Novel Tools to Predict and Prevent the Emergence of Resistance Against Targeted

有些癌症对抗癌治疗非常敏感。例如，肺部 腺癌在表皮生长因子受体（EGFR）酪氨酸激酶中具有特异性突变 区域经常经历选择性EGFR酪氨酸激酶的临床和放射学反应 抑制剂（TKI）吉非替尼（易瑞沙）和埃罗替尼（特罗凯）。髓母细胞瘤类似地非常 对放射治疗敏感。然而，在这两种情况下，疾病都会复发。在一半的肺癌患者中， 患者，Pao实验室已经证明肿瘤细胞在EGFR激酶结构域中具有第二个突变， 其改变ATP结合口袋中的“看门人”残基（T790 M）。在髓母细胞瘤患者中，数据 来自荷兰实验室的研究表明，血管周围小生境中的抗辐射细胞在24小时内经历G1期阻滞。 对治疗有反应，然后自我更新，引起复发。自从获得性的抵抗 严重的局限性，由于现有的治疗方案是根据经验建立的，我们提出了一个 跨学科的方法，利用数学建模和独特的实验系统来预测和 防止出现对靶向药物和放射治疗的耐药性。我们已经开发了一个 在靶向药物治疗期间出现耐药性的一般情况的数学框架 毒品我们现在将：1）扩大数学框架，以包括癌症中更复杂的情况 治疗; 2）应用模型，使用定量分析，最大限度地降低肺癌中EGFR TKI耐药的风险， 从适当的等基因细胞系和体内转基因肺肿瘤模型获得的测量值; 3）应用 使用定量测量最小化髓母细胞瘤放射抵抗风险的模型 总的来说，这些研究将导致合理的设计， 临床试验以防止耐药性的出现。该项目是多机构（范德比尔特和MSKCC）， 跨学科，并将依赖于从PS-OC核心（Altan-Bonnet）获得的物理测量。

Some cancers are exquisitely sensitive to anti-cancer treatment. For example, patients whose lung adenocarcinomas harbor specific mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain frequently experience clinical and radiographic responses to the selective EGFR tyrosine kinase inhibitors (TKIs) gefitinib (Iressa) and eriotinib (Tarceva). Medulloblastomas analogously are extremely sensitive to radiation treatment. However, in both instances, the disease returns. In half of such lung cancer patients, the Pao Lab has demonstrated that tumor cells harbor a second mutation in the EGFR kinase domain, which alters a 'gatekeeper' residue (T790M) in the ATP-binding pocket. In patients with medulloblastoma, data from the Holland Lab suggests that radiation-resistant cells in the perivascular niche undergo Gl arrest in response to treatment and then self-renew, giving rise to recurrence. Since acquired resistance to represent severe limitations, and since existing treatment schedules were established empirically, we propose an interdisciplinary approach utilizing mathematical modeling and unique experimental systems to predict and prevent the emergence of resistance against targeted drugs and radiation therapy. We have already developed a mathematical framework for the general scenario of drug resistance emerging during therapy with targeted drugs. We now will: 1) broaden the mathematical framework to include more complex scenarios in cancer therapy; 2) apply the models to minimize the risk of resistance to EGFR TKIs in lung cancer, using quantitative measurements obtained from appropriate isogenic cell lines and in vivo transgenic lung tumor models; 3) apply the models to minimize the risk of resistance to radiation in medulloblastoma, using quantitative measurements obtained from a novel transgenic mouse model Collectively, these studies will lead to the rational design of clinical trials to prevent the emergence of resistance. This project is multi-institutional (Vanderbilt and MSKCC), cross-disciplinary, and will rely upon physical measurements obtained from the PS-OC Core (Altan-Bonnet).