Citations

OBJECTIVE Congenital anomalies of the atlanto-occipital articulation may be present in patients with Chiari malformation type I (CM-I). However, it is unclear how these anomalies affect the biomechanical stability of the craniovertebral junction (CVJ) and whether they are associated with an increased incidence of occipitocervical fusion (OCF) following posterior fossa decompression (PFD). The objective of this study was to determine the prevalence of condylar hypoplasia and atlas anomalies in children with CM-I and syringomyelia. The authors also investigated the predictive contribution of these anomalies to the occurrence of OCF following PFD (PFD+OCF). METHODS The authors analyzed the prevalence of condylar hypoplasia and atlas arch anomalies for patients in the Park-Reeves Syringomyelia Research Consortium database who underwent PFD+OCF. Condylar hypoplasia was defined by an atlanto-occipital joint axis angle (AOJAA) ≥ 130°. Atlas assimilation and arch anomalies were identified on presurgical radiographic imaging. This PFD+OCF cohort was compared with a control cohort of patients who underwent PFD alone. The control group was matched to the PFD+OCF cohort according to age, sex, and duration of symptoms at a 2:1 ratio. RESULTS Clinical features and radiographic atlanto-occipital joint parameters were compared between 19 patients in the PFD+OCF cohort and 38 patients in the PFD-only cohort. Demographic data were not significantly different between cohorts (p > 0.05). The mean AOJAA was significantly higher in the PFD+OCF group than in the PFD group (144° ± 12° vs 127° ± 6°, p < 0.0001). In the PFD+OCF group, atlas assimilation and atlas arch anomalies were identified in 10 (53%) and 5 (26%) patients, respectively. These anomalies were absent (n = 0) in the PFD group (p < 0.001). Multivariate regression analysis identified the following 3 CVJ radiographic variables that were predictive of OCF occurrence after PFD: AOJAA ≥ 130° (p = 0.01), clivoaxial angle < 125° (p = 0.02), and occipital condyle–C2 sagittal vertical alignment (C–C2SVA) ≥ 5 mm (p = 0.01). A predictive model based on these 3 factors accurately predicted OCF following PFD (C-statistic 0.95). CONCLUSIONS The authors’ results indicate that the occipital condyle–atlas joint complex might affect the biomechanical integrity of the CVJ in children with CM-I and syringomyelia. They describe the role of the AOJAA metric as an independent predictive factor for occurrence of OCF following PFD. Preoperative identification of these skeletal abnormalities may be used to guide surgical planning and treatment of patients with complex CM-I and coexistent osseous pathology.

Abstract Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Abstract Introduction: Porcine myeloperoxidase (pMPO) is a cationic enzyme secreted by granulocytic leukocytes capable of selectively binding cancer cells due to their negative surface charge. In the presence of hydrogen peroxide (H2O2) and sodium chloride (NaCl), MPO catalyzes the production of electronically excited singlet molecular oxygen, which exerts focused cytotoxic activity against cancer cells. Methods: Human bladder non-cancer (SV-HUC1) and cancer cells (T24 and 5637) were used to assess differential cytotoxic activity of pMPO. pMPO was combined with associated reagents, including H2O2, NaCl, and L-proline and glycine for 45 minutes to support the generation of singlet oxygen. MTS assay was used to assessed cell viability at day 3 post-treatment. Non-cancer and cancer cells were labeled with GFP and mCherry, respectively, via infection with lentivirus and cultured in tandem to allow for simultaneous treatment of both cell types. After the treatment, flow cytometry and microscopy were used to monitor the growth of the cells. Results: No cytotoxicity was noted when SV-HUC1 cells were cultured with individual reagents pMPO, NaCl, L-Proline, or Glycine, and limited cytotoxicity was observed with H2O2. Notably, cell viability was nearly maximally preserved for SV-HUC1 cells. The combination of pMPO with associated reagents resulted in killing of 5637 and T24 cells, with tumoricidal activity maintained across increasing tumor cell density, with cell viability preserved for SV-HUC1 cells. Within the cell co-culture system, simultaneous treatment of both cell types with the pMPO treatment resulted in selective killing of tumor cells and preservation of SV-HUC1 cells. Mechanistic studies confirm specificity of pMPO binding to tumor cells, and further, high levels of expression of gammaH2AX and phospho-ATM in 5637 and T24 cells following treatment. Conclusion: pMPO demonstrates selective and efficient killing of bladder cancer cells with minimal cytotoxicity against non-cancer urothelial cells. Given its reliance on direct surface contact with tumor cells for activity, pMPO may be developed as an intravesical therapy for treatment of non-muscle invasive bladder cancers. Acknowledgements: This work was partially supported by Mayo funds provided to Dr. Haojie Huang. Citation Format: Junnan Liu, Robert C. Allen, Jackson T. Stephens, Paras Shah, Haojie Huang. Investigation of anti-tumor activity of porcine myeloperoxidase for urothelial carcinoma of the bladder [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2029.

