Volumetric Growth and Growth Curve Analysis of Residual Intracranial Meningioma.

BackgroundAfter meningioma surgery, approximately 1 in 3 patients will have residual tumor that requires ongoing imaging surveillance. The precise volumetric growth rates of these tumors are unknown.ObjectiveTo identify the volumetric growth rates of residual meningioma, growth trajectory, and factors associated with progression.MethodsPatients with residual meningioma identified at a tertiary neurosurgery center between 2004 and 2020 were retrospectively reviewed. Tumor volume was measured using manual segmentation, after surgery and at every follow-up MRI scan. Growth rates were ascertained using a linear mixed-effects model and nonlinear regression analysis of growth trajectories. Progression was defined according to the Response Assessment in Neuro-Oncology (RANO) criteria (40% volume increase).ResultsThere were 236 patients with residual meningioma. One hundred and thirty-two patients (56.0%) progressed according to the RANO criteria, with 86 patients being conservatively managed (65.2%) after progression. Thirteen patients (5.5%) developed clinical progression. Over a median follow-up of 5.3 years (interquartile range, 3.5-8.6 years), the absolute growth rate was 0.11 cm 3 per year and the relative growth rate 4.3% per year. Factors associated with residual meningioma progression in multivariable Cox regression analysis were skull base location (hazard ratio [HR] 1.60, 95% CI 1.02-2.50) and increasing Ki-67 index (HR 3.43, 95% CI 1.19-9.90). Most meningioma exhibited exponential and logistic growth patterns (median R 2 value 0.84, 95% CI 0.60-0.90).ConclusionAbsolute and relative growth rates of residual meningioma are low, but most meet the RANO criteria for progression. Location and Ki-67 index can be used to stratify adjuvant treatment and surveillance paradigms

Predicting drugs for epilepsy using genetic and genomic data

65 million people have epilepsy. Current antiepileptic drugs produce adverse effects in 88% of users and fail to prevent seizures in 30% of people with epilepsy. New drugs for epilepsy are therefore required. Traditional drug development methods are arduous and expensive, taking on average 10-15 years and $2.6 billion per drug. It is estimated that over 90% of drugs have a viable second indication and thus may be used for other purposes, making drug repurposing an attractive alternative. This thesis aims to create drug repurposing resources for epilepsy and generate drug predictions for both monogenic and polygenic epilepsies. We create and present the Seizure Associated Genes Across Species (SAGAS) database, the largest and most comprehensive existing database of epilepsy genes, containing over 9700 pieces of published evidence for the involvement of 3879 genes in the generation and potentiation of seizures across 6 species. We use genetic data from the SAGAS, alongside a publicly available network-based method of drug prediction, to generate drug prediction lists for polygenic focal and generalised epilepsies. A monogenic epileptic syndrome is caused by a single mutant gene. However, knowing the identity of the mutant gene underlying a monogenic epileptic syndrome is not sufficient for predicting the effect of antiseizure medications on the syndrome. Dravet syndrome (DS), the archetypal monogenic epileptic encephalopathy, is typically caused by mutations in SCN1A. Some antiseizure medications that alleviate seizures in Dravet syndrome do not affect SCN1A, whilst some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome. We are not aware of any genomics-based methods that can correctly predict the varying effects of different antiseizure medications on Dravet syndrome (or any other monogenic epileptic syndrome). We create a novel method to predict drugs for Dravet syndrome that takes into account not only the gene that causes Dravet syndrome but also other genes that can influence the expression of its phenotype and show that our predictions correctly identify the antiseizure drugs that are effective, aggravating and equivocal for Dravet syndrome

A systems medicine strategy to predict the efficacy of drugs for monogenic epilepsies.

ObjectiveMonogenic epilepsies are rare but often severe. Because of their rarity, they are neglected by traditional drug developers. Hence, many lack effective treatments. Treatments for a disease can be discovered more quickly and economically by computationally predicting drugs that can be repurposed for it. We aimed to create a computational method to predict the efficacy of drugs for monogenic epilepsies, and to use the method to predict drugs for Dravet syndrome, as (1) it is the archetypal monogenic catastrophic epilepsy, (2) few antiseizure medications are efficacious in Dravet syndrome, and (3) predicting the effect of drugs on Dravet syndrome is challenging-Dravet syndrome is typically caused by an SCN1A mutation, but some antiseizure medications that are efficacious in Dravet syndrome do not affect SCN1A, and some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome.MethodsWe have devised a computational method to predict drugs that could be repurposed for a monogenic epilepsy, based on a combined measure of drugs' effects upon (1) the function of the disease's causal gene and other genes predicted to influence its phenotype, and (2) the transcriptomic dysregulation induced by the casual gene mutation, and (3) clinical phenotypes.ResultsOur method correctly predicts drugs that are more effective, less effective, ineffective and aggravating for seizures in people with Dravet syndrome. Our method correctly predicts the positive 'hits' from large-scale screening of compounds in an animal model of Dravet syndrome. We predict the relative efficacy of 1,462 drugs. At least 38 drugs are ranked higher than one or more of the antiseizure drugs currently used for Dravet syndrome and have existing evidence of antiseizure efficacy in animal models.SignificanceOur predictions are a novel resource for identifying new treatments for seizures in Dravet syndrome, and our method can be adapted to other monogenic epilepsies

