An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach

使用多阶段综合组学方法研究癌症化疗相关疲劳的分子机制并预测其严重程度

• 批准号: 10516695
• 负责人: Kord Michael Kober
• 金额: $ 7.59万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-03 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10516695
• 关键词: Address; Candidate Disease Gene; Chemotherapy-Oncologic Procedure; Clinical; Clinical Research; Cyclophosphamide; Data; Development; Epigenetic Process; Exercise; Fatigue; Gene Expression; Gene Expression Profiling; Genetic; Intervention; Investigation; Knowledge; Malignant Neoplasms; Modeling; Molecular; Oncology; Operative Surgical Procedures; Pathway interactions; Patients; Quality of life; Radiation therapy; Recommendation; Sampling; Severities; Source; Symptoms; Therapeutic Intervention; associated symptom; base; cancer therapy; chemotherapy; common symptom; effective therapy; experience; high risk; insight; risk prediction model; targeted treatment; therapeutic target

Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. Moderate to severe CRF has a negative impact on patients’ ability to tolerate treatments as well as on their quality of life. In some patients, CRF is so severe, that they discontinue cancer treatment. Given its high occurrence and significant negative impact, it is imperative that effective treatments be developed for this devastating symptom. Two of the major knowledge gaps for CRF are a lack of a risk prediction model and a lack of knowledge of its underlying mechanisms. A sensitive and specific risk prediction model would assist clinicians to determine which patients are most likely to experience high levels of CRF and provide recommendations regarding activity modifying interventions (e.g., exercise). Increased knowledge of the mechanisms for CRF could identify potential targets for therapeutic interventions. Both of these knowledge gaps will be addressed in this application. This study will use multiple sources of “omics” data to investigate the molecular mechanisms associated with the severity of CRF in a well characterized sample of oncology patients (n=1343) who are experiencing low versus high levels of morning and evening CRF. Because these patients are undergoing chemotherapy (CTX), our study will investigate CTX-related fatigue (CTXRF). We will use a multi-staged analysis to integrate the gene expression, genetic, and epigenetic data. We will take advantage of the functional candidate genes identified in a gene expression profiling analyses to provide loci for analysis in subsequent genetic and epigenetic analyses. Candidate genes and pathways identified in this study will provide new and needed information on CTXRF mechanisms, as well as potential therapeutic targets. Prior studies suggest that patients will experience an increase in the severity of CTXRF in the week following CTX. However, no models exist to predict the magnitude of this increase. This inability to predict the severity of CTXRF during subsequent cycles of CTX limits the ability of clinicians to identify high-risk patients and provide them with recommendations to manage CTXRF. To address this knowledge gap, we propose to use demographic, clinical, and omics data to develop a model to predict the severity of morning and evening CTXRF experienced by a patient one week following CTX based on their profile for CTXRF in the week prior to the receipt of this cycle of CTX. This study will provide new insights to be able to identify high-risk patients as well as identify potential therapeutic targets. This project will guide the development and clinical studies to investigate additional mechanisms and therapeutic interventions for CTXRF and other types of fatigue associated with cancer and its treatment (e.g., radiation therapy, surgery).

癌症相关性疲劳（CRF）是与癌症及其治疗相关的最常见症状。 中度至重度CRF对患者耐受治疗的能力以及他们的生活质量有负面影响。 生活质量在一些患者中，CRF是如此严重，以至于他们停止癌症治疗。鉴于其高 的发生和重大的负面影响，必须为此开发有效的治疗方法。 毁灭性的症状通用报告格式的两个主要知识差距是缺乏风险预测模型和 缺乏对其潜在机制的了解。一个敏感和具体的风险预测模型将有助于 临床医生确定哪些患者最有可能经历高水平的CRF，并提供 关于活动修改干预的建议（例如，锻炼）。增加了对 CRF的机制可以确定治疗干预的潜在靶点。这两种知识 在本申请中将解决差距。本研究将使用多个来源的“组学”数据进行调查 在充分表征的肿瘤学样本中，与CRF严重程度相关的分子机制 患者（n=1343）经历低水平vs高水平的早晨和晚上CRF。因为这些 患者正在接受化疗（CTX），我们的研究将调查CTX相关疲劳（CTXRF）。我们将 使用多阶段分析来整合基因表达、遗传和表观遗传数据。我们将采取 在基因表达谱分析中鉴定的功能候选基因的优点是提供基因座 用于随后的遗传和表观遗传分析中的分析。本研究中确定的候选基因和途径 研究将提供新的和所需的信息CTXRF机制，以及潜在的治疗 目标的先前的研究表明，患者在一周内会经历CTXRF严重程度的增加， 在CTX之后然而，没有任何模型可以预测这一增长的幅度。这种无法预测 后续CTX周期中CTXRF的严重程度限制了临床医生识别高风险患者的能力 并为他们提供管理CTXRF的建议。为了弥补这一知识差距，我们建议 使用人口统计学、临床和组学数据开发一个模型来预测早晨和晚上的严重程度。 基于患者在CTX给药前一周的CTXRF特征，患者在CTX给药后一周经历的CTXRF 收到这一轮的CTX这项研究将提供新的见解，能够识别高风险患者， 并确定潜在的治疗靶点。该项目将指导开发和临床研究， 研究CTXRF和其他类型疲劳的其他机制和治疗干预措施 与癌症及其治疗相关的疾病（例如，放射治疗、手术）。