Treg Cells Promote the SREBP1-Dependent Metabolic Fitness of Tumor-Promoting Macrophages via Repression of CD8+ T Cell-Derived Interferon-γ

Liu, Chang; Chikina, Maria; Deshpande, Rahul; Menk, Ashley V.; Wang, Ting; Tabib, Tracy; Brunazzi, Erin A.; Vignali, Kate M.; Sun, Ming; Stolz, Donna B.; Lafyatis, Robert; Chen, Wei; Delgoffe, Greg M.; Workman, Creg J.; Wendell, Stacy G.; Vignali, Dario A.A.

doi:10.1016/j.immuni.2019.06.017

Cited by 227 publications

(199 citation statements)

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“…Among them, macrophages show the highest content in tumor tissues and most significant regulatory effect on tumors; these cells can promote the proliferation, invasion, and metastasis of tumor cells and induce tumor cells to develop immune tolerance. These cells are known as tumor-associated macrophages (TAMs) (most studies have suggested that TAMs are mainly the macrophage M2 type) [46]. TAMs are often distributed around CSCs, and the amount of infiltration is closely related to the tumor histological grade and number of CSCs.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the immune checkpoint molecule CD274 (PD-L1) was significantly up-regulated in the SCE_H subtype. This molecule suppresses the proliferation and differentiation of T lymphocytes, promotes the differentiation of Tregs, and induce the secretion of cytokines, thereby suppressing the immune response [46]. Prediction of the anti-PD-1 treatment response showed that SCE_H is more sensitive to anti-PD-1 than other subtypes, indicating that CD274 highly expressed in tumor/tumor stem cells and may be involved in the immune escape process of the tumor.…”

Section: Discussionmentioning

confidence: 99%

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Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Tang

Liu

et al. 2020

Preprint

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Background Bladder cancer is the fifth most common type of cancer worldwide, with high recurrence and progression rates. Although considerable progress has been made in the treatment of bladder cancer through accurate typing of molecular characteristics, little is known about the various genetic and epigenetic changes that have evolved in stem and progenitor cells. Thus, we developed a novel stem cell typing method to fill this gap. Methods Based on six published genomic data sets, we used 26 stem cell gene sets to classify each data set. Unsupervised and supervised machine learning methods were used to perform the classification. Results We classified BLCA into three subtypes—high stem cell enrichment (SCE_H), medium stem cell enrichment (SCE_M), and low stem cell enrichment (SCE_L)—based on multiple cross-platform data sets. The stability and reliability of the classification were verified. The stemness index obtained from the one-class logistic regression machine learning method showed that the degree of tumor stem cell enrichment was not proportional to the stemness index. Compared with the other subtypes, SCE_H showed the highest degree of cancer stem cell concentration, lowest stemness index, and highest level of immune cell infiltration and was the most sensitive to predicted immune checkpoint inhibitor treatment. However, this group showed the worst prognosis. Comparison of gene set enrichment analysis results for pathway enrichment of various subtypes revealed that the SCE_H subtype activates some important pathways regulating cancer occurrence, development, and even poor prognosis, including epithelial mesenchymal transition, hypoxia, angiogenesis, KRAS signal up-regulation, the interleukin 6-mediated Jak-STAT signaling pathway, and inflammatory response. Two identified pairs of transcription factors, GRHL2 and GATA6 and IRF5 and GATA3 , likely have opposite regulatory effects on SCE_H and SCE_L, respectively. Conclusions The identification of BLCA subtypes based on cancer stem cell gene sets revealed the complex mechanism of carcinogenesis causing BLCA and provides a new direction for the diagnosis and treatment of BLCA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Tang

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Among them, macrophages show the highest content in tumor tissues and had the most signi cant regulatory effect on tumors. These cells, which can promote the proliferation, invasion, and metastasis of tumor cells and induce tumor cells to develop immune tolerance, are known as tumor-associated macrophages (TAMs) (most studies have suggested that TAMs are primarily the M2 type) [57]. TAMs are often distributed around CSCs, and the amount of in ltration is closely correlated to the tumor histological grade and number of CSCs.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the immune checkpoint molecule, CD274 (PD-L1), was signi cantly upregulated in the SCE_H subtype. This molecule suppresses the proliferation and differentiation of T lymphocytes, promotes the differentiation of Tregs, and induce the secretion of cytokines, thereby suppressing the immune response [57]. Prediction of the anti-PD-1 treatment response showed that SCE_H is more sensitive to anti-PD-1 than other subtypes, indicating that CD274 is highly expressed in tumor / tumor stem cells and may be involved in the tumor immune escape process.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Tang

