With the significant shift in the classification, risk stratification, and standards of care for gliomas, we sought to understand how the overall survival of patients with these tumors is impacted by molecular features, clinical metrics, and treatment received. We assembled a cohort of patients with histopathologically diagnosed glioma from The Cancer Genome Atlas (TCGA), Project Genomics Evidence Neoplasia Information Exchange, and Dana-Farber Cancer Institute/Brigham and Women’s Hospital. This incorporated retrospective clinical, histological, and molecular data alongside a prospective assessment of patient survival. Of 4400 gliomas were identified: 2195 glioblastomas, 1198 IDH1/2-mutant astrocytomas, 531 oligodendrogliomas, 271 other IDH1/2-wild-type gliomas, and 205 pediatric-type glioma. Molecular classification updated 27.2% of gliomas from their original histopathologic diagnosis. Examining the distribution of molecular alterations across glioma subtypes revealed mutually exclusive alterations within tumorigenic pathways. Non-TCGA patients had significantly improved overall survival compared to TCGA patients, with 26.7%, 55.6%, and 127.8% longer survival for glioblastoma, IDH1/2-mutant astrocytoma, and oligodendroglioma, respectively (all P < .01). Several prognostic features were characterized, including NF1 alteration and 21q loss in glioblastoma, and EGFR amplification and 22q loss in IDH1/2-mutant astrocytoma. Leveraging the size of this cohort, nomograms were generated to assess the probability of overall survival based on patient age, the molecular features of a tumor, and the treatment received. By applying modern molecular criteria, we characterize the genomic diversity across glioma subtypes, identify clinically applicable prognostic features, and provide a contemporary update on patient survival to serve as a reference for ongoing investigations.

Chemogenetics enables precise, non-invasive, and reversible modulation of neural activity via the activation of engineered receptors that are pharmacologically selective to endogenous or exogenous ligands. With recent advances in therapeutic gene delivery, chemogenetics is poised to support novel interventions against neuropsychiatric diseases and disorders. To evaluate its translational potential, we performed a scoping review of applications of chemogenetics that led to the reversal of molecular and behavioral deficits in studies relevant to neuropsychiatric diseases and disorders. In this review, we present these findings and discuss the potential and challenges for using chemogenetics as a precision medicine-based neuromodulation strategy.

Background: Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management. Methods: We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS). Results: A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range −8 to 36). Conclusions: There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.

Background: Surgical intervention is a critical tool to address adult spinal deformity (ASD). Given the evolution of spinal surgical techniques, we sought to characterize developments in ASD correction and barriers impacting clinical outcomes. Methods: We conducted a literature review utilizing PubMed, Embase, Web of Science, and Google Scholar to examine advances in ASD surgical correction and ongoing challenges from patient and clinician perspectives. ASD procedures were examined across pre-, intra-, and post-operative phases. Results: Several factors influence the effectiveness of ASD correction. Standardized radiographic parameters and three-dimensional modeling have been used to guide operative planning. Complex minimally invasive procedures, targeted corrections, and staged procedures can tailor surgical approaches while minimizing operative time. Further, improvements in osteotomy technique, intraoperative navigation, and enhanced hardware have increased patient safety. However, challenges remain. Variability in patient selection and deformity undercorrection have resulted in heterogenous clinical responses. Surgical complications, including blood loss, infection, hardware failure, proximal junction kyphosis/failure, and pseudarthroses, pose barriers. Although minimally invasive approaches are being utilized more often, clinical validation is needed. Conclusions: The growing prevalence of ASD requires surgical solutions that can lead to sustained symptom resolution. Leveraging computational and imaging advances will be necessary as we seek to provide comprehensive treatment plans for patients.

Molecular features have been incorporated alongside histologic criteria to improve glioma diagnostics and prognostication. CDKN2A/B homozygous-loss associates with worse survival in IDH1/2-mutant astrocytomas (IDHmut-astrocytomas), the presence of which denotes grade 4 tumor independent of histologic features. However, no molecular features distinguish survival amongst histologically-defined grade 2 and 3 IDHmut-astrocytomas. We assembled a cohort of patients ≥19 years old diagnosed with an IDHmut-astrocytoma between 1989-2020 from public datasets and several academic medical centers. Multivariate modeling and unbiased clustering were used to stratify risk. We identified 998 IDHmut-astrocytoma patients (41.5% female; 85.6% white). Tumor grade, CDKN2A/B loss, and/or ≥1 focal amplification was associated with reduced survival. Grade 2/3 patients with intact CDKN2A/B and no focal amplifications survived the longest (OS 205.7 months). Survival for grade 2/3 cases with either CDKN2A/B hemizygous-loss or focal amplifications (80.4, 88.7 months respectively) did not differ significantly from grade 4 cases with intact CDKN2A/B and no amplifications (91.5 months, p=0.93). Grade 4 patients with either hemizygous or homozygous loss of CDKN2A/B had the shortest survival (OS 31.9, 32.5 months respectively), followed by grade 4 cases with intact CDKN2A/B and focal gene amplifications (OS 55.9 months). Integrating CDKN2A/B status and amplifications alongside histopathologic grade refined overall survival prediction. Unbiased clustering revealed 9 distinct molecular profiles, with differential survival. IDHmut-astrocytomas with any CDKN2A/B-loss clustered together, regardless of grade, and exhibited the poorest outcomes. Combining CDKN2A/B hemizygous-loss and focal gene amplifications reveals a group of IDHmut-astrocytoma patients with intermediate prognosis, refining IDHmut-astrocytoma classification.