학술논문

Modelling the tumor immune microenvironment for precision immunotherapy.
Document Type
Article
Source
Clinical & Translational Immunology. 2022, Vol. 11 Issue 6, p1-22. 22p.
Subject
*TUMOR microenvironment
*IMMUNOTHERAPY
*THERAPEUTICS
*CELL populations
*INDIVIDUALIZED medicine
Language
ISSN
2050-0068
Abstract
The complexity of the cellular and acellular players within the tumor microenvironment (TME) allows for significant variation in TME constitution and role in anticancer treatment response. Spatial alterations 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 determining disease progression and treatment response in cancer. Many current standard systemic antineoplastic treatments target the cancer cells and could be further refined to directly target commonly dysregulated cell populations of the TME. Recent developments in immuno‐oncology and bioengineering have created an attractive potential to model these complexities at the level of the individual patient. 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. [ABSTRACT FROM AUTHOR]