Differential Targeting of Gr-MDSCs, T Cells and Prostate Cancer Cells by Dactolisib and Dasatinib

Granulocytic myeloid-derived suppressor cells (Gr-MDSCs) promote immune evasion and resistance to immunotherapeutics in a variety of malignancies. Our previous study showed that dual PI3K/mTOR inhibitor Dactolisib impaired the viability and immunosuppressive function of Gr-MDSCs, and significantly synergized with immune checkpoint blockade (ICB) antibodies targeting PD1 and CTLA4 to eradicate metastatic castration-resistant prostate cancer (CRPC) in a preclinical transgenic mouse model. On the contrary, tyrosine kinase inhibitor Dasatinib diminished tumor-infiltrating T lymphocytes and showed no synergic activity with ICB. The understanding of the distinct effects of Dactolisib and Dasatinib on Gr-MDSCs, T cells and prostate neoplastic cells is inadequate, limiting the clinical translation of the combination immunotherapy. To address this question, we applied Reverse Phase Protein Array (RPPA) to profile 297 proteins and protein phosphorylation sites of Gr-MDSCs, T cells and prostate cancer cells isolated from the CRPC model. We found cell type-specific protein expression patterns and highly selective targets by the two drugs, including preferential inhibition of phospho-4E-BP1 in Gr-MDSCs by Dactolisib and preferential suppression of phospho-Src and phospho-p38 MAPK in T cells. Furthermore, transcriptomic profiling of Gr-MDSCs treated with the two inhibitors revealed downregulation of mitochondrial respiration pathways by Dactolisib but not Dasatinib. Overall, these results provide important mechanistic insight into the efficacious combination of Dactolisib and ICB as well as the detrimental effect of Dasatinib on anti-tumor immunity

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs

With recent study of the deep learning in scientific computation, the PINNs method has drawn widespread attention for solving PDEs. Compared with traditional methods, PINNs can efficiently handle high-dimensional problems, while the accuracy is relatively low, especially for highly irregular problems. Inspired by the idea of adaptive finite element methods and incremental learning, we propose GAS, a Gaussian mixture distribution-based adaptive sampling method for PINNs. During the training procedure, GAS uses the current residual information to generate a Gaussian mixture distribution for the sampling of additional points, which are then trained together with history data to speed up the convergence of loss and achieve a higher accuracy. Several numerical simulations on 2d to 10d problems show that GAS is a promising method which achieves the state-of-the-art accuracy among deep solvers, while being comparable with traditional numerical solvers