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Professor Ashley Grossman, Professor of Endocrinology at the Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), has been awarded the prestigious Geoffrey Harris Prize Lecture from the European Society of Endocrinology.
Focal deletions of a promoter tether activate the IRX3 oncogene in T-cell acute lymphoblastic leukemia.
Oncogenes can be activated in cis through multiple mechanisms including enhancer hijacking events and noncoding mutations that create enhancers or promoters de novo. These paradigms have helped parse somatic variation of noncoding cancer genomes, thereby providing a rationale to identify noncanonical mechanisms of gene activation. Here we describe a novel mechanism of oncogene activation whereby focal copy number loss of an intronic element within the FTO gene leads to aberrant expression of IRX3, an oncogene in T cell acute lymphoblastic leukemia (T-ALL). Loss of this CTCF bound element downstream to IRX3 (+224 kb) leads to enhancer hijack of an upstream developmentally active super-enhancer of the CRNDE long noncoding RNA (-644 kb). Unexpectedly, the CRNDE super-enhancer interacts with the IRX3 promoter with no transcriptional output until it is untethered from the FTO intronic site. We propose that 'promoter tethering' of oncogenes to inert regions of the genome is a previously unappreciated biological mechanism preventing tumorigenesis.
Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning
The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems.
Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.
Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.
Post-hospitalisation COVID-19 cognitive deficits at one year are global and associated with elevated brain injury markers and grey matter volume reduction.
The spectrum, pathophysiology, and recovery trajectory of persistent post-COVID-19 cognitive deficits are unknown, limiting our ability to develop prevention and treatment strategies. We report the one-year cognitive, serum biomarker, and neuroimaging findings from a prospective, national study of cognition in 351 COVID-19 patients who had required hospitalisation, compared to 2,927 normative matched controls. Cognitive deficits were global and associated with elevated brain injury markers, and reduced anterior cingulate cortex volume one year after COVID-19. The severity of the initial infective insult, post-acute psychiatric symptoms, and a history of encephalopathy were associated with greatest deficits. There was strong concordance between subjective and objective cognitive deficits. Longitudinal follow-up in 106 patients demonstrated a trend toward recovery. Together, these findings support the hypothesis that brain injury in moderate to severe COVID-19 may be immune-mediated, and should guide the development of therapeutic strategies.
Geography Influences Susceptibility to SARS-CoV-2 Serological Response in Patients With Inflammatory Bowel Disease: Multinational Analysis From the ICARUS-IBD Consortium.
BACKGROUND: Beyond systematic reviews and meta-analyses, there have been no direct studies of serological response to COVID-19 in patients with inflammatory bowel disease (IBD) across continents. In particular, there has been limited data from Asia, with no data reported from India. The ICARUS-IBD (International study of COVID-19 Antibody Response Under Sustained immunosuppression in IBD) consortium assessed serological response to SARS-CoV-2 in patients with IBD in North America, Europe, and Asia. METHODS: The ICARUS-IBD study is a multicenter observational cohort study spanning sites in 7 countries. We report seroprevalence data from 2303 patients with IBD before COVID-19 vaccination between May 2020 and November 2021. SARS-CoV-2 anti-spike and anti-nucleocapsid antibodies were analyzed. RESULTS: The highest and lowest SARS-CoV-2 anti-spike seropositivity rates were found in Asia (81.2% in Chandigarh and 57.9% in Delhi, India; and 0% in Hong Kong). By multivariable analysis, country (India: odds ratio [OR], 18.01; 95% confidence interval [CI], 12.03-26.95; P < .0001; United Kingdom: OR, 2.43; 95% CI, 1.58-3.72; P < .0001; United States: OR, 2.21; 95% CI, 1.27-3.85; P = .005), male sex (OR, 1.46; 95% CI, 1.07-1.99; P = .016), and diabetes (OR, 2.37; 95% CI, 1.04-5.46; P = .039) conferred higher seropositivity rates. Biological therapies associated with lower seroprevalence (OR, 0.22; 95% CI, 0.15-0.33; P < .0001). Multiple linear regression showed associations between anti-spike and anti-nucleocapsid titers with medications (P < .0001) but not with country (P = .3841). CONCLUSIONS: While the effects of medications on anti-SARS-CoV-2 antibody titers in patients with IBD were consistent across sites, geographical location conferred the highest risk of susceptibility to serologically detectable SARS-CoV-2 infection. Over half of IBD patients in India were seropositive prior to vaccination. These insights can help to inform shielding advice, therapeutic choices, and vaccine strategies in IBD patients for COVID-19 and future viral challenges.
Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis.
The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.
Advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma: A British Society of Haematology and UK Myeloma Society Good Practice Paper.
This Good Practice Paper provides recommendations for the use of advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma. It describes how advanced imaging contributes to optimal healthcare resource utilisation by in newly diagnosed and relapsed myeloma, and provides a perspective on future directions of myeloma imaging, including machine learning assisted reporting.
