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Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma

BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have...

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Autores principales: Li, Ying, Jiang, Wenbin, Li, Tianhao, Li, Mengyue, Li, Xin, Zhang, Zheyang, Zhang, Sainan, Liu, Yixin, Zhao, Wenyuan, Gu, Yunyan, Qi, Lishuang, Ao, Lu, Guo, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961230/
https://www.ncbi.nlm.nih.gov/pubmed/31937321
http://dx.doi.org/10.1186/s12967-019-02199-6
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author Li, Ying
Jiang, Wenbin
Li, Tianhao
Li, Mengyue
Li, Xin
Zhang, Zheyang
Zhang, Sainan
Liu, Yixin
Zhao, Wenyuan
Gu, Yunyan
Qi, Lishuang
Ao, Lu
Guo, Zheng
author_facet Li, Ying
Jiang, Wenbin
Li, Tianhao
Li, Mengyue
Li, Xin
Zhang, Zheyang
Zhang, Sainan
Liu, Yixin
Zhao, Wenyuan
Gu, Yunyan
Qi, Lishuang
Ao, Lu
Guo, Zheng
author_sort Li, Ying
collection PubMed
description BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients. METHODS: We developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm. RESULTS: A mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80–0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51–7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63–15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels. CONCLUSIONS: The small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications.
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spelling pubmed-69612302020-01-17 Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma Li, Ying Jiang, Wenbin Li, Tianhao Li, Mengyue Li, Xin Zhang, Zheyang Zhang, Sainan Liu, Yixin Zhao, Wenyuan Gu, Yunyan Qi, Lishuang Ao, Lu Guo, Zheng J Transl Med Research BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients. METHODS: We developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm. RESULTS: A mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80–0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51–7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63–15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels. CONCLUSIONS: The small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications. BioMed Central 2020-01-14 /pmc/articles/PMC6961230/ /pubmed/31937321 http://dx.doi.org/10.1186/s12967-019-02199-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Ying
Jiang, Wenbin
Li, Tianhao
Li, Mengyue
Li, Xin
Zhang, Zheyang
Zhang, Sainan
Liu, Yixin
Zhao, Wenyuan
Gu, Yunyan
Qi, Lishuang
Ao, Lu
Guo, Zheng
Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title_full Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title_fullStr Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title_full_unstemmed Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title_short Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
title_sort identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961230/
https://www.ncbi.nlm.nih.gov/pubmed/31937321
http://dx.doi.org/10.1186/s12967-019-02199-6
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