Improving Treatment Selection in Advanced Ovarian Cancer

项目概要/摘要 该提案的目标是改善晚期卵巢女性的治疗选择和生存率 癌症。卵巢癌是美国妇科癌症死亡的主要原因。大多数女性 患有晚期疾病，在诊断时已发生远处扩散。即便如此，超过十分之一的人 此类女性在初次诊断后可存活 10 年以上，但通常会定期复发。可能性 长期生存强调了每个治疗决策的至关重要性。 为个别患者确定最佳治疗策略很困难。对于新确诊的女性来说， 晚期卵巢癌，建议手术和化疗。然而，很多女性却做不到 如果两者都完成，手术死亡率很高，而且化疗往往受到毒性的限制。 旨在分子水平上靶向细胞的新疗法（“PARP抑制剂”）最近已被开发出来。 已获批准使用，但其益处因特定肿瘤突变的存在及其成本而异 超过 150,000 美元/年。关于手术和化疗的顺序、治疗类型的精心定制的决定 化疗以及化疗的给药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来预测女性的结果 治疗晚期卵巢癌，并用它来确定个性化治疗方法。我们建立了一个 初步模型预测了新诊断的晚期卵巢癌女性的结果。我们会 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、分期和 突变状态——影响生存以及新疗法和毒性（目标 1.1）。我们还将 模拟复发性癌症的治疗（目标 1.2）。使用我们的新建模框架，我们将确定 量身定制的治疗方法可以优化生存（目标 2.1），最大限度地减少治疗毒性（目标 2.2），并且 成本效益（目标 3）。最后，我们将确定未来可能产生最大影响的研究 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获取我们的研究结果，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）实时指导决策； 3）快速权衡新事物的好处的能力 具有长期风险和成本的疗法； 4）未来研究的优先顺序。所获得的知识将 为改善卵巢癌的治疗选择和生存提供新的机会。

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.