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Deciphering the tumour immune microenvironment cell by cell
Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie...
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Published in: | Immuno-oncology technology 2023-06, Vol.18, p.100383-100383, Article 100383 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.
•The TME shapes responses to ICIs.•We discuss how metabolites, hypoxia and cancer-associated fibroblasts sculpt the TME.•We discuss single-cell omics technologies to analyse and quantify the TME.•We survey spatial transcriptomics and proteomics technologies for analysing the TME.•We discuss the clinical impact of TME spatial and single-cell analysis. |
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ISSN: | 2590-0188 2590-0188 |
DOI: | 10.1016/j.iotech.2023.100383 |