Identification of genes with oscillatory expression in glioblastoma: the paradigm of SOX2.

Quiescence, a reversible state of cell-cycle arrest, is an important state during both normal development and cancer progression. For example, in glioblastoma (GBM) quiescent glioblastoma stem cells (GSCs) play an important role in re-establishing the tumour, leading to relapse. While most studies have focused on identifying differentially expressed genes between proliferative and quiescent cells as potential drivers of this transition, recent studies have shown the importance of protein oscillations in controlling the exit from quiescence of neural stem cells. Here, we have undertaken a genome-wide bioinformatic inference approach to identify genes whose expression oscillates and which may be good candidates for controlling the transition to and from the quiescent cell state in GBM. Our analysis identified, among others, a list of important transcription regulators as potential oscillators, including the stemness gene SOX2, which we verified to oscillate in quiescent GSCs. These findings expand on the way we think about gene regulation and introduce new candidate genes as key regulators of quiescence.

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis.

Traumatic brain injury (TBI) is a complex condition where heterogeneity impedes the advancement of care. Understanding the diverse presentations of TBI is crucial for personalized medicine. Our study aimed to identify clinically relevant patient endotypes in TBI using latent class analysis based on comorbidity data. We used the Medical Information Mart for Intensive Care III database, which includes 2,629 adult TBI patients. We identified five stable endotypes characterized by specific comorbidity profiles: Heart Failure and Arrhythmia, Healthy, Renal Failure with Hypertension, Alcohol Abuse, and Hypertension. Each endotype had distinct clinical characteristics and outcomes: The Heart Failure and Arrhythmia endotype had lower survival rates than the Renal Failure with Hypertension despite featuring fewer comorbidities overall. Patients in the Hypertension endotype had higher rates of neurosurgical intervention but shorter stays in contrast to the Alcohol Abuse endotype which had lower rates of neurosurgical intervention but significantly longer hospital stays. Both endotypes had high overall survival rates comparable to the Healthy endotype. Logistic regression models showed that endotypes improved the predictability of survival compared to individual comorbidities alone. This study validates clinical endotypes as an approach to addressing heterogeneity in TBI and demonstrates the potential of this methodology in other complex conditions.

Profiling medical specialties and informing aspiring physicians: a data-driven approach.

A detailed, unbiased perspective of the inter-relations among medical fields could help students make informed decisions on their future career plans. Using a data-driven approach, the inter-relations among different medical fields were decomposed and clustered based on the similarity of their working environments.Publicly available, aggregate databases were merged into a single rich dataset containing demographic, working environment and remuneration information for physicians across Canada. These data were collected from the Canadian Institute for Health Information, the Canadian Medical Association, and the Institute for Clinical Evaluative Sciences, primarily from 2018 to 2019. The merged dataset includes 25 unique medical specialties, each with 36 indicator variables. Latent Profile Analysis (LPA) was used to group specialties into distinct clusters based on relatedness.The 25 medical specialties were decomposed into seven clusters (latent variables) that were chosen based on the Bayesian Information Criterion. The Kruskal-Wallis test identified eight indicator variables that significantly differed between the seven profiles. These variables included income, work settings and payment styles. Variables that did not significantly vary between profiles included demographics, professional satisfaction, and work-life balance satisfaction.The 25 analyzed medical specialties were grouped in an unsupervised manner into seven profiles via LPA. These profiles correspond to expected and meaningful groups of specialties that share a common theme and set of indicator variables (e.g. procedurally-focused, clinic-based practice). These profiles can help aspiring physicians narrow down and guide specialty choice.

Cancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma.

MYC-driven medulloblastoma (MB) is an aggressive pediatric brain tumor characterized by therapy resistance and disease recurrence. Here, we integrated data from unbiased genetic screening and metabolomic profiling to identify multiple cancer-selective metabolic vulnerabilities in MYC-driven MB tumor cells, which are amenable to therapeutic targeting. Among these targets, dihydroorotate dehydrogenase (DHODH), an enzyme that catalyzes de novo pyrimidine biosynthesis, emerged as a favorable candidate for therapeutic targeting. Mechanistically, DHODH inhibition acts on target, leading to uridine metabolite scarcity and hyperlipidemia, accompanied by reduced protein O-GlcNAcylation and c-Myc degradation. Pyrimidine starvation evokes a metabolic stress response that leads to cell-cycle arrest and apoptosis. We further show that an orally available small-molecule DHODH inhibitor demonstrates potent mono-therapeutic efficacy against patient-derived MB xenografts in vivo. The reprogramming of pyrimidine metabolism in MYC-driven medulloblastoma represents an unappreciated therapeutic strategy and a potential new class of treatments with stronger cancer selectivity and fewer neurotoxic sequelae.

