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

基本信息

批准号：
8664820
负责人：
Vito Quaranta
金额：
$ 55.72万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-06-01 至 2018-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8664820
关键词：
Apoptosis Breast Cancer Cell Cancer Patient 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 Image In Situ In Vitro Individual Link Literature Magnetic Resonance Imaging Mammary Neoplasms Measurement Measures Mechanics Metabolism Methods Metric Modeling Motivation Mus Neoadjuvant Therapy Organism Outcome Patients Pharmaceutical Preparations Phase II Clinical Trials 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 pre-clinical preclinical study programs public health relevance response tissue fixing treatment response tumor tumor growth tumor xenograft

项目摘要

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）：在对特定的新辅助方案没有反应的治疗患者过程中，能够识别的能力将提供机会转向潜在的更有效的治疗方法并改变当前的实践。不幸的是，确定早期响应的现有方法不足。该程序的愿景是开发单个新辅助治疗周期后，用于预测个别乳腺癌患者的反应的肿瘤造成的方法。我们建议将时间分辨的药物反应细胞尺度数据与生理和组织量表成像数据相结合，以便初始化和约束多尺度的血管生成细胞增殖模型，旨在预测治疗完成时乳腺肿瘤的大小和空间特征。为了实现这一目标，我们将追求以下特定目标：1。（临床前验证） BT-474 HER2+人类乳腺癌细胞系，我们将获得：1a。（细胞尺度）体外数据量化增殖细胞进入静止和凋亡的速率； 1B。（生理量表）体内MRI和细胞性，血管和代谢的PET测量； 1C。（组织尺度）体内MR弹性学测量值，以量化肿瘤机械性能； 1d （所有尺度）原位数据从固定肿瘤组织到证实细胞和基于成像的指标。这些数据将集成到多尺度模型中，以预测靶向抗HER2剂曲妥珠单抗和拉帕替尼的一个循环后的肿瘤反应。 2。（临床应用）在接受新辅助曲妥珠单抗和拉帕替尼的HER2+患者中，我们将获得：2a。（生理规模）体内MRI和细胞性，血管性和代谢的PET测量； 2b。（组织尺度）体内MR弹性学测量值，以量化肿瘤机械性能。在AIM 1的结果的指导下，这些数据将集成到多尺度模型中，并在单个曲妥珠单抗和/或Lapatinib周期后对乳腺肿瘤反应结果进行预测。如果成功的话，我们的方法将是在临床环境中进行大型大规模应用的基础。