Proceedings from the First Onco Summit: LATAM Chapter, 19–20 May 2023, Rio de Janeiro, Brazil
The Onco Summit 2023: The Latin American (LATAM) Chapter took place over two days, from 19–20 May 2023, in Brazil. The event aimed to share the latest updates across various oncology disciplines, address critical clinical challenges, and exchange best practices to ensure optimal patient treatment. More than 30 international and regional speakers and more than 300 oncology specialists participated in the Summit. The Summit discussions centered on common challenges and therapeutic advances in cancer care, with a specific focus on the unique obstacles faced in LATAM and examples of adaptable strategies to address these challenges. The Summit also facilitated the establishment of a network of oncologists, hematologists, and scientists in LATAM, enabling collaboration to improve cancer care, both in this region and globally, through drug development and clinical research. This report summarizes the key discussions from the Summit for the global and LATAM oncology community.
Loss of electrical β-cell to δ-cell coupling underlies impaired hypoglycaemia-induced glucagon secretion in type-1 diabetes.
Diabetes mellitus involves both insufficient insulin secretion and dysregulation of glucagon secretion1. In healthy people, a fall in plasma glucose stimulates glucagon release and thereby increases counter-regulatory hepatic glucose production. This response is absent in many patients with type-1 diabetes (T1D)2, which predisposes to severe hypoglycaemia that may be fatal and accounts for up to 10% of the mortality in patients with T1D3. In rats with chemically induced or autoimmune diabetes, counter-regulatory glucagon secretion can be restored by SSTR antagonists4-7 but both the underlying cellular mechanism and whether it can be extended to humans remain unestablished. Here, we show that glucagon secretion is not stimulated by low glucose in isolated human islets from donors with T1D, a defect recapitulated in non-obese diabetic mice with T1D. This occurs because of hypersecretion of somatostatin, leading to aberrant paracrine inhibition of glucagon secretion. Normally, KATP channel-dependent hyperpolarization of β-cells at low glucose extends into the δ-cells through gap junctions, culminating in suppression of action potential firing and inhibition of somatostatin secretion. This 'electric brake' is lost following autoimmune destruction of the β-cells, resulting in impaired counter-regulation. This scenario accounts for the clinical observation that residual β-cell function correlates with reduced hypoglycaemia risk8.
Epigenetic regulation of hematopoietic stem cell fate.
Hematopoietic stem cells (HSCs) sustain blood cell production throughout the mammalian life span. However, it has become clear that at the single cell level a subset of HSCs is stably biased in their lineage output, and that such heterogeneity may play a key role in physiological processes including aging and adaptive immunity. Analysis of chromatin accessibility, DNA methylation, and histone modifications has revealed that HSCs with different lineage bias exhibit distinct epigenetic traits inscribed at poised, lineage-specific enhancers. This allows for lineage priming without initiating lineage-specific gene expression in HSCs, controlling lineage bias while preserving self-renewal and multipotency. Here, we review our current understanding of epigenetic regulation in the establishment and maintenance of HSC fate decisions under different physiological conditions.
Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer.
Testing for DNA mismatch repair deficiency (MMRd) is recommended for all colorectal cancers (CRCs). Automating this would enable precision medicine, particularly if providing information on etiology not captured by deep learning (DL) methods. We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples. AIMMeR shows an area under the receiver-operator curve (AUROC) of 0.98, and specificity of ≥75% at 98% sensitivity against pathologist ground truth in stage II/III in two trial cohorts, with positive predictive value of ≥98% for the commonest pattern of somatic MMRd. Lower agreement with microsatellite instability (MSI) testing (AUROC 0.86) reflects discordance between MMR and MSI PCR rather than AIMMeR misclassification. Analysis of the SCOT trial confirms MMRd prognostic value in oxaliplatin-treated patients; while MMRd does not predict differential benefit of chemotherapy duration, it correlates with difference in relapse by regimen (PInteraction = 0.04). AIMMeR may help reduce pathologist workload and streamline diagnostics in CRC.
Cardiovascular care with digital twin technology in the era of generative artificial intelligence
Abstract Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.
Data from GTP cyclohydrolase drives breast cancer development and promotes EMT in an enzyme-independent manner
<div>Abstract<p>GTP cyclohydrolase (GCH1) is the rate-limiting enzyme for tetrahydrobiopterin (BH4) biosynthesis. The catalysis of BH4 biosynthesis is tightly regulated for physiological neurotransmission, inflammation, and vascular tone. Paradoxically, BH4 has emerged as an oncometabolite regulating tumor growth, but the effects on tumor development remain controversial. Here we found that GCH1 potentiates the growth of triple-negative breast cancer (TNBC) and HER2+ breast cancer and transforms non-tumor breast epithelial cells. Independent of BH4 production, GCH1 protein induced epithelial-to-mesenchymal transition IEMT) by binding to Vim, which was mediated by HSP90. Conversely, GCH1 ablation impaired tumor growth, suppressed Vim in TNBC, and inhibited EGFR/ERK signaling while activating the p53 pathway in estrogen receptor-positive tumor cells. GCH1 deficiency increases tumor cell sensitivity to HSP90 inhibition and endocrine treatments. Additionally, high GCH1 corelated with poor breast cancer survival. Together, this study reveals an enzyme-independent oncogenic role of GCH1, presenting it as a potential target for therapeutic development.</p></div>
Supplementary Data from GTP cyclohydrolase drives breast cancer development and promotes EMT in an enzyme-independent manner
<p>Tables and figure legends</p>
Supplementary Figures from GTP cyclohydrolase drives breast cancer development and promotes EMT in an enzyme-independent manner
<p>Supplementary Figures S1-S11</p>