Matthew Tucker, Clinical Fellow at Vanderbilt University @Tucker_MatthewD @VUMC #kidneycancer Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma

Matthew Tucker, Clinical Fellow at Vanderbilt University @Tucker_MatthewD @VUMC #kidneycancer Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma

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Matthew Tucker, Clinical Fellow at Vanderbilt University Medical Center discusses Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma.


Quick Summary
Immunotherapy-based treatment methods for metastatic renal cell carcinoma have become the standard of care. The tumors in several patients will gradually progress, despite considerable improvement in overall survival with these therapies. This analysis highlights ongoing attempts to establish biomarkers to assist in determining which patients are most likely to benefit from immunotherapy care.

Abstract
The treatment environment for metastatic renal cell carcinoma (RCC) has been altered by immunotherapy-based combinations powered by PD-1, PD-L1, and CTLA-4 inhibitors. Many patients do not achieve deep or permanent benefits, despite major changes in clinical outcomes. Recent attempts have shown promise but have not yet influenced clinical practice to assess which patients are most likely to benefit from immunotherapy and immunotherapy-based combinations. In a few clinical trials, PD-L1 expression by immunohistochemistry ( IHC) has shown promise, while differences in the IHC assays as well as the use of different positivity values poses specific challenges for this potential biomarker. Several other candidate biomarkers have been studied, including tumor mutational burden, signatures of gene expression, single-gene mutations, endogenous human retroviruses, gastrointestinal microbiomes, and laboratory markers for peripheral blood. Although the heterogeneity of the treatment response to immunotherapy has yet to be clarified individually by these biomarkers, the use of aggregate information from these biomarkers-inform clinically useful predictive biomarkers.

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