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

• 批准号: 10097270
• 负责人: Jennifer J Hu
• 金额: $ 3.95万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10097270
• 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; ROC Curve; Radiation; Radiation Oncologist; Radiation Tolerance; Radiation therapy; Reaction; 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

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.

项目总结/摘要 乳腺癌是最常见的癌症，也是癌症死亡的第二大原因。 美国女人肿瘤切除术后放疗（RT）显着提高生存率。然而，在这方面， 大约42%-47%的患者出现更严重的早期不良皮肤反应（EASR）、疼痛、乳房水肿和不良反应。 影响生活质量的美容效果。RT诱导的不良反应发生的个体间差异 正常组织中的急性反应和迟发反应都有充分的记录。非裔美国人（AA） 服务不足的人群不太可能比白人接受推荐的辅助RT，如果治疗， 发生RT相关副作用和临床结局较差的风险较高。为了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 代谢组学方法来测试三个种族/民族人群中RT诱导的EASR/疼痛的预测模型。 我们将测试一种新的范式，即多种代谢物/途径有助于辐射敏感性， 预测RT诱导的EASR/疼痛。研究这一新的范式将开发出强大的工具，以确定高- 风险人群和精准干预和治疗的目标。目标1将评估全球代谢组学- RT诱导的EASR/疼痛的驱动预测模型。目标2将定量和验证重要代谢物 和Aim 1中的通路预测RT诱导的EASR/疼痛。目标3将制定和评估绩效 综合模型（即，代谢组学/来自目标2的途径、患者特征和临床变量） 预测RT诱导的EASRs/疼痛。我们的初步数据显示丙氨酸天冬氨酸和谷氨酸 代谢途径可能有助于RT诱导的EASRs/疼痛。因此，靶向肿瘤代谢， 转氨酶抑制剂除了具有抗肿瘤活性外，还可以预防RT诱导的EASR/疼痛。资本化 基于来自大型三种族/民族乳腺癌人群的现有尿液样本和临床数据，目标为480 患者（67%少数民族），有前途的试点数据，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，可以进行 提议的研究。据我们所知，这是第一次对RT诱导的EASRs/疼痛进行代谢组学研究。 在乳腺癌中。使用假设驱动的方法，结果将显著提高 乳腺癌中RT诱导的EASRs/疼痛的急需预测模型。拟议会议的成果 研究将推进我们在准确评估RT结果方面的科学知识， 干预它们还可能有助于制定获益-风险指数，以帮助决策， 结果将针对有效的干预和治疗策略，并最终 改善乳腺癌患者的生活质量和无进展生存期，特别是在 服务不足的少数民族，RT诱导的EASR/疼痛和临床结果更差。