Downregulation of adipose LPL by PAR2 contributes to the development of hypertriglyceridemia.

Lipoprotein lipase (LPL) hydrolyzes circulating triglycerides (TGs), releasing fatty acids (FA) and promoting lipid storage in white adipose tissue (WAT). However, the mechanisms regulating adipose LPL and its relationship with the development of hypertriglyceridemia are largely unknown. WAT from obese humans exhibited high PAR2 expression, which was inversely correlated with the LPL gene. Decreased LPL expression was also inversely correlated with elevated plasma TG levels, suggesting that adipose PAR2 might regulate hypertriglyceridemia by downregulating LPL. In mice, aging and high palmitic acid diet (PD) increased PAR2 expression in WAT, which was associated with a high level of macrophage migration inhibitory factor (MIF). MIF downregulated LPL expression and activity in adipocytes by binding with CXCR2/4 receptors and inhibiting Akt phosphorylation. In a MIF overexpression model, high-circulating MIF levels suppressed adipose LPL, and this suppression was associated with increased plasma TGs but not FA. Following PD feeding, adipose LPL expression and activity were significantly reduced, and this reduction was reversed in Par2-/- mice. Recombinant MIF infusion restored high plasma MIF levels in Par2-/- mice, and the levels decreased LPL and attenuated adipocyte lipid storage, leading to hypertriglyceridemia. These data collectively suggest that downregulation of adipose LPL by PAR2/MIF may contribute to the development of hypertriglyceridemia.

LDL receptor-related protein 5 selectively transports unesterified polyunsaturated fatty acids to intracellular compartments.

Polyunsaturated fatty acids (PUFAs), which cannot be synthesized by animals and must be supplied from the diet, have been strongly associated with human health. However, the mechanisms for their accretion remain poorly understood. Here, we show that LDL receptor-related protein 5 (LRP5), but not its homolog LRP6, selectively transports unesterified PUFAs into a number of cell types. The LDLa ligand-binding repeats of LRP5 directly bind to PUFAs and are required and sufficient for PUFA transport. In contrast to the known PUFA transporters Mfsd2a, CD36 and FATP2, LRP5 transports unesterified PUFAs via internalization to intracellular compartments including lysosomes, and n-3 PUFAs depend on this transport mechanism to inhibit mTORC1. This LRP5-mediated PUFA transport mechanism suppresses extracellular trap formation in neutrophils and protects mice from myocardial injury during ischemia-reperfusion. Thus, this study reveals a biologically important mechanism for unesterified PUFA transport to intracellular compartments.

Cancer drug sensitivity prediction from routine histology images.

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomised controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression-based mapping of drug effects on cancer cell lines to train a deep learning model that predicts patients' sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients.

Extracellular macrophage migration inhibitory factor (MIF) downregulates adipose hormone-sensitive lipase (HSL) and contributes to obesity.

Attenuation of adipose hormone sensitive lipase (HSL) may impair lipolysis and exacerbate obesity. We investigate the role of cytokine, macrophage migration inhibitory factor (MIF) in regulating adipose HSL and adipocyte hypertrophy. Extracellular MIF downregulates HSL in an autocrine fashion, by activating the AMPK/JNK signaling pathway upon binding to its membrane receptor, CD74. WT mice fed high fat diet (HFD), as well as mice overexpressing MIF, both had high circulating MIF levels and showed suppression of HSL during the development of obesity. Blocking the extracellular action of MIF by a neutralizing MIF antibody significantly reduced obesity in HFD mice. Interestingly, intracellular MIF binds with COP9 signalosome subunit 5 (Csn5) and JNK, which leads to an opposing effect to inhibit JNK phosphorylation. With global MIF deletion, adipocyte JNK phosphorylation increased, resulting in decreased HSL expression, suggesting that the loss of MIF's intracellular inhibitory action on JNK was dominant in Mif-/- mice. Adipose tissue from Mif-/- mice also exhibited higher Akt and lower PKA phosphorylation following HFD feeding compared with WT, which may contribute to the downregulation of HSL activation during more severe obesity. Both intracellular and extracellular MIF have opposing effects to regulate HSL, but extracellular actions predominate to downregulate HSL and exacerbate the development of obesity during HFD.

AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma.

BACKGROUND: Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein-Barr virus DNA, are not sensitive to locoregional recurrence. METHODS: We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n = 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score. RESULTS: Based on Kaplan-Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p < 0.001). This finding was also found in distant metastasis-free survival (p < 0.001), progression-free survival (p < 0.001), and regional recurrence-free survival (p < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, p < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, p < 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence. CONCLUSION: The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.

Cross-linking breast tumor transcriptomic states and tissue histology.

Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials.

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test=0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test<0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all with pone-sample t-test<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials.

Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test = 0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test < 0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all simulations with Cox regression-derived p value of < 0.01 for the effect of the intervention on the respective primary outcome). This adaptive framework has the potential to maximize RCT enrollment efficiency.

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images.

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this article, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole-slide images of routine hematoxylin and eosin-stained HNSCC sections. We show that our Digital-HPV score generated from hematoxylin and eosin slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated stroma. High levels of B cells and T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in patients with HNSCC.

A pref-1-controlled non-inflammatory mechanism of insulin resistance.

