Citations

Objective Myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD) can be monophasic or relapsing, with early relapse being a feature. However, the relevance of early relapse on longer‐term relapse risk is unknown. Here, we investigate whether early relapses increase longer‐term relapse risk in patients with MOGAD. Methods A retrospective analysis of 289 adult‐ and pediatric‐onset patients with MOGAD followed for at least 2 years in 6 specialized referral centers. “Early relapses” were defined as attacks within the first 12 months from onset, with “very early relapses” defined within 30 to 90 days from onset and “delayed early relapses” defined within 90 to 365 days. “Long‐term relapses” were defined as relapses beyond 12 months. Cox regression modeling and Kaplan–Meier survival analysis were used to estimate the long‐term relapse risk and rate. Results Sixty‐seven patients (23.2%) had early relapses with a median number of 1 event. Univariate analysis revealed an elevated risk for long‐term relapses if any “early relapses” were present (hazard ratio [HR] = 2.11, p < 0.001), whether occurring during the first 3 months (HR = 2.70, p < 0.001) or the remaining 9 months (HR = 1.88, p = 0.001), with similar results yielded in the multivariate analysis. In children with onset below aged 12 years, only delayed early relapses were associated with an increased risk of long‐term relapses (HR = 2.64, p = 0.026). Interpretation The presence of very early relapses and delayed early relapses within 12 months of onset in patients with MOGAD increases the risk of long‐term relapsing disease, whereas a relapse within 90 days appears not to indicate a chronic inflammatory process in young pediatric‐onset disease. ANN NEUROL 2023;94:508–517

PURPOSE In response to the COVID-19 pandemic, many cancer practices rapidly adopted telehealth services. However, there is a paucity of data regarding ongoing telehealth visit utilization beyond this initial response. The purpose of this study was to assess changes in variables associated with telehealth visit utilization over time. METHODS This is a cross-sectional, year-over-year, retrospective analysis of telehealth visits conducted across a multisite, multiregional cancer practice in the United States. Multivariable models examined the association of patient- and provider-level variables with telehealth utilization across outpatient visits conducted over three 8-week periods from July to August in 2019 (n = 32,537), 2020 (n = 33,399), and 2021 (n = 35,820). RESULTS The rate of telehealth utilization increased from <0.01% (2019) to 11% (2020) to 14% (2021). The most significant patient-level factors associated with increased telehealth utilization included nonrural residence and age ≤65 years. Among patients residing in rural settings, video visit utilization rates were significantly lower and phone visit utilization rates were significantly higher compared with patients from nonrural residences. Regarding provider-level factors, widening differences in telehealth utilization were observed at tertiary versus community-based practice settings. Increased telehealth utilization was not associated with duplicative care as per-patient and per-physician visit volumes in 2021 remained consistent with prepandemic levels. CONCLUSION We observed continuous expansion in telehealth visit utilization from 2020 to 2021. Our experiences suggest that telehealth can be integrated into cancer practices without evidence of duplicative care. Future work should examine sustainable reimbursement structures and policies to ensure accessibility of telehealth as a means to facilitate equitable, patient-centered cancer care.

Abstract Atypical Teratoid/Rhabdoid Tumors (ATRTs) are malignant tumors of the central nervous system in children. They are almost universally characterized by genetic loss of SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin Subfamily member B1 (SMARCB1) - a core subunit of the ATP-dependent SWI/SNF chromatin remodeling complex, and dysregulation of lineage restricted enhancers. Here, we show that tumor subgroup-specific upregulation of a distant member of the transforming growth factor-β (TGF-β), bone morphogenic factor (BMF) family of proteins drives insensitivity to NK-mediated cytolysis in vitro and in vivo. Mechanistically, subgroup-specific loss of chromatin occupancy by the RE1 Silencing Transcription Factor (REST) to its consensus RE1 motif, allowed deposition of enhancer-specific histone H3 lysine4 (K4) mono-methylation and histone H3K27 acetylation marks to activate an enhancer-promoter interaction. Restoration of REST binding or BET-domain inhibition resulted in a significant reduction in the enhancer marks and enhancer-promoter interaction and involved histone H3K27me3 deposition at the REST binding site, to facilitate gene silencing and sensitization of ATRTs to NK-mediated cytolysis. Thus, our study has identified a novel function for REST as a modulator of tumor-intrinsic enhancer activity and tumor-extrinsic immune microenvironment. Of equal significance, we highlight a role of REST in recruiting the polycomb repressive complex-2 (PRC2) in gene silencing in ATRTs.

