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Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy
The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302726/ https://www.ncbi.nlm.nih.gov/pubmed/37388743 http://dx.doi.org/10.3389/fimmu.2023.1187332 |
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author | Pickering, Chad Aiyetan, Paul Xu, Gege Mitchell, Alan Rice, Rachel Najjar, Yana G. Markowitz, Joseph Ebert, Lisa M. Brown, Michael P. Tapia-Rico, Gonzalo Frederick, Dennie Cong, Xin Serie, Daniel Lindpaintner, Klaus Schwarz, Flavio Boland, Genevieve M. |
author_facet | Pickering, Chad Aiyetan, Paul Xu, Gege Mitchell, Alan Rice, Rachel Najjar, Yana G. Markowitz, Joseph Ebert, Lisa M. Brown, Michael P. Tapia-Rico, Gonzalo Frederick, Dennie Cong, Xin Serie, Daniel Lindpaintner, Klaus Schwarz, Flavio Boland, Genevieve M. |
author_sort | Pickering, Chad |
collection | PubMed |
description | The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity. |
format | Online Article Text |
id | pubmed-10302726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103027262023-06-29 Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy Pickering, Chad Aiyetan, Paul Xu, Gege Mitchell, Alan Rice, Rachel Najjar, Yana G. Markowitz, Joseph Ebert, Lisa M. Brown, Michael P. Tapia-Rico, Gonzalo Frederick, Dennie Cong, Xin Serie, Daniel Lindpaintner, Klaus Schwarz, Flavio Boland, Genevieve M. Front Immunol Immunology The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity. Frontiers Media S.A. 2023-06-14 /pmc/articles/PMC10302726/ /pubmed/37388743 http://dx.doi.org/10.3389/fimmu.2023.1187332 Text en Copyright © 2023 Pickering, Aiyetan, Xu, Mitchell, Rice, Najjar, Markowitz, Ebert, Brown, Tapia-Rico, Frederick, Cong, Serie, Lindpaintner, Schwarz and Boland 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 | Immunology Pickering, Chad Aiyetan, Paul Xu, Gege Mitchell, Alan Rice, Rachel Najjar, Yana G. Markowitz, Joseph Ebert, Lisa M. Brown, Michael P. Tapia-Rico, Gonzalo Frederick, Dennie Cong, Xin Serie, Daniel Lindpaintner, Klaus Schwarz, Flavio Boland, Genevieve M. Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title | Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title_full | Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title_fullStr | Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title_full_unstemmed | Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title_short | Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
title_sort | plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302726/ https://www.ncbi.nlm.nih.gov/pubmed/37388743 http://dx.doi.org/10.3389/fimmu.2023.1187332 |
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