Citations

Abstract Background Brain tumors represent a significant global health challenge, with rising incidence and mortality impacting individuals worldwide and contributing to cancer-related morbidity and mortality. In Africa, this burden is exacerbated by limited access to advanced diagnostics, treatment options, and multidisciplinary care, compounded by the absence of standardized cancer registration and tumor biobanking. The introduction of molecular diagnostics, as outlined in the 2021 World Health Organization central nervous system (CNS) tumor classification, adds complexity to brain tumor management, particularly in regions with scarce resources. Methods To address these issues, the Brain Tumor Consortium for Africa (BTCA) was established in 2023, bringing together experts to improve CNS tumor diagnosis, patient care, and research. The initial project, conducted via an electronic questionnaire, aimed to assess neuro-oncology capacity across Sub-Saharan Africa. Results The study revealed significant gaps, with a limited number of institutions incorporating molecular subtyping into their diagnostic algorithms. The consortium’s efforts focus on enhancing local data use, informing public policy, and promoting collaboration to advance neuro-oncology practices in Africa. By fostering a network enlisting the expertise of collaborators in the fields of neurosurgery, neurology, neuropathology, anatomic pathology, and medical and radiation oncology, the BTCA seeks to improve brain tumor management through better diagnostics, infrastructure, and policy advocacy. Future directions include expanding molecular diagnostic capabilities, standardizing brain tumor biobanking, enhancing data collection, and advocating for improved brain tumor care in national health agendas. Conclusions The BTCA represents a pioneering model of collaboration and innovation in addressing the unique challenges of brain tumor care in Africa.

Abstract T cell immune tolerance is established in part through the activity of the Auto-immune Regulator (AIRE) transcription factor in the medullary Thymic Epithelial Cells (mTEC) of the thymus. AIRE induces expression of SELF peripheral tissue-specific antigens for presentation to naïve T cells to promote activation/deletion of potentially autoreactive T cells. We show, for the first time to our knowledge, that tumors mimic the role of AIRE in mTEC to evade immune rejection. Thus, by expressing a broad range of SELF epitopes against which minimal functional T cell reactivities exist because of thymic deletion, AIRE acts as a master controller of SELFNESS, effectively cloaking the tumor from T cell attack. Moreover, we describe a completely novel immunotherapy in which engineered changes in AIRE expression in tumor cells alters their profile of SELFNESS, exposing both AIRE-modified, and parental unmodified, tumor cells to T cell attack. Consistent with our studies, patient RNAseq shows expression of AIRE predicts response to immune therapies with a strong correlation between AIRE expression and markers of TCR signaling. Therefore, by re-setting the immunological SELFNESS of cancer cells, this novel AIRE-mediated immunotherapy 1). converts a highly tolerized T cell compartment into a heteroclitic tumor-reactive T cell population; 2) confers de novo sensitivity to immune checkpoint blockade upon non-immunogenic tumors; 3). completely removes the need to identify potentially immunogenic tumor-associated antigens as targets for generation of de novo CD8+ and helper CD4+ T cell responses; and 4) leads to potent T cell-mediated rejection of aggressive, immunologically cold, non-immunogenic tumors.

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).

Background: There is limited data on the safety and efficacy of ventricular tachycardia (VT) ablation in patients with chronic kidney disease (CKD). We examined the outcomes of patients with CKD undergoing VT ablation in a nationally representative cohort of patients. Methods: The National Inpatient Sample Database (NIS) was analyzed from 2018 to 2021 to identify patients ≥18 years old with VT undergoing ablation. Patients with atrial fibrillation, atrial flutter, supraventricular tachycardia, or pre-excitation syndrome were excluded. Patients were divided into those with CKD and without CKD. A multivariable logistic regression model was utilized to assess the association of CKD with in-hospital mortality and outcomes after adjusting for confounders. Results: Our cohort included 1608 VT ablation procedures, of which 428 (27%) were performed on CKD patients. Mean age was 63 (±13) years, 318 (19%) were female, and 1194 (74%) were White. 1475 (92%) of the procedures were done at an urban teaching hospital, and 1240 (77%) at a private non-profit hospital. On multivariable analysis, CKD was associated with significantly higher odds of death (adjusted odds ration [aOR]: 3.43; 95% confidence interval [CI]: 1.79-6.5; p=0.0002), acute decompensated heart failure (aOR: 3.1; 95% CI 2.24-4.56; p<.0001), bleeding (aOR: 1.53; 95% CI1.02-2.28; p<.03), and acute kidney injury (aOR: 4.49; 95% CI3.42-5.9; p<.0001). There was no difference in the odds of acute myocardial infarction, stroke, cardiogenic shock, vasopressor use, mechanical ventilation, and pericardial tamponade or pericardial procedures Figure . Conclusion: Patients with CKD undergoing VT ablation have an increased risk of mortality and adverse in-hospital outcomes when compared to patients without CKD. These hypothesis-generating findings need validation in future prospective studies for optimal pre-procedural risk stratification.

