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

USP5 motivates immunosuppressive microenvironment in multiple myeloma by activating STAT2-PFKFB4-mediated glycolysis

BACKGROUND

Glycolysis, a classic characteristic of cancer cells, can drive cancer progression by generating lactate, which play as a key immunosuppressive mediator. Currently, ubiquitin-specific proteases 5 (USP5) has been demonstrated to facilitate tumor cell survival in multiple myeloma (MM), whereas whether USP5 was involved in glycolysis-lactate production pathway and immunosuppressive microenvironment formation in MM remain unknown.

METHODS

The gene and protein expression characteristics were assessed via qRT-PCR and western blot. MM cell survival was determined by CCK-8 and flow cytometry analysis. Glycolysis was evaluated by examining glucose uptake, lactate production and ATP level via corresponding kits. Tumor-associated macrophages polarization was tested via measurement of M1/M2-like macrophage markers using qRT-PCR and flow cytometry methods. Dual-luciferase reporter, chromatin immunoprecipitation and co-immunoprecipitation assays were conducted to verified molecular relationship. Xenograft model was used for verified cellular findings.

RESULTS

USP5 was abnormally overexpressed in MM patients and cell lines. Knockdown of USP5 could restrain MM cell survival and glycolysis activity, thus reducing lactate-mediated immunosuppressive M2-like macrophage polarization in vitro and in vivo, whereas overexpression of USP5 play opposite impacts. Mechanistically, USP5 could downregulate the ubiquitination modification of STAT to stabilize STAT2 protein, thus activating PFKFB4 transcription. Moreover, STAT2 could overturn the regulatory role of USP5 on MM cell survival, glycolysis and lactate-mediated immunosuppressive M2-like macrophage polarization.

CONCLUSION

These findings elucidated that USP5 served as a regulator of glycolysis-lactate to stimulate M2-like macrophage formation by regulating STAT2-PFKFB4 signaling, which supported that USP5 could be a viable therapeutic target of MM treatment.

YY1-induced transcription of AKR1C3 activates the Hedgehog signalling pathway to enhance lenalidomide resistance and glycolytic activity in multiple myeloma cells

Lenalidomide (LEN) is a mainstay for treating multiple myeloma (MM), but its efficacy is often limited by resistance. We investigated the interaction between aldo-keto reductase family 1 member C3 (AKR1C3) and Yin Yang 1 (YY1) and their roles in LEN resistance. We induced LEN-resistant MM cell lines (H929R and U266R). Loss- or gain-of-function assays of AKR1C3 and YY1 were used to analyse the half maximal inhibitory concentration (IC50) values, cell senescence, DNA damage, and glycolytic activity under LEN treatment. Chromatin immunoprecipitation was used to determine the interaction between YY1 and AKR1C3. As results, AKR1C3 and YY1 were upregulated in H929R and U266R cells. AKR1C3 silencing decreased the LEN's IC50, slowed cell growth, enhanced senescence and DNA damage, reduced metabolic reprogramming. YY1 activated the transcription of AKR1C3 by binding to its promoter region. Similarly, silencing YY1 enhanced LEN sensitivity, suppressed glycolysis, and was counteracted by AKR1C3 overexpression. Mechanistically, YY1-AKR1C3 activated the Hedgehog pathway; fluticasone reversed the effects of AKR1C3 silencing on LEN resistance and glycolysis in H929R and U266R cells. Overall, YY1 activates AKR1C3 transcription and the Hedgehog pathway to increase LEN resistance and glycolytic activity in MM cells.

Glucose Metabolism and Tumor Microenvironment: Mechanistic Insights and Therapeutic Implications

Metabolic reprogramming in cancer cells involves changes in glucose metabolism, glutamine utilization, and lipid production, as well as promoting increased cell proliferation, survival, and immune resistance by altering the tumor microenvironment. Our study analyzes metabolic reprogramming in neoplastically transformed cells, focusing on changes in glucose metabolism, glutaminolysis, and lipid synthesis. Moreover, we discuss the therapeutic potential of targeting cancer metabolism, focusing on key enzymes involved in glycolysis, the pentose phosphate pathway, and amino acid metabolism, including lactate dehydrogenase A, hexokinase, phosphofructokinase and others. The review also highlights challenges such as metabolic heterogeneity, adaptability, and the need for personalized therapies to overcome resistance and minimize adverse effects in cancer treatment. This review underscores the significance of comprehending metabolic reprogramming in cancer cells to engineer targeted therapies, personalize treatment methodologies, and surmount challenges, including metabolic plasticity and therapeutic resistance.

