Mathematical modeling and molecular imaging to maximize response while minimizing toxicities from systemic therapies in preclinical models of breast cancer

项目总结 我们的首要目标是利用基于生物学的数学模型和先进的分子成像技术 在维持或加速肿瘤控制的同时，显著降低全身毒性 乳腺癌的临床前模型。系统治疗的进展提高了患者的长期存活率 然而，对于局部晚期乳腺癌，伴随而来的是与之相关的 长期副作用，包括认知缺陷和心脏问题。我们已经开发出实用的，生物学的- 基于数学模型，能够系统地研究时间、顺序、剂量和排序 联合疗法，以确定潜在的最大限度的反应，同时最小化的治疗方案 毒性。初步结果(包括实验和数学)显示，交替使用 联合化疗(阿霉素)和靶向治疗(赫赛汀)可以显著和协同作用 增强反应，同时将化疗剂量减少50%。此外，利用最优控制理论， 我们已经确定了治疗方案，表明我们可以在不增加增加的情况下将肿瘤控制速度提高1.6倍 化疗的量。我们建议发展数学形式主义，允许系统地 根据患者具体情况，确定最大限度地提高肿瘤反应和最大限度地减少副作用的治疗方案 效果。然后，我们选择最有希望的选项，并对它们进行实验测试，以对抗既定的治疗方法 治疗方案和试验以获得更好的结果和毒性。我们还寻求开发定量成像技术 能够表征大脑和心脏功能--已知的不利器官--的暂时变化 受化疗的影响。我们计划通过以下具体目标实现这一目标。AIM 1将验证 用最优化疗剂量维持肿瘤控制的数学预测 对照理论以确定和生物学验证(通过免疫组织化学和总体肿瘤负担 测量)是三种最有前景的联合治疗策略。AIM 2将实施高级 应用心脏成像技术定量评价治疗期间重要器官毒性变化的分子成像 膜电位(18F-TTP+-PET)和小胶质细胞活化的脑成像(TSPO，18F-DPA- 714-PET)，以确定使用该标准治疗的动物的长期影响之间的纵向差异 和优化的养生方案。这些目标的实现将带来一个实际的、实验性的计算 在临床前小鼠模型中确定最佳治疗策略的方法，并适用于 第1阶段临床试验中的前瞻性试验。由于毒性是癌症治疗中的主要剂量限制因素， 开发控制它的方法将极大地影响患者的健康。

PROJECT SUMMARY Our overarching goal is to utilize biology-based mathematical models and advanced molecular imaging to dramatically decrease systemic toxicities while either maintaining or accelerating tumor control in preclinical models of breast cancer. Advances in systemic therapies have improved long-term survival in patients with locally-advanced breast cancer, however there has been a concomitant increase in the associated their long-term side effects, including cognitive deficits and cardiac problems. We have developed practical, biology- based mathematical models capable of systematically investigating the timing, order, dosing, and sequencing of combination therapies to identify therapeutic regimens that can potentially maximize response while minimizing toxicity. Preliminary results (both experimental and mathematical) reveal that alternating the order and dosing of combination chemotherapy (doxorubicin) and targeted therapy (Herceptin) can significantly and synergistically enhance response while reducing the chemotherapy dose by 50%. Furthermore, using optimal control theory, we have identified therapeutic regimens suggesting we can achieve tumor control 1.6x faster without increasing the amount of chemotherapy. We propose to develop the mathematical formalism that allows for systematically determining, on a patient specific basis, therapeutic regimens that maximize tumor response and minimize side effects. We then select the most promising options and test them experimentally against established treatment regimens and test for superior outcomes and toxicity. We also seek to develop quantitative imaging technologies capable of characterizing the temporal alterations in brain and cardiac function—organs known to be adversely affected by chemotherapies. We plan to achieve this goal with the following Specific Aims. Aim 1 will validate mathematical predictions for maintaining tumor control with minimal chemotherapy dose by employing optimal control theory to identify and biologically validate (with immunohistochemistry and overall tumor burden measurements) the three most promising combination treatment strategies. Aim 2 will implement advanced molecular imaging to quantify toxicity changes in critical organs during therapy by employing cardiac imaging of membrane potential (18F-TTP+-PET) and brain imaging of microglia activation (TSPO, measured with 18F-DPA- 714-PET) to determine longitudinal differences between long-term effects in animals treated with the standard and the optimized regimens. Completion of these aims will deliver a practical, experimental-computational approach for identifying optimal treatment strategies in pre-clinical mouse models, and appropriate for prospective testing in phase 1 clinical trials. As toxicity is the main dose-limiting factor in cancer treatments, developing methods to control it will dramatically effect patient health.