Lysosome-Targeting Bacterial Outer Membrane Vesicles for Tumor Specific Degradation of PD-L1.

Increased expression of immune check point genes, such as PD-L1, is one of the main reasons for immunosuppression, especially for colon cancer. Development of novel therapeutic strategies is of great importance to improve the prognosis. In this study, outer membrane vesicles (OMV) derived from Gram-negative bacteria are engineered to immune checkpoint blockade nanosystem for efficient elicitation of anti-tumor immunity. Briefly, the OMVs are engineered with Lyp1-Traptavidin (S52G, R53D mutant of streptavidin) fusion protein displayed on the surface. The Lyp-1 endows the OMV with the capacity to target tumor tissues, while the Traptavidin ensures easy decoration of biotinylated anti-PD-L1 and biotinylated M6P (mannose 6-phosphate). The simultaneously anchored anti-PD-L1 and M6P (ligand for cation-independent mannose 6-phosphate receptor) on the engineered OMVs coordinately direct the membrane PD-L1 to lysosome for degradation, and thus unleash the anti-tumor immunity. With syngeneic tumor model, the engineered OMVs are confirmed to boost immunity, inhibit cancer growth, and thus prolong survival. Together, A proposed OMV-based modular nanosystem that enables assembly of biotinylated anti-PD-L1 and M6P on the surface for tumor-targeted immune checkpoint blockade.

Targeted Clearance of Senescent Cells Via Engineered Extracellular Vesicles Reprograms Tumor Immunosuppressive Microenvironment.

Unravelling the mechanisms for the immunosuppressive tumor microenvironment and developing corresponding therapeutic strategies are of great importance to improve the cancer immunotherapy. This study has revealed that there are abundant senescent cells accumulated in the colon cancer tissue, which contributes greatly to the immunosuppressive microenvironment. Oral delivery of Dasatinib and Quercetin (D+Q) eliminates the senescent cells with compromised efficiency due to the poor tumor penetration and short half-life. To improve the efficacy of senescent cell clearance, this work has developed an extracellular vesicle (EV) based senolytic strategy. The engineered senolytic EVs have anti-GPNMB (a senescent cell surface marker) displayed on the surface and D+Q loaded on the membrane. In a syngeneic mouse model, senolytic EVs efficiently and selectively eradicate the senescent cells and in turn unleashes the antitumor immunity. With the antitumor immunity boosted, cancer growth is inhibited and the survival is prolonged. In summary, this work has illuminated that senescent cells contribute to the immunosuppressive microenvironment in colon cancer and proposes a novel strategy to conquer the problem by EV-based senolytics.

Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter study.

Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE. Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists. Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; P < 0.001) and (AUC, 0.76; 95% CI: 0.70, 0.83; P < 0.001)] and two juniors radiologists [(AUC, 0.65; 95% CI: 0.58, 0.73; P < 0.001) and (AUC, 0.69; 95% CI: 0.62, 0.77; P < 0.001)]. DL model was significantly higher than clinical model [(AUC, 0.84; 95% CI: 0.79, 0.89; P = 0.019)], but there was no significant difference between DL model and clinical and DL combined model [(AUC, 0.94; 95% CI: 0.91, 0.97; P = 0.143)]. In the external test set, DL model also demonstrated the highest AUC (0.88, 95% CI: 0.81, 0.94), which was significantly higher than that of one of senior radiologists [(AUC, 0.75; 95% CI: 0.66, 0.84; P = 0.008) and (AUC, 0.81; 95% CI: 0.72, 0.89; P = 0.152)] and two junior radiologists [(AUC, 0.72; 95% CI: 0.62, 0.81; P = 0.002) and (AUC, 0.67; 95 CI: 0.57, 0.77; P < 0.001]. There was no significant difference between DL model and clinical model [(AUC, 0.85; 95% CI: 0.79, 0.91; P = 0.516)] and clinical + DL model [(AUC, 0.92; 95% CI: 0.87, 0.96; P = 0.093)]. Using DL model, the diagnostic ability of two junior radiologists was significantly improved. Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists. Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110).