Computational modeling of DLBCL predicts response to BH3-mimetics.
Computational modeling of DLBCL predicts response to BH3-mimetics.
复制标题
DOI：
10.1038/s41540-023-00286-5
复制
发表时间：
2023-06-06
影响因子：
4
通讯作者：
中科院分区：
文献类型：
--
作者：
在健康细胞中，促凋亡和抗凋亡的BCL2家族和仅bh3蛋白在微妙的平衡中表达。相反，由于抗凋亡BCL2家族蛋白的过度表达，这种稳态在癌细胞中经常被扰乱。这些蛋白在弥漫性大B细胞淋巴瘤（DLBCL）中表达和隔离的变异性可能是对bh3模拟物反应的变异性的原因。在DLBCL中成功应用bh3模拟物需要可靠的预测哪些淋巴瘤细胞会有反应。在这里，我们展示了计算系统生物学方法能够准确预测DLBCL细胞对bh3模拟物的敏感性。我们发现DLBCL的部分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细胞中遗传病变的知识相结合，我们的计算机模型准确地预测了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，然后我们通过实验验证了这一点。这些结果表明，在实验数据的约束下，凋亡信号的计算系统生物学模型可以促进B细胞恶性肿瘤中有效靶向抑制剂的合理分配，为开发更个性化的治疗方法铺平道路。
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.
登录
查看更多内容
影响因子：
9.8
作者：
Albeck JG;Burke JM;Spencer SL;Lauffenburger DA;Sorger PK
通讯作者：
Sorger PK
影响因子：
64.8
作者：
Kim, Min-Sik;Pinto, Sneha M.;Getnet, Derese;Nirujogi, Raja Sekhar;Manda, Srikanth S.;Chaerkady, Raghothama;Madugundu, Anil K.;Kelkar, Dhanashree S.;Isserlin, Ruth;Jain, Shobhit;Thomas, Joji K.;Muthusamy, Babylakshmi;Leal-Rojas, Pamela;Kumar, Praveen;Sahasrabuddhe, Nandini A.;Balakrishnan, Lavanya;Advani, Jayshree;George, Bijesh;Renuse, Santosh;Selvan, Lakshmi Dhevi N.;Patil, Arun H.;Nanjappa, Vishalakshi;Radhakrishnan, Aneesha;Prasad, Samarjeet;Subbannayya, Tejaswini;Raju, Rajesh;Kumar, Manish;Sreenivasamurthy, Sreelakshmi K.;Marimuthu, Arivusudar;Sathe, Gajanan J.;Chavan, Sandip;Datta, Keshava K.;Subbannayya, Yashwanth;Sahu, Apeksha;Yelamanchi, Soujanya D.;Jayaram, Savita;Rajagopalan, Pavithra;Sharma, Jyoti;Murthy, Krishna R.;Syed, Nazia;Goel, Renu;Khan, Aafaque A.;Ahmad, Sartaj;Dey, Gourav;Mudgal, Keshav;Chatterjee, Aditi;Huang, Tai-Chung;Zhong, Jun;Wu, Xinyan;Shaw, Patrick G.;Freed, Donald;Zahari, Muhammad S.;Mukherjee, Kanchan K.;Shankar, Subramanian;Mahadevan, Anita;Lam, Henry;Mitchell, Christopher J.;Shankar, Susarla Krishna;Satishchandra, Parthasarathy;Schroeder, John T.;Sirdeshmukh, Ravi;Maitra, Anirban;Leach, Steven D.;Drake, Charles G.;Halushka, Marc K.;Prasad, T. S. Keshava;Hruban, Ralph H.;Kerr, Candace L.;Bader, Gary D.;Iacobuzio-Donahue, Christine A.;Gowda, Harsha;Pandey, Akhilesh
通讯作者：
Pandey, Akhilesh
影响因子：
46.9
作者：
Fussenegger, M;Bailey, JE;Varner, J
通讯作者：
Varner, J
数学建模将细胞凋亡的抑制剂鉴定为阳性反馈和双重性的介体。
影响因子：
4.3
作者：
Legewie S;Blüthgen N;Herzel H
通讯作者：
Herzel H
Bcl-2凋亡开关中的两个独立的正反馈和双重性。
影响因子：
3.7
作者：
Cui, Jun;Chen, Chun;Lu, Haizhu;Sun, Tingzhe;Shen, Pingping
通讯作者：
Shen, Pingping