PO.ET01.01 · 实验与分子治疗
BCL-2与MEK/HDAC抑制在IBC和非IBC模型中的协同作用:首个IBC特异性疗法的潜力
Synergy of BCL-2 and MEK/HDAC inhibition in IBC and non-IBC models: Potential for a first IBC specific therapy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:炎性乳腺癌(IBC)是一种以快速进展为特征的侵袭性乳腺癌亚型。尽管具有独特的临床表现,但目前尚无IBC特异性疗法。IBC的生长速度并不快于非IBC,因此假设由B细胞淋巴瘤2(BCL-2)家族介导的凋亡逃逸是IBC持续存在的主要机制。BCL-2的异常表达有利于肿瘤存活。虽然BCL-2抑制可恢复凋亡活性,但单药活性仍受代偿性信号传导的限制。我们最初的高通量筛选表明,某些药物类别(包括HSP90、BCL-2、MEK和HDAC抑制剂)在IBC中相比非IBC模型显示出更高活性的趋势。这促使我们在IBC和非IBC乳腺癌模型面板中探索合理的联合策略。
方法:我们用一个包含1,200多种化合物的文库筛选了八种乳腺癌细胞系(3种IBC:SUM-149、SUM-190、IBC-1;5种非IBC:MDA-MB-231、JIMT-1-GFP、BT-474、MUM51、HCC1143),以识别在IBC中相对非IBC敏感性增加的药物类别。这一初步筛选确定了HSP90、BCL-2、MEK和HDAC抑制剂作为关注类别。随后我们使用Pimitespib(HSP90抑制剂)和Navitoclax(BCL-2抑制剂)(各自类别中的顶级命中)进行后续联合筛选,针对同一1,200多种化合物文库进行测试以识别协同伙伴。同时,我们合理地联合了多条活跃通路的抑制剂(如BCL-2+MEK或BCL-2+HDAC),这些组合在其他癌症中已显示出协同作用,以评估额外的协同相互作用。使用Chou-Talalay方法评估联合效应(CI<1表示协同)。
结果:我们的高通量筛选确定HSP90、BCL-2、MEK和HDAC抑制剂在IBC中相对非IBC更为敏感。将Pimitespib(HSP90抑制剂)或Navitoclax(BCL-2抑制剂)与同一化合物文库配对的二次筛选识别出协同伙伴。Pimitespib在MDA-231和BT474等特定细胞系中与紫杉烷类和PI3K/MAPK抑制剂协同,而Navitoclax在IBC模型中持续显示出与MEK和HDAC抑制剂的强协同作用。出现了明显的亚型依赖性趋势:BCL-2与MEK抑制之间的协同作用在IBC中最强,而BCL-2与HDAC的协同作用在非IBC细胞系中占主导。
结论:本研究首次显示出IBC特异性协同药物相互作用的趋势。这些发现提示凋亡调控中可能存在机制差异,并可能指导针对IBC的靶向联合疗法的合理开发。未来研究将评估这些体外协同趋势是否转化为体内亚型特异性治疗反应。
AI披露:AI仅用于语言编辑;内容由作者核实。
查看英文原文 English abstract
Background: Inflammatory breast cancer (IBC) is an aggressive subtype of breast cancer characterized by rapid progression. Despite its unique clinical presentation, there are no IBC specific therapies. IBC growth is not faster than non-IBC, so evasion of apoptosis, mediated by the B-cell lymphoma 2 (BCL-2) family, is hypothesized to be a major mechanism for IBC persistence. Aberrant BCL-2 expression favors tumor survival. Although BCL-2 inhibition can restore apoptotic activity, single-agent activity remains limited by compensatory signaling. Our initial high-throughput screen indicated that certain drug classes including: HSP90, BCL-2, MEK, and HDAC inhibitors showed a trend toward greater activity in IBC compared to non-IBC models. This led us to explore rational combination strategies across our panel of IBC and non-IBC breast cancer models.
Methods: We screened eight breast cancer cell lines (3 IBC: SUM-149, SUM-190, IBC-1; and 5 non-IBC: MDA-MB-231, JIMT-1-GFP, BT-474, MUM51, HCC1143) with a library of more than 1,200 compounds to identify drug classes with increased sensitivity in IBC relative to non-IBC. This initial screen identified HSP90, BCL-2, MEK, and HDAC inhibitors as classes of interest. We then performed follow-up combination screens using Pimitespib (HSP90 inhibitor) and Navitoclax (BCL-2 inhibitor), the top hits within their respective classes, testing them against the same 1,200+ compound library to identify synergistic partners. In parallel, we rationally combined inhibitors of multiple active pathways (e.g., BCL-2 + MEK or BCL-2 + HDAC), which have shown synergy in other cancers, to evaluate additional synergistic interactions. Combination effects were assessed using the Chou-Talalay method (CI < 1 indicating synergy).
Results: Our high-throughput screen identified HSP90, BCL-2, MEK, and HDAC inhibitors as more sensitive in IBC relative to non-IBC. Secondary screens pairing Pimitespib (HSP90 inhibitor) or Navitoclax (BCL-2 inhibitor) with the same compound library identified synergistic partners. Pimitespib synergized with taxanes and PI3K/MAPK inhibitors in select lines such as MDA-231 and BT474, whereas Navitoclax consistently showed strong synergy with MEK and HDAC inhibitors in IBC models. Distinct subtype-dependent trends emerged: synergy between BCL-2 and MEK inhibition was strongest in IBC, while BCL-2 and HDAC synergy predominated in non-IBC lines.
Conclusions: This study shows for the first time a trend towards IBC-specific synergistic drug interactions. These findings suggest potential mechanistic differences in apoptotic regulation and may guide rational development of targeted combination therapies for IBC. Future studies will assess whether these in vitro synergy trends translate into subtype-specific therapeutic responses in vivo.
AI disclosure: AI was used only for language editing; content was verified by the authors.
利益披露 Disclosure
H. Zbib, None..
H. Serhan, None..
M. Nakhjiri, None..
R. Raghavan, None..
T. Rastogi, None..
P. J. Ulintz, None..
N. Merrill, None..
S. Merajver, None.