Metabolomics: Novel Strategies to Improve Breast Cancer Radiotherapy Responses

代谢组学：改善乳腺癌放射治疗反应的新策略

基本信息

批准号：
9810248
负责人：
Jennifer J Hu
金额：
$ 23.37万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9810248
关键词：
Acute Address Adjuvant Radiotherapy Adverse reactions African American Alanine American Area Aspartate Benefits and Risks Bioinformatics Biological Markers Breast Breast Cancer Patient Breast Cancer survivor Cancer Etiology Cancer Survivor Cessation of life Characteristics Clinic Clinical Clinical Data Computational Biology Cosmetics DNA Repair Data Development Discipline Edema Epidemiologist Funding Genomics Glutamate Metabolism Pathway Glutaminase Goals Health Services Research Hispanics Individual Industry Inflammatory Interdisciplinary Study Intervention Knowledge Late Effects Logistic Regressions Metabolic Metabolic Pathway Minority Modeling Molecular Genetics Normal tissue morphology Not Hispanic or Latino Outcome Pain Parents Pathway interactions Patients Performance Population Positioning Attribute Postoperative Period Precision therapeutics Process Progression-Free Survivals Proteomics Quality of life Radiation Radiation Oncologist Radiation Tolerance Radiation therapy Reaction Receiver Operating Characteristics Recurrence Research Research Project Grants Resources Risk Sampling Skin Technology Testing Toxic effect Underserved Population United States National Institutes of Health Urine Validation Woman base biomarker-driven breast cancer survival breast lumpectomy cancer biomarkers cancer diagnosis cancer health disparity cancer radiation therapy clinical application clinical practice drug metabolism effective intervention evidence base experience high risk high risk population improved indexing inhibitor/antagonist innovation inter-individual variation malignant breast neoplasm metabolomics multidisciplinary novel novel strategies personalized intervention predictive modeling prevent racial and ethnic response side effect study population tool transcriptomics treatment strategy tumor tumor metabolism underserved minority

项目摘要

PROJECT SUMMARY/ABSTRACT Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death in American women. Lumpectomy followed by radiotherapy (RT) has significantly improved survival. However, about 42%-47% of patients develop worse early adverse skin reactions (EASRs), pain, breast edema and poor cosmetic results that impact quality of life. Inter-individual variability in the development of RT-induced adverse reactions in normal tissue is well-documented for both acute and late effects. African-American (AA) and underserved populations are less likely than Whites to receive the recommended adjuvant RT, if treated, have a higher risk for developing RT-related side effects and worse clinical outcome. To achieve our long-term goals in improving quality of life, clinical outcome, and overcoming breast cancer disparities, we will use a metabolomics approach to test prediction models for RT-induced EASRs/pain in three racial/ethnic populations. We will test a new paradigm that multiple metabolites/pathways contribute to radiation sensitivity that may predict RT-induced EASRs/pain. Investigating this new paradigm will develop powerful tools in identifying high- risk populations and targets for precision intervention and treatment. Aim 1 will evaluate global metabolomics - driven prediction models of RT-induced EASRs/pain. Aim 2 will quantitate and validate significant metabolites and pathways from Aim 1 in predicting RT-induced EASRs/pain. Aim 3 will develop and assess the performance of comprehensive model (i.e., metabolomics/pathways from Aim 2, patient characteristics, and clinical variables) in predicting RT-induced EASRs/pain. Our preliminary data showed that the alanine, aspartate and glutamate metabolism pathway may contribute to RT-induced EASRs/pain. Thus, targeting tumor metabolism by glutaminase inhibitors may prevent RT-induced EASRs/pain in addition to their anti-tumor activities. Capitalizing on existing urine samples and clinical data from a large tri-racial/ethnic breast cancer population to target 480 patients (67% minorities), promising pilot data, state-of-the-art metabolomics technologies, innovative bioinformatics approach, and skilled multidisciplinary team, we are in an exceptional position to carry out the proposed research. To the best of our knowledge, this is the first metabolomics study of RT-induced EASRs/pain in breast cancer. Using a hypothesis-driven approach, the outcomes will significantly improve the accuracy of urgently needed prediction models of RT-induced EASRs/pain in breast cancer. The outcomes of the proposed research will advance our scientific knowledge in the accurate assessment of RT outcomes and targeted intervention. They also may enable the development of benefit-risk indices that will aid in the decision and planning of RT. The outcome will target effective intervention and treatment strategies, and ultimately improve the quality of life and progression-free survival in breast cancer patients, particularly in underserved minorities with worse RT-induced EASRs/pain and clinical outcome.

项目概要/摘要乳腺癌是最常见的癌症，也是癌症死亡的第二大原因美国妇女。肿瘤切除术后进行放射治疗（RT）可显着提高生存率。然而，约 42%-47% 的患者出现更严重的早期皮肤不良反应 (EASR)、疼痛、乳房水肿和皮肤不良反应影响生活质量的美容效果。放疗引起的不良反应发生的个体差异正常组织中的急性反应和迟发反应都有详细记录。非裔美国人 (AA) 和服务不足的人群接受推荐的辅助放疗的可能性低于白人，如果接受治疗的话，发生放疗相关副作用和临床结果较差的风险更高。实现我们的长期目标在改善生活质量、临床结果和克服乳腺癌差异方面，我们将使用代谢组学方法在三个种族/族裔人群中测试 RT 诱导的 EASR/疼痛的预测模型。我们将测试一种新的范例，即多种代谢物/途径有助于辐射敏感性，这可能预测放疗引起的 EASR/疼痛。研究这一新范式将开发出强大的工具来识别高精准干预和治疗的风险人群和目标。目标 1 将评估全球代谢组学 - RT 引起的 EASR/疼痛的驱动预测模型。目标 2 将定量和验证重要的代谢物以及目标 1 预测 RT 引起的 EASR/疼痛的途径。目标 3 将制定和评估绩效综合模型（即目标 2 的代谢组学/途径、患者特征和临床变量）预测 RT 引起的 EASR/疼痛。我们的初步数据表明，丙氨酸、天冬氨酸和谷氨酸代谢途径可能导致 RT 诱导的 EASR/疼痛。因此，通过靶向肿瘤代谢谷氨酰胺酶抑制剂除了具有抗肿瘤活性外，还可以预防放疗引起的 EASR/疼痛。大写根据来自大型三种族/族裔乳腺癌人群的现有尿液样本和临床数据，目标是 480 患者（67% 少数族裔）、有希望的试点数据、最先进的代谢组学技术、创新生物信息学方法和熟练的多学科团队，我们处于特殊的地位来开展提出的研究。据我们所知，这是第一项针对放疗引起的 EASR/疼痛的代谢组学研究在乳腺癌中。使用假设驱动的方法，结果将显着提高迫切需要 RT 诱导的乳腺癌 EASR/疼痛的预测模型。拟议的结果研究将提高我们在准确评估 RT 结果和有针对性的方面的科学知识干涉。它们还可以促进收益-风险指数的开发，以帮助决策和 RT 的规划。结果将针对有效的干预和治疗策略，最终改善乳腺癌患者的生活质量和无进展生存期，特别是在服务不足的少数群体，其 RT 引起的 EASR/疼痛和临床结果较差。