Development of Novel Ovarian Cancer Biomarkers for Early Detection Algorithms

开发用于早期检测算法的新型卵巢癌生物标志物

• 批准号: 10226017
• 负责人: ROBERT C BAST
• 金额: $ 77.71万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10226017
• 关键词: Advisory Committees; Algorithms; Autoantibodies; Biological Markers; CA-125 Antigen; Clinical Trials; Combination Drug Therapy; Complement; Computer Models; Consensus; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early treatment; Epithelial ovarian cancer; Goals; Interdisciplinary Study; Intervention; Lead; Low Prevalence; Malignant neoplasm of ovary; Measures; Modality; Nurses' Health Study; Patient Care; Patients; Performance; Population; Postmenopause; Predictive Value; Preventive service; Probability; Prospective Studies; Recommendation; Resistance; Risk; Sampling; Screening for Ovarian Cancer; Serum; Specificity; Survival Rate; Symptoms; Testing; Time; Tumor Debulking; Ultrasonography; United Kingdom; Validation; Woman; base; blood-based biomarker; cancer biomarkers; cancer survival; candidate marker; care costs; classification algorithm; clinical Diagnosis; clinical practice; cohort; collaborative trial; early detection biomarkers; feasibility testing; follow-up; improved; mathematical model; mortality; novel; operation; population based; pre-clinical; prospective; screening; temporal measurement; tumor

ABSTRACT Ovarian cancer (OC) is a deadly but often silent disease, showing no specific signs until it reaches advanced stages. The 5-year survival rate for advanced OC is only 50%, as most tumors ultimately become resistant to treatment.1,2 Advances in cytoreductive surgery and combination chemotherapy have improved 5-year survival in patients with epithelial OC, but the rate of cure has not improved over the last two decades. Computer models suggest that detection of OC in early stages (I-II) could substantially improve cure rates, but the low prevalence of OC in the general postmenopausal population restricts early detection efforts. Definitive diagnosis requires operative intervention, but a consensus is that no more than 10 operations should be performed to diagnose a single OC (>10% positive predictive value, PPV). According to current requirements, a first-line biomarker-based screening test must achieve a sensitivity (SN) of at least 75% and a specificity (SP) of 98%, which can then be further increased to 99.6% by adding a second-line screening modality such as transvaginal sonography (TVS). 1,3-6 Because available screening tests remain inadequate to merit wide implementation, based on our strong preliminary findings the proposed project aims to develop a novel, widely translatable, and economically feasible test that can reduce OC mortality rates. Currently, the only promising strategy developed in the United Kingdom Collaborative Trial for OC screening (UKCTOCS), is sequential analysis of the marker CA125 in serum over time (Risk of OC Algorithm, ROCA), followed by TVS. UKCTOCS yielded only a modest 20% decrease in mortality, insufficient to prompt the US Preventive Services Task Force to change its recommendation against population-based OC screening. 1 The most likely reason for such modest mortality reduction by CA125 measures is their insufficient lead-time (estimated interval for detection prior to symptoms-based diagnosis). Bio- mathematical modeling suggests that OC progresses to late stages more than 1 year before symptoms onset, a time range when CA125 levels offer only limited diagnostic power. Therefore, to improve current clinical practice, novel screening algorithms allowing substantially longer lead-times are needed. Based on our strong preliminary findings, we aim to develop and validate a 2-pronged approach, whereby a first-line multi-biomarker test recognizes OC with high SN (>80%) and modest SP (>80%), followed by a second-line biomarker velocity-based test in women who tested positive in the first test, that then yields a combined SP of 98%. Supporting this approach, we have generated a preliminary classification algorithm (threshold-based algorithm, TBA) based on one-time measurement of multiple biomarker concentrations, that identifies with 80%SN-70%SP women who will develop OC 1-7 years later. We further identified several biomarkers that display robust temporal dynamics (velocity) associated with OC development in the 1-7 YTD interval. We thus hypothesize that we can generate a 2-step algorithm that provides >75%SN at >98%SP, by combining our novel TBA with a velocity-based algorithm (VBA). In this approach, similar to ROCA, the positive results of the TBA would trigger frequent follow- up screening with VBA. The crucial advantage of our proposed algorithm vs. UKCTOCS' ROCA is that our novel combined algorithm will recognize OC more than 1 YTD, increasing the probability of detecting OC at early, treatment-responsive stages. We have discovered, and will prioritize for integration into the tests, several promising candidate pre-diagnostic OC biomarkers, including autoantibodies (AAbs). Our long-term goal is to develop a robust, accurate and widely translatable early-stage screening algorithm for risk of OC. Our immediate objectives are to enhance our biomarker-based classifiers for pre-diagnostic samples, developed in preliminary studies, by adding new promising candidate biomarkers we have identified, and validate them in independent pre-diagnostic samples. The Specific Aims are: 1. Generate and validate an optimized first-line threshold-based classification algorithm with 1.5-7 years lead-time. We will assess whether new candidate biomarkers can further improve the algorithm we developed in preliminary studies, and then validate the optimized algorithms in pre-diagnostic PLCO samples. 2. Generate and validate a biomarker temporal dynamics (velocity)-based algorithm. We will validate the promising candidate velocity-based biomarkers identified in Aim 1 in pre-diagnostic serial samples from UKCTOCS and NROSS prospective studies and generate a velocity-based classification algorithm for detecting OC, to complement and enhance the cut-off- based algorithm(s) developed in Aim 1. 3. Determine the performance of a 2-step (threshold+velocity)– based OC screening algorithm with 1.5-7 years lead-time in serial samples. We will determine the cumulative performance of sequential algorithms including the threshold-based algorithm developed in Aim 1, followed by the velocity-based algorithm developed in Aim 2, for OC screening in the 1.5-7 YTD interval, in serial UKCTOCS samples. In summary, we anticipate our results will yield development and validation of the first blood biomarker-based algorithms with the required >75% SN, >98% SP, for reliably classifying OC in preclinical samples collected 1.5-7 YTD. These algorithms will be ready for validation in prospective screening clinical trials to evaluate the effect of early detection upon OC survival. The proposal is supported by extensive preliminary data and will be carried out by a highly qualified, multi-disciplinary research team.

