Systematically understanding the immunity leading to CRPC progression

Abstract

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.

Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer

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Targeting Inflammatory Signaling in Prostate Cancer Castration Resistance

“…The infiltration of lymphocytes is increased in late-stage and recurrent PCa, and the infiltration of B and T lymphocytes is very common in almost all human PCa samples with high Gleason scores [58][59][60][61]. A pre-clinical model demonstrates that androgen ablation elicits a tumor-associated inflammatory response.…”

The Optimal Tetralogy of Fallot Repair Using Generative Adversarial Networks

“…Sensitivity analysis ( Li et al, 2013 ) was performed to explore the model output variation upon perturbation of variables ( Iman et al, 1981 ; Saltelli et al, 2000 ) such as patch size D , shape S , location L , mesh related cross section area A , wall shear stress W , blood flow rate F , and parameters ( Ji et al, 2017 ) such as 10 coefficients which represent the important features via DX score ( Ji et al, 2019 ; Hu et al, 2020 ). While a perturbation over a range of 5% was imposed on all factor values, the output variance was bounded by 5%, indicating the high stability of our model.…”