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

• 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
• 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

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 该提案的总体目标是将高级成像和数学建模集成到 优化人表皮生长因子受体免疫治疗的联合治疗 2型阳性(HER2+)乳腺癌。目前的标准治疗方案甚至临床试验 是有限的，因为它们不是基于个体患者的肿瘤生物学进行个性化的，潜在地 降低治疗效果的。这项拟议的研究将使用非侵入性、定量的磁力 磁共振成像(MRI)和正电子发射断层扫描(PET)通知数学模型以指导 HER2+乳腺癌多模式治疗的时机选择。HER2的过度表达意味着更多 侵袭性疾病的转移风险增加五倍，乳房到脑转移的风险增加， 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 评估和预测抗癌治疗反应的技术。此外，我们已经证明了 在细胞毒治疗之前服用曲妥珠单抗(而不是同时服用联合疗法)有 HER2+乳腺癌肿瘤改善血管输送和氧合的潜力，这反过来又使其增敏 肿瘤用于细胞毒治疗，减少转移潜能，改善药物输送，并减少全身 毒性。随着免疫疗法成为许多实体肿瘤的主流，开发技术来治疗 两者都个性化和优化治疗效果，并减少全身毒性。因此，我们的中心假设 将定量成像与数学建模相结合可以增强治疗的个性化 联合治疗和免疫治疗的策略和提高疗效(相加和协同) HER2+乳腺癌。为了实现这一目标，我们确定了以下具体目标：1)量化生物 HER2+乳腺癌免疫和靶向治疗的定量成像变化，2)建立 HER2+乳腺癌免疫治疗生物学改变的数学模型，以及3)应用模型 预测和定量成像以指导联合治疗。我们将利用生物上的变化 变化，如血管输送(用动态增强(DCE)-MRI药代动力学评估 参数，KTrans)和氧合(用氟咪唑(FMISO)-PET成像指标(SUV)评估)以 通知数学模型以确定(和验证)最佳排序(顺序、时间、剂量)以 联合治疗(靶向治疗、免疫治疗)以增强协同效应。本项目竣工 提供了一种途径，以显著提高免疫治疗策略的疗效 HER2+乳腺癌。重要的是，建议的技术为患者提供了一条直接的途径 翻译和加强对HER2+乳腺癌患者的护理的潜力。