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Bladder cancer (BLCA) is the fifth most common type of cancer worldwide, with high recurrence and progression rates. Although considerable progress has been made in the treatment of BLCA through accurate typing of molecular characteristics, little is known regarding the various genetic and epigenetic changes that have evolved in stem and progenitor cells. To address this issue, we have developed a novel stem cell typing method.Methods: Based on six published genomic datasets, we used 26 stem cell gene sets to classify each dataset. Unsupervised and supervised machine learning methods were used to perform the classification. Results: We classified BLCA into three subtypes—high stem cell enrichment (SCE_H), medium stem cell enrichment (SCE_M), and low stem cell enrichment (SCE_L)—based on multiple cross-platform datasets. The stability and reliability of the classification were verified. Compared with the other subtypes, SCE_H had the highest degree of cancer stem cell concentration, highest level of immune cell infiltration, and highest sensitivity not only to predicted anti-PD-1 immunosuppressive therapy but also to conventional chemotherapeutic agents such as cisplatin, sunitinib, and vinblastine; however, this group had the worst prognosis. Comparison of gene set enrichment analysis results for pathway enrichment of various subtypes reveal that the SCE_H subtype activates the important pathways regulating cancer occurrence, development, and even poor prognosis, including epithelial mesenchymal transition, hypoxia, angiogenesis, KRAS signal upregulation, interleukin 6-mediated JAK-STAT signaling pathway, and inflammatory response. Two identified pairs of transcription factors, GRHL2 and GATA6 and IRF5 and GATA3, possibly have opposite regulatory effects on SCE_H and SCE_L, respectively.Conclusions: The identification of BLCA subtypes based on cancer stem cell gene sets revealed the complex mechanism of carcinogenesis of BLCA and provides a new direction for the diagnosis and treatment of BLCA.

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“…5). We find that on average our NIFA model performs better than NMF and ICA at this cell-type detection (Klein et al, 2015) inDrop Principal genes identified by PCA Silver Mouse: Embryonic Stem Cells 2,717 4 Kolodziejczyk (Kolodziejczyk et al, 2015) SMARTer Homogeneous cell line Gold Mouse: Embryonic Stem Cells 704 3 Li (Li et al, 2017) SMARTer Homogeneous cell line Gold Human: Colorectal Tumors 561 9 Liu (Liu et al, 2019) 10x drop-seq k-means clustering & specific markers Silver Mouse: Tumor immune cells 1,607 13 Nestorowa (Nestorowa et al, 2016) Smart-Seq2 hierachical clustering & specific markers Silver Mouse: Hematopoietic Stem Cells 1,656 9 Olsson (Olsson et al, 2016) SMARTer Flow cytometry & cell sorting Gold Mouse: Hematopoietic Stem Cells 382 4 SimKumar4easy (Duò et al, 2018) NA Simulated Gold NA 500 4 SimKumar4hard (Duò et al, 2018) NA Simulated Gold NA 499 4 SimKumar8hard (Duò et al, 2018) NA Simulated Gold NA 499 8 Zhengmix4eq (Duò et al, 2018) NA Simulated Gold NA 3,555 4 Zhengmix4uneq (Duò et al, 2018) NA Simulated Gold NA 6,414 4 Zhengmix8eq (Duò et al, 2018) NA Simulated Gold NA 3,971 8 Table 1. The complete set of datasets evaluated in this study.…”

Section: Cell-type Identificationmentioning

confidence: 98%

Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data

Mao

Pouyan

Kostka

et al. 2020

Preprint

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Motivation: Single cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the level of individual cells. With the emergence of high-throughput platforms datasets comprising tens of thousands or more cells have become routine, and the technology is having an impact across a wide range of biomedical subject areas. However, scRNA-seq data are high-dimensional and affected by noise, so that scalable and robust computational techniques are needed for meaningful analysis, visualization and interpretation. Specifically, a range of matrix factorization techniques have been employed to aid scRNAseq data analysis. In this context we note that sources contributing to biological variability between cells can be discrete (or multi-modal, for instance cell-types), or continuous (e.g. pathway activity). However, no current matrix factorization approach is set up to jointly infer such mixed sources of variability. Results: To address this shortcoming, we present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that combines features of complementary approaches like Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). NIFA simultaneously models uni-and multi-modal latent factors and can so isolate discrete cell-type identity and continuous pathway-level variations into separate components. Similar to NMF, NIFA constrains factor loadings to be non-negative in order to increase biological interpretability. We apply our approach to a range of data sets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA and NMF in terms of cell-type identification and biological interpretability. Studying an immunotherapy dataset in detail, we show that NIFA identifies biomedically meaningful sources of variation, derive an improved expression signature for regulatory T-cells, and identify a novel myeloid cell subtype associated with treatment response. Overall, NIFA is a general approach advancing scRNA-seq analysis capabilities and it allows researchers to better take

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Treg Cells Promote the SREBP1-Dependent Metabolic Fitness of Tumor-Promoting Macrophages via Repression of CD8+ T Cell-Derived Interferon-γ

Cited by 227 publications

References 42 publications

Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Development and validation of a novel stem cell subtype for bladder cancer based on stem genomic profiling

Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data

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