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

基本信息

批准号：
8403713
负责人：
Jennifer J Hu
金额：
$ 49.71万
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：
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/8403713
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 racial and ethnic response standard care tool treatment strategy tumor

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 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.

项目摘要/摘要乳腺癌是最常见的癌症，也是癌症死亡的第二大原因在美国妇女。乳房切除术后进行放疗（RT）的生存率显着提高。然而，大约30％的患者患有2级或更糟的早期或晚期皮肤反应，疼痛，乳房水肿和差影响生活质量的美容结果。 RT引起的不良发展的个体间变异性正常组织中的反应均有充分记录在急性和晚期作用方面。非裔美国人（AA）和服务不足的人群比白人接受建议的辅助RT（如果接受治疗）的可能性较小发展与RT相关的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时，我们将使用基因组 - 测试RT诱导的不良反应和复发的基因组预测模型的广泛方法种族/族裔人口。我们将测试一个新的范式，该范式多种遗传变异和功能表型有助于辐射敏感性，这可能预测了RT诱导的副作用和临床结果。调查这一新范式将开发出强大的工具，以识别高风险人群和目标个性化干预和治疗。 AIM 1将评估RT诱导的早期不良模型的多基因模型 1000名乳腺癌患者的皮肤反应（EASR）全面评估了全基因组非同义单核苷酸多态性（NSSNP； n = 21,877）。 AIM 2将评估协会在RT诱导的EASR和三个功能性DNA损伤/修复表型之间。 AIM 3将发展全基因组NSSNP的多基因模型预测RT诱导的晚期副作用和/或复发乳腺癌队列的850名女性中位随访（4-12岁）。结果拟议的研究将推进我们在准确评估癌症预后的科学知识患者，这对于控制乳腺癌引起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。这些预测模型可能会识别出可能受益的不良反应或复发的高风险的人来自目标治疗或其他干预措施。他们还可以使利益风险指数的发展这将有助于设计和计划临床治疗。拟议的研究将使用假设 - 在开发RT-的最佳预测模型中，以遗传和功能生物标志物整合的驱动方法引起不良反应和复发。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的质量，特别是在少数民族和服务不足的人群中，患有更具侵略性的疾病和临床结果较差。这将是迄今为止与RT相关临床结果的最大，最完整的遗传分析，并将显着将领域移动到针对乳腺癌患者更有效，个性化治疗的目标。