Citations

Abstract Brain metabolism perturbation can contribute to traits and diseases. We conducted the first large-scale CSF and brain genome-wide association studies, which identified 219 independent associations (59.8% novel) for 144 CSF metabolites and 36 independent associations (55.6% novel) for 34 brain metabolites. Most of the novel signals (97.7% and 70.0% in CSF and brain) were tissue specific. We also integrated MWAS-FUSION approaches with Mendelian Randomization and colocalization to identify causal metabolites for 27 brain and human wellness phenotypes and identified eight metabolites to be causal for eight traits (11 relationships). Low mannose level was causal to bipolar disorder and as dietary supplement it may provide therapeutic benefits. Low galactosylglycerol level was found causal to Parkinson’s Disease (PD). Our study expanded the knowledge of MQTL in central nervous system, provided insights into human wellness, and successfully demonstrates the utility of combined statistical approaches to inform interventions.

Background Hematologic toxicities, including coagulopathy, endothelial activation, and cytopenias, with CD19-targeted chimeric antigen receptor (CAR) T-cell therapies correlate with cytokine release syndrome (CRS) and neurotoxicity severity, but little is known about the extended toxicity profiles of CAR T-cells targeting alternative antigens. This report characterizes hematologic toxicities seen following CD22 CAR T-cells and their relationship to CRS and neurotoxicity. Methods We retrospectively characterized hematologic toxicities associated with CRS seen on a phase 1 study of anti-CD22 CAR T-cells for children and young adults with relapsed/refractory CD22+ hematologic malignancies. Additional analyses included correlation of hematologic toxicities with neurotoxicity and exploring effects of hemophagocytic lymphohistiocytosis-like toxicities (HLH) on bone marrow recovery and cytopenias. Coagulopathy was defined as evidence of bleeding or abnormal coagulation parameters. Hematologic toxicities were graded by Common Terminology Criteria for Adverse Events V.4.0. Results Across 53 patients receiving CD22 CAR T-cells who experienced CRS, 43 (81.1%) patients achieved complete remission. Eighteen (34.0%) patients experienced coagulopathy, of whom 16 had clinical manifestations of mild bleeding (typically mucosal bleeding) which generally subsided following CRS resolution. Three had manifestations of thrombotic microangiopathy. Patients with coagulopathy had higher peak ferritin, D-dimer, prothrombin time, international normalized ratio (INR), lactate dehydrogenase (LDH), tissue factor, prothrombin fragment F1+2 and soluble vascular cell adhesion molecule-1 (s-VCAM-1). Despite a relatively higher incidence of HLH-like toxicities and endothelial activation, overall neurotoxicity was generally less severe than reported with CD19 CAR T-cells, prompting additional analysis to explore CD22 expression in the central nervous system (CNS). Single-cell analysis revealed that in contrast to CD19 expression, CD22 is not on oligodendrocyte precursor cells or on neurovascular cells but is seen on mature oligodendrocytes. Lastly, among those attaining CR, grade 3–4 neutropenia and thrombocytopenia were seen in 65% of patients at D28. Conclusion With rising incidence of CD19 negative relapse, CD22 CAR T-cells are increasingly important for the treatment of B-cell malignancies. In characterizing hematologic toxicities on CD22 CAR T-cells, we demonstrate that despite endothelial activation, coagulopathy, and cytopenias, neurotoxicity was relatively mild and that CD22 and CD19 expression in the CNS differed, providing one potential hypothesis for divergent neurotoxicity profiles. Systematic characterization of on-target off-tumor toxicities of novel CAR T-cell constructs will be vital as new antigens are targeted. Trial registration number NCT02315612 .

