Evaluation and Validation of Imaging Biomarkers of Tumor Response to Treatment

描述(申请人提供)：拟议研究的目的是对选定的多模式成像指标进行批判性评估、比较和验证，作为乳腺肿瘤对特定治疗反应的定量(替代)生物标记物，为将其转化为患者管理和临床试验提供科学基础。近年来，成像方法提供的信息范围急剧增加，因此有许多技术可以用来定量监测肿瘤的生长和治疗反应。其中一些已经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它们，结果喜忧参半。我们建议在适当的动物模型中系统地评估新出现的、临床上可行的成像指标，以确定哪种方法组合在预测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，HER2阳性肿瘤和ER/PR/HER2三阴性肿瘤，以及既有的和新兴的治疗方法。对于每个类别，我们将通过进行结合PET、SPECT和MRI的纵向研究来评估治疗反应，以提供乳腺肿瘤对治疗的反应的功能评估。我们还将把成像与组织学数据联系起来，以了解每种测量提供的信息的机制基础。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。为了验证这一假设，我们将追求以下具体目标：1.[HER2癌症]在对Herceptin耐药和不耐药的BT-474小鼠乳腺癌模型中(分别模拟应答者和无应答者)，测量PET、SPECT和MRI指标报告的对Herceptin或Herceptin Lapatinib的治疗反应的效果。这项研究也将在人类乳腺癌的多瘤中T(PYMT)自发小鼠模型中进行。2.[三阴性癌症]在对多西紫杉醇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PET、SPECT和MRI指标报告的多西紫杉醇或多西紫杉醇孙尼替尼的治疗效果。 公共卫生相关性：我们建议系统地评估新出现的临床可行的成像指标，以确定哪些方法在预测临床使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记物的范围。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，从而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记物。

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.