Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis

摘要 乳腺摄影密度和代谢基因分型用于预测癌症预后 本项目将研究定量乳腺摄影密度（MD）和细胞色素P450的作用 CYP 2D 6代谢基因分型在乳腺癌（BC）患者预后中的作用，最终目标是 使用它们作为改善治疗的预后预测因子， 病人乳腺摄影密度是发展乳腺癌的一个既定的危险因素， 有证据表明，来自致密组织区域的癌症更具侵袭性;因此， 他们认为MD可以作为一种预后预测因子。在这个项目中，我们将利用一个单一的机构，所有- 中国，在一家医院使用类似策略治疗的患者队列（台中退伍军人总医院 台湾医院）。他们的乳房护理中心建立了详细的登记册，每个病人的 个人因素、TNM分期、分子生物标志物、成像发现和治疗方案（手术， 放疗、化疗和激素疗法）都在数据库中有详细记录。患者 新诊断的I、II和III期浸润性乳腺癌将从登记研究中确定为入选 的搜索.大多数患者在同一家医院持续接受随访，因此很容易 寻找其预后信息，包括复发、继发BC、远处转移的发展情况 和BC特异性死亡。该登记为研究MD与以下疾病的关联提供了很好的资源： 患者预后（Aim-1）。对于诊断为激素受体阳性的BC患者， 给予激素治疗，如绝经前和围绝经期妇女服用他莫昔芬。虽然 治疗在改善无病生存期和总生存期方面非常有效， 根据统计学，许多患者仍然发展为疾病进展，提出了个体化的问题。 响应能力。激素治疗药物与各种副作用有关;因此， 感兴趣的是找到可以预测每个个体患者的反应性的生物标志物，以确保 有利的获益风险比。MD减少已被证明是预测 他莫昔芬反应，这将是非常有趣的理解为什么有些妇女会回应， 显示MD减小，但其它没有。Aim-2旨在预测他莫昔芬治疗的疗效， MD减少和CYP 2D 6基因分型已知会影响他莫昔芬的代谢， 对雌激素受体具有高亲和力的化合物。患者返回医院进行面对面治疗 将邀请一名随访者提供血样，采用新方法进行CYP 2D 6基因分型 基于已在中国女性中验证的高分辨率熔解曲线分析（HRM） 并且被证明是高效和低成本的。根据基因等位基因，患者将被确定为广泛的 代谢者或中间代谢者。CYP 2D 6代谢状态将与MD相关 减少，然后两者都与预后相关。此外，他们将被合并，以调查是否 这两个因素可以相加以改善预后的预测。

ABSTRACT Title: Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis This project will investigate the role of quantitative mammographic density (MD) and cytochrome P450 CYP2D6 metabolic genotyping in the prognosis of breast cancer (BC) patients, with the ultimate goal of using them as prognostic predictors for improving the treatment that can be provided to each individual patient. Mammographic density is an established risk factor for developing breast cancer, and there is also evidence suggesting that cancers arising from dense tissue area are more aggressive; therefore collectively they suggest MD may serve as a prognostic predictor. In this project we will utilize a single-institution, all- Chinese, patient cohort that is treated in one hospital using similar strategies (Taichung Veteran's General Hospital in Taiwan). Their Breast Care Center has established a detailed registry, and each patient's personal factors, TNM staging, molecular biomarkers, imaging findings, and treatment protocols (surgery, radiation, chemotherapy and hormonal therapy) are all well documented in the database. Patients with newly diagnosed Stage I, II, and III invasive breast cancer will be identified from the registry as the inclusion criteria. The majority of patients are continuingly being followed in the same hospital, so it is very easy to find their prognostic information, including development of recurrence, secondary BC, distant metastasis and BC-specific death. This registry provides a great resource for investigating the association of MD with patients' prognosis (Aim-1). For patients diagnosed with hormonal receptor positive BC, it is the standard of care to give them hormonal therapy, e.g. tamoxifen for pre- and peri-menopausal women. Although the treatment has been shown very effective in improving disease-free survival and overall survival on a statistical basis, many patients still develop progressive disease, raising the question of individual responsiveness. The hormonal therapy drugs are associated with various side effects; thus there is a strong interest to find biomarkers that can predict the responsiveness of each individual patient to ensure a favorable benefit-to-risk ratio. MD reduction has been shown as a valid surrogate marker for predicating tamoxifen response, and it would be very interesting to understand why some women would respond and show MD reduction but others not. Aim-2 was designed to predict tamoxifen treatment efficacy based on MD reduction and the CYP2D6 genotyping that are known to affect the metabolism of tamoxifen to active compounds that have a high affinity for estrogen receptors. Patients returning to hospital for in-person follow-up will be invited to provide blood samples for the CYP2D6 genotyping, by using a new method based on the high-resolution melting curve analysis (HRM), which has been validated in Chinese women and proven to be efficient and low-cost. Based on the gene alleles patients will be determined as extensive metabolizers or intermediate metabolizers. The CYP2D6 metabolic status will be correlated with MD reduction, and then both correlated with prognosis. Further, they will be combined to investigate whether these two factors can be added to improve the prediction of prognosis.