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Modelling the tumor immune microenvironment for precision immunotherapy
- Source :
- Clinical and Translational Immunology
- Publication Year :
- 2022
-
Abstract
- The complexity of the cellular and acellular players within thetumor microenvironment (TME) allows for significant variation inTME constitution and role in anticancer treatment response. Spatialalterations in populations of tumor cells and adjacent non-malignant cells, including endothelial cells, fibroblasts and tissue-infiltrating immune cells, often have a major role in determiningdisease progression and treatment response in cancer. Many currentstandard systemic antineoplastic treatments target the cancer cellsand could be further refined to directly target commonlydysregulated cell populations of the TME. Recent developments inimmuno-oncology and bioengineering have created an attractivepotential to model these complexities at the level of the individualpatient. These developments, along with the increasing momentum in precision medicine research and application, have catalysed exciting new discoveries in understanding drug–TME interactions, target identification, and improved efficacy of therapies. While rapid progress has been made, there are still many challenges to overcome in the development of accurate in vitro, in vivo and ex vivo models incorporating the cellular interactions that take place in the TME. In this review, we describe how advances in immuno-oncology and patient-derived models, such as patient-derived organoids and explant cultures, have enhanced the landscape of personalised immunotherapy prediction and treatment of solid organ malignancies. We describe and compare different immunological targets and perspectives on two-dimensional and three-dimensional modelling approaches that may be used to better rationalise immunotherapy use, ultimately providing a knowledge base for the integration of the autologous TME into these predictive models.
Details
- Database :
- OAIster
- Journal :
- Clinical and Translational Immunology
- Notes :
- application/pdf
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1343978809
- Document Type :
- Electronic Resource