Subversion of infiltrating prostate macrophages to a mixed immunosuppressive tumor‐associated macrophage phenotype

Boibessot, Clovis; Molina, Oscar Eduardo; Lachance, Gabriel; Tav, Christophe; Champagne, Audrey; Neveu, Bertrand; Pelletier, Jean‐François; Pouliot, Frédéric; Fradet, Vincent; Bilodeau, Steve; Fradet, Yves; Bergeron, Alain; Toren, Paul

doi:10.1002/ctm2.581

Cited by 13 publications

(10 citation statements)

References 35 publications

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“…Furthermore, researchers observed that when PCa cells were present, M1 phenotype could not be fully restored even with M1-like cytokine stimulation, usually accompanied by diminished cytotoxicity. This finding implies that attempts to repolarize prostate TAMs will be sufficiently effective with concomitantly destroying adjacent tumor cells [ 183 ].…”

Section: Promising Strategies For Pca Treatment Via Targeting Tamsmentioning

confidence: 99%

The Roles of Tumor-Associated Macrophages in Prostate Cancer

Han

Deng

et al. 2022

Journal of Oncology

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The morbidity of prostate cancer (PCa) is rising year by year, and it has become the primary cause of tumor-related mortality in males. It is widely accepted that macrophages account for 50% of the tumor mass in solid tumors and have emerged as a crucial participator in multiple stages of PCa, with the huge potential for further treatment. Oftentimes, tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) behave like M2-like phenotypes that modulate malignant hallmarks of tumor lesions, ranging from tumorigenesis to metastasis. Several clinical studies indicated that mean TAM density was higher in human PCa cores versus benign prostatic hyperplasia (BPH), and increased biopsy TAM density potentially predicts worse clinicopathological characteristics as well. Therefore, TAM represents a promising target for therapeutic intervention either alone or in combination with other strategies to halt the “vicious cycle,” thus improving oncological outcomes. Herein, we mainly focus on the fundamental aspects of TAMs in prostate adenocarcinoma, while reviewing the mechanisms responsible for macrophage recruitment and polarization, which has clinical translational implications for the exploitation of potentially effective therapies against TAMs.

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Section: Promising Strategies For Pca Treatment Via Targeting Tamsmentioning

confidence: 99%

The Roles of Tumor-Associated Macrophages in Prostate Cancer

Han

Deng

et al. 2022

Journal of Oncology

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“…In addition, we were able to find supporting evidences for the role of the some cell types in the specific cancer types listed in the Fig. 4b [64][65][66][67][68].…”

Section: Global Interpretationmentioning

confidence: 82%

“…5d. Prostate cancer cells decrease the expression of CCR7 in M1 macrophages and increase the expression of M2 markers, which switches inflammatory M1 macrophages to immunosuppressive M2 macrophages [68]. Additionally, CRC tumors are highly infiltrated by effector CD8 T cells, which have lower CCR7 expression than naive and central memory.…”

Section: Local Interpretationmentioning

confidence: 99%

scCDX: enhancing cancer driver gene identification and model interpretability with single-cell RNA sequencing data and extreme gradient boosting

Lee,

Kim,

Kim

2024

Preprint

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Background: Identifying cancer driver genes is a key step toward better understanding the molecular mechanism of cancers, which kill countless people worldwide. In recent years, numerous computational methods using biological data and networks have been developed for cancer driver gene identification. However, despite the rising number of single-cell RNA sequencing (scRNA-seq) data, none of the existing computational approaches have made use of scRNA-seq data. Moreover, it has been reported that deep learning models do not always outperform other machine learning models when applied to tabular data. Therefore, the current task of utilizing omics features, which can be presented in a tabular format, requires experimentation with various models to determine the best one. Results: In this study, we propose scCDX, extreme gradient boosting-based cancer driver gene identification method utilizing scRNA-seq data as well as bulk multi-omics data, topological feature in protein-protein interaction network, and systems-level features from various databases. The experimental results reveal that scCDX provides superior performance compared to graph neural network (GNN)-based state-of-the-art methods for cancer driver gene identification. The baselines were also retrained using the same features to ensure a fair evaluation, as they were trained on different set of features. The experimental results demonstrate that scCDX consistently outperforms, indicating that GNN-based deep learning is unnecessary. Furthermore, the scCDX prediction results were interpreted using the Shapley additive explanation method, which illustrates the contribution of each feature to the output. Conclusions: The study results suggest that selecting the appropriate model based on feature characteristics can achieve better performance than deep learning-based methods. Additionally, demonstrating the ability to interpret the model at the cell type level using high-resolution data, which was previously impossible, can ensure model reliability.

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“…For instance, M2 macrophages consume more glutamine intracellularly than M1 macrophages, and glutamine also promotes macrophage polarization to M2 type by modifying the correspondent gene expression [ 19 ]. Additionally, PCa cells can alter the phenotype of recruited macrophages by releasing chemokines to polarize them toward a pro-tumor macrophage mixed state [ 41 ]. Notably, in the current study, we found a significantly higher infiltration of M2 type macrophages and Tregs cells in the high-risk group.…”

Section: Discussionmentioning

confidence: 99%

A Five Glutamine-Associated Signature Predicts Prognosis of Prostate Cancer and Links Glutamine Metabolism with Tumor Microenvironment

Wang

Chen

Zhao

et al. 2023

JCM

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Glutamine has been recognized as an important amino acid that provide a variety of intermediate products to fuel biosynthesis. Glutamine metabolism participates in the progression of the tumor via various mechanisms. However, glutamine-metabolism-associated signatures and its significance in prostate cancer are still unclear. In this current study, we identified five genes associated with glutamine metabolism by univariate and Lasso regression analysis and constructed a model to predict the biochemical recurrence free survival (BCRFS) of PCa. Further validation of the prognostic risk model demonstrated a good efficacy in predicting the BCRFS in PCa patients. Interestingly, based on the CIBERSORTx, ssGSEA and ESTIMATE algorithms predictions, we noticed a distinct immune cell infiltration and immune pathway pattern in the prediction of the two risk groups stratified by the risk model. Drug sensitivity prediction revealed that patients in the high-risk group were more suitable for chemotherapy. Last but not least, glutamine deprivation significantly inhibited cell growth in GLUL or ASNS knock down prostate cancer cell lines. Therefore, we proposed a novel prognostic model by using glutamine metabolism genes for PCa patients and identified potential mechanism of PCa progression through glutamine-related tumor microenvironment remodeling.

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Subversion of infiltrating prostate macrophages to a mixed immunosuppressive tumor‐associated macrophage phenotype

Cited by 13 publications

References 35 publications

The Roles of Tumor-Associated Macrophages in Prostate Cancer

The Roles of Tumor-Associated Macrophages in Prostate Cancer

scCDX: enhancing cancer driver gene identification and model interpretability with single-cell RNA sequencing data and extreme gradient boosting

A Five Glutamine-Associated Signature Predicts Prognosis of Prostate Cancer and Links Glutamine Metabolism with Tumor Microenvironment

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