Posterior fossa ependymoma comprises two distinct molecular variants termed EPN_PFA and EPN_PFB that have a distinct biology and natural history. The therapeutic value of cytoreductive surgery and radiation therapy for posterior fossa ependymoma after accounting for molecular subgroup is not known.

Transected axons fail to regrow across anatomically complete spinal cord injuries (SCI) in adults. Diverse molecules can partially facilitate or attenuate axon growth during development or after injury1-3, but efficient reversal of this regrowth failure remains elusive4. Here we show that three factors that are essential for axon growth during development but are attenuated or lacking in adults-(i) neuron intrinsic growth capacity2,5-9, (ii) growth-supportive substrate10,11 and (iii) chemoattraction12,13-are all individually required and, in combination, are sufficient to stimulate robust axon regrowth across anatomically complete SCI lesions in adult rodents. We reactivated the growth capacity of mature descending propriospinal neurons with osteopontin, insulin-like growth factor 1 and ciliary-derived neurotrophic factor before SCI14,15; induced growth-supportive substrates with fibroblast growth factor 2 and epidermal growth factor; and chemoattracted propriospinal axons with glial-derived neurotrophic factor16,17 delivered via spatially and temporally controlled release from biomaterial depots18,19, placed sequentially after SCI. We show in both mice and rats that providing these three mechanisms in combination, but not individually, stimulated robust propriospinal axon regrowth through astrocyte scar borders and across lesion cores of non-neural tissue that was over 100-fold greater than controls. Stimulated, supported and chemoattracted propriospinal axons regrew a full spinal segment beyond lesion centres, passed well into spared neural tissue, formed terminal-like contacts exhibiting synaptic markers and conveyed a significant return of electrophysiological conduction capacity across lesions. Thus, overcoming the failure of axon regrowth across anatomically complete SCI lesions after maturity required the combined sequential reinstatement of several developmentally essential mechanisms that facilitate axon growth. These findings identify a mechanism-based biological repair strategy for complete SCI lesions that could be suitable to use with rehabilitation models designed to augment the functional recovery of remodelling circuits.

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.

Surgical resection and systemic chemotherapy with temozolomide remain the mainstay for treatment of glioblastoma. However, many patients are not candidates for surgical resection given inaccessible tumor location or poor health status. Furthermore, despite being first line treatment, temozolomide has only limited efficacy.

Over the past decade, wingless-activated (WNT) medulloblastoma has been identified as a candidate for therapy de-escalation based on excellent survival; however, a paucity of relapses has precluded additional analyses of markers of relapse. To address this gap in knowledge, an international cohort of 93 molecularly confirmed WNT MB was assembled, where 5-year progression-free survival is 0.84 (95%, 0.763-0.925) with 15 relapsed individuals identified. Maintenance chemotherapy is identified as a strong predictor of relapse, with individuals receiving high doses of cyclophosphamide or ifosphamide having only one very late molecularly confirmed relapse (p = 0.032). The anatomical location of recurrence is metastatic in 12 of 15 relapses, with 8 of 12 metastatic relapses in the lateral ventricles. Maintenance chemotherapy, specifically cumulative cyclophosphamide doses, is a significant predictor of relapse across WNT MB. Future efforts to de-escalate therapy need to carefully consider not only the radiation dose but also the chemotherapy regimen and the propensity for metastatic relapses.

In cancer, recurrent somatic single-nucleotide variants-which are rare in most paediatric cancers-are confined largely to protein-coding genes1-3. Here we report highly recurrent hotspot mutations (r.3A>G) of U1 spliceosomal small nuclear RNAs (snRNAs) in about 50% of Sonic hedgehog (SHH) medulloblastomas. These mutations were not present across other subgroups of medulloblastoma, and we identified these hotspot mutations in U1 snRNA in only <0.1% of 2,442 cancers, across 36 other tumour types. The mutations occur in 97% of adults (subtype SHHδ) and 25% of adolescents (subtype SHHα) with SHH medulloblastoma, but are largely absent from SHH medulloblastoma in infants. The U1 snRNA mutations occur in the 5' splice-site binding region, and snRNA-mutant tumours have significantly disrupted RNA splicing and an excess of 5' cryptic splicing events. Alternative splicing mediated by mutant U1 snRNA inactivates tumour-suppressor genes (PTCH1) and activates oncogenes (GLI2 and CCND2), and represents a target for therapy. These U1 snRNA mutations provide an example of highly recurrent and tissue-specific mutations of a non-protein-coding gene in cancer.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a shift to virtual residency interviews for the 2020-2021 neurosurgery match, with unknown implications for stakeholders. This study seeks to analyze the perceptions of residency program directors (PDs) and associate program directors (APDs) regarding the current virtual format used for residency selection and interviews.

