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Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type
Immune checkpoint inhibitors (ICIs) have improved the care of patients in multiple cancer types. However, PD-L1 status, high Tumor Mutational Burden (TMB), and mismatch repair deficiency are the only validated biomarkers of efficacy for ICIs. These markers remain imperfect, and new predictive marker...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144126/ https://www.ncbi.nlm.nih.gov/pubmed/37108755 http://dx.doi.org/10.3390/ijms24087592 |
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author | Dalens, Lorraine Lecuelle, Julie Favier, Laure Fraisse, Cléa Lagrange, Aurélie Kaderbhai, Courèche Boidot, Romain Chevrier, Sandy Mananet, Hugo Derangère, Valentin Truntzer, Caroline Ghiringhelli, François |
author_facet | Dalens, Lorraine Lecuelle, Julie Favier, Laure Fraisse, Cléa Lagrange, Aurélie Kaderbhai, Courèche Boidot, Romain Chevrier, Sandy Mananet, Hugo Derangère, Valentin Truntzer, Caroline Ghiringhelli, François |
author_sort | Dalens, Lorraine |
collection | PubMed |
description | Immune checkpoint inhibitors (ICIs) have improved the care of patients in multiple cancer types. However, PD-L1 status, high Tumor Mutational Burden (TMB), and mismatch repair deficiency are the only validated biomarkers of efficacy for ICIs. These markers remain imperfect, and new predictive markers represent an unmet medical need. Whole-exome sequencing was carried out on 154 metastatic or locally advanced cancers from different tumor types treated by immunotherapy. Clinical and genomic features were investigated using Cox regression models to explore their capacity to predict progression-free survival (PFS). The cohort was split into training and validation sets to assess validity of observations. Two predictive models were estimated using clinical and exome-derived variables, respectively. Stage at diagnosis, surgery before immunotherapy, number of lines before immunotherapy, pleuroperitoneal, bone or lung metastasis, and immune-related toxicity were selected to generate a clinical score. KRAS mutations, TMB, TCR clonality, and Shannon entropy were retained to generate an exome-derived score. The addition of the exome-derived score improved the prediction of prognosis compared with the clinical score alone. Exome-derived variables could be used to predict responses to ICI independently of tumor type and might be of value in improving patient selection for ICI therapy. |
format | Online Article Text |
id | pubmed-10144126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101441262023-04-29 Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type Dalens, Lorraine Lecuelle, Julie Favier, Laure Fraisse, Cléa Lagrange, Aurélie Kaderbhai, Courèche Boidot, Romain Chevrier, Sandy Mananet, Hugo Derangère, Valentin Truntzer, Caroline Ghiringhelli, François Int J Mol Sci Article Immune checkpoint inhibitors (ICIs) have improved the care of patients in multiple cancer types. However, PD-L1 status, high Tumor Mutational Burden (TMB), and mismatch repair deficiency are the only validated biomarkers of efficacy for ICIs. These markers remain imperfect, and new predictive markers represent an unmet medical need. Whole-exome sequencing was carried out on 154 metastatic or locally advanced cancers from different tumor types treated by immunotherapy. Clinical and genomic features were investigated using Cox regression models to explore their capacity to predict progression-free survival (PFS). The cohort was split into training and validation sets to assess validity of observations. Two predictive models were estimated using clinical and exome-derived variables, respectively. Stage at diagnosis, surgery before immunotherapy, number of lines before immunotherapy, pleuroperitoneal, bone or lung metastasis, and immune-related toxicity were selected to generate a clinical score. KRAS mutations, TMB, TCR clonality, and Shannon entropy were retained to generate an exome-derived score. The addition of the exome-derived score improved the prediction of prognosis compared with the clinical score alone. Exome-derived variables could be used to predict responses to ICI independently of tumor type and might be of value in improving patient selection for ICI therapy. MDPI 2023-04-20 /pmc/articles/PMC10144126/ /pubmed/37108755 http://dx.doi.org/10.3390/ijms24087592 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dalens, Lorraine Lecuelle, Julie Favier, Laure Fraisse, Cléa Lagrange, Aurélie Kaderbhai, Courèche Boidot, Romain Chevrier, Sandy Mananet, Hugo Derangère, Valentin Truntzer, Caroline Ghiringhelli, François Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title | Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title_full | Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title_fullStr | Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title_full_unstemmed | Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title_short | Exome-Based Genomic Markers Could Improve Prediction of Checkpoint Inhibitor Efficacy Independently of Tumor Type |
title_sort | exome-based genomic markers could improve prediction of checkpoint inhibitor efficacy independently of tumor type |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144126/ https://www.ncbi.nlm.nih.gov/pubmed/37108755 http://dx.doi.org/10.3390/ijms24087592 |
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