抽象的 卵巢癌 (OC) 是一种致命但往往无声无息的疾病，在发展到晚期之前不会出现任何具体症状 晚期 OC 的 5 年生存率仅为 50%，因为大多数肿瘤最终会产生耐药性。 治疗。1,2 细胞减灭术和联合化疗的进展提高了 5 年生存率 上皮性 OC 患者的治愈率在过去二十年中并未提高。 表明在早期（I-II）阶段检测 OC 可以显着提高治愈率，但患病率较低 一般绝经后人群中 OC 的发生限制了早期检测工作的需要。 手术干预，但共识是诊断一个疾病的手术次数不应超过 10 次。 单一 OC（>10% 阳性预测值，PPV） 根据当前要求，基于一线生物标志物。 筛选测试必须达到至少 75% 的敏感性 (SN) 和 98% 的特异性 (SP)，然后才能进行 通过增加经阴道超声检查等二线筛查方式进一步提高至 99.6% (TVS)。根据我们的研究，现有的筛选测试仍然不足以值得广泛实施。 强有力的初步结果拟议项目旨在开发一种新颖的、可广泛翻译的、经济的 目前，唯一有前途的策略是联合开发，可以降低OC率。 Kingdom Collaborative Trial for OC 筛查 (UKCTOCS)，是对血清中标记物 CA125 进行序贯分析 随着时间的推移（OC 算法的风险，ROCA），其次是 UKCTOCS，仅产生了 20% 的适度下降。 死亡率，不足以促使美国预防服务工作组改变其反对的建议 1 基于人群的 OC 筛查是 CA125 死亡率适度降低的最可能原因。 措施的不足是其提前时间（基于症状的生物诊断之前的估计检测间隔）。 数学模型表明，OC 在症状出现前一年多就进展到晚期， CA125 水平仅提供有限诊断能力的时间范围因此，为了改善当前的临床实践， 基于我们强有力的初步研究，需要能够显着延长交货时间的新颖筛选算法。 根据研究结果，我们的目标是开发和验证一种双管齐下的方法，其中一线多生物标志物测试 识别具有高 SN (>80%) 和适度 SP (>80%) 的 OC，然后是基于速度的二线生物标志物 对第一次测试呈阳性的女性进行测试，得出的综合 SP 为 98%，支持这一点。 方法，我们生成了一个基于阈值的初步分类算法（TBA） 一次性测量多种生物标志物浓度，可识别 80%SN-70%SP 女性 将在 1-7 年后发展为 OC 我们进一步确定了几个表现出强大时间动态的生物标志物。 （速度）与 1-7 YTD 区间内的 OC 发展相关，因此我们追求我们可以生成。 一种 2 步算法，通过将我们新颖的 TBA 与基于速度的算法相结合，提供 >75%SN 和 >98%SP 在这种方法中，与 ROCA 类似，TBA 的积极结果将触发频繁的后续操作。 使用 VBA 进行筛选 与 UKCTOCS 的 ROCA 相比，我们提出的算法的关键优势在于我们的新颖性。 组合算法将识别 OC 超过 1 YTD，增加早期检测到 OC 的概率， 我们已经发现了几个治疗反应阶段，并将优先考虑将其纳入测试中。 有前途的候选预诊断 OC 生物标志物，包括自身抗体 (AAb)，我们的长期目标是 开发一种稳健、准确且可广泛翻译的 OC 风险早期筛查算法。 近期目标是增强我们基于生物标记的预诊断样本分类器，该分类器是在 初步研究，通过添加我们已经确定的新的有前途的候选生物标志物，并在 独立的预诊断样本的具体目标是： 1. 生成并验证优化的一线。 基于阈值的分类算法，需要 1.5-7 年的准备时间，我们将评估是否有新的候选者。 生物标志物可以进一步改进我们在初步研究中开发的算法，然后验证 预诊断 PLCO 样本中的优化算法 2. 生成并验证生物标记时间。 我们将验证有前途的基于速度的候选生物标志物。 在目标 1 中从 UKCTOCS 和 NROSS 前瞻性研究的诊断前系列样本中鉴定出 生成用于检测 OC 的基于速度的分类算法，以补充和增强截止值 基于目标 1 中开发的算法。 3. 确定 2 步（阈值 + 速度）的性能 – 基于 OC 筛选算法，我们将在系列样品中确定 1.5-7 年的交付周期。 顺序算法的累积性能，包括目标 1 中开发的基于阈值的算法， 接下来是目标 2 中开发的基于速度的算法，用于 1.5-7 YTD 区间内的 OC 筛查，连续进行 总之，我们预计我们的结果将产生第一个样本的开发和验证。 基于血液生物标志物的算法，需要 >75% SN、>98% SP，用于在临床前可靠地对 OC 进行分类 今年 1.5-7 月收集的样本这些算法将准备好在前瞻性筛选临床试验中进行验证。 评估早期检测对 OC 存活的影响 该提案得到了广泛的初步支持。 数据，并将由高素质的多学科研究团队进行。