T cells expressing a HER2-specific chimeric antigen receptor as treatment for breast cancer.

PURPOSE: Human endothelial growth factor receptor-2 (HER2) is a leucine kinase receptor that is closely related to cell growth and differentiation. It is very weakly expressed in a few epithelial cells in normal tissue. Abnormal expression of HER2 usually leads to sustained activation of downstream signaling pathways, enabling epithelial cell growth, proliferation, and differentiation; this disturbs normal physiological processes and causes tumor formation. Overexpression of HER2 is related to the occurrence and development of breast cancer. HER2 has become a well-established immunotherapy target for breast cancer. We chose to construct a second-generation CAR targeting HER 2 to test whether it kills breast cancer. METHODS: We constructed a second-generation CAR molecule targeting HER2, and we generated cells expressing this second-generation CAR through lentivirus infection of T lymphocytes. LDH assay and flow cytometry were perform to detect the effect of cells and animal models. RESULTS: The result indicated that the CARHER2 T cells could selectively kill cells with high Her2 expression. The PBMC-activated/CARHer2 cells had stronger in vivo tumor suppressive activity than PBMC-activated cells, and administration of PBMC-activated/CARHer2 cells significantly improved the survival of tumor-bearing mice, and induced the production of more Th1 cytokines in tumor-bearing NSG mice. CONCLUSIONS: We prove that the generated T cells carrying the second-generation CARHer2 molecule could effectively guide immune effector cells to identify and kill HER2-positive tumor cells and inhibit tumors in model mice.

Non-Fatal Drowning Risk Prediction Based on Stacking Ensemble Algorithm.

Drowning is a major public health problem and a leading cause of death in children living in developing countries. We seek better machine learning (ML) algorithms to provide a novel risk-assessment insight on non-fatal drowning prediction. The data on non-fatal drowning were collected in Qingyuan city, Guangdong Province, China. We developed four ML models to predict the non-fatal drowning risk, including a logistic regression model (LR), random forest model (RF), support vector machine model (SVM), and stacking-based model, on three primary learners (LR, RF, SVM). The area under the curve (AUC), F1 value, accuracy, sensitivity, and specificity were calculated to evaluate the predictive ability of the different learning algorithms. This study included a total of 8390 children. Of those, 12.07% (1013) had experienced non-fatal drowning. We found the following risk factors are closely associated with the risk of non-fatal drowning: the frequency of swimming in open water, distance between the school and the surrounding open waters, swimming skills, personality (introvert) and relationality with family members. Compared to the other three base models, the stacking generalization model achieved a superior performance in the non-fatal drowning dataset (AUC = 0.741, sensitivity = 0.625, F1 value = 0.359, accuracy = 0.739 and specificity = 0.754). This study indicates that applying stacking ensemble algorithms in the non-fatal drowning dataset may outperform other ML models.