Brighton and Sussex Medical School, University of Sussex, Brighton, UK.
Department of Molecular and Cell Biology, University of Leicester, Leicester, UK.
NPJ Syst Biol Appl. 2023 Jun 6;9(1):23. doi: 10.1038/s41540-023-00286-5.
In healthy cells, pro- and anti-apoptotic BCL2 family and BH3-only proteins are expressed in a delicate equilibrium. In contrast, this homeostasis is frequently perturbed in cancer cells due to the overexpression of anti-apoptotic BCL2 family proteins. Variability in the expression and sequestration of these proteins in Diffuse Large B cell Lymphoma (DLBCL) likely contributes to variability in response to BH3-mimetics. Successful deployment of BH3-mimetics in DLBCL requires reliable predictions of which lymphoma cells will respond. Here we show that a computational systems biology approach enables accurate prediction of the sensitivity of DLBCL cells to BH3-mimetics. We found that fractional killing of DLBCL, can be explained by cell-to-cell variability in the molecular abundances of signaling proteins. Importantly, by combining protein interaction data with a knowledge of genetic lesions in DLBCL cells, our in silico models accurately predict in vitro response to BH3-mimetics. Furthermore, through virtual DLBCL cells we predict synergistic combinations of BH3-mimetics, which we then experimentally validated. These results show that computational systems biology models of apoptotic signaling, when constrained by experimental data, can facilitate the rational assignment of efficacious targeted inhibitors in B cell malignancies, paving the way for development of more personalized approaches to treatment.
在健康细胞中，促凋亡和抗凋亡 BCL2 家族和 BH3 蛋白仅在微妙的平衡中表达。相比之下，由于抗凋亡 BCL2 家族蛋白的过度表达，这种体内平衡在癌细胞中经常受到干扰。在弥漫性大 B 细胞淋巴瘤（DLBCL）中，这些蛋白的表达和隔离的可变性可能导致对 BH3 模拟物反应的可变性。BH3 模拟物在 DLBCL 中的成功应用需要对淋巴瘤细胞的反应进行可靠预测。在这里，我们展示了一种计算系统生物学方法，可准确预测 DLBCL 细胞对 BH3 模拟物的敏感性。我们发现，DLBCL 的分数杀伤可以通过信号蛋白分子丰度的细胞间变异性来解释。重要的是，通过将蛋白质相互作用数据与 DLBCL 细胞中的遗传损伤知识相结合，我们的计算模型可以准确预测 BH3 模拟物的体外反应。此外，通过虚拟 DLBCL 细胞，我们预测了 BH3 模拟物的协同组合，然后通过实验验证了这些组合。这些结果表明，凋亡信号的计算系统生物学模型，在受到实验数据约束时，可以促进在 B 细胞恶性肿瘤中合理分配有效的靶向抑制剂，为开发更个性化的治疗方法铺平道路。
