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

• Published in: Nature communications Vol. 13; no. 1; p. 5268
• Main Authors: Li, Minghao, Zhang, Zicheng, Wang, Qianrong, Yi, Yan, Li, Baosheng
• Format: Journal Article
• Language: English
• 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
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32962-1

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.