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Review
. 2010 Dec;9(12):3115-25.
doi: 10.1158/1535-7163.MCT-10-0873. Epub 2010 Nov 15.

Breaking through a plateau in renal cell carcinoma therapeutics: development and incorporation of biomarkers

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Review

Breaking through a plateau in renal cell carcinoma therapeutics: development and incorporation of biomarkers

Sumanta Kumar Pal et al. Mol Cancer Ther. 2010 Dec.

Abstract

With the Food and Drug Administration approval of 6 novel targeted agents since December 2005 and limited comparative trials to discern relative efficacy, the treatment of metastatic renal cell carcinoma (RCC) has become immensely complex. The research community must look to novel ways in which to identify appropriate candidates for selected targeted therapies; one potential strategy is the use of clinical and molecular biomarkers. A growing body of knowledge-related von Hippel Lindau-driven pathways in this disease has highlighted the potential role of hypoxia-inducible factor subtypes in distinguishing RCC patients clinically. Techniques applied in other malignancies, such as gene expression and proteomic profiling, may also ultimately allow for clinical stratification. An emerging understanding of immunologic phenomena that may affect cancer progression (i.e., tumor infiltration by CD68 lymphocytes, memory T-cells, etc.) has unveiled a number of other potential biomarkers of response. Several vascular endothelial growth factor receptor-directed therapies classically thought to function as antiangiogenics may also have complex effects upon the tumor microenvironment including the associated immune cell milieu. As such, immunologic parameters could potentially predict response to current therapies. Finally, clinical biomarkers, such as hypertension, may predict the efficacy of several currently available targeted agents, although implementation of such biomarkers remains challenging. Herein, the clinical relevance of putative RCC biomarkers is examined in detail.

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Figures

Figure 1
Figure 1
Agents such as sunitinib may act through a multitude of mechanisms, yielding numerous potential biomarkers of response.
Figure 2
Figure 2
A proposed marker-based strategy design to evaluate predictive biomarkers.

References

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