BMC BiologyGenomicsPeer-reviewed journal article
Single cell transcriptomics reveals the effect of PD-L1/TGF-β blockade on the tumor microenvironment
Yoong Wearn LimiD, Garry L. Coles, Savreet K. SandhuiD, David S. JohnsoniD, Adam S. AdleriD, Erica Lyn StoneiD
Abstract
BackgroundThe anti-tumor activity of anti-PD-1/PD-L1 therapies correlates with T cell infiltration in tumors. Thus, a major goal in oncology is to find strategies that enhance T cell infiltration and efficacy of anti-PD-1/PD-L1 therapy. TGF-{beta} has been shown to contribute to T cell exclusion and anti-TGF-{beta} improves anti-PD-L1 efficacy in vivo. However, TGF-{beta} inhibition has frequently been shown to induce toxicity in the clinic, and the clinical efficacy of combination PD-L1 and TGF-{beta} blockade has not yet been proven. To identify strategies to overcome resistance to PD-L1 blockade, the transcriptional programs associated with PD-L1 and/or TGF-{beta} blockade in the tumor microenvironment should be further elucidated. ResultsFor the first time, we used single-cell RNA sequencing to characterize the transcriptomic effects of PD-L1 and/or TGF-{beta} blockade on nearly 30,000 single cells in the tumor and surrounding microenvironment. Combination treatment led to upregulation of immune response genes, including multiple chemokine genes such as CCL5, in CD45+ cells, and down-regulation of extracellular matrix genes in CD45-cells. Analysis of publicly available tumor transcriptome profiles showed that the chemokine CCL5 was strongly associated with immune cell infiltration in various human cancers. Further investigation with in vivo models showed that intratumorally administered CCL5 enhanced cytotoxic lymphocytes and the anti-tumor activity of anti-PD-L1. ConclusionsTaken together, our data could be leveraged translationally to improve anti-PD-L1 plus anti-TGF-{beta} combination therapy, for example through companion biomarkers, and/or to identify novel targets that could be modulated to overcome resistance.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: cell type annotation
- research focus: cell-cell communication
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 24
- FWCI
- 1.55
- Field percentile
- top 13%
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