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

数学建模和分子成像可最大限度地提高乳腺癌临床前模型中全身治疗的反应，同时最大限度地降低毒性

• 批准号: 10564905
• 负责人: Anna C. Sorace
• 金额: $ 47.78万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10564905
• 关键词: Acceleration; Address; Affect; Aftercare; Animals; Biological; Biology; Brain; Brain imaging; Breast Cancer Model; Cardiac; Cardiotoxicity; Cognitive deficits; Combination Drug Therapy; Combined Modality Therapy; Cytotoxic Chemotherapy; Disease; Dose; Dose Limiting; Doxorubicin; ERBB2 gene; Goals; Health; Heart; Histology; Human; Image; Imaging technology; Immunohistochemistry; Immunotherapy; In complete remission; Injury; Long-Term Effects; Mathematics; Measurement; Measures; Membrane Potentials; Methods; Microglia; Neoadjuvant Therapy; Organ; Outcome; Pathologic; Patients; Phase I Clinical Trials; Positron-Emission Tomography; Pre-Clinical Model; Radiation therapy; Regimen; Risk; Schedule; Site; Standardization; Systemic Therapy; Testing; Therapeutic; Time; Toxic effect; Trastuzumab; Treatment Protocols; Tumor Burden; Xenograft procedure; advanced breast cancer; alternative treatment; cancer therapy; chemotherapy; clinically relevant; cognitive ability; glial activation; heart function; heart imaging; imaging modality; improved; malignant breast neoplasm; mathematical methods; mathematical model; molecular imaging; mouse model; neuroinflammation; optimal control theory; optimal treatments; patient derived xenograft model; pre-clinical; predictive modeling; prospective test; quantitative imaging; response; side effect; systemic toxicity; targeted treatment; treatment strategy; tumor; uptake

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.

项目概要 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 显着降低全身毒性，同时维持或加速肿瘤控制 乳腺癌的临床前模型。全身治疗的进步改善了患者的长期生存 局部晚期乳腺癌，然而，相关的乳腺癌风险也随之增加。 长期副作用，包括认知缺陷和心脏问题。我们开发了实用的生物学- 基于数学模型，能够系统地研究时间、顺序、剂量和顺序 联合疗法以确定可以最大化反应同时最小化反应的治疗方案 毒性。初步结果（实验和数学）表明，交替使用顺序和剂量 联合化疗（阿霉素）和靶向治疗（赫赛汀）可显着协同作用 增强反应，同时减少 50% 的化疗剂量。此外，利用最优控制理论， 我们已经确定的治疗方案表明我们可以将肿瘤控制速度提高 1.6 倍，而无需增加 化疗量。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治疗方案，以最大限度地提高肿瘤反应并最大限度地减少副作用 影响。然后，我们选择最有希望的选项，并根据现有的治疗方法对它们进行实验测试 治疗方案并测试优越的结果和毒性。我们还寻求开发定量成像技术 能够描述大脑和心脏功能的时间变化——已知对这些器官产生不利影响 受化疗影响。我们计划通过以下具体目标来实现这一目标。目标 1 将验证 通过采用最佳方案以最小化疗剂量维持肿瘤控制的数学预测 控制理论来识别和生物学验证（通过免疫组织化学和总体肿瘤负荷 测量）三种最有希望的联合治疗策略。目标2将实施先进 分子成像通过采用心脏成像来量化治疗期间关键器官的毒性变化 膜电位 (18F-TTP+-PET) 和小胶质细胞激活的脑成像 (TSPO，用 18F-DPA- 测量) 714-PET）以确定用标准治疗的动物的长期影响之间的纵向差异 以及优化的治疗方案。完成这些目标将提供实用的实验计算 在临床前小鼠模型中确定最佳治疗策略的方法，并且适用于 一期临床试验中的前瞻性测试。由于毒性是癌症治疗中主要的剂量限制因素， 开发控制它的方法将极大地影响患者的健康。