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: 8011343
• 负责人: Jennifer J Hu
• 金额: $ 55.9万
• 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 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8011343
• 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; Resistance; 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; public health relevance; racial and ethnic; response; standard care; tool; treatment strategy; tumor

DESCRIPTION (provided by applicant): 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 genome- wide 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 hypothesis- driven approach to integrate genetic and functional biomarkers in developing optimal prediction models of RT- induced 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. PUBLIC HEALTH RELEVANCE: Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death in American women. The long-term goal of this study is to improve quality of life, clinical outcome, and breast cancer disparities. The proposed research will use a genome-wide approach to develop and validate genomic prediction models for radiotherapy-induced early and late side effects as well as local-regional recurrence in three racial/ethnic populations. The outcome of the proposed research will advance our scientific knowledge in the accurate assessment of prognosis and response to therapy in cancer patients, which is crucial to controlling the suffering and death due to breast cancer.

描述（由申请人提供）： 乳腺癌是最常见的癌症，也是美国女性癌症死亡的第二大原因。肿瘤切除术后放疗（RT）显着提高生存率。然而，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、乳房水肿和不良美容效果，影响生活质量。在正常组织中，RT诱导的不良反应发展的个体间差异性在急性和晚期效应方面均有充分记录。非洲裔美国人（AA）和服务不足的人群不太可能比白人接受推荐的辅助RT，如果接受治疗，发生RT相关副作用的风险更高，临床结局更差。为了实现我们改善生活质量、临床结局和克服乳腺癌差异的长期目标，我们将使用全基因组方法在三个种族/民族人群中测试RT诱导的不良反应和复发的基因组预测模型。我们将测试一种新的范式，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，用于识别高危人群和个性化干预和治疗的目标。目的1将在1000例乳腺癌患者中评估RT诱导的早期不良皮肤反应（EASR）的多基因模型，并对全基因组非同义单核苷酸多态性（nsSNPs; n= 21，877）进行综合评价。目的2将评估RT诱导的EASRs与三种功能性DNA损伤/修复表型之间的关联。目标3将开发全基因组nsSNP的多基因模型，用于预测850名女性乳腺癌队列中RT诱导的晚期副作用和/或复发，中位随访时间为8年（范围4-12年）。拟议研究的结果将推进我们在准确评估癌症患者预后方面的科学知识，这对控制乳腺癌造成的痛苦和死亡至关重要。预测模型提供了评估癌症风险、进展、生活质量和预后的重要方法。这些预测模型可以识别可能受益于靶向治疗或其他干预措施的处于发生不良反应或复发的高风险的个体。它们还可能有助于开发获益-风险指数，以帮助临床治疗的设计和规划。拟议的研究将使用假设驱动的方法，整合遗传和功能生物标志物，以开发RT诱导的不良反应和复发的最佳预测模型。研究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，并将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。 公共卫生关系： 乳腺癌是最常见的癌症，也是美国女性癌症死亡的第二大原因。这项研究的长期目标是改善生活质量，临床结果和乳腺癌的差异。拟议的研究将使用全基因组方法开发和验证基因组预测模型，用于放射治疗诱导的早期和晚期副作用以及三个种族/民族人群的局部复发。拟议研究的结果将推进我们在准确评估癌症患者预后和治疗反应方面的科学知识，这对控制乳腺癌造成的痛苦和死亡至关重要。