Dissecting the tumor microenvironment in response to immune checkpoint inhibitors via single-cell and spatial transcriptomics
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10585_2023_Article_10246.pdf
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Author(s)
Liu, Wendi
Puri, Anusha
Fu, Doris
Chen, Lee
Wang, Cassia
Kellis, Manolis
Yang, Jiekun
Date Issued
December 8, 2023
Publisher
Springer Netherlands
Citation
Liu, Wendi, Puri, Anusha, Fu, Doris, Chen, Lee, Wang, Cassia et al. 2023. "Dissecting the tumor microenvironment in response to immune checkpoint inhibitors via single-cell and spatial transcriptomics."
Version
Final published version
Abstract
Cancer is a disease that undergoes selective pressure to evolve during its progression, becoming increasingly heterogeneous. Tumoral heterogeneity can dictate therapeutic response. Transcriptomics can be used to uncover complexities in cancer and reveal phenotypic heterogeneity that affects disease response. This is especially pertinent in the immune microenvironment, which contains diverse populations of immune cells, and whose dynamic properties influence disease response. The recent development of immunotherapies has revolutionized cancer therapy, with response rates of up to 50% within certain cancers. However, despite advances in immune checkpoint blockade specifically, there remains a significant population of non-responders to these treatments. Transcriptomics can be used to profile immune and other cell populations following immune-checkpoint inhibitor (ICI) treatment, generate predictive biomarkers of resistance or response, assess immune effector function, and identify potential immune checkpoints. Single-cell RNA sequencing has offered insight into mRNA expression within the complex and heterogeneous tumor microenvironment at single-cell resolution. Spatial transcriptomics has enabled measurement of mRNA expression while adding locational context. Here, we review single-cell sequencing and spatial transcriptomic research investigating ICI response within a variety of cancer microenvironments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
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DOI of Published Version
https://doi.org/10.1007/s10585-023-10246-2