Abstract Enhanced DNA repair is an important mechanism of inherent and acquired resistance to DNA targeted therapies, including poly ADP ribose polymerase inhibition. Spleen associated tyrosine kinase (Syk) is a non-receptor tyrosine kinase known to regulate immune cell function, cell adhesion, and vascular development. Here, we report that Syk can be expressed in high grade serous ovarian cancer and triple negative breast cancers and promotes DNA double strand break resection, homologous recombination (HR) and therapeutic resistance. We found that Syk is activated by ATM following DNA damage and is recruited to DNA double strand breaks by NBS1. Once at the break site, Syk phosphorylates CtIP, a key mediator of resection and HR, at Thr-847 to promote repair activity, specifically in Syk expressing cancer cells. Syk inhibition or genetic deletion abolished CtIP Thr-847 phosphorylation and overcame the resistant phenotype. Collectively, our findings suggest that Syk drives therapeutic resistance by promoting DNA resection and HR through a novel ATM-Syk-CtIP pathway, and that Syk is a new tumor-specific target to sensitize Syk-expressing tumors to PARPi and other DNA targeted therapy.

7569 Background: B-cell lymphoma 2 inhibitors (BCL-2i) are well tolerated and effective in treating B-cell lymphoproliferative disorders such as CLL. Investigational agent lisaftoclax is a novel, oral, highly selective and potent BCL-2i. Preclinical data show a strong synergistic effect of lisaftoclax plus ibrutinib in an ibrutinib-insensitive PDXWM1 model. Methods: This global, open-label, multicenter study evaluates the safety and efficacy of lisaftoclax alone and combined with ibrutinib or rituximab in pts with WM. Pts were enrolled in 3 arms: Arm A (lisaftoclax), BTKi-resistant/intolerant pts; Arm B (lisaftoclax plus ibrutinib), treatment-naïve pts; and Arm C (lisaftoclax plus rituximab), BTKi-naïve relapsed/refractory pts. Lisaftoclax was escalated from 400 to 1,200 mg using a modified toxicity probability interval design. A 3-day ramp-up was used for pts at high TLS risk. Primary objectives were pharmacokinetics (PK), safety, and efficacy assessments; responses were assessed per IWWM criteria. Results: As of January 25, 2023, 46 pts were enrolled (Arm A [n = 14]; up to 1,000 mg; Arm B [n = 24]; up to 1,200 mg; Arm C [n = 8]; up to 800 mg). Baseline characteristics are shown in the table. Median (range) treatment duration was 6 (1-18; Arm A), 14 (1-27; B), and 8 (2-26; C) months. Overall response rates (ORRs) were: Arm A, 25% (median time to response [MTTR], 4.3 months); Arm B, 90.9% (MTTR, 1.9); Arm C, 37.5% (MTTR, 4.4). No significant difference was observed between pts with and without CXCR4-mut. At 1,200 mg, 1 DLT (grade 3 clinical TLS), due to pre-existing renal impairment, was reported. At 1,000 mg, 1 grade 3 laboratory TLS occurred in Arm B because of dehydration and active symptomatic therapies; abnormal electrolytes resolved after 1 day of drug interruption, without recurrence. Grade ≥ 3 lisaftoclax-related AEs included neutropenia (13%), leukocytopenia (4.3%), anemia (2.2%), weight loss (2.2%), and septic shock (2.2%). Ventricular arrhythmias were not observed. Only 1 pt required dose reduction (due to neutropenia). MTD has not been reached. A total of 14 pts discontinued study treatment because of disease progression (n = 5) and AE (n = 1). Lisaftoclax plus ibrutinib showed a PK exposure comparable to ibrutinib alone, indicating no potential DDI. Conclusions: Lisaftoclax alone or combined with ibrutinib/rituximab demonstrated measurable effects in pts with naïve or BTKi-treatment-failed WM. Internal study identifier: APG2575WU101. *SA and ML are co-first authors. Clinical trial information: NCT04260217 . [Table: see text]

e17009 Background: Decreased circulating levels of ether lipids (glycerophospholipids with an ether-linked fatty alcohol, e.g. plasmalogens) have been observed in obesity, diabetes and cardiovascular disease. In vitro and in vivo studies suggest that ether lipids are anti-inflammatory and protective against obesity. A recent landmark study revealed that ether lipids can kill cancer cells through ferroptosis. However, the role of ether lipids in prostate cancer has not been thoroughly investigated despite the known association of dysregulated lipid metabolism with poorer clinical outcomes. The aim of this study was to assess the relationship between the circulating profiles of ether lipids and clinical outcomes in localised and metastatic prostate cancer. Methods: The profiles of ~150 ether lipids (8 lipid classes) in plasma lipidomic data from localised prostate cancer (389 men in Australia) and metastatic castration-resistant prostate cancer (mCRPC)(2 cohorts –147 men in USA, 128 men in Australia) were analysed for associations with disease progression or overall survival (OS). Open-access genomic data of mCRPC tumours (108 men, CbioPortal) were analysed for aberrations in genes associated with ether lipid metabolism in relation to clinical outcome. Results: Previously in these study cohorts, we had identified men with poorer prognosis based on their plasma lipidome and our validated sphingolipid signature. These men with poorer prognosis have lower plasma levels of ether lipids than those with better prognosis (t-test p < 0.05, localised disease – 110 ether lipids, mCRPC – 54 ether lipids). Decreased plasma levels of up to 46 ether lipid species were associated with higher rates of clinical relapse in localised disease (hazard ratio (HR) range 0.37-0.73, p-value range 0.006-0.049), shorter radiological progression-free survival in mCRPC (HR range 0.35-0.73, p-value range 0.00009-0.049), and shorter OS in mCRPC (HR range 0.33-0.79, p-value range 0.000004-0.0497). Chromosomal loss of tumour GNPAT, an essential gene in ether lipid synthesis, was associated with shorter OS in mCRPC (HR = 1.36, 95% CI 1.02-1.81, p = 0.03). Conclusions: Lower circulating levels of ether lipids were associated with worse prognosis in localised and metastatic prostate cancer, suggesting that these lipids are protective against disease progression. Further research is required to understand the role of ether lipids in disease progression, and to determine if modulation of ether lipid levels through supplementation is beneficial.

