Receptor-interacting protein kinase 3 (RIPK3) mediates necroptosis, a form of programmed cell death that promotes inflammation in various pathological conditions, suggesting that it might be a privileged pharmacological target. However, its function in glucose homeostasis and obesity has been unknown. Here we show that RIPK3 is over expressed in the white adipose tissue (WAT) of obese mice fed with a choline-deficient high-fat diet. Genetic inactivation of Ripk3 promotes increased Caspase-8-dependent adipocyte apoptosis and WAT inflammation, associated with impaired insulin signalling in WAT as the basis for glucose intolerance. Similarly to mice, in visceral WAT of obese humans, RIPK3 is overexpressed and correlates with the body mass index and metabolic serum markers. Together, these findings provide evidence that RIPK3 in WAT maintains tissue homeostasis and suppresses inflammation and adipocyte apoptosis, suggesting that systemic targeting of necroptosis might be associated with the risk of promoting insulin resistance in obese patients.

Background & AimsHepatocellular carcinoma (HCC) typically arises in fibrotic or cirrhotic livers, which are characterized by pathogenic angiogenesis. Myeloid immune cells, specifically tumor-associated macrophages (TAMs), may represent potential novel therapeutic targets in HCC, complementing current ablative or immune therapies. However, the detailed functions of TAM subsets in hepatocarcinogenesis have remained obscure.MethodsTAM subsets were analyzed in-depth in human HCC samples and a combined fibrosis–HCC mouse model, established by i.p. injection with diethylnitrosamine after birth and repetitive carbon tetrachloride (CCl4) treatment for 16 weeks. Based on comprehensively phenotyping TAM subsets (fluorescence-activated cell sorter, transcriptomics) in mice, the function of CCR2+ TAM was assessed by a pharmacologic chemokine inhibitor. Angiogenesis was evaluated by contrast-enhanced micro–computed tomography and histology.ResultsWe show that human CCR2+ TAM accumulate at the highly vascularized HCC border and express the inflammatory marker S100A9, whereas CD163+ immune-suppressive TAM accrue in the HCC center. In the fibrosis–cancer mouse model, we identified 3 major hepatic myeloid cell populations with distinct messenger RNA profiles, of which CCR2+ TAM particularly showed activated inflammatory and angiogenic pathways. Inhibiting CCR2+ TAM infiltration using a pharmacologic chemokine CCL2 antagonist in the fibrosis–HCC model significantly reduced pathogenic vascularization and hepatic blood volume, alongside attenuated tumor volume.ConclusionsThe HCC microenvironment in human patients and mice is characterized by functionally distinct macrophage populations, of which the CCR2+ inflammatory TAM subset has pro-angiogenic properties. Understanding the functional differentiation of myeloid cell subsets in chronically inflamed liver may provide novel opportunities for modulating hepatic macrophages to inhibit tumor-promoting pathogenic angiogenesis.

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts.

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