New horizons in our understanding of precursor multiple myeloma and early interception

Multiple myeloma is an incurable plasma cell malignancy that evolves over decades through the selection and malignant transformation of monoclonal plasma cells. The evolution from precursor states to symptomatic disease is characterized by an increasing complexity of genomic alterations within the plasma cells and a remodelling of the microenvironment towards an immunosuppressive state. Notably, in patients with advanced disease, similar mechanisms of tumour escape and immune dysfunction mediate resistance to modern T cell-based therapies, such as T cell-engaging bispecific antibodies and chimeric antigen receptor (CAR)-T cells. Thus, an increasing number of clinical trials are assessing the efficiency and safety of these therapies in individuals with newly diagnosed multiple myeloma and high-risk smoldering multiple myeloma. In this Review, we summarize the current knowledge about tumour intrinsic and extrinsic processes underlying progression from precursor states to symptomatic myeloma and discuss the rationale for early interception including the use of T cell-redirecting therapies.

New insights into the stromal interaction molecule 2 function and its impact on the immunomodulation of tumor microenvironment

Immune cells-enhanced immunotherapy exhibits unprecedented overall survival-prolongation even curable in some cancer patients. Although so, most of the patients show no response. Tumor microenvironment (TME) where immune cells settle down has multi-faceted influences, but usually creates an immunosuppressive niche that facilitating tumor cells escape from immune attack. The metabolites and malnutrition of TME exert enormous effects on the resident immune cells, but the underlying mechanism is largely unknown. The stromal interaction molecules 2 (STIM2) is an endoplasmic reticulum (ER) calcium (Ca2+) sensor to maintain Ca2+ homeostasis. Notably, the cytosol STIM2 C-terminus is long with various domains that are available for the combination or/and molecular modification. This distinct structure endows STIM2 with a high susceptibility to numerous permeable physico-chemical molecules or protein interactions. STIM2 and its variants are extensively expressed in various immune cells, especially in T immune cells. STIM2 was reported closely correlated with the function of immune cells via regulating Ca2+ signaling, energy metabolism and cell fitness. Herein, we sum the latest findings on the STIM2 structure, focusing on its distinct characteristics and profound effect on the regulation of Ca2+ homeostasis and multi-talented functionality. We also outline the advancements on the underlying mechanism how STIM2 anomalies influence the function of immune cells and on the turbulent expression or/and amenably modification of STIM2 within the tumor niches. Then we discuss the translation of these researches into antitumor approaches, emphasizing the potential of STIM2 as a therapeutic target for direct inhibition of tumor cells or more activation towards immune cells driving to flare TME. This review is an update on STIM2, aiming to rationalize the potential of STIM2 as a therapeutic target for immunomodulation, engaging immune cells to exert the utmost anti-tumor effect.

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies.

METHODS

This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified.

RESULTS

High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077-2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207-5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL.

CONCLUSIONS

This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

Metabolic reprogramming and its clinical implication for liver cancer

Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated β-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.

Lipid metabolic vulnerabilities of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy worldwide, characterized by abnormal proliferation of malignant plasma cells within a tumor-permissive bone marrow microenvironment. Metabolic dysfunctions are emerging as key determinants in the pathobiology of MM. In this review, we highlight the metabolic features of MM, showing how alterations in various lipid pathways, mainly involving fatty acids, cholesterol and sphingolipids, affect the growth, survival and drug responsiveness of MM cells, as well as their cross-talk with other cellular components of the tumor microenvironment. These findings will provide a new path to understanding the mechanisms underlying how lipid vulnerabilities may arise and affect the phenotype of malignant plasma cells, highlighting novel druggable pathways with a significant impact on the management of MM.

