Systemic Nos2 Depletion and Cox inhibition limits TNBC disease progression and alters lymphoid cell spatial orientation and density

Antitumor immune polarization is a key predictor of clinical outcomes to cancer therapy. An emerging concept influencing clinical outcome involves the spatial location of CD8+ T cells, within the tumor. Our earlier work demonstrated immunosuppressive effects of NOS2 and COX2 tumor expression. Here, we show that NOS2/COX2 levels influence both the polarization and spatial location of lymphoid cells including CD8+ T cells. Importantly, elevated tumor NOS2/COX2 correlated with exclusion of CD8+ T cells from the tumor epithelium. In contrast, tumors expressing low NOS2/COX2 had increased CD8+ T cell penetration into the tumor epithelium. Consistent with a causative relationship between these observations, pharmacological inhibition of COX2 with indomethacin dramatically reduced tumor growth of the 4T1 model of TNBC in both WT and Nos2- mice. This regimen led to complete tumor regression in ∼20–25% of tumor-bearing Nos2- mice, and these animals were resistant to tumor rechallenge. Th1 cytokines were elevated in the blood of treated mice and intratumoral CD4+ and CD8+ T cells were higher in mice that received indomethacin when compared to control untreated mice. Multiplex immunofluorescence imaging confirmed our phenotyping results and demonstrated that targeted Nos2/Cox2 blockade improved CD8+ T cell penetration into the 4T1 tumor core. These findings are consistent with our observations in low NOS2/COX2 expressing breast tumors proving that COX2 activity is responsible for limiting the spatial distribution of effector T cells in TNBC. Together these results suggest that clinically available NSAID’s may provide a cost-effective, novel immunotherapeutic approach for treatment of aggressive tumors including triple negative breast cancer

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.Comment: Project website: https://droid-dataset.github.io