Clinical outcomes after MRI connectivity-guided radiofrequency thalamotomy for tremor

OBJECTIVE: Radiofrequency thalamotomy (RF-T) is an established treatment for refractory tremor. It is unclear whether connectivity-guided targeting strategies could further augment outcomes. The aim of this study was to evaluate the efficacy and safety of MRI connectivity-guided RF-T in severe tremor. METHODS: Twenty-one consecutive patients with severe tremor (14 with essential tremor [ET], 7 with Parkinson's disease [PD]) underwent unilateral RF-T at a single institution between 2017 and 2020. Connectivity-derived thalamic segmentation was used to guide targeting. Changes in the Fahn-Tolosa-Marin Rating Scale (FTMRS) were recorded in treated and nontreated hands as well as procedure-related side effects. RESULTS: Twenty-three thalamotomies were performed (with 2 patients receiving a repeated intervention). The mean postoperative assessment time point was 14.1 months. Treated-hand tremor scores improved by 63.8%, whereas nontreated-hand scores deteriorated by 10.1% (p < 0.01). Total FTMRS scores were significantly better at follow-up compared with baseline (mean 34.7 vs 51.7, p = 0.016). Baseline treated-hand tremor severity (rho = 0.786, p < 0.01) and total FTMRS score (rho = 0.64, p < 0.01) best correlated with tremor improvement. The most reported side effect was mild gait ataxia (n = 11 patients). CONCLUSIONS: RF-T guided by connectivity-derived segmentation is a safe and effective option for severe tremor in both PD and ET

MMSAT: Automated quantification of metabolites in selected reaction monitoring experiments

Selected reaction monitoring (SRM) is a mass spectrometry-based approach commonly used to increase analytical sensitivity and selectively for specific compounds in complex metabolomic samples. While the goal of well-designed SRM methods is to monitor for unique precursor-product ion pairs, in practice this is not always possible due to the diversity of the metabome and the resolution limits of mass spectrometers that are capable of SRM. Isobaric or near-isobaric precursor ions with different chromatographic properties but identical product ions often arise in complex samples. Without analytical standards, such metabolites will go undetected by conventional data analysis methods. Furthermore, a single SRM method may include simultaneous monitoring of tens to hundreds of different metabolites across multiple samples making quantification of all detected ions a challenging task. To facilitate the analysis of SRM data from complex metabolomic samples, we have developed the Metabolite Mass Spectrometry Analysis Tool (MMSAT). MMSAT is a web-based tool that objectively quantifies every metabolite peak detected in a set of samples and aligns peaks across multiple samples to enable quantitative comparison of each metabolite between samples. The analysis incorporates quantification of multiple peaks/ions that have different chromatographic retention times but are detected within a single SRM transition. We compare the performance of MMSAT against existing tools using a human glioblastoma tissue extract and illustrate its ability to automatically quantify multiple precursors within each of three different transitions. The Web-interface and source code is avaliable at http://www.cancerresearch.unsw.edu.au/crcweb.nsf/page/MMSAT. © 2011 American Chemical Society.Link_to_subscribed_fulltex

Investigating Sphingolipid Metabolism in Glioblastoma

Glioblastoma (GBM), a genetically heterogeneous disease, has a significant burden onour society. Currently, the standard treatment for newly diagnosed GBM patientsconsists of surgery followed by concomitant radiotherapy and temozolomidechemotherapy, resulting in a median survival of 12-15 months. Targeted therapies arebeing developed to inhibit oncogenes based upon GBM molecular profiling, thoughhave not been as successful as expected. Our understanding of DNA and RNAalterations in GBM has grown considerably over the past few years. However, ourunderstanding of the lipid biology, specifically sphingolipids, in GBM is lagging andmay prove useful in the arsenal of targeted therapies. The sphingolipid pathwaycontains lipid signalling molecules, which modulate cellular survival through thebalance of ceramide, a pro-apoptotic metabolite, and Sphingosine-1-Phosphate (S1P), apro-survival metabolite.Herein, I characterise for the first time the sphingolipid profile of normal grey matter(NGM), diffuse astrocytomas (AII), anaplastic astrocytomas (AIII), and GBM usingliquid chromatography tandem mass spectrometry. The lipid profile is supported by anenzyme expression profile favouring ceramide catabolism and S1P formation, includingupregulation of acid ceramidase (ASAH1) and sphingosine kinase 1 (SPHK1), and adown regulation of S1P phosphatase 2 (SGPP2). Significantly, C18 ceramide wasreduced 5-fold in GBM compared to NGM, while S1P was increased in GBM byapproximately 9-fold compared to NGM. Based on the sphingolipid profiles, ASAH1and SPHK1 were assessed for functional relevance in vitro. Using gene silencing andpharmacological inhibition, I found SPHK1 to be critical for U87MG-inducedangiogenesis through S1P paracrine signalling, which was independent of VEGF levels.EGFR mutations were associated with increased C16 and C22 ceramide levels. For thefirst time, I measured sphingolipid metabolites in plasma extracted from GBM patientsand healthy controls. Elevated levels of S1P were found in GBM plasma and togetherwith tumour S1P levels, were associated with a poor survival outcome. In contrast lowS1P levels in tissue combined with MGMT methylation was associated with a goodsurvival outcome.Overall, the data presented in this thesis reaffirm the importance of sphingolipidmetabolism in GBM biology, reflected by a shift in the ceramide-S1P balance,favouring the pro-angiogenic S1P. Additionally, sphingolipid interactions with alteredgenetic pathways and potential biomarker capacity are novel findings that requirefurther validation, with the hope of informing and monitoring therapeutic responses forGBM patients

<i>MMSAT</i>: Automated Quantification of Metabolites in Selected Reaction Monitoring Experiments

Selected reaction monitoring (SRM) is a mass spectrometry-based approach commonly used to increase analytical sensitivity and selectively for specific compounds in complex metabolomic samples. While the goal of well-designed SRM methods is to monitor for unique precursor–product ion pairs, in practice this is not always possible due to the diversity of the metabome and the resolution limits of mass spectrometers that are capable of SRM. Isobaric or near-isobaric precursor ions with different chromatographic properties but identical product ions often arise in complex samples. Without analytical standards, such metabolites will go undetected by conventional data analysis methods. Furthermore, a single SRM method may include simultaneous monitoring of tens to hundreds of different metabolites across multiple samples making quantification of all detected ions a challenging task. To facilitate the analysis of SRM data from complex metabolomic samples, we have developed the Metabolite Mass Spectrometry Analysis Tool (<i>MMSAT</i>). <i>MMSAT</i> is a web-based tool that objectively quantifies every metabolite peak detected in a set of samples and aligns peaks across multiple samples to enable quantitative comparison of each metabolite between samples. The analysis incorporates quantification of multiple peaks/ions that have different chromatographic retention times but are detected within a single SRM transition. We compare the performance of <i>MMSAT</i> against existing tools using a human glioblastoma tissue extract and illustrate its ability to automatically quantify multiple precursors within each of three different transitions. The Web-interface and source code is avaliable at http://www.cancerresearch.unsw.edu.au/crcweb.nsf/page/MMSAT