Histone Methyltransferase NSD2 Activates PKCα to Drive Metabolic Reprogramming and Lenalidomide Resistance in Multiple Myeloma

Multiple myeloma cells undergo metabolic reprogramming in response to the hypoxic and nutrient-deprived bone marrow microenvironment. Primary oncogenes in recurrent translocations might be able to drive metabolic heterogeneity to survive the microenvironment that can present new vulnerabilities for therapeutic targeting. t(4;14) translocation leads to the universal overexpression of histone methyltransferase NSD2 that promotes plasma cell transformation through a global increase in H3K36me2. Here, we identified PKCα as an epigenetic target that contributes to the oncogenic potential of NSD2. RNA sequencing of t(4;14) multiple myeloma cell lines revealed a significant enrichment in the regulation of metabolic processes by PKCα, and the glycolytic gene, hexokinase 2 (HK2), was transcriptionally regulated by PKCα in a PI3K/Akt-dependent manner. Loss of PKCα displaced mitochondria-bound HK2 and reversed sensitivity to the glycolytic inhibitor 3-bromopyruvate. In addition, the perturbation of glycolytic flux led to a metabolic shift to a less energetic state and decreased ATP production. Metabolomics analysis indicated lactate as a differential metabolite associated with PKCα. As a result, PKCα conferred resistance to the immunomodulatory drugs (IMiD) lenalidomide in a cereblon-independent manner and could be phenocopied by either overexpression of HK2 or direct supplementation of lactate. Clinically, t(4;14) patients had elevated plasma lactate levels and did not benefit from lenalidomide-based regimens. Altogether, this study provides insights into the epigenetic-metabolism cross-talk in multiple myeloma and highlights the opportunity for therapeutic intervention that leverages the distinct metabolic program in t(4;14) myeloma.

SIGNIFICANCE

Aberrant glycolysis driven by NSD2-mediated upregulation of PKCα can be therapeutically exploited using metabolic inhibitors with lactate as a biomarker to identify high-risk patients who exhibit poor response towards IMiD-based regimens.

Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature

BACKGROUND

Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming.

METHODS

HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted ¹H Nuclear Magnetic Resonance (¹H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS).

RESULTS

Principal component analysis (PCA) within our ¹H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS.

CONCLUSIONS

Together, ¹H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.

CDK7 controls E2F- and MYC-driven proliferative and metabolic vulnerabilities in multiple myeloma

Therapeutic targeting of CDK7 has proven beneficial in preclinical studies, yet the off-target effects of currently available CDK7 inhibitors make it difficult to pinpoint the exact mechanisms behind MM cell death mediated by CDK7 inhibition. Here, we show that CDK7 expression positively correlates with E2F and MYC transcriptional programs in cells from patients with multiple myeloma (MM); its selective targeting counteracts E2F activity via perturbation of the cyclin-dependent kinases/Rb axis and impairs MYC-regulated metabolic gene signatures translating into defects in glycolysis and reduced levels of lactate production in MM cells. CDK7 inhibition using the covalent small-molecule inhibitor YKL-5-124 elicits a strong therapeutic response with minimal effects on normal cells, and causes in vivo tumor regression, increasing survival in several mouse models of MM including a genetically engineered mouse model of MYC-dependent MM. Through its role as a critical cofactor and regulator of MYC and E2F activity, CDK7 is therefore a master regulator of oncogenic cellular programs supporting MM growth and survival, and a valuable therapeutic target providing rationale for development of YKL-5-124 for clinical use.

The Interplay between Dysregulated Metabolism and Epigenetics in Cancer

Cellular metabolism (or energetics) and epigenetics are tightly coupled cellular processes. It is arguable that of all the described cancer hallmarks, dysregulated cellular energetics and epigenetics are the most tightly coregulated. Cellular metabolic states regulate and drive epigenetic changes while also being capable of influencing, if not driving, epigenetic reprogramming. Conversely, epigenetic changes can drive altered and compensatory metabolic states. Cancer cells meticulously modify and control each of these two linked cellular processes in order to maintain their tumorigenic potential and capacity. This review aims to explore the interplay between these two processes and discuss how each affects the other, driving and enhancing tumorigenic states in certain contexts.

