Tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology and treatment

Metabolic fingerprinting by nuclear magnetic resonance of hepatocellular carcinoma cells during p53 reactivation-induced senescence

Cellular senescence is characterized by stable cell cycle arrest. Senescent cells exhibit a senescence-associated secretory phenotype that can promote tumor progression. The aim of our study was to identify specific nuclear magnetic resonance (NMR) spectroscopy-based markers of cancer cell senescence. For metabolic studies, we employed murine liver carcinoma Harvey Rat Sarcoma Virus (H-Ras) cells, in which reactivation of p53 expression induces senescence. Senescent and nonsenescent cell extracts were subjected to high-resolution proton (1H)-NMR spectroscopy-based metabolomics, and dynamic metabolic changes during senescence were analyzed using a magnetic resonance spectroscopy (MRS)-compatible cell perfusion system. Additionally, the ability of intact senescent cells to degrade the extracellular matrix (ECM) was quantified in the cell perfusion system. Analysis of senescent H-Ras cell extracts revealed elevated sn-glycero-3-phosphocholine, myoinositol, taurine, and creatine levels, with decreases in glycine, o-phosphocholine, threonine, and valine. These metabolic findings were accompanied by a greater degradation index of the ECM in senescent H-Ras cells than in control H-Ras cells. MRS studies with the cell perfusion system revealed elevated creatine levels in senescent cells on Day 4, confirming the 1H-NMR results. These senescence-associated changes in metabolism and ECM degradation strongly impact growth and redox metabolism and reveal potential MRS signals for detecting senescent cancer cells in vivo.

An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma

Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.

Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

BACKGROUND

Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated.

METHODS

We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach.

RESULTS

[18 F]FDG (2-deoxy-2-[fluorine-18]fluoro-d-glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one-carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18 F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade.

CONCLUSION

Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18 F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

The genomic alterations in glioblastoma influence the levels of CSF metabolites

Cerebrospinal fluid (CSF) analysis is underutilized in patients with glioblastoma (GBM), partly due to a lack of studies demonstrating the clinical utility of CSF biomarkers. While some studies show the utility of CSF cell-free DNA analysis, studies analyzing CSF metabolites in patients with glioblastoma are limited. Diffuse gliomas have altered cellular metabolism. For example, mutations in isocitrate dehydrogenase enzymes (e.g., IDH1 and IDH2) are common in diffuse gliomas and lead to increased levels of D-2-hydroxyglutarate in CSF. However, there is a poor understanding of changes CSF metabolites in GBM patients. In this study, we performed targeted metabolomic analysis of CSF from n = 31 patients with GBM and n = 13 individuals with non-neoplastic conditions (controls), by mass spectrometry. Hierarchical clustering and sparse partial least square-discriminant analysis (sPLS-DA) revealed differences in CSF metabolites between GBM and control CSF, including metabolites associated with fatty acid oxidation and the gut microbiome (i.e., carnitine, 2-methylbutyrylcarnitine, shikimate, aminobutanal, uridine, N-acetylputrescine, and farnesyl diphosphate). In addition, we identified differences in CSF metabolites in GBM patients based on the presence/absence of TP53 or PTEN mutations, consistent with the idea that different mutations have different effects on tumor metabolism. In summary, our results increase the understanding of CSF metabolites in patients with diffuse gliomas and highlight several metabolites that could be informative biomarkers in patients with GBM.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Advances in PET imaging of cancer

Molecular imaging has experienced enormous advancements in the areas of imaging technology, imaging probe and contrast development, and data quality, as well as machine learning-based data analysis. Positron emission tomography (PET) and its combination with computed tomography (CT) or magnetic resonance imaging (MRI) as a multimodality PET-CT or PET-MRI system offer a wealth of molecular, functional and morphological data with a single patient scan. Despite the recent technical advances and the availability of dozens of disease-specific contrast and imaging probes, only a few parameters, such as tumour size or the mean tracer uptake, are used for the evaluation of images in clinical practice. Multiparametric in vivo imaging data not only are highly quantitative but also can provide invaluable information about pathophysiology, receptor expression, metabolism, or morphological and functional features of tumours, such as pH, oxygenation or tissue density, as well as pharmacodynamic properties of drugs, to measure drug response with a contrast agent. It can further quantitatively map and spatially resolve the intertumoural and intratumoural heterogeneity, providing insights into tumour vulnerabilities for target-specific therapeutic interventions. Failure to exploit and integrate the full potential of such powerful imaging data may lead to a lost opportunity in which patients do not receive the best possible care. With the desire to implement personalized medicine in the cancer clinic, the full comprehensive diagnostic power of multiplexed imaging should be utilized.

Most recent update of preclinical and clinical data on radioresistance and radiosensitivity of high-grade gliomas-a radiation oncologist's perspective

PURPOSE

This review article discusses the studies concerning advances in radiotherapy of high-grade gliomas published in the second half of 2021.

METHODS

A literature search was performed in PubMed using the terms ("gliom* and radio*") and time limits 1 July 2021-31 December 2021. The articles were then manually selected for relevance to the analyzed topics.

