Reduction-responsive RNAi nanoplatform to reprogram tumor lipid metabolism and repolarize macrophage for combination pancreatic cancer therapy. (January 2022)

• Record Type: Journal Article
• Authors: Cao, Shuwen; Saw, Phei Er; Shen, Qian; Li, Rong; Liu, Yun; Xu, Xiaoding
• Is Part Of: Biomaterials. Volume 280(2022)
• Publication Date: 2022-01
• Subjects: Lipid metabolism -- TAMs -- Nanoplatform -- siRNA delivery -- Combination PAC therapy; Biomedical materials -- Periodicals; Biocompatible Materials -- Periodicals; Biomatériaux -- Périodiques; 610.28
• DOI: 10.1016/j.biomaterials.2021.121264
• Languages: English
• ISSNs: 0142-9612

Abstract

Abstract: Pancreatic cancer (PAC) is one of the most lethal malignant neoplasms with poor prognosis and high mortality. Emerging evidence has revealed that abnormal tumor lipid metabolism and tumor-associated macrophages (TAMs) significantly contribute to PAC development and progression. Therefore, concurrently reprogramming tumor lipid metabolism and regulating TAMs function could be a promising strategy for effective PAC therapy. Herein, we identified an important enzyme catabolizing lipids (monoacylglycerol lipase, MGLL) and a key receptor regulating macrophage phenotype (endocannabinoid receptor-2, CB-2) that are over-expressed in PAC cells and on TAMs, respectively. Based on this finding, we developed a reduction-responsive poly (disulfide amide) (PDSA)-based nanoplatform for systemic co-delivery of MGLL siRNA (siMGLL) and CB-2 siRNA (siCB-2). This nanoplatform could utilize its reduction-responsive characteristic to rapidly release siRNA for efficient silencing of MGLL and CB-2, inducing concurrent suppression of free fatty acids (FFAs) generation in PAC cells and repolarization of TAMs into tumor-inhibiting M1-like phenotype. With this suppressed FFAs generation to inhibit nutrient supply for tumor cells and repolarized TAMs to secrete tumoricidal cytokines such as TNF-α and IL-12, a combinational anticancer effect could be achieved in both xenograft and orthotopic PAC tumor models. Graphical abstract: Image 1