Mutation load estimation model as a predictor of the response to cancer immunotherapy

Cancer genetics: Predicting patient response to immunotherapy Estimating patients’ mutation load from a small set of genes can accurately predict their response to cancer immunotherapy. Harnessing patients’ immune response to target tumor cells is an effective treatment approach in some cases but no...

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Autores principales:	Guan-Yi Lyu, Yu-Hsuan Yeh, Yi-Chen Yeh, Yu-Chao Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/5a1a16e91f2c4ec3a23a2894000b3764
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id	oai:doaj.org-article:5a1a16e91f2c4ec3a23a2894000b3764
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spelling	oai:doaj.org-article:5a1a16e91f2c4ec3a23a2894000b37642021-12-02T16:09:00ZMutation load estimation model as a predictor of the response to cancer immunotherapy10.1038/s41525-018-0051-x2056-7944https://doaj.org/article/5a1a16e91f2c4ec3a23a2894000b37642018-04-01T00:00:00Zhttps://doi.org/10.1038/s41525-018-0051-xhttps://doaj.org/toc/2056-7944Cancer genetics: Predicting patient response to immunotherapy Estimating patients’ mutation load from a small set of genes can accurately predict their response to cancer immunotherapy. Harnessing patients’ immune response to target tumor cells is an effective treatment approach in some cases but not others. A patient’s number of deleterious genetic mutations across all their protein-coding genes has been shown to correlate with their responsiveness to immunotherapy. However, whole-exome sequencing is time-consuming and costly. Yu-Chao Wang at the National Yang-Ming University, Taiwan, and colleagues have developed cancer-specific mutation load estimation models for adenocarcinoma, melanoma and colorectal cancer that require sequencing only a small number of genes. They show that the mutation load in lung adenocarcinoma patients can be estimated from 24 genes and that they can predict immunotherapy responsiveness with similar accuracy to that obtained using whole-exome sequencing.Guan-Yi LyuYu-Hsuan YehYi-Chen YehYu-Chao WangNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 3, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Genetics QH426-470
spellingShingle	Medicine R Genetics QH426-470 Guan-Yi Lyu Yu-Hsuan Yeh Yi-Chen Yeh Yu-Chao Wang Mutation load estimation model as a predictor of the response to cancer immunotherapy
description	Cancer genetics: Predicting patient response to immunotherapy Estimating patients’ mutation load from a small set of genes can accurately predict their response to cancer immunotherapy. Harnessing patients’ immune response to target tumor cells is an effective treatment approach in some cases but not others. A patient’s number of deleterious genetic mutations across all their protein-coding genes has been shown to correlate with their responsiveness to immunotherapy. However, whole-exome sequencing is time-consuming and costly. Yu-Chao Wang at the National Yang-Ming University, Taiwan, and colleagues have developed cancer-specific mutation load estimation models for adenocarcinoma, melanoma and colorectal cancer that require sequencing only a small number of genes. They show that the mutation load in lung adenocarcinoma patients can be estimated from 24 genes and that they can predict immunotherapy responsiveness with similar accuracy to that obtained using whole-exome sequencing.
format	article
author	Guan-Yi Lyu Yu-Hsuan Yeh Yi-Chen Yeh Yu-Chao Wang
author_facet	Guan-Yi Lyu Yu-Hsuan Yeh Yi-Chen Yeh Yu-Chao Wang
author_sort	Guan-Yi Lyu
title	Mutation load estimation model as a predictor of the response to cancer immunotherapy
title_short	Mutation load estimation model as a predictor of the response to cancer immunotherapy
title_full	Mutation load estimation model as a predictor of the response to cancer immunotherapy
title_fullStr	Mutation load estimation model as a predictor of the response to cancer immunotherapy
title_full_unstemmed	Mutation load estimation model as a predictor of the response to cancer immunotherapy
title_sort	mutation load estimation model as a predictor of the response to cancer immunotherapy
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/5a1a16e91f2c4ec3a23a2894000b3764
work_keys_str_mv	AT guanyilyu mutationloadestimationmodelasapredictoroftheresponsetocancerimmunotherapy AT yuhsuanyeh mutationloadestimationmodelasapredictoroftheresponsetocancerimmunotherapy AT yichenyeh mutationloadestimationmodelasapredictoroftheresponsetocancerimmunotherapy AT yuchaowang mutationloadestimationmodelasapredictoroftheresponsetocancerimmunotherapy
_version_	1718384470293217280

Mutation load estimation model as a predictor of the response to cancer immunotherapy

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