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:10570913
- 负责人:
- 金额:$ 40.5万
- 依托单位:
- 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 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:BiologicalBiological ModelsBlood VesselsBreastBreast Cancer PatientCalibrationCaringClinicalClinical TrialsCombined Modality TherapyCytotoxic ChemotherapyDataDiseaseDoseDrug Delivery SystemsDrug KineticsDrug SynergismERBB2 geneEpidermal Growth Factor ReceptorEquilibriumFlow CytometryGoalsHealthcareHistologyHumanHypoxiaImageImaging TechniquesImmune responseImmune systemImmunotherapyInfiltrationMagnetic Resonance ImagingMainstreamingMalignant NeoplasmsMammary NeoplasmsMapsMeasuresMedical ImagingMetastatic malignant neoplasm to brainMethodsModalityModelingMusMyelogenousMyeloid CellsNecrosisNeoplasm MetastasisPathway interactionsPatient CarePatient-Focused OutcomesPatientsPerfusionPositron-Emission TomographyPrediction of Response to TherapyRegimenResearchRiskRouteScheduleSolid NeoplasmSystemic TherapyT-LymphocyteTechniquesTestingTherapeuticTherapeutic EffectToxic effectTranslationsTrastuzumabTreatment EfficacyTreatment ProtocolsTumor BiologyTumor Cell InvasionValidationanti-PD-1anti-cancercancer carecancer therapycell killingchemotherapyclinically relevantcontrast enhancedcytotoxicexperiencehigh riskhuman modelimmunogenicimprovedin vivoindividual patientmalignant breast neoplasmmathematical modelmouse modelneoplastic cellneovascularizationoptimal control theoryoverexpressionpatient derived xenograft modelpersonalized approachpersonalized immunotherapypersonalized medicinequantitative imagingresponseserial imagingstandard of caresynergismsystemic toxicitytargeted treatmenttreatment optimizationtreatment responsetreatment strategytumortumor growthtumor 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种简单的途径。
翻译和潜力,以加强对HER 2+乳腺癌患者的护理。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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:
- 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:
- 影响因子:0
- 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:
- 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:
- 影响因子:0
- 作者:Tobias Duswald;E. Lima;J. Oden;B. Wohlmuth
- 通讯作者:Tobias Duswald;E. Lima;J. Oden;B. Wohlmuth
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Anna C. Sorace其他文献
Anna C. Sorace的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Anna C. Sorace', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 40.5万 - 项目类别:
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 - 财政年份:2020
- 资助金额:
$ 40.5万 - 项目类别:
相似海外基金
Nonlocal Variational Problems from Physical and Biological Models
物理和生物模型的非局部变分问题
- 批准号:
2306962 - 财政年份:2023
- 资助金额:
$ 40.5万 - 项目类别:
Standard Grant
Point-of-care optical spectroscopy platform and novel ratio-metric algorithms for rapid and systematic functional characterization of biological models in vivo
即时光学光谱平台和新颖的比率度量算法,可快速、系统地表征体内生物模型的功能
- 批准号:
10655174 - 财政年份:2023
- 资助金额:
$ 40.5万 - 项目类别:
Multi-scale stochastic systems motivated by biological models
由生物模型驱动的多尺度随机系统
- 批准号:
RGPIN-2015-06573 - 财政年份:2022
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Micro-electrofluidic platforms for monitoring 3D human biological models
用于监测 3D 人体生物模型的微电流体平台
- 批准号:
DP220102872 - 财政年份:2022
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Projects
Multi-scale stochastic systems motivated by biological models
由生物模型驱动的多尺度随机系统
- 批准号:
RGPIN-2015-06573 - 财政年份:2021
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Multi-scale stochastic systems motivated by biological models
由生物模型驱动的多尺度随机系统
- 批准号:
RGPIN-2015-06573 - 财政年份:2020
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Harnessing machine learning and cloud computing to test biological models of the role of white matter in human learning
利用机器学习和云计算来测试白质在人类学习中的作用的生物模型
- 批准号:
2004877 - 财政年份:2020
- 资助金额:
$ 40.5万 - 项目类别:
Fellowship Award
A Portable low-cost, Point of Investigation CapCell Scope to Image and Quantify the Major Axes of Metabolism and the Associated Vasculature in In vitro and In vivo Biological Models
便携式低成本调查点 CapCell 示波器,用于对体外和体内生物模型中的主要代谢轴和相关脉管系统进行成像和量化
- 批准号:
9899988 - 财政年份:2019
- 资助金额:
$ 40.5万 - 项目类别:
Multi-scale stochastic systems motivated by biological models
由生物模型驱动的多尺度随机系统
- 批准号:
RGPIN-2015-06573 - 财政年份:2019
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
A Portable low-cost, Point of Investigation CapCell Scope to Image and Quantify the Major Axes of Metabolism and the Associated Vasculature in In vitro and In vivo Biological Models
便携式低成本调查点 CapCell 示波器,用于对体外和体内生物模型中的主要代谢轴和相关脉管系统进行成像和量化
- 批准号:
9753458 - 财政年份:2019
- 资助金额:
$ 40.5万 - 项目类别: