Evaluation and Validation of Imaging Biomarkers of Tumor Response to Treatment

描述（由申请人提供）：拟议研究的目的是严格评价、比较和验证选定的多模态成像指标，作为乳腺肿瘤对特定治疗反应的定量（替代）生物标志物，为将其转化为患者管理和临床试验提供科学依据。 近年来，成像方法提供的信息范围急剧增加，因此有许多技术可用于定量监测肿瘤生长和治疗反应。其中一些已用于临床前和临床研究，但由于缺乏标准化，对潜在机制的理解不足以及缺乏适当的验证来帮助解释，结果好坏参半。我们建议在适当的动物模型中系统地评估新兴的、临床上可行的成像指标，以确定哪种方法的组合在预测对与乳腺癌相关的特定治疗的反应方面最准确。我们选择实现这些目标的范例考虑了两大类人类乳腺癌，HER 2阳性肿瘤和ER/PR/HER 2三阴性肿瘤以及已建立和新兴的治疗方法。对于每个类别，我们将通过进行纵向研究来评估治疗反应，这些纵向研究结合了联合收割机PET、SPECT和MRI，以提供乳腺肿瘤对治疗反应的功能评估。我们还将把成像与组织学数据相关联，以了解每种测量方法提供的信息的机械基础。我们假设治疗类型将决定哪些成像指标对早期反应最敏感。为了检验这个假设，我们将追求以下具体目标：1。[HER2在对赫赛汀耐药和不耐药的乳腺癌BT-474小鼠模型中（分别模拟应答者和非应答者），测量通过PET、SPECT和MRI指标报告的对赫赛汀或赫赛汀+拉帕替尼治疗应答的影响。本研究还将在人乳腺癌的多发性T细胞瘤（PyMT）自发小鼠模型中进行。 2. [三阴性癌症]在对多西他赛耐药和不耐药的三阴性乳腺癌的MDA-231小鼠模型中（分别模拟应答者和无应答者），测量多西他赛或多西他赛+舒尼替尼治疗的效果，如PET、SPECT和MRI指标所报告。 公共卫生相关性：我们建议系统地评估新兴的临床可行的成像指标，以确定哪些方法在预测临床采用的乳腺癌治疗的治疗反应方面最准确。通过这种方式，我们希望扩大可以在临床环境中实施的定量成像生物标志物的范围。我们假设，治疗类型将决定哪些成像指标对早期反应最敏感，使特定的治疗类别与特定的成像方法配对，以便在设计临床试验时选择最敏感的成像生物标志物。

DESCRIPTION (provided by applicant): The aims of the proposed research are to critically evaluate, compare and validate selected multi-modality imaging metrics as quantitative (surrogate) biomarkers of the response of breast tumors to specific treatments to provide the scientific basis for their translation into patient management and clinical trials. Recent years have seen a dramatic increase in the range of information available from imaging methods so that a number of techniques are available to quantitatively monitor tumor growth and treatment response. Several of these have been used in both pre-clinical and clinical studies, but with mixed results confounded by lack of standardization, inadequate understanding of underlying mechanisms and absence of appropriate validation to assist their interpretation. We propose to systematically evaluate emerging, clinically-viable imaging metrics in appropriate animal models to establish which combination of methods is most accurate at predicting response to specific treatments that are relevant for breast cancer. The paradigm we have chosen to achieve these ends considers two major categories of human breast cancer, HER2 positive tumors and ER/PR/HER2 triple-negative tumors and both established and emerging therapies. For each category we will assess treatment response by performing longitudinal studies that combine PET, SPECT, and MRI to provide functional assessments of the response of breast tumors to treatment. We will also correlate imaging with histology data to understand the mechanistic underpinnings of the information provided by each type of measurement. We hypothesize that treatment type will determine which imaging metrics are most sensitive to early response. To test this hypothesis we will pursue the following specific aims: 1. [HER2+ cancer] In the BT-474 mouse model of breast cancer with and without resistance to Herceptin (to simulate responders and nonresponders, respectively), measure the effects of treatment response to Herceptin or Herceptin+lapatinib as reported by PET, SPECT, and MRI metrics. This study will also be performed in the polymoma middle T (PyMT) spontaneous mouse model of human breast cancer. 2. [Triple negative cancer] In the MDA-231 mouse model of triple-negative breast cancer with and without resistance to Docetaxel (to simulate responders and nonresponders, respectively), measure the effects of treatment to Docetaxel or Docetaxel+sunitinib as reported by PET, SPECT, and MRI metrics. PUBLIC HEALTH RELEVANCE: We propose to systematically evaluate emerging clinically-viable imaging metrics to establish which methods are most accurate at predicting treatment response with clinically employed breast cancer treatments. In this way we hope to expand the range of quantitative imaging biomarkers that can be implemented in the clinical setting. We hypothesize that treatment type will determine which imaging metrics are the most sensitive to early response enabling specific treatment classes to be paired with specific imaging approaches so that the most sensitive imaging biomarkers can be selected when designing clinical trials.