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Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma

SIMPLE SUMMARY: Immunotherapy-based treatment options have become standard of care in metastatic renal cell carcinoma. Despite significant improvement in overall survival with these therapies, the tumors of many patients will eventually progress. This review highlights the ongoing efforts to develop...

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Autores principales: Tucker, Matthew D., Rini, Brian I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565517/
https://www.ncbi.nlm.nih.gov/pubmed/32961934
http://dx.doi.org/10.3390/cancers12092662
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author Tucker, Matthew D.
Rini, Brian I.
author_facet Tucker, Matthew D.
Rini, Brian I.
author_sort Tucker, Matthew D.
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description SIMPLE SUMMARY: Immunotherapy-based treatment options have become standard of care in metastatic renal cell carcinoma. Despite significant improvement in overall survival with these therapies, the tumors of many patients will eventually progress. This review highlights the ongoing efforts to develop biomarkers to help predict which patients are most likely to benefit from treatment with immunotherapy. ABSTRACT: Immunotherapy-based combinations, driven by PD-1, PD-L1, and CTLA-4 inhibitors, has altered the treatment landscape for metastatic renal cell carcinoma (RCC). Despite significant improvements in clinical outcomes, many patients do not experience deep or lasting benefits. Recent efforts to determine which patients are most likely to benefit from immunotherapy and immunotherapy-based combinations have shown promise but have not yet affected clinical practice. PD-L1 expression via immunohistochemistry (IHC) has shown promise in a few clinical trials, although variations in the IHC assays as well as the use of different values for positivity presents unique challenges for this potential biomarker. Several other candidate biomarkers were investigated including tumor mutational burden, gene expression signatures, single gene mutations, human endogenous retroviruses, the gastrointestinal microbiome, and peripheral blood laboratory markers. While individually these biomarkers have yet to explain the heterogeneity of treatment response to immunotherapy, using aggregate information from these biomarkers may inform clinically useful predictive biomarkers.
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spelling pubmed-75655172020-10-26 Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma Tucker, Matthew D. Rini, Brian I. Cancers (Basel) Review SIMPLE SUMMARY: Immunotherapy-based treatment options have become standard of care in metastatic renal cell carcinoma. Despite significant improvement in overall survival with these therapies, the tumors of many patients will eventually progress. This review highlights the ongoing efforts to develop biomarkers to help predict which patients are most likely to benefit from treatment with immunotherapy. ABSTRACT: Immunotherapy-based combinations, driven by PD-1, PD-L1, and CTLA-4 inhibitors, has altered the treatment landscape for metastatic renal cell carcinoma (RCC). Despite significant improvements in clinical outcomes, many patients do not experience deep or lasting benefits. Recent efforts to determine which patients are most likely to benefit from immunotherapy and immunotherapy-based combinations have shown promise but have not yet affected clinical practice. PD-L1 expression via immunohistochemistry (IHC) has shown promise in a few clinical trials, although variations in the IHC assays as well as the use of different values for positivity presents unique challenges for this potential biomarker. Several other candidate biomarkers were investigated including tumor mutational burden, gene expression signatures, single gene mutations, human endogenous retroviruses, the gastrointestinal microbiome, and peripheral blood laboratory markers. While individually these biomarkers have yet to explain the heterogeneity of treatment response to immunotherapy, using aggregate information from these biomarkers may inform clinically useful predictive biomarkers. MDPI 2020-09-18 /pmc/articles/PMC7565517/ /pubmed/32961934 http://dx.doi.org/10.3390/cancers12092662 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Tucker, Matthew D.
Rini, Brian I.
Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title_full Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title_fullStr Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title_full_unstemmed Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title_short Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
title_sort predicting response to immunotherapy in metastatic renal cell carcinoma
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565517/
https://www.ncbi.nlm.nih.gov/pubmed/32961934
http://dx.doi.org/10.3390/cancers12092662
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