Seizure-Associated Genes Acrosss Species database offers insights into epilepsy genes, pathways, and treatments

ObjectiveDecades of genetic studies on people with many different epilepsies, and on many nonhuman species, using many different technologies, have generated a huge body of literature about the genes associated with seizures/epilepsy. Collating these data can help uncover epilepsy genes, pathways, and treatments that would otherwise be overlooked. We aimed to collate and structure these data into a database, and use the database to identify novel epilepsy genes and pathways, and to prioritize promising treatments.MethodsWe collated all the genes associated with all types of seizures/epilepsy in all species, and quantified the supporting evidence for each gene, by manually screening ~10 000 publications, and by extracting data from existing databases.ResultsThe largest published dataset of epilepsy genes includes only 977 genes, whereas our database (www.sagas.ac) includes 2876 genes, which demonstrates that the number of genes that can potentially contribute to seizures/epilepsy is much higher than previously envisaged. We use our database to identify 12 hitherto unreported polygenic epilepsy genes, 479 high-confidence monogenic epilepsy genes, and 394 more biological pathways than identified using the previously largest epilepsy gene dataset. We use a unique feature of Seizure-Associated Genes Across Species-the number of citations for each gene-to demonstrate that a drug is more likely to affect seizures if there is more evidence that the genes it affects are associated with seizures, and we use these data to identify promising candidate antiseizure drugs.SignificanceThis database offers insights into the causes of epilepsy and its treatments, and can accelerate future epilepsy research

External validation of brain arteriovenous malformation haemorrhage scores, AVICH, ICH and R2eD

PURPOSE: To externally validate the arteriovenous malformation-related intracerebral haemorrhage (AVICH), intracerebral haemorrhage (ICH), and novel haemorrhage presentation risk score (R2eD) in brain arteriovenous malformations. METHODS: Adult patients diagnosed radiologically with an arteriovenous malformation (AVM) at a tertiary neurosurgical centre between 2007 and 2018 were eligible for inclusion. Both the AVICH and ICH scores were calculated for AVM-related symptomatic haemorrhage (SH) and compared against the modified Rankin scale (mRS) at discharge and last follow-up, with unfavourable outcome defined as mRS > 2. R2eD scores were stratified based on presentation with SH. External validity was assessed using Harrel’s C-statistic. RESULTS: Two hundred fifty patients were included. Mean age at diagnosis was 46.2 years [SD = 16.5]). Eighty-seven patients (34.8%) had a SH, with 83 included in the analysis. Unfavourable mRS outcome was seen in 18 (21.6%) patients at discharge and 18 (21.6%) patients at last follow-up. The AVICH score C-statistic was 0.67 (95% confidence interval [CI], 0.53–0.80) at discharge and 0.70 (95% CI, 0.56–0.84) at last follow-up. The ICH score C-statistic was 0.78 (95% CI 0.67–0.88), at discharge and 0.80 (95% CI 0.69–0.91) at last follow-up. The R2eD score C-statistic for predicting AVM haemorrhage was 0.60 (95% CI, 0.53–0.67). CONCLUSIONS: The AVICH score showed fair-poor performance, while the ICH score showed good-fair performance. The R2eD score demonstrated poor performance, and its clinical utility in predicting AVM haemorrhage remains unclear

Predictors of future haemorrhage from cerebral cavernous malformations: a retrospective cohort study.