Redefining critical illness.

Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence-supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside.

Inflammatory Biomarkers and Intracranial Hemorrhage after Endovascular Thrombectomy.

BACKGROUND: Intracranial hemorrhage after endovascular thrombectomy is associated with poorer prognosis compared with those who do not develop the complication. Our study aims to determine predictors of post-EVT hemorrhage - more specifically, inflammatory biomarkers present in baseline serology. METHODS: We performed a retrospective review of consecutive patients treated with EVT for acute large vessel ischemic stroke. The primary outcome of the study is the presence of ICH on the post-EVT scan. We used four definitions: the SITS-MOST criteria, the NINDS criteria, asymptomatic hemorrhage, and overall hemorrhage. We identified nonredundant predictors of outcome using backward elimination based on Akaike Information Criteria. We then assessed prediction accuracy using area under the receiver operating curve. Then we implemented variable importance ranking from logistic regression models using the drop in Naegelkerke R2 with the exclusion of each predictor. RESULTS: Our study demonstrates a 6.3% SITS (16/252) and 10.0% NINDS (25/252) sICH rate, as well as a 19.4% asymptomatic (49/252) and 29.4% (74/252) overall hemorrhage rate. Serologic markers that demonstrated association with post-EVT hemorrhage were: low lymphocyte count (SITS), high neutrophil count (NINDS, overall hemorrhage), low platelet to lymphocyte ratio (NINDS), and low total WBC (NINDS, asymptomatic hemorrhage). CONCLUSION: Higher neutrophil counts, low WBC counts, low lymphocyte counts, and low platelet to lymphoycyte ratio were baseline serology biomarkers that were associated with post-EVT hemorrhage. Our findings, particularly the association of diabetes mellitus and high neutrophil, support experimental data on the role of thromboinflammation in hemorrhagic transformation of large vessel occlusions.

Earlier Surgery Reduces Complications in Acute Traumatic Thoracolumbar Spinal Cord Injury: Analysis of a Multi-Center Cohort of 4108 Patients.

Early surgical intervention to decompress the spinal cord and stabilize the spinal column in patients with acute traumatic thoracolumbar spinal cord injury (TLSCI) may lessen the risk of developing complications and improve outcomes. However, there has yet to be agreement on what constitutes "early" surgery; reported thresholds range from 8 to 72 h. To address this knowledge gap, we conducted an observational cohort study using data from the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) from 2010 to 2016. The association between time from hospital arrival to surgical intervention and risk of major complications was assessed using restricted cubic splines. Propensity score matching was then used to assess the association between delayed surgery and risk of complications. Across 354 trauma centers 4108 adult TLSCI patients who underwent surgery were included. Median time-to-surgery was 18.8 h (interquartile range [IQR]: 7.4-40.9 h). The spline model suggests the risk of major complication rises consistently after a 12-h surgical wait-time. After propensity score matching, the odds of major complication were significantly lower for those receiving surgery within 12 h (odds ratio [OR] 0.77, 95% confidence interval [CI]: 0.64 to 0.94). This was also true for immobility-related complications (OR 0.79, 95% CI: 0.64 to 0.97). Patients in the early group spent 1.5 fewer days in the critical care unit on average (95% CI: -2.09 to -0.88). Although surgery within 12 h may not always be feasible, these data suggest that whenever possible surgeons should strive to reduce the amount of time between hospital arrival and surgical intervention, and health care systems should support this endeavor.

Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma.

Medulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from "stroma-active" to "stroma-suppressed" cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.

Aquaporin-4 Reduces Post-Traumatic Seizure Susceptibility by Promoting Astrocytic Glial Scar Formation in Mice.

