and Affiliations

  1. Department of Urology, University of Montreal Health Centre, Montreal, QC, Canada

    Helen Davis Bondarenko

  2. Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany

    Raisa S. Pompe

  3. Division of Oncology/Unit of Urology, Urological Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy

    Emanuele Zaffuto

  4. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada

    Pierre I. Karakiewicz

  5. Departement of Urology, Weill Cornell Medical Center, New York, NY, USA

    Shahrokh F. Shariat

Authors
  1. Helen Davis Bondarenko
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  2. Raisa S. Pompe
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  3. Emanuele Zaffuto
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  4. Shahrokh F. Shariat
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  5. Pierre I. Karakiewicz
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Corresponding author

Correspondence to Pierre I. Karakiewicz .

Editor information

Editors and Affiliations

  1. Dept. of Urology & Urologic Oncology, Hannover Medical School, Hannover, Germany

    Axel S. Merseburger

  2. Dept. of Urology, University of Regensburg St. Josef Medical Centre, Regensburg, Germany

    Maximilian Burger

Section Editor information

  1. Klinik für Urologie, Ratzeburger Allee 160, 23538, Lübeck, Germany

    Mario W. Kramer

  2. Department of Urology, University Hospital Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany

    Axel S. Merseburger

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Davis Bondarenko, H., Pompe, R.S., Zaffuto, E., Shariat, S.F., Karakiewicz, P.I. (2017). Prognostic and Predictive Markers, and Stratifications Tables, for the Detection and Treatment of Renal Cell Carcinoma. In: Merseburger, A., Burger, M. (eds) Urologic Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-42603-7_57-1

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  • DOI: https://doi.org/10.1007/978-3-319-42603-7_57-1

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  • Print ISBN: 978-3-319-42603-7

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