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM's great heterogeneity and incurability, risk assessment and treatment choices are still difficult. Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes. Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study. Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

Metabolic Alterations in Multiple Myeloma: From Oncogenesis to Proteasome Inhibitor Resistance

Despite significant improvements in treatment strategies over the past couple of decades, multiple myeloma (MM) remains an incurable disease due to the development of drug resistance. Metabolic reprogramming is a key feature of cancer cells, including MM, and acts to fuel increased proliferation, create a permissive tumour microenvironment, and promote drug resistance. This review presents an overview of the key metabolic adaptations that occur in MM pathogenesis and in the development of resistance to proteasome inhibitors, the backbone of current MM therapy, and considers the potential for therapeutic targeting of key metabolic pathways to improve outcomes.

A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma which that highly aggressive and heterogeneous. Glycolysis has been implicated in the regulation of tumor microenvironment (TME) and development. In this study, we aimed to establish a glycolysis-related prognostic model for the risk stratification, prognosis prediction, and immune landscape evaluation in patients with DLBCL. Methods: Three independent datasets GSE181063, GSE10846, and GSE53786 containing gene expression profiles and clinical data were downloaded from the Gene Expression Omnibus (GEO) database. The glycolysis-related prognostic model was developed with Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression and validated. A nomogram integrating clinical factors and glycolytic risk scores was constructed. The composition of the TME was analyzed with the ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA). Results: A glycolytic risk model containing eight genes was developed. The area under the receiver operating characteristic (ROC) curve (AUC) for the 1-, 3-, and 5-year was 0.718, 0.695, and 0.688, respectively. Patients in the high-risk group had significantly lower immune scores, elevated tumor purity, and poorer survival compared with those in the low-risk group. The nomogram constructed based on glycolytic risk score, age, Eastern Cooperative Oncology Group performance status (ECOG-PS), use of rituximab, and cell of origin (COO) displayed better prediction performance compared with the International Prognostic Index (IPI) in DLBCL. The glycolytic risk score was negatively correlated with the infiltration level of activated CD8 T cells, activated dendritic cells, natural killer cells, and macrophages and immune checkpoint molecules including PD-L2, CTLA4, TIM-3, TIGIT, and B7-H3. Conclusion: These results suggested that the glycolytic risk model could accurately and stably predict the prognosis of patients with DLBCL and might unearth the possible explanation for the glycolysis-related poor prognosis.

The Tumor Microenvironment Reprograms Immune Cells

Tumor tissue comprises a highly complex network of diverse cell types. The tumor microenvironment (TME) can be mainly subdivided into cancer cells and stromal cell compartments, the latter include different types of immune cells, fibroblasts, endothelial cells, and pericytes. Tumor cells reprogram immune cells and other stromal cells in the TME to constrain their antitumor capacity by creating an immunosuppressive milieu and metabolism competition. Moreover, the reprogramming effect on immune cells is localized not only in the tumor but also at the systemic level. With wide application of single-cell sequencing technology, tumor-specific characteristics of immune cells and other stromal cells in the TME have been dissected. In this review, we mainly focus on how tumor cells reprogram immune cells both within the TME and peripheral blood. This information can further help us to improve the efficiency of current immunotherapy as well as bring up new ideas to combat cancer.

Identification of fatty acid metabolism-related molecular subtype biomarkers and their correlation with immune checkpoints in cutaneous melanoma

PURPOSE

Fatty acid metabolism (FAM) affects the immune phenotype in a metabolically dynamic tumor microenvironment (TME), but the use of FAM-related genes (FAMGs) to predict the prognosis and immunotherapy response of cutaneous melanoma (CM) patients has not been investigated. In this study, we aimed to construct FAM molecular subtypes and identify key prognostic biomarkers in CM.

METHODS

We used a CM dataset in The Cancer Genome Atlas (TCGA) to construct FAM molecular subtypes. We performed Kaplan-Meier (K-M) analysis, gene set enrichment analysis (GSEA), and TME analysis to assess differences in the prognosis and immune phenotype between subtypes. We used weighted gene co-expression network analysis (WGCNA) to identify key biomarkers that regulate tumor metabolism and immunity between the subtypes. We compared overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between CM patients with high or low biomarker expression. We applied univariable and multivariable Cox analyses to verify the independent prognostic value of the FAM biomarkers. We used GSEA and TME analysis to investigate the immune-related regulation mechanism of the FAM subtype biomarker. We evaluated the immune checkpoint inhibition (ICI) response and chemotherapy sensitivity between CM patients with high or low biomarker expression. We performed real-time fluorescent quantitative PCR (qRT-PCR) and semi-quantitative analysis of the immunohistochemical (IHC) data from the Human Protein Atlas to evaluate the mRNA and protein expression levels of the FAM biomarkers in CM.

