Citations

Abstract Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high‐throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi‐marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Background Inflammatory bowel disease (IBD) management has become increasingly complex, and education varies across fellowship programs. IBD 101 was designed to introduce first-year gastroenterology (GI) fellows to IBD care and training. Methods In 2019, a cohort of fellows participated in a 1-day course with small group learning and group observed structured clinical examinations. Pre- and postcourse surveys were administered to evaluate the course. To assess the long-term impact, surveys were emailed in May 2022 to all third-year fellows from previously participating programs. The primary outcome was comfort managing IBD scenarios and information regarding each fellow’s exposure to IBD education. Results Fifty-five fellows from 32 programs participated. A total of 49 (89%) of 55 completed pre- and postcourse surveys. All fellows agreed that the course content was appropriate. In the postcourse survey, all fellows reported increased comfort managing IBD patients. Ninety-six percent of attendees stated that they would strongly recommend this course. Thirty-six fellows completed surveys in 2022, 21 (58%) attendees and 15 (42%) nonattendees. Attendees reported equivalent or higher levels of comfort compared with nonattendees. Higher global competence was noted among attendees (odds ratio, 5.21; 95% confidence interval, 0.91-29.9; P = .06) after adjusting for presence of a local IBD specialist, number of IBD patients seen monthly (≤5 vs >5), and rotation through an IBD service. Conclusions IBD 101, an introductory course for first-year GI trainees, was associated with increased comfort managing IBD with a durable benefit independent of individual access to IBD education. Continuation of this program will further enhance the IBD education of future GI fellows.

Abstract Study Objectives Rapid eye movement sleep behavior disorder (RBD) is strongly associated with phenoconversion to an overt synucleinopathy, e.g. Parkinson’s disease (PD), Lewy body dementia, and related disorders. Comorbid traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD)—henceforth “neurotrauma” (NT)—increase the odds of RBD by ~2.5-fold and are associated with an increased rate of service-connected PD in Veterans. Thus, RBD and NT are both independently associated with PD; however, it is unclear how NT influences neurological function in patients with RBD. Methods Participants ≥18 years with overnight polysomnogram-confirmed RBD were enrolled between 8/2018 to 4/2021 through the North American Prodromal Synucleinopathy Consortium. Standardized assessments for RBD, TBI, and PTSD history, as well as cognitive, motor, sensory, and autonomic function, were completed. This cross-sectional analysis compared cases (n = 24; RBD + NT) to controls (n = 96; RBD), matched for age (~60 years), sex (15% female), and years of education (~15 years). Results RBD + NT reported earlier RBD symptom onset (37.5 ± 11.9 vs. 52.2 ± 15.1 years of age) and a more severe RBD phenotype. Similarly, RBD + NT reported more severe anxiety and depression, greater frequency of hypertension, and significantly worse cognitive, motor, and autonomic function compared to RBD. No differences in olfaction or color vision were observed. Conclusions This cross-sectional, matched case:control study shows individuals with RBD + NT have significantly worse neurological measures related to common features of an overt synucleinopathy. Confirmatory longitudinal studies are ongoing; however, these results suggest RBD + NT may be associated with more advanced neurological symptoms related to an evolving neurodegenerative process.

ABSTRACT In the United States (US), biosafety and biosecurity oversight of research on viruses is being reappraised. Safety in virology research is paramount and oversight frameworks should be reviewed periodically. Changes should be made with care, however, to avoid impeding science that is essential for rapidly reducing and responding to pandemic threats as well as addressing more common challenges caused by infectious diseases. Decades of research uniquely positioned the US to be able to respond to the COVID-19 crisis with astounding speed, delivering life-saving vaccines within a year of identifying the virus. We should embolden and empower this strength, which is a vital part of protecting the health, economy, and security of US citizens. Herein, we offer our perspectives on priorities for revised rules governing virology research in the US.

AbstractBackgroundCognitive decline with aging and neurodegeneration is caused by various neurobiological processes at molecular, cellular, and organ levels. Advanced imaging techniques that detect tissue microstructural alterations may predict cognitive decline earlier than atrophy measures. Tissue‐level mechanical properties are thought to primarily reflect the rigidity and tension of extracellular matrix‐cytoskeleton matrix (https://doi.org/10.1038/s41586‐019‐1516‐5) and can be measured noninvasively by Magnetic Resonance Elastography (MRE) (https://doi.org/10.1126/science.7569924). These properties are sensitive to pathophysiological processes that characterize neurological disease including dementias. This study examines the role of medial temporal lobe stiffness in predicting global cognitive decline in participants along the Alzheimer’s disease spectrum.MethodSeventy‐five participants underwent MRE, MRI, amyloid PET imaging, and cognitive assessments with informed consent, and the study was approved by the Mayo Clinic IRB. MRE stiffness maps were calculated by neural network inversion, and medial temporal (MT) stiffness extracted (https://doi.org/10.1002/mrm.29226https://doi.org/10.1016/j.nicl.2023.103328). White matter hyperintensity (WMH) load, average cortical thickness of AD signature regions, PiB‐PET standardized uptake ratio (PiB‐SUVr), and global cognition z‐scores were each computed (https://doi.org/10.1016/j.neurobiolaging.2020.09.005). Linear mixed effect models were used to assess the effects of MT‐stiffness and its interaction with follow‐up time on global cognition. The additional effects of cortical thickness, WMH, and PiB‐SUVr, and their interactions with cognition follow‐up time were evaluated.ResultTable 1 summarizes results from three mixed effect models. MT‐stiffness and its interaction with time were significant predictors in models 1 (MT‐stiffness only) and 2 (MT‐stiffness+cortical thickness). In model 3 (MT‐stiffness+cortical thickness+PiB‐ratio+WMH), MT‐stiffness alone did not reach significance (P= 0.058), but its interaction with time was a significant predictor. Fig.1 shows global cognition z‐scores with age. Figs. 2A,2B, and 2C show global cognition prediction trajectories. These results suggest that softer MT‐stiffness predicts faster cognitive decline. MT‐stiffness was a significant predictor of cognitive score even when controlling for other biomarkers such as cortical thickness, PiB‐SUVr, and WMH. These results indicate that stiffness measurements can add predictive value regarding cognitive decline in addition to atrophy and amyloidosis.ConclusionBrain mechanical alterations capture microstructural changes that may aid in understanding disease progression and provide information about future cognitive decline in elderly participants along the Alzheimer’s disease continuum.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

Background: Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods: To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y. Results: The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions: These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development. Funding: This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/ .

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.