Seizures are important neurological complications after traumatic brain injury (TBI) and are reported for up to 50% of patients with TBI. Despite several studies, no drug strategy has been able to alter the biological events leading to epileptogenesis. The glial water channel, aquaporin-4 (AQP4), was shown to facilitate cytotoxic cell swelling in ischemia and glial scar formation after stab wound injury. In this study, we examined post-traumatic seizure susceptibility of AQP4-deficient mice (AQP4-/-) after injection of pentylenetetrazole (PTZ) 1 month after controlled cortical impact (CCI) and compared them to wild-type sham injury controls. After PTZ injection, AQP4-/- mice demonstrated dramatically shortened seizure latency (120 ± 40 vs. 300 ± 70 sec; p < 0.001) and increased seizure severity (grade 7.5 ± 0.4 vs. 5.8 ± 0.4; p < 0.001) compared to their wild-type counterparts. Morphometric analysis demonstrated a significant 2-fold reduction in astrocytosis, with a concomitant increase in microgliosis in injured AQP4-null mice compared to their injured wild-type counterparts (44 ± 2 vs. 24 ± 3 cells per high power field [cells/hpf], respectively; p < 0.0001). Minocycline, an inhibitor of microglia, reversed the post-TBI epilepsy phenotype of AQP4-null mice. After minocycline treatment, AQP4-/- mice demonstrated similar latency of seizures evoked by PTZ (723 ± 35 vs. 696 ± 38 sec; p > 0.05) and severity of seizures evoked by PTZ (grade 4.0 ± 0.5 vs. 3.81 ± 0.30; p > 0.05) compared to wild-type counterparts. Immunohistochemical analysis demonstrated decreased immunostaining of microglia to levels comparable to wild-type (12 ± 2 vs. 11 ± 4 cells/hpf, respectively; p > 0.05). Taken together, these results suggest a protective role of AQP4 in post-traumatic seizure susceptibility by promoting astrogliosis, formation of a glial scar, and preventing microgliosis.

Distinct regional ontogeny and activation of tumor associated macrophages in human glioblastoma.

Tumor-associated macrophages (TAMs) constitute up to 50% of tumor bulk in glioblastoma (GBM) and play an important role in tumor maintenance and progression. The recently discovered differences between invading tumour periphery and hypoxic tumor core implies that macrophage biology is also distinct by location. This may provide further insight into the observed treatment resistance to immune modulation. We hypothesize that macrophage activation occurs through processes that are distinct in tumor periphery versus core. We therefore investigated regional differences in TAM recruitment and evolution in GBM by combining open source single cell and bulk gene expression data. We used single cell gene expression data from 4 glioblastomas (total of 3589 cells) and 122 total bulk samples obtained from 10 different patients. Cell identity, ontogeny (bone-marrow derived macrophages-BMDM vs microglia), and macrophage activation state were inferred using verified gene expression signatures. We captured the spectrum of immune states using cell trajectory analysis with pseudotime ordering. In keeping with previous studies, TAMs carrying BMDM identity were more abundant in tumor bulk while microglia-derived TAMs dominated the tumor periphery across all macrophage activation states including pre-activation. We note that core TAMs evolve towards a pro-inflammatory state and identify a subpopulation of cells based on a gene program exhibiting strong, opposing correlation with Programmed cell Death-1 (PD-1) signaling, which may correlate to their response to PD-1 inhibition. By contrast, peripheral TAMs evolve towards anti-inflammatory phenotype and contains a population of cells strongly associated with NFkB signaling. Our preliminary analysis suggests important regional differences in TAMs with regard to recruitment and evolution. We identify regionally distinct and potentially actionable cell subpopulations and advocate the need for a multi-targeted approach to GBM therapeutics.

Gene Expression Signatures Identify Biologically Homogenous Subgroups of Grade 2 Meningiomas.