RESULTS

Considerable progress has been made in the preclinical field on the mechanisms of radioresistance and radiosensitization of high-grade gliomas (HGG). However, fewer early-phase (I/II) clinical trials have been performed and, of the latter, even fewer have produced results that justify moving to phase III. In the 6‑month period under consideration, no studies were published that would lead to a change in clinical practice and the overall survival (OS) of patients remained similar to that of 2005, the year in which it increased significantly for the last time thanks to introduction of the alkylating agent temozolomide.

CONCLUSION

After 17 years of stalemate in improving the OS of patients with HGG, an in-depth analysis of the causes should be carried out in order to identify whether the research efforts conducted so far, including in the radiotherapeutic field, have been the most effective or require improvement. In our opinion, in addition to the therapeutic difficulties related to the biology of HGG tumors (e.g., high infiltrating capacity, multiple resistance mechanisms, blood-brain barrier), some public research policy choices may also play a role, especially in consideration of the limited interest of the pharmaceutical industry in the field of rare cancers.

Acute and chronic inflammation alter immunometabolism in a cutaneous delayed-type hypersensitivity reaction (DTHR) mouse model

T-cell-driven immune responses are responsible for several autoimmune disorders, such as psoriasis vulgaris and rheumatoid arthritis. Identification of metabolic signatures in inflamed tissues is needed to facilitate novel and individualised therapeutic developments. Here we show the temporal metabolic dynamics of T-cell-driven inflammation characterised by nuclear magnetic resonance spectroscopy-based metabolomics, histopathology and immunohistochemistry in acute and chronic cutaneous delayed-type hypersensitivity reaction (DTHR). During acute DTHR, an increase in glutathione and glutathione disulfide is consistent with the ear swelling response and degree of neutrophilic infiltration, while taurine and ascorbate dominate the chronic phase, suggesting a switch in redox metabolism. Lowered amino acids, an increase in cell membrane repair-related metabolites and infiltration of T cells and macrophages further characterise chronic DTHR. Acute and chronic cutaneous DTHR can be distinguished by characteristic metabolic patterns associated with individual inflammatory pathways providing knowledge that will aid target discovery of specialised therapeutics.

Nuclear magnetic resonance spectroscopy-based tissue metabolomics is used to define detailed temporal signatures of acute and chronic inflammation in cutaneous delayed-type hypersensitivity reaction.

Multiparametric Longitudinal Profiling of RCAS-tva-Induced PDGFB-Driven Experimental Glioma

Glioblastomas are incurable primary brain tumors harboring a heterogeneous landscape of genetic and metabolic alterations. Longitudinal imaging by MRI and [18F]FET-PET measurements enable us to visualize the features of evolving tumors in a dynamic manner. Yet, close-meshed longitudinal imaging time points for characterizing temporal and spatial metabolic alterations during tumor evolution in patients is not feasible because patients usually present with already established tumors. The replication-competent avian sarcoma-leukosis virus (RCAS)/tumor virus receptor-A (tva) system is a powerful preclinical glioma model offering a high grade of spatial and temporal control of somatic gene delivery in vivo. Consequently, here, we aimed at using MRI and [18F]FET-PET to identify typical neuroimaging characteristics of the platelet-derived growth factor B (PDGFB)-driven glioma model using the RCAS-tva system. Our study showed that this preclinical glioma model displays MRI and [18F]FET-PET features that highly resemble the corresponding established human disease, emphasizing the high translational relevance of this experimental model. Furthermore, our investigations unravel exponential growth dynamics and a model-specific tumor microenvironment, as assessed by histology and immunochemistry. Taken together, our study provides further insights into this preclinical model and advocates for the imaging-stratified design of preclinical therapeutic interventions.

Targeted inhibition of ubiquitin signaling reverses metabolic reprogramming and suppresses glioblastoma growth

Glioblastoma multiforme (GBM) is the most frequent and aggressive form of primary brain tumor in the adult population; its high recurrence rate and resistance to current therapeutics urgently demand a better therapy. Regulation of protein stability by the ubiquitin proteasome system (UPS) represents an important control mechanism of cell growth. UPS deregulation is mechanistically linked to the development and progression of a variety of human cancers, including GBM. Thus, the UPS represents a potentially valuable target for GBM treatment. Using an integrated approach that includes proteomics, transcriptomics and metabolic profiling, we identify praja2, a RING E3 ubiquitin ligase, as the key component of a signaling network that regulates GBM cell growth and metabolism. Praja2 is preferentially expressed in primary GBM lesions expressing the wild-type isocitrate dehydrogenase 1 gene (IDH1). Mechanistically, we found that praja2 ubiquitylates and degrades the kinase suppressor of Ras 2 (KSR2). As a consequence, praja2 restrains the activity of downstream AMP-dependent protein kinase in GBM cells and attenuates the oxidative metabolism. Delivery in the brain of siRNA targeting praja2 by transferrin-targeted self-assembling nanoparticles (SANPs) prevented KSR2 degradation and inhibited GBM growth, reducing the size of the tumor and prolonging the survival rate of treated mice. These data identify praja2 as an essential regulator of cancer cell metabolism, and as a potential therapeutic target to suppress GBM growth.

The E3 ubiquitin ligase praja2  is expressed in glioblastoma (GBM), targets the kinase suppressor of Ras 2 for degradation and attenuates oxidative metabolism. Nanoparticle-mediated delivery of praja2 siRNA to GBM-bearing mice reduces tumour size.