Predicting risk of cardiotoxicity among young and emerging adult breast cancer patients from treatment to survivorship

预测年轻和新兴成年乳腺癌患者从治疗到生存的心脏毒性风险

• 批准号: 10383402
• 负责人: Elizabeth Anne Hibler
• 金额: $ 15.64万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-06 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10383402
• 关键词: Address; Affect; Age; Animals; Bioinformatics; Breast Cancer Patient; Breast Cancer Treatment; Breast Cancer survivor; Breast Cancer therapy; Cancer Patient; Cancer Survivor; Cardiac; Cardiac Myocytes; Cardiotoxicity; Cardiovascular Diseases; Cause of Death; Chemotherapy-Oncologic Procedure; Clinical; Consumption; DNA; DNA Sequence Alteration; Data; Development; Diagnosis; Diet; Echocardiography; Electronic Health Record; Emotional; Epidemiology; Etiology; Exposure to; Face; Future; Gene Expression; Gene Expression Profile; Gene Mutation; Genetic Predisposition to Disease; Goals; Health; Health behavior; Heart Injuries; Heart failure; Hypertension; Impairment; Knowledge; Left Ventricular Ejection Fraction; Longevity; Longitudinal cohort; Longitudinal cohort study; Machine Learning; Malignant Neoplasms; Measures; Medical center; Medicine; Methods; Modeling; Molecular; Morbidity - disease rate; Mutation; National Heart, Lung, and Blood Institute; Nested Case-Control Study; Obesity; Outcome; Participant; Patients; Pattern; Phenotype; Population; Predictive Factor; Psychosocial Factor; Public Health Informatics; Questionnaires; Research Personnel; Risk; Risk Factors; Time; Tissues; Training; Variant; Woman; aged; base; brca gene; breast cancer diagnosis; breast cancer survival; cancer diagnosis; cancer epidemiology; cardiogenesis; cardiovascular disorder risk; career; career development; case control; chemotherapy; childhood cancer survivor; circulating biomarkers; clinical care; design; emerging adult; experience; follow-up; high risk; improved; malignant breast neoplasm; modifiable risk; molecular marker; molecular modeling; mortality; novel; physical inactivity; predictive marker; predictive modeling; psychosocial; recruit; repaired; risk prediction model; risk sharing; skills; social; survivorship; young adult; young cancer survivor

Breast cancer is the most common cancer among women and cardiovascular disease (CVD) is prevalent among breast cancer survivors. This is due to shared risk factors between CVD and cancer, but also that breast cancer therapies are often cardiotoxic, which may later cause heart failure (HF). Cardiotoxicity from breast cancer chemotherapy affects between 10-20% of patients with enhanced risk in the presence of traditional CVD risk factors. However, there is a significant gap in our knowledge of cardiotoxicity among the rapidly growing population of young and emerging adult (YEA) breast cancer survivors, which comprise 5-12% of all breast cancer diagnoses. As survival from breast cancer increases, exposure to cardiotoxic chemotherapies at a younger age may enhance HF risk among YEA breast cancer survivors. Moreover, YEA breast cancer patients are more likely to have gene mutations that may also impair cardiac tissue function combined with a unique pattern of health behaviors and CVD risk factors. However, we are currently unable to predict which patients are at highest risk of cardiotoxicity. Studies suggest that gene expression may refine identification of women at increased risk of cardiotoxicity. To date, no studies examined whether combining gene expression and genetic mutations with CVD risk factors can identify YEA patients at increased risk of cardiotoxic effects of chemotherapy. To address this issue, I will complete the following specific aims: 1) Develop a predictive model combining psychosocial and traditional CVD risk factors to identify YEA breast cancer patients at increased risk of cardiotoxicity as defined by a decline in global longitudinal strain (GLS) or left ventricular ejection fraction (LVEF); 2) Investigate if the risk factor profile at diagnosis is associated with trajectory of GLS and LVEF during and after breast cancer treatment; and 3) investigate the impact of molecular biomarkers to risk prediction models. We will recruit a longitudinal cohort of n=300 YEA breast cancer patients treated at Northwestern Medicine. Among these participants, in a nested case-control design, we will select cases diagnosed with decline in GLS during chemotherapy (n=50) with age-matched controls without cardiotoxicity (n=50). For all participants, we will combine electronic health record (EHR) data with psychosocial and traditional CVD risk factors at three timepoints. For the nested case-control study, we will additionally measure gene expression at two timepoints. This directly informs my short-term career development goals to 1) Gain experience in HF and CVD etiology, epidemiology, and risk factors; 2) Develop skills in machine learning and bioinformatics approaches for prediction; and 3) Refine health informatics methods to integrate EHR with epidemiologic and molecular data. The skills and pilot data generated through this K01 will enable me to address the NHLBI compelling question (5.CQ.10) to reduce cardiac morbidity and mortality in cancer survivors. I will thus achieve my long-term career goal to identify and intervene on the CVD threats to the health and longevity of YEA cancer survivors.

乳腺癌是女性中最常见的癌症，心血管疾病（CVD）在女性中普遍存在。 乳腺癌幸存者这是由于心血管疾病和癌症之间的共同风险因素，而且乳腺癌 癌症治疗通常是心脏毒性的，其随后可能导致心力衰竭（HF）。乳房毒性 癌症化疗影响了10-20%的患者，在传统化疗的存在下， CVD危险因素。然而，我们对心脏毒性的认识在快速发展的心脏病患者中存在显著的差距。 越来越多的年轻和新兴成年（是的）乳腺癌幸存者，占所有乳腺癌幸存者的5-12%。 乳腺癌的诊断随着乳腺癌生存率的增加，暴露于心脏毒性化疗， 在是的乳腺癌幸存者中，较年轻的年龄可能增加HF风险。而且，是的乳腺癌 患者更可能有基因突变，也可能损害心脏组织功能， 独特的健康行为模式和CVD危险因素。然而，我们目前无法预测 患者的心脏毒性风险最高。研究表明，基因表达可能会改善识别 心脏毒性风险增加的女性。到目前为止，还没有研究检测是否结合基因表达， 具有心血管疾病危险因素的基因突变可以识别出是的YEA患者， 化疗为了解决这个问题，我将完成以下具体目标：1）开发一个预测模型 结合心理社会和传统的心血管疾病风险因素来识别是的乳腺癌患者 心脏毒性风险，定义为总体纵向应变（GLS）或左心室射血功能下降 2）研究诊断时的风险因素特征是否与GLS的轨迹相关， 乳腺癌治疗期间和之后的LVEF;以及3）研究分子生物标志物对风险的影响 预测模型我们将招募n=300例接受以下治疗的是的乳腺癌患者的纵向队列： 西北医学在这些参与者中，在嵌套的病例对照设计中，我们将选择病例 被诊断为化疗期间GLS下降（n=50），年龄匹配的对照组无心脏毒性 （n=50）。对于所有参与者，我们将联合收割机电子健康记录（EHR）数据与心理社会和 传统的CVD危险因素在三个时间点。对于巢式病例对照研究，我们将额外测量 两个时间点的基因表达。这直接告诉我的短期职业发展目标1）获得 HF和CVD病因学、流行病学和风险因素方面的经验; 2）培养机器学习技能， 用于预测的生物信息学方法;以及3）改进健康信息学方法，将EHR与 流行病学和分子数据。通过K 01生成的技能和试点数据将使我能够 解决NHLBI迫切的问题（5.CQ.10），以降低癌症的心脏病发病率和死亡率 幸存者因此，我将实现我的长期职业目标，以确定和干预CVD的威胁， 是的癌症幸存者的健康和长寿。