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): National Institute of Health Research St George's, University of London Background SCN5A-E1784K (c.5350G>A) is the most common variant associated with the LQT3 long QT syndrome (LQTS) and Brugada syndrome (BrS). It exhibits incomplete penetrance and can manifest variably as a mixed clinical phenotype of LQTS, BrS and/or conduction disorders (1-4). This presents a challenge for risk stratification and optimal management. We aimed to describe the clinical characteristics and risk markers in a large, well characterised international cohort of SCN5A-E1784K subjects and identify demographic and/or ECG parameters associated with event free survival. Methods Comprehensive clinical data, including initial presentation and follow-up data, were collected from SCN5A-E1784K subjects. Outcome ‘events’ included sudden death, aborted cardiac arrest, and documented VF or VT or arrhythmic syncope. ‘Lethal events’ included sudden death, aborted cardiac arrest or documented VF or VT. Associations between clinical characteristics and event free survival were investigated using Cox proportional hazard regression models. Results SCN5A-E1784K subjects (n=231) were recruited from 16 centres. Median follow up was 24.9 years (15.2 - 43.3) and 45 individuals (19%) experienced an event. Lethal events were observed in 14 subjects (6%). Figure 1 shows event (A) and lethal event (B) free survival in SCN5A-E1784K subjects. Higher QRS duration (corrected for age) was associated with shorter event-free interval. QRS duration was the only characteristic associated with increased risk with a hazard ratio (HR) of 1.04 or 1.05 per ms for events and lethal events, respectively. The observed survival for events and lethal events for QRS tertiles are shown in Figure 2. Discussion Ventricular myocardial conduction, as evidenced by QRS duration, appears to play a role in the risk of arrhythmic events in patients with SCN5A-E1784K. Corrected QT interval and BrS phenotype were, however, not associated with risk. This provides an important opportunity for the personalisation of risk stratification of these patients, whose management can otherwise be very challenging. Figures

Abstract Purpose: Clinical trials reported 25% to 30% pathologic complete response (pCR) rates in HER2+ patients with breast cancer treated with anti-HER2 therapies without chemotherapy. We hypothesize that a multiparameter classifier can identify patients with HER2-“addicted” tumors who may benefit from a chemotherapy-sparing strategy. Experimental Design: Baseline HER2+ breast cancer specimens from the TBCRC023 and PAMELA trials, which included neoadjuvant treatment with lapatinib and trastuzumab, were used. In the case of estrogen receptor–positive (ER+) tumors, endocrine therapy was also administered. HER2 protein and gene amplification (ratio), HER2-enriched (HER2-E), and PIK3CA mutation status were assessed by dual gene protein assay (GPA), research-based PAM50, and targeted DNA-sequencing. GPA cutoffs and classifier of response were constructed in TBCRC023 using a decision tree algorithm, then validated in PAMELA. Results: In TBCRC023, 72 breast cancer specimens had GPA, PAM50, and sequencing data, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio ≥ 4.6 and %3+ IHC staining ≥ 97.5%. With PAM50 and sequencing data, the model added HER2-E and PIK3CA wild-type (WT). For clinical implementation, the classifier was locked as HER2 ratio ≥ 4.5, %3+ IHC staining ≥ 90%, and PIK3CA-WT and HER2-E, yielding 55% and 94% positive (PPV) and negative (NPV) predictive values, respectively. Independent validation using 44 PAMELA cases with all three biomarkers yielded 47% PPV and 82% NPV. Importantly, our classifier's high NPV signifies its strength in accurately identifying patients who may not be good candidates for treatment deescalation. Conclusions: Our multiparameter classifier differentially identifies patients who may benefit from HER2-targeted therapy alone from those who need chemotherapy and predicts pCR to anti-HER2 therapy alone comparable with chemotherapy plus dual anti-HER2 therapy in unselected patients.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women’s worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women’s brains and provide initial evidence for neuroscience-informed policies for gender equality.

Recruitment of endothelial cells to cardiovascular device surfaces could solve issues of thrombosis, neointimal hyperplasia, and restenosis. Since current targeting strategies are often nonspecific, new technologies to allow for site-specific cell localization and capture in vivo are needed. The development of cytocompatible superparamagnetic iron oxide nanoparticles has allowed for the use of magnetism for cell targeting. In this study, a magnetic polyurethane (PU)-2205 stainless steel (2205-SS) nanofibrous composite biomaterial was developed through analysis of composite sheets and application to stent-grafts. The PU nanofibers provide strength and elasticity while the 2205-SS microparticles provide ferromagnetic properties. Sheets were electrospun at mass ratios of 0–4:1 (2205-SS:PU) and stent-grafts with magnetic or nonmagnetic stents were coated at the optimal ratio of 2:1. These composite materials were characterized by microscopy, mechanical testing, a sessile drop test, magnetic field measurement, magnetic cell capture assays, and cytocompatibility after 14 days of culturing with endothelial cells. Results of this study show that an optimal ratio of 2:1 2205-SS:PU results in a hydrophobic material that balanced mechanical and magnetic properties and was cytocompatible up to 14 days. Significant cell capture required a thicker material of 0.5 mm thickness. Stent-grafts fabricated from a magnetic coating and a magnetic stent demonstrated uniform cell capture throughout the device surface. This novel biomaterial exhibits a combination of mechanical and magnetic properties that enables magnetic capture of cells and other therapeutic agents for vascular and other tissue engineering applications.