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Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data

BACKGROUND: Tumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient. METHODS: We analyzed associations of somat...

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Detalles Bibliográficos
Autores principales: Li, Mengyuan, Gao, Xuejiao, Wang, Xiaosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998480/
https://www.ncbi.nlm.nih.gov/pubmed/36911742
http://dx.doi.org/10.3389/fimmu.2023.1090838
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author Li, Mengyuan
Gao, Xuejiao
Wang, Xiaosheng
author_facet Li, Mengyuan
Gao, Xuejiao
Wang, Xiaosheng
author_sort Li, Mengyuan
collection PubMed
description BACKGROUND: Tumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient. METHODS: We analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy. RESULTS: Among the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes (ARID1A, ARID1B, BRIP1, NOTCH2, NOTCH4, EPHA5, ROS1, FAT1, SPEN, NSD1,and PTPRT) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes (ROS1, SPEN, and PTPRT), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score’s ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and PD-L1 expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts. CONCLUSIONS: The molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management.
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spelling pubmed-99984802023-03-11 Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data Li, Mengyuan Gao, Xuejiao Wang, Xiaosheng Front Immunol Immunology BACKGROUND: Tumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient. METHODS: We analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy. RESULTS: Among the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes (ARID1A, ARID1B, BRIP1, NOTCH2, NOTCH4, EPHA5, ROS1, FAT1, SPEN, NSD1,and PTPRT) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes (ROS1, SPEN, and PTPRT), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score’s ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and PD-L1 expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts. CONCLUSIONS: The molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management. Frontiers Media S.A. 2023-02-24 /pmc/articles/PMC9998480/ /pubmed/36911742 http://dx.doi.org/10.3389/fimmu.2023.1090838 Text en Copyright © 2023 Li, Gao and Wang 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
Li, Mengyuan
Gao, Xuejiao
Wang, Xiaosheng
Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title_full Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title_fullStr Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title_full_unstemmed Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title_short Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
title_sort identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998480/
https://www.ncbi.nlm.nih.gov/pubmed/36911742
http://dx.doi.org/10.3389/fimmu.2023.1090838
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