Background: Guidelines recommend shared decision-making (SDM) for patients with atrial fibrillation (AF) making decisions regarding anticoagulation for stroke prevention. Decision support tools (e.g., Decision Aids [DAs]) facilitate SDM. However, little research has compared the effectiveness of the two most common types of DAs: (1) encounter decision aid (EDA) and (2) pre-encounter patient decision aid (PDA). Our study assessed the comparative effectiveness of PDAs and EDAs on SDM during AF clinical encounters where stroke prevention strategies were discussed. Research Question: Do decision aids (PDA or EDA), alone or in combination, improve patients’ AF decision-making experience compared to usual care? Methods: A cluster randomized multi-center trial in patients with AF comparing usual care (no DA) vs. one DA (EDA or PDA) or both DAs. Clinicians and patients were randomized independently into 4 study arms. Clinicians were randomized to use or not use the EDA for all study visits, and patients were randomized to use or not use the PDA. Co-primary outcome domains were: (1) quality of SDM (2) patient knowledge, and (3) decisional conflict. Secondary outcomes included: (1) treatment choice, (2) treatment initiation, and (3) treatment persistence. Patient characteristics were assessed. We will present comparisons of the randomized DAs on the co-primary and secondary outcomes and subgroup analyses. Results: Between December 2020-July 2023, investigators in 6 U.S. healthcare systems enrolled 1117 patients (mean (M) age 69, 63% male, 89% White) and 107 clinicians. Compared to usual care (no DA, M=31.6), SDM was better in patients receiving PDA (M=35.4) and EDA (44.5) alone or together (M=43.7; p’s<0.001). Knowledge about AF was greatest for those receiving both DAs or the PDA alone (84% accurate responses for PDA and both vs 76% with no DA, p<0.001), followed closely by the EDA alone (82%, p=0.003). Finally, both DAs (M=19.5) and EDA alone (M=20.0) had better decisional conflict scores compared to usual care (M=25.8; p’s<0.001). We will present analyses that assess the impact of DAs on secondary outcomes and interactions with DA, patient, and clinical characteristics across each outcome. Conclusions: In one of the first randomized trials examining the comparative effectiveness of EDAs and PDAs on SDM in clinical medicine, both PDAs and EDAs improved shared decision-making, decisional conflict, and knowledge compared to patients receiving usual care.

Abstract Purpose: Craniopharyngiomas are rare parasellar tumors, and papillary craniopharyngiomas (PCP) represent 8-20% of cases. We assessed surgical outcomes of these very rare tumors among patients treated in the last decade. Methods: Retrospective data from the Registry of Adenomas of the Pituitary and Related Disorders (RAPID) were used to evaluate patients with PCP treated between 2011 and 2023 at 14 US-based academic skull base centers. Demographic, imaging, surgical, and outcome variables were analyzed. Results: Ninety-nine patients with PCP were included. Mean age was 51.8±14.7 and 57.6% were male. Most tumors showed mixed cystic/solid components (67.5%), were trans-infundibular (50.0%), and were treated endoscopically (74.5%), with 51.1% demonstrating gross total resection (GTR). Pituitary stalk was preserved in 51.9%. Permanent arginine vasopressin deficiency was seen in 68.8% and was associated with pituitary stalk sacrifice (p=0.02). Cerebrospinal fluid leak was the most common nonendocrine postsurgical, 30-day complication (9.1%), reason for return to the operating room (3.1%), and reason for 90-day readmission (8.1%). Return to the operating room was associated with discharge to a skilled nursing or rehabilitation facility (p=0.0001). Most patients (63.9%) received no adjuvant therapy; GTR was associated with decreased radiotherapy use (p=0.0001). BRAFV600E mutation was detected in 64/69 tumors tested, although only 1 patient was treated with a BRAF inhibitor. Conclusion: To our knowledge, the RAPID consortium enabled the largest real-world clinical information dataset for PCP. Our data can be used as a benchmark for short-term surgical outcomes. Longer-term follow-up studies are needed to understand how to optimize outcomes from each treatment modality.

