Citations

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.

Abstract Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treat-ment effects and surgical cavities are not present. Thus, existing automatic segmenta-tion models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmenta-tion on glioma images with different phenotypic and treatment appearance. We as-sessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained techni-cians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Abstract Oligodendroglioma is characterized by oligodendrocyte-like histology, mutation in the IDH genes, along with codeletion of chromosome arms 1p and 19q. Treatment for oligodendrogliomas combines surgery, followed by observation, and chemotherapy and/or radiotherapy when tumors progress. Most oligodendrogliomas eventually recur with a more aggressive phenotype, leading to patient mortality but molecular mechanisms remain poorly understood. To investigate the molecular trajectory of oligodendroglioma, we collected more than 270 longitudinal samples from over 130 oligodendroglioma patients, to perform whole genome or whole exome sequencing, bulk RNA, and single-nucleus RNA sequencing (n = 30 samples). Common mutations in IDH1/2, TERT promoter, CIC, and FUBP1 were observed. About half of the CIC and FUBP1 mutations occurred in the initial sample and persisted stably throughout recurrence. The majority of PIK3CA and PIK3R1 mutations were either shared or present in recurrences only implicating the PI3K pathway in oligodendroglioma recurrence. Mutational signature analysis revealed 34.5% of tumors treated with alkylating agents acquired SBS11/SBS31 associated hypermutation. By integrating signatures with clonality estimates, we approximated the timing of mutational processes and found that clonal timings of treatment related mutations correlate with the treatment administration date. We found that aging signature-related mutations were mostly clonal, and treatment signature-related mutations were more likely to be sub-clonal, supporting the late emergence of these mutations following treatment. We observed increased somatic copy number alterations in oligodendroglioma recurrences, reflected in notable chromosome 4 losses. Associating treatment with snRNAseq derived cellular states, we observed a significant increase in the fraction of proliferating stem-like cells in the hypermutator versus non-hypermutator recurrent samples. These findings shed light on the molecular changes occurring in oligodendrogliomas following treatment and provide insights that may aid in targeting treatment resistance.

Abstract Background Antitachycardia pacing (ATP) found in most implantable cardioverter defibrillators (ICDs) is an important therapy for painless termination of ventricular tachycardias (VT). In ICDs programmed to guideline-recommended detection and therapy settings typical ATP success rates are 50-70%.[1-3] The first closed-loop, automated ATP (AATP) algorithm, introduced in 2021, applies an S1 train derived from the detected VT, followed by an S2 derived initially from a refractory period estimator and then adjusted using analysis of the post-pacing interval if ATP fails. The termination efficacy of AATP in the real-world population is unknown. Purpose To assess the effectiveness AATP in a large real-world population. Methods A database of de-identified ICD transmissions from subjects in the United States, Australia, Canada, and New Zealand was queried for AATP treated episodes. ICDs were randomly sampled, biased toward ICDs with multiple treated VT episodes. For rhythms classified as monomorphic ventricular tachycardia, each AATP pulse train was reviewed for: rhythm at delivery, effect of ATP, and any non-capturing pulses. Acceleration was defined as a post-ATP decrease in MVT cycle length (VTCL) of at least 10% and at least 30ms. Acceleration to polymorphic VT or ventricular fibrillation (PVT/VF) was assessed. Rates of termination and acceleration were calculated for all MVT and dichotomized using a 320ms VTCL cutoff. The method of generalized estimating equations (GEE) was used to correct event rates for multiple episodes from a single patient. Results There were 2281 episodes of MVT in 340 patients; 63% of patients had 2 or more episodes. There were 724 episodes with initial VTCL <320ms and 1557 episodes with VTCL ≥320ms. The rate of termination of MVT by AATP was 78% for all VTCL [95% CI: 74%-81%]. In 300 patients (88%) AATP terminated at least one MVT. For MVT with VTCL ≥320ms the termination rate was 80% [95% CI: 75%-83%] and for VTCL <320ms the termination rate was 75% [95% CI: 69%-80%]. The rate of acceleration was 3.6% [95% CI: 2.7%-4.9%], with a 0.8% rate of acceleration to PVT/VF [95% CI: 0.4%-1.8%]. Acceleration led to shock therapy in 63% of accelerated episodes. All MVT accelerated to PVT/VF either spontaneously terminated (4/14) or were successfully converted by a single shock therapy (10/14). All accelerations that remained MVT were successfully treated by the device with either subsequent ATP (35%) or shock therapy (61%), or spontaneously terminated (4%). Conclusion In this large, real-world cohort analysis, automated ATP’s termination rate exceeded the typical success rate of burst ATP, particularly for MVT <320ms. Acceleration to PVT/VF was low and effective first-shock defibrillation was consistent. Subsequent ATP for accelerated MVT had incremental benefit, leading to successful termination one-third of the time. Additional data are currently being prospectively collected in a separate post-approval study.

