Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer

图像驱动的多尺度建模来预测乳腺癌的治疗反应

• 批准号: 8920097
• 负责人: Vito Quaranta
• 金额: $ 54.14万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-01-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8920097
• 关键词: Apoptosis; Breast Cancer Cell; Breast Cancer Patient; Breast Cancer cell line; Cell Proliferation; Cells; Cellularity; Characteristics; Clinical; Clinical Trials; Communities; Cultured Cells; Data; Dimensions; ERBB2 gene; Early Diagnosis; Early treatment; Exposure to; Foundations; Glucose; Goals; Health; Image; In Situ; In Vitro; Individual; Link; Literature; Magnetic Resonance Imaging; Mammary Neoplasms; Measurement; Measures; Mechanics; Metabolism; Methods; Modeling; Motivation; Mus; Neoadjuvant Therapy; Organism; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Physiological; Positioning Attribute; Positron-Emission Tomography; Proliferating; Property; Prospective Studies; Recording of previous events; Regimen; Research Design; Resolution; Roche brand of trastuzumab; Therapeutic; Time; Tissues; Toxic effect; Translating; Trastuzumab; Treatment outcome; Tumor Tissue; Validation; Vision; Xenograft procedure; angiogenesis; animal data; base; biophysical model; cellular imaging; clinical application; clinical practice; clinically relevant; data modeling; design; elastography; imaging modality; in vivo; lapatinib; malignant breast neoplasm; mathematical model; model design; multi-scale modeling; neoplastic cell; phase II trial; pre-clinical; preclinical study; programs; quantitative imaging; response; tissue fixing; treatment response; tumor; tumor growth; tumor xenograft

DESCRIPTION (provided by applicant): The ability to identify-early in the course of therapy-patients that are not responding to a particular neoadjuvant regimen would provide the opportunity to switch to a potentially more efficacious treatment and transform current practice. Unfortunately, existing methods of determining early response are inadequate. The vision for this program is to develop tumor-forecasting methods for predicting response in individual breast cancer patients after a single cycle of neoadjuvant therapy. We propose to combine time-resolved drug- response cell scale data with physiological and tissue scale imaging data in order to initialize and constrain a multi-scale angiogenesis-cell proliferation model designed to predict both size and spatial characteristics of breast tumors at the completion of therapy. To achieve this goal, we will pursue the following specific aims: 1. (Pre-clinical validation) In the BT-474 HER2+ human breast cancer cell line, we will obtain: 1a. (cell scale) in vitro data quantifying rates of entry of proliferating cells into quiescence and apoptosis; 1b. (physiologica scale) in vivo MRI and PET measurements of cellularity, vascularity, and metabolism; 1c. (tissue scale) in vivo MR elastography measurements to quantify the tumor mechanical properties; 1d. (all scales) in situ data from fixed tumor tissue to corroborate cell and imaging-based metrics. These data will be integrated into the multi-scale model to predict tumor response after one cycle of the targeted anti-HER2 agents trastuzumab and lapatinib. 2. (Clinical application) In HER2+ patients receiving neoadjuvant trastuzumab and lapatinib, we will obtain: 2a. (physiological scale) in vivo MRI and PET measurements of cellularity, vascularity, and metabolism; 2b. (tissue scale) in vivo MR elastography measurements to quantify tumor mechanical properties. Guided by the results from Aim 1, these data will be integrated into the multi-scale model and make predictions on breast tumor response outcomes after a single cycle of trastuzumab and/or lapatinib. If successful, our approach would be the foundation for high-impact, large-scale application in clinical settings.

描述（由申请人提供）：在治疗过程的早期识别对特定新辅助治疗方案没有反应的患者的能力将提供转向可能更有效的治疗并改变当前实践的机会。不幸的是，现有的确定早期反应的方法是不够的。该计划的愿景是开发肿瘤预测方法，用于预测单个乳腺癌患者在单周期新辅助治疗后的反应。我们提出将联合收割机时间分辨的药物反应细胞尺度数据与生理和组织尺度成像数据相结合，以便初始化和约束多尺度血管生成细胞增殖模型，该模型被设计用于预测治疗完成时乳腺肿瘤的尺寸和空间特征。为实现这一目标，我们将努力实现以下具体目标：1.（临床前验证） BT-474 HER 2+人乳腺癌细胞系，我们将获得：1a. (cell量化增殖细胞进入静止期和凋亡的速率的体外数据; 1b.（生理学尺度）细胞结构、血管分布和代谢的体内MRI和PET测量; 1c.（组织尺度）体内MR弹性成像测量以量化肿瘤机械特性; 1d. (all规模）来自固定肿瘤组织的原位数据，以证实细胞和基于成像的指标。这些数据将整合到多尺度模型中，以预测靶向抗HER 2药物曲妥珠单抗和拉帕替尼治疗一个周期后的肿瘤缓解。2.（临床应用）在接受新辅助曲妥珠单抗和拉帕替尼的HER 2+患者中，我们将获得：2a.（生理尺度）细胞结构、血管分布和代谢的体内MRI和PET测量; 2b.（组织尺度）体内MR弹性成像测量以量化肿瘤机械特性。 在目标1结果的指导下，这些数据将被整合到多尺度模型中，并对曲妥珠单抗和/或拉帕替尼单周期治疗后的乳腺肿瘤缓解结局进行预测。如果成功，我们的方法将成为临床环境中高影响力，大规模应用的基础。