Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer
Abstract
Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.
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- Authors
- Li, J., Li, L., You, P., Wei, Y., & Xu, B.
- Affiliations
- Jiangxi Provincial Cancer Hospital (CN)
- Nanchang University (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- Corresponding author
- Wei, Yiping
- Xu, Bin
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 91
- Pages
- 35-49
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W4322754149
- Cited by
- 55
- References
- 152
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 21 · 2023: 3
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Seminars in Cancer Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Esophageal Cancer Research and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- esophageal cancer, tumor heterogeneity, multi-omics integration, artificial intelligence, machine learning, deep learning, single-cell sequencing, spatial transcriptomics, tumor microenvironment, tumor stroma, intratumoral heterogeneity, precision oncology, tumor cell of origin
- MeSH terms
- Multiomics, Artificial Intelligence, Esophageal Neoplasms, Humans, Genomics, Precision Medicine
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