Back

Bola-amphiphilic dendrimer empowers imatinib to target metastatic ovarian cancer stem cells via beta-catenin-HRP2 signaling axis

Shi, Z.; Artemenko, M.; Zhang, M.; Yi, C.; Chen, P.; Lin, S.; Bian, Z.; Lian, B.; Meng, F.; Chen, J.; Roussel, T.; Li, Y.; Chan, K. K. L.; Ip, P. P. C.; Lai, H.-C.; Liu, X.; Peng, L.; Wong, A. S.-T.

2024-06-09 cancer biology
10.1101/2024.06.07.597857 bioRxiv
Show abstract

Ovarian cancer is the leading cause of death among all gynecological malignancies, and drug resistance renders the current chemotherapy agents ineffective for patients with advanced metastatic tumors. We report an effective treatment strategy for targeting metastatic ovarian cancer involving a nanoformulation (Bola/IM) - bola-amphiphilic dendrimer (Bola)-encapsulated imatinib (IM) - to target the critical mediator of ovarian cancer stem cells (CSCs) CD117 (c-Kit). Bola/IM offered significantly more effective targeting of CSCs compared to IM alone, through a novel and tumor-specific {beta}-catenin/HRP2 axis, allowing potent inhibition of cancer cell survival, stemness and metastasis in metastatic and drug-resistant ovarian cancer cells. Promising results were also obtained in clinically relevant patient-derived ascites and organoids, alongside high tumor-oriented accumulation and favorable pharmacokinetic properties in mouse models. Furthermore, Bola/IM displayed synergistic anticancer activity when combined with the first-line chemotherapeutic drug cisplatin in patient-derived xenograft mouse models, without any adverse effects. Our findings support the use of Bola/IM as a nanoformulation to empower IM, providing targeted and potent treatment of metastatic ovarian cancer. Our study thus represents a significant advancement towards addressing the unmet medical need for improved therapies targeting this challenging disease.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.