2002 Background: Elderly patients with glioblastoma have a poor prognosis with phase III trials reporting median overall survival (OS) and global time to deterioration of 6–9 and 1.2 months respectively. 18F-DOPA (3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. Proton beam therapy (PBT) can spare more healthy tissue that may improve quality of life for this patient population. With improved targeting of disease along with dosimetric advantages of PBT, this phase II trial tested whether hypofractionated PBT can offer both improved survival and quality of life. Methods: Patients with newly diagnosed glioblastoma aged ≥65 years without contraindications to 18F-DOPA were eligible. Target volumes were defined by PET (tumor/normal brain SUV ≥2.0) and MRI areas of contrast enhancement including surgical cavity. Patients received dose escalated hypofractionated PBT of 35-40 GyE to PET/MRI over 5-10 treatments with 10 mm margin. Patients received concurrent/adjuvant temozolomide. With an alpha of 0.1, 18 of 39 patients would be required to be alive at 12 months for the study to be considered a success. Kaplan-Meier/Cox regression analysis was performed for progression free survival (PFS) and OS. Toxicities were evaluated with CTCAE v4.0. Patients completed EORTC BN20, QLQ-C30, and MMSE questionnaires. Time to deterioration, defined as a 10-point decrease in the score of the function domain or 10-point increase in score of symptom domain, was evaluated. Results: Between 5/2019-6/2021, 43 patients were enrolled, with 4 patients withdrawing due to progression/insurance denial prior to beginning protocol therapy. The median age was 70.2 yrs. and median follow-up was 12.5 months. MGMT methylation was present in 13 (33%). Tumors were multifocal in 10 (26%) and unifocal in 29 (74%). The primary endpoint was met with median PFS/OS was 6.5/12.9 months for all patients and for MGMT methylated patients 11.9/29.8 months respectively. Grade 3 baseline adjusted treatment related adverse events included 1 (3%) confusion, 2 (5%) fatigue, and 1 (3%) seizure. There were no grade 4/5 events. Multivariate analysis revealed MGMT unmethylated, ECOG 2 and biopsy only patients had worse survival. PET volume was more predictive than MRI volume for OS with PET volume>26 cc having a hazard ratio of 3.7. The median time to global deterioration per QLC-C30 was 6.3 months. Conclusions: This phase II trial incorporating 18F-DOPA PET-guided dose escalated hypofractionated proton beam treatment for elderly patients with glioblastoma met its primary endpoint for improved OS and patient reported quality of life compared to historical controls. Further investigation is warranted. Clinical trial information: NCT03778294 .

OBJECTIVE Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis. METHODS The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features. RESULTS Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998. CONCLUSIONS Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.

OBJECTIVE The purpose of this study was to evaluate the performance of different supervised machine learning algorithms to predict achievement of minimum clinically important difference (MCID) in neck pain after surgery in patients with cervical spondylotic myelopathy (CSM). METHODS This was a retrospective analysis of the prospective Quality Outcomes Database CSM cohort. The data set was divided into an 80% training and a 20% test set. Various supervised learning algorithms (including logistic regression, support vector machine, decision tree, random forest, extra trees, gaussian naïve Bayes, k–nearest neighbors, multilayer perceptron, and extreme gradient boosted trees) were evaluated on their performance to predict achievement of MCID in neck pain at 3 and 24 months after surgery, given a set of predicting baseline features. Model performance was assessed with accuracy, F1 score, area under the receiver operating characteristic curve, precision, recall/sensitivity, and specificity. RESULTS In total, 535 patients (46.9%) achieved MCID for neck pain at 3 months and 569 patients (49.9%) achieved it at 24 months. In each follow-up cohort, 501 patients (93.6%) were satisfied at 3 months after surgery and 569 patients (100%) were satisfied at 24 months after surgery. Of the supervised machine learning algorithms tested, logistic regression demonstrated the best accuracy (3 months: 0.76 ± 0.031, 24 months: 0.773 ± 0.044), followed by F1 score (3 months: 0.759 ± 0.019, 24 months: 0.777 ± 0.039) and area under the receiver operating characteristic curve (3 months: 0.762 ± 0.027, 24 months: 0.773 ± 0.043) at predicting achievement of MCID for neck pain at both follow-up time points, with fair performance. The best precision was also demonstrated by logistic regression at 3 (0.724 ± 0.058) and 24 (0.780 ± 0.097) months. The best recall/sensitivity was demonstrated by multilayer perceptron at 3 months (0.841 ± 0.094) and by extra trees at 24 months (0.817 ± 0.115). Highest specificity was shown by support vector machine at 3 months (0.952 ± 0.013) and by logistic regression at 24 months (0.747 ± 0.18). CONCLUSIONS Appropriate selection of models for studies should be based on the strengths of each model and the aims of the studies. For maximally predicting true achievement of MCID in neck pain, of all the predictions in this balanced data set the appropriate metric for the authors’ study was precision. For both short- and long-term follow-ups, logistic regression demonstrated the highest precision of all models tested. Logistic regression performed consistently the best of all models tested and remains a powerful model for clinical classification tasks.