RESULTS

We identified 2 FAM molecular subtypes (cluster 1 and cluster 2). K-M analysis showed that cluster 2 had better OS and PFS than cluster 1 did. GSEA showed that, compared with cluster 1, cluster 2 had significantly upregulated immune response pathways. The TME analysis indicated that immune cell subpopulations and immune functions were highly enriched in cluster 2 as compared with cluster 1. WGCNA identified 6 hub genes (ACSL5, ALOX5AP, CD1D, CD74, IL4I1, and TBXAS1) as FAM biomarkers. CM patients with high expression levels of the six biomarkers had better OS, PFS, and DSS than those with low expression levels of the biomarkers. The Cox regression analyses verified that the 6 FAM biomarkers can be independent prognostic factors for CM patients. The single-gene GSEA showed that the high expression levels of the 6 genes were mainly enriched in T-cell antigen presentation, the PD-1 signaling pathway, and tumor escape. The TME analysis confirmed that the FAM subtype biomarkers were not only related to immune infiltration but also highly correlated with immune checkpoints such as PD-1, PD-L1, and CTLA-4. TIDE scores confirmed that patients with high expression levels of the 6 biomarkers had worse immunotherapy responses. The 6 genes conveyed significant sensitivity to some chemotherapy drugs. qRT-PCR and IHC analyses verified the expression levels of the 6 biomarkers in CM cells.

CONCLUSION

Our FAM subtypes verify that different FAM reprogramming affects the function and phenotype of infiltrating immune cells in the CM TME. The FAM molecular subtype biomarkers can be independent predictors of prognosis and immunotherapy response in CM patients.

Metabolic features of myeloma cells in the context of bone microenvironment: Implication for the pathophysiology and clinic of myeloma bone disease

Multiple myeloma (MM) is a hematological malignancy characterized by the accumulation of malignant plasma cells (PCs) into the bone marrow (BM). The complex interaction between the BM microenvironment and MM PCs can lead to severe impairment of bone remodeling. Indeed, the BM microenvironment exerts a critical role in the survival of malignant PCs. Growing evidence indicates that MM cells have several metabolic features including enhanced glycolysis and an increase in lactate production through the upregulation of glucose transporters and enzymes. More recently, it has been reported that MM cells arehighly glutamine addicted. Interestingly, these metabolic changes in MM cells may affect BM microenvironment cells by altering the differentiation process of osteoblasts from mesenchymal stromal cells. The identification of glutamine metabolism alterations in MM cells and bone microenvironment may provide a rationale to design new therapeutic approaches and diagnostic tools. The osteolytic lesions are the most frequent clinical features in MM patients, often characterized by pathological fractures and acute pain. The use of the newer imaging techniques such as Magnetic Resonance Imaging (MRI) and combined Positron Emission Tomography (PET) and Computerized Tomography (CT) has been introduced into clinical practice to better define the skeletal involvement. Currently, the PET/CT with ¹⁸F-fluorodeoxyglucose (FDG) is the diagnostic gold standard to detect active MM bone disease due to the high glycolytic activity of MM cells. However, new tracers are actively under investigation because a portion of MM patients remains negative at the skeletal level by ¹⁸F-FDG. In this review, we will summarize the existing knowledge on the metabolic alterations of MM cells considering their impact on the BM microenvironment cells and particularly in the subsequent formation of osteolytic bone lesions. Based on this, we will discuss the identification of possible new druggable targets and the use of novel metabolic targets for PET imaging in the detection of skeletal lesions, in the staging and treatment response of MM patients.

