Precursor exhausted T cells: key to successful immunotherapy?

Kallies, Axel; Zehn, Dietmar; Utzschneider, Daniel T.

doi:10.1038/s41577-019-0223-7

Cited by 303 publications

(301 citation statements)

References 124 publications

Supporting

Mentioning

296

Contrasting

Order By: Relevance

“…PD-1 + Tcf7:GFP + and PD-1 + TIM3 -SLAMF6 + ) and were named "memory-like" (Siddiqui et al, 2019) or "progenitor exhausted" (Miller et al, 2019). Memory-like CD8 TILs cells have the capacity to expand, self-renew and give rise to terminally differentiated cells (PD-1 + TIM3 + Tcf1 -, GZMB + ; termed "exhausted") in the context of chronic antigenic stimulation, similarly to progenitor CD8 T cell subsets previously described in chronic infection (He et al, 2016;Im et al, 2016;Utzschneider et al, 2016;Wu et al, 2016;Kallies, Zehn and Utzschneider, 2019). However, there are still many unknowns regarding how T cells committed to the exhaustion lineage develop from pre-exhausted T cell states and which of these states are present in the tumor.…”

Section: Although Initially Considered Hypofunctional Effector T Cellmentioning

confidence: 86%

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Carmona

Siddiqui

Bilous

et al. 2019

Preprint

View full text Add to dashboard Cite

Recent studies have proposed that tumor-specific tumor-infiltrating CD8 + T lymphocytes (CD8 TIL) can be classified into two main groups: "exhausted" TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and "memory-like" TILs, with self-renewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within both tumor-specific and total CD8 TILs has yet to be clearly established.To investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene signature analysis revealed four distinct CD8 TIL states -exhausted, memory-like, naïve and effector memory-like (EM-like) -and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, TCR repertoire sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNAseq data suggested that anti-PD-1 treatment induces expansion of EM-like cells.Our reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8 TIL subpopulations in response to therapeutic interventions.2

show abstract

Section: Although Initially Considered Hypofunctional Effector T Cellmentioning

confidence: 86%

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Carmona

Siddiqui

Bilous

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…CD8 + T cells are key effectors of anti-tumor immunity (29). PD-1 blockade disrupts adaptive immune resistance resulting in increased intratumor infiltration of CD8 + T cells (45). Recently, scRNA-seq profiling of melanoma has revealed unique CD8 T cell states associated with response and resistance to PD-1 blockade (20).…”

Section: Discussionmentioning

confidence: 99%

Mapping the Immune Landscape of Clear Cell Renal Cell Carcinoma by Single-Cell RNA-seq

Vishwakarma

Borcherding

Chimenti

et al. 2019

Preprint

View full text Add to dashboard Cite

One Sentence Summary: Single-cell RNA-sequencing reveals unique lymphoid and myeloid gene programs with putative functions in clear cell renal cancer patients Abstract: The immune cells within the tumor microenvironment are considered key determinants of response to cancer immunotherapy. Immune checkpoint blockade (ICB) has transformed the treatment of clear cell renal cell carcinoma (ccRCC), although the role of specific immune cell states remains unclear. To characterize the tumor microenvironment (TME) of ccRCC, we applied single-cell RNA sequencing (scRNA-seq) along with paired T cell receptor sequencing to map the transcriptomic heterogeneity of 24,904 individual CD45 + lymphoid and myeloid cells in matched tumor and blood from patients with ccRCC. We identified multiple distinct immune cell phenotypes for B and T (CD4 and CD8) lymphocytes, natural kill (NK) cells, and myeloid cells. Evaluation of T cell receptor (TCR) sequences revealed limited shared clonotypes between patients, whereas tumor-infiltrating T cell clonotypes were frequently found in peripheral blood, albeit in lower abundance. We further show that the circulating CD4 + T cell clonality is far less diverse than peripheral CD8 + . Evaluation of myeloid subsets revealed unique gene programs defining monocytes, dendritic cells and tumor-associated macrophages. In summary, here we have leveraged scRNA-seq to refine our understanding of the relative abundance, diversity and complexity of the immune landscape of ccRCC. This report represents the first characterization of ccRCC immune landscape using scRNA-seq. With further characterization and functional validation, these findings may identify novel sub-populations of immune cells amenable to therapeutic intervention. [Main Text:]

show abstract

“…In particular, we identified a cluster of Naive-like CD8 T cells and a smaller one of Naive-like CD4 T cells, co-expressing Tcf7 and Ccr7 while lacking any activation features; a cluster of Effector Memory-like CD8 T cells, co-expressing Tcf7 and granzymes (most prominently Gzmk), with low to intermediate expression of Pdcd1; an "Early activation" state of CD8 T cells, with an intermediate profile between the Naive-like and the EM-like CD8 types; a CD8 Terminally-Exhausted (Tex) effector cluster, characterized by high expression of granzymes, multiple inhibitory receptors (Pdcd1, Ctla4, Lag3, Tigit, Havcr2/TIM-3, etc.) and Tox 25,26 ; a CD8 Precursor-Exhausted (Tpex) cluster, with co-expression of Tcf7, Pdcd1, Ctla4, Tox but not Havcr2 or granzymes 25,27,28 ; a cluster of CD4 Th1 cells, expressing IFN-gamma receptor 1 (Ifngr1) and Fasl 29 ; a CD4 follicular-helper (Tfh) population 29,30 , with a pronounced expression level of Cxcr5, Tox and Slamf6; and a cluster of regulatory T cells (Treg), identified by Foxp3 ( Figure 1D…”

Section: A Cross-study Reference Atlas Of Tumor-infiltrating T Cell Smentioning

confidence: 99%

Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

Andreatta

Corría-Osorio

Müller³

et al. 2020

Preprint

View full text Add to dashboard Cite

Single-cell transcriptomics is a transformative technology to explore heterogeneous cell populations such as T cells, one of the most potent weapons against cancer and viral infections. Recent advances in this technology and the computational tools developed in their wake provide unique opportunities to build reference atlases that can be used to systematically compare new single-cell RNA-seq (scRNA-seq) datasets derived from different models or therapeutic conditions. We have developed ProjecTILs (https://github.com/carmonalab/ProjecTILs), a novel computational tool to project new scRNA-seq data into a reference map of T cells, allowing their direct comparison in a stable, annotated system of coordinates. ProjecTILs enables the classification of query cells into curated, discrete states, but also over a continuous space of intermediate states. We illustrate the projection of several datasets from recent publications over two novel cross-study murine T cell reference atlases: the first describing tumor-infiltrating T lymphocytes (TILs), the second characterizing acute and chronic viral infection. ProjecTILs accurately predicted the effects of multiple perturbations, including the ablation of genes controlling T cell differentiation, such as Tox, Ptpn2, miR-155 and Regnase-1, and identified novel gene programs that were altered in these cells (such as a Lag3-Klrc1 inhibitory module), revealing mechanisms of action behind these immunotherapeutic targets and opening new opportunities for the identification of novel targets. By comparing multiple samples over the same reference map, and across alternative embeddings, our method allows exploring the effect of cellular perturbations (e.g. as the result of therapy or genetic engineering) in terms of transcriptional states and altered genetic programs. *

show abstract

Precursor exhausted T cells: key to successful immunotherapy?

Cited by 303 publications

References 124 publications

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Mapping the Immune Landscape of Clear Cell Renal Cell Carcinoma by Single-Cell RNA-seq

Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

Contact Info

Product

Resources

About