Improving Treatment Selection in Advanced Ovarian Cancer

改善晚期卵巢癌的治疗选择

• 批准号: 10604317
• 负责人: Pari Vijay Pandharipande
• 金额: $ 53.27万
• 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/10604317
• 关键词: Address; Affect; Aftercare; Age; Cancer Etiology; Caring; Cause of Death; Cells; Cessation of life; Characteristics; Data; Data Sources; Databases; Death Rate; Decision Making; Diagnosis; Disease model; Distant; Epithelial ovarian cancer; Future; Goals; Harm Reduction; Knowledge; Life Expectancy; Malignant Female Reproductive System Neoplasm; Malignant Neoplasms; Malignant neoplasm of ovary; Medicare; Modeling; Molecular; Mutation; Newly Diagnosed; Operative Surgical Procedures; Outcome; Patients; Periodicals; Physicians; Policy Maker; Poly(ADP-ribose) Polymerase Inhibitor; 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; cancer care; cancer recurrence; cancer survival; cancer therapy; chemotherapy; comorbidity; cost; cost effective; cost effectiveness; cost estimate; improved; individual patient; individualized medicine; mathematical model; models and simulation; mortality; mutational status; new therapeutic target; novel therapeutic intervention; 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.

项目摘要/摘要 该建议的目的是改善卵巢晚期女性的治疗选择和生存 癌症。卵巢癌是美国妇科癌症死亡的主要原因 出现晚期疾病，在诊断时具有远处传播。即便如此，超过十分之一 这些妇女在初次诊断后的10年后将生存>，通常会定期回报。可能性 长期生存强调了每个治疗决策的重要性。 为个别患者确定最佳治疗策略是困难的。对于有新诊断的女性 建议进行晚期卵巢癌，包括手术和化学疗法。但是，许多女性没有 完全完成手术的死亡率很高，化学疗法的递送通常受到毒性的限制。 最近设计用于分子水平靶向细胞的新疗法（“ PARP抑制剂”）已是 被批准用于使用，但它们的益处因特定肿瘤突变的存在及其成本而有所不同 超过$ 150,000/年。精心量身定制有关手术和化学疗法序列的决定，类型 化学疗法以及化疗的方式可以改善长期结局并降低成本。 为了解决这个问题，我们将建立一个模拟建模框架，以投影女性的结果 治疗晚期卵巢癌，并使用它来识别个性化治疗方法。我们已经建立了一个 初步模型，该模型为新诊断的晚期卵巢癌的女性提供结果。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，阶段和 突变状态 - 影响生存以及新的疗法和毒性（目标1.1）。我们也会 模拟复发性癌症的治疗（AIM 1.2）。使用我们的新建模框架，我们将确定 量身定制的治疗方法优化生存（AIM 2.1），最小化治疗毒性（AIM 2.2），并且是 具有成本效益（目标3）。最后，我们将确定未来的研究，这些研究可能会对 改善治疗决策（AIM 4）。 为了确保患者，医师和政策制定者可以访问我们的发现，我们将在线创建一个， 我们的建模框架的交互式版本可以投射成果，量化权衡和支持 实时决策。拟议的研究将导致：1）个性化治疗 建议； 2）实时决策指南； 3）新的重量益处的能力 长期风险和成本的治疗； 4）未来研究的优先级。获得的知识将 提供新的机会来改善卵巢癌的治疗选择和生存。