INTRODUCTION: Meningiomas are the most common brain tumor, with prevalence of approximately 3%. Histological grading has a major role in determining treatment choice and predicting outcome. While indolent grade 1 and aggressive grade 3 meningiomas exhibit relatively homogeneous clinical behavior, grade 2 meningiomas are far more heterogeneous, making outcome prediction challenging. We hypothesized two subgroups of grade 2 meningiomas which biologically resemble either World Health Organization (WHO) grade 1 or WHO grade 3. Our aim was to establish gene expression signatures that separate grade 2 meningiomas into two homogeneous subgroups: a more indolent subtype genetically resembling grade 1 and a more aggressive subtype resembling grade 3. METHODS: We carried out an observational meta-analysis on 212 meningiomas from six distinct studies retrieved from the open-access platform Gene Expression Omnibus. Microarray data was analyzed with systems-level gene co-expression network analysis. Fuzzy C-means clustering was employed to reclassify 34 of the 46 grade 2 meningiomas (74%) into a benign "grade 1-like" (13/46), and malignant "grade 3-like" (21/46) subgroup based on transcriptomic profiles. We verified shared biology between matching subgroups based on meta-gene expression and recurrence rates. These results were validated further using an independent RNA-seq dataset with 160 meningiomas, with similar results. RESULTS: Recurrence rates of "grade 1-like" and "grade 3- like" tumors were 0 and 75%, respectively, statistically similar to recurrence rates of grade 1 (17%) and 3 (85%). We also found overlapping biological processes of new subgroups with their adjacent grades 1 and 3. CONCLUSION: These results underpin molecular signatures as complements to histological grading systems. They may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials.

Meta-gene markers predict meningioma recurrence with high accuracy.

Meningiomas, the most common adult brain tumors, recur in up to half of cases. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors which constitute the majority of cases. Moreover, attempts to find molecular markers to predict their recurrence have been impeded by low or heterogenous genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. We apply gene co-expression networks to a cohort of 252 adult patients from the publicly available genetic repository Gene Expression Omnibus. Resultant gene clusters ("modules") were represented by the first principle component of their expression, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using two independent samples: one merged microarray-based cohort with a total of 108 patients and one RNA-seq-based cohort with 145 patients, using the same modules. We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes (AUC of 0.81 ± 0.09 and 0.77 ± 0.10 in external validation using microarray and RNA-seq data, respectively). This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research.

Landscape of immune cell gene expression is unique in predominantly WHO grade 1 skull base meningiomas when compared to convexity.

Modulation of tumor microenvironment is an emerging frontier for new therapeutics. However in meningiomas, the most frequent adult brain tumor, the correlation of microenvironment with tumor phenotype is scarcely studied. We applied a variety of systems biology approaches to bulk tumor transcriptomics to explore the immune environments of both skull base and convexity (hemispheric) meningiomas. We hypothesized that the more benign biology of skull base meningiomas parallels the relative composition and activity of immune cells that oppose tumor growth and/or survival. We firstly applied gene co-expression networks to tumor bulk transcriptomics from 107 meningiomas (derived from 3 independent studies) and found immune processes to be the sole biological mechanism correlated with anatomical location while correcting for tumour grade. We then derived tumor immune cell fractions from bulk transcriptomics data and examined the immune cell-cytokine interactions using a network-based approach. We demonstrate that oncolytic Gamma-Delta T cells dominate skull base meningiomas while mast cells and neutrophils, known to play a role in oncogenesis, show greater activity in convexity tumors. Our results are the first to suggest the importance of tumor microenvironment in meningioma biology in the context of anatomic location and immune landscape. These findings may help better inform surgical decision making and yield location-specific therapies through modulation of immune microenvironment.

Data Driven Analysis Reveals Shared Transcriptome Response, Immune Cell Composition, and Distinct Mortality Rates Across Differing Etiologies of Critical Illness.

OBJECTIVES: Sepsis and trauma are common health problems and provide great challenges in critical care. Diverse patient responses to these conditions further complicate patient management and outcome prediction. Whole blood transcriptomics provides a unique opportunity to follow the molecular response in the critically ill. Prior results show robust and diverse genomic signal in the acute phase and others have found shared biological mechanisms across divergent disease etiologies. We hypothesize that selected transcriptomics responses, particularly immune mechanisms are shared across disease etiologies. We further hypothesize that these processes may identify homogenous patient subgroups with shared clinical course in critical illness deciphering disease heterogeneity. These processes may serve as universal markers for predicting a complicated clinical course and/or risk of a poor outcome. DESIGN: We present a system level, data driven, genome-wide analysis of whole blood gene expression for a total of 382 patients suffering from either abdominal sepsis (49), pulmonary sepsis (107) or trauma (158) and compare these to gene expression in healthy controls (68). PATIENTS AND SETTING: We relied on available open genetic data from gene expression omnibus for patients diagnosed with abdominal sepsis, community-acquired pneumonia, or trauma which also included healthy control patients. MEASUREMENTS AND MAIN RESULTS: Our results confirm that immune processes are shared across disease etiologies in critical illnesses. We identify two consistent and distinct patient subgroups through deconvolution of serum transcriptomics: 1) increased neutrophils and naïve CD4 cell fractions and 2) suppressed neutrophil fraction. Furthermore, we found immune and inflammatory processes were downregulated in subgroup 2, a configuration previously shown to be more susceptible to multiple organ failure. Correspondingly, this subgroup had significantly higher mortality rates in all three etiologies of illness (0% vs 6.1%, p = 3.1 × 10 for trauma; 15.0% vs 25.4%, p = 4.4 × 10 for community-acquired pneumonia, and 7.1% vs 20.0%, p = 3.4 × 10 for abdominal sepsis). CONCLUSIONS: We identify two consistent subgroups of critical illness based on serum transcriptomics and derived immune cell fractions, with significantly different survival rates. This may serve as a universal predictor of complicated clinical course or treatment response and, importantly, may identify opportunities for subgroup-specific immunomodulatory intervention.

Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression.

BACKGROUND: It is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture. METHODS: We applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture. RESULTS: Network analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation. CONCLUSIONS: Our results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.

Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care.

BACKGROUND: Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients' medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations ("multimorbidity"). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. METHODS: We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. RESULTS: We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The "cardiopulmonary" and "cardiac" subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The "young" subgroup included 23.5% of the cohort composed of young and healthy patients. The "hepatic/addiction" subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85-52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The "complicated diabetics" and "uncomplicated diabetics" subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6-69% and 12.5-46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4-23.8% and 11.7-27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. CONCLUSION: We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design.

Reclassification of breast cancer: Towards improved diagnosis and outcome.

BACKGROUND: The subtyping of breast cancer based on features of tumour biology such as hormonal receptor and HER2 status has led to increasingly patient-specific treatment and thus improved outcomes. However, such subgroups may not be sufficiently informed to best predict outcome and/or treatment response. The incorporation of multi-modal data may identify unexpected and actionable subgroups to enhance disease understanding and improve outcomes. METHODS: This retrospective cross-sectional study used the cancer registry Surveillance, Epidemiology and End Results (SEER), which represents 28% of the U.S. population. We included adult female patients diagnosed with breast cancer in 2010. Latent class analysis (LCA), a data-driven technique, was used to identify clinically homogeneous subgroups ("endophenotypes") of breast cancer from receptor status (hormonal receptor and HER2), clinical, and demographic data and each subgroup was explored using Bayesian networks. RESULTS: Included were 44,346 patients, 1257 (3%) of whom had distant organ metastases at diagnosis. Four endophenotypes were identified with LCA: 1) "Favourable biology" had entirely local disease with favourable biology, 2) "HGHR-" had the highest incidence of HR- receptor status and highest grade but few metastases and relatively good outcomes, 3) "HR+ bone" had isolated bone metastases and uniform receptor status (HR+/HER2-), and 4) "Distant organ spread" had high metastatic burden and poor survival. Bayesian networks revealed clinically intuitive interactions between patient and disease features. CONCLUSIONS: We have identified four distinct subgroups of breast cancer using LCA, including one unexpected group with good outcomes despite having the highest average histologic grade and rate of HR- tumours. Deeper understanding of subgroup characteristics can allow us to 1) identify actionable group properties relating to disease biology and patient features and 2) develop group-specific diagnostics and treatments.

Using artificial neural networks to identify patients with concussion and postconcussion syndrome based on antisaccades.

OBJECTIVEArtificial neural networks (ANNs) have shown considerable promise as decision support tools in medicine, including neurosurgery. However, their use in concussion and postconcussion syndrome (PCS) has been limited. The authors explore the value of using an ANN to identify patients with concussion/PCS based on their antisaccade performance.METHODSStudy participants were prospectively recruited from the emergency department and head injury clinic of a large teaching hospital in Toronto. Acquaintances of study participants were used as controls. Saccades were measured using an automated, portable, head-mounted device preprogrammed with an antisaccade task. Each participant underwent 100 trials of the task and 11 saccade parameters were recorded for each trial. ANN analysis was performed using the MATLAB Neural Network Toolbox, and individual saccade parameters were further explored with receiver operating characteristic (ROC) curves and a logistic regression analysis.RESULTSControl (n = 15), concussion (n = 32), and PCS (n = 25) groups were matched by age and level of education. The authors examined 11 saccade parameters and found that the prosaccade error rate (p = 0.04) and median antisaccade latency (p = 0.02) were significantly different between control and concussion/PCS groups. When used to distinguish concussion and PCS participants from controls, the neural networks achieved accuracies of 67% and 72%, respectively. This method was unable to distinguish study patients with concussion from those with PCS, suggesting persistence of eye movement abnormalities in patients with PCS. The authors' observations also suggest the potential for improved results with a larger training sample.CONCLUSIONSThis study explored the utility of ANNs in the diagnosis of concussion/PCS based on antisaccades. With the use of an ANN, modest accuracy was achieved in a small cohort. In addition, the authors explored the pearls and pitfalls of this novel approach and identified important future directions for this research.