Abstract Brain tumors constitute significant morbidity and mortality in sub-Saharan Africa (SSA). There is no previous large-scale effort focusing on brain tumor genetics in SSA. This project aims to map out the current neuro-oncology capacity in terms of workforce and available services in SSA. This is an ongoing online survey among a cross section of neuro-oncology practitioners spread across SSA countries to capture information on the distribution, number of available neuro-oncology care practitioners, capacity for neuropathology and molecular diagnosis, management of brain tumors, cancer registration, and tumor biobanking. The survey was disseminated by the Society for Neuro-oncology in SSA (SNOSSA). There were 83 respondents representing 15 countries and 52 health institutions: 46 tertiary, 4 secondary, 1 private and 1 other. Here, we present data on the 52 institutions. The prevalence of specialties was 87% neurosurgery, 75% anatomic pathology, 69% medical oncology, 64% neurology, 60% radiation oncology, and 39% neuropathology. For radiologic evaluation, 81% of institutions were reported to have both CT and MRI, 17% CT only, and one institution neither. For molecular testing, 46% have access to IHC or NGS testing, whereas 52% were reported to have neither. A neuro-oncology tumor board was reported at 58% of institutions. A general cancer registry is available in 64% of institutions, 31% have a brain tumor specific registry and 10% have a brain tumor biorepository. This study shows that SSA has substantial potential in neuro-oncology care practice capacity and facilities. Most significant gaps remain in areas such as neuropathology and molecular testing, which makes widespread implementation of the WHO 2021 diagnosis challenging. Understanding the prevalence of CNS tumor subtypes is also difficult due to low number of brain tumor registries and biobanks in SSA. The present findings reveal strategic opportunities, especially in neuropathology, tumor registries, biobanks, and infrastructure for IHC and NGS in SSA.

Abstract Glioblastoma (GBM), the most common and deadly primary brain tumor, exerts profound local and systemic immunosuppressive effects through a variety of mechanisms. Tumor-derived extracellular vesicles (EVs) are increasingly appreciated as critical mediators of these effects. In surveying the tumor-immune microenvironment in samples of ultrasonic aspirate collected during surgical resection of GBM (n=13), we identified several key immunomodulatory markers expressed across a range of myeloid cell populations. Particularly, we found TIGIT, classically understood as a T cell inhibitory protein, expressed across a range of myeloid cell populations in patient samples, including in myeloid-derived suppressor cells (MDSCs, 46.84±16.78% of total monocytic MDSCs). Using EVs collected from patient-derived cell lines, we were able to recapitulate these effects upon co-culture with monocytes derived from the peripheral blood of healthy donors (n=7). Furthermore, Induced MDSCs significantly inhibit T cells proliferation (70.97±8.454%, compared to control 19.4±5.71; p&lt;0.03), concomitant with a surge in Th2 immune responses via TIGIT/CD155 axis. This effect was reversed after TIGIT silencing, indicating a TIGIT-dependent immune suppression in the GBM microenvironment. Our study indicates EV-induced TIGIT upregulation in myeloid cells as a crucial immunosuppressive mechanism in glioblastoma, with potential therapeutic applications.

Abstract Despite exhibiting longer overall survival that IDH-wt counterparts, IDH-mutant gliomas remain uniformly deadly and impact patients in the prime of adult life. The immune microenvironment of IDH-mutant gliomas remains incompletely characterized, particularly with regard to distinctions between IDH-mutant astrocytoma and IDH-mutant, 1p/19q codeleted oligodendroglioma. To address this knowledge gap, we subjected dissociated tumor suspensions derived from 7 astrocytoma and 7 oligodendroglioma clinical samples to flow cytometry-based sorting for the pan-immune marker, CD45, and the microglial marker, P2RY12. This process yielded both microglial and non-microglial immune cell populations, which were then analyzed by scRNA sequencing. This workflow revealed a single large cluster of microglia, characterized by expression of Galectin-1 and lysosomal Lipase A, which was present at a significantly higher level in oligodendroglioma. While the biological significance of this cluster is unclear, gene set enrichment analysis exploring phenotypic differences between microglia in astrocytoma and oligodendroglioma revealed significant increases in expression of gene sets related to antigen presentation in astrocytoma. Parallel analysis of the non-microglial immune fraction showed an increase in the expression of pro-inflammatory gene sets, such as genes related to cytokine secretion, within the CD8 T-cell compartment of astrocytomas. We also observed upregulation of the expression of inflammatory gene sets within the dendritic cells of astrocytomas. Taken together, these changes suggest that the immune compartment of astrocytoma is more active and inflamed, and targeting the immune system in these tumors may represent a potential therapeutic avenue.