Dataset Information

Tumor Infiltrating Effector Memory Antigen-Specific CD8⁺ T Cells Predict Response to Immune Checkpoint Therapy.

ABSTRACT: Immune checkpoint therapy (ICT) results in durable responses in individuals with some cancers, but not all patients respond to treatment. ICT improves CD8⁺ cytotoxic T lymphocyte (CTL) function, but changes in tumor antigen-specific CTLs post-ICT that correlate with successful responses have not been well characterized. Here, we studied murine tumor models with dichotomous responses to ICT. We tracked tumor antigen-specific CTL frequencies and phenotype before and after ICT in responding and non-responding animals. Tumor antigen-specific CTLs increased within tumor and draining lymph nodes after ICT, and exhibited an effector memory-like phenotype, expressing IL-7R (CD127), KLRG1, T-bet, and granzyme B. Responding tumors exhibited higher infiltration of effector memory tumor antigen-specific CTLs, but lower frequencies of regulatory T cells compared to non-responders. Tumor antigen-specific CTLs persisted in responding animals and formed memory responses against tumor antigens. Our results suggest that increased effector memory tumor antigen-specific CTLs, in the presence of reduced immunosuppression within tumors is part of a successful ICT response. Temporal and nuanced analysis of T cell subsets provides a potential new source of immune based biomarkers for response to ICT.

SUBMITTER: Principe N

PROVIDER: S-EPMC7688517 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Tumor Infiltrating Effector Memory Antigen-Specific CD8<sup>+</sup> T Cells Predict Response to Immune Checkpoint Therapy.

Principe Nicola N Kidman Joel J Goh Siting S Tilsed Caitlin M CM Fisher Scott A SA Fear Vanessa S VS Forbes Catherine A CA Zemek Rachael M RM Chopra Abha A Watson Mark M Dick Ian M IM Boon Louis L Holt Robert A RA Lake Richard A RA Nowak Anna K AK Lesterhuis Willem Joost WJ McDonnell Alison M AM Chee Jonathan J

Frontiers in immunology 20201112

Immune checkpoint therapy (ICT) results in durable responses in individuals with some cancers, but not all patients respond to treatment. ICT improves CD8<sup>+</sup> cytotoxic T lymphocyte (CTL) function, but changes in tumor antigen-specific CTLs post-ICT that correlate with successful responses have not been well characterized. Here, we studied murine tumor models with dichotomous responses to ICT. We tracked tumor antigen-specific CTL frequencies and phenotype before and after ICT in respond ...[more]

PMID: 33262762

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Project description:BackgroundStratification of patients who could benefit from immune checkpoint inhibitor (ICI) therapy is of much importance. PD-1hiCD8+ T cells represent a newly identified and effective biomarker for ICI therapy response biomarker in lung cancer. Accurately quantifying these T cells using commonly available RNA sequencing (RNA-seq) data may extend their applications to more cancer types.MethodWe built a transcriptome signature of PD-1hiCD8+ T cells from bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data of tumor-infiltrating immune cells. The signature was validated by flow cytometry and in independent datasets. The clinical applications of the signature were explored in non-small-cell lung cancer, melanoma, gastric cancer, urothelial cancer, and a mouse model of breast cancer samples treated with ICI, and systematically evaluated across 21 cancer types in The Cancer Genome Atlas (TCGA). Its associations with other biomarkers were also determined.ResultsSignature scores could be used to identify the PD-1hiCD8+ T subset and were correlated with the fraction of PD-1hiCD8+ T cells in tumor tissue (Pearson correlation, R=0.76, p=0.0004). Furthermore, in the scRNA-seq dataset, we confirmed the capability of PD-1hiCD8+ T cells to secrete CXCL13, as well as their interactions with other immune cells. In 581 clinical samples and 204 mouse models treated with ICIs, high signature scores were associated with increased survival, and the signature achieved area under the receiver operating characteristic curve scores of 0.755 (ranging from 0.61 to 0.91) in predicting therapy response. In TCGA pan-cancer datasets, our signature scores were consistently correlated with therapy response (R=0.78, p<0.0001) and partially explained the diverse response rates among different cancer types. Finally, our signature generally outperformed other mRNA-based predictors and showed improved predictive performance when used in combination with tumor mutational burden (TMB). The signature score is available in the R package "PD1highCD8Tscore" (https://github.com/Liulab/PD1highCD8Tscore).ConclusionThrough estimating the fraction of the PD-1hiCD8+ T cell, our signature could predict response to ICI therapy across multiple cancers and could serve as a complementary biomarker to TMB.

Dataset Information

Tumor Infiltrating Effector Memory Antigen-Specific CD8⁺ T Cells Predict Response to Immune Checkpoint Therapy.

Publications

Tumor Infiltrating Effector Memory Antigen-Specific CD8<sup>+</sup> T Cells Predict Response to Immune Checkpoint Therapy.

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Dataset Information

Tumor Infiltrating Effector Memory Antigen-Specific CD8+ T Cells Predict Response to Immune Checkpoint Therapy.

Publications

Tumor Infiltrating Effector Memory Antigen-Specific CD8<sup>+</sup> T Cells Predict Response to Immune Checkpoint Therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Tumor Infiltrating Effector Memory Antigen-Specific CD8⁺ T Cells Predict Response to Immune Checkpoint Therapy.