The schematic diagram of EMM@DJHAD NPs destroys tumor cells via the synergism of DAC and JTC. The former enriches the level of gasdermin E in tumor by demethylation of the DFNA5 gene and the latter activates the caspase-3 pathway, triggering pyroptosis of tumor cells. Importantly, JTC-mediated µ-opioid receptor (MOR) inhibition is responsible for both tumor suppression and pain relief. Interestingly, highly-inflammatory pyroptosis makes tumor an ideal decoy for EMM, amplifying the nanodrug targeting effect.
Abstract:
Breast cancer with bone metastasis accounts for serious cancer-associated pain which significantly reduces the quality of life of affected patients and promotes cancer progression. However, effective treatment using nanomedicine remains a formidable challenge owing to poor drug delivery efficiency to multiple cancer lesions and inappropriate management of cancer-associated pain. In this study, using engineered macrophage membrane (EMM) and drugs loaded nanoparticle, we constructed a biomimetic nanoplatform (EMM@DJHAD) for the concurrent therapy of bone metastatic breast cancer and associated pain. Tumor tropism inherited from EMM provided the targeting ability for both primary and metastatic lesions. Subsequently, the synergistic combination of decitabine and JTC801 boosted the lytic and inflammatory responses accompanied by a tumoricidal effect, which transformed the tumor into an ideal decoy for EMM, resulting in prolonged troop migration toward tumors. EMM@DJHAD exerted significant effects on tumor suppression and a pronounced analgesic effect by inhibiting µ-opioid receptors in bone metastasis mouse models. Moreover, the nanoplatform significantly reduced the severe toxicity induced by chemotherapy agents. Overall, this biomimetic nanoplatform with good biocompatibility may be used for the effective treatment of breast cancer with bone metastasis.
Author list:
Cuixia Zheng, Dandan Zhang, Yueyue Kong, Mengya Niu, Hongjuan Zhao, Qingling Song, Qianhua Feng, Xingru Li, Lei Wang*
How to cite:
C. Zheng, D. Zhang, Y. Kong, M. Niu, H. Zhao, Q. Song, Q. Feng, X. Li, L. Wang, Exploration 2023, 3, 20220124.
https://doi.org/10.1002/EXP.20220124