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Review

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

by 1,2,* and 3,*
1
Gerhard-Domagk-Institute for Pathology, University Hospital Münster, 48149 Münster, Germany
2
Institute for Pathology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
3
Edwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02478, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: David Wong
Cancers 2021, 13(19), 4919; https://doi.org/10.3390/cancers13194919
Received: 22 August 2021 / Revised: 27 September 2021 / Accepted: 28 September 2021 / Published: 30 September 2021
(This article belongs to the Special Issue Current and Future Treatment Strategies for Esophageal Adenocarcinoma)
We summarize the main components of the tumor microenvironment in gastro-esophageal adenocarcinomas (GEA). In addition, we highlight past and present applications of machine learning in GEA to propose ways to facilitate its clinical use in the future.
Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA. View Full-Text
Keywords: gastric cancer; esophageal cancer; gastro-esophageal; machine learning; tumor microenvironment; deep learning; artificial intelligence; immunotherapy; omics gastric cancer; esophageal cancer; gastro-esophageal; machine learning; tumor microenvironment; deep learning; artificial intelligence; immunotherapy; omics
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MDPI and ACS Style

Klein, S.; Duda, D.G. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers 2021, 13, 4919. https://doi.org/10.3390/cancers13194919

AMA Style

Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers. 2021; 13(19):4919. https://doi.org/10.3390/cancers13194919

Chicago/Turabian Style

Klein, Sebastian, and Dan G. Duda. 2021. "Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas" Cancers 13, no. 19: 4919. https://doi.org/10.3390/cancers13194919

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