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Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

Nature Reviews Urology volume 20, pages 133–157 (2023)Cite this article

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Abstract

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Key points

  • No prognostic and predictive biomarkers of response to immune checkpoint inhibitors (ICIs) are available in clinical practice.

  • Current prognostic models for renal cell carcinoma (RCC) should be improved by integrating novel markers.

  • Programmed cell death 1 ligand 1 (PDL1) expression level is a negative prognostic factor with an unclear predictive role for ICI response in patients with RCC.

  • Tumour mutational burden and gene expression profile are promising predictive factors of response to ICIs that deserve to be further explored.

  • Research is in progress to identify additional biomarkers, which, however, are still far from validation and approval.

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Fig. 1: Evolution of prognostic markers in metastatic renal cell carcinoma — towards integrated prognostic models.
Fig. 2: Tumour microenvironment features to predict response to immunotherapy in patients with mRCC.
Fig. 3: Most promising predictive biomarkers of response to immunotherapy in mRCC.

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Author notes

  1. These authors contributed equally: Matteo Rosellini, Andrea Marchetti.

  2. These authors jointly supervised this work: Matteo Santoni, Francesco Massari.

Authors and Affiliations

  1. Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy

    Matteo Rosellini, Andrea Marchetti, Veronica Mollica & Francesco Massari

  2. Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy

    Alessandro Rizzo

  3. Oncology Unit, Macerata Hospital, Macerata, Italy

    Matteo Santoni

  4. Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy

    Francesco Massari

Authors
  1. Matteo Rosellini
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  2. Andrea Marchetti
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  3. Veronica Mollica
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  4. Alessandro Rizzo
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  5. Matteo Santoni
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  6. Francesco Massari
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Contributions

F.M. researched data for the article. F.M., V.M. and A.R. contributed substantially to discussion of the content. F.M., M.R. and A.M. wrote the article. F.M. and M.S. reviewed and edited the manuscript before submission.

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Correspondence to Francesco Massari.

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Nature Reviews Urology thanks R. Saliby, E. Jonasch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Rosellini, M., Marchetti, A., Mollica, V. et al. Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma. Nat Rev Urol 20, 133–157 (2023). https://doi.org/10.1038/s41585-022-00676-0

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