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FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients
A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational bur...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216110/ https://www.ncbi.nlm.nih.gov/pubmed/34164344 http://dx.doi.org/10.3389/fonc.2021.683419 |
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author | Wang, Ye Tong, Zhuang Zhang, Wenhua Zhang, Weizhen Buzdin, Anton Mu, Xiaofeng Yan, Qing Zhao, Xiaowen Chang, Hui-Hua Duhon, Mark Zhou, Xin Zhao, Gexin Chen, Hong Li, Xinmin |
author_facet | Wang, Ye Tong, Zhuang Zhang, Wenhua Zhang, Weizhen Buzdin, Anton Mu, Xiaofeng Yan, Qing Zhao, Xiaowen Chang, Hui-Hua Duhon, Mark Zhou, Xin Zhao, Gexin Chen, Hong Li, Xinmin |
author_sort | Wang, Ye |
collection | PubMed |
description | A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response. |
format | Online Article Text |
id | pubmed-8216110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82161102021-06-22 FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients Wang, Ye Tong, Zhuang Zhang, Wenhua Zhang, Weizhen Buzdin, Anton Mu, Xiaofeng Yan, Qing Zhao, Xiaowen Chang, Hui-Hua Duhon, Mark Zhou, Xin Zhao, Gexin Chen, Hong Li, Xinmin Front Oncol Oncology A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response. Frontiers Media S.A. 2021-06-07 /pmc/articles/PMC8216110/ /pubmed/34164344 http://dx.doi.org/10.3389/fonc.2021.683419 Text en Copyright © 2021 Wang, Tong, Zhang, Zhang, Buzdin, Mu, Yan, Zhao, Chang, Duhon, Zhou, Zhao, Chen and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Wang, Ye Tong, Zhuang Zhang, Wenhua Zhang, Weizhen Buzdin, Anton Mu, Xiaofeng Yan, Qing Zhao, Xiaowen Chang, Hui-Hua Duhon, Mark Zhou, Xin Zhao, Gexin Chen, Hong Li, Xinmin FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title | FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title_full | FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title_fullStr | FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title_full_unstemmed | FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title_short | FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients |
title_sort | fda-approved and emerging next generation predictive biomarkers for immune checkpoint inhibitors in cancer patients |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216110/ https://www.ncbi.nlm.nih.gov/pubmed/34164344 http://dx.doi.org/10.3389/fonc.2021.683419 |
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