Impact of Genomics on Disparities in Breast Cancer Radiosensitivity

基因组学对乳腺癌放射敏感性差异的影响

• 批准号: 8205701
• 负责人: Jennifer J Hu
• 金额: $ 14.1万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8205701
• 关键词: Acute; Adjuvant Radiotherapy; Adverse effects; Adverse reactions; African American; American; Amino Acid Sequence; Amino Acids; Angiogenesis Pathway; Apoptosis; Benefits and Risks; Biological Assay; Biological Markers; Breast; Cancer Etiology; Cancer Patient; Cell Cycle; Cell Cycle Regulation; Cessation of life; Clinical; Clinical Treatment; Comet Assay; Cosmetics; DNA Damage; DNA Repair; Data; Development; Developmental Process; Disease; Edema; Epidemiologist; Ethnic group; Evaluation; Follow-Up Studies; Funding; Genes; Genetic; Genetic Models; Genetic Variation; Genomics; Goals; Hispanics; Immunology; Individual; Inflammation; Intensity-Modulated Radiotherapy; Interdisciplinary Study; Intervention; Ionizing radiation; Knowledge; Late Effects; Lead; Learning; Logit Models; Malignant Neoplasms; Minority; Modeling; Molecular; Mutation; Normal tissue morphology; Not Hispanic or Latino; Oncologist; Outcome; Pain; Pathway interactions; Patients; Peptide Sequence Determination; Phenotype; Phosphorylation; Population; Population Study; Positioning Attribute; Postoperative Period; Progression-Free Survivals; Quality of life; Radiation Oncologist; Radiation Tolerance; Radiation therapy; Reaction; Recruitment Activity; Recurrence; Research; Sampling; Second Primary Cancers; Signal Transduction; Single Nucleotide Polymorphism; Skin; Staging; Surgeon; Target Populations; Testing; Trees; Underserved Population; Woman; Work; breast lumpectomy; cancer diagnosis; cancer health disparity; cancer risk; cell motility; cohort; data mining; design; dosimetry; effective intervention; effective therapy; experience; follow-up; functional genomics; gene interaction; genetic analysis; genome-wide; high risk; high throughput technology; improved; indexing; intercellular communication; malignant breast neoplasm; medically underserved population; mortality; outcome forecast; racial and ethnic; response; standard care; tool; treatment strategy; tumor

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 30% of patients develop a Grade 2 or worse early or late skin reaction, 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 genomewide approach to test genomic prediction models for RT-induced adverse reactions and recurrence in three racial/ethnic populations. We will test a new paradigm that multiple genetic variations and functional phenotypes contribute to radiation sensitivity that may predict RT-induced side effects and clinical outcome. Investigating this new paradigm will develop powerful tools in identifying high-risk populations and targets for personalized intervention and treatment. Aim 1 will evaluate polygenic models of RT-induced early adverse skin reactions (EASRs) in 1000 breast cancer patients with a comprehensive evaluation of genome-wide nonsynonymous single nucleotide polymorphisms (nsSNPs; n=21,877). Aim 2 will evaluate the association between RT-induced EASRs and three functional DNA damage/repair phenotypes. Aim 3 will develop polygenic models of genome-wide nsSNPs in predicting RT-induced late side effects and/or recurrence in a breast cancer cohort of 850 women with a median follow up of 8 years (range 4-12 years). The outcome of the proposed research will advance our scientific knowledge in the accurate assessment of prognosis in cancer patients, which is crucial to controlling the suffering and death due to breast cancer. Prediction models provide an important approach to assessing cancer risk, progression, quality of life, and prognosis. These prediction models may identify individuals at high risk of developing adverse reactions or recurrence who may benefit from targeted treatment or other interventions. They also may enable the development of benefit-risk indices that will aid in the design and planning of clinical treatment. The proposed research will use a hypothesisdriven approach to integrate genetic and functional biomarkers in developing optimal prediction models of RTinduced adverse reactions and recurrence. The outcome will target effective intervention and treatment strategies, and ultimately improve quality of life and progression-free survival in breast cancer patients, particularly in minority and underserved populations with more aggressive disease and worse clinical outcome. This will be the largest and most complete genetic analysis of RT-related clinical outcome to date, and will move the field significantly towards the goal of more effective, personalized therapy for breast cancer patients.

乳腺癌是最常见的癌症，也是癌症死亡的第二大原因 在美国妇女。肿瘤切除术后放疗（RT）显着提高生存率。然而，在这方面， 大约30%的患者出现2级或更严重的早期或晚期皮肤反应、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，我们将使用全基因组 一种方法来测试基因组预测模型的RT诱导的不良反应和复发，在三个 种族/民族人口。我们将测试一种新的模式，即多种遗传变异和功能 表型有助于辐射敏感性，可以预测RT诱导的副作用和临床结果。 对这一新模式的研究将开发出强有力的工具，用于确定高危人群和目标， 个性化的干预和治疗。目的1将评价RT诱导的早期不良反应的多基因模型 1000例乳腺癌患者的皮肤反应（EASRs）， 非同义单核苷酸多态性（nsSNPs; n= 21，877）。目标2将评估关联 RT诱导的EASRs和三种功能性DNA损伤/修复表型之间的关系。Aim 3将开发 全基因组nsSNP的多基因模型在预测RT诱导的晚期副作用和/或复发中的应用 850名女性的乳腺癌队列，中位随访时间为8年（范围4-12年）。的结果 拟议的研究将推进我们在准确评估癌症预后方面的科学知识 这对控制乳腺癌患者的痛苦和死亡至关重要。预测模型提供 评估癌症风险、进展、生活质量和预后的重要方法。这些预测 模型可以识别出具有发生不良反应或复发的高风险的个体， 有针对性的治疗或其他干预措施。它们还可能有助于开发获益-风险指数 这将有助于临床治疗的设计和规划。拟议的研究将使用假设驱动 一种整合遗传和功能生物标志物的方法，用于开发RT诱导的最佳预测模型 不良反应和复发。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， 特别是在少数民族和服务不足的人群中，疾病更具侵袭性，临床结果更差。 这将是迄今为止对RT相关临床结果进行的最大和最完整的遗传分析， 使该领域朝着为乳腺癌患者提供更有效、个性化治疗的目标迈进。