Musculoskeletal Pain Among Breast Cancer Survivors: Through Bio-behavioral and Imaging Lenses

乳腺癌幸存者的肌肉骨骼疼痛：通过生物行为和影像镜片 肌肉骨骼疼痛(MP)是身体残疾的主要原因，可由以下因素引发或加剧 全身癌症治疗，如化疗(如紫杉烷)、内分泌治疗(芳香酶) 抑制物[AIS])和免疫疗法(检查点抑制物)。MP在服药的女性中尤其常见 人工智能疗法，一般为激素敏感型绝经后妇女开5-10年 乳腺癌。MP(包括关节痛、肌肉酸痛和关节/肌肉僵硬)在高达85%的人工智能患者中会出现 是治疗中止率高(高达73%)的头号贡献者。而当 用人工智能治疗MP的策略已经被提出，但仍然没有始终如一的有效 预防或管理问题的干预措施，主要是因为MP的表型尚未 MP的特征和潜在的机制还没有清楚地阐明。目标是做得更好 描述MP的表型，并对MP的潜在机制有更好的理解，我们建议 一系列的研究。这些研究的中心假设是人工智能治疗对表型的影响 在深部组织和中枢神经系统中MP和MP相关的改变被多个 遗传变异。本学位论文项目(F99研究)将调查Long-Rate的个体间变异性。 使用生物行为框架的MP与人工智能治疗乳腺癌的长期轨迹(18个月)。国际间- MP的个体变异性及其相关的表型和背景因素将被研究。该协会 MP的个体间变异性与遗传因素(雌激素相关基因的DNA变异)之间的关系 将探索生物合成、AI新陈代谢、炎症和肌肉骨骼疾病)。在博士后期间 项目(K00研究)，成像技术将被纳入为一个全新的镜头来描述表型 MP及其在深层组织(关节和肌肉)和中枢神经系统(大脑)中的相关变化。 自述MP与结构关节/肌肉病理、结构和功能的关系 将首先在乳腺癌幸存者中描述和探索大脑的变化。建议数 F99/K00的培训和研究旨在建立一种独特的模式，将组学、成像和 行为数据提供了对MP的表型和机制的更深入的了解，并作为基础 通过人工智能治疗和个体化干预的发展来预测MP。

Musculoskeletal Pain among Breast Cancer Survivors: Through Bio-behavioral and Imaging Lenses Musculoskeletal pain (MP) is a leading cause of physical disability and can be triggered or exacerbated by systemic cancer treatments, such as chemotherapy (e.g., taxane-based), endocrine therapy (aromatase inhibitors [AIs]), and immunotherapy (checkpoint inhibitors). MP is particularly common among women taking AI therapy, which is generally prescribed for 5-10 years for postmenopausal women with hormone-sensitive breast cancer. MP (including arthralgias, myalgia, and joint/muscle stiffness) is experienced by up to 85% of AI users, and is the number one contributor to the high treatment discontinuation rate (up to 73%). While strategies have been proposed to manage MP with AI therapy, there are still no consistently effective interventions to prevent or manage the problem, due in large part because the phenotype of MP has not been well-characterized and the mechanisms underlying MP have not been clearly explicated. Aiming to better characterize the phenotype of MP and gain a greater understanding of mechanism underlying MP, we propose a line of studies. The central hypothesis of these studies is that the influences of AI therapy on the phenotype of MP and MP-related alterations in deep tissues and the central nervous system are moderated by multiple genetic variabilities. The dissertation project (F99 study) will investigate the inter-individual variability of long- term trajectories (18 months) of MP with AI for breast cancer using a biobehavioral framework. The inter- individual variability of MP, and its related phenotypic and contextual factors will be examined. The association between inter-individual variability of MP and genotypic factors (DNA variation in genes related to estrogen biosynthesis, AI metabolism, inflammation, and musculoskeletal disorders) will be explored. In the postdoctoral project (K00 study), imaging techniques will be incorporated as a brand-new lens to describe the phenotype of MP and its related alterations in deep tissues (joints and muscles) and the central nervous system (the brain). The relationship among self-reported MP, structural joints/muscles pathology, and structural and functional alterations in the brain will be first described and explored among breast cancer survivors. The proposed F99/K00 training and studies are aimed at establishing a unique model integrating omics, imaging and behavioral data to provide greater insight into the phenotype and mechanisms of MP, and serve as the basis for predicting MP with AI therapy and development of individualized interventions.