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Conditional gene regulation in Drosophila through binary expression systems like the LexA-LexAop system provides a superb tool for investigating gene and tissue function. To increase the availability of defined LexA enhancer trap insertions, we present molecular, genetic and tissue expression studies of 301 novel Stan-X LexA enhancer traps derived from mobilization of the index SX4 line. This includes insertions into distinct loci on the X, II and III chromosomes that were not previously associated with enhancer traps or targeted LexA constructs, an insertion into ptc, and seventeen insertions into natural transposons. A subset of enhancer traps was expressed in CNS neurons known to produce and secrete insulin, an essential regulator of growth, development and metabolism. Fly lines described here were generated and characterized through studies by students and teachers in an international network of genetics classes at public, independent high schools, and universities serving a diversity of students, including those underrepresented in science. Thus, a unique partnership between secondary schools and university-based programs has produced and characterized novel resources in Drosophila, establishing instructional paradigms devoted to unscripted experimental science.

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Background: The unique biology and expression pattern of tumor necrosis factor receptor-2 (TNFR2) make it an attractive therapeutic target for immuno-oncology. TNFR2 highly expresses on a subset of regulatory T cells (Tregs) and MDSCs within tumor microenvironment that can activate these cells through nuclear factor kappa B (NF-kB) pathway. TNFR2+ Treg has been shown to be most suppressive among all Treg populations in tumor. On the other hand, TNFR2 is also abundantly expressed on the surface of many human tumors. TNFR2 blocking antibody is expected to relieve TNFR2-mediated immunosuppression and inhibit TNFR2-expressing tumor cell survival. AN3025 is a novel humanized IgG1 (variant) anti-hTNFR2 antibody under preclinical development. The immunomodulatory and anti-tumor activity of AN3025 were evaluated both in vitro and in vivo. Materials and methods: AN3025 was generated through rabbit immunization followed by phage display, then humanized by CDRs grafting. The binding affinity and specificity were studied by ELISA and FACS. The ability of AN3025 to mitigate TNF/TNFR2 signaling pathway was characterized using TNFR2 overexpressing Jurkat cell line in vitro. The in vivo anti-tumor activity was evaluated in TNFR2 humanized mouse model bearing MC38 tumor. The tumor samples from control and AN3025 treated mice were taken for further FACS and RNA seq analysis. Results: AN3025 binds to the extracellular domain of human TNFR2 with sub-nanomolar affinity and specificity. It cross-reacts with cynomolgus TNFR2 with similar affinity, but not with mouse or rat TNFR2. Mechanistically, AN3025 partially competes with TNFα for binding to TNFR2 receptor and inhibits TNFα induced hTNFR2 overexpressing Jurkat cell death, however it lacks of agonist activity towards TNFR2 even in the presence of hFC crosslinking. In vivo AN3025 significantly inhibits MC38 tumor growth as a monotherapy in hTNFR2 mouse model, while no impact on body weight. Subsequent FACS analysis suggests decreased Tregs% in AN3025 treated tumor. RNA seq suggests immune activation such as increased IFN-gamma and Granzyme expression. In addition, AN3025 enhances anti-tumor efficacy of mPD-1 antibody in a combination study. Conclusions: AN3025 is a novel anti-hTNFR2 antibody and demonstrates immunomodulatory activity and potent anti-tumor efficacy in vivo, supporting its clinical development for the treatment of human cancers. Citation Format: Yonglin Chen, Manxue Jia, Sherry Xu, Yanhui Zhao, Eric Chan, Meng Zhang, Emma Chen, Yi Zhang. AN3025: A novel anti-human TNFR2 antibody that exhibits immune activation and strong anti-tumor activity in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1451.

Importance: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Objective: To determine whether ML models for allocating readmission-mitigating interventions are ranked differently based on their overall utility and their discriminative ability. Design: A retrospective analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart. Setting: Health plan claims from all 50 states for commercially-insured individuals. Participants: 513,495 patients who were admitted as inpatients over the period January 2016 through January 2017. Main Outcomes and Measures: Maximum utility achieved by three machine learning models for allocating readmission-mitigating interventions, determined using cost accrued in the 90 days post-discharge of an index admission and estimated counterfactual cost. Data were analyzed between April 2019 and March 2020. Results: The study sample consisted of 513,495 patients (mean [SD] age 69 [19] years; 294,895 [57%] Female) mean 90 day cost of $11,552 for the study period. Allocating readmission-mitigating interventions based on a LightGBM model trained to predict readmissions achieved a maximum utility of $-12,645 per patient, and an AUC of 0.74 (95% CI 0.74, 0.75); allocating interventions based on a model trained to predict cost as a proxy achieved a higher maximum utility of $-12,472 per patient, and an AUC of 0.63 (95% CI 0.62, 0.63). A hybrid model combining both intervention strategies achieved a maximum utility of $-12,472, and an AUC of 0.71 (95% CI 0.71, 0.71), comparable with the best models on either metric. Conclusion and Relevance: We demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.