Metabolism in tumor microenvironment: Implications for cancer immunotherapy

Shi, Rongchen; Tang, Yueming; Miao, Hongming

doi:10.1002/mco2.6

Cited by 116 publications

(88 citation statements)

References 294 publications

(299 reference statements)

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“…All these alterations contribute greatly to an immunosuppressive TME, thereby impairing the antitumor immune response and further promoting the tumor progression ( Biswas, 2015 ). Targeting metabolic pathways is therefore an immunotherapeutic approach to inhibit the tumor progression via restoring the TME environment ( Shi et al, 2020 ). In the present study, several signaling pathways related to metabolism were mainly enriched in subtype Sub2, for example, tyrosine metabolism, cytochrome p450 metabolism, and arachidonic acid metabolism.…”

Section: Discussionmentioning

confidence: 99%

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Mai

et al. 2021

Front. Cell Dev. Biol.

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Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes.Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed.Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways.Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.

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Section: Discussionmentioning

confidence: 99%

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Mai

et al. 2021

Front. Cell Dev. Biol.

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“…One way how tumors can influence Th2 cell-mediated immunity to neoplastic cells is by consuming nutrients and thereby starving Th2 effectors. This mechanism was meanwhile indicated for several immune cells within the TME including macrophages, CD8 + T cells, dendritic cells (DCs), and natural killer cells but not yet for Th2 cells (130,(146)(147)(148)(149). Tumors consume high levels of glucose to perform glycolysis.…”

Section: Interactions Of Th2 Cell Metabolism Tme and Potential Therapeutic Strategiesmentioning

confidence: 99%

Metabolic Interdependency of Th2 Cell-Mediated Type 2 Immunity and the Tumor Microenvironment

et al. 2021

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The function of T cells is critically dependent on their ability to generate metabolic building blocks to fulfil energy demands for proliferation and consecutive differentiation into various T helper (Th) cells. Th cells then have to adapt their metabolism to specific microenvironments within different organs during physiological and pathological immune responses. In this context, Th2 cells mediate immunity to parasites and are involved in the pathogenesis of allergic diseases including asthma, while CD8+ T cells and Th1 cells mediate immunity to viruses and tumors. Importantly, recent studies have investigated the metabolism of Th2 cells in more detail, while others have studied the influence of Th2 cell-mediated type 2 immunity on the tumor microenvironment (TME) and on tumor progression. We here review recent findings on the metabolism of Th2 cells and discuss how Th2 cells contribute to antitumor immunity. Combining the evidence from both types of studies, we provide here for the first time a perspective on how the energy metabolism of Th2 cells and the TME interact. Finally, we elaborate how a more detailed understanding of the unique metabolic interdependency between Th2 cells and the TME could reveal novel avenues for the development of immunotherapies in treating cancer.

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“…While it is well known that cancer cells undergo metabolic reprogramming, as described above, there is a growing recognition that such metabolic alterations also occur in immune cells, which affects the function of immune cells and contribute to tumor immune escape ( 30 – 32 ).…”

Section: Metabolic Changes In Immune Cellsmentioning

confidence: 99%

Metabolic Reprogramming Induces Immune Cell Dysfunction in the Tumor Microenvironment of Multiple Myeloma

Kuang

Jin³

et al. 2021

Front. Oncol.

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Tumor cells rewire metabolism to meet their increased nutritional demands, allowing the maintenance of tumor survival, proliferation, and expansion. Enhancement of glycolysis and glutaminolysis is identified in most, if not all cancers, including multiple myeloma (MM), which interacts with a hypoxic, acidic, and nutritionally deficient tumor microenvironment (TME). In this review, we discuss the metabolic changes including generation, depletion or accumulation of metabolites and signaling pathways, as well as their relationship with the TME in MM cells. Moreover, we describe the crosstalk among metabolism, TME, and changing function of immune cells during cancer progression. The overlapping metabolic phenotype between MM and immune cells is discussed. In this sense, targeting metabolism of MM cells is a promising therapeutic approach. We propose that it is important to define the metabolic signatures that may regulate the function of immune cells in TME in order to improve the response to immunotherapy.

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Metabolism in tumor microenvironment: Implications for cancer immunotherapy

Cited by 116 publications

References 294 publications

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Metabolic Interdependency of Th2 Cell-Mediated Type 2 Immunity and the Tumor Microenvironment

Metabolic Reprogramming Induces Immune Cell Dysfunction in the Tumor Microenvironment of Multiple Myeloma

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