Immune checkpoint inhibitors (ICIs) have improved the metastatic melanoma (MM) treatment. However, a significant proportion of patients show resistance to immunotherapy, and predictive biomarkers for Show more
Immune checkpoint inhibitors (ICIs) have improved the metastatic melanoma (MM) treatment. However, a significant proportion of patients show resistance to immunotherapy, and predictive biomarkers for non-responders or high-risk recurring patients are currently lacking. Recent studies have shown that tumor-related metabolic fingerprints can be useful in predicting prognosis and response to therapy in various cancer types. Our study aimed to identify serum-derived metabolomic signatures that could predict clinical responses in MM patients treated with ICIs. A multivariable model was used to identify distinct prognostic factors for OS. Negative factors included glucose, high-density lipoprotein (HDL) cholesterol, and apolipoprotein B-very low-density lipoprotein (ApoB-VLDL), whereas glutamine and free HDL cholesterol emerged as positive factors. They were then used to construct a risk score model able to stratify patients in prognostic groups. Similarly, a separate predictive risk score model for PFS was developed, focusing solely on glucose and apolipoprotein A1 (ApoA1) HDL. Threefold cross validation resulted in mean concordance indices of 0.72 and 0.74 for PFS and OS, respectively. Importantly, this analysis was replicated in patients who received first-line ICIs. Interestingly, the prognostic score for OS included glutamine, glucose, and LDL (low-density lipoprotein) triglycerides, whereas only glucose negatively influenced PFS. In this subset, the concordance indices increased to 0.81 and 0.9 for PFS and OS, respectively. Our data identified glycolipid signatures as robust predictors of distinct therapeutic outcomes in MM patients treated with ICIs. These results could pave the way for novel therapeutic approaches. Show less
Cannabidiolic (CBDA) and cannabigerolic (CBGA) acids are naturally occurring compounds from Cannabis sativa plant, previously identified by us as dual PPARα/γ agonists. Since the development of multit Show more
Cannabidiolic (CBDA) and cannabigerolic (CBGA) acids are naturally occurring compounds from Cannabis sativa plant, previously identified by us as dual PPARα/γ agonists. Since the development of multitarget-directed ligands (MTDL) represents a valuable strategy to alleviate and slow down the progression of multifactorial diseases, we evaluated the potential ability of CBDA and CBGA to also inhibit enzymes involved in the modulation of the cholinergic tone and/or β-amyloid production. A multidisciplinary approach based on computational and biochemical studies was pursued on selected enzymes, followed by behavioral and electrophysiological experiments in an AD mouse model. The β-arrestin assay on GPR109A and qPCR on TRPM7 were also carried out. CBDA and CBGA are effective on both acetyl- and butyryl-cholinesterases (AChE/BuChE), as well as on β-secretase-1 (BACE-1) enzymes in a low micromolar range, and they also prevent aggregation of β-amyloid fibrils. Computational studies provided a rationale for the competitive (AChE) vs. noncompetitive (BuChE) inhibitory profile of the two ligands. The repeated treatment with CBDA and CBGA (10 mg/kg, i.p.) improved the cognitive deficit induced by the β-amyloid peptide. A recovery of the long-term potentiation in the hippocampus was observed, where the treatment with CBGA and CBDA also restored the physiological expression level of TRPM7, a receptor channel involved in neurodegenerative diseases. We also showed that these compounds do not stimulate GPR109A in β-arrestin assay. Collectively, these data broaden the pharmacological profile of CBDA and CBGA and suggest their potential use as novel anti-AD MTDLs. Show less
Despite extensive studies on the neurobiological correlates of traumatic brain injury (TBI), little is known about its molecular determinants on long-term consequences, such as dementia and Alzheimer' Show more
Despite extensive studies on the neurobiological correlates of traumatic brain injury (TBI), little is known about its molecular determinants on long-term consequences, such as dementia and Alzheimer's disease (AD). Here, we carried out behavioural studies and an extensive biomolecular analysis, including inflammatory cytokines, gene expression and the combination of LC-HRMS and MALDI-MS Imaging to elucidate the targeted metabolomics and lipidomics spatiotemporal alterations of brains from wild-type and APP-SWE mice, a genetic model of AD, at the presymptomatic stage, subjected to mild TBI. We found that brain injury does not affect cognitive performance in APP-SWE mice. However, we detected an increase of key hallmarks of AD, including Aβ Mild TBI induces biochemical changes in AD genetically predisposed mice and the eCBome may play a role in the pathogenetic link between brain injury and neurodegenerative disorders also by interacting with the serotonergic system. Show less