Targeting REGNASE-1 programs long-lived effector T cells for cancer therapy

Wei, Jun; Long, Ling; Zheng, Wenting; Dhungana, Yogesh; Lim, Seon Ah; Guy, Cliff; Wang, Yanyan; Wang, Yongdong; Qian, Chenxi; Xu, Beisi; Kc, Anil; Saravia, Jordy; Huang, Hongling; Yu, Jiyang; Doench, John G.; Geiger, Terrence L.; Chi, Hongbo

doi:10.1038/s41586-019-1821-z

Cited by 272 publications

(201 citation statements)

References 61 publications

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“…190 Also, LKB1 and AMPK signaling can influence mitochondrial fitness and biogenesis in a potentially mTORC1-independent manner. 228,229,231,232 Thus, the crosstalk between PI3K-Akt, mTOR, LKB1 and AMPK signaling (along with other immunometabolic signaling networks, such as those mediated by Regnase-1 244 ), likely promotes metabolic adaptations that allow cells to meet energetic and biosynthetic demands to function in unique microenvironments.…”

Section: Immunometabolic Signaling Networkmentioning

confidence: 99%

Signaling networks in immunometabolism

Saravia¹,

Raynor²,

Chapman³

et al. 2020

Cell Res

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130

107

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Adaptive immunity is essential for pathogen and tumor eradication, but may also trigger uncontrolled or pathological inflammation. T cell receptor, co-stimulatory and cytokine signals coordinately dictate specific signaling networks that trigger the activation and functional programming of T cells. In addition, cellular metabolism promotes T cell responses and is dynamically regulated through the interplay of serine/threonine kinases, immunological cues and nutrient signaling networks. In this review, we summarize the upstream regulators and signaling effectors of key serine/threonine kinase-mediated signaling networks, including PI3K-AGC kinases, mTOR and LKB1-AMPK pathways that regulate metabolism, especially in T cells. We also provide our perspectives about the pending questions and clinical applicability of immunometabolic signaling. Understanding the regulators and effectors of immunometabolic signaling networks may uncover therapeutic targets to modulate metabolic programming and T cell responses in human disease.

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Section: Immunometabolic Signaling Networkmentioning

confidence: 99%

Signaling networks in immunometabolism

Saravia¹,

Raynor²,

Chapman³

et al. 2020

Cell Res

Self Cite

130

107

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“…ICA dimensions where the two sets deviate significantly suggest gene modules that are up-or down-regulated in the query set, and may aid the biological interpretation of phenotypic and functional experimental observations. As an illustrative example of this approach, we re-analyzed the scRNA-seq from Wei et al 38 . In this study, the authors show that KO of Zc3h12a (which encodes Regnase-1) in CD8 T-cells improved the therapeutic efficacy of adoptively transferred, tumor-specific cells in mouse models of melanoma and leukemia.…”

Section: Includes B Cells and Dendritic Cells Among Others; Non-t Cementioning

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

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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. *

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“…The transcriptional landscape of SLAMF6 Δ17-65 is governed by the SLAMF7, GZMB, and NKG2D genes, which confer cytotoxic activity, and by the transcription factors Tbet and Runx3. These features are encountered in helper and exhausted progenitor phenotypes, cell states that develop in activated T cells and that characterize subsets critical for an effective immune response 42,43,44,45,46 . Thus, while SLAMF6 is an identifier of exhausted T cell precursors, it is also a key player in heightened T cell functionality.…”

Section: Discussionmentioning

confidence: 99%

Alternative splicing of SLAMF6 in human T cells creates a co-stimulatory isoform that counteracts the inhibitory effect of the full-length receptor

Hajaj

Zisman

Tzaban

et al. 2020

Preprint

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SLAMF6 is a homotypic receptor of the Ig-superfamily associated with progenitor exhausted T cells. In humans, SLAMF6 has three splice isoforms involving its V-domain. While the canonical 8-exon receptor inhibits T cell activation through SAP recruitment, the short isoform SLAMF6Δ17-65 has a strong agonistic effect. The costimulatory action depends on protein phosphatase SHP-1 and leads to a cytotoxic molecular profile governed by transcription factors Tbet, Runx3, and Tcf7. In T cells from individual patients treated with immune checkpoint blockade, a shift was noted towards SLAMF6Δ17-65. Splice-switching antisense oligonucleotides designed to target the SLAMF6 splice junction, enhanced SLAMF6Δ17-65 in human tumor-infiltrating lymphocytes and improved their capacity to inhibit human melanoma in mice. The possible emergence of two opposing isoforms from the SLAMF6 gene may represent an immune-modulatory mechanism that can be exploited for cancer immunotherapy.

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Targeting REGNASE-1 programs long-lived effector T cells for cancer therapy

Cited by 272 publications

References 61 publications

Signaling networks in immunometabolism

Signaling networks in immunometabolism

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

Alternative splicing of SLAMF6 in human T cells creates a co-stimulatory isoform that counteracts the inhibitory effect of the full-length receptor

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