Personalizing immunotherapy in HER2+ breast cancer through quantitative imaging

通过定量成像对 HER2 乳腺癌进行个性化免疫治疗

基本信息

批准号：
10338122
负责人：
Anna C. Sorace
金额：
$ 41.39万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10338122
关键词：
Biological Biological Models Blood Vessels Breast Breast Cancer Patient Caring Clinical Clinical Trials Combined Modality Therapy Cytotoxic Chemotherapy Data Disease Dose Drug Delivery Systems Drug Kinetics Drug Synergism ERBB2 gene Epidermal Growth Factor Receptor Equilibrium Flow Cytometry Goals Healthcare Histology Human Hypoxia Image Imaging Techniques Immune response Immune system Immunotherapy Infiltration Magnetic Resonance Imaging Mainstreaming Malignant Neoplasms Mammary Neoplasms Measures Medical Imaging Metastatic malignant neoplasm to brain Methods Modality Modeling Mus Myeloid Cells Necrosis Neoplasm Metastasis Pathway interactions Patient Care Patient-Focused Outcomes Patients Perfusion Positron-Emission Tomography Prediction of Response to Therapy Regimen Research Risk Route Schedule Solid Neoplasm Systemic Therapy T-Lymphocyte Techniques Testing Therapeutic Therapeutic Effect Time Toxic effect Translations Trastuzumab Treatment Efficacy Treatment Protocols Tumor Biology Tumor Cell Invasion Validation anti-PD-1 anti-cancer base cancer care cancer therapy cell killing chemotherapy clinically relevant contrast enhanced cytotoxic experience high risk immunogenic improved in vivo individual patient malignant breast neoplasm mathematical model mouse model neoplastic cell neovascularization optimal control theory overexpression patient derived xenograft model personalized approach personalized immunotherapy personalized medicine quantitative imaging response serial imaging standard of care synergism systemic toxicity targeted treatment treatment optimization treatment response treatment strategy tumor tumor growth tumor microenvironment

项目摘要

PROJECT SUMMARY/ABSTRACT The overall goal of this proposal is to integrate advanced imaging and mathematical modeling to optimize combination treatments involving immunotherapy in human epidermal growth factor receptor type 2 positive (HER2+) breast cancer. Current standard-of-care therapeutic regimens and even clinical trials are limited because they are not personalized based on the tumor biology of the individual patient, potentially diminishing the efficacy of the treatment. This proposed research will employ noninvasive, quantitative magnetic resonance imaging (MRI) and positron emission tomography (PET) to inform mathematical models to direct timing for multi-modal therapies in HER2+ breast cancer. Overexpression of HER2 is indicative of more aggressive disease with five times higher risk of metastasis, with increased risk of breast-to-brain metastases, compared to HER2- patients. We have extensive experience and expertise in using quantitative medical imaging techniques to assess and predict treatment response to anti-cancer therapies. Additionally, we have shown that trastuzumab dosing prior to cytotoxic treatment (instead of simultaneous dosing of combination therapies) has potential to improve vascular delivery and oxygenation in HER2+ breast cancer tumors, which in turns sensitizes the tumor for cytotoxic therapies, reduces metastatic potential, improves drug delivery and reduces systemic toxicity. As immunotherapy becomes mainstream for many solid tumors, it is essential to develop techniques to both personalize and optimize therapeutic efficacy and decrease systemic toxicity. Thus, our central hypothesis is that quantitative imaging integrated with mathematical modeling can enhance personalization of treatment strategies and increase efficacy (additive and synergistic) of combination therapies with immunotherapy in HER2+ breast cancer. To achieve this goal, we have identified the following specific aims: 1) Quantify biological changes to immuno- and targeted therapy in HER2+ breast cancer with quantitative imaging, 2) Build a mathematical model of biological alterations to immunotherapy in HER2+ breast cancer, and 3) Employ model forecasting and quantitative imaging to guide combination therapy. We will exploit the alterations in biological changes, such as vascular delivery (evaluated with dynamic contrast enhanced (DCE)- MRI pharmacokinetic parameter, Ktrans) and oxygenation (evaluated with fluoromisonidazole (FMISO)-PET imaging metric, SUV) to inform a mathematical model in order to identify (and validate) optimal sequencing (order, timing, dose) to combination therapy (targeted, immunotherapy) for enhanced synergistic effects. Completion of this project provides a pathway to dramatically improve the efficacy of treatment strategies with immunotherapy for primary HER2+ breast cancer. Importantly, the proposed techniques provide a straightforward route for patient translation and potential to enhance care for HER2+ breast cancer patients.

项目摘要/摘要该建议的总体目标是将高级成像和数学建模集成到优化涉及人类表皮生长因子受体免疫疗法的组合处理 2型阳性（HER2+）乳腺癌。当前的护理标准治疗方案甚至临床试验受到限制，因为它们不是基于个体患者的肿瘤生物学个性化的，可能是降低治疗的功效。这项拟议的研究将采用无创，定量磁性共振成像（MRI）和正电子发射断层扫描（PET）通知数学模型 HER2+乳腺癌中多模式疗法的时机。 HER2的过表达表明更多侵略性疾病的转移风险高五倍，乳腺转移的风险增加，与HER2-患者相比。我们在使用定量医学成像方面拥有丰富的经验和专业知识评估和预测对抗癌疗法的治疗反应的技术。此外，我们已经表明曲妥珠单抗在细胞毒性治疗之前给药（而不是同时给予联合疗法的给药）在HER2+乳腺癌肿瘤中改善血管递送和氧合的潜力，该肿瘤又迅速敏感细胞毒性疗法的肿瘤，降低转移性潜力，改善药物输送并降低全身性毒性。随着免疫疗法成为许多实体瘤的主流，开发技术至关重要个性化和优化治疗功效并降低全身毒性。因此，我们的中心假设是与数学建模集成的定量成像可以增强治疗的个性化策略并提高组合疗法与免疫疗法的疗效（添加剂和协同作用） HER2+乳腺癌。为了实现这一目标，我们确定了以下特定目的：1）量化生物学通过定量成像，对HER2+乳腺癌的免疫和靶向疗法的变化，2）建立 HER2+乳腺癌免疫疗法的生物学改变的数学模型，3）采用模型预测和定量成像以指导组合疗法。我们将利用生物学的改变变化，例如血管递送（通过动态对比度增强（DCE） - MRI药代动力学评估参数，ktrans）和氧合（用荧光症（FMISO）-PET成像度量，SUV评估）告知数学模型，以识别（和验证）最佳测序（顺序，时机，剂量）联合疗法（有针对性的免疫疗法）可增强协同作用。该项目的完成提供了一种通过免疫疗法来大大提高治疗策略的疗效的途径 HER2+乳腺癌。重要的是，建议的技术为患者提供了直接的途径翻译和增强HER2+乳腺癌患者护理的潜力。