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的第五大原因。尽管取得了进展，但 手术方法和治疗方案，在过去30年中，总体存活率仅略有改善 好几年了。虽然近80%的卵巢癌患者将通过手术实现临床完全缓解 和系统治疗在最初诊断时，超过50%的人在五年后会复发 诊断。然而，人们对卵巢癌复发风险的影响因素知之甚少。卵巢 癌症是一种异质性疾病，具有不同的组织类型，决定着预后。高级别浆液性癌 是最常见的组织类型，约占所有卵巢癌诊断的70%。最近，有三个健壮基因 已经开发出有可能告知患者预后和生物标记物的表达特征。 驱动型治疗方法。这些肿瘤基因表达特征包括：a)Milstein预后 区分存活概率高和低的个体的分数；b)原型分类器，它 将四种生物学亚型分类；以及c)牛津分类器，该分类器识别预后不良的上皮到上皮 间充质转化评分。每个特征都与生存相关，这表明 签名可能在告知患者预后方面具有临床实用价值；然而，评分还有待评估。 在以人口为基础的环境中。因此，这项建议的首要目标是理解患者 与卵巢癌复发模式相关的人口学、临床病理和分子特征 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 以下研究的目的是：(1)描述卵巢复发和死亡率的类型 临床病理特征；以及(2)比较三种预后肿瘤基因表达的表现 与(A)死亡率和(B)高级别浆液性卵巢癌患者复发有关的特征。初级阶段 培训经验将集中在三个方面：第一，在开发和验证 使用多数据流识别复发的算法；第二，发展转录学专业知识 和用于基因表达谱分析的数据分析管道；第三，促进专业和职业 通过领导、科学交流实现发展，然后过渡到独立。这个 研究和培训将由詹妮弗·多尔蒂博士领导的跨学科指导团队提供支持， 由卵巢癌和遗传流行病学、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风险和时机选择因素的理解。 并为高级别浆液性卵巢癌的基因表达特征提供证据 癌症可以纳入临床风险评估。累积起来，从这项工作中收集到的信息 可以通过包含预后因素来实现卵巢癌疾病管理的个性化方法 临床护理中的标记物和生物标记物驱动疗法的发展。