Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications.

Autor:	Kumar G; Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States., Pandurengan RK; Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States., Parra ER; Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States., Kannan K; Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States., Haymaker C; Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in immunology [Front Immunol] 2023 Dec 20; Vol. 14, pp. 1288802. Date of Electronic Publication: 2023 Dec 20 (Print Publication: 2023).
DOI:	10.3389/fimmu.2023.1288802
Abstrakt:	Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data. Competing Interests: CH declares research funding to institution from Sanofi, Dragonfly, BTG, Iovance, Obsidian and Avenge; scientific advisory board member of Briacell with stock options; personal fees from Nanobiotix and speaker fees/honorarium from the Hope Foundation and SITC outside the scope of the submitted work. EP is a pathology consultant for Nucleai LTD. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2023 Kumar, Pandurengan, Parra, Kannan and Haymaker.)
Databáze:	MEDLINE
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