While insulin resistance (IR) is associated with inflammation in white adipose tissue, we report a non-inflammatory adipose mechanism of high fat-induced IR mediated by loss of Pref-1. Pref-1, released from adipose Pref-1+ cells with characteristics of M2 macrophages, endothelial cells or progenitors, inhibits MIF release from both Pref-1+ cells and adipocytes by binding with integrin ß1 and inhibiting the mobilization of p115. High palmitic acid induces PAR2 expression in Pref-1+ cells, downregulating Pref-1 expression and release in an AMPK-dependent manner. The loss of Pref-1 increases adipose MIF secretion contributing to non-inflammatory IR in obesity. Treatment with Pref-1 blunts the increase in circulating plasma MIF levels and subsequent IR induced by a high palmitic acid diet. Thus, high levels of fatty acids suppress Pref-1 expression and secretion, through increased activation of PAR2, resulting in an increase in MIF secretion and a non-inflammatory adipose mechanism of IR.

A Case of Fulminant Right Heart Failure Owing to Tumoral Pulmonary Hypertension.

Tumoral pulmonary hypertension is a rare cause of pulmonary hypertension. We report a patient who was thought to have idiopathic pulmonary arterial hypertension, but later developed fulminant right heart failure ultimately leading to death. Autopsy revealed substantial pulmonary tumor embolism burden originating from liver adenocarcinoma. (Level of Difficulty: Advanced.).

Differential developmental blueprints of organ-intrinsic nervous systems.

The organ-intrinsic nervous system is a major interface between visceral organs and the brain, mediating important sensory and regulatory functions in the body-brain axis and serving as critical local processors for organ homeostasis. Molecularly, anatomically, and functionally, organ-intrinsic neurons are highly specialized for their host organs. However, the underlying mechanism that drives this specialization is largely unknown. Here, we describe the differential strategies utilized to achieve organ-specific organization between the enteric nervous system (ENS) 1 and the intrinsic cardiac nervous system (ICNS) 2 , a neuronal network essential for heart performance but poorly characterized. Integrating high-resolution whole-embryo imaging, single-cell genomics, spatial transcriptomics, proteomics, and bioinformatics, we uncover that unlike the ENS which is highly mobile and colonizes the entire gastrointestinal (GI) tract, the ICNS uses a rich set of extracellular matrix (ECM) genes that match with surrounding heart cells and an intermediate dedicated neuronal progenitor state to stabilize itself for a 'beads-on-the-necklace' organization on heart atria. While ICNS- and ENS-precursors are genetically similar, their differentiation paths are influenced by their host-organs, leading to distinct mature neuron types. Co-culturing ENS-precursors with heart cells shifts their identity towards the ICNS and induces the expression of heart-matching ECM genes. Our cross-organ study thus reveals fundamental principles for the maturation and specialization of organ-intrinsic neurons.

Functional Implications of Epstein-Barr Virus Lytic Genes in Carcinogenesis.

Epstein-Barr virus (EBV) is associated with a diverse range of tumors of both lymphoid and epithelial origin. Similar to other herpesviruses, EBV displays a bipartite life cycle consisting of latent and lytic phases. Current dogma indicates that the latent genes are key drivers in the pathogenesis of EBV-associated cancers, while the lytic genes are primarily responsible for viral transmission. In recent years, evidence has emerged to show that the EBV lytic phase also plays an important role in EBV tumorigenesis, and the expression of EBV lytic genes is frequently detected in tumor tissues and cell lines. The advent of next generation sequencing has allowed the comprehensive profiling of EBV gene expression, and this has revealed the consistent expression of several lytic genes across various types of EBV-associated cancers. In this review, we provide an overview of the functional implications of EBV lytic gene expression to the oncogenic process and discuss possible avenues for future investigations.

Central vs site outcome adjudication in the IRIS trial.

BACKGROUND: Central adjudication of outcome events is the standard in clinical trial research. We examine the benefit of central adjudication in the Insulin Resistance Intervention after Stroke (IRIS) trial and show how the benefit is influenced by outcome definition and features of the adjudicated events. METHODS: IRIS tested pioglitazone for prevention of stroke and myocardial infarction in patients with a recent transient ischemic attack or ischemic stroke. We compared the hazard ratios for study outcomes classified by site and central adjudication. We repeated the analysis for an updated stroke definition. RESULTS: The hazard ratios for the primary outcome were identical (0.76) and statistically significant regardless of adjudicator. The hazard ratios for stroke alone were nearly identical with site and central adjudication, but only reached significance with site adjudication (HR, 0.80; p = 0.049 vs. HR, 0.82; p = 0.09). Using the updated stroke definition, all hazard ratios were lower than with the original IRIS definition and statistically significant regardless of adjudication method. Agreement was higher when stroke type was certain, subtype was large vessel or cardioembolic, signs or symptoms lasted > 24 h, imaging showed a stroke, and when NIHSS was ≥3. DISCUSSION: Central stroke adjudication caused the hazard ratio for a main secondary outcome in IRIS (stroke alone) to be higher and lose statistical significant compared with site review. The estimate of treatment effects were larger with the updated stroke definition. There may be benefit of central adjudication for events with specific features, such as shorter symptom duration or normal brain imaging.