Targeting oncometabolism to maximize immunotherapy in malignant brain tumors

Extracellular vesicles set the stage for brain plasticity and recovery by multimodal signalling

Extracellular vesicles (EVs) are extremely versatile naturally occurring membrane particles that convey complex signals between cells. EVs of different cellular sources are capable of inducing striking therapeutic responses in neurological disease models. Differently from pharmacological compounds that act by modulating defined signalling pathways, EV-based therapeutics possess multiple abilities via a variety of effectors, thus allowing the modulation of complex disease processes that may have very potent effects on brain tissue recovery. When applied in vivo in experimental models of neurological diseases, EV-based therapeutics have revealed remarkable effects on immune responses, cell metabolism and neuronal plasticity. This multimodal modulation of neuroimmune networks by EVs profoundly influences disease processes in a highly synergistic and context-dependent way. Ultimately, the EV-mediated restoration of cellular functions helps to set the stage for neurological recovery. With this review we first outline the current understanding of the mechanisms of action of EVs, describing how EVs released from various cellular sources identify their cellular targets and convey signals to recipient cells. Then, mechanisms of action applicable to key neurological conditions such as stroke, multiple sclerosis and neurodegenerative diseases are presented. Pathways that deserve attention in specific disease contexts are discussed. We subsequently showcase considerations about EV biodistribution and delineate genetic engineering strategies aiming at enhancing brain uptake and signalling. By sketching a broad view of EV-orchestrated brain plasticity and recovery, we finally define possible future clinical EV applications and propose necessary information to be provided ahead of clinical trials. Our goal is to provide a steppingstone that can be used to critically discuss EVs as next generation therapeutics for brain diseases.

Choroid plexus tumors: A spectrum from benign to malignant

Choroid plexus tumors (CPT) are believed to originate from outgrowths of the choroid plexus. Despite their broad spectrum of symptoms, invasive nature, and prognosis, most CPTs typically exhibit similar presentations due to their relationship with the cerebral ventricles, as well as the mechanical obstruction and mass effect associated with their growth. In addition, these tumors mainly affect the pediatric population, further complicating the differentiation between benign and malignant subtypes. The World Health Organization classifies CPTs into three grades, namely, grades I, II, or III, based on their mitotic activity, which determine the benign or malignant nature of the tumors. CPTs classified by the World Health Organization (WHO) include choroid plexus papillomas (CPP), atypical CPPs (aCPP), and malignant choroid plexus carcinomas (CPC). Choroid plexus adenomas represent an additional category of benign CPTs not officially classified by the WHO. Despite the variations in histology, immunohistochemistry, imaging, treatment, and prognosis, CPTs cannot be reliably distinguished based solely on clinical presentation. Therefore, in this review, we aim to provide a comprehensive overview of each tumor subtype, along with the current management approach and emerging treatments.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Glutamine-dependent effects of nitric oxide on cancer cells subjected to hypoxia-reoxygenation

Limited O₂ availability can decrease essential processes in energy metabolism. However, cancers have developed distinct metabolic adaptations to these conditions. For example, glutaminolysis can maintain energy metabolism and hypoxia signaling. Additionally, it has been observed that nitric oxide (NO) possesses concentration-dependent, biphasic effects in cancer. NO has potent anti-tumor effects through modulating events such as angiogenesis and metastasis at low physiological concentrations and inducing cell death at higher concentrations. In this study, Ewing Sarcoma cells (A-673), MIA PaCa, and SKBR3 cells were treated with DetaNONOate (DetaNO) in a model of hypoxia (1% O₂) and reoxygenation (21% O₂). All 3 cell types showed NO-dependent inhibition of cellular O₂ consumption which was enhanced as O₂-tension decreased. L-Gln depletion suppressed the mitochondrial response to decreasing O₂ tension in all 3 cell types and resulted in inhibition of Complex I activity. In A-673 cells the O₂ tension dependent change in mitochondrial O₂ consumption and increase in glycolysis was dependent on the presence of L-Gln. The response to hypoxia and Complex I activity were restored by α-ketoglutarate. NO exposure resulted in the A-673 cells showing greater sensitivity to decreasing O₂ tension. Under conditions of L-Gln depletion, NO restored HIF-1α levels and the mitochondrial response to O₂ tension possibly through the increase of 2-hydroxyglutarate. NO also resulted in suppression of cellular bioenergetics and further inhibition of Complex I which was not rescued by α-ketoglutarate. Taken together these data suggest that NO modulates the mitochondrial response to O₂ differentially in the absence and presence of L-Gln. These data suggest a combination of metabolic strategies targeting glutaminolysis and Complex I in cancer cells.

Trends in metabolic signaling pathways of tumor drug resistance: A scientometric analysis

Background

Cancer chemotherapy resistance is one of the most critical obstacles in cancer therapy. Since Warburg O first observed alterations in cancer metabolism in the 1950s, people gradually found tumor metabolism pathways play a fundamental role in regulating the response to chemotherapeutic drugs, and the attempts of targeting tumor energetics have shown promising preclinical outcomes in recent years. This study aimed to summarize the knowledge structure and identify emerging trends and potential hotspots in metabolic signaling pathways of tumor drug resistance research.

Methods

Publications related to metabolic signaling pathways of tumor drug resistance published from 1992 to 2022 were retrieved from the Web of Science Core Collection database. The document type was set to articles or reviews with language restriction to English. Two different scientometric software including Citespace and VOS viewer were used to conduct this scientometric analysis.

Results

A total of 2,537 publications including 1,704 articles and 833 reviews were retrieved in the final analysis. The USA made the most contributions to this field. The leading institution was the University of Texas MD Anderson Cancer Center. Avan A was the most productive author, and Hanahan D was the key researcher with the most co-citations, but there is no leader in this field yet. Cancers was the most influential academic journal, and Oncology was the most popular research field. Based on keywords occurrence analysis, these selected keywords could be roughly divided into five main topics: cluster 1 (study of cancer cell apoptosis pathway); cluster 2 (study of resistance mechanisms of different cancer types); cluster 3 (study of cancer stem cells); cluster 4 (study of tumor oxidative stress and inflammation signaling pathways); and cluster 5 (study of autophagy). The keywords burst detection identified several keywords as new research hotspots, including “tumor microenvironment,” “invasion,” and “target”.

Conclusion

Tumor metabolic reprogramming of drug resistance research is advancing rapidly. This study serves as a starting point, providing a thorough overview, the development landscape, and future opportunities in this field.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.