High PARP-1 expression predicts poor survival in acute myeloid leukemia and PARP-1 inhibitor and SAHA-bendamustine hybrid inhibitor combination treatment synergistically enhances anti-tumor effects
PARP-1 plays a critical role in DNA damage repair and contributes to progression of cancer. To explore the role of PARP-1 in acute myeloid leukemia (AML), we analyzed the expression of PARP-1 in AML and its relation to the clinical prognosis. Then, we investigated the efficacy and mechanism of PARP inhibitor BMN673 (Talazoparib) combined with NL101, a novel SAHA-bendamustine hybrid in vitro and in vivo.
The expression of PARP-1 in 339 cytogenetically normal AML (CN-AML) cases was evaluated using RT-PCR. According to the expression of PARP-1, the clinical characteristics and prognosis of the patients were grouped and compared. The combination effects of BMN673 and NL101 were studied in AML cells and B-NSG mice xenograft model of MV4-11.
We found patients in high PARP-1 expression group had higher levels of blast cells in bone marrow (P = .003) and white blood cells (WBC) in peripheral blood (P = .008), and were associated with a more frequent FLT3-ITD mutation (28.2% vs 17.3%, P = .031). The overall survival (OS) and event free survival (EFS) of the high expression group were significantly shorter than those in the low expression group (OS, P = .005 and EFS, P = .004). BMN673 combined with NL101 had a strong synergistic effect in treating AML. The combination significantly induced cell apoptosis and arrested cell cycle in G2/M phase. Mechanistically, BMN673 and NL101 combinatorial treatment promoted DNA damage. In vivo, the combination effectively delayed the development of AML and prolonged survival.
High PARP-1 expression predicts poor survival in CN-AML patients. The synergistic effects of PARP inhibitor BMN673 in combination with SAHA-bendamustine hybrid, NL101, provide a new therapeutic strategy against AML.
National Natural Science Foundation of China and Zhejiang Provincial Key Innovation Team.
Authors: Xia Lia,b,1, Chenying Lia,b,1, Jingrui Jina,b,1, Jinghan Wanga, Jiansong Huanga,b, Zhixin Maa,b, Xin Huanga,b, Xiao Hea,b, Yile Zhoua,b, Yu Xua, Mengxia Yuc, Shujuan Huanga,b, Xiao Yana,b, Fenglin Lia,b, Jiajia Pana,b, Yungui Wanga,b, Yongping Yud, Jie Jin
Influence Factor: 6.18
Citation: EBioMedicine 38, 47-56 (2018).