New drug candidates for treatment of atypical meningiomas: An integrated approach using gene expression signatures for drug repurposing.

BACKGROUND: Atypical meningiomas are common central nervous system neoplasms with high recurrence rate and poorer prognosis compared to their grade I counterparts. Surgical excision and radiotherapy remains the mainstay therapy but medical treatments are limited. We explore new drug candidates using computational drug repurposing based on the gene expression signature of atypical meningioma tissue with subsequent analysis of drug-generated expression profiles. We further explore possible mechanisms of action for the identified drug candidates using ingenuity pathway analysis (IPA). METHODS: We extracted gene expression profiles for atypical meningiomas (12 samples) and normal meningeal tissue (4 samples) from the Gene Expression Omnibus, which were then used to generate a gene signature comprising of 281 differentially expressed genes. Drug candidates were explored using both the Board Institute Connectivity Map (cmap) and Library of Integrated Network-Based Cellular Signatures (LINCS). Functional analysis of significant differential gene expression for drug candidates was performed with IPA. RESULTS: Using our integrated approach, we identified multiple, already licensed, drug candidates such as emetine, verteporfin, phenoxybenzamine and trazodone. Analysis with IPA revealed that these drugs target signal cascades potentially relevant in pathogenesis of meningiomas, particular examples are the effect on ERK by trazodone, MAP kinases by emetine, and YAP-1 protein by verteporfin. CONCLUSION: Gene expression profiling and use of drug expression profiles have yielded several plausible drug candidates for treating atypical meningioma, some of which have already been suggested by preceding studies. Although our analyses suggested multiple anti-tumour mechanisms for these drugs, further in vivo studies are required for validation. IMPORTANCE OF THE STUDY: To our knowledge this is the first study which combines relatively new, yet established computational techniques to identify additional treatments for a difficult to manage cerebral neoplasm. Beyond proposing already approved drug candidates in the management of atypical meningioma the study highlights the promise held by computational techniques in improving our management strategies.

Combined Minimally Invasive Supraciliary and Transfacial Approach for Large Tumors with Skull Base and Sinonasal Involvement.

BACKGROUND: Tumors invading both the anterior skull base and the sinonasal area have traditionally been accessed via largely invasive open craniofacial approaches. Minimally invasive extended endoscopic endonasal approaches have recently become increasingly available but have anatomical limitations and require incremental experience and thus high patient volume. Our objective was to assess the applicability of a novel combination of the minimally invasive supraciliary incision and the limited maxillofacial osteotomy as a combined surgical approach for large tumors invading both the anterior skull base and the sinonasal area. METHODS: The well-established technique of supraciliary incision with a 2.5 × 3.0-cm craniotomy was combined for the first time with limited facial translocation approach. RESULTS: This series involves 11 cases (female/male ratio 4:7; ranging in age from 6 to 61 years). Intracranial tumor propagation with intranasal and ethmoidal extension was detected in all patients. The pathologic diagnoses included adenocarcinomas, esthesioneuroblastoma, rhabdomyosarcoma, sinonasal papilloma, meningioma, and neurofibroma. The postoperative approach-related mortality rate was zero. No case of cerebrospinal fluid leak was detected. The 3-year survival rate was 70%. CONCLUSIONS: The limited transfacial approach in combination with a supraciliary extension is associated with minimal mortality and morbidity and facilitates gross total tumor removal. We highly recommend this approach for the surgical treatment of large tumors invading both the anterior skull base and the sinonasal area, especially for those being out of indication for extended endoscopic endonasal surgery.