Metabolic cross-talk within the bone marrow milieu: focus on multiple myeloma

Cancer cells are well-known for their capacity to adapt their metabolism to their increasing energy demands which is necessary for tumor progression. This is no different for Multiple Myeloma (MM), a hematological cancer which develops in the bone marrow (BM), whereby the malignant plasma cells accumulate and impair normal BM functions. It has become clear that the hypoxic BM environment contributes to metabolic rewiring of the MM cells, including changes in metabolite levels, increased/decreased activity of metabolic enzymes and metabolic shifts. These adaptations will lead to a pro-tumoral environment stimulating MM growth and drug resistance In this review, we discuss the identified metabolic changes in MM and the BM microenvironment and summarize how these identified changes have been targeted (by inhibitors, genetic approaches or deprivation studies) in order to block MM progression and survival.

Metabolic Reprogramming Induces Macrophage Polarization in the Tumor Microenvironment

Macrophages are one of the most important cells in the innate immune system, they are converted into two distinct subtypes with completely different molecular phenotypes and functional features under different stimuli of the microenvironment: M1 macrophages induced by IFN-γ/lipopolysaccharides(LPS) and M2 macrophages induced by IL-4/IL-10/IL-13. Tumor-associated macrophages (TAMs) differentiate from macrophages through various factors in the tumor microenvironment (TME). TAMs have the phenotype and function of M2 macrophages and are capable of secreting multiple cytokines to promote tumor progression. Both tumor cells and macrophages can meet the energy needs for rapid cell growth and proliferation through metabolic reprogramming, so a comprehensive understanding of pro-tumor and antitumor metabolic switches in TAM is essential to understanding immune escape mechanisms. This paper focuses on the functions of relevant signaling pathways and cytokines during macrophage polarization and metabolic reprogramming, and briefly discusses the effects of different microenvironments and macrophage pathogenicity, in addition to describing the research progress of inhibitory drugs for certain metabolic and polarized signaling pathways.

Metabolic Vulnerabilities in Multiple Myeloma

Multiple myeloma (MM) remains an incurable malignancy with eventual emergence of refractory disease. Metabolic shifts, which ensure the availability of sufficient energy to support hyperproliferation of malignant cells, are a hallmark of cancer. Deregulated metabolic pathways have implications for the tumor microenvironment, immune cell function, prognostic significance in MM and anti-myeloma drug resistance. Herein, we summarize recent findings on metabolic abnormalities in MM and clinical implications driven by metabolism that may consequently inspire novel therapeutic interventions. We highlight some future perspectives on metabolism in MM and propose potential targets that might revolutionize the field.

Alteration in glycolytic/cholesterogenic gene expression is associated with bladder cancer prognosis and immune cell infiltration

Background

Oncogenic metabolic reprogramming contributes to tumor growth and immune evasion. The intertumoral metabolic heterogeneity and interaction of distinct metabolic pathways may determine patient outcomes. In this study, we aim to determine the clinical and immunological significance of metabolic subtypes according to the expression levels of genes related to glycolysis and cholesterol-synthesis in bladder cancer (BCa).

Methods

Based on the median expression levels of glycolytic and cholesterogenic genes, patients were stratified into 4 subtypes (mixed, cholesterogenic, glycolytic, and quiescent) in an integrated cohort including TCGA, GSE13507, and IMvigor210. Clinical, genomic, transcriptomic, and tumor microenvironment characteristics were compared between the 4 subtypes.

Results

The 4 metabolic subtypes exhibited distinct clinical, molecular, and genomic patterns. Compared to quiescent subtype, mixed subtype was more likely to be basal tumors and was significantly associated with poorer prognosis even after controlling for age, gender, histological grade, clinical stage, and molecular phenotypes. Additionally, mixed tumors harbored a higher frequency of RB1 and LRP1B copy number deletion compared to quiescent tumors (25.7% vs. 12.7 and 27.9% vs. 10.2%, respectively, both adjusted P value< 0.05). Furthermore, aberrant PIK3CA expression level was significantly correlated with those of glycolytic and cholesterogenic genes. The quiescent subtype was associated with lower stemness indices and lower signature scores for gene sets involved in genomic instability, including DNA replication, DNA damage repair, mismatch repair, and homologous recombination genes. Moreover, quiescent tumors exhibited lower expression levels of pyruvate dehydrogenase kinases 1-3 (PDK1-3) than the other subtypes. In addition, distinct immune cell infiltration patterns were observed across the 4 metabolic subtypes, with greater infiltration of M0/M2 macrophages observed in glycolytic and mixed subtypes. However, no significant difference in immunotherapy response was observed across the 4 metabolic subtypes.

