Journal
CANCERS
Volume 13, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/cancers13194919
Keywords
gastric cancer; esophageal cancer; gastro-esophageal; machine learning; tumor microenvironment; deep learning; artificial intelligence; immunotherapy; omics
Categories
Funding
- NIH [R01CA260872, R01CA260857, R01CA247441, R03CA256764, P01CA261669]
- Department of Defense grant [W81XWH-19-1-0284]
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This article summarizes the main components of the tumor microenvironment in gastro-esophageal adenocarcinomas (GEA) and the applications of machine learning in this area, highlighting the potential of TME targeting and reprogramming for future therapies.
Simple Summary 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.
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