Improving Treatment Selection in Advanced Ovarian Cancer

改善晚期卵巢癌的治疗选择

• 批准号: 10345161
• 负责人: Pari Vijay Pandharipande
• 金额: $ 64.06万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-06 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10345161
• 关键词: Address; Affect; Aftercare; Age; Cancer Etiology; Cancer Survivor; Caring; Cause of Death; Cells; Cessation of life; Characteristics; Data; Data Sources; Databases; Death Rate; Decision Making; Diagnosis; Disease model; Distant; Ensure; Epithelial ovarian cancer; Future; Goals; Harm Reduction; Knowledge; Life Expectancy; Light; Malignant Female Reproductive System Neoplasm; Malignant Neoplasms; Malignant neoplasm of ovary; Medicare; Modeling; Molecular; Mutation; Newly Diagnosed; Operative Surgical Procedures; Outcome; Patients; Periodicity; Physicians; Poly(ADP-ribose) Polymerases; Publishing; Quality-Adjusted Life Years; Recommendation; Recurrence; Recurrent Malignant Neoplasm; Research; Research Activity; Research Priority; Resources; Risk; SEER Program; Selection for Treatments; Survivors; Techniques; Time; Toxic effect; Treatment Failure; Treatment outcome; Treatment-related toxicity; Woman; Work; advanced disease; base; cancer care; cancer therapy; chemotherapy; comorbidity; cost; cost effective; cost effectiveness; cost estimate; improved; individual patient; individualized medicine; inhibitor; mathematical model; models and simulation; mortality; mutational status; new therapeutic target; novel therapeutics; patient stratification; personalized approach; personalized medicine; prevent; programs; side effect; targeted treatment; therapy design; treatment strategy; tumor

PROJECT SUMMARY/ABSTRACT The goal of this proposal is to improve treatment selection and survival in women with advanced ovarian cancer. Ovarian cancer is the leading cause of deaths from gynecologic cancers in the U.S. Most women present with advanced disease, with distant spread at the time of diagnosis. Even so, more than 1 in 10 of such women will survive >10 years after an initial diagnosis, usually with periodic recurrences. The possibility of long-term survival underscores the paramount importance of each treatment decision. Determining the best treatment strategy for an individual patient is difficult. For women with newly diagnosed, advanced ovarian cancer, both surgery and chemotherapy are recommended. However, many women fail to complete both, rates of death from surgery are high, and chemotherapy delivery is often limited by toxicities. New therapies that are designed to target cells at the molecular level (“PARP inhibitors”) have recently been approved for use, but their benefits vary based on the presence of specific tumor mutations, and their costs exceed $150,000/year. Carefully tailored decisions about the sequence of surgery and chemotherapy, types of chemotherapy, and the way chemotherapy is delivered, could improve long-term outcomes and reduce costs. To address this problem, we will build a simulation modeling framework that projects the outcomes of women treated for advanced ovarian cancer, and use it to identify personalized treatment approaches. We have built a preliminary model that projects outcomes for women with newly diagnosed, advanced ovarian cancer. We will extend our model to include detailed patient and tumor characteristics – age, comorbidities, stage, and mutation status – that influence survival, as well as new therapies and toxicities (Aim 1.1). We will also simulate the treatment of recurrent cancer (Aim 1.2). Using our new modeling framework, we will identify tailored treatment approaches that optimize survival (Aim 2.1), minimize treatment toxicities (Aim 2.2), and are cost-effective (Aim 3). Finally, we will identify future studies that are likely to have the greatest impact in improving treatment decisions (Aim 4). To ensure that our findings are accessible to patients, physicians, and policymakers, we will create an online, interactive version of our modeling framework that can project outcomes, quantify trade-offs, and support decision-making in real time. The proposed research will result in: 1) personalized treatment recommendations; 2) real-time guidance for decision-making; 3) the capacity to rapidly weigh benefits of new therapies with long-term risks and costs; and 4) prioritization of future research. The knowledge gained will provide new opportunities to improve treatment selection and survival in ovarian cancer.

项目总结/摘要 该提案的目的是改善晚期卵巢癌患者的治疗选择和生存率。 癌卵巢癌是美国妇科癌症死亡的主要原因。 目前患有晚期疾病，在诊断时已远距离传播。即便如此， 这些妇女在初次诊断后将存活>10年，通常伴有周期性复发。的可能性 长期生存的重要性强调了每一个治疗决定的至关重要性。 为个别患者确定最佳治疗策略是困难的。对于新诊断的女性， 晚期卵巢癌，建议手术和化疗。然而，许多女性未能 如果两者都完成，手术死亡率很高，化疗的实施往往受到毒性的限制。 最近，被设计为在分子水平上靶向细胞的新疗法（“PARP抑制剂”）已经被发现。 批准使用，但它们的益处因特定肿瘤突变的存在及其成本而异 超过15万元/年。精心定制的决定，手术和化疗的顺序，类型， 化疗和化疗的方式可以改善长期结果并降低成本。 为了解决这个问题，我们将建立一个模拟建模框架， 治疗晚期卵巢癌，并使用它来确定个性化的治疗方法。我们已经建立了一个 初步模型，预测新诊断的晚期卵巢癌妇女的结局。我们将 扩展我们的模型，以包括详细的患者和肿瘤特征-年龄，合并症，阶段， 突变状态-影响生存率以及新疗法和毒性（目标1.1）。我们还将 模拟复发性癌症的治疗（目标1.2）。使用我们新的建模框架，我们将识别 优化生存率（目标2.1）、最小化治疗毒性（目标2.2）， 成本效益（目标3）。最后，我们将确定未来的研究，可能有最大的影响， 改善治疗决策（目标4）。 为了确保患者、医生和政策制定者能够访问我们的研究结果，我们将创建一个在线， 我们的建模框架的交互式版本，可以预测结果，量化权衡，并支持 真实的决策。拟议的研究将导致：1）个性化治疗 建议; 2）决策的实时指导; 3）快速权衡新的 具有长期风险和成本的治疗;以及4）未来研究的优先次序。获得的知识将 为改善卵巢癌的治疗选择和生存率提供了新的机会。