Conclusion

This study proposed a new metabolic subtyping method for BCa based on genes involved in glycolysis and cholesterol synthesis pathways. Our findings may provide novel insight for the development of personalized subtype-specific treatment strategies targeting metabolic vulnerabilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09064-0.

Development and Validation of a Combined Glycolysis and Immune Prognostic Model for Melanoma

Background

Glycolytic effects and immune microenvironments play important roles in the development of melanoma. However, reliable biomarkers for prognostic prediction of melanoma as based on glycolysis and immune status remain to be identified.

Methods

Glycolysis-related genes (GRGs) were obtained from the Molecular Signatures database and immune-related genes (IRGs) were downloaded from the ImmPort dataset. Prognostic GRGs and IRGs in the TCGA (The Cancer Genome Atlas) and GSE65904 datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Glycolysis expression profiles and the infiltration of immune cells were analyzed and compared. Finally, in vitro experiments were performed to assess the expression and function of these CIGI genes.

Results

Four prognostic glycolysis- and immune-related signatures (SEMA4D, IFITM1, KIF20A and GPR87) were identified for use in constructing a comprehensive glycolysis and immune (CIGI) model. CIGI proved to be a stable, predictive method as determined from different datasets and subgroups of patients and served as an independent prognostic factor for melanoma patients. In addition, patients in the high-CIGI group showed increased levels of glycolytic gene expressions and exhibited immune-suppressive features. Finally, SEMA4D and IFITM1 may function as tumor suppressor genes, while KIF20A and GPR87 may function as oncogenes in melanoma as revealed from results of in vitro experiments.

Conclusion

In this report we present our findings on the development and validation of a novel prognostic classifier for use in patients with melanoma as based on glycolysis and immune expression profiles.

Targeting Reactive Oxygen Species Metabolism to Induce Myeloma Cell Death

Multiple myeloma (MM) is a common hematological disease characterized by the accumulation of clonal malignant plasma cells in the bone marrow. Over the past two decades, new therapeutic strategies have significantly improved the treatment outcome and patients survival. Nevertheless, most MM patients relapse underlying the need of new therapeutic approaches. Plasma cells are prone to produce large amounts of immunoglobulins causing the production of intracellular ROS. Although adapted to high level of ROS, MM cells die when exposed to drugs increasing ROS production either directly or by inhibiting antioxidant enzymes. In this review, we discuss the efficacy of ROS-generating drugs for inducing MM cell death and counteracting acquired drug resistance specifically toward proteasome inhibitors.

Overcoming the Immunosuppressive Tumor Microenvironment in Multiple Myeloma

Simple Summary

This article provides a comprehensive review of new and emerging treatment strategies against multiple myeloma that employ precision medicines and/or drugs capable of improving the ability of the immune system to prevent or slow down the progression of multiple myeloma. These rationally designed new treatment methods have the potential to change the therapeutic landscape in multiple myeloma and improve the long-term survival outcome.

Abstract

SeverFigurel cellular elements of the bone marrow (BM) microenvironment in multiple myeloma (MM) patients contribute to the immune evasion, proliferation, and drug resistance of MM cells, including myeloid-derived suppressor cells (MDSCs), tumor-associated M2-like, “alternatively activated” macrophages, CD38+ regulatory B-cells (Bregs), and regulatory T-cells (Tregs). These immunosuppressive elements in bidirectional and multi-directional crosstalk with each other inhibit both memory and cytotoxic effector T-cell populations as well as natural killer (NK) cells. Immunomodulatory imide drugs (IMiDs), protease inhibitors (PI), monoclonal antibodies (MoAb), adoptive T-cell/NK cell therapy, and inhibitors of anti-apoptotic signaling pathways have emerged as promising therapeutic platforms that can be employed in various combinations as part of a rationally designed immunomodulatory strategy against an immunosuppressive tumor microenvironment (TME) in MM. These platforms provide the foundation for a new therapeutic paradigm for achieving improved survival of high-risk newly diagnosed as well as relapsed/refractory MM patients. Here we review the scientific rationale and clinical proof of concept for each of these platforms.