Integrated cohort of esophageal squamous cell cancer reveals genomic features underlying clinical characteristics

Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets....

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Published inNature communications Vol. 13; no. 1; p. 5268
Main Authors Li, Minghao, Zhang, Zicheng, Wang, Qianrong, Yi, Yan, Li, Baosheng
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 07.09.2022
Nature Publishing Group
Nature Portfolio
Subjects
45
45/47
631/67/1504/1477
631/67/69
692/4028/67/1857
Cancer
Carcinoma, Squamous Cell - pathology
Carrier Proteins
Datasets
Epithelial Cells - pathology
Esophageal cancer
Esophageal Neoplasms - pathology
Esophageal Squamous Cell Carcinoma - genetics
Esophagus
Genomes
Genomics
Homogeneity
Humanities and Social Sciences
Humans
Medical prognosis
multidisciplinary
Nuclear Proteins
Science
Science (multidisciplinary)
Signatures
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Abstract Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets. The data process pipelines lead to well homogeneity of this integrated cohort for further analysis. We identified 11 mutational signatures in ESCC, some of which are related to clinical features, and firstly detect the significant mutated hotspots in TGFBR2 and IRF2BPL . We screen the survival related mutational features and found some genes had different prognostic impacts between early and late stage, such as PIK3CA and NFE2L2 . Based on the results, an applicable approach of mutational score is proposed and validated to predict prognosis in ESCC. As an open-sourced, quality-controlled and updating mutational landscape, the ESCC-META dataset could facilitate further genomic and translational study in this field. The mutational landscape of esophageal squamous cell cancer (ESCC) remains poorly characterised. Here, the authors integrate multiple published datasets and systematically evaluate the mutational signatures and the related genomic and clinical features in ESCC.
AbstractList Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets. The data process pipelines lead to well homogeneity of this integrated cohort for further analysis. We identified 11 mutational signatures in ESCC, some of which are related to clinical features, and firstly detect the significant mutated hotspots in TGFBR2 and IRF2BPL. We screen the survival related mutational features and found some genes had different prognostic impacts between early and late stage, such as PIK3CA and NFE2L2. Based on the results, an applicable approach of mutational score is proposed and validated to predict prognosis in ESCC. As an open-sourced, quality-controlled and updating mutational landscape, the ESCC-META dataset could facilitate further genomic and translational study in this field.The mutational landscape of esophageal squamous cell cancer (ESCC) remains poorly characterised. Here, the authors integrate multiple published datasets and systematically evaluate the mutational signatures and the related genomic and clinical features in ESCC.
Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets. The data process pipelines lead to well homogeneity of this integrated cohort for further analysis. We identified 11 mutational signatures in ESCC, some of which are related to clinical features, and firstly detect the significant mutated hotspots in TGFBR2 and IRF2BPL . We screen the survival related mutational features and found some genes had different prognostic impacts between early and late stage, such as PIK3CA and NFE2L2 . Based on the results, an applicable approach of mutational score is proposed and validated to predict prognosis in ESCC. As an open-sourced, quality-controlled and updating mutational landscape, the ESCC-META dataset could facilitate further genomic and translational study in this field. The mutational landscape of esophageal squamous cell cancer (ESCC) remains poorly characterised. Here, the authors integrate multiple published datasets and systematically evaluate the mutational signatures and the related genomic and clinical features in ESCC.
Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets. The data process pipelines lead to well homogeneity of this integrated cohort for further analysis. We identified 11 mutational signatures in ESCC, some of which are related to clinical features, and firstly detect the significant mutated hotspots in TGFBR2 and IRF2BPL. We screen the survival related mutational features and found some genes had different prognostic impacts between early and late stage, such as PIK3CA and NFE2L2. Based on the results, an applicable approach of mutational score is proposed and validated to predict prognosis in ESCC. As an open-sourced, quality-controlled and updating mutational landscape, the ESCC-META dataset could facilitate further genomic and translational study in this field.
Abstract Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational features underlying clinical characteristics, we establish the integrated dataset of ESCC-META that consists of 1930 ESCC genomes from 33 datasets. The data process pipelines lead to well homogeneity of this integrated cohort for further analysis. We identified 11 mutational signatures in ESCC, some of which are related to clinical features, and firstly detect the significant mutated hotspots in TGFBR2 and IRF2BPL . We screen the survival related mutational features and found some genes had different prognostic impacts between early and late stage, such as PIK3CA and NFE2L2 . Based on the results, an applicable approach of mutational score is proposed and validated to predict prognosis in ESCC. As an open-sourced, quality-controlled and updating mutational landscape, the ESCC-META dataset could facilitate further genomic and translational study in this field.
The mutational landscape of esophageal squamous cell cancer (ESCC) remains poorly characterised. Here, the authors integrate multiple published datasets and systematically evaluate the mutational signatures and the related genomic and clinical features in ESCC.
ArticleNumber 5268
Author Wang, Qianrong
Li, Minghao
Yi, Yan
Li, Baosheng
Zhang, Zicheng
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  organization: Cheeloo College of Medicine, Shandong University, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36071046$$D View this record in MEDLINE/PubMed
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Snippet Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the mutational...
Abstract Esophageal squamous cell cancer (ESCC) is the major pathologic type of esophageal cancer in Asian population. To systematically evaluate the...
The mutational landscape of esophageal squamous cell cancer (ESCC) remains poorly characterised. Here, the authors integrate multiple published datasets and...
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StartPage 5268
SubjectTerms 45
45/47
631/67/1504/1477
631/67/69
692/4028/67/1857
Cancer
Carcinoma, Squamous Cell - pathology
Carrier Proteins
Datasets
Epithelial Cells - pathology
Esophageal cancer
Esophageal Neoplasms - pathology
Esophageal Squamous Cell Carcinoma - genetics
Esophagus
Genomes
Genomics
Homogeneity
Humanities and Social Sciences
Humans
Medical prognosis
multidisciplinary
Nuclear Proteins
Science
Science (multidisciplinary)
Signatures
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Title Integrated cohort of esophageal squamous cell cancer reveals genomic features underlying clinical characteristics
URI https://link.springer.com/article/10.1038/s41467-022-32962-1
https://www.ncbi.nlm.nih.gov/pubmed/36071046
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https://search.proquest.com/docview/2711843272
https://pubmed.ncbi.nlm.nih.gov/PMC9452532
https://doaj.org/article/ecdab92024c54c49b6c3954ba12159e4
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