OBJECTIVE The goal of this study was to characterize local tumor control (LC), overall survival (OS), and safety of stereotactic radiosurgery for colorectal brain metastasis (CRBM). METHODS Ten international institutions participating in the International Radiosurgery Research Foundation provided data for this retrospective case series. This study included 187 patients with CRBM (281 tumors), with a median age of 62 years and 56.7% being male. Most patients (53.5%) had solitary tumors, although 10.7% had > 5 tumors. The median tumor volume was 2.7 cm3 (IQR 0.22–8.1 cm3), and the median margin dose was 20 Gy (IQR 18–22 Gy). RESULTS The 3-year LC and OS rates were 72% and 20%, respectively. Symptomatic adverse radiation effects occurred in 1.6% of patients. In the multivariate analysis, age > 65 years and tumor volume > 4.0 cm3 were significant predictors of tumor progression (hazard ratio [HR] 2.6, 95% CI 1.4–4.9; p = 0.003 and HR 3.4, 95% CI 1.7–6.9; p < 0.001, respectively). Better performance status (Karnofsky Performance Scale score > 80) was associated with a reduced risk of tumor progression (HR 0.38, 95% CI 0.19–0.73; p = 0.004). Patient age > 62 years (HR 1.6, 95% CI 1.1–2.3; p = 0.03) and the presence of active extracranial disease (HR 1.7, 95% CI 1.1–2.4; p = 0.009) were significantly associated with worse OS. CONCLUSIONS Stereotactic radiosurgery offers a high LC rate and a low rate of symptomatic adverse radiation effects for the majority of CRBMs. The OS and LC favored younger patients with high functional performance scores and inactive extracranial disease.

The current study evaluated work- and client-related burnout, perceived stress, and race-based stress with a focus on Black, Indigenous, or Persons of Color (BIPOC) mental health providers. An online survey ( n = 82) was distributed in the United States between July 17, 2020, and November 5, 2020. Demographic variables, racial discrimination during coronavirus disease 2019 (COVID-19), perceived stress, and the coping variables were assessed. The majority of respondents were women (85%), BIPOC identifying (72%), aged 25 to 34 years (43%), psychologists (50%), and had been practicing for 9 years. In total, 68% and 27% of providers reported moderate to severe work- and client-related burnout, respectively. Another 87% reported moderate to high stress. Factors associated with a higher level of burnout include being younger, having fewer years of experience, and being a social worker or family therapist. Among BIPOC providers, greater experiences of racial discrimination were associated with higher work-related burnout and stress. Greater use of maladaptive coping was associated with higher burnout and stress, yet we found no associations with the use of adaptive coping. Racial discrimination during COVID-19 was significantly associated with work-related burnout and perceived stress within a sample of predominantly BIPOC mental health providers. These findings reinforce the need for more focused support for mental health providers to ensure a healthy and resilient future workforce.

AbstractBackgroundYoung‐onset multiple system atrophy (YOMSA) is defined as the onset of multiple system atrophy (MSA) before the age of 40 years old. YOMSA is rare and there is much uncertainty of the phenotype and natural history in patients with YOMSA.ObjectiveThe objective is to evaluate the characteristics and disease course of patients with YOMSA.MethodsWe retrospectively reviewed medical records of patients with MSA who were evaluated at all Mayo Clinic sites from 1998 to 2021. We identified patients with YOMSA and evaluated clinical characteristics, autonomic function testing results, and disease course.ResultsOf 1496 patients with a diagnosis of clinically probable or clinically established MSA, 20 patients had YOMSA. The median age of onset was 39.1 (interquartile range [IQR] = 37.1, 40.1) years; 13 patients (65%) were male. MSA‐parkinsonism was the most common subtype (65%). The median duration of symptom onset to YOMSA diagnosis was 4.9 (IQR = 3.7, 9) years. At the time of medical record review, 17 patients were deceased with a median survival of 8.3 (IQR = 7, 10.9) years. Univariate analysis showed that initial onset of autonomic failure predicted unfavorable survival (hazard ratio = 2.89, P = 0.04) compared to those who presented with motor impairment only at onset. At the time of YOMSA diagnosis, composite autonomic severity score was available in 19 patients with a median of 5 (IQR = 4, 6.5).ConclusionsYOMSA resembles MSA in most aspects including phenotype and prognosis, although the diagnosis is usually delayed. The presence of autonomic failure at symptom onset may be a poor predictor for survival.

In this paper we report on the inaugural meetings of the Musical Care International Network held online in 2022. The term “musical care” is defined by Spiro and Sanfilippo (2022) as “the role of music—music listening as well as music-making—in supporting any aspect of people's developmental or health needs” (pp. 2–3). Musical care takes varied forms in different cultural contexts and involves people from different disciplines and areas of expertise. Therefore, the Musical Care International Network takes an interdisciplinary and international approach and aims to better reflect the disciplinary, geographic, and cultural diversity relevant to musical care. Forty-two delegates participated in 5 inaugural meetings over 2 days, representing 24 countries and numerous disciplines and areas of practice. Based on the meetings, the aims of this paper are to (1) better understand the diverse practices, applications, contexts, and impacts of musical care around the globe and (2) introduce the Musical Care International Network. Transcriptions of the recordings, alongside notes taken by the hosts, were used to summarise the conversations. The discussions developed ideas in three areas: (a) musical care as context-dependent and social, (b) musical care's position within the broader research and practice context, and (c) debates about the impact of and evidence for musical care. We can conclude that musical care refers to context-dependent and social phenomena. The term musical care was seen as useful in talking across boundaries while not minimizing individual disciplinary and professional expertise. The use of the term was seen to help balance the importance and place of multiple disciplines, with a role to play in the development of a collective identity. This collective identity was seen as important in advocacy and in helping to shape policy. The paper closes with proposed future directions for the network and its emerging mission statement.

Abstract The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.