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: 10570913
• 负责人: Anna C. Sorace
• 金额: $ 40.5万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570913
• 关键词: Biological; Biological Models; Blood Vessels; Breast; Breast Cancer Patient; Calibration; 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; Maps; Measures; Medical Imaging; Metastatic malignant neoplasm to brain; Methods; Modality; Modeling; Mus; Myelogenous; 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; Toxic effect; Translations; Trastuzumab; Treatment Efficacy; Treatment Protocols; Tumor Biology; Tumor Cell Invasion; Validation; anti-PD-1; anti-cancer; cancer care; cancer therapy; cell killing; chemotherapy; clinically relevant; contrast enhanced; cytotoxic; experience; high risk; human model; 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.

项目总结/摘要 该提案的总体目标是整合先进的成像和数学建模， 优化涉及人表皮生长因子受体免疫疗法的联合治疗 2型阳性（HER 2+）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而个性化的， 降低了治疗的效果。这项拟议的研究将采用非侵入性，定量磁 核磁共振成像（MRI）和正电子发射断层扫描（PET），以告知数学模型， HER 2+乳腺癌多模式治疗的时机。HER 2的过度表达表明 侵袭性疾病，转移风险高5倍，乳腺至脑转移风险增加， 与HER 2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，我们还表明， 在细胞毒性治疗前给予曲妥珠单抗（而不是联合治疗的同时给药）， 改善HER 2+乳腺癌肿瘤血管输送和氧合的潜力，这反过来又使 用于细胞毒性治疗的肿瘤，降低转移潜能，改善药物递送并降低全身性 毒性随着免疫疗法成为许多实体瘤的主流，开发技术以 都使治疗效果个性化和优化，并降低全身毒性。因此，我们的中心假设 定量成像与数学建模相结合可以增强治疗的个性化 策略和增加与免疫疗法的联合疗法的功效（相加和协同）， HER 2+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 在定量成像中，HER 2+乳腺癌免疫和靶向治疗的变化，2）建立一个 HER 2+乳腺癌中免疫疗法的生物学改变的数学模型，以及3）使用模型 预测和定量成像来指导组合治疗。我们将利用生物学上的改变 变化，如血管输送（用动态对比增强（DCE）- MRI药代动力学评价 参数，Ktranss）和氧合（用氟咪唑（FMISO）-PET成像指标，SUV评价）， 通知数学模型，以确定（和验证）最佳排序（顺序、时间、剂量）， 联合治疗（靶向，免疫治疗），以增强协同效应。完成本项目 提供了一种途径，以显着提高治疗策略的疗效与免疫治疗原发性 HER 2+乳腺癌。重要的是，所提出的技术为患者提供了一条简单的途径 翻译和潜力，以加强对HER 2+乳腺癌患者的护理。

项目成果

Optimizing combination therapy in a murine model of HER2+breast cancer?

• DOI: 10.1016/j.cma.2022.115484
• 发表时间: 2022-11-25
• 期刊: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
• 影响因子: 7.2
• 作者: Lima, Ernesto A. B. F.;Wyde, Reid A. F.;Yankeelov, Thomas E.
• 通讯作者: Yankeelov, Thomas E.

CD4 T-cell immune stimulation of HER2 + breast cancer cells alters response to trastuzumab in vitro.

• DOI: 10.1186/s12935-020-01625-w
• 发表时间: 2020-11-10
• 期刊: Cancer cell international
• 影响因子: 5.8
• 作者: Song PN;Mansur A;Dugger KJ;Davis TR;Howard G;Yankeelov TE;Sorace AG
• 通讯作者: Sorace AG

Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer.

• DOI: 10.3934/mbe.2023783
• 发表时间: 2023-09-15
• 期刊: Mathematical biosciences and engineering : MBE
• 作者: Lima EABF;Song PN;Reeves K;Larimer B;Sorace AG;Yankeelov TE
• 通讯作者: Yankeelov TE

Predicting Schwannoma Growth in a Tumor Model Using Targeted Imaging.

• DOI: 10.1097/mao.0000000000003063
• 发表时间: 2021-06-01
• 期刊: OTOLOGY & NEUROTOLOGY
• 影响因子: 2.1
• 作者: Morrison, Daniel R.;Sorace, Anna G.;Hamilton, Ellis;Moore, Lindsay S.;Houson, Hailey A.;Udayakumar, Neha;Ovaitt, Alyssa;Warram, Jason M.;Walsh, Erika M.
• 通讯作者: Walsh, Erika M.

Bridging Scales: a Hybrid Model to Simulate Vascular Tumor Growth and Treatment Response

• DOI: 10.1016/j.cma.2023.116566
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Tobias Duswald;E. Lima;J. Oden;B. Wohlmuth