Citations

AbstractBackgroundRates of cognitive and biomarker change in Autosomal Dominant Alzheimer disease (ADAD) vary substantially across individuals. Prior cross‐sectional work suggests that the location of the pathogenic variant within PSEN1, specifically whether the underlying variant affects transmembrane (TM) or cytoplasmic (CY) domains in PSEN1, may be a key determinant in these differential rates of progression. Here we use longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN‐Obs) to examine whether variants affecting TM versus CY domains in PSEN1 have differential rates of change in key cognitive and neurodegenerative markers, and whether these differences are relevant to ADAD clinical trials.MethodsUsing longitudinal clinical, cognitive, and MRI data from PSEN1 pathogenic variant carriers [TM group N=76 and CY group N=44; Table 1], we assessed rates of change in Mini‐Mental State Exam (MMSE), Clinical Dementia Rating® Sum of Boxes (CDR®‐SOB), and hippocampal volume (HV) using linear mixed effects models accounting for disease stage (estimated years to symptom onset [EYO]). We further assessed how PSEN1 mutation location (TM versus CY) impacts sample size and detectable effect size in a potential ADAD clinical trial (modeled as a 4‐year trial with annual assessments; 80% power; α = 0.05).Results PSEN1 TM and PSEN1 CY groups did not differ on baseline age, EYO, or CDR®. The PSEN1 TM group had significantly greater rates of change on MMSE (B[SE] = ‐0.42[0.1], p=0.002), CDR®‐SOB (B[SE] = 0.23[0.1], p=0.001), and HV atrophy (B[SE] = ‐58.93[14.3], p=0.0006 compared to the PSEN1 CY group (Fig.1). Consistent with these differential rates of change, power analyses indicated the required sample size to detect a 30% treatment effect on MMSE or HV would be reduced by 59.6% for MMSE and 91.0% for HV for a trial population comprised of PSEN1 TM versus CY carriers (Fig.2).ConclusionsIndividuals who had a variant affecting the transmembrane domains of PSEN1 had greater rates of cognitive decline and neurodegeneration compared to those with variants affecting cytoplasmic domains. In addition to having implications for ADAD pathophysiology, these results suggest that incorporating information regarding the location of PSEN1 variants may be beneficial in analyzing and designing stratification approaches for ADAD trials.

Abstract Bone marrow (BM) niche plays critical roles in promoting progression and chemoresistance of multiple myeloma (MM), but the iron metabolism bridging the malignant plasma cells and BM stromal cells (BMSCs) has not been well elucidated. Using in vitro and in vivo models of interaction of MM and BMSCs, we identified that iron level was augmented due to the elevated expression of transferrin in MM cells, and the accumulation of iron desensitized MM cells to proteasome inhibitors (PIs) treatment and promoted productions of cholesterol and lanosterol. Simultaneously, direct contact with BMSCs remarkably upregulated the protein level of GPX4, a ROS eraser, to obviate ferroptosis in MM cells. Mechanistic studies revealed that CD40/CD40 ligand (CD40L) signaling promoted the expressions of transferrin and SUMO-specific protease 3 (SENP3), and SENP3 stabilized GPX4 protein via deSUMOylation modification. GPX4 inhibitor induced ferroptosis was cell-cell contact dependent, since blocking CD40/CD40L interaction, or depletion of Cd40l in BMSCs from Cd40lfl/fl;Prx1Cre/+ mice, eliminated the anti-MM efficacy of RSL3 in the Vk*MYC mouse model of myeloma, but not in the patient-derived xenografts (PDX) model. Our study deciphers mechanism of iron metabolism in regulating MM chemoresistance, and clarifies the therapeutic potential of non-apoptosis strategies in managing refractory or relapsed MM patients.

Cross-talk between Rho- and Arf-family guanosine triphosphatases (GTPases) plays an important role in linking the actin cytoskeleton to membrane protrusions, organelle morphology, and vesicle trafficking. The central actin regulator, WAVE regulatory complex (WRC), integrates Rac1 (a Rho-family GTPase) and Arf signaling to promote Arp2/3-mediated actin polymerization in many processes, but how WRC senses Arf signaling is unknown. Here, we have reconstituted a direct interaction between Arf and WRC. This interaction is greatly enhanced by Rac1 binding to the D site of WRC. Arf1 binds to a previously unidentified, conserved surface on the Sra1 subunit of WRC, which, in turn, drives WRC activation using a mechanism distinct from that of Rac1. Mutating the Arf binding site abolishes Arf1-WRC interaction, disrupts Arf1-mediated WRC activation, and impairs lamellipodia formation and cell migration. This work uncovers a new mechanism underlying WRC activation and provides a mechanistic foundation for studying how WRC-mediated actin polymerization links Arf and Rac signaling in cells.

AbstractSince the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on‐board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk‐based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi‐institutional analysis on tomotherapy‐related failure modes and their effect ranking.

Objectives: Prostate cancer is well known to express high levels of somatostatin receptors and preliminary data suggests that PET imaging with the somatostatin analog, [68Ga]Ga-DOTATATE, may allow for whole body staging of patients with metastatic castration resistant prostate cancer (mCRPC) and neuroendocrine prostate cancer (NePC). This study explores the utility of [68Ga]Ga-DOTATATE PET-CT to identify metastatic deposits in men with mCRPC and NePC and prognosticate disease progression. Methods: [68Ga]Ga-DOTATATE PET-CT was performed in 17 patients with mCRPC and of those, 2/17 had NePC. A semiquantitative analysis with standardized uptake values (SUV) (e.g., SUVmax, SUVmean) was performed for each metastatic lesion and reference background tissues. [68Ga]Ga-DOTATATE uptake in metastatic deposits was further classified as: mild (less than liver), moderate (up to liver average), or marked (greater than liver). Serial prostate-specific antigen measurements and patient survival were followed up to 3 years after PET imaging to assess response to standard of care treatment. Results: All patients had at least one metastatic lesion with identifiable [68Ga]Ga-DOTATATE uptake. Marked [68Ga]Ga-DOTATATE uptake was found in 7/17 patients, including both NePC patients, and all were non-responders to systemic therapy and died within the follow up period, with a mean time to death of 8.1 months. Three patients had mild [68Ga]Ga-DOTATATE uptake, and all were responders to systemic therapy and were alive 36 months after [68Ga]Ga-DOTATATE imaging. Conclusions: [68Ga]Ga-DOTATATE is able to identify mCRPC and NePC metastatic deposits, and lesions with [68Ga]Ga-DOTATATE uptake > liver may portend poor outcomes in patients with mCRPC.

AbstractAn increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP‐assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP‐derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP‐assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.

AbstractObjectiveThe aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.Materials and MethodsA sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.ResultsMost of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27.DiscussionDespite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints.ConclusionsThe phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.