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

• 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
• 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

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大原因。尽管取得了进展， 手术方法和治疗方案，总生存率在过去的30年中仅略有改善 年虽然近80%的卵巢癌患者会通过手术达到临床完全缓解 如果在初次诊断时进行全身治疗，超过50%的患者在治疗后五年内会复发。 诊断.然而，对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，其具有不同的组织型，可告知预后。高级别浆液性癌 是最常见的组织型，占所有卵巢癌诊断的约70%。最近，三个强大的基因 已经开发出了具有告知患者预后和生物标志物的潜力的表达特征， 驱动的治疗方法。这些肿瘤基因表达特征包括： 区分具有高和低存活概率的个体的评分; B）PrOTYPE分类器，其 分类四个生物亚型;和c）牛津分类器，其鉴定预后不良的上皮- 间充质转化评分。每一个信号都与生存率的差异相关，这表明 签名可能在告知患者预后方面具有临床实用性;然而，尚未对评分进行评估 in a population人口based基础setting设置.因此，该提案的首要目标是了解患者 与卵巢癌复发模式相关的人口统计学、临床病理学和分子特征 and mortality.为此，我将利用犹他州人口数据库的强大资源， 本研究的目的如下：（1）通过患者描述卵巢复发和死亡的模式， 临床病理特征;（2）比较三种预后肿瘤基因表达的表现 高级别浆液性卵巢癌患者中具有（a）死亡率和（B）复发率的特征。主 培训经验将集中在三个领域：第一，发展制定和验证 使用多个数据流识别复发的算法;第二，发展转录组学方面的专业知识 和数据分析管道基因表达谱;第三，培养专业和职业 通过领导力，科学交流，然后过渡到独立发展。的 研究和培训将得到由詹妮弗·多尔蒂博士领导的跨学科导师团队的支持， 由卵巢癌、遗传流行病学、计算生物学和生物统计学方面的专家组成。 这些目标的结果将扩大我们对卵巢癌风险和时间因素的理解。 并提供关于高级别浆液性卵巢癌的基因表达特征如何 可以将癌症纳入临床风险评估。累积起来，从这项工作中收集的信息 通过纳入预后因素， 临床护理中的生物标志物和生物标志物驱动疗法的发展。