Blocking iASPP/Nrf2/M-CSF axis improves anti-cancer effect of chemotherapy-induced senescence by attenuating M2 polarization.
Hao LiuDong ZhaoHuayi LiWenxin ZhangQingyu LinXingwen WangShanliang ZhengLei ZhangLi LiShaoshan HuYing HuPublished in: Cell death & disease (2022)
The complex interaction between cancer cells and the immune microenvironment is a central regulator of tumor growth and the treatment response. Chemotherapy-induced senescence is accompanied by the senescence-associated secretion phenotype (SASP). However, the mechanisms underlying the regulation of the SASP remain the most poorly understood element of senescence. Here, we show that nuclear erythroid factor 2-like factor 2 (Nrf2), a master antioxidative transcription factor, accumulates upon doxorubicin-induced senescence. This is due to the increased cytoplasmic Inhibitor of Apoptosis Stimulating Protein of P53, iASPP, which binds with Keap1, interrupting Keap1/Nrf2 interaction and promoting Nrf2 stabilization and activation. Activated Nrf2 transactivates a novel target gene of SASP factor, macrophage colony-stimulating factor (M-CSF), which subsequently acts on macrophages and induces polarization from M1 to M2 via a paracrine mechanism. Genetic inhibition of iASPP-Nrf2 suppresses the growth of apoptosis-resistant xenografts, with further analysis revealing that M-CSF/M-CSFR-regulated macrophage polarization is critical for the functional outcomes delineated above. Overall, our data uncover a novel function of iASPP-Nrf2 in skewing the immune microenvironment under treatment-induced senescence. Targeting the iASPP-Nrf2 axis could be a powerful strategy for the implementation of new chemotherapy-based therapeutic opportunities.
Keyphrases
- oxidative stress
- dna damage
- diabetic rats
- chemotherapy induced
- transcription factor
- endothelial cells
- stress induced
- stem cells
- healthcare
- endoplasmic reticulum stress
- signaling pathway
- drug delivery
- cancer therapy
- adipose tissue
- gene expression
- protein protein
- squamous cell carcinoma
- machine learning
- rectal cancer
- drug induced
- cerebrospinal fluid
- amino acid
- binding protein