Histone deacetylase (HDAC) inhibitors are a class of agents that have potent antitumor activity with a reported ability to upregulate MHC and costimulatory molecule expression. We hypothesized that epigenetic pharmacological immunomodulation could sensitize tumors to immune mediated cell death with an adoptive T cell therapy.

Patients with incomplete surgical resection of medulloblastoma are controversially regarded as having a marker of high-risk disease, which leads to patients undergoing aggressive surgical resections, so-called second-look surgeries, and intensified chemoradiotherapy. All previous studies assessing the clinical importance of extent of resection have not accounted for molecular subgroup. We analysed the prognostic value of extent of resection in a subgroup-specific manner.

Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures that are anatomically consistent across a large population of potential target subjects (i.e., multi-atlas segmentation is limited to "in-atlas" applications). Herein, the authors propose a technique to determine the likelihood that a multi-atlas segmentation estimate is representative of the problem at hand, and, therefore, identify anomalous regions that are not well represented within the atlases.

Identifying risk factors for negative postoperative outcomes is an important part of providing quality care. Here, we build machine learning (ML) ensembles to model the independent impact of presurgical comorbidities on discharge disposition and length of stay (LOS) following brain tumor resection from the HCUP National Inpatient Sample (NIS).

Biomaterials hold promise for therapeutic applications in the central nervous system (CNS). Little is known about molecular factors that determine CNS foreign body responses (FBRs) in vivo, or about how such responses influence biomaterial function. Here, we probed these factors in mice using a platform of injectable hydrogels readily modified to present interfaces with different physiochemical properties to host cells. We found that biomaterial FBRs mimic specialized multicellular CNS wound responses not present in peripheral tissues, which serve to isolate damaged neural tissue and restore barrier functions. We show that the nature and intensity of CNS FBRs are determined by definable properties that significantly influence hydrogel functions, including resorption and molecular delivery when injected into healthy brain or stroke injuries. Cationic interfaces elicit stromal cell infiltration, peripherally derived inflammation, neural damage and amyloid production. Nonionic and anionic formulations show minimal levels of these responses, which contributes to superior bioactive molecular delivery. Our results identify specific molecular mechanisms that drive FBRs in the CNS and have important implications for developing effective biomaterials for CNS applications.

The COVID-19 pandemic has profoundly disrupted medical education and the residency application process.

Sonic hedgehog medulloblastoma encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. Here, we use unbiased sequencing of the transcriptome across a large cohort of 250 tumors to reveal differences among molecular subtypes of the disease, and demonstrate the previously unappreciated importance of non-coding RNA transcripts. We identify alterations within the cAMP dependent pathway (GNAS, PRKAR1A) which converge on GLI2 activity and show that 18% of tumors have a genetic event that directly targets the abundance and/or stability of MYCN. Furthermore, we discover an extensive network of fusions in focally amplified regions encompassing GLI2, and several loss-of-function fusions in tumor suppressor genes PTCH1, SUFU and NCOR1. Molecular convergence on a subset of genes by nucleotide variants, copy number aberrations, and gene fusions highlight the key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma and open up opportunities for therapeutic intervention.

Radiographic contact of glioblastoma (GBM) tumors with the lateral ventricle and adjacent stem cell niche correlates with poor patient prognosis, but the cellular basis of this difference is unclear. Here, we reveal and functionally characterize distinct immune microenvironments that predominate in subtypes of GBM distinguished by proximity to the lateral ventricle. Mass cytometry analysis of isocitrate dehydrogenase wild-type human tumors identified elevated T cell checkpoint receptor expression and greater abundance of a specific CD32+CD44+HLA-DRhi macrophage population in ventricle-contacting GBM. Multiple computational analysis approaches, phospho-specific cytometry, and focal resection of GBMs validated and extended these findings. Phospho-flow quantified cytokine-induced immune cell signaling in ventricle-contacting GBM, revealing differential signaling between GBM subtypes. Subregion analysis within a given tumor supported initial findings and revealed intratumor compartmentalization of T cell memory and exhaustion phenotypes within GBM subtypes. Collectively, these results characterize immunotherapeutically targetable features of macrophages and suppressed lymphocytes in GBMs defined by MRI-detectable lateral ventricle contact.

Immunotherapeutic approaches, such as dendritic cell (DC) vaccination, have emerged as promising strategies in the treatment of glioblastoma. Despite their promise, however, the absence of objective biomarkers and/or immunological monitoring techniques to assess the clinical efficacy of immunotherapy still remains a primary limitation. To address this, we sought to identify a functional biomarker for anti-tumor immune responsiveness associated with extended survival in glioblastoma patients undergoing DC vaccination.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.