Noninvasive prediction of tumor response to gemcitabine using MRI

通过阐明药物的有效性可以预测肿瘤对化疗的反应。 靶向肿瘤细胞的递送，以及递送的药物被激活并作用于肿瘤的有效性 细胞。一种能够回答这些问题的非侵入性手段对于设计高效和 个体化治疗，对提高胰管治疗效果尤为关键 腺癌(PDAC)，人类最致命的恶性肿瘤之一。在本研究中，我们建议 开发一种高度可翻译的MRI技术来回答吉西他滨中的上述两个问题 PADC的治疗，并因此预测肿瘤反应。特别是，我们的方法是基于所谓的 药物直接成像的化学交换饱和转移(CEST)磁共振成像机制 通过其固有携带的可交换质子(OH、NH或NH2)，其可探测性可与 以GD为基础的代理商。根据我们的初步结果，我们假设含有以下物质的制剂 胞嘧啶和胞苷，例如吉西他滨，可以使用CEST MRI，即细胞CEST来检测。我们 预期我们的方法可用于预测肿瘤对吉西他滨治疗的反应 药物在肿瘤中的积累、生物分布和滞留，而不需要成像标记或 其他代理。为了实现我们的目标，我们将首先优化和验证cytCEST MRI对肿瘤的检测 吉西他滨的吸收和生物分布。然后，我们将开发细胞CEST磁共振作为一种有效的手段来检测 脱氧胞苷酶(DCK)是最重要的耐药相关酶之一。终于 CytCEST MRI预测胰腺肿瘤治疗反应的潜力将在 使用三种不同的基于吉西他滨的治疗方法对KPC基因工程小鼠模型进行治疗。 该项目的成功完成将带来一种预测肿瘤反应的成像工具 吉西他滨使用药物或其类似物脱氧胞苷直接作为显像剂，即无标签，因为 不需要进行化学修饰。预计这样的无标签方法可以快速地转化为 临床，允许临床医生在服用吉西他滨之前(或之后)对患者进行分层 或其他胞嘧啶或胞苷为基础的化疗药物，并选择个性化的治疗计划 每组患者。

The prediction of tumor response to chemotherapy can be achieved by elucidating the efficiency of drug delivery to the targeted tumor cells, and the effectiveness of the delivered drug to be activated and act on tumor cells. A non-invasive means that can answer these questions is essential for designing efficient and personalized therapy, and is especially crucial to improve the efficacy of treating pancreatic ductal adenocarcinoma (PDAC), one of the most lethal human malignancies. In the present study, we propose to develop a highly translatable MRI technology to answer the two questions mentioned above in the gemcitabine treatment of PADC, and hence to predict tumor responses. In particular, Our approach is based on a so-called Chemical Exchange Saturation Transfer (CEST) MRI contrast mechanism, by which drugs are imaged directly by their inherently carried exchangeable protons (OH, NH or NH2), at a detectability comparable to that for Gd-based agents. Formulated on the basis of our preliminary results, we hypothesize that agents that contain cytosine and cytidine, for instance gemcitabine, can be detected using CEST MRI, namely cytCEST. We anticipate our approach can be used to predict tumor response to the gemcitabine treatment by assessing the accumulation, biodistribution and retention of the drug in the tumor, without the need for imaging tags or additional agents. To achieve our goal, we will first optimize and validate the cytCEST MRI detection of tumor uptake and biodistribution of gemcitabine. Then we will develop cytCEST MRI as an effective means to detect the activity of deoxycytidine kinase (dCK), one of the most important drug-resistance-related enzymes. Finally the potential of cytCEST MRI to predict the response of pancreatic tumors to therapy will be examined on the treatment in KPC genetically engineered mouse models using three different gemcitabine-based treatments. Successful completion of this project will result in an imaging tool for the prediction of tumor response to gemcitabine using the drug or its analog deoxycytidine directly as the imaging agent, namely label-free because no chemical-modification is needed. It is expected that such a label-free approach can be rapidly translated to the clinic, allowing clinicians to stratify patients prior to (or immediately after) the administration of gemcitabine or other cytosine- or cytidine-based chemotherapeutic drugs and to choose the personalized treatment plan for each group of patients.