TRAIL-Armed ER Nanosomes Induce Drastically Enhanced Apoptosis in Resistant Tumor in Combination with the Antagonist of IAPs (AZD5582).
Huan HouKui SuChaohong HuangQian YuanShuyi LiJianwu SunYue LinZhiyun DuChanghong KeZhengqiang YuanPublished in: Advanced healthcare materials (2021)
Although mesenchymal stem cells (MSCs) can be engineered to deliver the TNF-related apoptosis-inducing ligand (TRAIL) as an effective anticancer therapy, the clinical application is hampered by the costly manufacturing of therapeutic MSCs. Therefore, it is needed to find an alternative cell-free therapy. In this study, TRAIL-armed endoplasmic reticulum (ER)-derived nanosomes (ERN-T) are successfully prepared with an average size of 70.6 nm in diameter from TRAIL transduced MSCs. It is demonstrated that the ERN-T is significantly more efficient for cancer cell killing than the soluble recombinant TRAIL (rTRAIL). AZD5582 is an antagonist of the inhibitors of apoptosis proteins (IAPs), and its combination with ERN-T induces strikingly enhanced apoptosis in cancerous but not normal cells. AZD5582 sensitizes resistant cancer cells to TRAIL through concomitant downregulation of IAP members like XIAP and the Bcl2 family member Mcl-1. Intravenously infused ERN-Ts accumulate in tumors for over 48 h indicating good tumor tropism and retention. The combination of ERN-T and AZD5582 drastically promotes therapeutic efficacy comparing with the cotreatment by rTRAIL and AZD5582 in a subcutaneous MDA-MB-231 xenograft tumor model. The data thus demonstrate that ERN-T can be a novel cell-free alternative to TRAIL-expressing MSC-based anticancer therapy and its efficacy can be drastically enhanced through combination with AZD5582.
Keyphrases
- cell cycle arrest
- cell free
- mesenchymal stem cells
- endoplasmic reticulum
- endoplasmic reticulum stress
- cell death
- induced apoptosis
- oxidative stress
- pi k akt
- umbilical cord
- signaling pathway
- breast cancer cells
- rheumatoid arthritis
- cell proliferation
- cell therapy
- stem cells
- electronic health record
- estrogen receptor
- artificial intelligence
- machine learning
- photodynamic therapy
- deep learning