Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer.
Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer.
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DOI：
10.1021/mp200270v
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发表时间：
2011-12-05
影响因子：
4.9
通讯作者：
Michor F
中科院分区：
文献类型：
--
作者：
Mumenthaler SM;Foo J;Leder K;Choi NC;Agus DB;Pao W;Mallick P;Michor F
许多最初成功的抗癌疗法随着时间的推移而失去效力，最终，癌细胞获得对疗法的抗性。获得性耐药仍然是提高缓解率和延长无病生存期的主要障碍。因此，克服或预防耐药性的新方法具有重要的临床意义。在用EGFR靶向治疗剂（例如，厄洛替尼）和细胞毒性治疗剂（例如，紫杉醇）;然而，由多种机制驱动的对埃罗替尼的获得性耐药性仍然是治疗成功的障碍。在约50%的病例中，耐药是由于EGFR中的T790 M点突变，并且含有T790 M的细胞最终主导肿瘤组成并导致肿瘤再生长。我们采用了一种结合实验和数学建模的方法来确定治疗策略，阻止原发性T790 M介导的耐药在NSCLC人群中的生长。我们的数学模型预测了敏感细胞和耐药细胞混合物的群体动力学，从而描述了肿瘤组成、耐药细胞的初始分数和选择压力的程度如何影响疾病进展的时间。模型开发依赖于使用新的显微镜方法对细胞增殖和死亡进行定量实验测量。使用这种方法，我们系统地探索了联合治疗策略的空间，并证明了最佳时间的序贯策略相对于相同浓度的单药治疗在生存结局方面有很大的改善。我们的研究揭示了治疗空间的区域，在临床前验证后，低剂量序贯联合策略可能导致T790 M介导的耐药患者的肿瘤减少和生存结局改善。
Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance.
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影响因子：
17.1
作者：
Chmielecki J;Foo J;Oxnard GR;Hutchinson K;Ohashi K;Somwar R;Wang L;Amato KR;Arcila M;Sos ML;Socci ND;Viale A;de Stanchina E;Ginsberg MS;Thomas RK;Kris MG;Inoue A;Ladanyi M;Miller VA;Michor F;Pao W
通讯作者：
Pao W
影响因子：
158.5
作者：
Lynch, TJ;Bell, DW;Haber, DA
通讯作者：
Haber, DA
影响因子：
45.3
作者：
Giaccone, G;Herbst, RS;Johnson, DH
通讯作者：
Johnson, DH
在连续和脉冲给药策略期间对靶向抗癌疗法的抵抗力的演变。
影响因子：
4.3
作者：
Foo J;Michor F
通讯作者：
Michor F
影响因子：
158.5
作者：
Kobayashi, S;Boggon, TJ;Halmos, B
通讯作者：
Halmos, B