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Acute myeloid leukemia (AML) is a malignant disorder derived from neoplastic myeloid progenitor cells characterized by abnormal proliferation and differentiation. Although novel therapeutics have recently been introduced, AML remains a therapeutic challenge with insufficient cure rates. In the last years, immune-directed therapies such as chimeric antigen receptor (CAR)-T cells were introduced, which showed outstanding clinical activity against B-cell malignancies including acute lymphoblastic leukemia (ALL). However, the application of CAR-T cells appears to be challenging due to the enormous molecular heterogeneity of the disease and potential long-term suppression of hematopoiesis. Here we report on the generation of CD33-targeted CAR-modified natural killer (NK) cells by transduction of blood-derived primary NK cells using baboon envelope pseudotyped lentiviral vectors (BaEV-LVs). Transduced cells displayed stable CAR-expression, unimpeded proliferation, and increased cytotoxic activity against CD33-positive OCI-AML2 and primary AML cells in vitro. Furthermore, CD33-CAR-NK cells strongly reduced leukemic burden and prevented bone marrow engraftment of leukemic cells in OCI-AML2 xenograft mouse models without observable side effects.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.