CD300ld on neutrophils is required for tumour-driven immune suppression.

The immune-suppressive tumour microenvironment represents a major obstacle to effective immunotherapy1,2. Pathologically activated neutrophils, also known as polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), are a critical component of the tumour microenvironment and have crucial roles in tumour progression and therapy resistance2-4. Identification of the key molecules on PMN-MDSCs is required to selectively target these cells for tumour treatment. Here, we performed an in vivo CRISPR-Cas9 screen in a tumour mouse model and identified CD300ld as a top candidate of tumour-favouring receptors. CD300ld is specifically expressed in normal neutrophils and is upregulated in PMN-MDSCs upon tumour-bearing. CD300ld knockout inhibits the development of multiple tumour types in a PMN-MDSC-dependent manner. CD300ld is required for the recruitment of PMN-MDSCs into tumours and their function to suppress T cell activation. CD300ld acts via the STAT3-S100A8/A9 axis, and knockout of Cd300ld reverses the tumour immune-suppressive microenvironment. CD300ld is upregulated in human cancers and shows an unfavourable correlation with patient survival. Blocking CD300ld activity inhibits tumour development and has synergistic effects with anti-PD1. Our study identifies CD300ld as a critical immune suppressor present on PMN-MDSCs, being required for tumour immune resistance and providing a potential target for cancer immunotherapy.

Sufu limits sepsis-induced lung inflammation via regulating phase separation of TRAF6.

Rationale: Sepsis is a potentially life-threatening condition caused by the body's response to a severe infection. Although the identification of multiple pathways involved in inflammation, tissue damage and aberrant healing during sepsis, there remain unmet needs for the development of new therapeutic strategies essential to prevent the reoccurrence of infection and organ injuries. Methods: Expression of Suppressor of Fused (Sufu) was evaluated by qRT-PCR, western blotting, and immunofluorescence in murine lung and peritoneal macrophages. The significance of Sufu expression in prognosis was assessed by Kaplan-Meier survival analysis. The GFP-TRAF6-expressing stable cell line (GFP-TRAF6 Blue cells) were constructed to evaluate phase separation of TRAF6. Phase separation of TRAF6 and the roles of Sufu in repressing TRAF6 droplet aggregation were analyzed by co-immunoprecipitation, immunofluorescence, Native-PAGE, FRAP and in vitro assays using purified proteins. The effects of Sufu on sepsis-induced lung inflammation were evaluated by cell function assays, LPS-induced septic shock model and polymicrobial sepsis-CLP mice model. Results: We found that Sufu expression is reduced in early response to lipopolysaccharide (LPS)-induced acute inflammation in murine lung and peritoneal macrophages. Deletion of Sufu aggravated LPS-induced and CLP (cecal ligation puncture)-induced lung injury and lethality in mice, and augmented LPS-induced proinflammatory gene expression in cultured macrophages. In addition, we identified the role of Sufu as a negative regulator of the Toll-Like Receptor (TLR)-triggered inflammatory response. We further demonstrated that Sufu directly interacts with TRAF6, thereby preventing oligomerization and autoubiquitination of TRAF6. Importantly, TRAF6 underwent phase separation during LPS-induced inflammation, which is essential for subsequent ubiquitination activation and NF-κB activity. Sufu inhibits the phase-separated TRAF6 droplet formation, preventing NF-κB activation upon LPS stimulation. In a septic shock model, TRAF6 depletion rescued the augmented inflammatory phenotype in mice with myeloid cell-specific deletion of Sufu. Conclusions: These findings implicated Sufu as an important inhibitor of TRAF6 in sepsis and suggest that therapeutics targeting Sufu-TRAF6 may greatly benefit the treatment of sepsis.

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer.

Background: The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer. Methods: One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness. Results: Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95). Conclusion: The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

Identification of a rare Gli1+ progenitor cell population contributing to liver regeneration during chronic injury.

In adults, hepatocytes are mainly replenished from the existing progenitor pools of hepatocytes and cholangiocytes during chronic liver injury. However, it is unclear whether other cell types in addition to classical hepatocytes and cholangiocytes contribute to hepatocyte regeneration after chronic liver injuries. Here, we identified a new biphenotypic cell population that contributes to hepatocyte regeneration during chronic liver injuries. We found that a cell population expressed Gli1 and EpCAM (EpCAM+Gli1+), which was further characterized with both epithelial and mesenchymal identities by single-cell RNA sequencing. Genetic lineage tracing using dual recombinases revealed that Gli1+ nonhepatocyte cell population could generate hepatocytes after chronic liver injury. EpCAM+Gli1+ cells exhibited a greater capacity for organoid formation with functional hepatocytes in vitro and liver regeneration upon transplantation in vivo. Collectively, these findings demonstrate that EpCAM+Gli1+ cells can serve as a new source of liver progenitor cells and contribute to liver repair and regeneration.

Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia.

Objective: Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia. Materials and Methods: One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results: The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77-0.92) and testing set (AUC = 0.84; 95% CI: 0.72-0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85-0.97) and testing set (AUC = 0.90; 95% CI: 0.81-0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion: The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.