Identifying factors associated with ovarian cancer recurrence using a population-based approach

使用基于人群的方法识别与卵巢癌复发相关的因素

基本信息

批准号：
10581186
负责人：
Lindsay Jane Collin
金额：
$ 13.96万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10581186
关键词：
Affect Algorithms Area Asian Award Biological Biological Markers Biometry Black race Cancer Etiology Cancer Patient Cancer Prognosis Cancer Survivor Carcinoma Categories Characteristics Classification Clinical Clinical Data Clinical Trials Communication Computational Biology Data Data Sources Development Diagnosis Disease Disease Management Distress Epithelial ovarian cancer Event Fostering Foundations Fright Future Gene Expression Gene Expression Profile Gene Expression Profiling Glean Goals Guidelines Hispanic Hospitalization Individual Intervention Knowledge Leadership Link Malignant Neoplasms Malignant neoplasm of ovary Medical Records Mentorship Molecular Morbidity - disease rate Operative Surgical Procedures Ovarian Pathology Report Patient Care Patients Pattern Performance Population Population Database Probability Prognosis Prognostic Marker Publishing Recurrence Recurrent Malignant Neoplasm Research Research Personnel Research Project Grants Resources Risk Risk Assessment Risk Factors Serous Source Systemic Therapy Therapeutic Tissue Sample Training Treatment Protocols United States Update Utah Validation Woman Work World Health Organization biobank biomarker development biomarker driven brca gene cancer diagnosis cancer epidemiology cancer recurrence career development clinical care clinical decision-making clinical remission clinical risk clinically relevant cohort data analysis pipeline data streams epithelial to mesenchymal transition experience follow-up genetic epidemiology high risk improved mortality multiple data sources patient biomarkers patient prognosis personalized approach population based prognostic transcriptomics treatment response tumor

项目摘要

Ovarian cancer is the fifth leading cause of cancer related mortality in the United States. Despite advances in surgical approaches and treatment regimens, overall survival has improved only marginally over the past thirty years. Although nearly 80% of ovarian cancer patients will achieve complete clinical remission through surgery and systemic therapy at their initial diagnosis, more than 50% will experience a recurrence by five years after diagnosis. However, little is known about factors contributing to risk of ovarian cancer recurrence. Ovarian cancer is a heterogeneous disease with distinct histotypes that inform prognosis. High grade serous carcinoma is the most common histotype, comprising ~70% of all ovarian cancer diagnoses. Recently, three robust gene expression signatures have been developed that have the potential to inform patient prognosis and biomarker- driven therapeutic approaches. These tumor gene expression signatures include: a) the Milstein prognostic score that distinguishes individuals with high and low probability of survival; b) the PrOTYPE classifier, which categorizes four biologic subtypes; and c) the Oxford classifier, which identifies a poor prognosis epithelial-to- mesenchymal transition score. Each signature correlates to differential with survival, suggesting that the signatures may have clinical utility in informing patient prognosis; however, the scores have yet to be evaluated in a population-based setting. Thus, the overarching goal of this proposal is to understand patient demographic, clinicopathologic, and molecular features associated with patterns of ovarian cancer recurrence and mortality. To do this, I will leverage the robust resources through the Utah Population Database to achieve the following study aims: (1) Characterize patterns of ovarian recurrence and mortality by patient and clinicopathologic characteristics; and (2) Compare the performance of three prognostic tumor gene expression signatures with (a) mortality and (b) recurrence among high-grade serous ovarian cancer patients. The primary training experience will focus on three areas: first, to develop expertise in the development and validation of an algorithm to identify recurrence using multiple data streams; second, to develop expertise in transcriptomics and data analysis pipelines for gene expression profiling; and third, to foster professional and career development through leadership, scientific communication, and then transitioning to independence. The research and training will be supported by an interdisciplinary mentorship team led by Dr. Jennifer Doherty, and comprised of experts in ovarian cancer and genetic epidemiology, computational biology, and biostatistics. The results from these aims will expand our understanding of factors contributing to risk and timing of ovarian cancer recurrence and provide evidence on how gene expression signatures of high-grade serous ovarian cancer can be incorporated into clinical risk assessment. Cumulatively, information gleaned from this work could lead to a personalized approach to ovarian cancer disease management through inclusion of prognostic markers in clinical care and the development of biomarker-driven therapies.

卵巢癌是美国癌症相关死亡率的第五个主要原因。尽管进步手术方法和治疗方案，整体生存率仅在过去三十次略有改善年。尽管近80％的卵巢癌患者将通过手术实现完全的临床缓解和全身疗法在初次诊断时，超过50％的人会在五年后复发诊断。但是，关于导致卵巢癌复发风险的因素知之甚少。卵巢癌症是一种异质性疾病，具有明显的组织型，可为预后提供依据。高级浆液性癌是最常见的组织型，占所有卵巢癌诊断的约70％。最近，三个健壮的基因已经开发了表达特征，有可能告知患者预后和生物标志物驱动的治疗方法。这些肿瘤基因表达特征包括：a）米尔斯坦预后得分可以区分生存率高和低概率的个体； b）原型分类器，该分类器对四种生物学亚型分类； c）牛津分类器，它标识了预后不良的上皮到 - 间充质转变评分。每个签名与生存的差异相关，表明签名可能具有临床效用，以告知患者预后；但是，分数尚未评估在基于人群的环境中。因此，该提议的总体目标是了解患者人口统计学，临床病理学和分子特征与卵巢癌复发有关和死亡率。为此，我将通过犹他州人口数据库利用强大的资源来实现以下研究的目的：（1）表征患者的卵巢复发和死亡模式，临床病理特征；（2）比较三个预后肿瘤基因表达的性能（a）死亡率和（b）高级浆液卵巢癌患者的签名。主要培训经验将重点关注三个领域：首先，在开发和验证方面发展专业知识算法使用多个数据流识别复发；第二，发展转录组学专业知识以及基因表达分析的数据分析管道；第三，要培养专业人士和职业通过领导力，科学沟通，然后过渡到独立性。这研究和培训将得到由詹妮弗·多赫蒂（Jennifer Doherty）博士领导的跨学科指导团队的支持由卵巢癌和遗传流行病学，计算生物学和生物统计学专家组成。这些目标的结果将扩大我们对造成卵巢风险和时机的因素的理解癌症复发，并提供有关高级浆液卵巢基因表达特征的证据癌症可以纳入临床风险评估中。累积地，从这项工作中收集的信息通过纳入预后，可能导致卵巢癌疾病管理的个性化方法临床护理和生物标志物驱动疗法的发展。