Related Experiment Video

Updated: Jun 1, 2026

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
06:38

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Published on: April 12, 2017

[Current status in molecular-targeted therapy and IFNα for renal cell carcinoma in Japan]

Masahiro Nozawa1

  • 1The Department of Urology, Kinki University School of Medicine

Hinyokika Kiyo. Acta Urologica Japonica
|May 19, 2011
PubMed

Insights

Molecular-targeted therapy, including sunitinib and temsirolimus, shows promise for advanced renal cell carcinoma (RCC). Future research will confirm if these treatments improve patient prognosis compared to traditional therapies like interferon-alfa (IFNα).

Area of Science:

  • Oncology
  • Pharmacology

Background:

  • Advanced renal cell carcinoma (RCC) treatment landscape in Japan has evolved with the introduction of molecular-targeted therapy.
  • The long-term impact of these novel therapies on patient prognosis requires validation.

Purpose of the Study:

  • To review pivotal phase III clinical studies comparing molecular-targeted agents with interferon-alfa (IFNα) for advanced RCC.
  • To discuss future optimal therapeutic strategies for advanced RCC, considering biomarkers and existing treatments.

Main Methods:

  • Review of two key phase III clinical trials.
  • Analysis of data comparing sunitinib and temsirolimus efficacy against IFNα.
  • Discussion of future treatment paradigms in the context of emerging biomarkers.

Main Results:

  • Sunitinib and temsirolimus demonstrated superiority over IFNα in phase III clinical studies.
  • These targeted therapies represent a significant advancement in managing advanced RCC.

Conclusions:

  • Molecular-targeted therapies have shown significant benefits in advanced RCC.
  • Future treatment strategies should integrate these agents and consider biomarker-driven approaches for optimal patient outcomes.

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