Cerebral cavernous malformations (CCMs) are commonly diagnosed, with a low reported rate of haemorrhage on long-term follow-up. The identification of factors predictive of future haemorrhage risk would assist in guiding the management of patients with CCM. The aim of this study was to identify variables associated with haemorrhage, and calculate haemorrhage risk in CCM. We conducted a retrospective study of patients diagnosed with a CCM, managed at a specialist tertiary neuroscience centre (2007-2019). The primary outcome was symptomatic haemorrhage, and secondary outcomes were variables associated with increased risk of haemorrhage, using multivariable Cox regression analysis. Included were 545 patients, with 734 confirmed cavernomas. Median age at diagnosis was 47 (interquartile range [IQR] 35-60), with a median follow-up duration after diagnosis of 46 months (IQR 19-85). Of the patients, 15.0% had multiple lesions (N = 82/545). Symptomatic presentation was observed in 52.5% of patients (N = 286/545). The annual haemorrhage rate was 1.00% per lesion-year (25 events in 2512 lesion-years), and higher in those with symptoms at presentation (1.50% per lesion-year, 22 events vs 0.29%, 3 events, P < 0.001). The variables associated with symptomatic haemorrhage were increased size (hazard ratio [HR] 1.04, 95% confidence interval [CI] 1.01-1.07, P = 0.004), eloquent location (HR 2.63, 95% CI 1.12-6.16, P = 0.026), and symptomatic haemorrhage at presentation (HR 5.37, 95% CI 2.40-11.99, P < 0.001). This study demonstrated that CCMs have a low haemorrhage rate. Increased size, eloquent location, and haemorrhage at presentation appear to be predictive of a higher risk of haemorrhage, and could be used to stratify management protocols

Sporadic multiple intracranial meningioma does not infer worse patient outcomes: results from a case control study

BackgroundSporadic multiple meningioma are uncommon. Population-based data suggests that these patients have a reduced overall survival when compared to patients with solitary meningioma. The aim of this study was to investigate the clinical outcomes in multiple and solitary meningioma.MethodsA single-center matched cohort study (2008-2018) was performed. Patients with synchronous multiple meningioma at presentation, with no history of prior intracranial radiation, concurrent hormone replacement therapy or features of NF2-schwannomatosis were included. Eligible patients were matched 1:1 to patients with solitary meningioma. Outcomes of interest were occurrence of an intervention, recurrence, new meningioma development and mortality.ResultsThirty-four patients harboring 76 meningioma at presentation were included. Mean age was 59.3 years (SD = 13.5). Thirty-one (91.2%) were female. The median number of meningioma per patient was 2 (range 2-6). Eighteen patients (52.9%) were symptomatic at presentation. Median overall follow-up was 80.6 months (IQR 44.1-99.6). Compared to patients with a sporadic meningioma, there was no difference in intervention rates (67.6% vs 70.6%, P = 0.792). Eight patients (34.8%) with a multiple meningioma had a WHO grade 2 meningioma compared to 7 (29.2%) with a solitary meningioma (P = 0.679). Median recurrence-free survival was 89 months (95% CI 76-104) with no difference between the two groups (P = 0.209). Mean overall survival was 132 months (95% CI 127-138) with no difference between the two groups (P = 0.860). One patient with multiple meningioma developed two further new meningioma 36 months following diagnosis.ConclusionSporadic multiple meningioma may not have worse clinical outcomes. Management of patients with sporadic multiple meningioma should be tailored towards the symptomatic meningioma or high-risk asymptomatic meningioma

Volumetric growth of residual meningioma - A systematic review

Surgical resection of meningioma leaves residual solid tumour in over 25% of patients. Selection for further treatment and follow-up strategy may benefit from knowledge of volumetric growth and factors associated with re-growth. The aim of this review was to evaluate volumetric growth and variables associated with growth in patients that underwent incomplete resection of a meningioma without the use of adjuvant radiotherapy. A systematic review was conducted in accordance with the PRISMA statement and registered a priori with PROSPERO (registration number: CRD42020177052). Six databases were searched up to May 2020. Full text articles analysing volumetric growth rates in at least 10 patients who had residual meningioma after surgery were assessed. Four single-centre, retrospective studies totalling 238 patients were included, of which 99% of meningioma were WHO grade 1. The absolute tumour growth rate ranged from 0.09 to 4.94 cm3 per year. The relative growth rate ranged from 5.11 to 14.18% per year. Varying methods of volumetric assessment and definitions of growth impeded pooled analysis. Pre-operative and residual tumour volume, and hyperintensity on T2 weighted MRI were identified as variables associated with residual meningioma growth, however this was inconsistent across studies. Risk of bias was high in all studies. Radiological regrowth occurred in 42-67% of cases. Our review identified that volumetric growth of residual meningioma is scarcely reported. Sufficiently powered studies are required to delineate volumetric growth and prognostic factors to stratify management.</p