Abstract Magnetic resonance imaging (MRI) is a centerpiece of clinical management of brain tumor patients, yet the biology underlying even commonly interpreted imaging changes is unclear. The purpose of this project was to understand what biology is associated with brain tumor edema abundance and if that association was sex distinct. We have developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Multiparametric MRIs were preprocessed with bias field correction and intensity normalization. The images were co-registered and segmented for abnormal regions and normal tissue. The segmentations and preprocessed images were utilized in the edema mathematical model to estimate the fraction of extracellular space (Fecs) and fraction of intracellular space (Fics). Through an ongoing image-localized biopsy collection protocol, we analyzed 179 samples (Female: 75; Male: 104) from 55 patients (Female: 21; Male: 34) for bulk RNA-Seq. Samples were characterized by their edema Fecs scores and underwent differential expression, gene set enrichment analysis using MSigDB hallmarks and cellular deconvolution using CIBERSORTx. Through differential expression and gene set enrichment analyses of these spatial biopsies, we have identified sex-distinct gene expression and pathways corresponding to regions of high vs low edema. Within highly edematous regions, male samples were amplified for respiratory pathways, while female samples were suppressed. Connecting changes on MRIs with molecular markers from image-localized biopsies provides an opportunity to identify the biological drivers of brain tumor associated imageable edema. Specifically, elucidating the sex-distinct patterns connecting changes on MRI with cellular populations and molecular pathways could be used to better interpret imaging of patients and interpreting treatment response. Citation Format: Pamela R. Jackson, Lee Curtin, Sara Ranjbar, Kyle W. Singleton, Maciej M. Mrugala, Richard S. Zimmerman, Bernard R. Bendok, Peter Canoll, Kristin R. Swanson. Sex-distinct patterns of molecular pathways associated with brain tumor edema [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6168.

Abstract Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Abstract Cerebrospinal fluid (CSF) is a clear, transparent fluid derived from blood plasma that protects the brain and spinal cord against mechanical shock, provides buoyancy, clears metabolic waste and transports extracellular components to remote sites in the brain. Given its contact with the brain and the spinal cord, CSF is the most informative biofluid for studies of the central nervous system (CNS). In addition to other components, CSF contains extracellular vesicles (EVs) that carry bioactive cargoes (e.g., lipids, nucleic acids, proteins), and that can have biological functions within and beyond the CNS. Thus, CSF EVs likely serve as both mediators of and contributors to communication in the CNS. Accordingly, their potential as biomarkers for CNS diseases has stimulated much excitement for and attention to CSF EV research. However, studies on CSF EVs present unique challenges relative to EV studies in other biofluids, including the invasive nature of CSF collection, limited CSF volumes and the low numbers of EVs in CSF as compared to plasma. Here, the objectives of the International Society for Extracellular Vesicles CSF Task Force are to promote the reproducibility of CSF EV studies by providing current reporting and best practices, and recommendations and reporting guidelines, for CSF EV studies. To accomplish this, we created and distributed a world‐wide survey to ISEV members to assess methods considered ‘best practices’ for CSF EVs, then performed a detailed literature review for CSF EV publications that was used to curate methods and resources. Based on responses to the survey and curated information from publications, the CSF Task Force herein provides recommendations and reporting guidelines to promote the reproducibility of CSF EV studies in seven domains: (i) CSF Collection, Processing, and Storage; (ii) CSF EV Separation/Concentration; (iii) CSF EV Size and Number Measurements; (iv) CSF EV Protein Studies; (v) CSF EV RNA Studies; (vi) CSF EV Omics Studies and (vii) CSF EV Functional Studies.